From c77a9791b537c1bc94d3e9bdb9bc2745419cdda1 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 16:37:20 +0100 Subject: [PATCH 1/9] Accept GPy tutorial CIPs and add implementation backlog. CIP-0001 is In Progress; CIP-0002 and CIP-0003 are Accepted with tasks for the Python 3 port, plotting switch, upstream Plotly issue, and CI runner. --- .../2026-10-24_gpy-plotly-upstream-issue.md | 52 ++++++++++++++++++ ...-10-24_gpy-tutorials-matplotlib-backend.md | 48 ++++++++++++++++ ...4_gpy-tutorials-numpy-matplotlib-config.md | 55 +++++++++++++++++++ .../2026-10-24_gpy-tutorials-python3.md | 49 +++++++++++++++++ .../2026-10-24_gpy-tutorials-ci-runner.md | 53 ++++++++++++++++++ cip/cip0001.md | 8 +-- cip/cip0002.md | 6 +- cip/cip0003.md | 6 +- 8 files changed, 267 insertions(+), 10 deletions(-) create mode 100644 backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md create mode 100644 backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md create mode 100644 backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md create mode 100644 backlog/features/2026-10-24_gpy-tutorials-python3.md create mode 100644 backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md diff --git a/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md b/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md new file mode 100644 index 0000000..19103a9 --- /dev/null +++ b/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md @@ -0,0 +1,52 @@ +--- +id: "2026-10-24_gpy-plotly-upstream-issue" +title: "File GPy issue for Plotly online iplot and string marker sizes" +status: "Ready" +priority: "Medium" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "bugs" +related_cips: ["0002"] +owner: "Neil Lawrence" +dependencies: [] +tags: +- backlog +- gpy +- plotly +--- + +# Task: File GPy Plotly backend issue + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +Open an issue on SheffieldML/GPy listing the two blockers that remain even after notebooks stop calling `'plotly'`: + +1. `GPy/plotting/plotly_dep/plot_definitions.py` — `PlotlyPlotsOnline` uses `plotly.plotly.iplot` / `.plot` (Chart Studio is a separate package). +2. `GPy/plotting/plotly_dep/defaults.py` — `size="5"` / `size="0"` strings; Plotly 7 rejects `marker.size` as a string. +3. `change_plotting_library('plotly')` aliases the online class. + +Link the issue from CIP-0002. Do not patch GPy from this repository. + +## Acceptance Criteria + +- [ ] GitHub issue exists on SheffieldML/GPy covering iplot and string marker sizes +- [ ] CIP-0002 References section links to that issue + +## Implementation Notes + +Track as a GPy issue or CIP against SheffieldML/GPy. Notebook-side workaround is matplotlib (sibling task). + +## Related + +- CIP: 0002 +- PRs: +- Documentation: SheffieldML/GPy plotting backends + +## Progress Updates + +### 2026-10-24 + +Task created as Ready after CIP-0002 was accepted. diff --git a/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md b/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md new file mode 100644 index 0000000..fd1d188 --- /dev/null +++ b/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md @@ -0,0 +1,48 @@ +--- +id: "2026-10-24_gpy-tutorials-matplotlib-backend" +title: "Switch GPy tutorial plotting from Plotly online to matplotlib" +status: "Ready" +priority: "High" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "features" +related_cips: ["0002"] +owner: "Neil Lawrence" +dependencies: +- "2026-10-24_gpy-tutorials-python3" +tags: +- backlog +- gpy +- plotting +--- + +# Task: Switch tutorial plotting to matplotlib + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +In `basic_gp.ipynb`, `models_basic.ipynb`, and `MagnificationFactor.ipynb`, replace `GPy.plotting.change_plotting_library('plotly')` with `'matplotlib'`. CIP-0002's default is matplotlib for all three so CI does not need Chart Studio or Plotly widgets. Remove nearby Chart Studio / `plotly.plotly` instructions if present. + +## Acceptance Criteria + +- [ ] No `GPy/*.ipynb` cell calls `change_plotting_library('plotly')` +- [ ] The three named notebooks select matplotlib +- [ ] Nearby markdown does not tell readers to use Plotly Cloud / Chart Studio + +## Implementation Notes + +Do not patch GPy from this repository. GPy-side `plotly.plotly.iplot` and string marker sizes are a separate upstream issue. + +## Related + +- CIP: 0002 +- PRs: +- Documentation: `GPy/basic_gp.ipynb` + +## Progress Updates + +### 2026-10-24 + +Task created as Ready after CIP-0002 was accepted. diff --git a/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md b/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md new file mode 100644 index 0000000..436e796 --- /dev/null +++ b/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md @@ -0,0 +1,55 @@ +--- +id: "2026-10-24_gpy-tutorials-numpy-matplotlib-config" +title: "Fix NumPy 2, matplotlib fmt, config.ipynb, and climin guards" +status: "Ready" +priority: "High" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "features" +related_cips: ["0001"] +owner: "Neil Lawrence" +dependencies: +- "2026-10-24_gpy-tutorials-python3" +tags: +- backlog +- gpy +- numpy2 +--- + +# Task: NumPy 2, matplotlib, config, and climin guards + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +Finish the CIP-0001 cell-content port beyond print/xrange: + +1. `Integral_kernel.ipynb`: `"%0.2f" % speed` must use `.item()` (NumPy 2 ndarray is not a float). +2. `Poisson regression tutorial.ipynb`: `float(rates[r])` must use `.item()` or `float(np.asarray(x).reshape(-1)[0])`. +3. `heteroscedastic_regression.ipynb`: `fmt=None` → `fmt="none"`. +4. `config.ipynb`: move `defaults.cfg` / `installation.cfg` INI samples from code cells to markdown (or raw) so the notebook is not executed as Python. +5. `SVI.ipynb` and `optimizer-implementation.ipynb`: short markdown plus an early cell so `import climin` fails with a readable message that climin is an optional extra, not a GPy dependency. + +## Acceptance Criteria + +- [ ] Integral and Poisson notebooks format/cast scalars via `.item()` (or equivalent) +- [ ] Heteroscedastic `errorbar` uses `fmt="none"` +- [ ] `config.ipynb` has no INI in code cells +- [ ] Missing `climin` raises a clear ImportError naming the extra in both climin notebooks + +## Implementation Notes + +Do not add climin as a hard dependency. Skip policy for CI is CIP-0003. + +## Related + +- CIP: 0001 +- PRs: +- Documentation: `GPy/config.ipynb` + +## Progress Updates + +### 2026-10-24 + +Task created as Ready after CIP-0001 was accepted. diff --git a/backlog/features/2026-10-24_gpy-tutorials-python3.md b/backlog/features/2026-10-24_gpy-tutorials-python3.md new file mode 100644 index 0000000..c0570e5 --- /dev/null +++ b/backlog/features/2026-10-24_gpy-tutorials-python3.md @@ -0,0 +1,49 @@ +--- +id: "2026-10-24_gpy-tutorials-python3" +title: "Port GPy tutorial cells to Python 3 print/xrange and python3 kernelspec" +status: "Ready" +priority: "High" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "features" +related_cips: ["0001"] +owner: "Neil Lawrence" +dependencies: [] +tags: +- backlog +- gpy +- python3 +--- + +# Task: Port GPy tutorials to Python 3 syntax + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +Update `GPy/*.ipynb` source cells so they are valid Python 3: convert `print` statements and `xrange` to `print()` / `range`, and set every tutorial `kernelspec` to `python3`. Do not rewrite the teaching narrative. + +Notebooks with Python 2 `print` / `xrange` (CIP-0001 inventory): `GPyCrashCourse`, `basic_classification`, `basic_kernels`, `coregionalized_regression_tutorial`, `models_basic`, `multiple outputs`, `sparse_gp_regression`, `sampling_hmc`, `heteroscedastic_regression`. Apply kernelspec `python3` to every file in `GPy/`. + +## Acceptance Criteria + +- [ ] No code cell in `GPy/*.ipynb` uses Python 2 `print` or `xrange` +- [ ] Every `GPy/*.ipynb` kernelspec name is `python3` +- [ ] Teaching markdown is unchanged except where it quotes the old `print` syntax as a command to type + +## Implementation Notes + +Mechanical port only. IPython magics (`%matplotlib`, `%pylab`, `?` help) must be left intact. Prefer lib2to3 print/xrange fixers on cell source with magics stripped and restored. + +## Related + +- CIP: 0001 +- PRs: +- Documentation: `GPy/index.ipynb` + +## Progress Updates + +### 2026-10-24 + +Task created as Ready after CIP-0001 was accepted. diff --git a/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md b/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md new file mode 100644 index 0000000..f59ac05 --- /dev/null +++ b/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md @@ -0,0 +1,53 @@ +--- +id: "2026-10-24_gpy-tutorials-ci-runner" +title: "Add nbclient runner and GitHub Actions for GPy tutorials" +status: "Ready" +priority: "High" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "infrastructure" +related_cips: ["0003"] +owner: "Neil Lawrence" +dependencies: +- "2026-10-24_gpy-tutorials-python3" +- "2026-10-24_gpy-tutorials-numpy-matplotlib-config" +- "2026-10-24_gpy-tutorials-matplotlib-backend" +tags: +- backlog +- gpy +- ci +--- + +# Task: Execute GPy tutorials in CI + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +Add `scripts/run_gpy_tutorials.sh` (nbclient, `MPLBACKEND=Agg`, per-notebook timeout, climin skip policy) and a GitHub Actions workflow: Python 3.12, install GPy pinned to the 1.15.0 candidate (`gpy==1.15.0` once published; until then `release/1.15.0` or the #1235 SHA) plus `gpy[plotting]`, Jupyter (`nbclient`, `ipykernel`). Upload executed notebooks on failure. Point the README GPy tutorial section at the workflow badge. + +Skip `SVI.ipynb` and `optimizer-implementation.ipynb` when climin is not installed, with the skip documented. `index.ipynb` has no required code. Fail the job if a required notebook raises. + +## Acceptance Criteria + +- [ ] Runner script executes `GPy/*.ipynb` with documented climin skip +- [ ] GitHub Actions workflow pins GPy and runs the script on this repository +- [ ] README GPy tutorial section shows the workflow badge +- [ ] Job does not use Plotly Cloud credentials + +## Implementation Notes + +Run with cwd such that `import GPy` resolves the library, not this repo's `GPy/` tutorial folder. Pin must not float on GPy `devel`. Failures should block merges that touch `GPy/`. + +## Related + +- CIP: 0003 +- PRs: +- Documentation: `README.md` + +## Progress Updates + +### 2026-10-24 + +Task created as Ready after CIP-0003 was accepted. diff --git a/cip/cip0001.md b/cip/cip0001.md index e60b7eb..26e4811 100644 --- a/cip/cip0001.md +++ b/cip/cip0001.md @@ -3,7 +3,7 @@ author: Neil Lawrence created: "2026-10-24" id: "0001" last_updated: "2026-10-24" -status: Proposed +status: In Progress compressed: false related_requirements: [] related_cips: @@ -22,8 +22,8 @@ title: Port GPy tutorials to current Python ## Status - [x] Proposed - Initial idea documented -- [ ] Accepted - Approved, ready to start work -- [ ] In Progress - Actively being implemented +- [x] Accepted - Approved, ready to start work +- [x] In Progress - Actively being implemented - [ ] Implemented - Work complete, awaiting verification - [ ] Closed - Verified and complete - [ ] Rejected - Will not be implemented @@ -132,7 +132,7 @@ non-Plotly notebooks that failed only on `print` / `xrange` / NumPy / matplotlib ## Implementation Status - [x] Execute tutorials against GPy 1.15.0; record pass/fail -- [ ] Accept CIP after review +- [x] Accept CIP after review - [ ] Python 3 + kernelspec - [ ] NumPy 2, matplotlib, config, climin guards - [ ] Green execute with CIP-0002 + CIP-0003 diff --git a/cip/cip0002.md b/cip/cip0002.md index 5b99613..51f777c 100644 --- a/cip/cip0002.md +++ b/cip/cip0002.md @@ -3,7 +3,7 @@ author: Neil Lawrence created: "2026-10-24" id: "0002" last_updated: "2026-10-24" -status: Proposed +status: Accepted compressed: false related_requirements: [] related_cips: @@ -23,7 +23,7 @@ title: Plotting in GPy tutorials ## Status - [x] Proposed - Initial idea documented -- [ ] Accepted - Approved, ready to start work +- [x] Accepted - Approved, ready to start work - [ ] In Progress - Actively being implemented - [ ] Implemented - Work complete, awaiting verification - [ ] Closed - Verified and complete @@ -118,7 +118,7 @@ credentials in CI. ## Implementation Status - [x] Diagnose Plotly failures against GPy 1.15.0 -- [ ] Accept CIP after review +- [x] Accept CIP after review - [ ] Switch tutorial library cells - [ ] File GPy issue for online `iplot` + string marker sizes - [ ] Green execute with CIP-0001 + CIP-0003 diff --git a/cip/cip0003.md b/cip/cip0003.md index 8ad2530..07fa15e 100644 --- a/cip/cip0003.md +++ b/cip/cip0003.md @@ -3,7 +3,7 @@ author: Neil Lawrence created: "2026-10-24" id: "0003" last_updated: "2026-10-24" -status: Proposed +status: Accepted compressed: false related_requirements: [] related_cips: @@ -22,7 +22,7 @@ title: Execute GPy tutorials in CI ## Status - [x] Proposed - Initial idea documented -- [ ] Accepted - Approved, ready to start work +- [x] Accepted - Approved, ready to start work - [ ] In Progress - Actively being implemented - [ ] Implemented - Work complete, awaiting verification - [ ] Closed - Verified and complete @@ -110,7 +110,7 @@ check when they edit `GPy/` tutorials. ## Implementation Status - [x] Manual execute against GPy 1.15.0; inventory failures -- [ ] Accept CIP after review +- [x] Accept CIP after review - [ ] Runner script + Actions workflow - [ ] Green run on GPy 1.15.0 candidate - [ ] README badge; close CIP From 678b062943cbbb08326f762a54f34bef2b49a0cf Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 16:39:39 +0100 Subject: [PATCH 2/9] CIP-0001: Port GPy tutorial cells to current Python. Convert print/xrange, set kernelspec to python3, fix NumPy 2 scalars and matplotlib errorbar fmt, move config INI samples to markdown, and guard optional climin imports. --- GPy/GPyCrashCourse.ipynb | 14 +++--- GPy/Integral_kernel.ipynb | 12 ++--- GPy/MagnificationFactor.ipynb | 10 ++--- GPy/ParametricNonParametricInference.ipynb | 10 ++--- GPy/Poisson regression tutorial.ipynb | 16 +++---- GPy/SVI.ipynb | 26 ++++++++--- GPy/Space_time_effects.ipynb | 10 ++--- GPy/basic_classification.ipynb | 20 ++++----- GPy/basic_gp.ipynb | 10 ++--- GPy/basic_kernels.ipynb | 20 ++++----- GPy/config.ipynb | 18 +++----- GPy/coregionalized_regression_tutorial.ipynb | 16 +++---- GPy/heteroscedastic_regression.ipynb | 22 +++++----- GPy/index.ipynb | 10 ++--- GPy/models_basic.ipynb | 44 +++++++++---------- GPy/multiple outputs.ipynb | 20 ++++----- GPy/optimizer-implementation.ipynb | 25 ++++++++--- GPy/sampling_hmc.ipynb | 24 +++++----- GPy/sparse_gp_regression.ipynb | 18 ++++---- ...4_gpy-tutorials-numpy-matplotlib-config.md | 12 ++--- .../2026-10-24_gpy-tutorials-python3.md | 10 +++-- cip/cip0001.md | 4 +- 22 files changed, 198 insertions(+), 173 deletions(-) diff --git a/GPy/GPyCrashCourse.ipynb b/GPy/GPyCrashCourse.ipynb index 52ba495..4b8a909 100644 --- a/GPy/GPyCrashCourse.ipynb +++ b/GPy/GPyCrashCourse.ipynb @@ -1234,7 +1234,7 @@ "kern1 = GPy.kern.RBF(1, variance=1., lengthscale=2.)\n", "kern2 = GPy.kern.Matern32(1, variance=0.5, lengthscale=0.2)\n", "kern = kern1 + kern2\n", - "print kern\n", + "print(kern)\n", "\n", "kern.plot()" ] @@ -1287,7 +1287,7 @@ ], "source": [ "kern = kern1*kern2\n", - "print kern\n", + "print(kern)\n", "kern.plot()" ] }, @@ -1807,21 +1807,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" }, "widgets": { "state": { diff --git a/GPy/Integral_kernel.ipynb b/GPy/Integral_kernel.ipynb index 0ed38d3..a0443e8 100644 --- a/GPy/Integral_kernel.ipynb +++ b/GPy/Integral_kernel.ipynb @@ -93,7 +93,7 @@ "\n", "#prediction for after five seconds\n", "(speed, var) = m.predict_noiseless(np.array([[5.0]]))\n", - "print(\"After 5 seconds the robot was travelling at %0.2f m/s\" % speed);" + "print(\"After 5 seconds the robot was travelling at %0.2f m/s\" % speed.item());" ] }, { @@ -356,21 +356,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/MagnificationFactor.ipynb b/GPy/MagnificationFactor.ipynb index 9876748..da52f16 100644 --- a/GPy/MagnificationFactor.ipynb +++ b/GPy/MagnificationFactor.ipynb @@ -513,21 +513,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/ParametricNonParametricInference.ipynb b/GPy/ParametricNonParametricInference.ipynb index 4af3dee..cb1a4f5 100644 --- a/GPy/ParametricNonParametricInference.ipynb +++ b/GPy/ParametricNonParametricInference.ipynb @@ -499,21 +499,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/Poisson regression tutorial.ipynb b/GPy/Poisson regression tutorial.ipynb index 2d06eeb..c1ad325 100644 --- a/GPy/Poisson regression tutorial.ipynb +++ b/GPy/Poisson regression tutorial.ipynb @@ -103,7 +103,7 @@ "\n", "#Plot each rate as a function of counts K\n", "for r in rates_plot:\n", - " pb.plot(K, rates_count[r, :], label='rate = {}'.format(float(rates[r])))\n", + " pb.plot(K, rates_count[r, :], label='rate = {}'.format(float(rates[r].item())))\n", "\n", "pb.title('Poisson probability mass distribution for different rates')\n", "pb.ylabel('PMF')\n", @@ -480,7 +480,7 @@ ], "source": [ "m = GPy.core.GP(X=X, Y=Y, likelihood=poisson_likelihood, inference_method=laplace_inf, kernel=kernel)\n", - "print m\n", + "print(m)\n", "\n", "#Predictions of counts of buses, including noise arising from Poisson noise\n", "m.plot()\n", @@ -541,7 +541,7 @@ ], "source": [ "m.optimize()\n", - "print m\n", + "print(m)\n", "\n", "#Predictions of counts of buses, including noise arising from Poisson noise\n", "m.plot()\n", @@ -657,21 +657,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/SVI.ipynb b/GPy/SVI.ipynb index 89c089f..6db7d1c 100644 --- a/GPy/SVI.ipynb +++ b/GPy/SVI.ipynb @@ -25,6 +25,13 @@ "First lets import some of the essentials, first we must note that within the model we rely on a optimization library called [climin](http://climin.readthedocs.org/en/latest/), so this must first be [installed](http://climin.readthedocs.org/en/latest/installation.html)." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook needs [climin](https://github.com/BRML/climin) for stochastic optimisation. Climin is an **optional extra**, not a GPy dependency. Install it with `pip install climin` before running the cells below." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -36,7 +43,13 @@ "import numpy as np\n", "import GPy\n", "from matplotlib import pyplot as plt\n", - "import climin\n", + "try:\n", + " import climin\n", + "except ImportError as exc:\n", + " raise ImportError(\n", + " \"This notebook needs climin, an optional extra that is not a GPy \"\n", + " \"dependency. Install it with: pip install climin\"\n", + " ) from exc\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "#plt.rcParams['figure.figsize'] = 8,8" @@ -11967,7 +11980,6 @@ } ], "source": [ - " X_variance = None\n", "plt.figure()\n", "plt.plot(X, Y1, 'bx', alpha=0.2)\n", "plt.xlabel('X')\n", @@ -24623,21 +24635,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/Space_time_effects.ipynb b/GPy/Space_time_effects.ipynb index f741974..65eb9f2 100644 --- a/GPy/Space_time_effects.ipynb +++ b/GPy/Space_time_effects.ipynb @@ -433,21 +433,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/basic_classification.ipynb b/GPy/basic_classification.ipynb index a14278d..086b266 100644 --- a/GPy/basic_classification.ipynb +++ b/GPy/basic_classification.ipynb @@ -3766,7 +3766,7 @@ " kernel=k, \n", " inference_method=GPy.inference.latent_function_inference.expectation_propagation.EP(),\n", " likelihood=lik)\n", - "print m" + "print(m)" ] }, { @@ -7355,12 +7355,12 @@ } ], "source": [ - "print m, '\\n'\n", + "print(m, '\\n')\n", "for i in range(5):\n", " m.optimize('bfgs', max_iters=100) #first runs EP and then optimizes the kernel parameters\n", - " print 'iteration:', i,\n", - " print m\n", - " print \"\"\n", + " print('iteration:', i, end=' ')\n", + " print(m)\n", + " print(\"\")\n", " " ] }, @@ -45977,21 +45977,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/basic_gp.ipynb b/GPy/basic_gp.ipynb index 2997f2c..f8895e5 100644 --- a/GPy/basic_gp.ipynb +++ b/GPy/basic_gp.ipynb @@ -699,21 +699,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/basic_kernels.ipynb b/GPy/basic_kernels.ipynb index ce4c8d7..e259b58 100644 --- a/GPy/basic_kernels.ipynb +++ b/GPy/basic_kernels.ipynb @@ -85,7 +85,7 @@ } ], "source": [ - "print ker2" + "print(ker2)" ] }, { @@ -6712,7 +6712,7 @@ "k1 = GPy.kern.RBF(1,1.,2.)\n", "k2 = GPy.kern.Matern32(1, 0.5, 0.2)\n", "k_prod = k1 *k2\n", - "print k_prod\n", + "print(k_prod)\n", "k_prod.plot()\n", "\n", "\n", @@ -6721,7 +6721,7 @@ "k1 = GPy.kern.RBF(1,1.,2.)\n", "k2 = GPy.kern.Matern32(1, 0.5, 0.2)\n", "k_add = k1 + k2\n", - "print k_add\n", + "print(k_add)\n", "k_add.plot()" ] }, @@ -6754,9 +6754,9 @@ } ], "source": [ - "print k1, '\\n'\n", + "print(k1, '\\n')\n", "k_add.rbf.variance = 12.\n", - "print k1" + "print(k1)" ] }, { @@ -12622,21 +12622,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/config.ipynb b/GPy/config.ipynb index 8f8612e..8ac2f36 100644 --- a/GPy/config.ipynb +++ b/GPy/config.ipynb @@ -10,12 +10,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": false }, - "outputs": [], "source": [ "# This is the default configuration file for GPy\n", "\n", @@ -47,12 +45,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": false }, - "outputs": [], "source": [ "# This is the local installation configuration file for GPy\n", "[parallel]\n", @@ -71,21 +67,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/coregionalized_regression_tutorial.ipynb b/GPy/coregionalized_regression_tutorial.ipynb index 3b9bc68..ca82d89 100644 --- a/GPy/coregionalized_regression_tutorial.ipynb +++ b/GPy/coregionalized_regression_tutorial.ipynb @@ -268,7 +268,7 @@ "K=GPy.kern.RBF(1)\n", "B = GPy.kern.Coregionalize(input_dim=1,output_dim=2) \n", "multkernel = K.prod(B,name='B.K')\n", - "print multkernel" + "print(multkernel)" ] }, { @@ -310,9 +310,9 @@ ], "source": [ "#Components of B\n", - "print 'W matrix\\n',B.W\n", - "print '\\nkappa vector\\n',B.kappa\n", - "print '\\nB matrix\\n',B.B" + "print('W matrix\\n',B.W)\n", + "print('\\nkappa vector\\n',B.kappa)\n", + "print('\\nB matrix\\n',B.B)" ] }, { @@ -346,7 +346,7 @@ ], "source": [ "icm = GPy.util.multioutput.ICM(input_dim=1,num_outputs=2,kernel=GPy.kern.RBF(1))\n", - "print icm" + "print(icm)" ] }, { @@ -419,7 +419,7 @@ "m = GPy.models.GPCoregionalizedRegression([X1,X2],[Y1,Y2],kernel=icm)\n", "m['.*Mat32.var'].constrain_fixed(1.) #For this kernel, B.kappa encodes the variance now.\n", "m.optimize()\n", - "print m\n", + "print(m)\n", "plot_2outputs(m,xlim=(0,100),ylim=(-20,60))" ] }, @@ -726,7 +726,7 @@ "source": [ "newX = np.arange(100,110)[:,None]\n", "newX = np.hstack([newX,np.ones_like(newX)])\n", - "print newX" + "print(newX)" ] }, { @@ -831,7 +831,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/GPy/heteroscedastic_regression.ipynb b/GPy/heteroscedastic_regression.ipynb index d156c09..378b4d4 100644 --- a/GPy/heteroscedastic_regression.ipynb +++ b/GPy/heteroscedastic_regression.ipynb @@ -207,7 +207,7 @@ "m.optimize()\n", "\n", "m.plot_f() #Show the predictive values of the GP.\n", - "pb.errorbar(X,Y,yerr=np.array(m.likelihood.flattened_parameters).flatten(),fmt=None,ecolor='r',zorder=1)\n", + "pb.errorbar(X,Y,yerr=np.array(m.likelihood.flattened_parameters).flatten(),fmt=\"none\",ecolor='r',zorder=1)\n", "pb.grid()\n", "pb.plot(X,Y,'kx',mew=1.5)" ] @@ -233,7 +233,7 @@ " m.optimize()\n", " \n", " m.plot_f() \n", - " pb.errorbar(X.flatten(),Y.flatten(),yerr=np.array(m.likelihood.flattened_parameters).flatten(),fmt=None,ecolor='r',zorder=1) \n", + " pb.errorbar(X.flatten(),Y.flatten(),yerr=np.array(m.likelihood.flattened_parameters).flatten(),fmt=\"none\",ecolor='r',zorder=1) \n", " pb.plot(X[1:],Y[1:],'kx',mew=1.5)\n", " pb.plot(X[:1],Y[:1],'ko',mew=.5)\n", " pb.grid()" @@ -351,10 +351,10 @@ "m2.plot_f()\n", "pb.title('Heteroscedastic model')\n", "\n", - "print \"Kernel parameters (optimized) in the heteroscedastic model\"\n", - "print m1.kern\n", - "print \"\\nKernel parameters (optimized) in the homoscedastic model\"\n", - "print m2.kern" + "print(\"Kernel parameters (optimized) in the heteroscedastic model\")\n", + "print(m1.kern)\n", + "print(\"\\nKernel parameters (optimized) in the homoscedastic model\")\n", + "print(m2.kern)" ] }, { @@ -489,21 +489,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/index.ipynb b/GPy/index.ipynb index ea81170..d910d72 100644 --- a/GPy/index.ipynb +++ b/GPy/index.ipynb @@ -73,21 +73,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/models_basic.ipynb b/GPy/models_basic.ipynb index 878347b..5f3fa15 100644 --- a/GPy/models_basic.ipynb +++ b/GPy/models_basic.ipynb @@ -50,7 +50,7 @@ "\n", "To see the current state of the model parameters, and the model’s (marginal) likelihood just print the model\n", "\n", - " print m\n", + " print(m)\n", "\n", "The first thing displayed on the screen is the log-likelihood value of the model with its current parameters. Below the log-likelihood, a table with all the model’s parameters is shown. For each parameter, the table contains the name of the parameter, the current value, and in case there are defined: constraints, ties and prior distrbutions associated." ] @@ -256,7 +256,7 @@ ], "source": [ "m.rbf.lengthscale = 0.2\n", - "print m" + "print(m)" ] }, { @@ -294,7 +294,7 @@ } ], "source": [ - "print m['.*var']\n", + "print(m['.*var'])\n", "#print \"variances as a np.array:\", m['.*var'].values()\n", "#print \"np.array of rbf matches: \", m['.*rbf'].values()" ] @@ -346,9 +346,9 @@ ], "source": [ "m['.*var'] = 2.\n", - "print m\n", + "print(m)\n", "m['.*var'] = [2., 3.]\n", - "print m" + "print(m)" ] }, { @@ -385,7 +385,7 @@ } ], "source": [ - "print m['']" + "print(m[''])" ] }, { @@ -426,10 +426,10 @@ ], "source": [ "new_params = np.r_[[-4,-2,0,2,4], [.1,2], [.7]]\n", - "print new_params\n", + "print(new_params)\n", "\n", "m[:] = new_params\n", - "print m " + "print(m) " ] }, { @@ -461,7 +461,7 @@ ], "source": [ "m.inducing_inputs[2:, 0] = [1,3,5]\n", - "print m.inducing_inputs" + "print(m.inducing_inputs)" ] }, { @@ -488,7 +488,7 @@ ], "source": [ "precision = 1./m.Gaussian_noise.variance\n", - "print precision" + "print(precision)" ] }, { @@ -521,8 +521,8 @@ } ], "source": [ - "print \"all gradients of the model:\\n\", m.gradient\n", - "print \"\\n gradients of the rbf kernel:\\n\", m.rbf.gradient" + "print(\"all gradients of the model:\\n\", m.gradient)\n", + "print(\"\\n gradients of the rbf kernel:\\n\", m.rbf.gradient)" ] }, { @@ -550,7 +550,7 @@ ], "source": [ "m.optimize()\n", - "print m.gradient" + "print(m.gradient)" ] }, { @@ -592,7 +592,7 @@ ], "source": [ "m.rbf.variance.unconstrain()\n", - "print m" + "print(m)" ] }, { @@ -623,7 +623,7 @@ ], "source": [ "m.unconstrain()\n", - "print m" + "print(m)" ] }, { @@ -674,9 +674,9 @@ "source": [ "m.inducing_inputs[0].fix()\n", "m.rbf.constrain_positive()\n", - "print m\n", + "print(m)\n", "m.unfix()\n", - "print m" + "print(m)" ] }, { @@ -2544,21 +2544,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/multiple outputs.ipynb b/GPy/multiple outputs.ipynb index 9808f02..fbf796c 100644 --- a/GPy/multiple outputs.ipynb +++ b/GPy/multiple outputs.ipynb @@ -66,7 +66,7 @@ "data = pods.datasets.olympic_sprints()\n", "X = data['X']\n", "y = data['Y']\n", - "print data['info'], data['details']" + "print(data['info'], data['details'])" ] }, { @@ -92,7 +92,7 @@ } ], "source": [ - "print data['citation']" + "print(data['citation'])" ] }, { @@ -118,7 +118,7 @@ } ], "source": [ - "print data['output_info']" + "print(data['output_info'])" ] }, { @@ -150,9 +150,9 @@ ], "source": [ "print('First column of X contains the olympic years.')\n", - "print(np.unique(data['X'][:, 0]))\n", + "print((np.unique(data['X'][:, 0])))\n", "print('Second column of X contains the event index.')\n", - "print(np.unique(data['X'][:, 1]))" + "print((np.unique(data['X'][:, 1])))" ] }, { @@ -1068,21 +1068,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/optimizer-implementation.ipynb b/GPy/optimizer-implementation.ipynb index fcebff5..4263f18 100644 --- a/GPy/optimizer-implementation.ipynb +++ b/GPy/optimizer-implementation.ipynb @@ -13,6 +13,13 @@ "%matplotlib inline" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The RProp example below needs [climin](https://github.com/BRML/climin). Climin is an **optional extra**, not a GPy dependency. Install it with `pip install climin`." + ] + }, { "cell_type": "code", "execution_count": 12, @@ -21,7 +28,13 @@ }, "outputs": [], "source": [ - "import climin" + "try:\n", + " import climin\n", + "except ImportError as exc:\n", + " raise ImportError(\n", + " \"This notebook needs climin, an optional extra that is not a GPy \"\n", + " \"dependency. Install it with: pip install climin\"\n", + " ) from exc\n" ] }, { @@ -299,21 +312,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/sampling_hmc.ipynb b/GPy/sampling_hmc.ipynb index d3327ea..06474bb 100644 --- a/GPy/sampling_hmc.ipynb +++ b/GPy/sampling_hmc.ipynb @@ -207,7 +207,7 @@ "xmin = samples.min()\n", "xmax = samples.max()\n", "xs = np.linspace(xmin,xmax,100)\n", - "for i in xrange(samples.shape[1]):\n", + "for i in range(samples.shape[1]):\n", " kernel = stats.gaussian_kde(samples[:,i])\n", " plot(xs,kernel(xs),label=labels[i])\n", "_ = legend()" @@ -308,7 +308,7 @@ "m.kern.variance[:] = samples[:,0].mean()\n", "m.kern.lengthscale[:] = samples[:,1].mean()\n", "m.likelihood.variance[:] = samples[:,2].mean()\n", - "print m\n", + "print(m)\n", "_=m.plot()" ] }, @@ -396,7 +396,7 @@ ], "source": [ "x_new,mi = m.infer_newX(y_new)\n", - "print mi\n", + "print(mi)\n", "m.plot()\n", "plot(x_new,y_new,'or')" ] @@ -480,7 +480,7 @@ "xmin = samples_new.min()\n", "xmax = samples_new.max()\n", "xs = np.linspace(xmin,xmax,100)\n", - "for i in xrange(samples_new.shape[1]):\n", + "for i in range(samples_new.shape[1]):\n", " kernel = stats.gaussian_kde(samples_new[:,i])\n", " plot(xs,kernel(xs))" ] @@ -542,7 +542,7 @@ "#set prior for lengthscale and variance.\n", "m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(25.,150.))\n", "m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(120.,2000.))\n", - "print m" + "print(m)" ] }, { @@ -617,8 +617,8 @@ } ], "source": [ - "print t.mean(axis=0)\n", - "print t.std(axis=0)\n", + "print(t.mean(axis=0))\n", + "print(t.std(axis=0))\n", "_=hist(t[:,:2],50)" ] }, @@ -1174,21 +1174,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/GPy/sparse_gp_regression.ipynb b/GPy/sparse_gp_regression.ipynb index e79ae4c..a31c2a5 100644 --- a/GPy/sparse_gp_regression.ipynb +++ b/GPy/sparse_gp_regression.ipynb @@ -1547,7 +1547,7 @@ "m_full = GPy.models.GPRegression(X,y)\n", "m_full.optimize('bfgs')\n", "m_full.plot()\n", - "print m_full" + "print(m_full)" ] }, { @@ -3096,7 +3096,7 @@ "m = GPy.models.SparseGPRegression(X,y,Z=Z)\n", "m.likelihood.variance = noise_var\n", "m.plot()\n", - "print m" + "print(m)" ] }, { @@ -4637,7 +4637,7 @@ "m.inducing_inputs.fix()\n", "m.optimize('bfgs')\n", "m.plot()\n", - "print m" + "print(m)" ] }, { @@ -9255,7 +9255,7 @@ "m.optimize('bfgs')\n", "m.plot()\n", "m_full.plot()\n", - "print m.log_likelihood(), m_full.log_likelihood()" + "print(m.log_likelihood(), m_full.log_likelihood())" ] }, { @@ -9277,21 +9277,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.12.0" } }, "nbformat": 4, diff --git a/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md b/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md index 436e796..a8fcadd 100644 --- a/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md +++ b/backlog/features/2026-10-24_gpy-tutorials-numpy-matplotlib-config.md @@ -1,7 +1,7 @@ --- id: "2026-10-24_gpy-tutorials-numpy-matplotlib-config" title: "Fix NumPy 2, matplotlib fmt, config.ipynb, and climin guards" -status: "Ready" +status: "Completed" priority: "High" created: "2026-10-24" last_updated: "2026-10-24" @@ -33,10 +33,10 @@ Finish the CIP-0001 cell-content port beyond print/xrange: ## Acceptance Criteria -- [ ] Integral and Poisson notebooks format/cast scalars via `.item()` (or equivalent) -- [ ] Heteroscedastic `errorbar` uses `fmt="none"` -- [ ] `config.ipynb` has no INI in code cells -- [ ] Missing `climin` raises a clear ImportError naming the extra in both climin notebooks +- [x] Integral and Poisson notebooks format/cast scalars via `.item()` (or equivalent) +- [x] Heteroscedastic `errorbar` uses `fmt="none"` +- [x] `config.ipynb` has no INI in code cells +- [x] Missing `climin` raises a clear ImportError naming the extra in both climin notebooks ## Implementation Notes @@ -53,3 +53,5 @@ Do not add climin as a hard dependency. Skip policy for CI is CIP-0003. ### 2026-10-24 Task created as Ready after CIP-0001 was accepted. + +Applied `.item()` in Integral and Poisson tutorials, `fmt="none"` in heteroscedastic errorbars, moved `config.ipynb` INI samples to markdown, and guarded `import climin` in SVI and optimizer notebooks. Also dropped a leftover indented fragment in `SVI.ipynb` so that plotting cell parses. diff --git a/backlog/features/2026-10-24_gpy-tutorials-python3.md b/backlog/features/2026-10-24_gpy-tutorials-python3.md index c0570e5..1edf62b 100644 --- a/backlog/features/2026-10-24_gpy-tutorials-python3.md +++ b/backlog/features/2026-10-24_gpy-tutorials-python3.md @@ -1,7 +1,7 @@ --- id: "2026-10-24_gpy-tutorials-python3" title: "Port GPy tutorial cells to Python 3 print/xrange and python3 kernelspec" -status: "Ready" +status: "Completed" priority: "High" created: "2026-10-24" last_updated: "2026-10-24" @@ -28,9 +28,9 @@ Notebooks with Python 2 `print` / `xrange` (CIP-0001 inventory): `GPyCrashCourse ## Acceptance Criteria -- [ ] No code cell in `GPy/*.ipynb` uses Python 2 `print` or `xrange` -- [ ] Every `GPy/*.ipynb` kernelspec name is `python3` -- [ ] Teaching markdown is unchanged except where it quotes the old `print` syntax as a command to type +- [x] No code cell in `GPy/*.ipynb` uses Python 2 `print` or `xrange` +- [x] Every `GPy/*.ipynb` kernelspec name is `python3` +- [x] Teaching markdown is unchanged except where it quotes the old `print` syntax as a command to type ## Implementation Notes @@ -47,3 +47,5 @@ Mechanical port only. IPython magics (`%matplotlib`, `%pylab`, `?` help) must be ### 2026-10-24 Task created as Ready after CIP-0001 was accepted. + +Converted `print` / `xrange` with lib2to3 (IPython magics preserved) and set kernelspec to `python3` on every `GPy/*.ipynb`. Code cells parse as Python 3. diff --git a/cip/cip0001.md b/cip/cip0001.md index 26e4811..72bfe7b 100644 --- a/cip/cip0001.md +++ b/cip/cip0001.md @@ -133,8 +133,8 @@ non-Plotly notebooks that failed only on `print` / `xrange` / NumPy / matplotlib - [x] Execute tutorials against GPy 1.15.0; record pass/fail - [x] Accept CIP after review -- [ ] Python 3 + kernelspec -- [ ] NumPy 2, matplotlib, config, climin guards +- [x] Python 3 + kernelspec +- [x] NumPy 2, matplotlib, config, climin guards - [ ] Green execute with CIP-0002 + CIP-0003 - [ ] Close CIP From 4b391bd05a7f62aa12261c5015c36a7238df6809 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 16:40:53 +0100 Subject: [PATCH 3/9] CIP-0002: Use matplotlib in GPy tutorials instead of Plotly online. Tutorials no longer call change_plotting_library('plotly'); Chart Studio iplot and string marker sizes are tracked on SheffieldML/GPy#1243. --- GPy/MagnificationFactor.ipynb | 2 +- GPy/basic_gp.ipynb | 2 +- GPy/models_basic.ipynb | 2 +- backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md | 9 ++++++--- .../2026-10-24_gpy-tutorials-matplotlib-backend.md | 10 ++++++---- cip/cip0002.md | 9 +++++---- 6 files changed, 20 insertions(+), 14 deletions(-) diff --git a/GPy/MagnificationFactor.ipynb b/GPy/MagnificationFactor.ipynb index da52f16..c9ffc4a 100644 --- a/GPy/MagnificationFactor.ipynb +++ b/GPy/MagnificationFactor.ipynb @@ -40,7 +40,7 @@ "from matplotlib import pyplot as plt\n", "import GPy # import GPy package\n", "np.random.seed(12345)\n", - "GPy.plotting.change_plotting_library('plotly')" + "GPy.plotting.change_plotting_library('matplotlib')" ] }, { diff --git a/GPy/basic_gp.ipynb b/GPy/basic_gp.ipynb index f8895e5..83c6305 100644 --- a/GPy/basic_gp.ipynb +++ b/GPy/basic_gp.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "GPy.plotting.change_plotting_library('plotly')" + "GPy.plotting.change_plotting_library('matplotlib')" ] }, { diff --git a/GPy/models_basic.ipynb b/GPy/models_basic.ipynb index 5f3fa15..5ba430b 100644 --- a/GPy/models_basic.ipynb +++ b/GPy/models_basic.ipynb @@ -2527,7 +2527,7 @@ } ], "source": [ - "GPy.plotting.change_plotting_library('plotly')\n", + "GPy.plotting.change_plotting_library('matplotlib')\n", "fig = m.plot(plot_density=True)\n", "GPy.plotting.show(fig, filename='gpy_sparse_gp_example')" ] diff --git a/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md b/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md index 19103a9..5136c42 100644 --- a/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md +++ b/backlog/bugs/2026-10-24_gpy-plotly-upstream-issue.md @@ -1,7 +1,7 @@ --- id: "2026-10-24_gpy-plotly-upstream-issue" title: "File GPy issue for Plotly online iplot and string marker sizes" -status: "Ready" +status: "Completed" priority: "Medium" created: "2026-10-24" last_updated: "2026-10-24" @@ -32,8 +32,8 @@ Link the issue from CIP-0002. Do not patch GPy from this repository. ## Acceptance Criteria -- [ ] GitHub issue exists on SheffieldML/GPy covering iplot and string marker sizes -- [ ] CIP-0002 References section links to that issue +- [x] GitHub issue exists on SheffieldML/GPy covering iplot and string marker sizes +- [x] CIP-0002 References section links to that issue ## Implementation Notes @@ -42,6 +42,7 @@ Track as a GPy issue or CIP against SheffieldML/GPy. Notebook-side workaround is ## Related - CIP: 0002 +- Issue: [SheffieldML/GPy#1243](https://github.com/SheffieldML/GPy/issues/1243) - PRs: - Documentation: SheffieldML/GPy plotting backends @@ -50,3 +51,5 @@ Track as a GPy issue or CIP against SheffieldML/GPy. Notebook-side workaround is ### 2026-10-24 Task created as Ready after CIP-0002 was accepted. + +Opened https://github.com/SheffieldML/GPy/issues/1243 and linked it from CIP-0002. diff --git a/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md b/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md index fd1d188..fb56e4d 100644 --- a/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md +++ b/backlog/features/2026-10-24_gpy-tutorials-matplotlib-backend.md @@ -1,7 +1,7 @@ --- id: "2026-10-24_gpy-tutorials-matplotlib-backend" title: "Switch GPy tutorial plotting from Plotly online to matplotlib" -status: "Ready" +status: "Completed" priority: "High" created: "2026-10-24" last_updated: "2026-10-24" @@ -27,9 +27,9 @@ In `basic_gp.ipynb`, `models_basic.ipynb`, and `MagnificationFactor.ipynb`, repl ## Acceptance Criteria -- [ ] No `GPy/*.ipynb` cell calls `change_plotting_library('plotly')` -- [ ] The three named notebooks select matplotlib -- [ ] Nearby markdown does not tell readers to use Plotly Cloud / Chart Studio +- [x] No `GPy/*.ipynb` cell calls `change_plotting_library('plotly')` +- [x] The three named notebooks select matplotlib +- [x] Nearby markdown does not tell readers to use Plotly Cloud / Chart Studio ## Implementation Notes @@ -46,3 +46,5 @@ Do not patch GPy from this repository. GPy-side `plotly.plotly.iplot` and string ### 2026-10-24 Task created as Ready after CIP-0002 was accepted. + +Switched `basic_gp.ipynb`, `models_basic.ipynb`, and `MagnificationFactor.ipynb` from `'plotly'` to `'matplotlib'`. No tutorial markdown required Chart Studio. diff --git a/cip/cip0002.md b/cip/cip0002.md index 51f777c..26586a6 100644 --- a/cip/cip0002.md +++ b/cip/cip0002.md @@ -3,7 +3,7 @@ author: Neil Lawrence created: "2026-10-24" id: "0002" last_updated: "2026-10-24" -status: Accepted +status: In Progress compressed: false related_requirements: [] related_cips: @@ -24,7 +24,7 @@ title: Plotting in GPy tutorials - [x] Proposed - Initial idea documented - [x] Accepted - Approved, ready to start work -- [ ] In Progress - Actively being implemented +- [x] In Progress - Actively being implemented - [ ] Implemented - Work complete, awaiting verification - [ ] Closed - Verified and complete - [ ] Rejected - Will not be implemented @@ -119,8 +119,8 @@ credentials in CI. - [x] Diagnose Plotly failures against GPy 1.15.0 - [x] Accept CIP after review -- [ ] Switch tutorial library cells -- [ ] File GPy issue for online `iplot` + string marker sizes +- [x] Switch tutorial library cells +- [x] File GPy issue for online `iplot` + string marker sizes - [ ] Green execute with CIP-0001 + CIP-0003 - [ ] Close CIP @@ -129,3 +129,4 @@ credentials in CI. - `GPy/basic_gp.ipynb`, `GPy/models_basic.ipynb`, `GPy/MagnificationFactor.ipynb` - GPy: `GPy/plotting/__init__.py` (`'plotly'` → `PlotlyPlotsOnline`) - GPy: `GPy/plotting/plotly_dep/plot_definitions.py`, `defaults.py` +- Upstream: https://github.com/SheffieldML/GPy/issues/1243 From fc4cae8a85d4a529f70ef6a5d8da075c91cd0b68 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 17:15:39 +0100 Subject: [PATCH 4/9] CIP-0001: Finish tutorial execute compatibility on current libraries. Fix matplotlib bar shapes, Python 2 stdlib (cPickle/urllib), ipywidgets, seaborn/parameter names, and skip the dead East Timor download so required notebooks run under nbclient. --- GPy/GPyCrashCourse.ipynb | 73 +++++++++++++++------------ GPy/Integral_kernel.ipynb | 9 ++-- GPy/MagnificationFactor.ipynb | 19 ++----- GPy/Poisson regression tutorial.ipynb | 4 +- GPy/basic_gp.ipynb | 16 +----- GPy/heteroscedastic_regression.ipynb | 2 +- GPy/sampling_hmc.ipynb | 13 ++--- cip/cip0001.md | 2 +- 8 files changed, 60 insertions(+), 78 deletions(-) diff --git a/GPy/GPyCrashCourse.ipynb b/GPy/GPyCrashCourse.ipynb index 4b8a909..e893153 100644 --- a/GPy/GPyCrashCourse.ipynb +++ b/GPy/GPyCrashCourse.ipynb @@ -23,6 +23,7 @@ "outputs": [], "source": [ "import GPy, numpy as np\n", + "import pods\n", "from matplotlib import pyplot as plt\n", "np.random.seed(1)\n", "%matplotlib inline\n", @@ -1498,8 +1499,7 @@ ], "source": [ "# Download the marathon data from yesterday and plot\n", - "GPy.util.datasets.authorize_download = lambda x: True # prevents requesting authorization for download.\n", - "data = GPy.util.datasets.olympic_marathon_men()\n", + "data = pods.datasets.olympic_marathon_men()\n", "X = data['X']\n", "Y = data['Y']\n", "\n", @@ -1630,14 +1630,17 @@ "source": [ "from matplotlib.patches import Polygon\n", "from matplotlib.collections import PatchCollection\n", - "import cPickle as pickle\n", - "import urllib\n", + "import pickle\n", + "from urllib.request import urlretrieve\n", "\n", - "urllib.urlretrieve('http://staffwww.dcs.sheffield.ac.uk/people/M.Zwiessele/gpss/lab2/EastTimor.pickle', 'EastTimor2.pickle')\n", - "\n", - "#Load the data\n", - "with open(\"./EastTimor2.pickle\",\"rb\") as f:\n", - " Xeast,Yeast,polygons = pickle.load(f)" + "east_timor_available = False\n", + "try:\n", + " urlretrieve('http://staffwww.dcs.sheffield.ac.uk/people/M.Zwiessele/gpss/lab2/EastTimor.pickle', 'EastTimor2.pickle')\n", + " with open(\"./EastTimor2.pickle\", \"rb\") as f:\n", + " Xeast, Yeast, polygons = pickle.load(f, encoding=\"latin1\")\n", + " east_timor_available = True\n", + "except Exception as exc:\n", + " print(\"East Timor map data is no longer hosted; skipping this demonstration:\", exc)\n" ] }, { @@ -1676,22 +1679,23 @@ } ], "source": [ - "ep = GPy.inference.latent_function_inference.EP()\n", - "kern = GPy.kern.RBF(2, lengthscale=0.6, ARD=True)\n", - "bern = GPy.likelihoods.Bernoulli()\n", - "m_ep = GPy.core.GP(Xeast, Yeast, kernel=kern, inference_method=ep, likelihood=bern)\n", + "if east_timor_available:\n", + " ep = GPy.inference.latent_function_inference.EP()\n", + " kern = GPy.kern.RBF(2, lengthscale=0.6, ARD=True)\n", + " bern = GPy.likelihoods.Bernoulli()\n", + " m_ep = GPy.core.GP(Xeast, Yeast, kernel=kern, inference_method=ep, likelihood=bern)\n", "\n", "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "for p in polygons:\n", - " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", - "ax.set_xlim(124.,127.5)\n", - "ax.set_ylim(-9.6,-8.1)\n", - "ax.set_xlabel(\"longitude\")\n", - "ax.set_ylabel(\"latitude\")\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(111)\n", + " for p in polygons:\n", + " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", + " ax.set_xlim(124.,127.5)\n", + " ax.set_ylim(-9.6,-8.1)\n", + " ax.set_xlabel(\"longitude\")\n", + " ax.set_ylabel(\"latitude\")\n", "\n", - "m_ep.plot(ax=ax)" + " m_ep.plot(ax=ax)\n" ] }, { @@ -1709,7 +1713,8 @@ }, "outputs": [], "source": [ - "m_ep.optimize()" + "if east_timor_available:\n", + " m_ep.optimize()\n" ] }, { @@ -1741,16 +1746,17 @@ } ], "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "for p in polygons:\n", - " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", - "ax.set_xlim(124.,127.5)\n", - "ax.set_ylim(-9.6,-8.1)\n", - "ax.set_xlabel(\"longitude\")\n", - "ax.set_ylabel(\"latitude\")\n", + "if east_timor_available:\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(111)\n", + " for p in polygons:\n", + " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", + " ax.set_xlim(124.,127.5)\n", + " ax.set_ylim(-9.6,-8.1)\n", + " ax.set_xlabel(\"longitude\")\n", + " ax.set_ylabel(\"latitude\")\n", "\n", - "m_ep.plot(ax=ax)\n" + " m_ep.plot(ax=ax)\n" ] }, { @@ -1801,7 +1807,8 @@ } ], "source": [ - "m_ep" + "if east_timor_available:\n", + " m_ep\n" ] } ], diff --git a/GPy/Integral_kernel.ipynb b/GPy/Integral_kernel.ipynb index a0443e8..d541d9f 100644 --- a/GPy/Integral_kernel.ipynb +++ b/GPy/Integral_kernel.ipynb @@ -18,11 +18,12 @@ "def plot_hist(bins,bin_ranges,errs,alpha):\n", " \"\"\"Plots a histogram\"\"\"\n", " assert len(bins)==(len(bin_ranges)-1)\n", - " yvals = bins/(np.diff(bin_ranges)[:,None])\n", - " evals = 1.96*errs/(np.diff(bin_ranges)[:,None]) #the error is in the area, which is 'spread'\n", - " plt.bar(bin_ranges[0:-1],yvals,np.diff(bin_ranges),lw=1,alpha=alpha,color='#ccccff')\n", + " widths = np.diff(bin_ranges).astype(float)\n", + " yvals = np.asarray(bins, dtype=float).reshape(-1) / widths\n", + " evals = 1.96 * np.broadcast_to(np.asarray(errs, dtype=float).reshape(-1), yvals.shape) / widths\n", + " plt.bar(bin_ranges[0:-1], yvals, widths, linewidth=1, alpha=alpha, color='#ccccff')\n", " midpoints = .5*(bin_ranges[0:-1] + bin_ranges[1:])\n", - " plt.errorbar(midpoints,yvals,evals,elinewidth=1,capthick=1,color='black',alpha=alpha,lw=0)\n", + " plt.errorbar(midpoints, yvals, evals, elinewidth=1, capthick=1, color='black', alpha=alpha, linewidth=0)\n", " \n", "def histogram_data(age,height,weight,age_step,height_step):\n", " inputs = []\n", diff --git a/GPy/MagnificationFactor.ipynb b/GPy/MagnificationFactor.ipynb index c9ffc4a..86d2978 100644 --- a/GPy/MagnificationFactor.ipynb +++ b/GPy/MagnificationFactor.ipynb @@ -36,6 +36,8 @@ ], "source": [ "import numpy as np\n", + "if not hasattr(np, \"NaN\"):\n", + " np.NaN = np.nan # pods mocap still uses np.NaN\n", "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import GPy # import GPy package\n", @@ -219,13 +221,7 @@ } ], "source": [ - "figure = GPy.plotting.plotting_library().figure(1, 2, \n", - " shared_yaxes=True,\n", - " shared_xaxes=True,\n", - " subplot_titles=('Latent Space', \n", - " 'Magnification',\n", - " )\n", - " )\n", + "figure = GPy.plotting.plotting_library().figure(1, 2)\n", "\n", "canvas = m_gplvm.plot_latent(labels=labels, figure=figure, col=(1), legend=False)\n", "canvas = m_gplvm.plot_magnification(labels=labels, figure=figure, col=(2), legend=False)\n", @@ -264,14 +260,7 @@ } ], "source": [ - "figure = GPy.plotting.plotting_library().figure(1, 3, \n", - " shared_yaxes=True,\n", - " shared_xaxes=True,\n", - " subplot_titles=('Full Magnification', \n", - " 'Magnification Mean',\n", - " 'Magnification Variance'\n", - " )\n", - " )\n", + "figure = GPy.plotting.plotting_library().figure(1, 3)\n", "\n", "canvas = m_gplvm.plot_magnification(figure=figure,\n", " covariance=True, \n", diff --git a/GPy/Poisson regression tutorial.ipynb b/GPy/Poisson regression tutorial.ipynb index c1ad325..8d21ce4 100644 --- a/GPy/Poisson regression tutorial.ipynb +++ b/GPy/Poisson regression tutorial.ipynb @@ -371,9 +371,9 @@ "pb.plot(X, intensities)\n", "pb.title('Real underlying intensities (rate at which buses are issued)')\n", "\n", - "Y = np.array([[sp.random.poisson(intensity) for intensity in intensities]]).T\n", + "Y = np.random.poisson(intensities.ravel())[:, None]\n", "pb.figure()\n", - "pb.bar(X, Y, width=0.5)\n", + "pb.bar(X.ravel(), Y.ravel(), width=0.5)\n", "pb.xlabel('Time')\n", "pb.ylabel('Buses in half an hour')\n", "pb.title('Observed (noisey) counts of buses throughout day')" diff --git a/GPy/basic_gp.ipynb b/GPy/basic_gp.ipynb index 83c6305..0f30838 100644 --- a/GPy/basic_gp.ipynb +++ b/GPy/basic_gp.ipynb @@ -615,13 +615,7 @@ ], "source": [ "slices = [-1, 0, 1.5]\n", - "figure = GPy.plotting.plotting_library().figure(3, 1, \n", - " shared_xaxes=True,\n", - " subplot_titles=('slice at -1', \n", - " 'slice at 0', \n", - " 'slice at 1.5', \n", - " )\n", - " )\n", + "figure = GPy.plotting.plotting_library().figure(3, 1)\n", "for i, y in zip(range(3), slices):\n", " canvas = m.plot(figure=figure, fixed_inputs=[(1,y)], row=(i+1), plot_data=False)\n", "GPy.plotting.show(canvas, filename='basic_gp_regression_notebook_slicing')" @@ -677,13 +671,7 @@ ], "source": [ "slices = [-1, 0, 1.5]\n", - "figure = GPy.plotting.plotting_library().figure(3, 1, \n", - " shared_xaxes=True,\n", - " subplot_titles=('slice at -1', \n", - " 'slice at 0', \n", - " 'slice at 1.5', \n", - " )\n", - " )\n", + "figure = GPy.plotting.plotting_library().figure(3, 1)\n", "for i, y in zip(range(3), slices):\n", " canvas = m.plot(figure=figure, fixed_inputs=[(0,y)], row=(i+1), plot_data=False)\n", "GPy.plotting.show(canvas, filename='basic_gp_regression_notebook_slicing_vertical')" diff --git a/GPy/heteroscedastic_regression.ipynb b/GPy/heteroscedastic_regression.ipynb index 378b4d4..be5ddf4 100644 --- a/GPy/heteroscedastic_regression.ipynb +++ b/GPy/heteroscedastic_regression.ipynb @@ -275,7 +275,7 @@ } ], "source": [ - "from IPython.html.widgets import *\n", + "from ipywidgets import interact\n", "interact(noise_effect, noise=(0.1,2.))" ] }, diff --git a/GPy/sampling_hmc.ipynb b/GPy/sampling_hmc.ipynb index 06474bb..e65b598 100644 --- a/GPy/sampling_hmc.ipynb +++ b/GPy/sampling_hmc.ipynb @@ -662,10 +662,10 @@ ], "source": [ "df = pd.DataFrame(t, columns=m.parameter_names_flat())\n", - "ax = sns.kdeplot(df['rbf.variance'],\n", - " color=\"b\", shade=True, shade_lowest=False)\n", - "ax = sns.kdeplot(df['rbf.lengthscale'],\n", - " color=\"r\", shade=True, shade_lowest=False)" + "var_col = next(c for c in df.columns if c.endswith('rbf.variance'))\n", + "ls_col = next(c for c in df.columns if c.endswith('rbf.lengthscale'))\n", + "ax = sns.kdeplot(df[var_col], color=\"b\", fill=True)\n", + "ax = sns.kdeplot(df[ls_col], color=\"r\", fill=True)" ] }, { @@ -688,10 +688,7 @@ ], "source": [ "sns.set(style=\"white\", color_codes=True)\n", - "_ = sns.jointplot(data=df, x='rbf.variance', y='rbf.lengthscale', kind=\"hex\", \n", - " marginal_kws=dict(kde=True, hist=True, kde_kws=dict(shade=False)),\n", - " stat_func=None\n", - " )" + "_ = sns.jointplot(data=df, x=var_col, y=ls_col, kind=\"hex\")" ] }, { diff --git a/cip/cip0001.md b/cip/cip0001.md index 72bfe7b..befcaf5 100644 --- a/cip/cip0001.md +++ b/cip/cip0001.md @@ -135,7 +135,7 @@ non-Plotly notebooks that failed only on `print` / `xrange` / NumPy / matplotlib - [x] Accept CIP after review - [x] Python 3 + kernelspec - [x] NumPy 2, matplotlib, config, climin guards -- [ ] Green execute with CIP-0002 + CIP-0003 +- [ ] Green execute with CIP-0002 + CIP-0003 (local nbclient run passed required notebooks; Actions still pending) - [ ] Close CIP ## References From 2f8f24df71ee90c1b89afbf8200638fa42473898 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 17:15:39 +0100 Subject: [PATCH 5/9] CIP-0003: Add nbclient runner and GitHub Actions for GPy tutorials. Pin the job to the GPy 1.15.0 candidate (PR 1235), skip climin-only notebooks when that extra is missing, and show the workflow badge on the README. --- .github/workflows/gpy-tutorials.yml | 66 ++++++++++ README.md | 2 +- .../2026-10-24_gpy-tutorials-ci-runner.md | 12 +- cip/cip0003.md | 9 +- scripts/run_gpy_tutorials.py | 116 ++++++++++++++++++ scripts/run_gpy_tutorials.sh | 12 ++ 6 files changed, 207 insertions(+), 10 deletions(-) create mode 100644 .github/workflows/gpy-tutorials.yml create mode 100755 scripts/run_gpy_tutorials.py create mode 100755 scripts/run_gpy_tutorials.sh diff --git a/.github/workflows/gpy-tutorials.yml b/.github/workflows/gpy-tutorials.yml new file mode 100644 index 0000000..9a4e5dd --- /dev/null +++ b/.github/workflows/gpy-tutorials.yml @@ -0,0 +1,66 @@ +# Execute GPy/*.ipynb against a pinned GPy (CIP-0003). +# +# GPy pin: 1.15.0 candidate (SheffieldML/GPy#1235, branch release/1.15.0). +# Switch the install line to `gpy==1.15.0` once that release is on PyPI. +# Do not float on GPy devel. +# +# climin is not installed here: SVI.ipynb and optimizer-implementation.ipynb +# are skipped by scripts/run_gpy_tutorials.py (optional extra). +name: GPy tutorials + +on: + push: + branches: [master, main] + paths: + - "GPy/**" + - "scripts/run_gpy_tutorials.py" + - "scripts/run_gpy_tutorials.sh" + - ".github/workflows/gpy-tutorials.yml" + pull_request: + paths: + - "GPy/**" + - "scripts/run_gpy_tutorials.py" + - "scripts/run_gpy_tutorials.sh" + - ".github/workflows/gpy-tutorials.yml" + workflow_dispatch: + +permissions: + contents: read + +jobs: + execute: + runs-on: ubuntu-latest + timeout-minutes: 90 + env: + MPLBACKEND: Agg + MPLCONFIGDIR: ${{ github.workspace }}/.mplconfig + PYTHONUNBUFFERED: "1" + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install GPy 1.15.0 candidate, plotting, Jupyter + run: | + python -m pip install --upgrade pip + python -m pip install "gpy[plotting] @ git+https://github.com/SheffieldML/GPy.git@refs/pull/1235/head" + python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas + python -c "import GPy; print('GPy', getattr(GPy, '__version__', '?'))" + + - name: Register kernel + run: python -m ipykernel install --user --name python3 + + - name: Execute GPy tutorials + run: bash scripts/run_gpy_tutorials.sh --output-dir executed-gpy + + - name: Upload executed notebooks + if: failure() + uses: actions/upload-artifact@v4 + with: + name: executed-gpy-notebooks + path: executed-gpy/ + if-no-files-found: ignore diff --git a/README.md b/README.md index 2ddf918..9ea454d 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ We have split the notebooks into groups. [Computational Biology Notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/compbio/index.ipynb) Give some examples of work completed in different projects and implemented in GPy. -[GPy Tutorial Notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb) These notebooks give short tutorials on various features of GPy. +[GPy Tutorial Notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb) These notebooks give short tutorials on various features of GPy. [![GPy tutorials](https://github.com/SheffieldML/notebook/actions/workflows/gpy-tutorials.yml/badge.svg)](https://github.com/SheffieldML/notebook/actions/workflows/gpy-tutorials.yml) [Lab Classes](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/lab_classes/index.ipynb) Gives a summary of notebooks that have been used in lab classes for teaching using our software. They include notebooks for undergraduate, postgraduate and summer school courses on Gaussian processes and machine learning. diff --git a/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md b/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md index f59ac05..24267e5 100644 --- a/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md +++ b/backlog/infrastructure/2026-10-24_gpy-tutorials-ci-runner.md @@ -1,7 +1,7 @@ --- id: "2026-10-24_gpy-tutorials-ci-runner" title: "Add nbclient runner and GitHub Actions for GPy tutorials" -status: "Ready" +status: "In Progress" priority: "High" created: "2026-10-24" last_updated: "2026-10-24" @@ -31,10 +31,10 @@ Skip `SVI.ipynb` and `optimizer-implementation.ipynb` when climin is not install ## Acceptance Criteria -- [ ] Runner script executes `GPy/*.ipynb` with documented climin skip -- [ ] GitHub Actions workflow pins GPy and runs the script on this repository -- [ ] README GPy tutorial section shows the workflow badge -- [ ] Job does not use Plotly Cloud credentials +- [x] Runner script executes `GPy/*.ipynb` with documented climin skip +- [x] GitHub Actions workflow pins GPy and runs the script on this repository +- [x] README GPy tutorial section shows the workflow badge +- [x] Job does not use Plotly Cloud credentials ## Implementation Notes @@ -51,3 +51,5 @@ Run with cwd such that `import GPy` resolves the library, not this repo's `GPy/` ### 2026-10-24 Task created as Ready after CIP-0003 was accepted. + +Added `scripts/run_gpy_tutorials.py` / `.sh` and `.github/workflows/gpy-tutorials.yml` (Python 3.12, GPy from SheffieldML/GPy#1235, climin skip). Local execute against the installed GPy passed required notebooks (climin pair skipped). First Actions green run is still pending. diff --git a/cip/cip0003.md b/cip/cip0003.md index 07fa15e..83a8465 100644 --- a/cip/cip0003.md +++ b/cip/cip0003.md @@ -3,7 +3,7 @@ author: Neil Lawrence created: "2026-10-24" id: "0003" last_updated: "2026-10-24" -status: Accepted +status: In Progress compressed: false related_requirements: [] related_cips: @@ -23,7 +23,7 @@ title: Execute GPy tutorials in CI - [x] Proposed - Initial idea documented - [x] Accepted - Approved, ready to start work -- [ ] In Progress - Actively being implemented +- [x] In Progress - Actively being implemented - [ ] Implemented - Work complete, awaiting verification - [ ] Closed - Verified and complete - [ ] Rejected - Will not be implemented @@ -111,9 +111,10 @@ check when they edit `GPy/` tutorials. - [x] Manual execute against GPy 1.15.0; inventory failures - [x] Accept CIP after review -- [ ] Runner script + Actions workflow +- [x] Runner script + Actions workflow - [ ] Green run on GPy 1.15.0 candidate -- [ ] README badge; close CIP +- [x] README badge +- [ ] Close CIP ## References diff --git a/scripts/run_gpy_tutorials.py b/scripts/run_gpy_tutorials.py new file mode 100755 index 0000000..bdb25c1 --- /dev/null +++ b/scripts/run_gpy_tutorials.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""Execute GPy tutorial notebooks (CIP-0003). + +Runs every ``GPy/*.ipynb`` with nbclient, ``MPLBACKEND=Agg``, and the notebook +directory as cwd so ``import GPy`` resolves the library, not this folder. + +Skip policy: ``SVI.ipynb`` and ``optimizer-implementation.ipynb`` are skipped +when climin is not installed (optional extra, not a GPy dependency). +""" +from __future__ import annotations + +import argparse +import os +import sys +import traceback +from pathlib import Path + +import nbformat +from nbclient import NotebookClient +from nbclient.exceptions import CellExecutionError + +ROOT = Path(__file__).resolve().parents[1] +TUTORIAL_DIR = ROOT / "GPy" +CLIMIN_OPTIONAL = frozenset({"SVI.ipynb", "optimizer-implementation.ipynb"}) +DEFAULT_TIMEOUT = 900 + + +def climin_available() -> bool: + try: + import climin # noqa: F401 + except ImportError: + return False + return True + + +def execute_notebook(path: Path, timeout: int, output_dir: Path | None) -> None: + nb = nbformat.read(path, as_version=4) + client = NotebookClient( + nb, + timeout=timeout, + kernel_name="python3", + resources={"metadata": {"path": str(path.parent)}}, + ) + try: + client.execute() + except CellExecutionError: + if output_dir is not None: + output_dir.mkdir(parents=True, exist_ok=True) + failed = output_dir / path.name + nbformat.write(nb, failed) + print(f" wrote executed notebook to {failed}", file=sys.stderr) + raise + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--timeout", + type=int, + default=DEFAULT_TIMEOUT, + help="Per-notebook timeout in seconds (default: %(default)s)", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=None, + help="Write executed notebooks here on failure (CI artifact dir)", + ) + args = parser.parse_args() + + os.environ.setdefault("MPLBACKEND", "Agg") + + notebooks = sorted(TUTORIAL_DIR.glob("*.ipynb")) + if not notebooks: + print(f"No notebooks found in {TUTORIAL_DIR}", file=sys.stderr) + return 1 + + have_climin = climin_available() + print(f"climin installed: {have_climin}", flush=True) + print( + f"GPy pin check happens in the environment; executing {len(notebooks)} notebooks", + flush=True, + ) + + failed: list[str] = [] + skipped: list[str] = [] + passed: list[str] = [] + + for path in notebooks: + if path.name in CLIMIN_OPTIONAL and not have_climin: + print(f"SKIP {path.name} (climin not installed; optional extra)", flush=True) + skipped.append(path.name) + continue + print(f"RUN {path.name}", flush=True) + try: + execute_notebook(path, timeout=args.timeout, output_dir=args.output_dir) + except Exception as exc: + print(f"FAIL {path.name}: {exc}", file=sys.stderr, flush=True) + traceback.print_exc() + failed.append(path.name) + else: + print(f"PASS {path.name}", flush=True) + passed.append(path.name) + + print(flush=True) + print(f"passed={len(passed)} skipped={len(skipped)} failed={len(failed)}", flush=True) + if skipped: + print("skipped:", ", ".join(skipped), flush=True) + if failed: + print("failed:", ", ".join(failed), file=sys.stderr, flush=True) + return 1 + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/run_gpy_tutorials.sh b/scripts/run_gpy_tutorials.sh new file mode 100755 index 0000000..5674203 --- /dev/null +++ b/scripts/run_gpy_tutorials.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Execute GPy/*.ipynb (CIP-0003). +# +# Skip SVI.ipynb and optimizer-implementation.ipynb when climin is not +# installed. GPy version is pinned by the caller (CI workflow or local env). +set -euo pipefail + +ROOT="$(cd "$(dirname "$0")/.." && pwd)" +export MPLBACKEND="${MPLBACKEND:-Agg}" +export PYTHONUNBUFFERED="${PYTHONUNBUFFERED:-1}" + +exec python3 "$ROOT/scripts/run_gpy_tutorials.py" "$@" From ef5a6f1501a7987e5c3bf2d7a3fd94b1295f1886 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 17:54:57 +0100 Subject: [PATCH 6/9] CIP-0004: Keep GPy tutorials as source cells with Colab badges. Strip outputs on commit, open each tutorial from this repository in Colab, and finish the execute path (East Timor mirror, climin SVI shim, Actions with climin plus an informational latest-GPy job) so the set can gate GPy 1.15. --- .github/workflows/gpy-tutorials.yml | 54 +- .pre-commit-config.yaml | 7 + GPy/GPyCrashCourse.ipynb | 1465 +- GPy/Integral_kernel.ipynb | 164 +- GPy/MagnificationFactor.ipynb | 265 +- GPy/ParametricNonParametricInference.ipynb | 294 +- GPy/Poisson regression tutorial.ipynb | 363 +- GPy/SVI.ipynb | 24492 +-------- GPy/Space_time_effects.ipynb | 224 +- GPy/basic_classification.ipynb | 45792 +--------------- GPy/basic_gp.ipynb | 456 +- GPy/basic_kernels.ipynb | 12472 +---- GPy/config.ipynb | 11 +- GPy/coregionalized_regression_tutorial.ipynb | 277 +- GPy/heteroscedastic_regression.ipynb | 251 +- GPy/index.ipynb | 39 +- GPy/models_basic.ipynb | 2260 +- GPy/multiple outputs.ipynb | 604 +- GPy/optimizer-implementation.ipynb | 200 +- GPy/sampling_hmc.ipynb | 868 +- GPy/sparse_gp_regression.ipynb | 9128 +-- README.md | 2 +- .../2026-10-24_gpy-colab-badges.md | 48 + .../2026-10-24_gpy-nbstripout-pre-commit.md | 47 + cip/cip0001.md | 3 +- cip/cip0003.md | 8 +- cip/cip0004.md | 94 + scripts/run_gpy_tutorials.py | 22 +- scripts/run_gpy_tutorials.sh | 4 +- 29 files changed, 1128 insertions(+), 98786 deletions(-) create mode 100644 .pre-commit-config.yaml create mode 100644 backlog/documentation/2026-10-24_gpy-colab-badges.md create mode 100644 backlog/infrastructure/2026-10-24_gpy-nbstripout-pre-commit.md create mode 100644 cip/cip0004.md diff --git a/.github/workflows/gpy-tutorials.yml b/.github/workflows/gpy-tutorials.yml index 9a4e5dd..576fed6 100644 --- a/.github/workflows/gpy-tutorials.yml +++ b/.github/workflows/gpy-tutorials.yml @@ -2,10 +2,16 @@ # # GPy pin: 1.15.0 candidate (SheffieldML/GPy#1235, branch release/1.15.0). # Switch the install line to `gpy==1.15.0` once that release is on PyPI. -# Do not float on GPy devel. +# Do not float on GPy devel for the release-gate job. # -# climin is not installed here: SVI.ipynb and optimizer-implementation.ipynb -# are skipped by scripts/run_gpy_tutorials.py (optional extra). +# East Timor in GPyCrashCourse is loaded from datasets_mirror (PyPI pods 0.1.18 +# does not yet ship pods.datasets.east_timor). +# +# climin is installed so SVI.ipynb and optimizer-implementation.ipynb are +# required (CIP-0003 skip only applies when the extra is absent). +# +# execute-latest is a second job against current PyPI GPy. It is not the 1.15 +# release gate; keep that pin in execute. name: GPy tutorials on: @@ -48,7 +54,7 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install "gpy[plotting] @ git+https://github.com/SheffieldML/GPy.git@refs/pull/1235/head" - python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas + python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas climin python -c "import GPy; print('GPy', getattr(GPy, '__version__', '?'))" - name: Register kernel @@ -64,3 +70,43 @@ jobs: name: executed-gpy-notebooks path: executed-gpy/ if-no-files-found: ignore + + execute-latest: + # Informational: current PyPI GPy, not the 1.15 release gate. + # continue-on-error keeps a latest-GPy regression from blocking 1.15. + continue-on-error: true + runs-on: ubuntu-latest + timeout-minutes: 90 + env: + MPLBACKEND: Agg + MPLCONFIGDIR: ${{ github.workspace }}/.mplconfig + PYTHONUNBUFFERED: "1" + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install latest PyPI GPy, plotting, Jupyter + run: | + python -m pip install --upgrade pip + python -m pip install "gpy[plotting]" + python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas climin + python -c "import GPy; print('GPy', getattr(GPy, '__version__', '?'))" + + - name: Register kernel + run: python -m ipykernel install --user --name python3 + + - name: Execute GPy tutorials + run: bash scripts/run_gpy_tutorials.sh --output-dir executed-gpy-latest + + - name: Upload executed notebooks + if: failure() + uses: actions/upload-artifact@v4 + with: + name: executed-gpy-latest-notebooks + path: executed-gpy-latest/ + if-no-files-found: ignore diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..a142a42 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,7 @@ +# Strip executed outputs from the GPy tutorial notebooks in this repo. +repos: + - repo: https://github.com/kynan/nbstripout + rev: 0.9.1 + hooks: + - id: nbstripout + files: ^GPy/.*\.ipynb$ diff --git a/GPy/GPyCrashCourse.ipynb b/GPy/GPyCrashCourse.ipynb index e893153..40829d4 100644 --- a/GPy/GPyCrashCourse.ipynb +++ b/GPy/GPyCrashCourse.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## GPy crash course" + "## GPy crash course\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/GPyCrashCourse.ipynb)\n" ] }, { @@ -16,10 +18,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import GPy, numpy as np\n", @@ -48,32 +48,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "Model: GP regression
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\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve
Gaussian_noise.variance 1.0 +ve
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indexGP_regression.rbf.lengthscaleconstraintspriors
[0] 1.00000000 +ve
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qcxVDho3Gp53Y0odjTR2K1T0E3EPIzS0iFg7gr9+Cr2YD/r3fgI5RVHQya9eu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\nUVpaylNPPcW6det59NFHycvLZdiwfKq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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "_ = m.plot()" ] @@ -331,59 +161,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed properly. Did you enable the widgetsnbextension? If not, then run \"jupyter nbextension enable --py --sys-prefix widgetsnbextension\"\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
\n", - "Objective: -151.496450286
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "
indexGP_regression.rbf.lengthscaleconstraintspriors
[0] 1.00000000 +ve
\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
rbf.variance 0.130864941346 +ve
rbf.lengthscale 1.36805842166 +ve
Gaussian_noise.variance0.010323494102 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.optimize(messages=1, ipython_notebook=True) # Messages indicates you wish to see the progress of the optimizer, needs ipywidgets to be installed\n", "m" @@ -391,22 +171,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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IhYWFjB8/npiYGJ555hmcTic+n499+/a1aq54OvQ4D6oRnVZDZa2DWINWDkvI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\n/tQhDM1P53+XfMj2vYe57/F3uOuKU5k8suu9sN7Q2trO926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NX58x7SucMe2rHTWz3/F4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\ncyOw93sQR9cAAAAASUVORK5CYII=\n", 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\n", - "Model: GP regression
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GP_regression. valueconstraintspriors
rbf.variance 0.130864941346 +ve
rbf.lengthscale 0.1 +ve
Gaussian_noise.variance0.010323494102 +ve
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\n", - "Model: GP regression
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GP_regression. valueconstraintspriors
rbf.variance 0.3 +ve
rbf.lengthscale 0.3 +ve
Gaussian_noise.variance0.010323494102 +ve
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\n", - "Model: GP regression
\n", - "Objective: -92.1828527437
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 2
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
rbf.variance 1.0 +ve
rbf.lengthscale 0.3 0.1,0.5
Gaussian_noise.variance 0.01 +ve fixed
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.Gaussian_noise[:] = 0.01\n", "m.Gaussian_noise.fix()\n", @@ -603,69 +234,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed properly. Did you enable the widgetsnbextension? If not, then run \"jupyter nbextension enable --py --sys-prefix widgetsnbextension\"\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
\n", - "Objective: -139.107192827
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\n", - "Number of Optimization Parameters: 2
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
rbf.variance 0.051783879034 +ve
rbf.lengthscale 0.499999991553 0.1,0.5
Gaussian_noise.variance 0.01 +ve fixed
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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82/Fs3ryZoqIiPvtsJ+XlOygpKUm43cbR0JXg3LjxpUitPr3rAMH0SbzzaX3k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\n1CIJrZTw2LPvcOCYmdx0M9ecP7TJ56FMiCZTMk888pt2fZeKjiEvw4J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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.optimize(messages=1, ipython_notebook=True) # Messages indicates you wish to see the progress of the optimizer\n", "_ = m.plot()\n", @@ -688,76 +259,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: reconstraining parameters GP_regression.rbf.lengthscale\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " /Users/alansaul/Work/Code/GPy/GPy/core/parameterization/priors.py:301: RuntimeWarning:divide by zero encountered in log\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
\n", - "Objective: 199.465650082
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraints priors
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve Ga(3.2, 2.1)
Gaussian_noise.variance 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Reset model\n", "m.unconstrain()\n", @@ -773,62 +277,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
\n", - "Objective: -150.515620364
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraints priors
rbf.variance 0.12881177115 +ve
rbf.lengthscale 1.36155433892 +ve Ga(3.2, 2.1)
Gaussian_noise.variance0.0103243560503 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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gxowZc0qNnBhK5OePjr1RBIPGQEEo1FfT1M6EkRkDvRTBIMbqiN53L3JKbkND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\nFJe2mPRcOWUEM88dGSqXFg8GhEBBoF3HXd+ZyMiiDF5+p5x31+ykYm8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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.optimize() # Messages indicates you wish to see the progress of the optimizer\n", "_ = m.plot()\n", @@ -851,62 +302,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: gp
\n", - "Objective: -151.496450286
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
gp. valueconstraintspriors
rbf.variance 0.130864941346 +ve
rbf.lengthscale 1.36805842166 +ve
Gaussian_noise.variance0.010323494102 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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IhYWFjB8/npiYGJ555hmcTic+n499+/a1aq54OvQ4D6oRnVZDZa2DWINWDkvI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\ncyOw93sQR9cAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "rbf_kern = GPy.kern.RBF(input_dim=1, lengthscale=1.0, variance=1.0)\n", "gauss = GPy.likelihoods.Gaussian(variance=1.0)\n", @@ -933,22 +331,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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S2e/g4GCSkpJwcXHBycnp2jqop59+Gp1Ox8qVK3nllVe4evUqvXr1on79+qSn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\nwHW33Ua7k092XbNm1SrGjBjByuXLWbxwIV98/jkpHtxvpdi7YwcHEhK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1sn5377kheRw0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PUMIF9SOOm0AAldPtC76/ccRsgF7m+h6Qto+hyCVbL5Vj8E9IMlgASg/51g6OD7DPmg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3n4+VNXmyjt9yi+/nlrTl9tt1cIyP+7uRn5amr2NjdWj+fL9yRdg6e7ZGVKumvYtK8KLpgdQj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\nLn/Rurv6iY2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dCD3fhqsePLsHOb4GNn0ACb9D8/uh9RCo3Kh0Y7kckPqHIaukn6DqNVDt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\nwZWVRf6iReS5CYugIMKaNCGsQQN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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# sample inputs and outputs from 2d model\n", "X2 = np.random.uniform(-3.,3.,(50,2))\n", @@ -981,10 +366,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "d = 1 # input dimension\n", @@ -1002,57 +385,18 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "
rbf. valueconstraintspriors
variance 1.0 +ve
lengthscale 0.2 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "k" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mrbf. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mvariance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mlengthscale\u001b[0;0m | 0.2 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(k)" ] @@ -1068,32 +412,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Osr9640p/fm8VxV4/N13Tmu42nVFU6dWxMTf0aklRiY8/v7fa6TjGQVagjOss3rCHhev3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\ncLHnRhq/X5kZ/MUc1d8GRxg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q1gvSEqILNLPsPT1ERAC+okK14269R+y9TRjqkuVSz8lRavb5LAWx3OYjW61m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\nWiaFMXzjEs2H/dW86/yEoCUXyHQE/Icm/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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.matshow(k.K(X))\n", "plt.colorbar()" @@ -1194,43 +469,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1msum. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.0 | +ve | \n", - " \u001b[1mMat32.variance \u001b[0;0m | 0.5 | +ve | \n", - " \u001b[1mMat32.lengthscale\u001b[0;0m | 0.2 | +ve | \n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "kern1 = GPy.kern.RBF(1, variance=1., lengthscale=2.)\n", "kern2 = GPy.kern.Matern32(1, variance=0.5, lengthscale=0.2)\n", @@ -1249,43 +490,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mmul. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.0 | +ve | \n", - " \u001b[1mMat32.variance \u001b[0;0m | 0.5 | +ve | \n", - " \u001b[1mMat32.lengthscale\u001b[0;0m | 0.2 | +ve | \n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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AVswIR74WyOuB7caYVb3vEJFbhxWRctWR+lZqGttIHZlA9tgU1+KYlZOBCOwr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\nPV1xjlg4nTOOWlDu5VgKiOll/o9FJNPmUkQuwHBXrYgsF5FXReR1Ebna5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peYI8heD1Jfrzb49pbsos2Lyg9SQ4twoO/Jn281O04619eP4/Q1AKFyhpofhQ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\noF9CxFzXC5+Nfxi3cXGryO8lRSIuGFt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MgEA/3acOT4Rd/eDWRVDS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\nTlZ5PBoMxH8OrNoJnE4AujYA3p0AZFy0b06zXDwHv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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# sample inputs and outputs from 2d model\n", "X2 = np.random.uniform(-3.,3.,(50,2))\n", @@ -1399,22 +580,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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zeP2U1bZhMBqwmPs/Y62nDp1qYPWGfRyvbAEgO83KrVeNZOolBWj7aGgr3jZV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\nZpYkB5SZmSXJAWVmZkn6L5P0tLT+tfGAAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "_ = m2.plot(fixed_inputs=[(1, 0.2)])" ] @@ -1428,22 +596,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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rnM6VJctonINS0X9iDDvbsp0wp45uPzsPNERSwGvqaW6PaXGuYMakgsiy3awy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\n6pIUD2swdwN7R+vJMiJAAWuBv0/1IIY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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "_ = m2.plot(fixed_inputs=[(0, 1.0)])" ] @@ -1471,32 +626,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Download the marathon data from yesterday and plot\n", "data = pods.datasets.olympic_marathon_men()\n", @@ -1517,10 +649,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "GPy.likelihoods.StudentT?" @@ -1535,64 +665,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: gp
\n", - "Objective: 0.155064820671
\n", - "Number of Parameters: 5
\n", - "Number of Optimization Parameters: 5
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
gp. valueconstraintspriors
sum.rbf.variance 0.247079340096 +ve
sum.rbf.lengthscale 18.0531700475 +ve
sum.bias.variance 5.77057683482 +ve
Student_T.t_scale2 0.00664234394947 +ve
Student_T.deg_free 2.27411786114 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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0hFPDPFNRFGVoUspsgOrVY3IciV3gqiYVoPrZ3hTm5ntfwLQcTj1mPp8+dfFE\nN2lIXrfOVy84jsoyP+/s6uSnf3hlQNbMcDKWxLYdtjeG8Xh9BPweTKuwTzmKoii9mZaNpok+PaaM\nZaMVWMxABahemtrjfLvu7yS7U8k/e8aRk2pYbzAVpT6uXXMcPo/Ohs0NPPzc1oKer7t13qnvwO31\n5CYvM7bEccZvAbeiKPsXy3LQNA2HvcN6KSOTW+aSLxWgusWSaW745d+JJNIcechMvvRvH0SfwGy9\nQiyYM40vfeqDAPzqiTf5wR33AhKQ1NWto7W1dcjn+jxuvH5vLvUTQHdpJA1zjFutKMr+yjAtNE1D\n07RcGnnStHAXuDxnxFu+708yls13f/UiLZ0JFswp4yurj8Xtmlqx+9jFczjzxEN46LmtPLsNrB/c\nhkdzWL/+foAhd+YVQuBx9/018HrcRJMmoYB3wPHxVBq3rudKlCiKovSXSmdwu1wIAYlUhoDPQypt\n4fEOvKbsy9S6Co8BKSU//cMrbNnZ0T1ctmzEW1tMtNWnLmZRbSUuXwnPbpWsX/8Aq1efz9q1aws6\nj65pGObgCRdNbXGiCZXlpyjK0HrWO7ldOinTwszYpDOFz20f8AHqd8+8yzOvNOB161x74TIqy4q3\nT5Jl2cTjKVIpAyM99kNmLl3jqvM+iFvY+CoXULbwxBGfqycj0EhncsN9ZsbGciClFvgqitItEjdI\n9ZsSsLvnsIUQWLakpSNOYJARmeEc0AHq+dd2s/6pLQgBV573IQ6aM62o5zcMk8NqKji0ugLbHp8F\nsX/8/QPs3vQAIClbeAK/f/wf1NXVFXweKQRdsRTvNUfZ2RIlY9m0dGZ/ySyVQKEoSreOSIqmjr5l\n0+xe1wjTskkYVsEJEnAAz0G929DJTx58CYCLVi7hmEWzi3p+23EI+ly5zLjSgIe0ZeNyFV7DzzAz\n+Dz57d20atXHAQjULOLXf3mb6mPP44STjyn4NT1uF43tCcpKAjiOZEdTGEdCMOjGLHCxnaIo+y/T\ncnDIjrb07DFn91rwlLEc/L7Ce09wgPag9nQluPnef2BaDh/70HxOP/7gor9GMplmVmUo9/WMaQFS\nIxjmk1JimRniSSOv46uqqli79mLOWXE4i+dXkrY1HtjQUHDauMftoqwkW1xW0wQujxtP9y9f73Il\nXdGUqoquKAeolJEBTSPo99HcEQeyaeW9d3aaVhoccVLVARegEkaGG+95gUgizREHz+CzZ3yg6Gud\npJR43Fq070ocAAAgAElEQVSfiudul453BJmByZRJbVUpQY+OVUDdPF0TXHHuhwj53bzybiuP/P29\ngl+7N7fLhdvVvbmYJnJzVF0xg66oqjyhKAeizlgKn9eNpgmMjEMiZWLZ2TVQxXBABSjbdvjh/Rtp\n2BNj3owSvnL+MbiKtE277TjEEymklQHbYk5lcMAxM6cFiMWTBfVmdCEJ+D3MnVFScKLF9DI/Xzx7\nKQD3PfEG7zWGC3r+kG3SNdJmtgikZUuSQ2T8KYqyf0v2mlsKBf3sbIkSTaaL9qH/gAlQUkru/vNr\nbN66h9KAh2svWkbQ7ynKuS3LxjJNDplXTs2sMmpmlRHwDTx3SdDLIfPKyZhpjDwWwqYMk5nTssNs\nuq4RGEH6+zGL57DyuIOwbMktD2wsSgaeW9dJGtnNxixJwTUAFUWZ+mzbGZAwVRLys3tPHM8I5toH\nk3eAEkJoQoiXhRB/HOLxHwshtgohNgshjixK64ro0Re28/iLO3DpGl9dcxyzKgb2cEbKMEwWzJ6W\nV2/M7dI5eF4F04JuYvFUn9p54WgSw9xbnt6xbcp67drr9+oj2h7jwpVLqJ1VSnNHgl/86dWCn9+f\ny6VjmBbxRBqXS0cKMWDTsc5oatjdMvMRTxZvzVXatAjH8pvLUxRlcJbt0NgWZUdzGE+/5C0hBNMr\nSkaUDDaYQnpQXwK2DPaAEGIlsFBKeQhwKXBnEdpWNP98u4W6R14D4ItnL2VRbWXRzu04koDPhV7g\nUOGM8iA1M0O5NUZSSkI+HaenLEgqzex+w4QBr5uMVXgPyOvWc7sA//XlXWzYnP9OvEOxHEkkaeLz\nZOemov22++iIJNnTNbode6WUNOwZ2V5Xg9kTTtDYHiu45L+iKHtF4gZJU+L1+QZUoSm2vK6qQoh5\nwCrgF0Mc8gngXgAp5YtAmRCiqigtHKUdzRFueWATjoRPnXI4y4+sLur5kymDWYPMN+UjFPCiye5C\nimmTyjI/syuDJFJpNBzKQr4+x3s9LpxBLq759FSqq0r5zL9mt4v/2cObaenOuBkpy5aYGRshBF6P\ni0S/nTNtB2LJwrMWewcPM2MTT2b2cXRhkmmLgN9HY3usaOdUlANNKp0Z88DUI9+P/bcCXyFbgXQw\nc4HeH8sbu++bUJ1Rg5vufQHDtDjxiHmcW+R9naSUuF0a3lH8sKaVeEmbFo7tUBLwUhryoUubudNL\nBhzrdukDfgJSSto787vgfvSD81m2ZA6ptMUtv/7ngM3ECuE4ss9eL2avMiZp00IiEJpGJFHYkNq7\nDR252wkjg9s9smHN/tKmheMIXC6dhGFhpIsX+BTlQNJ72/axNuyVVQjxcaBVSrlZCHEy+9qfNw+3\nfO/G3O1lx5/IsuOXj+Z0Q+rZabYjkuKwmgouP3tp0dPJk6k0NTNCwx+4D9PLgrRHOgn5XLn2HVIz\nfcjjdb3v95BKm5QF3diOM+xKbSEEXzjrKLbu7mLb7i4e+L8trDltyYjaLXSN3r+jlswW3XW7dCIJ\nA4/Hhcftoj2coizoG/pEvaQzFvFUhnTGwut2EU+a+Hwe0qbVp9r6SLSFE/i7k2ICfh97upLUzCoD\nIJZI09qVQNcE82aWDvlaXdEU00p8k34LFkUZS7YjGe0M0wvPP8sLzz8HQCYz9LRFPh/9jwfOEEKs\nAvxAiRDiXinlhb2OaQR6j53N675vgKv+67o8XnJ0bNvhlgc2sr0pzKyKIFdfcFyfNUlFeQ3HwesS\nBEaZCahpgoBHY3qeNQD7J2I4tsOcyhCtEQO/d/i2hPwerjz3Q3z958/xh2e38v6FMznykJkFtzvo\n77sy3O9109oZZ97MMpKGhcedbUvakrmAM5xwzKA0FCASN5hZHiJjO3jcLpLpzKCV1XtLpEyklHg9\nrkEDTDJt4fdnJ3Q1TZAw9v5R7Akn8fp8ZCybzmiKqorBP3TUt0TweVz4fflV9VCU/ZFd6La4g1h2\n/PJc5ySZNLj9tu8NetywQ3xSymullDVSyoOA84Cn+wUngD8CFwIIIY4DwlLKoTchGkNSSn7x59d4\n6Z1WQn43X/v3ZZSFRlZmY19SqTTzZpYW5Vzz55TnHejcushl/jmOJOh1EfC5sQsYBltUW8m5p2SH\nO3/8238Sjo8+U87l0omlLBxH9hnuCwW8NOzJDkFKKXmnvp1kavC5qYRhEfB7SKSywSNjObh0DSO9\n7+8tmTLZ3hylsSPFtt3hAevMonED2e9XXQidRCpNxrJJdw91ul36kPNmiVQat8etKrkrB5y0aRHr\nlU1rj2MtzhGvgxJCXCqE+ByAlPJRYIcQYhtwF3BZkdpXsIef28oTL+7A7dK45sJlzBlkLme0LMum\nxD/4J/WxFvC5c+uOUmmT6WV+3C694HHXT558GO9bMJ1wPM1PHnypKDvout1uWjtjWL1OJYTAdgSR\nuEFDaxSXx0N968BMOikl6e4Fv6blkLFsZPfze6+16J/ODtAWSVEa8hPwe/D5POxq2bsgOZZIs7s9\nQaBfj8/nddMRMWgLJ/v0PDOW7JP6n3uNcIqSoI+UKuukHGDCcSO3PMOyHeQ4DnEXFKCklBuklGd0\n375LSvmzXo99UUp5sJTyA1LKl4vd0Hz8/fVG7n38TQD+3zlHFzWdvDfDMJldWfzAl4+A17235JF0\ncj2vQipitLa2cu89dXzpU0fnSiE98MTmUbfN63GxJ5wasAYi4PdQ3xrFsLJDdj6/l/qWSJ9jkoaJ\n1v092FISiRvo3efpyVK0bIct29v7BDcpJcm0lZu0dbl0TAe27e5ke1OYhvY4JaGBw6eaJkimLWJJ\ns097NZc2YO2V40hS6WzGojmKxBJFmYpSaSv3e5/J2GjjGKD2m2rmb9V38KPf/hOANae9j+OPmDcm\nr5OxbMpCnoLXPRWL1+PCcRwMw6Squ8oEgNulIaXMawL/0Ucf4fbb76Crq4tqV4C3mMXvntvBsUcs\nYOHc0W05UhoKDPrelJftnddx6RqGrdHaEaequ6BuV8zA212M1uN209weo6Q0+1jPmHckbhAIZNPE\na6qyCQ7ReDoXyHr0rpzs3sfIqTZIO30eD12xNCVBHzubujBtiSbA3V3s0nYkjjN+WUyKMt4MM0Mm\n41ASzP4dmZZDz6BC2rTG9dq3X5Q6amyL8Z37/kHGcviXYxZw5omHjNlrpQ2TqvLiVaEolKYJNAHS\nsSkv3dszCPYa+hvO2rVrWb36fNavv58nfnc3s30RJKK7FNLo0q9dLj2vIOnzuOmMp4nGDfZ0xYml\n9tb08npcpDO9shJF9oNBOJ4m4PcQN6zc99oeTeWVHDIYv887YBuAbNFLi6b2GBk0/H4fHq8Xb/eK\neaFpucXVirK/cRzJzuYIe8LJ3Ne2I7GlxLYdDNMqWpWIfEz5ANUWTvLNdc8TS5ocfVgVl5x+xJil\nAZsZi/IJ7D31kNJh3oy+Q4whvxur36aI+c4rLQh29iqF9FrR2jmcUNDP7o4E0ZRNKNh3GK6ifO/3\np+kaadPKJV8E/T62NXbx7q7OPvNdxWJLQcywcntw9f598nrcxEewAFlRpoJdLWG8Xi9mxkFKiWFm\n0DQNXddIpTOkM3bRCmznY0oHqHA8zTfXPU97JMXhtRV8+fxjxjR4mGaGmUOkII+nhXMrBmT9eT0u\nZJ8qDBZdkcGrRdTV1bF+/f2sXn0+q1efzwP3r2eBp6GopZDyVRL053onvfVOQHHpOm1dSbTu7Uo0\nTVASCuAP+AiMcCO0fQnt47wuXSNl2t2fNMOqtp+y30ibFqmMxOXS0VwasWSaWMLE43bhcWXXJY73\nbtpTdg4qYWT49i+fp6k9zvzZZVx34bIRb4o1FCklHV1xSkv82LZDVXlgUsw9DLamSwiBrpGbH7HM\nDBUlvkHnpXp23V27di0A5eXlrFq1kkW7kvz0D5v52cObWTinrGhp9KPlduk0t8WYPWN082PFks7Y\nbG3oxOP10Bo26IymKAt5CfrcuR1FFWWqCfeaB/Z7s3OxjiNxebIfho2MOa4p5jBFe1Bp0+Lme19g\nR1OEWRVBvv7vHy7a1hm9GabFglmleHWJdGwqSvNbTDtR5s+ehpEyiMSSzJ0Zoizk7VMdvUfPrrvZ\noiCCtWsvpqqqio9+cD7Hv38uqbTFzff9g/gQ65UK0draSl3dOrI1miR1detobS1siZwQglBg8lRw\ncLld+Pw+XC6dgN+Dy+OhK2GxvSXGrpYITncihSqnpEwlifTeii1CCAzTItN7VMZyirIcpRBTrgeV\nNi1uvPcFtuzsoKLUx/UXH095SX6ldAplWTbBgIdppf5B18ZMNh63ziHVFUTjBiG/F9t2aOxIQJ6j\nYEIILj97KU3tcXY0R/jh/Zv42kXLRjVs2jtjEGD9+vsBugNk/koHSRWfKP2HJHsK5no9LjKWzdu7\nOrLrt2yHxbWVEz5nqSj5yGTsPlmvjhTQ67pnFaGCRKGmVIAyzGx9vTe2tzOtxMs3Lj6BqiLu69Sf\nLujziWIq0DTBtO6enq5ruAockvR5XHz1guP4rzv+yqvb9nDv42+w9uNHjLg9a9eupaurKxeYVq8+\nPze0uD9yu3TcoWz6f9q06IgmmVk+8fOWirIv6YyF0+8a5/d7+vSYirWNeyGmzEe7/sHpW585kXkz\nx3axrNs1Zd6eIXlH8D3MLA/w1U8fi0sX/On593j6pfoxaNn+z+txEY6rjD9l8ovG0wO20NA1rU+y\nks/nxjfOdSinxBU4lc5w0z17g9O3Lxn74GTZDgHP+JcyKrZQIFsNvFCL5k/ns2d8AIA7H3qFzVtH\nVlqxf8bg+vX3U1dXN6JzTUW2ZETvv6KMp4Qx/B5PuqYNu2NCsU36Ib5wPM0Nv/w725vCueA0d8bY\nlxkyzQxVMyZuQW6xlAS87AmnRpTh+LEPLWD3nhh/ev49vvurF/nGZ47nsJrCykcNnjH48YLbMlX5\nfR5auxK5yheKMhmlMw6BSZiAKsZz8l8IIXe15b8NeGtngm/VPU9zR4KqiiDXr/0wsyrHZzw/Hs+u\nrZoqc0/78k59B/4RZsE5juT237/MX1/eRdDn5tufPZH5s9XFthCJZIrDuwO7lJJtDR0snFc5KZYs\nKIqZsXmvKTxgsfx4SSYNDq+tREo54A9i0g7x7WiOcM1dG2juSLBgdhk3X7p83IITgEsX+0VwAlgw\nZxrxRArIZiaa+9ggrD9NE1x21lEcu3g2CSPT/YFhdNvFH3CElttiJJEySduwuy06wY1SlKyuWArv\nCMuFjbVJGaD++XYzX/vZs4RjaZYcNJ1vf/ZEpo1RKvlgHEfiLfIGhxPJ49ZZMKuUZCKF1wVWprD1\nObquceW5H+KIhTMIx9Ncf/ffVJAqgN/roT2a/YDQETUoDflJph3aw0nauhKqGoUyoWJJc0K2DsrH\npApQUkoefOYdbr7vH6TSFh9+/1y+dtGHCYxz5kjKMJlZHhj+wCnE7/NwWG0lc6aX4BnBL6PHrfPV\nC47lsJoK2sMprvvZs9S3qF5APjRN5PaRSqUthBAEA1464yZRw6a5M5FbZ2fZDq2dKvgr48NxJJkJ\nWN+Ur0kToNKmxS0PbGL9k1uQElZ/bDH/ed6Hir5Vez404ezXJWuC/uyC0kL5vW7+e+2Hef9BMwjH\n0nztZ8/y+nttY9DC/Y+Ugs5IEsTePzm/z4PH7UJ36bleVEtHjC7Vo1LGSSwxcLuayWRSBKiG1ihX\n37mB519vxO91cfWa4zhnxWETMgeUsWxKx6Bs0mRSGvAWNA/Vm9/r5rqLlvWZk3py444pUWmjmAot\n4eT1etjRHMHvG/i71TME6DiSWNLCQQzYcVhRxkI4buSq9k9GE5pmLqXkyY07qXvkNUzLYXZlkKsv\nOI7qqokrUmqkTaqnT46ipGOlf+XzQnncOl9ZfSy/euJNHnpuK3c+tJm36jv43BlH4vdO+pULRVFo\nCSeXrhEK+YbM3MtYksa2KB6vG0dKYol0riJIb5btYJrWgGr2ijIcI51hW2MXh9VU4nbpSClJZWyC\n+9rVc4JN2NUkmkhzx+9fYeNbzQCsWFrDJacfgX8Ch9YcR+JxaZN2wrBYhBCjrpKhaYILVy6hZlYp\ndz20mQ2vNPDurk4uO2sp7ztoepFaOnmNpITTvrYG8fu9dMaSVEwLIaUkkjQHDVCJlEkkkaZGBSil\nQA17ooRCAbbu7mJORZDmruSkzd7rMe4BSkrJs6/upu6R14gmTAI+N5//xJGc8IGx2aK9EImkwcGj\n3PJ8qvAUsEX8vpx8VA0L50zjll9vor4lytd/8RwfObqW8z+2mIrS/DIvW1tbefTRR3IX+Lq6Olat\n+jhVVVUFtyeVttjRFKaxPU5HJEVHNEVHJEUilcGREkdKpAS3rlEa9FBR6md2ZZCaqlIOnldOabD4\n+0vlw6VrVEzLLqMQIrur72BiyTTpPHdOVpQendEUCB1d0wgG/LRGjAlb91SIcQ9Q36x7nte2ZSfW\nF8+v5P/929HMnMAt1HskUmnmVAYnJCljIpQGPLTFzKIkg1RXlfK9y1bw+w3v8Ltn3uEvL9Xzt9d2\ns3LZQXx82UIqy/b9hzDSiueptMXO5jDbGsNsbwzzXmOYxvYYo5kOmzs9xAcOmckHD5/N+w+aPmQl\n8t4lnHraXF5eXnCV9qFYTnY4z6X3/SBhWhJnEmddKZNLIpWmI2qQMKxcQNI0QcA/MR/ECjXuAeq1\nbW2E/G4uWrmEFUtrJ81qepfGuK61mmjBgIemzmTRshXdLo1zP7KIEz8wj/sef5MXtzTz0LNb+fPz\n2zhm0RyWHzmPDxxSNej6snyGy+obmvjtn57i8A8cz3uNXby0pZ6EqdP/Uq1rgppZpdTOKmX6tACV\npX4qy/yUBNxoQqBp2QXYZsYmmkjTHknR3J5gR3NPgIvT2B7n0Re2Uxb0svzIefzLsQuYM71vea2x\nLuHkdrlobotiZBx8Hp3q7lJJluVgA7btqG08lH1yHMmO5igloQCh4ORNhNiXcS919JWfPsuFpy2h\nLDR5Irhl2QTcgqpxrFQxGWxt6MTnH5ugvLWhk4ef28Y/3mykp2K/26VxeE0lB8+bRs2sMmaU+ZlW\n4sXj1vnFz3/GQ398DM3t45SPncaKj66ksS3O7rYYDXti3WuD+n6YEUjmz57GQXOncfDccg6aO43a\nqtIR94Jt2+Hd3V288k4rz7++m+aOvWW5Pnj4LM776CIOmjN+Q8CGmcHncZNKGRxaXYHjSN5p6ETT\nNeZVBsdkk05l/xGOpmiLm5M6Sw/2XepoUtfiGy+JZJramSH847wgeKJ1RlN0jOIXOJ+5o/Zwkude\n283zrzWyvSk84rbqmsAnDFrr38KMNrNi2RFcc9Xnh63APFJSSrY1hnly4w6ee3U3Zve8z7GLZ3Pe\nRxdRO2v86hHGEikOnVdOJmNTvyeO1+M6ID9QKYXZ0dSFyzN5OgJD2VeAOjByggeRsexctp7jOPgO\nkPTo3spLfOzpSsIIA1Q+c0fTpwU4a/mhnLX8UKKJNFt2tLOzJUrDniidUYNIPI1hZkilDCqmlRAK\neIh07GFBzRwWzJvOvBklzJtZwuzKID/58W2sf+wRAErch43pfKEQgkPmlXPIvHI+fer7eOjZd3n8\nH9t5cUszL25pZsXSGi5auWRckio8bjfRRBrLtnG7dVwunZSp9pnKVzGSgaYaKSXpjINrineyD9ge\nVGt7hBkVpWiawDDSHDKvfKKbNCGa22MYFrhGlFovueWWW/rMHV111VX0H4orhrq6ddx++x19khIu\nv/yyPsGwmNmAg+mMGvxhw7s8sXEHlu1QEvDw76uWcPJRNWN+AbQzJgLQutesHMi/s4XasqONw2un\nT5r57vEQT6Zp7EhOiWQI1YMaRInfTcowCfg9I9p1dn8xozzIOw2dBAO+cd+MrBD5JCWMNBswXxWl\nPj5z+hGsXLaAux56lde3t/GTB1/mmZcb+MJZR45ptX3DtHHpGj05LZalKk3kozOawkEQjhtUDLKu\nbH/VGTXwTfI1TvkYtgclhPACzwIesgHtQSnlN/sdcxLwMLC9+67fSylvGORck6IHZdkOfl0STZkI\nzcWMEs+giyIPFJbtsLM5jNBdBc3p5NOrGV/j16OTUvLMKw388tHXiSVN/F4Xn/vEkZx0ZHXRXwuy\na/Rs26G0JFvEOJ40WDi77IBZFrEvjiOH7B29Xd9BMOjHTKdZOPfA6HEaZoYdTVFCoalxTRtVD0pK\nmRZCrJBSJoUQOvC8EOIxKeXGfoc+K6U8o1iNHkuWZePrzoDaEzEIVh3Yk80uXePgeRXsaomQNjN4\n85yTOpB3yxVCsGJpDUcfVsVdD2/mhTea+NFv/smrW1v57BkfKHpFFK/H3ad+okvXicQNZkyCNYQT\nbWdzF3NnluLt9+GqK5pC6x66Ni1nn4Fsf2GYGbY3RabEItx85DWmI6VMdt/0kg1qg3W7psxP3u5O\nipheHkQ69n5f2ihfNbPKcAtJKr13An5fPeyqqqru3pIABGvXXly0+Z6R6L14dvXq81m//n7q6urG\n9DVLg16+fP4xfOGso/C4dZ55pYEv/89fR5WxOBiXS+8zn+DzummLpgnHB1Y+NzM22xu7Dph9phKG\nRWSQ96EjmsLfPczlcrsGPWZ/4jgy23MK+vebpJC8xnOEEBrwErAQuF1KuWmQw5YJITYDjcBXpJRb\nitfM4nIcB7dLR9c1Dq2unOjmTCq1s6dl088jKWxH4nVrGBlnSnwim6genRCCj31oPotqK/jhA9mS\nT9feuYEvfHLpmA35AZQEfTS1J/C6tFyV9EjcYHdbglDQR1NHgoDPPemHAW3bwZFyRB8U06aFy+Ui\nlswws9cInpTZfY56QrrP46YzZlC+Hw/lN7VH8fo8+01wggKz+IQQpcBDwBd7ByAhRAhwuocBVwI/\nklIeOsjz5RVfuTb39bLjT2TZ8ctH0/4RSSRSHF6rAlO+du+JkHG0EWb6HVjMjM3P//Qqf/lnPQCn\nn3AwF/7L+8as6oOUEss0Oah7fmXr7k58vuzia8eRpA2DQ6orJvVFq6E1gq5rA6p1DMVIZ3IfMFva\nY6Ss7C4Ei3r9TUcTBi1dRp/tTeLxFIfXTu73olCRuIHf68ZxHLa3xCgJTo1qOC88/ywvPP8cAJmM\nxe23fa84C3WFEF8HElLKW/ZxzA7gaCllZ7/7J0WSRM/KfCU/tu3wTkMXJVNk0nWiSSl54sUd3P3n\n17AdyfsPmsGXV3+IksDAlN9ipMbHEikOmTsNR8oB8w9JI82scj9lk/TCZdkOWxu68Hr0vKt0vFPf\ngd/noqaqjG27O/H6fMQTBgfNLsXryQ4K7WqJIDVXnzmnpJFmdrmf0kn6XvSWNi08bn2fwTRj2bzT\n0IUmBFJKSkJTc2hvVEkSQojpQEZKGRFC+IGPAd/pd0yVlLK1+/YxZANf58CzTQ4uVcOsILquMS3k\nIZWxxqxyw/5ECMFpxx1E7axSvr9+I69vb+Pqn27guouWDeglFCM13u/z0toZx5HZbTv6POb1EI4a\n4xKgpJRkLKegIcWmthg+v5dMOr+Fx5G4AZpG3LBJGWZuGM/rdROOG1RVZBOejIyF3983UcXv9dAV\nSw8IULGEMel6HjtbIpQFPMzaR6+yuT1OKDi5l4eMVj7f2Wzgr93zSy8CT0gpHxVCXCqE+Fz3MecI\nId4QQrwC3AacO0btLQp9P8/kGQuzK0Nojk3azGA7DrF4krSZmehmTWqL5k/n+5evYMGcMpo7Elz9\n0w28uaO9zzFr167NJXT0JHgMt69Ufy5dI5aySBjWgIuVEILUGG7PEU+mSRomkZjBu7s6ea+xK+/n\nWrZDIm3h0jUsR+a1i3BrZ4KA30so4OW9pjC6ng2GbpdOwshmOWYsG8sZ+DcuhMAw92ZCSinZ0djF\n9pZo7rVt2+GtHW19njcWxQxiiTTxVHrQxyzbwZbQmTAx0oP/jVm2M+jPe38z7HcnpXxdSrlUSnmk\nlPIIKeWN3fffJaX8Wfft26WUS6SUR0kpPyylfHGsGz5SlmXjm+STxpOREIIFc8vxuwXCsTl4bjkZ\nc2Tbxh9IKsv83PDZ5Xzw8FnEUxm+ue5vbHhlV9Ffx+Nx4xlyeYA25IWuR1c0hTVIgNjZFKYrmhr0\nOYmUyc7WGA3tCVojBoGgH03XiSbyy5Zr70rkFpPqLo3UEG3sjCTZtruL7Y2doGX/doUQ+P2+PtX4\nDdMmljDoihlD9vQlgrRpIaVke1MXUtcJBXy0R7KJym1dCaSm09H9dTRu8Mb2drbt7iSWGDygjER7\nJMmu1vig5+yIJPF5PYQCPnY0R9jRFOatne19AnhLR7bnub/bv8PvIDK2g8+nhqlGau6MUmpnZReI\n+r36mHy63F+0trZSV7cOv1fnqxccy4JpBpYt+dFvX+KB/3sLKWXRUuM97qEXWft87twFuD8zY7O1\noYM9UYOtDV20h/vOEadMm7Zoml0tkQHPbWyLUVYSIODz5pIR/D4PbeHBA1p/0ZSZS7zxuN3Ek4MP\n8+2JpPD6vLi9vj5JDx63q8+cSzDgo7nLoD1q5Oai+vN63HRGU+xsyi5Md7uy71skng0U4YRJSdBH\nWzgbsBvb40wrC+L1+Whoi5MyilMD0cg4lIT8NLTFiSX7BqlowsTtys4/BYN+XB4PXp+Xtq7sz8a2\nHWIp64CYqjjgrtSO7eBT8yhFMb3MP2XqfU2E/vNLG35zP6s+fSVb2gP85um3aelMcM6pK4GxTY3X\nNY1kysS2HdojSSrLArmL247mMH6/L3eh3xNOUV7iR9ezvS6hZVPY40mDtGnlLvxtXQmEa+DfkRCC\ntOX0KcY8mJRh4si9F1iXrg1aALcrmkLT8xvx0DTRJ4ANxuXS2RPOzt14erXfQaO5PZZ7LbfHzZbt\nbZSU7l0IXRLys6M5yiHV2YzJZCpDezRFwOtidp4ZiEDufe05Z8OeOAfPdeFx62Qsm4wDPTNiPT8X\ntzmGGB4AACAASURBVEsnnExRJSUtnfH9ooxRPg64YrHxRIrDqiv2+xXl4+XtXR0EAyq7b3CDl156\n6Z1Wfnj/JgzTYlFtJV+94Ngxr4qeSBpIR+Jyu9BxOGhuOR2RJJ2JTJ/tVox0hsoSDxWlfvZ0xYmn\ns+uTHEeCnaF29rRc5t1QpXQs28GFndtksbeewDVYlt1g2bXvNnTiH6M9y3qTUtLWEWXm9L1tth1n\nwByP7Tgkk2mEAN2l4/d6iMVTHFZdjq5rROMGSSOTS25IGtkiv70DZ2NbFNPRch8SpJTEkylK/W5S\naQvd7Rk0uKfSJpUhD3u6UlOmjFE+9pXFt//3EfsRoIJTEYV8rj4leCaS40hiec5/TKSjD5vFjZcu\np6LUx1v1HVxz5wYa22Jj+prBgI9QyI/P68ZG0B5OsqcrOWAvMJ83u7UHQCJl5S6UmiZImjaW7bCr\nJYJ/kJT5Hi5dI9adZddbysjw5o526pvDJNLWgL9Dy5Y4jiQcN9jZHOa9xq5cT2OsCSH6BCdg0AQE\nXdMoCfkJBf25KhU+n4fWrgSOI2lsjxMxLLqiKYx0hp0tMXa1xvoMhSeNvsNzQghCAT82Lnx+/5A9\nT7/XQ31LdjHugeKAC1AHwrjteJozvZSQVyORGHySfTylzQyadAZtR9q0SKTSZKyxy2jrb1/zSwtm\nl/HdL5ycy/C75s4NvP5e2zBnHJ2eOTGf101zZ4IHfn0/ra2tA44zzOx7lOlXMd3n9bC1oRNLDn7x\n7i0U8FLfGsv2vLp1xQ3KSoNI3TXoEJWmaWzb3UFrl4Hu9uDxeqfEUJbbpfP/27vz4Eiu+7Dj39fd\n09NzY3ANjsWevC/xkERSB8VIlkRRPsSSY8mmHYmKY0uWy05sJ6647IrjlCq2U1Fil6mS5UjUkaIt\nleJIikQ6LEWUeR/muRJJkcslubvALnYX5xzd09fLHzOLBbCDxWAXxwD4fapQO5jpmenZxvSv33u/\n93szFZ9Dx2ZwnCRpJ8nRyRoHj86QzTgkkglGT8wCjfGjIDqz10op1daFc293bluVZtt2gzEJU1pP\nq8kwFKXuLPl0kkMnqlgbuAx5GDayCw+MTi+YVFxzfbrSFrmMw6HxMpbprMuExuVKL/UUUnzm127i\nv339SZ588Rh/ctfDfPJDV/OeN+9ek/1pNefKNNSZc66UQaVWJ1x0HrUsEzNhzbUczkYphZ20OTpR\nZrgvD0DNC0g2q1y0ujROOTYavSlTp5OOTT2ISNuN4DH/7y9hWVRqHgfHpgnDiOR5FBLejBNxz8e2\nGoPyg5C8Y0oF6DWgtealNyY3tG/81BjG2MkybqDnstpqNZeLdzbK4Hj1gINHZ8llU9T9ANM4XcIp\nimMMpeZOAjXXx26uYLuWoljztX/4Ed956AAAH7rpQn75fZevQVd0e8uRhFGM57qYCfu8q7JXqi6X\n7GyMK734xqRUIxFnkDGopiCMyKU7v8tgM1JKkZi38OOpuSbryW6+/0B3Fr85p8YPQroyC6uA9xUc\nPNejmEng133iWDcGv6seNff0uEkchQsqu68V01B8/NYr+dRtV2Maim898Ar/5e7HF0wqXU+WaVD1\nQpwlUrVXwrRMpsseVdfHkO51scixySpf/8FPlnx8W3Xx6Thecn6EOH+ObRA2M5/8ep0gMMkuKiGz\nXPrxUqZnqmQyDgnLpFz1sJRGGSZOc8C47od0NS8+DEMxUspy5EQVHWt6+xcuVNdXzMy1ovOZJC83\n65ldtLObg2ON7q8wiilkk1TctQ9Qp7z3LXsoFTP8+d1P8PgLR/mjv3mQ37/9enq70qvy+vPHxKDR\nxVcsFluWVeop5lalOymVtJmYdXFss62uQbH1RbHm6Z8c497HDvLcgeOc7Tp2W52tLUNtuz7c9VTI\nJBmbcklYFj35FLOLJl66db9Rcy29dKZSK0EY0d/lUA9jZmc9dpby5DKNiYsTZY9M2iEMQ3KZ0yfy\nbCpJPu3j++FZK4knLJN9QwUMo5H2m03Z1KMYr+4zNJgnjuO5oLserrqgnz/95E185quP8eroNL93\n5/383i++lSv29p33a69kOZLVTCbyIwjdkExmdRdxFJtL1fX5f0+9wb2Pvcb4ZGOoJ2EZXH/pAP+0\nxHO2/BjU/LkMUeCze7C9isli5eJY85NDk2jgopEilZrP8ZnGkgeeH5A0Yag3x8tHpsgtsb6U6/mE\nUYxpqLkJwJVas1J1wkJrveAiY3yywowbEofRGUuoaN1IW17JUhd+EDVqvBlw0UhPI1V4vELmLGnV\na6Fcq/Nf/+5Jnj9wAsNQfOyWK/jpt+/blBdYYRTjen7HFWQV6+PQ+Cz3PnqQHz5ziHqzLmR/Mc0t\nN+zlPdftwiQ+92rmm5VX99FxjKkgMi0MpUhL996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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t_distribution = GPy.likelihoods.StudentT(deg_free=5.0, sigma2=2.0)\n", "laplace = GPy.inference.latent_function_inference.Laplace()\n", @@ -1622,10 +697,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from matplotlib.patches import Polygon\n", @@ -1633,14 +706,9 @@ "import pickle\n", "from urllib.request import urlretrieve\n", "\n", - "east_timor_available = False\n", - "try:\n", - " urlretrieve('http://staffwww.dcs.sheffield.ac.uk/people/M.Zwiessele/gpss/lab2/EastTimor.pickle', 'EastTimor2.pickle')\n", - " with open(\"./EastTimor2.pickle\", \"rb\") as f:\n", - " Xeast, Yeast, polygons = pickle.load(f, encoding=\"latin1\")\n", - " east_timor_available = True\n", - "except Exception as exc:\n", - " print(\"East Timor map data is no longer hosted; skipping this demonstration:\", exc)\n" + "urlretrieve('https://github.com/lawrennd/datasets_mirror/raw/main/east_timor/EastTimor2.pickle', 'EastTimor2.pickle')\n", + "with open(\"./EastTimor2.pickle\", \"rb\") as f:\n", + " Xeast, Yeast, polygons = pickle.load(f, encoding=\"latin1\")\n" ] }, { @@ -1652,50 +720,26 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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svNrH3Xlw9HNoeZ/xNKWEtX+D91MgbQGc9w5ctgqShhr3A7V4db0Npu6D5LHw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2LNaTmeWmO0IKjrVeW0FkQXI9ZC5ccmqJTjZouVB+dEIVWBtZ6FQCrLRXRNQja7Itcq/QmyOQ9N\n8EXMyfUli6o61G7ZmIsiGRjlTVRQLh/8ZuS347kUl5VQW57JmE8zqDNZ0ORdxOwTyS9Hr/D41/t5\nIkyFGBdPsJcTc/20OIb7kyzVUzzUmtCuPEnVvb2R6xPTnqU1BOZXcLaTX7NOX0uNicSPduEQ4Eb4\ngwPILKpCbBT6GPLaFnafy2XV34bd1F9L62qg4wN9iXvzVqaqRI6vPUOEvytxIR68/IvS+CmIIi6P\nD8GSXUrNZpvQo1aNcfYAtMuPNWugM43sgepSNmKOkuje/Gc2Azr5ow4JQXJ2QZOciKCxWRR1TliG\nPoku9RDYYfz1dTWw+3wuibllPLhK5uOdxYSrDpFpcqRKVGhAnnrySX74cTl9+/ZlzW8bWLb8J6IG\nTSRPE0z8879xocaTt9//hILCYvr1a5SrsNTC5tug4+3Q7an23UBJgou/Q2wrRrkoB9z9buh+2MVf\n7EFAfR4irfXCnPYaCAdZlm1iHjdB/P9/EQYHheRv2eewx47MZ2u473XISoCDrSS8e9+ncMOftcO+\nqtZDj+dbyEVMA+PV5l6E4zhAhmobcj+xOwjxINkSAmqA6bTsRUwCtgEt5WJEYAhwgpaa4VqCWm1g\nQP/ZVGelow6JxGHSDPQjJqKOjkN080SoF7SZ1CeUsqo63GctJ+ypdbxypJLyQZPRRHbBdPowdTvW\nYM3NrH9XAcGlA6JPNIJT01jv/sR8nIz1sp4+NViLHEASkU3VbDp0jnk/nSC5pJRps8MwNWp2lyty\nEV33YC1QwgNydTGC9gcsuSHItfU03l6pWMqDwarnWEoO04fBpmMOPH97HLueCWbdBTUHk0sY/9Ym\nVIVpWPxjWHcskxej1OhDg5GdncEqoTl8gfUGV7xdHXBx0KL97SzWLh3QdQ2gc5A7yDJ9z+VwJtYf\nyaChe0fPG/O01JhI+mgXDoFudJzVH0EUmDE0gsxrFeQUV2OVJMqqTXz72CDiQhuuuxnofV1YGexB\n50sFlCXk8d1jgzmdXsiJVOW+inoNbs+NpGbPJepOZDa5VursjzU+EO2vNkwCeh2mMb3RbjlKYnYx\n649n8vgYpdPY1KsPmqQEpILmC5jkHY7VPQhdxvFm56YPDudkWhF95m1EHzGIEQuO03nS2/xyZwEZ\n5w8gyzJffPnljfE6nY577rmHTz75O5u3bOXChQts37mbOXPmoFY3WrCtJtg2DVzCYOD77b9xV06B\ngxt4t8L9VpDZev4BFA/C0EbllaH9OQgAdUQE5pSWFCXrx7TzvYoEQQinPp4gCMKd2O+2ajcEQYgH\nFqOUxpiBx2VZbsZFIQjCM8CDKGUzF4BZsiy3zUj1V8AvEBath0dvg9pqmDCtfddp9fDsMnhjAoR0\ngeDOzceIKrjzc1g2HaJHgINNGKXrHDj9CeQfA/9GuxhRDZHzIXkBDDzUkCATBPBcAIWvguPYpnXZ\nqtfBcgeID4DQ+CGbDCxF6ZC2nWMgSnPcVpTSWHvwrB+zDxhPa+R8jSEIAo56PWnHTtDpymVQqVH5\nB6MKCEET2RXB4IC1MB9NYT7zHq9D4+PPM09OpSQnh9kPv8ttIWruHxrZ+A1Rhw1AcGzg0pdKs7Hm\nKCy17g46sjLSkKUURGc3zJc8kWtKWb5yLXO+3Issw5YNoyBdRG5E/SN6V4OqCtOxdBCyQVWNw7gL\n1O6ZcP1TUHsnYUxXNKcjOuRTXuXNrtduBcmIQ+lvBI+bxKB+sGX1WrblOjKkl55zqQWsHQrmLkrZ\no/pCGrK7MxcFLc56FWJ6Eeqj6dQsVEgax/YMomPqNVRaNbpB4dwb6YOPm7KbNFfWkfjBLpwjvQm7\nvy9CPflhl2BPDDoNRRV1FFbUIgO3xLUcwsgvqeZIcgGlVUYCPR0ZHOt/o2kP4EBiPrtySnll9gBS\n/nGArq+Px9lBS1Vtwz5R5e6A29PDKfv7brQx/oiNwmCmu3tieHkD4uBrSBENnejWLmGoT11i/Vc7\neXhUZ4bFKiXLsrML5vBI1HvWIPmE3jD6ssWI9cppLCG9MJzbiKo0B6t7w/f69UgGVknm9OnTjYjy\n+oDaAOtHwZ0HwbFpgcONv7co0qVLl+Ynqq/C1jvAwQdG/9B2z0NjnFkDcZNaH3M1Dfw6tj6mprrt\nRrmbSFIDaKKiMF9qPQLQ3m/6BLAE6CQIQi4KJehj7Z6JfXwIvC7LcnfgdaBZLEcQhA7AU0APWZbj\nUAzavze0Fd8HftwNH76oeBPtRXQfmP0hvHU7VLbQqR05FOImwlpbhlVA4wgD34P9c5s3CAVOB0sl\nXLXRhHCeqhD2VaxqelzsC+JAkP5u8yEOwBzgE+znEu5EKWltjTu+O0rAvjXp0ubo4O9Hfj2xG1YL\n1px0TMf3UrvlZ2o2/YQlNRFBZ+CZv01Dq9VQ8ftqnP7cxRAfmQWrTlFS1eDZCC4dmhgHANE9GEGv\n9FlM6RdKsaMOTScH6nafw5ywHUvaAX7adYYB0X6MHt2B4YP9MV9sOkdNzFXMSb7KrZEsaCKTsOQF\nIVcr5aoqjwxkkxNSjRcg8fitEtdK6yk36i5h1QYgq5wIdlXzRLyKlw6YmPb5PuYEy2gDApDd3EGS\n0By8gGlIPKXVJtLzytEuPYJpWm9wqa97rzTieSCVsL+N4NY+ofjVU2SYSmtIeGc7bl07EPZAg3G4\njjqTBQ9nHWarhABcVxqVZZnq4mpyzlzh/IZzHF52jNU7k0nPL6e0so4LmcWsOZTWJJ+QVlBJgKcT\nsYPCCZzcjeRP/8BcYyTcz7npPQv3QtcnlKpfbRoqHXWY7u6Fdtmxpg2lgsCVfl2ZqzPy+oSmmxRz\nbByq3CtIf27CnLIXS/ohLMk7kGvLQKXG2LE/2oxjYDVTXWdm9Ds7OZ9XQ15eXlMWVYAez0Cn6bB2\nsJLfyzuseAVt4epJWNUbgkfDreuUHGF7IVnhz1+gz/2tj8u5BIHRrY+pqQJH59bHODpCdfu9eW1s\nLKaLF1sd0y4DIctyuizLo1B4nzvJsjxIluXMds/EPiQUCTIANxRtS3tQAY6CIKhRVrS2Gab+akR3\nVRTqfvla0ZRob+J69EzoMwHev7vF0jwmvg/ph+C8nTBWp+mKZ5D8U9Pjggo6vw/JL9OEh1oQwOcj\nKHwFJJvQkOq9+oom20aoySjEevb6ItxROq9X2Tl3HSIKG+wFwE4DYAsICAykoKyFeKmxVjEYpw9R\nvXMDr8z7guxM5c/+2JhOxId68sjihgqs64agGeqPd+0VwrJlM6nbl4RUUg1WMwVlNZxMu8acsdF8\n9VU/EjeXK3bu+nu61iJ61ii03gBqE5qIJMxJ15udJCyef/L4wlqcp//II98uJzUPdCpfkC2oa5Mw\nGbpitkhs2bCV5YlWkorM/HEmm5cjRCxx3QBQJWQgO+mRQv0Y0cWfeRoVsqsey4CGHWXVzycxDI1E\n0yhZX3etkoS3t+E9sCMhd/e0SzpnMltx0qmI1GmIs1jZuOgAu97fwerHV7H11c0k7UzCXGemqLwW\n152JOKRcu7F4F5bXklPcsNhE+rtQWatUvPiNisYp3JvnJCitbJ4HcJraA+PZHEypTZ8H64COoNeg\n/qNp34tXVAd+N4rUbLch5tNqMfXoje7oIaguRa4ubvLbk9w6IDn7oLlyltmLj1Kn8yQnNx9//xYo\ns/u8CkO/gLpS2PcUfOMF62+Bk+8rnrpk8xtNWg6bxivX9Hvj5jwHgJT94OQN/vYrxm4g93L7DISD\nU+tjnJyg2j77rT1oY2Mx/ysGQhCEZxu/ULabDzf6/7+CZ4CPBUHIRvEmXrYdIMtyHsr2NhvFgJTJ\nsrz7X/zcfw7XZUyP7oH5D4GlnSmYB+sdo5UtiQY5wfQf4NfHoMqmQ1kQYejncPhlMNk0I/mMA50f\nXLGpz3YYCvo4KLXpfxA6gjgTrG/YTEADvAp8jP2y1dEokqT2ktnX4QQMAvbQcuVTU4ii6kbHbGtw\n0KuJDnBnXn1CVCWKLJzWk4NJV7FYlMVMrrHvock1pYheTsTd1ZuXnlqF3GjB23r6CiaLxF0zQrmS\nUcPnSxObXKuJKcByyfuG5oMmMglLfiBytWJ01F6pnEsxceyMmpenxPPCZPhhj44QbyfUxjQktSey\n2p0l284x2quSyL6DKV32AEmPxqLy90d2V/IK2oPnMQ+JB0Eg4WQWz0gSppn9b4QOVy89QuaxDAYc\nTmfRdmWONXllJLy7Hf9xMQTe3rw7V5Ik0lIKeUqW2fLUGv749A9izBIGJx2dx8Zy28KJTF10N6Nf\nGkPPe3rhNjyakiGRaAur8NxzGW1949v1+wvgatBirO9TEQSBjjP74SFA6Y7EZp8vOmhxmtaLyqVH\nka1NvQXjjH5o15+BiobNgSiKfGE24Jmag1DUlDHYGtpRSVhfsO+hGkN7Y82/jNpSx/6Dh9HrW+k2\nFgQIHQtDPoF7T8OsTIh7DGquwh9zYIknbBgPpz6CfU/D8bdgyl6ImNzye7aGEyugdxveAygeREAb\nxIHtMRA36UGoAgKQ26DmaMskOte/eqGElALqX48CPVq5DgBBEHYJgnC+0etC/b+31b/fXFmWg1GM\nxVI717sBE1H4pjsAToIg3NvW5/6PwcMLlv8BBXnw5BSlsqAtqNTw7I+w5R+Q3QIbasQQ6D4V1j7d\n/JxfXwgepRD5NYYgQMyHcPlNsNg8FN4fQMn7YLXphlXNVyRJZdsfdRwwEvjMzuQ0wDRgJS03z4HS\nnR1Ee0pfZVnmyNGj7abaeHlyHGczim6EPGKD3AnwdOLDTYrRkiuvIpU1dUCt1y4jOsroh0STve08\nK9aewtootPHRxvMM6umLtrPAB2+c56cDqThN/5H0ggoERyPqgHLMKfXJblvvQbCg8T/LomWOTOkb\nyoKpvsSFuPH1g/egVQloahIwO3Th9dVnqEw7zzW1F/3iwlFJEkH56Zi7KiI4qsQsZJ0Ga8cOmC0S\nQ45nkDs0EtlbCSXIJguhey/zmizz0NgYXvrpBHO+2EfiB7sIvqM7/qOb57Yqakz0fmI1G9/cSoIM\nn7k5MOLtCfzqoGGrJBPYLRAHd4cmHkenQDckFz1l/cOo6uKP85kcPE9m4dHIKblWUYuuUeWUqFHx\nR5QvlpPZlNjhZNL3D0N01lOzq+kzLwe6YxkYgdYmBOXh68pPohPanTZpSEHA2Kc/6rTLiMVKWW1V\nnZmHlxwhcM5K3tmUzIKTapbf5oKzw82RP6L3gPBJMPQzuO8czEyD2AehIgtMFXDPCfCyk5NoD0w1\ncH4D9Gojb2kxKz0QHdoQ46qpBsd2eBBV7fcgBEFAExPT6phWDYQsy2/KsvwmSsayhyzLz8my/BzQ\nE4Wqs1XIsjxaluW4Rq+u9f9uBmbIsryhftxaFA4HW4wC0mVZLpFl2YpCIjSgtc984403brz27dvX\n1hRvHg6OsHgTOLkqTLBlzSkJmsGzg1LZ1FqX9e0LIesEXNjS/NyA9yDhWyizqehw6w0egyDdZmHX\ndQbnO6DY1qh4gGoeWF6yM4GnUBZ3e/KjcShkfjvtz/0G+gGlKIyxLePLLz/H0yDQJ6J1Ocfr6B3u\nTVm1kZR8xcMRBIHnJnRh+YGGhinrlT+xpB7AmnMW8+U/kGsz0Q3vjPFkBkEWCZ1aRW6JYtBzi6u5\nUlzNLz8Ow5wEb97W80aX7dwfjqLtcwVzkg+YlAVRE5GMtaADcpUSEVV7X0Kq8eTaVSfSCqrQ6BIw\nm2IBAZUpG1nUU4cX64+m8HJvNcE9lRp4dcolJG9vKg3OWK1WtAfOYR7aDQSB1MOpxMgyodP73vhO\n1VsSyFCJPDR3OHNv7cJDIzvhcCITS5A7PkOaLyh1Jgvj523kYaOZwVO788OSaVypqOXYpWvMmxTP\nD3sv220K83V3YFyvYJwddRj9XVFP7UGn+AC2v76Vc+vPYjFZOJdZgr9b0yTpw5PjeUOWSf3uMDW5\nTXf+giDgPLMf1RvPY7URJjJNjkeVkId4uSG3dc/Ajjx7qRyhqBxVio3YlMGAqXs8aOwAACAASURB\nVGdfdEcOgtXCwg0JnMwzM3TUWPZeEXEd+xwuHSLhx/uaa6rcDAxeSm/D8K9gzI+KAflncWGz0hzn\n2kK46zquZoBnQMs04NfxF3oQ+/btu7FGftYGd1N7g2q+NI0dmGiQDvtnkSsIwlAAQRBGonBJ2yIb\n6CcIgl5QtjwjaUOUoLGBGDZs2L84xRag0cCHyyCuNzw0rn061+MfA1Mt/L7Y/nmtA9z5Bfz2jDKu\nMZw6QI/nYP/TzQ1Mp3ch/VOos0nNeL0B5T+CyaaMTXxC8SAk28XeGXgReAv7pa33ohiI1qoe1CiE\nf4koXdrNw3BlZWW88vLLfDWzV7vFWqZ/uZ8+kb509G1I0k3uG0pReS3JjRYmubYUqTQLQWtGP6Iz\nptNZWK8oBlynVVNSaeTJ7w4TPXcNLz0eh1+AHnMSxIV4kvvNvXz76GCi+1sQ9CbMiX71X8mMJjIR\n03XvQTSh8buAKbcHRrOVzoFWRLEUi7kjyDKamguYDV04kVbEbR1q0Xr4Iju4g8WC6uIF1la58o+t\nCez+5TDVFivWCKW/onbln6zxdgaN0vVsySujZmcS76lV+NVXLL05PIKxgsArBfY72B96bwf3VdQy\n5N7e9J4Yj0WSsVhlenT05NExnRkc48+E93fZvTY22IM5Y2N4ZlI8D4zuxIBpvbn1ndsozS5l07yN\nGIqqqDU37ZQe1NmPSg8nzkf6kPzpH1hrmy7Oan9XHEZ2ovIXm9yCQaskrH86AZLM+hOZnMkook6C\nU53C0e482YwZ2RoSiuTmhnDmDD/svczefQfYsGEDx06e5rU334YHV4OxClbMbJ1V+d+FYz9An+lt\nj8tJbjv/YLEozXRtkfW104MYNmzYjTVywYwZrY5tr4FYDpwQBOENQRDeAI4Dy9p5bUt4GPhEEIQz\nwDvAIwCCIPgLgrAFoL73Yi3KtvYcSh2lbVvw/w5EEV7+O0TGwlN3tK0Fq1LB8ytgxWuQZm+XDsSO\ng8BusM0OmV+P56A6Dy7aROIcIyBkDiTMbXpc7aeUveY/3LQKStCB+iuwPAaybYhsNArVhr1qLR+U\nP9HXtC4n6oLSQ2FEESdqyKtIksSUiROY2DuU+Juox88oqGTepDjUjZhMHXRqxvcM4XWbUIXgpEM/\nMgbzhRysmUU3jiseRDU/H0wjPtyDhR/2wHiCJpyDk4f5885CH7593wyyYrw0kYmK91CpdDNrfC9i\nrQhArnMnxNuJXtE59d6DCpU5FwErVm0wBaVVPN9NxhJUn4xOTiRNMpBcKyLIMoNy89jk7ElWYRVi\nRhHhtSbEUUoPgCxJVHxzmNVuBnTezvTo6IksyWQvO4F6ZBQn8itYc6RpY+DVSwX0SykkZko3YkZ1\n4njKNeYuPYqHsx5fNwfUKpHlTw4lu7DyRi7DFoIgNLnHTl5ODJs7nD7390F9MI2Jdui2Az0NZPq7\n4trZj7SlR5p1Ujve1hVzSiGmy00r4az9wzBJEl+9toX7v9jH1zuTUatEjOEByI561OdsvGVBoLpb\nH+ouJjIxPpzAQJuSXY0eHloHJVmwaZ7d7/dvQ845hd67+11tj804D6Gt6EQAVFcqFUxtbaicnaGy\n8qbYH7TxzTW/G6O9VUzvArNQ4gelKL0IC1u/qs33PCLLci9ZlrvLstxfluUz9cfzZVme0Gjcm7Is\nd64PTc2QZflf8CH/YggCvP2NIvX38qy2dy6B0fDoF/DeXVDTAo/R1K+U3Ue2TZmgSgtjVsDhl6A8\no+m5qFeh4hxctamEcn8K5FootxEPEscppa/NEtYCsABFWc4ODQixKFXGn9I65bcOpbIpHthKVtZW\nLl1KZuqdd5CfeZlFs9rgpbFBVZ2ZQM/mTUKPjYnm8KWGShnBsd44JORgSWs4LkkSpVV1fLM7GbNV\n4viW27BeFUCORB0xTOmhcPbBbWguJ7ep+Mfq6zoFRsV7SFQWedS1aHySmfJiGd6zV3AqI41BMRKy\n5x2oo0ejlTIxew4DQSDInEdKpQrJyQtMRtSJCewSlPr/LuVlGEWRFCdnLueWoVl/ll1h3ny5R3Gi\na7YngVpkn6cTSTkl1JqsXNufgizLjHigHw+OiGbBqlNkXC1HkmTyEvLY+O52drsZ6D8+FqPZyi3v\nbCOtoJKPpjcwiHq76Jk9Ipqtp29Ojz2wexCmiV1xLagk42hTw2SySGjUItrxXajOKeXavqYeq6BT\n43RXDypWnOCbnUl8tiWB3vM2MPKt7TxvFbgvt5TEjyZR9dNMREEgq7ga08ieaPadbVYIsuRwNp8X\nuvJdbyf7i6DWAI9shAubYN+Xzc//u7DrfRjxTNthI4CMc9CxW+tjqirAqR35Op1OWZPaqQkBoO/f\nutBRe7mYgoEilC3heqC4/th/oVbDp6sgLxsWPtO29R42DeJHwmcP2R/r4guTP4GfZ4PFxivx6gK9\nXoJdM5t6BSo9xC2BC0+CuZHhEVTg9x0UzgezTRWx6jOQloFk6824ooSZXkfZC9iiH4r63N+B1txZ\nAYhi8yaRgoJDpKUtoiDjNGufH40+KA512EBEv5h21ZWrVQLVdc33BfEhnlTXmamoMSE4aBXjkJSH\nxaa8stZoxSJJ7LuYz/Elt6MJEbDmx6Dyi0EwuCI4eaPtKYBWxYmtqhu7YE1UotL3UKX8OGWv06za\nKnL2Uh3b54/j5McBqF0nIHp1QqxKRZBqEWKmIzh4MkB/lflHlJCMJukiNb4BFIl6RFlm+LWr7PH1\nB0HAu6gKMasEp/GxFJXXYrlaQfWm87g8PJC1L4zC2aAlK7OYrDWnCZ/dn9JqIwGuemprTAyev5lJ\nc35h4wc7+VaA158biVat4njKNZz1Wo4tvJ0p/cKa3IsakxWV2L7QXmPc0jeMpcCJ5cepuNrwjM0Z\n1YmPNp6n60vreby4hqQVx3n0nW2MfGsbn2y+wJ4LuST5upBRWMWJlX/y474UPJ10FFfWcVAU8Yzx\np+NZ5dl8enwsb64+gxTkg+TvgfpkQzizzmTh8x2XGLdoCzqrEfb+bH+iTp7w+HZlkT6z7qa/57+M\nwjRI3gWDHm3f+PSzChN0a7juQbQHrq5Q0X4STUHXuhFrb4hpKwp72xaUWsZ0FB6G/wKUDsclm+H4\nXlj8Xtvj53wGeSlKZZM99L4P3AKVh9wW3Z9V6rUv2IgBeQ1XhIWS59vMrSu4PQYFT9joBPuA6gOw\nPtScBZa+wDgUQ2HP4N2Ckrj+gtbKWtPS0pgx40nO/XCYUUHuHDj8AlG33ILoFY7g5IXKOxJ1x4G0\n1oG9/rjiLXUOdGt2zkGnJtDfhcogD/RjumC+lI/lcvOmvl0JeVisMsc+uJ1eUz0wnRERnRt1ruoK\nUfnux5IxHheDTnEEtXVowpMxJ8Yz7bO99H/jJ4yOKXz2g5rN88bQO8qKg74c2Vvp21RlrcYacgcI\nKjSV16hTO3DsKlSWVaG5fAl1zx4EeTvRrbSEMo2WTCcnNGoVPc5cUbSc1Spq6kyUfXMIp0nxqH0V\no6TXqilZfw6fQeE4Bnuw90IeAvDgyCjGOuoYXWtmb6Ab7z03iu5hXsiyzK9HMtDr7JMk9Azz4HR6\nMRtOti0W0xgmi5UCjYr4Kd048OU+rPUlr1MHdOSde3pyZOHtPHV/Hza7GRiZWUJ8kBvrT2Tx2LdH\nGPLGVp6tMvK2g4Yz70xg+4JxnP14MkffnYB8b2+0Wy5AtZHhXfzJK6kmu6gK04geaA9fAKPyfH2/\n9zJd47vTb8BARXtl6YtQVWZ/sp6h8OgWpXQ8zR5j8f8gdn8Egx8DfTsW9JpKKM5tu8S1vR4EgIvL\nTRmIttDeEFPXRpVIkSgVR0fbuu7/Kbi4wfc7YM138KsdnejG0OrhlTXw8xuQYkfCUxDgnsWw/0vI\ns6kIElUw8hs49poiLtQYMR9B/looPdb0uOd8MF2GyrU27zUDcAfJXnnrUyi5BnvypABTUcSFltCS\neNDcp54gyFXNPQM6Yj5/BfP5ctShJlT+lSAohkfQuyA4t1zvcDC5gDAfF7TqpjTfoocj2r7hnD7/\nOq4d3DAevIzlkn01tNziasIDnek23R1rKVizVY2anqyog9ZjvTocTF509HWmtMbIQdNOtm/TMOG1\nI+w6n8PKhS78vEHPh/cOIdLfBa3+JCZLXxC0CGWJCHVFSD5DQLKiStxOiWcMMpCx7xiWwCBkJ2cm\n9w5hdGkRZyPD6BTkzsxQD3R5ZViGRDImPoCnnfVkXK3AMEYpX5Ukiag6M2JOKUFTlDBETr2Mp1dm\nCQPrzJhHd2JQ/46MjlOS3TvP5bL+RCa7Xx174/un5Zez93wup9MKmdg7hFemdGPu1wdIuZjP+cNp\nnD2QitXaenjUbJFQq0SiR3XCyceZP1c2lKP+bUJXYgLduXdwBB9/fAfRHVx5OdKHQ29P4PIXUzny\nzm188OpYDJ39qd7a9HmWA9yw9AxGs/kC/aN86RzkwWPfHkH2cccSEYDmyEWMZiufb7/Ma2/WMxxH\n94F+E2F5K4TSQd1hxk/w3R1wtdW6lr8O5flwZrXC1tweZF6A4NjWSfrgP99A2EKW5dMo28z/ojF8\n/GHpDvjyddjRhnvbIQKe+IeSj7C3E3IPUhhff57dvAvbMxa6PKLQcDSG1gNiP4VzDzfhwUfUKaGm\na3Ob9kYIAqiXgPUDkGxLU7XAQpSEtb2YtQjMRklG/4Stp7Fnzx7+PH6U7fNvQawPaUgVYE7xQNBI\nqCNKEd3qEPRmhBaqMy5eKWX5/hTmT6l3wVUi6jBv9Ld0QTckGrmylmH932f7NweQilsOdz0yLopf\n1g4lJ6sa01HAakauUhLYos9BsOqRSnojleUS4u2MrK2m94gS1q1wQ6vRsOOd3nSOruXB+DvoFe6N\nSp0ByFjrApGrilBlrsEaMgVEFWLaIWS1jlqDF85qiK3Ku8G55Hg+FU2QN7dM7MltfULx3Z2MeUIc\naNWUFldzX5WRJ6qNmCUrkiQx47O9PGyyEDWrP6p6XiR3Jx2OSVcxZJdQMiQcq5MOd+eG+7d41yUm\n9Q4mxNuZ0muVrF53ll9XHOfwujNs/fognzy7jrLVp7jPIvHz1we4dOYKZw6k8N0bWym40gIlDKDV\nqDBbJARBYMBDA8g9m0OWDSkfKMJDodP7kLX6FNb6sGBcqCcDov1wvqcnNbuSsRY3rfozT+6GZn8K\nQlEVy58czMHEfIxmK+Zh3dCcTGbd7kQ6x3ZlwIBGFe4zFsLhdXC5GX1bAzqPgYkfwtfjlcX7fxp7\nP4Pe01sXBmqMjHMQ1kZ4CW4uxPS/YSBsOqqfFwThF/43KC/+LyA0Er7ZCm89CT+3EEK6jsFTod/t\n8NZERXPWFgMeBoMbbJ7f/FyfBVB4Di7Z0GB0uBscOipkfo3hMACc76qvamocagoH1cdgmQKy7QIR\nidIT+RyKGpwt1Cg0XZkodBzK+0qSxMOzZ/LaHV1xqe8v2JuQR9ycr0nPq8KS7YJU6IDoYkQVVIF+\npC+GST3Qj4xB2ycMdSd/VAFuHL9aQY8IX8aNjUXbPxyHyT1RhXhiTsilduNpzIl53NLZnwWrTjfQ\ngdtA0IPzODVnTpeQvLmhG91y5RSynIDK6wSW7AlYC1ORSjII93XixHY3Fi2GMCdf1j03nIFDszDn\nx9fLjVrQ6k9hqusFCEipmxGqs5B8hyIbqxBPrMDkH4OfuyNzQ0DqEIjs7AJGE5qD5zEPVziCxMsF\niDmlWOpJBz9+/jeOAolWmTlLDrPjbC4ep6/g3tEL714NQjJxBi2GzBJKBkcgGbSoRJHBMX71913m\nREoBt/cO4fT+FJa8uplLh9OQKo2IBg3qYHdU3YMYNnc4e4M8MIyKZuqTw5g5fyy9RkSzbOF2Tu29\n3KwSCcDDSYfFKnE85RpaRx1DnhzK8R+PUVPa/LlwifLBOdKH/J1Nd+4qLycMw6Oo+q0pjb3s7oB5\nZDSajecI8XZGkmU8Z63A7OyIOT4c96MXef4lG4EtZ3eY8S4smdt63q/fDOg3C76/s3lO769EUQYc\n+Q5GPt/+a1L+hIg2+42hsvwmPAhXKGsh9PZPoL0ehHOjlw4lJzHxL5vFvwtpabBsGezbB1ev3pwY\n0M0gprtCy7HiS3hnLlitLY996BOlke6De5p7CqIIM39R3NbTNmJAagOMW6n0RjSuahIEiP8ecldB\ngU2ayPt9MKdDmU0vhmqGIk9qmWYnH3E30AklaW3vfhlQDEgKSre1zMqVK5FN1TxQ38yVX1rDHR/v\nJjUzl2kvfsqxpDykMj3mdD11u9Op+fU4dbsSMF3MRSqrQXTUoY7y495549h/bgHabsFIJdXUbDmL\ncV8y1tzSG1N5eVIcogBf/N6cU0ZwAv0YsGTJzH36GD6ujTwVuQZN6CaMx/0wnzuEdFUp/dR2TqCD\nlw6fggF8vesSKtccBE0NliIlTqzRJiJZvZCs/gpVRuVxTNoYzInb4dBiZJUaydWf4uIK5oaCuqey\nAGgOJWCNCEDy81CuW30K85TuoFFhrKxhhiRz66vjeO/e3mw7k0NXlcBoIP7RwTemLFkksjeeo+e9\nvekbH0i/Tn7MGBVNqK8LtSYLvedtxGKVcM2vYP9vZxn31FD0Azui6xaIJtIHdYAbKjcDiQUVpF0t\n58ERneofGYGew6OYtWAcx3Yksn7xQYw2RQG+rgbuGRTOMz8q4UuvcG+iRkZz9Pumpa01RgvVdWaC\n7+xO3raLWGwa8xwndMF4OhtLXtNFzDw2FvWf2QjXKtn12jgEAX4+mMo+Pz+GiCbG+NtpWhs5A4zV\niifRGm5ZoEh+bnix9XH/LGRZYUEY+Rx43ETtTuJh6Nxq36+CyjJwsS+a1Qzu7lDasid4s2ivgUi8\n3lUty/K7siz/DNz2l83ifxKZmfDxR9CnFwwaANu3weuvQlwX8PKAgf3hwdnw0YeweTOkpLSfZ6k1\nBIfD6qOQchEevR2qmgu7A4oReHaZ0gjz+UPNS2WdvOCh32D1E5BrI8bi00OpatpxX1OiMZ0X9PgZ\nzs2C2kbVS6IeAn6FotegzobbRvUxYAWrbZf19dLXXBTdKHtwBJ4H0oBfOHr4ILOGhDFgwVbcZywn\n8qnVCALkf3sfIwKtTHnkReou7lSYOSsUR1SuNiFdLcdyuQDTqUyMe5MZ0ud9Fk7/nrrtF5T8gp1K\nJo1aZFiXDvyZXtTkuOgOulHw9EvH0HdfhskqkVfcsNvVds9FKnHAmul6Y6MgehWgjkzEeHwIU3qH\nUWuqo9Cwn/ykroCIINSg0V6s9x5AZc5DkGux6MJBsqLJOYspqAcIAu6Zl9lYAJ/sz0Yor0Zz6hLm\nEYqxUJ3NQagxYRmoJMoLNyVwSADPcG9GxnXAXGfm6rLjfAloXBu6l5N2JGJwc6Dr8EgGx/ozONb/\nhvTnqsPpFFcZ+fW2LpzYlczMBWOJivJFrW7+E48N9sBilW6wu16Hd4AbD79xK6Io8u1rW5qFnB6/\npTNXiqv54nclHNl1Yhw1pTWkHkjFZLGy8VgG/9iawD+2JrAlrQiXboHkbm1quEVHHY7ju1C11qZ6\nzkmneBGbz9M/ypcwHxee+O4IXx+6wp9DJiMu/JsdMSyVssFa+pLy+2kJoggPLIeEzXDq15bH/bM4\nvxEKU2/OeygvguK8tnsgACrKlBxne+DuDmX/fgPRjEivhWP/GcjOhr9/Av37Qr8+iufw/odwJRdW\nroL9B+FaEVxKUY737ad4FF//A8aOAVdnGDIIduz417wMFzf4bht4+8Fjt7fcTKfRwvy1CmnXr3ba\nS4K6w5RP4dvJYEtM1/0Z0LrAcZvmOs/BEPoEnJkOciMPRhsFPp9C3t0gNYrbC2pQ/wrSJrDa9kDq\nUHofNgPft/BlHVA8iQwmTb7Gon2JJGQVseGl0cwcHkXy53fhbNAwOj6Aiuo6ZFN1q/c2t7iaxCsl\n9Axru6GurNqERyPtAdEH9CNg389XWfR1MksfH8Lgzv4s26/0Gaj8K1AFlmH8s5Gms7YOXd/9mE4O\nQq51JL+0hvsnSmRflYmcdZRJH+wkvegP6uoikGVnkCW01ScwOShSpuprl5EMbkguvlBXh+eVdD7N\nUaNVi2j2ncXcMwrZxREkCe3qU5im9gRRRKo2oj2YxleC0u+RXlDJbCBNJZDsrOeXQwqdSHVxNQmb\nL9B3Zj+7HehvrznN3DAPEo5kMOvVcbh7O2PQqhkVH4iqkSWICnAjwMMRQRBusLM2hlavYdKcQQyc\n0IVlC7dz5kBDX0NMoDu9wn3ILFSeG5VaxaBHB3N61Z/sPZLO5dyyG95EZkEFaRHeFPxxCVN50/Cp\nw5jOmFMKMWc0NermsbGoTylexNmPFA2FDUdT6L1wEZjNsMlOaWu3kRDUCba2Ec51cIcH1yobrfUv\nQHU7KHLaA2OV4j3c/Q9Q3wQVePIxiO6rGLm2UFEGzjdhIP5dHoQgCOMEQfgSCBAE4YtGrx/5T1WU\nGzQAevWA5GR46x3IyYOvF8OIEUrPQmN4ecHgwfDII/DJ3+H3bZCWAYXF8OTT8OzfYPhQOHjwn5+P\nRqM007m4KyywLS2Kekd4ZS1sWQTn9zU/32c6dJmg8M1IjXmpRRi9DC5+Dzk210XWx21TbIyO63RF\nx/qqjSyq4AGajWB9ASSbSih8UYzDZpRuanvfwwF4gcTEAi5cmMSaRSMYGuPPVw8OwLte2yDzWiWh\nvq52d7aN8d3uZADu/GQPh5LsVyddx9WyWjrUaySoAkE/GOoOwbtfnmNk1w5M7R/GgyOi2HEul9yq\ncrT9sjAeDbnBtQQy1Z138+NPEh98U8LKQ6mMfX8Tbz4l0MNwK7+/MpZeUQIeLkX4z77IU0uPoK5L\nRhYdsGqDwWpBk3MBc1B9OCkpAUtwCKnVMmMCXVBfysY8QCF9Ux9OQ3bS8XrKNfrP38zOT//A2NmP\nHFHEIsl0rDUzCPhbfgUVNSbeWXuGEW9s5bfP/iB6dCdc7BAcWqxWulTUoSupYdaCcbi4N0hTdg31\nZM64GCb2C2PGyGgm9gvjx/2puDvqCPFuOfHZfUgkM+eP5fCWBBZ/sJPca5VYrBK7zl1hYq+GMIp7\nkDsx42LJ23i+2bN9qdKI94CO5G5qygQs6NQ43t6VqnU2krpOOswjO6HZfB5RFHnm1i706tEdb19f\nePUL+Ogl+5747A+VjVVJG4nooO7w8nmoq4S3o2HXB81pbW4Wv78JkcMgavjNXZd4GGJa0alujMqb\n8CA8PKDkLzJ+tO1B5AF/opDznGr02oRSDP+fhwWvQW4+fPMtjB7d3Ci0Bw4OcNddcO4CzJwNMx+A\n8ePglJ2S1PZApYKPf1I0rr96q+VxXgEK8+tH0xURc1tM/khhidz6etPjjr4wainsuF8RVb8OQaWE\nmjIXQbGNkfP9CuqOQ/mKpseFGFAvVRToZFuJDh8UI7EHpQfCnpHQ8e7bSVzbbGLSncHob1HCPdfR\nJ9Kb3OJKTK2E8R795hCfbLnAt48O5oWJcTzYSPsBnRMq/y6oArohOCmdyfGhHpxILUTdEbR9oG4v\nmHIlLudXMCY+EEEQmNQnhP5Rvlz0PoM1yx2poNFCG3GejNJiNv/kzUcbz/P00qOsfs8TB1MYUq0H\nvSO8eOc+M2ZTd54Y243Nxy8jl5/B6NhXUbMrSEZy9kZy8oTaWjSpKZi7xOOk1+BxMhFzn05g0IHJ\ngrDmNPdcq+LDTRfo5uVAx0sFjDmZxV0DwnFVidSuO0PYrH6c/vIuriyZxhPjYgk3WynMKGbi9kRm\nLtrf5F4l5ZTy1oLNdBAEZi0Yi6NL84owR72GqAC3Gyp0NUYLFbVtJ2xFVwNbnXTsv5jPe8+u43xC\nHhqViNmmJDb21i6oJBlDetPuegetmoCJcRQeTsdY1LTKzDAsCsuVUsyphU2Om2+JueFFHEkv58V5\n9YGK7v2h/0hYbMfDDomF25+GtyaBqXXyOdw6wLTF8LeDkHkC3oqCI9+3rNfSEqwWOL0Gji+DyR/f\n3LUASUcgph35B7i5EJPbv9GDkGX5nCzLy4BwWZaXNXr9JsvNSl7+MzB2rLJr/yugVsPMmZCYDBMm\nwKTbYdJE2Lat9cSzPRgclGa69T/CqiUtj+t5C4yaCR9Max5XVWkUUrITy5sLDIWOhci7YNespl3W\n+g4QvxRO3wfGRkZHdIQOv8K1Z8Fok+AVJ4DqKbBMBNl2x+YJfAccAd6nicoOUFdXR2FJGV6ygbpd\nYEkB/XDQ9lYqiqI7uBHm48wLy20I3Oox86t9/HQglW8fHcy0QeE8PT6WkspaErKLEQxuaCKGIXqF\nI3qEoA7rj+gVzoS+gQyb6kdFqJHq7RJSCTywaD8GrZq76+P8KjX8vCyEwCA1/Sad4d11Sgzc5JxP\nTeBZHnlEw5ezBzFzeCRhgRL9+5ZizqvPGagzQLDg5diVV6Z047c57qw+J5BSKIDFhDYvAVOQUp1U\nefwkm0o1HMiqxM9cR3BRCeZ+CqXysS/28ntZLdYIH5Y8Moj3fV3wHRbFxg8n8/msfmT9egrXGH/i\nRnbC390BJ72G58bGMK7GxOjHBrPimRHsOpfLzK/2AWCxWPn87e1czS2n7/19MDi2g9oBeO3OboiC\nwIGklnfcpdVGbnl7OxVmK98tuZckrZrtiw8yJNqP3eebFjCKKpEu9/fFKfkqqqqGpHSvKG+0rgZ8\nR0RzZX3TnJegUSlexG/2vYjSFcfJKzdx++23N5x7/n1Y/S1kN6X7AOCe+eAXpjAmtwd+neDhdfBg\n/SL/Xjyk2aOXaQTJqoz57Xl4PQz2faYUkTj7tH6dLcwmSD0Fnfq1PRb+o0NM10tnztjoOpwXBKE1\nBZn/f0Gng8efUHIW48YrSe6IjvDWm5B3E9W+Xr7wwy745n345oOWx01/Axxc4LPZzZPWzj4wezX8\n8hDk25CuDXxP4bE/bENW5jsegmfByclgbbTD0seBz98hZ0JzKg7xJRC6hwax9wAAIABJREFUg2Uy\nyLbcLm4oCet04Fkal8CuWbOGfp0DcTYoRtqSDjVbASsYJoAmHl65O57v/7jEqbSmu8dF2xNZeyyT\nlX8bzt0DFZF3g1aNh5OejMIqRJ8opVEQQDQjul1B9/+xd95RUZ1r2//t6UOV3kFAUawo9t4LdqPG\nEo2aY0zR9FhjotEc003Uk6JJTDQae4ux94YVsIsNpYOA1GH6/v7YoDAMtuTkPd9532stFou9n92G\n2c/93O26WhXRb1Ygz/UJJ6rZFga+v4eLybnsik9hxaSOeDprQGVG0+UGCkcLQ/qn4iBTsmjHZWZv\nPkpR/V1MfVfNhlcH4uao5kxSFruXqrBkRCOaHAATKs0ZjPqWgAyZKYeWgTpeX28k6t1NGG/GYa4R\niOhQA6G4CM2dJCaeKKb3P3fyfg34MNPM8G+PQ2EpTS6kcbFdLVZM7sjw+n4YDt/AaWBjIvxdEfNK\nyDmRRMjw6EqfSfzaOLwjvGnYNpwO9fxY81YXjlzNYuTne/l8xlaKSgxM/3gg43rY0T2vBo4aJXUC\narA+Nsnu/rxiA93m7MBZq+LInD44apXkezmTIhNoUKIn9trdKsc0bRpEaLe6+CRmUdPbmT4tatKs\nTHvaP6Y+eWeTq3oRHWpLXsSdyiERU49IXC6mM+OFF1FXpILw8YfRk2HR7Ko3LQjwxo+QeBL2rai6\nvzqEtoI3DkGfD+HHobDhLclLv38zeri0HVZNgJn+sPYVUDvCy9vhrWNQt9vjX6scl49JWvUOj1m6\nWpAHNR6TetzTE3JzHj3uMfGoEFN5J1ZfpKol25//XXBwgIkT4dQZ2LgZsrKgSWNYseLxk9khtWDV\nUdiyAj551/5xcgVM/Q2ybkuUArYIbQUDP4MlAyon2+QqSTf35ma4aEPQFzEbtMFw7oXK13QdDTVe\ngpQeYKkYnhJA/h1QA8yjKie6AYm19Zuy3+MBadL4ZuGXvN6zduWhBjDGQel2ELQwfG5NflzQji/3\nX7zPsmG1WpmzLo65w6PpV6Huvxw6vRlBrUJwTUcRfBpl5B5kNdKxFgSh26rA/ZID6ek69l/MoNWM\n33mxRz0ahrgjuJSi7XUVS44jylN1uPz5cHa+14ueTQLpNvYGN856sjDmWfzcHACRj6eUkpyixZwj\nUTCr1AlYzP5YLT5lielYRJfm3P5uDE391Whzb2Aq8x5UCXEsThHYOmcAKwY3YJCPhj6v9mJ7XDLb\np2ziFxE0ZSy2xevicOhaB3kZCWHK5vP4dq+LskLTW+blDJJP36H5mAc9qe3q+rJhUkdcElI5lV6A\nT9c6hPq78qR4u28Ddp+ruri5W6iny+ztuDio2P9BL2RlCe5Xe9Zlc5Ee0508YmrZLxxoPywaJ4tI\nC7WCehXkUZVOarw71ibNphRZUMpx6BmJzra72knNT2YrI3OqGiLGvQlHd0nVgbbQOMK0NbD0LUi7\nXnV/dRAEaPIMzDgPhRkwPwoOfwM/DoMZvrD7Y/CJhLdjYfo56DMHAh6j+qg6nN0J0b0ePa4c+blQ\n4zHZjz094a6dz+0p8agQU7kP+oooincq/gCv/GV38f8jmjSBf30Du/bAZ5/As8Mg92EMpxXgGwAr\nD8PZozDjBftltWotfLAVzuyAjV9U3d/qeWjQD36y0bvWekD/bRD7HqTsf7BdECBqGZTcgOvzKp/L\nYyo49YWUGLBUCCkJclCsBArAYk/sSInE19QVGI3VmkjChct0rGdfJEXUgfEE6PdAn25B/GtlK7TD\nQDsAClsY+PmX9kyZ0RBFLZD7geAiXWLW5Cj8empQRcUid7+DtcgH09VumG+3xJLhDAapGqdugBsN\ngt3Z9G435j7bFLl/Adru1zFdCsCS0Q1FaDvkAY1B5cCqxTXoFuVDS3PM/aoghc8lvDxNLFvlDQgI\nsnsolDcxGqRVvUJ/DVBgVocjCDAn2swZgxdt5u5nxtfbMaen8/ktK7X9XBiuy8PaKYr6tbzZ/3xL\nOulNfCqX0bdpEKbbuRgvpOPQV5pkSjMKuJeQgn+vB+peplITx5ceo9X41qgrhI7MJgvHlp2gduMA\nDsjg/WGP0WhlB01CPaqIB+UU6ekxdwfeLhr2vf/AOACM71KHxO9G0m5AI7yT79ltppMpZDQb1ZyE\nDQmI1sr7/XvVI+fYzSoVTdoudTCcT8NSwbu4kJzHQkGGy7q1UFBQ+SJOLjBhKnw1y/6DhTaCUbOl\nviLT47OaSuf2hHG/STrxNw5DZE94/xq8eRi6vgWeYY8+x+Pg7E5o9pgGQhQlD8L1MT0ILy/I+fs8\niHJ0t7Ot9192F/8/IypK8iiCgyGqEezc+XjH1XCHn/dCVhq8NhQMdpJrzu4wdyds+Rr2/1p1/8BP\npYl/87uVt7tFQO/VsGME3Ksg8CPXQvMtkPwDpNs03nl9DOpGkDYQrBXuRVCDYhOI58HyVuX8hjQA\nSdrjNURxAj17ON0PL1UHsRCczqmZNeYcGofltGz7O8+/chhjmhWsIPMAZSRoOoLDYIgZHsDx49no\ntoHpQm2s94LBokI0lWJJjbt/3oNzenMrs5AADwc09e+iapmM/lAYMAC5dwSCoycy95ooG7mhCE/E\ncKIjiNIrIHNOR+cUT++XzLzeqyEgotacwGiIAlEL1lJUugQMTq1AEIg/e45IVwvXlTU5dzuX0do8\nXo83EFXbH01KNrK8IsxNJU+qXVwyGV3qkmmy4u2qpvi3MzgOaoys7HNK2XQOv571UFQwBGdXn8En\n0pfAJhVKcYFjf1zE3dcZt+hg/D2cqfGYeQdbqBRyRLOFe/d0FBcbyCoopeuc7Xi6aNj5Xs9KxqEc\nLg4q+j4ThaHUxIVqwlP+Df2RK+WkxqdUvp6bA56tw8iw0aKQOajQdqyNbscDj2Dz6WS6jB0NPXvB\nUjvyLyNfhgun4bz9PBZ9XwGvYFj2lJX4UYNh/Gpo88KT5xcehdx0yEmF2s0fPRYkHia1BlSPWULr\n5CQtOEv/ZHVWGR5a4iMIwstInkKYTc7BGfuCAf87odHA519An74wfizE9IFPP5MkAB8GB0f47nd4\ndzRMiIFvNldtqfcKgg93wPQu4OolJbHLIVfA+DXwWQsIaAytxj7YF9gJ2nwEW/vBsydBU+bya3yh\n+VY40R20NcGtTJtBEMD3O0gfIf0ErJN6I0BqSVZsl5LWluEg/0WKFVVCDKtW7WHZsnyUyZmYrnqD\n9eHrj0XjW9EgyJW3fzkJQAtXbwba4e37dkMi2+NSmNaoCeakY6B2RpArEXX3qFhJ5aRREVXLjdIG\nN1GEOaHfHQH4I/hVKKNS5KEM/AbjpWGIZXZQUBWjDj3Ms5NlpGRYSM0toVZANghmzKYIBGcfVAUH\nsXi2Am04FGfTkhs8d0rB4cx4JjVwprGvQKBPOBObBaPacQxjpyiQy5FdzUTILsIyojnsvkL+6RTk\neSVoO0md2bq0fPIvphM+7gEvf/qFdFITUuk/vzJZQV52ESd2XmbivH68sz4BL5fqjUNRkZ5Vq85w\nL1+HXm/GYDCh15vR600YDGaMRgudRJEZM7diMlu5JJcREOTGtmnd7RqHcshkMvqMbcXahQeJiApE\n41B54hIEgQb9GnLh9wsENg2q1LPh36c+52dtI6Bvg0rG0KFnJLnTt+A4KAqZk5qj1/OZ8tEgCAmB\nfn1g8mtSHrAcGi28MgsWzIRldmRwy/MRk6KgSTdoHlPt8/ztOLsTmnR/vP4HkCSNHze8BNKze3lJ\nYabgP6/I8CgPYhVSrmErlXMP0aIoPoae3v8ydO4M8eck2b/oJnDy5KOPUangy1UQWgdGd4Y8O+5h\nSH2YuRE+Hw3XbFZNDm4wcatEI3DLhmC3wT+gZh/YPrSyVq9rY4mO48wg0FUg4hPk4LdCigVl2irR\neYBiL6AEc2cQK5fhiqLIxBe/5faaQGSeJWj7X0YRlnuftbU6TOweSfqSkbg5qWka5ml3TIS/K7ey\nK4S+DEWIujxsy2wLzSXM/8YZfz8V+t0RiCVqkFeYWAQDCv8vsOT3xFpYltQVzKjDDpB2LZx9J0SC\nPJ34cN0JlOozGEtbI/MIR1nDBXnJDax1X0QR1g5lzm0Uzh7kKL3JK9IzL9yKpWlz3hrQmHpFRWC2\nYGkoSZCq1sVhGhRFidmCHDCui8NpeDOEsj6QlI0J+Peuz4HrWaw+egOjzkjsD8do8482qCpMvqIo\nsuOXk7SJqU8NTyfeG9yYy6n5VRL9APfu6Zg7bydarZKY3vUZMSKalya2Z9rU7nw8fwDf/OtZlv8y\nhgR3R3qPa80ddwdCSo1MbhL4UONQjqDa3oQ39OeQTWVSOYKbB2MoNpCdWPk7ovFyxq1JIBm7r1ba\nLnd3RB0djG7fVRKScjmflE2HDh0k77xhI/jJTnPmM+Mg5RacOGD/Jp3d4d2V8NUL0qr9PwVn/o35\nh3KUG4i/AI/KQRSIonhbFMURZXmHUqS30un/BIOqQY0a8MtymPdPGDQAnh8Ddx7BvS+Xw+xvoE03\nGNcd7tnJZdRvC6//ALP7wlUbw+MbCaN+gh8GV6UHb/+ZNEnuHV95wvftB2HvwMleYKggsCNTQ8BG\niR4888XKyWlBA/JfJUU6U3OwPnAik5KScHFQE+Hqg+FILUznW6CMLEU7IBFF7RyQ2yfT+yMumbqv\nr6eWrys9GwfaHdMh0heT+eF01DL3EtS9rnLjiojflUZgllZoYnG2REOizEIRMgPR5Ik1dwhiYQYg\nog45Tk6OmuAhl+jeOJAdM7rz3oh8Tl9zwWr1ROYZhvzad1hqjZP4rwozJEqN0Bak5hQzIQisajWW\ngECwWFHtOytRaggC8nNllBqtQ2kS6skLGgUpehPqpkEcuJjO2g0JFF7NYp3JwpAv9vHi98eY9fo6\nzhgtrLGp7Ll86g737hbROqY+ADW9nXF1UNHro8ohzczMQuZ8uJ22bcMYM6YljRoFUCfCh5AQd3x8\nXHB11aLRKJHJBHzdHPly20Uu5emY/X5v9h+4xqbN9id9W3R7NpqEIzfIrSAedP9/IZNRP6Y+l2yS\nzwAB/RqSsefKfT2Jcjj2aYBu9xWe+XQvM997Dze3Mq9v3kfwz49AbxOCVSph8mxYaNMTVBEN2kPv\nF2Hhi/8+3rUngVEPCXsrRwEehacyEN6Qnf3ocY+Bx2Vz7ScIwnUgCTiERN/5f4JBD8OQIXD1muQm\nN2sKn3z88C+pIEh13u16wpgukGdnBdCqP7z+I8zpB1dsvIWGfaWGncU9IKtC3kGmgJh1UJQM+1+u\nfA/hb4L/UIjtZmMkHCFol0Tsl/4cVFR5FQSQfwCKb8E8GNH8OcuX/0yXTh0Y1V4qTVXUbIPg2B1z\n8qtYMoajqCVDO+AiyvqZoKw8MUz59TQj2oWzfUaP+7TgtvB00WCximTYMIcKjgYUdbPQ9EhE0/kG\nH83OY9rUDCoJEFnNWHWrUQZPwVrQCUvqq1jSLyGW5qP0uYigKaDN2Hwm967PdxPaEhF0lchAVzrM\nyGLTqdso0raB1herVxsQRRSHv8XSaACi2gl3pchn9eWo27eTGuZOX8Hq6oildpmxWHMW47AySo0i\nPS8ZLfzm54ogCIz6+gC5mxJYIROYtTGBbg0D2P1cc0KsIlcDazB7bRw95m7HZDajK9KzY/lJBkxo\ni6KCLsYf03tSarSg00uf6a1bOcydu4MB/RsxoH8ju59lRTQJ9SA+KYdZz0RRN8yLmTN6cuzoTbZt\nqzqx28LJVUvr3vXZvz7O7v6wtuHcvX6XogqenyiKHL1bTIZawS+LD7F4xyWW7pW8hu3pBdwSBIZ7\neDFtegXm1qZNIaoJLFtW9SJ9hks5vDMPEQV6diZkJcHR9dWP+btwZockL+puv4jDLnKzweMJ8yC+\nvpDx19CbP26Seh6S1uQ1URRDkcpWbLkY/g+2cHGBD+dCXAJs2QzDhkrhp+ogCPDOfOjSTzISuXZW\nAS37SuR+cwdCkg15X/NR0G8eLOomSR+WQ+kgVTbdTYDDb1U2EhGzwW8wHOtgQ+znBIF/gLUI0oZU\nTlwDyPqA8hT5eUvxcv0Hb/b24b1BDRCcfRCcHoSKRF0IltRxGE60QnDR4zDgEsomqQhaqQkwI6+E\naYMao1VVnw6Ty2R4umg4dycXwUmPsl4mml5X0fZKROaqx3TBF92mhmiyvbhbqOfc7XIPzIqyfjyq\nejvQH2mH4YQG05XdWHOTkLumoPC+wsa1tbhXZGHeiGjcXG4iV6TjpY1h9rDm/LLnFLLU7ZgjJoIg\nIEvcB2Y95sDGbDyRxMvuxeR4+iO6uUOJHtWR8xh7tpCMxdEbiM5qLFGSV1Sw5iybrCL1yyRAY5zU\neCtluLcJx0Wr5KNhTUndfolnpnRn44yefPBME04nZvPer2f44esD1GkWTFDtyhNFhL8rfm4OrDx6\ngwsX0vns872MG9+azp0foVBWhrf71icysAaz18WzOyGVGjUcmD69J/v2J7LLhqrbHlr1jOTO1SzS\nk6qGRRVqBeHta3FtfyKiKLJkz1Wip2/jtV8vcKJWA5RxKSzan8xX+1JoN+t3pq2/SkKzNrzmYUdL\nYcZMqVLQZMMdpVDAhCnw/UNUHJUqmLwElrxRvQLd34WDq6DTyCc7JjcbPKoX1LILPz/I/HsNhEkU\nxVxAJgiCTBTFA0Czv+QO/jcgKAgOHJL0Ytu2lsgDq4MgwBtzoftgKSeRY4dyo3lvmPg1vN8LMmzO\n1Xo89JwJi7pCXoXQlsoZBu6E1AOSGl3F69WZDcEvwPEOoKtQnSLTQuBGqZIptR9YKwu9IITw/LgA\nPOSeTJ5/DY3nPQRVNYl5ox/G2JqU7qiLIBPR9rkCXS6xbn0wAV2zUTbMQFErB3lAPjL3EsmAlKvO\nuZQy+a0adH/zHpoe10gqzuWbz4vIWx2J8WQIlgxXsMro00SajCd8dwRUejTt9yL3zKJ0bz+sud5S\nA5RoQdDcQx1yDMOtTqw7kk27SD+UyiyU6gT0uq6AmpHtQpnfW8fV4tqIVgXo7iE/tQJT9HAs2dfY\nvfssgwLkeHWW6LhVB+IwNwxD9KoBBhPKjQkYn20GgoDpVg66M8ksEqBH4wCsZgv983Vsd3dk9oho\n0peOwno1C/dgN3zq+HD25l1OXcvCWaOkhs7AveR7GKrJz0wb2Jila+P45tvDvP5aZ5pFP37kN8jT\niaNz+/Fitzq8+lMseqMZDw9HZszoyfbtF9m/P/Ghx6s0SjoObMzeNfa9iPDOtTm36wqB/1jFW7+c\nYPLU2dy6k8LXx47TKDKS8yt/43ZyKleuXOHWnRTe2rETeXY2hgSb7urWraFWLVhph6xv0PNwJR4u\nJ1TdV476baFlf1g2rfox/27oCiFuN7R95smOy3sKD8LP72/3IPIFQXACDgMrBUH4Gih5xDH/h4pQ\nq2HpD/DSyxKh4K5d1Y8VBHh9DsQ8C891gmw7/+yOw2HE+zCze9UkXPuXoPMbsLBrZSUtjRsM2g3X\n18NpG73rWu9C+NtwrCMUV5gYBBX4rwJlICR3AVNldbl7ecXc3l8f4/lmZRPyfuxxNJUruIklaoxn\ng9Btrc8PX5RyZJcFa4EWBBGZRwmK2jmoWiaj6X0Vh+HxOAw5h6brDQL91Py0QM+8cSJRXeP4dOlt\nOs/ewb4LD569pCyuHRCuQ9vtdyz57ugP9wBDhYoruR5Nrf0YU5tjLfGme6MAsgvT0WgPYSjtiChK\nVWS+yiQctFpazD7P1SNbkO36JyavWpgLUsFkYJZfCWkRjUGlQpaZi+JqMsaOkiyoctdlrBHeWMO9\nEEWRopWniavni7uPK2E+LmTtSyRfqcAcKk36ZoOZi1vP0/gZqeHuSko+eSVGPB2VGBNSUUUFcju3\nBKO5Kr2Ln9FMqMHMGYWcEuWTC0Qq5DLe6teQe0V6WkzfAoCXpxMzpvdk0+ZzHD5846HHN+0Uwb3s\nIm5drPwd3BmfQp1Zf3ATgSUvv4ooikyaNAmlUokgCARNnkzKv/4FQN26dZHL5QgKBS4TJ1JYtr0S\nps+Ej/9ZleJGrYFxb0nsBA/D2Plw6ne49DdrVJfj+CZo1FkSOnoSPE2Iyc//yRgeHoLH/UYNQEpQ\nvwnsRCL+/9/XSf1nIQjw8iuwdj28MA4+/eTheYlJ78OA0TC6E2TaEucBMROh14vwXg8osmFw7PyG\n5E0s7g7FFZLeDt4weC9cWgoJiyofU/MVqPshxHaBwgrhK0EBvj+C8xC43QKKtwOg0+m4cOkyYb4u\nWNJqUnogBnnwOeTeUxG0lyg3FJbsa4h6m4Yno4IdB4pZ/GMmnyy4i+m8P8aTIRgO1kK/I5LSjY3Q\nrW5C6cFOmBImoNaNYeVBB2avieeT0S059GFfgj2dGfrFXtYdl7h5pvx6mnnTHPjtNxPvv6/CdKHZ\n/T4HAGQmNOEHMd8LwZwn5Uu2nb3BhulWjIamkggQIJjzUenOE1yrF0GeTmzfcxRZUTYmP6mRTXnp\nAoklIpbAYKlSaecpqaxVq4bCUpQ7L2McIjWwGc+nYS3Ss89RQ6iXI+YSA8mbz/GLQn5fyyFx31W8\nanvjUdZlrZQLBLo7EJKrI1cpR+HjjEwmIKtQMiqKIqvXnGX//kS+mN+f1lFB9Ji3kwXbbMKOjwE3\nRzUrX+/MrawiTpeR5/n4uDB9Wk9WrznLb1vOczu7iEKdsUqDnFwho8vQJuxdG4coimTe0zFi4WFG\nLDzEZ19+xZubtyAcPlzlOL/nniNv/370aZW/1y4TJlCyfj0WWz6hTp2k6px166o+wLMT4cR+uBxf\ndV85nGpIXvdXL0h9CH83nia8BJKBcH8KD+LvDDGJolgiiqJFFEVzGVnfwrKQ01NDEIRGgiAcFwTh\nnCAIW8o8FHvjegmCcFUQhGuCINiq2fz/ifbtIfYkbFj/6A7sl2fAkBckI5FsJzQ1dCo0i4FZvavG\nWHtMh/p94NvelXUknAJg8D44+xlcsGlEChoL9b6U+iTuVUgzCTLweBcC1kPmRMieyvKflxAZ6EZk\ngEQkJha7oN/XF/MtT+QenyL3mYy18Fus2VVpEbILSjlx/S6ffPY5vx6+YbczV3AJQOHXAcHJh4Ed\no9ixeApZW+fyQpcIfFy1rHmzM+8PjWbSj8cJf30FU2bn8vorcs4ub83SFZW7aAVNPpo6f5BwzUD3\nV++Sla9j3fHrvDEoi6ISX8ymsri9VY+mcB9Gx2YISldkFhMvhtzDENYa5AqEgnyU164w5YaCUpMF\n+eU7oDdibiodr9p8DnObMEQfF0SrleI1Z3Ea2hQXRxU6g5mUzefZXmzgZGEpHev5Yio1cWnbRRoP\njrp/r1FhntTWKImUy9hnkT6XhiHuKOTS62o2W1m69BiXL2fwwfsxBPrXYOH4VmyZ2p35m85V8qoe\nF90bBQDcb3JMTM9n0uoENhWUsn5dHM98sJPAl1bjN3ENvT89xORlJ/hm12WuZxRQv0VNrBYr7y84\nQKtZO6jTbgAZGZm88sorBPfqhTE/nyybkm+FszO+w4eTtrSyCJXc2xuHPn0osk1KCwLMeE/yImy/\nK07OMOUzmDq2ap6iIto+Az3Gw5stIfHUE39GT417WXDtFLTo++TH5maD+2NqXJfDz+/v8SAEQSgS\nBKHQzk+RIAh/Vhn7B2CKKIqNgU1AFdIhQRBkwGIkavH6wAhBEOr+yes+EqLZjPHqVcxpaViLi+1O\nXn8aQUGScFFgIDRuKK2MqrvOhCmSG/1sGzi+r/I+QYDxn0jUwVM6VA43CYJEGxDWDr7qWDnc5FJT\nMhKn/ymFmypeO+BZiQH2VH9JurQiHNpBzTgwXqNHxEzeGmRTgmeVY06qQ+mOgZiu1kYZeg5tn/Uo\n6yUgaB5UId3KKiI8NISRI0diFDT8fqZy6ApA7hle6W8XrRLXwDoVL8bkUe6sXurMyVgLJTrQ7Yuh\nnlsIdwt0ZNzTYbVa2HBtJ/rALbz8cT7dJhZTordQ7411KLXHCfd2J6hGF+l0ohlN4T7M6pqYNVIX\n9IaBjqy9ZqbL1/FgtaKOPYqxURPSDAJysxnV7lMYe7cEmQzZ7VwUp25jHCAJ0euP3kRQK1A3C+bY\ntWy6+rpw9+gNfpUJRPi70i86mIvbLuDf0B+3CtxFoR5OKC6mY6rrg1Emo209P7o0liZwURRZtOgg\nBQV6ZkzviXMF7qZ2dX35eFQLnlt4gELdk+kvlzsn8zYk8POBa0RP2UydNjHczM5m0alTtFBruXXu\nHDeT7vDeZ98R2nEke9MdaPLuJlyeX843d/IovpTJ5m07+OyLL/HwkL4XgkxGvRdf5OqPVXsZAiZM\nIP3nnxFtCCmdJ06k6Mcfq753vcr6B/burfoAg8ZIZJgr7YSnKj7k0Knw6rdSufilv6nXd9u/oNVA\n0Dg8eqwtcjLB+wmqngD8/SUBtCdlnLaDR/VBOIui6GLnx1ksD9Y+PWqLolgeENwL2MvetACul/E/\nmYDV/Bu1sK0FBeR/8QUptWqR2bs3aS1acMfHhySVitseHiSHh5PatCnpnTuTOWgQ2WPHcm/+fEyP\n6nOoDhoNfLkA1m2AOR/AM4Orr18e+TJ8tQbeGQXLF1ae0AUBJnwpubDvtIXUa5X3Df4Cmj4LC9pB\nTgWqZLfaMOwYJK6SSmArNtP5xEDrvXBlGiTOqXw9hRel7iuZs7iUwc9eRemXANj2KciwpNZEf7A3\n+sPdETSlaHtuRt3qIDKvDEqNJlQqFZ6enowa8zz7L9kRBZLZqWySm5EHJKNqdhSHfmtxbH6KHg2D\ncbsSw2uvy7mdqcdVq0IETt1KJVa/hqbNMvnmuzCejYzhzrcj2D87hlnDRbo3csJD2xuQgWhFXXQY\nUe6MyaGM5jv3NrUdSonq0psT17IpOHkGlEqKQ8IpLjVR//JNLGH+WEN8wSqi+uWEFFpy1iDqTRSv\ni8dpVHMEQeBmRj7tk3IJGtwEvVrJyHbh6PN1JO5LJGpoZT6lHStjEax1AAAgAElEQVROEtEwgOHD\nojEBbSJ976vCnTiRRPbdIt58swsaTVVKk/FdImgX6UfdN9YzetHBqp9fNRAEgefa12LzqdtM/S2e\n2NhYFi5ajJeXF7WbN2fMJ5+waNw4vLy86NGjB7NmzWLX3gNYrVby8vI4m5dHRHgtvHS6KueOGD2a\nm+vXYyqpnLZ0jopC4eLCPRtBLk27dohGI4ZTNqt8QZCEvBZ+be8B4L2F8O1H9gs7KqJVf3hnhVQJ\neOHQw8f+WZzdBbt+gNEP0YGpDlYr5GY9eRWTRiMJB/0Fieonz2r9dbgkCEI52fswwF6XVABQkdQl\ntWzbXwrTzZvkvPYayaGhGOPi8F63juCkJELS0ggtKSFUpyMoMRG/3bvxWroUt1mzcB4zBm3HjpiT\nk0mLjia9c2eKli3DWvgUjlXr1nA2HiIiJJnUi9XUobfsBGtiJU58WyZYQYBh06TE9dSOlTuuBQF6\nzZQ0c7/qUFnb2ikAhh6V+iS29gFDhVyBSyNodwKyd0D8qEpU4b9v28aVq/4YrvZH5ngXTd0/EDT2\neejFQjeMca3R/TEEy10f1E1P0PLFI3z6mQXMs3nrDS1mv1u8u3sbVwpvSp6GYMVaUOYNyfOQue5G\nETgPZe3xKMOvYM13p3RfH/R7BmC61AR5kTd+bk5sPnUHmUxg0jMetOlznDvpZjTJg3mzW3taRXgj\nl8nQWy4yrB04KvoACimPUHIKQTRhcGoLgoBg1KFKOoGhVnsahfvQJ9QJ16RrGFq1Jfb6Xbq5ylEn\nJmPsIXHqKMqkOc0dJM+jZPsllHV8UNXyRhRF2pusZOcW03jlKUqNZp5pFUr8+ngiOkfg5Pkgunr5\n1G2SE7PoNboFdQJcyS/W03TKZmqM+YW3lx1n5aozjB3b6qGKfGvf7MyMwVFsP5vM6z8dr3ZcRXy/\n5yorj96kcaOG3LyVRKtWlbUKuowdi66ggMtHKyd5BUHAzc2NGm5udJo6lQMfV00WO/r749u6NUmb\nNlU51m/MGDKWL6+y3Xn8eIp++qnqjY4aBadPSdrxtgivC4PHwuePUa0U3ROmrYaPhkC8HY/kr0D6\nDfhijMQw6xX06PG2yLsr6UA8Lg9TRYSESNLLfxL/VgMhCMIeGw2JC2W/+yFxRL8qCMJpJNX7J/OJ\nq8Hs2bPv/xw8eLDacaIoUnrwIJkDB5LWqhUyJycCL1zAe+VKNM0rE2kJSiVyT0+U4eGoo6PRdumC\n46BBOI8bh9e33xKSlobr5MmUbNlCcnAw2aNGodu5E/EhqmlVoFbDx59IMqndulRP+hcUCr8ekphg\nZ02s6kb2GAevLYEP+khldRXR/mUY+Dks7ga3KkwcahfovxXc6sDaNlB4+8E+jS+0OSB1YR9rB0US\n2ZrVaiXhZiZWowOGG90w59RBG7ETpc8FqnoTZTCrMN+MRLdzAHM+cEHr0BQQ8XDLYOHXXXhzqhqX\n9sfRdN+Kw+AVaFp/gLLmeJRhryE4XsCcFYVu+7PoD/fEfKMeoq6yZGar2p6cTbqL0vccX39QgiG5\nBYOCR+JX44GzK1ck41HjCjFzICNPuk9F6SXkpiz0zp0luhFRRHXzOGbvCKzO3mC18lFAMcsKXRAd\nnUjLKuA7TxFjTCspMV2kR7U+DuPYViATsNzTodt9BednJc/g3JVMhutNfKgz8Xq/hqR+PxLXUhNp\n51Jp0O8BbXRhXgl//HyCwS93QK1R4qRRsuiFNozvXJs1b3Xl+IHrJBst+AQ8XDxGEAQm9arHlmk9\nWBub9FD1PoAFf1zineUnOXbsGAnnzuPjU3XFKpPJ6PvGG/y+YEG152kyYgQ516+TcroqiV7dceO4\n+vPPVbb7jRpF9saNWGw8D+cxYyhZtw6rrUei1cIL/4DFNgUW5Xh1FhzdDQmP0aYV1RVmbpDEuc48\nJsnm40JXBB8OgFFzpI7up0F2Onj7P92xQcHVMjgcPHiw0jz5MAj/lvj6E0IQhNrAClEUW9lsbwXM\nFkWxV9nf0wBRFEW7ajuCIIiPeh7RYKB49WoKvvoKUa/H9Y03cBo9GpnDU8QH7cCSk0PxmjUUL1+O\nOTkZp1GjcBozBnWjR3e23sfx4zBsCEybDpMm2x9TXASvDJAqHD5bUVVF79Ix+GgwvLwY2g+tvO/y\nTlg+BsYsh3o2vDAJi+DMfOizEfwq/DtEEe4sgcT3oNZU7rmNxd/fn5wfR90fIqiKUIccQ1DpMGVH\nYs4NB2vV1c/e82lM33ST85cTUVSQhBVFkX4xPWnkkMPMwY1AaURQGxBLXUBUSJQZD0GneWvY9LUC\nvxqOGG63RzRV7MkQUShvoFKfQa/rTr/58Ry7ksm5eY2p5ZCI3jUGUS6NV6aeQ56XjL5BH5DJUJ5P\n4NyZq7TYr2fe8GhcjiRQV6OgzZwRAKh+Og4qOcbnJN2GgiVHkblocB4utQqd//4o64/foqRrHb4o\n03bY8/FugqKDqNv9gdDPb1/uwy/Ug06DorBFWlo+H3y4ndMaBS/FNODlnvWqjLGFKIq0mL6Vbg39\nmD+qhd0x3+2+wuwNF4iLT6B27dp2x5RDX1LChJAQPj15Er/wcLtjDn35Jenx8YxYUVm0x6zXsyIw\nkCFnz+IcUlnvI653b/xGj8ZvZOUqn4yYGJxGjsT5ORvat9RUiTn5ZpLUW2SLLb/CL1/BupOPR4p3\nJVaazN/4EVr+BcWZVit89IxErjn5+wcJnifFgT+knMoP25/82CnvgocHTH20NyUIAqIo2r3J/7EQ\nkyAIXmW/ZcB7wHd2hp0GagmCECIIggoYjkQc+FTQx8aSHBZG8apVuM+fT+ClS7hMnPiXGQcAuacn\nrq++SsDJk/gdOICgVpPVrx8ZPXtiun378U7Spg0cOQbffweTXrWvF+HkDEu3g14Hrw4CvQ29b/22\n8NEe+P512G3jqtfrBS9uhhXPw9k1lfdFTYauS+H3/nCtAiW4IEDNidDuJGRtQ3WmBwHulStGRKMz\n+us9Mdxpg9wpC4eG61EFx1YKPaXllvDC97HM+Wh+JeMgXULgy68Xs/TATU7fyAGTGrHYRVI1rcY4\nrI+9xZRfY5m5fS3blpbiYg1Df72HjXEwoNYeQqm6zN28rhiM7ozrXJsuEeBNAnqXbveNgyL3Noqc\nWxijnwWZHFleLsprV7kdIU3amzadZYTGyvPJBrILSpHdyEaekIJxsNTDYLqTi/FcKo5lVBelmYXo\n41PY7KBicIua0mdwPo2SnGIiOj9IuKfcyCbjTh7t+lYVohFFkV+Wn+SZQY1pWS+AlYdvsPn0HcyP\n4KcSBIHxXSJYsjeRraerriY3nrzNzNVx7Ny1+5HGAUDj6EiPCRP4Y+HCasdEP/ccl3//HaNNvkGh\n0VBr+HASbcJJgN0wE4DzuHH2w0yBgdC9B/xsh34DoP8oSU9lvZ1j7SGyNcz5A77+Bxzd8HjHPAy/\nzYX8bGlx9rTGAco8iCdMUJfj/4cQ0yMwQhCEROAykCaK4s8AgiD4CYKwDUAURQswCdgNXAJWi6L4\naA4AOzAkJJA5YABeS5bgt2sXDr16ITwGc+WfgapuXdw/+oigGzfQdulCWnQ0udOmVa3xtofQUDh6\nHG7dgq6dwZ5xUWtg0QZwdpWoObJsSttCG8HHB2HVh/Dr7MrypWFtYNJe2PS2pJhV0fMK7QOD9sDR\nd+HY9MqTs2MYtN7P1tMKTnwGStV5KutSC1iLfTEkdaL08kBEkxZN7T1o6mxD5nGV9zedYvjIUQwd\nOszuY0dERPD9Dz8xfOERUnMf0oupKGV/ykGcah/h47nXeW20hYKrHZHnNgGlo6SuB8jk6WidtiKK\nGnTFMQS9uJO5GxKI8DKxbKSF51YI5JRKjXRyfRGq5HjMfecir9sdRc02qI8fxRDdnG+P3CHSx4kj\njZ1xeqYdGSbIzdeh+jkW4/Bm4KBCtIoUrTgl0VaXMbHeWXMW9251uWu24KCWYzFbOLPyNE1HNENW\nIY+wf108HQY0QqGsuuI9fvwWhYV6enSP5JUedZHJ5bz47RHGflN9gtVstvL7oevUdXNgcHQw822Y\nVw9fzmDMooOs37CRtm3bVv852yBm0iQOLF9OaTWUMU7e3tRs04ZLW6uu4+o8/zzXli+vUp3kPXAg\nBSdPYsisXKjg2L8/xvPn7ReBTH4NvvmX/co/QYBZi+DrWZKn/TiIaA5zd8G3r1YNzT4uRBE2LYDd\nP0qhK+VT5A4qIivt6UNMwSH254wnxP+YgSjrpagjimJdURRnVNieIYpi3wp/7ywbV1sUxUe0S9qH\nOSODzP798Vy8GIc+ff6K238iCEolNaZOJfDcOax5eaRERJD/8cdV46u2cHWFbX9A/wHQqgX8+mvV\nF0KplEJMnfvCkBYQb0PiFxgBX56A+N3wz6FQWuHFDmgIb5+A85th6SDQVeij8GoMw89Adhxs6ALF\nFctnZRxPj2bFvnrI5NloHbcik1etmBBNDpgyokiL7cfP6104nBzPks9TWDA1B4p3gaXQ7gs+ePAz\nTHrjbUb9q2IZohWZYzZKv3g0dX/Hof5GlG5p1BDDkN8agk/uCAJc6qEI74Cybg+UdTuh9ryJWnsM\nY2kbjPpWnLpxD6PZSlHRXSLVp3hvp4aTKSpOXr+LrDAT9eXdmHtOAzcpoajYsw7RPxRLzTDu5Oj4\nNcIRpb8HloZh1HBUYd18HtFFi6W1pDRWeug6osmCtovUE5F7KZ1717IZfvwmkYFuNAn15PIfl3D2\ndiKo6YOk5c2L6RTkFNOkQ9VVfH6+jl9XnubFCW2Ry2VEhXpw+MM+LHu1A8cS7Ve8GYxmZiw8wNaD\n19iyP5HbF9KwpuQx6Z+7+PrXU6zedZkxCw7y1YIF9O37ZLX5HgEBRLZrR+yG6lfaDQYN4rIdA+HV\nrBmi1UqODZWGXKvFMyaG7M2bK20X1Gqchg2j2CZcBUiFHWo1HD5s/ybqRUHrrrCimlyFPYRHwfR1\n8NlzkPyE61CLpcxb/xE+Owruvk92vD1kJIPfU5Jmh4XBrYdQ+jwm/ic9iL8F1pISMvv1w2XiRJyG\n2V+1/l1QBAbitWQJ/seOYYiPJ6VWLQq//RbxYc09Mhm8/Q7s3A2fzIeRI8DWA5HJ4OWZ8OH38PIA\nWG3T/ObuCx8fkETS32kraV2Xwy0Q3jgM7iHwaTSkVODVcfCCgTsgpAf8Fg13HtCDDHpmKFOWXyU5\nsy1GQzRq7VHU2kMIQmWjt/roTYJfWsMn6yxkqr9H738RpUtnyJkFNwMgUQXXveFWXbjTBlL6QvoY\nxve7yPNDClB4X0IdegiHxmtQB8eCzIIxtTk5p4Yy+DUrAUITRJND2efbBMHBDUF/GWXGWwhKBQaH\nd7BYpMK3iUuO0TXSmVmdMnhpjYmuzdtQqDMQ5WlFc+0g5q5vI/pIbTay88eQ3byAuf8LIAh0VZoI\nzc3D0Lc1CAKja3pS91wKxvFtQBCwFJRSvC4OlxfaIMhkXE25R9x3R1irlpOSq2NYyxAKMwu5vOMS\nLZ5vdV9IRxRF9q2No8vQpshtKpNEUWTZshN06libMBsupm6NAsgpLK2SgC4pNfH253tRq+R8M7MX\nX03twcavhpIoQkCIO24uGn7bm0hNpYpfp09nWJMmfD19+hP1+nQZO5b9dhLO5Yjs14/EnTsxGyvX\nnQiCQPiwYdxcu7bKMT5DhpC9virjqvP48RQtW1alVwJBkJLVP/5Q5Zj7mPQB/LwACp+ApK9Bexj/\nKbzdGub0lzyCW+cqe9+20OuknMPti5Jx8P6LlBDS7kBAVX32x0JYmJSk/pO9EP/VBkK0WskeMwZV\n/frUmDHj0Qf8TVBFROCzZg2+v/9OyebNpERGUrxqVdWXoCLKpU29vaFJY/tiRJ37wG9HpQTd+y9B\nxRdUqYY3f4Lu4+Ct1nCxQu25QgVDvob+H8O/esLRJQ9W9oIMWrwHvVbBvomSjGlxOp07d8ZssXLs\nahYWczClxQOxWp3QOm5BqUpAEIpZe/wW//juCMuWLePazds8//zzePtFgturUPMURBRBnRIIvQAB\nm8DrM3B7CRy7obf4IWJFUBVhKfKl9HJ/Sq8MwJTWDGuxL++viaN+sAdB5SWighzBwQV53jIUOZ9j\ndhuHxfM1ZK7S6j67oBRvTRHbJpj5/oSG7VfVhPk4E+ZsJSwrFkNoG6y+ZT2Yd9OQb1mCecTbiJgR\ninXMU5dyrkVD0KjBbOG1pLt87qxBLLt+0YpTaDvWRhnsjt5o5vTGeCwI3HJ3wN1Zw/lbORz4/ggN\n+jWsVNZ65UwyVouVemX5iYo4cSKJjMxCBg+umrRWK+U4qBVcTH4w+RUU6Zk8fxdBvi7MfqUDyjJq\ncGetkq6NA1l+6jb9u9YhQWfh5+OxHM7NZdb333N0xw5+t7dKrwbN+/blzoULZCXZlx118fXFq04d\nbh2qGgILHzqUm+vWVTFIHr16UXjmDEYboRtVdDQyR0f09jyFUc/Btt8hvxoDEBohMSMv+/LxHqwc\n3cfCkkToNApSr0qe90hvqSR22zeSd1F+//nZMK2ztPiau1Oi9PirkJEM/k9pILRaqWHuxsO5tB6F\n/2oDkTdjBta7d/FasqSS9OF/CtTR0fjt2oXX0qUULFxIWtOm6I8/pG5dq4WvF8LCxdC/L9h7qUMj\nYN0JqVlojA0brCDAwDfg7V/gn0NgZ2WaA5oOhTePwqFFUpWToUIOIKgzjL4MrmGwshFCwtfUDg1i\nzKKD3MgsBJSYDNGU6nojyPQoNFsI9jvMV2815vlRQ6p/JkEFCh9QR4JDW3DqC65jqNlkAR99ryHx\nbD3MOXVsks6w9Uwqr/R40FQvk2WizHgbLHmY/L5CdCir2jEbSM8t4uip7fw+QQTX5rw+ZAiXFjxD\nfU8Fu/pBao36WDyCsWZcgoIclD99iKXXaETfQCyZiai2HGODRc3hUuk7pNxyHo2vC58W6Fm84xKG\nhFTMt3JwGiR1UKekFxB4IZ2kRv54OGvJzNdx7XgS2ZlFRFaoPrJYrOxfF0fXYU2RyQSy83XsO5fK\n7vgUriTlsHzFKSa+2BalnbwEQJNQLyYuOYbVaiU7r4SX5+2kWX0/poxrfb+xrhyDWoagM1qYv/ki\n/foPoH79+mgdHGjYogVzfvqJL999l9ysRzSYlUGpVtN+xAgO2Eksl6P+gAFc2rKlynbPJlIyPye+\nMm+SXKvFo2fPqmGmh/VEeHpKutW/rar+Zl+ZJVUC2VNqfBjcfKDjs1IV0g/XYHECtBog9RfN6gXP\n+cMnI6XFVnRP6Z36szmHirBaISMF/J6if6IcjaPAlhn3CfFfayAKf/qJkvXr8dm4EaGinu1/ILSd\nO+MfG0uNmTPJGjyYvNmzH95D0b8/7D8IH86GqVOqupFOLrB4A7TuBkNbwtXzlfc37SG5wpu+hG8n\ng7liB3UdePckyOTweUvIrBCLVTpIOtdDj0LSNk6+LdAmFD5Yc5YDZWyeorUGSWkNcRluIj6nKa8N\nC0DYFwQJ4yH38GMre8lkMtSqqp3CAIcupWMw6RncWoFKfRKt4wbU2gOYhPZYvN4G+YO+B2taLOLd\nLQQ5l5Kt6Y1FHYaDRoGrwozy4i6+SIBi15rS2MzrKL6dgrlBNKYAH0zX9qM4egah1EBi3VA2n05G\ndisH5f5E3N/qykcjm/PhytMkLtjHvX4NEMo0LczHblLo6USxuyMI4AAMEmCPi6ZSYvrc0Zs4umio\n1SiA29lF/HrgOnE37nLuVg6LvztC/ahAwsOr5+FZ+VpH0nKL2XTsFi/P3UFM+3BeeTba7mKofaQv\npUYzvx2/zceffVFpX72mTRk4bhwfv/baY/1vQAozHfjlF6zVeL31Bwzg8tatVTwFQRDuexG2qC7M\n5PTcc+i2bsVaUFBlH+NfeHiYKSgUeg+DHz97+AM9Cp6B0HU0vLUMfr4Nnx+T+ij+8bnUJf1XL0Bz\ns6X3WPsnKiwbN4bzj6cQWB3+Kw1E6eHD3Js+Hd8//kDuaZ9H/z8NgiDgNHQoAfHxGGJjSe/QAWPi\nQ/j469eHE6fgzGkY0B/ybNhcZTKJMvztj+H5rrC7chcrAbWl5HXmLYkNtiKHk8oBnlsGnd+UOq9P\n2STH3evCoD24dfuE3a+70r/WHf7xr92sPX6Lmb+d5eClDLp37cY7X51FaPkHdLoMzvXgwiuwv5ZE\n3VF8jYfh2rVr5N4rwFn7YFUmCEUYxYuoHfaT8pMVJ8eLiKIWQ2knSouHYsrVYE45i7UoC2t+MsR/\ngTJtJV/ut1C7wRCCvMtYMU16NFf2cN7gwQ+JCixWEUxGNAf3YvYPwBjsgzX3FkJ6Jqoj5zAM7sCb\n/Rtz5dZdVEuOYHiuBWINB17qEcmlbnW47aJlwkGps9eQU0zRkZvIukolrHX9XZnk7cTdGg7kVHgW\nk9HMoY0JdB0mTeixVzKxlE2291LzKS3Uo3hEQ5yHs4Z6Pq4s/PkEo/s15Dk7JbLliE/KRWcws+ib\nb+02wr30wQdcjY/ngJ1Vvz2EN22K2tGRyzY0GeXwjoxEoVaTZuMpAPfzEFXCTL17kx8bi9GGvFLu\n6Ym2e3eKV9vwggF07Sp99+Psa1IAUn5u3Q9w1w6dy9NAEMAvDHq+AG0G/TXntEX6HfD/k7mM//Mg\nqkI0GLj7wgt4/vgjqjp1Hn3AfxgUfn747tiB04gRpLdtK4XJSqop9/TwkJLXERFSXmL//qpj+g6H\nJX/A/Ddh7mtgrMBy6ugK72+Fhp1gUhQcqvACCgK0eQEm7YHd/4SfhkFxTuX9dYbj+FIyY158gzvz\nHLl14zBL91zg7V/P8vLkNx+M1fhB+DvQ8QJErwVTHhzvCDtcYE8wHGwkKdqd6g/xY+Dia8iuz+az\ncY74edxBpYlF67QBq2Iru87FsfOsmttpMeh1MZiMjbBaPSiXGRXzUxGvb0N5fjayklt8eDiEQ3dc\nqeFURmpn0qO5vAtzjQAIicLH1YHOMzaSsXojVncPTFHR0ji9Ec3agxh6tkB0d8EqiiySgcHPFUuZ\nKpzxejacTuZKi5qcuZlD9znbubEsFr+ekcR0rUPPpsHUcdYQVGQgztORFrUfeAPHtl3EP8yT4AjJ\naBWUkesZS02knU8juGkwxYaHNwbGnktFzCogVRTp0aZq45rRbGH10ZtM+P4409ZdYdu2bYwcOcrO\nmUCj1TJt0SK+mjYNy2MkNgVBoMPIkRy3R79dtr9unz5cs6N74hkVhWixcO/y5UrbFU5OuHfuTK4d\nFgGnMWMoticYJJPBmOfh14fkUHwDoc+IJ6to+p9GShIE1Pxz52jaFM6e+VN63P91BqJgwQJUkZE4\nPmH53tPAajJhyMpC/AtYEytCkMlwnTyZwPPnMd2+TWq9epRs2mS/0kSplAj/lv4Iz4+Gd98BQ2Wq\naxq3gM3xUl31sNaQVGH1LpfDqA+kRqFVc2D+s1BYYQUXGAVTzoJbMMyrBwe+AlOF86tdUHRagPr5\n88x8aQBFi/25d3A2MR0a23kwAWpEQ4OvoXs6dEuFtkeh6a9Qd56kaufZFRzCca3hjkwoQia/i9Xq\nwh8n6uA7xkqprh0fPDOEYHueodWEqvgk6sK9mLQNMLh0x83FgwKdFEITSvPRXt6FxS0QU3A0dQPd\nuPBxX+4MdGNPqp7z/nWlexRF1FuPYQnzw9JImnj9LqbTWSawuUkQCAJWvYnC747gPLYVbwyPZkS7\nWvil53PhQjqdd1/l/dVnaRTqgdOFdJoOjuJsWgHNwqV7zssq5OTuK/Qc9YDSJcTbGVEUSYlLxjPM\nA0d3B0K8nas+Yxk270/koyXH+PLdbji6OfLbsaoljWuOSUUCOy9kE3fuAn0eUeLdpkcPXNzc2F3N\npG+LlgMGcMpOGKkctbt25fq+fVW2C4JAUK9eJNsxBJ59+pCzvWrnsEOPHhgvXsRsox8BwPARsHbN\nwyt2xr0Ja5ZAyUMkf/+TkHwDQh7dvPhQBAZKsqx/oh/iv85A5H/+OR4P4Yv5sxCtVu4dOcKVl1/m\nsL8/sfXqsU+j4ZCfHyeaNCEuJoZL48dzfcYMkhcuJGvdOu4dOYKpukqLh0Dh///YO+/oKMr2/X9m\ns8mmd9IbIaQCoSeEFnrvIiBFQEBpgihIkyKIKEUEUbooTZrSe28h9NADoaVQ0tsmu8nuzu+PoaTs\nJqD46vv+vtc5HHJmnpmdnZ157udu1+WG8/r1VFi9mvRJk3jarh2FhqoSWraEyzGSnGm9MCixOsPG\nDhZtgXcHQ8/6sK3Eisu/Diy8BA7uMLwanNv9ap+JmcQI+/ERuH0QZgbBhd+Kl/7Z+GLaZRuyDr8j\nz7gJa6vC5kYQsxiUepKfggDG1mDuJZECOjQCl07g+T74juLY48Z8+4cVBaoG3HjoRtc5F5nbrx7v\nRviWjrGLWozUjzDL3AZiAfl2ndGaVgJBoLKrNWq1GuOEK5hd30uhsz+FnjWlzy9QY3rkAApXZw5b\nejNo6WkQRYwPXkDIVkr60oCQlIli/Tm+9nFgRdRDAHLXncfY3xnTOt4ojI1Y3KsWw2QCF4Jd6Vyv\nIov332L3jmtkP8kioEUgPk7WfLzyDKdvP2Xvr+eIaBuCbZFqpkYhrhQ8zUFbqMPZ3xl7K1OaV9fH\nXwnLt15m/Z4b/PRFG0L9nalR0ZGlh0qHI1cckcJeiUlJVKhQvqaAIAh8NHUqy2bMeC0vwiMoCGOF\ngvt6wkgAvo0bkxAdTaFKVWqfR4sWJB48WGq7Y5s2pO3fX2rRJSgUmHfsiFJPjoLAQKlzuKzwmFcl\nqNsYthrovv634eFd8PmLBkIQoG6Y/orH18T/nIGw/ugjjA3wxPxZiKJIzpUr3Bk3jlM+PtweNgxT\nLy/Czp0jMi2Npnl5hF24QPCKFXgOG4ZNRARG5uYoY2N5+ttv3B03jlPe3sR0707y9u3oStSHlwez\nJk3wuHIFsyZNSAoPJ33qVMSSXgJIVR1bf4dhI6BJ49Kdpr6bF3kAACAASURBVIIg0Yb/chiWzIJx\n7xdfUSnMYMh8GLsOfhoBCwZJWrov4FYFhu6G91bA4TkwLxzulihldAmDlqth0BOo9Rk8OQO/BsDW\nZpI4Uf7rVZNMnjiBB0/SGbnyNN/vvoFGK9K3sZ+0UxQRNBnI82+gyDqIefpvGOdfQ21ZnwKrhiB7\npZFwPCqGs910yJRp5FfriMYlqIhxOIjOsQIFtcOoH+iCUlWA8ZHLGN1/jKp3C5AbgboQ0x+OUfBu\nLY4rC6nuY4/qYjwF1x9j1fcVv9HDNedwaejH3PEt+bZPXcZ1rMqNzZeo1bM2RnIjTs9sj6ejFSNm\n7uPa7aeovIpLTwo6kcQbjxk5rBF9mgYwsEUgdpaliytu3Ethx9E7LJvaFk8XKRm/aGA4TzOUrC0i\nD7r17APup6pIT0/H/A2oZCJatsTcyoqD+ibiEhAEgbqdOnHOwMRsam2Nc5UqPNJTmefRtClPT59G\nU8J4mHp6onB3J0vPpGbZowe5enooAPh0LMwpR6Fx4Kfw87z/Di8iPg68/f76ecLC4Nz/GYiXsJ0w\n4a2dKy8ujvszZhAVEsKVzp0R5HKq795NvWvXqDhhAmYVpVi0zNgYU3d3rGvVokL79ngMGoTv5MkE\nLV5M6Nat1I2KosHDhzi0bMmj+fM54ebGraFDyTx9+rUblAQTE2zHjsXjyhUKYmJICgujoKSXANLk\nN2iQRNOx+mdJY6JkY11gNfj9guR+dq1VWqqxWiQsfl79MDy0NGd+QFMYex4iR8Pa/rCkIzwpcS1y\nBfh2hNbrYPATqDYMEg7B6kqwrTXcXA0pVyE7HtTZEltsEVhZKBjcPJBfjsWx4dR93GzgwtXTJNzd\nhVn6RkyzjyDTZqExrUye3TuobNujMylCS6ApwOR+FJ9XTmVnqiPqgKaIiuelsmo1pocPSMahVl0Q\nBPLUGj5WFCKPjUfVt5XE0gqY/HIWrY8DmkaVyckvpEPlCuSsisL6o4Yv6TTSL8aT+yAVz+ea0jKZ\nQBcbM3JFkSM5rziyTk5rS3OZwHa1hi5zD5OW82py3LvvJtVDPagW5IKbg4XeSiRRFFm47jxDutfE\ntohQkLW5CeM6h/LV79JvlqFUM279ZXbt2Yud3ZtpIL/wIpbOmGGwQqko6nbqRHQZK3dDYSaFnR12\nwcE8i4oqtc+xbVu9YSazZs0ojI1Fk5BQah+dOkn9EIY6qwFq1IPwpjDlw78Ul/+P4FEceL0NAxH+\nfwaiKGQWFuUPKgeZZ88SHRbG+fr1KUhOJnjlSho8eEDlWbOwqmq4UqQsGNvZ4TF4MHWOHyfs4kVM\nvby4OXgwpytVIu6LL1CWVbFUBHIPD5z/+APr4cN53Lix1Imt72GvXFki/PPxgVo14GwJ+mNzC5i1\nUuo2HdASln1TPIZrbg2jV0iEY9++B8vGgLoIIaBMBnXeg8m3oXIkfB8J64cUV617edFmULkbtN0E\nHyRBUH+4vwP2vweb68NKd1hkDEvs4OeKsK46C5veYW67LG5NdeDJVwqujxdISb7LipNZNP/JmJua\nlhRYRqBV+ICsyEpbFDFKe4RZzDZAZENBdb4+nfOqDFEthZV0Ts4vjQPASAsNzckntk0EWEiTr/zE\nXYwepFHQP5xcdSEFBRrst1/FtJEfJgFSJZBGqeb+6rNU+iACI4VU5lqYX8j1P2Iwb+zHymOvVvUn\nt1/F3a8CYwfWA+C305J4k1Kp5sCBW3TurCdvUwSHox+Sr9bQtmFpD3lgE3+UqgJm/X6Fz9dfpss7\n3YmIiCjzfIbQoHVrzCwsOFQGncYLBEZEkJ6URLIBamm/pk2J02MgQAozJegLM7VrR+ru3aW2CyYm\nWHTurN+LMDKCMZ9JOu9lYcoPcOealI/4tyI3G/Jy/zxRX1HUrg1XrxZvmn0D/M8ZiL+K1P37udKh\nA96ffELDpCQCFy3Ctl69t9poZ+btTcUJE6h34wbVtmxBq1RyITKSszVqkLhsWbkhKEEQsB48GLdT\np8hesULKTegl81NICezvvocuneDL6aUflA7vSd7EyX1SbqJkz0TddrD4KqQlwbCqcHhNcUNirIBm\nY2BKLJjZwFdVYONwiD1cXKHuBUwsIaAntP8d+lyHDxJgWA6MUEP/e9D1ME+rzuLL/aA1rYy3RyDm\nzs1QO/SkRZN+fNbrXdydXWk8Rc8EospBEXsYk4RLqCs3psA3gk71KpOek49Op0PIysT08H7JONSs\n89I4GJ+6iuX1+7ybZUpSoWRsZQ/TMNl4AdWISFAYM3JlFEPNjLEp1GLZ7VVn88P1F7Cr4YFN8KuX\n+eq2GFyrumHpYfdyoZqSlMmFI7EMHBnJwKYBbB7TjCkbL2LZZzUffbmXGjU8cHExLNKoKtDw48aL\njOpdp1QTHIC5Qs64TtVYsOsax2PT+WbOG3YPF72PgsBHU6aw4uuvyx1rZGRErXbtuLBrl9793hER\nPL1+HZUeIS3PFi1I0iMfahMeTv6jR6j1KKJZvPsuSkNJ9H79IOYK3Citgf4SZuawcAt8Nxni9Hjg\n/wY8ipNyJm9jzrG0hEqV/nS56/8ZiCLIPHOG6336UH3bNlx69kRWgo76bUMQBKxr1iRg/nwaJiTg\nP28eyVu3cjoggMQVK9CVxdEEmAQE4B4VhVmjRiTVrk3mnDn6eZ06dYILl+D8eYn0r2RS0d1byku8\n8wH0bw7fjoO8IqW11g4wYSN8vBz2LYOhIXBsQwmPww66zIEJV8DWA3ZMgImusKY/XN0BBSXoyEtC\nJgdTe7Dx5YHSjpin5ghmPmhM/dAZO2JlrkBuJMPKzJhFA8IxksHw5adB1CHLSeHpxWPoLmxjR6yK\n/God0VlLK/w7T7LwtjXF9MpFzA7uRVOp8ivjIIoYH7qA/Op9VP3b8FgDcpkMITMPxcIjqN+vh/i8\nF0F2P43eSjV2o5ogPKewSL+UQNbNJ/j0elWNlHo/lXsn46jVqzZKlQYTuYBOJ7Jj5Rkiu1bH2k7K\nB7Sp6cmvIxvjoJCjSspEXUbFEsCandcIquhIrWDDq8ozd5LJK9CyfdcerK3/miJwgzZtyEhJ4c61\na+WOrdasGdf10GoAGJua4hoaSuKFC6X2OdWtS/qNGxSWIK2UyeXYNW5M+tGjpY4xi4yUwkz6ur4V\nComfadnSsi+4oj98PB0mDymbY+mfwt0b4Bfy9s7XqDEcK30vXwf/ZyCeI+fqVWK6dKHKmjXYvgH9\n8duCTC7HvmlTau7fT9W1a3n222+cCQgg6eef0ZXRVS2YmGA7fjzu0dHkHzpEUu3aqEqGkwDc3WHH\nThg9Btq0gqlTinsTMhn0GAy7rknlsO2rwLESceDQJvDtCfhoEWz/HkaEwqktxV8yO09oNQHGnoPx\nl8GzFhz9Dia6wMrucH495OvpiC0CV1dXnqZnE/tEzzhRhyI/jSMDPehtew/T6PUknNnH9nMPCVqr\nZcDubKkLHEAUeXrxGlG11KBWk9euM5qA50lqnQ6T3VEYPXhKfv/WZMiMyFKqqWalwHTWPjRNAtA+\n50fSpikZk5xDfJsQ5M7SxFuQkce9laep/GFDjMykjm+tRsuZ5aep0bM2a8494rcz96nt68ilY3cQ\nRZE6zYv35bSr6cXUBpVIMTZi0tYr3NX3fYHEZ9lsPXibj4uUxRZFclY+bWcfYsf5R/z888/UqlWr\nzPv7OpDJZLTr3Zs9+noPSiC4YUNunjhhMJ/mFRZGvJ6ks9zMDPuqVUnRYzzsmzQhQ4+BEExMMGvR\ngnw9OQpAMhDr10F5TMm9PpLyXv/GUNPdG1D5LRqIps3090i9Bv7PQAB59+5xuU0bAr7/HsfWrcs/\n4G+Gbf361Dp0iJDVq3ny66+cCQzk8S+/lGkojCtVwmXfPmzHj+dZly6kDh9emppAECQ3/NIVyeWs\nUwtKvpyOzjBvHcxYBjM/ho+7F9eZEASo2QLmR8HAObD5GxhZA85sK534s/OEyJEw6ihMuwfBbeHi\nBvjCC35sA9vHw/6v4fhiqVv72k6IO8HW76fQ2NcSd0sZ6LTIclORP76O4tYhzM9vQHHvNP4Oclbd\nAqflGtoesKBRx85sHN8BjVbHvssJ7Nh/CXH3LqpkJREbUIuCeg0kLisArQ7FHyeRpWWj6tcKzE1Z\nsPs6LT3ssJ1zEE2jyhR2kAR/xEItmd8fZY0AHg2lpKGoE4ldchLr+pWwCnB6+XVv7LyOhb0FpzRa\npm66SKC7DRM7VOXo1su06x+OrERo6MmTLKKjH7J9QTdM5DJqjH3V7X73SRaf/nqeIUtPM3r+Ed5t\nFYSzQ+n8WoFGS/cFJ9BYupOamkr//v0NPiNvivZ9+7J73bpyk9VO3t7ITUx4rE8nGsMGAsAlIoKn\neqqc7Jo00etBAJi3b4/SQEgLLy8IrweGqp1eQCaDmcthwReldVT+ady9/nY9iMaNIerMn8pD/H9v\nIFSPH3OpRQsqfvEFLj17/tOXUwx2jRpR++hRglesIGnVKqKCg3mydq3BxjxBELDs1QuPmzcRNRoS\ngoNR6uHlx80Ntm2HceOhQzuYOAHyS4SA6reQvAnfQOgYCmt+KB5SEgSo0wYWnIO+M2D9NBhVW+qf\n0LeStHSEegPgo50wMxHqfQBmtqDKhqc34OZeOLUUdk7iPe1+tjZX4353N+bRa1HEnUKmykXj5Ede\njW7k1+pOYcV6fDKgA8tGNCNmXleC3G0J9XEg0s+BJ3sP0+zxVT49nUatUyJV67wi9aNQg2LjESjQ\noOrdHBTS6t8hv5CfkjK4X92DwueUFaIokr36LCpLBcsF8H7et3Dq5ygeP8lmh7ERy/ff4nGakszE\nDG4duEn4wHo8zconwM2GDaObcn7fTfyre+Lq7VDqlmzafIl2bUOwsTEnxNMOWwupKiohNZc647eT\n61CVXJkbDx5n8dn2q4xcFcW5uGQKNTqy8grIL9DQbd5RMgpNOHr8BA4OpT/jr8AvJAQ7R0cuGAgf\nvYAgCIQ0asRNAxVE3uHhxEdH6/UwDBkIy5AQNFlZ5OtRRTNv04b8Q4f0l3oDDPkQlpcTZgJpld7r\nI5hhQNb3n0LcDfCv8vbOZ2sr9YroiyyUg/+vDURhejqXWrbEffBgPD/66J++HIOwj4yk9rFjBP70\nE4lLlnAmJIRUPV2oL2BkZ0eFpUtx2rCBtDFjSO7dG20JfhsEAXr3lprr7t+HalWg5DlNzWD0DFh3\nAvZugq614dD20r0V4R2lJrsek+Dn8fBhkPT/rSj9MV5TK6jxDrQcD52+hh4/Qv91MHQXfHKSkw0W\n0fWsO3l13yMvvB/51TtT4BuOzqM68uDWGAe3xTioFVVDa9K6xnO2S1FEfu8u6z1TcbZWIH+nO4OG\nd2L1iMaYPM8lyTQKzLZEIzh5oRk9EkykaiXhWTbjriTwm70F1Y/d5XaS1NSYfySWwnspXGnoi62l\nKTKZwPXoB2hP3ye2thfIBLKUanZEPeDM8tNUf6cGFg4WHLz6hMgQVzJScrh07C5N3qlR6hbExaVw\n924KrZ6zu8oEgUxlAUlpShpO3cvEiRNZuepnChMSWLl3Lxs3b+WpsRdNp+3Brv+vuA9ZT4WBa1Ga\nVCD6/AVMTN4ik2gRtOvTh91r15Y7LrhRI24YMBC2Xl6IokimnsneJSKCZ2fOlCb1k8mwi4zUG2Yy\nqlABk5AQ8g2VtLZpI+lWX72qf39RDJ0krdgPbit/7H8CylyJgdnT9+2et2kzOPrmYab/bw2EVqnk\ncrt2OLZpg8/48oW9/2kIgoBDs2bUPnkS//nzuT18OFd79ECVmGjwGLNGjfC4ehWZkxOJVauSu2VL\n6VWciwv8thEWLYaRw6HHu9LLVRR+QbDuOIycBoumQeeaEvlf0clfJoP6XaX+iU9/lfIACwdDXzf4\nfjBE75SEVV4DERERnI99jE4nvqrkEASMvMMQTJ+L1MsVGFXwxygzF+OYy5ju3Yky5jodLgpUaN0c\nhZUFwR52dA2vCIUajKPuYLpwPboatdGO+hSZU2Xk3nURnmRh+vU+CjtWY/h371CxgiW1P9/G8Ik7\niP8lGu0HEfwa9ZDmoR5oVYVkrDnHg1A31BavJmTtjcdoBeGlxrSFQo5WK3J44yXCWgW9TEy/gCiK\n/LbxIt26VkehkPM0I4+L91NpW9OTtSfjaNqiFdO/nMHP335LQPXq1G/Vis6dO7Nn3wFEUUSpVKLV\nasnNzSX6wiUc/0ZCyja9enHo999RlfQwSyCkUSODxH2CIBgMM1m4uWFsaUnmndLkjfblhJnyDIWZ\n5PLXS1aDJNs7Y5nkReSWrrT6jyPupuS1G+mneP/TaNL0/wzEm+DuhAmY+vhQ+dtv/5VaEYYgCAIV\n2ral3vXrmPv7czY0lLhJk9DoKSMEkJmb4/jddzhv3kzGlCk8bdOGAj0vI61bw9XrEBAgEf/N+LJ4\nok8QoHkn2HZJMhRLvoL2VeGPX6Fo5ZRMBgF14f2v4KfrEi2yV7BELd7bBaZ1gD1LIVUPp85zuLq6\n4uRoT8yjV16PYO6AUFCAcCMao92rkS8eh/GM/phcvgA6HYU1avEdPhTa2VPH7zmthChidO0+Zov/\nwCgll8LZc9B1f1e6RkB2Px3TWfso7FYDzfPJffWISEbX8eLTZ9l8balgaUwSN5OyaVHVlbtLToKn\nHaker5rPjLJVWMQ+o2a/MASZ9Bx5OFrw7F4yCXeTiWhTOpYcHf2QnBwVjRr5ce1ROtU+k3IPey4l\nMGPLZfoPHMS9mzfZsGgR4/TQxpibmyOTybB4Cz0/5cHJzQ2/KlW4WFYDGuAeEIAyM5PMZP0yqB61\na5NkgHHVKSyM5HPnSm23a9yYzFOn9B5j3rYt+WV40QwYCBt/K81Lpg91G0sNdCv+IiX428CtK+D/\n53qtykSDBlLesWTTbDn4e+s4/6XIPH2aZ1u2EHH9+t9mHNSZmSSfO0fyuXM8O3cOdDpsg4KwDw7G\nLjgYu6AgTP5CKaKRmRl+M2bgMWQIcV98wWl/f3y/+AL3IUOQGZfWUTCtXx+PmBiyFi7kcUQE1kOG\nYDtpUvHGQjMz+HKGxLH/+TgICYJZs6Fnz2IreZp3gmYd4cxhWPq1JA4/8DPo/kFp/nrXStDlE+lf\nTgZc2i95E79MlEJVFjZSU565zcu/jSxs+MjyKT5P7iMvSEGWmYFRaipCfj6iVwA6n2B0bfohelZG\nm/EI3TNJsyJb9YT4FIlGQZaQjMn+c6ATUXdpiNCgHTLbVzTXwrFzyKcvRj20Bdpg+5fbK2l0jIpL\nxbJvXazvpXD2bgppOfn43npKYbaK4JGRXDoZR0GhFnQ6bC7GY1m/Eu5FJEFretoSezKOtqOaYGJa\n/LdQKtWsXXeeUR9H0mHuMU5ci2fNml/p06cv69et5djx47Ro0YK+9eox8quvcPV6S/KVfwG1GjXi\nyunT1G/VyuAYQRDwrlqVR9euYdusWan9LlWqcF6f6A8Su2taTAz07Vtsu0VgIIUpKRSkpWFSIr9i\nUq0a2rQ0NImJyD30cFZ5ekKVKrB/v6SfUh4+ni6xCvQdCQ5O5Y//u3D9AlTVX632l2BuLnkRu3dD\nnz6vfZjwJlq0/3YIgiCW9320KhXRNWpQaeZMnLt1eyufqy0oIP3aNZ5FR/MsOprk6GiUSUk41qyJ\nc1gYTmFhyIyNybh589W/27dR2NlhHxxczHA41amD0Z8QOHrBFaV6+BC/2bNx6tLFoPHTPH5M2mef\noT59Gvv587Ho2lX/2JMnYczo5w13C6Bu3dJjAGLOSYbiSpT0gvX4EOzLCXtotaDMBGUW5GVJnE/K\nLFBmocvNYPKYUTSoZEeLICd01tboKjgjq90RwbII6ZxOQ+Hdo1AgeToPknPoO30nsx1EmljL0bWs\njbaqLwgCgpUzcp9wAGSb92G0bBOF33xMofGrDuCCO8/IWnAUq/fDMQ3z4UlGHlU+2UJ9jY4pjpZU\nm94OExszUrNVXIxLIe1ILKZKNR0nt0ZeJCSweXUUu47c4Zdf3y/2lUVRZPqcQ7jamaPxsufzNdFk\nZ2djZVW8B2L5rFlcOHaMJfv3/yu825N79rB67lxWllMquXT4cFwrV6bj6NGl9qXcucPyVq2YqEem\n9NHevcTMm0dHPU1zF5s3x+uTT6igh4n2WffumHfogFW/fvovaMkSOHEc1m8o87pf4suRIDeGiX++\nyfAvo1MN+HIJhIa9/XOvXi1JtG4p3iEvCAKiKOp90P6/MxBxkyejvHWL0NegESgPcRs3cvX770mL\nicHa1xensDCc69bFKSwM+5AQg412y5ctZflPP+AgkyNLSaZj3XpUsrQk88YNch48wLd7dwL69cM5\nPPyNJ4jU/fu5O3YscmtrKs+Zg229egbH5h87RuqIEchdXXGYPx8TfTQiOh388gt8MUlquBn3uaSP\nrfeG3EJc/g0c/INcF29uOVckqVI1Gg4ZyY9LljBkyBBcXFwMXs+TJ09wdHTE2NgYOytzMnPzcbEz\np2mIK+M6VcPPwxEjpwAES0dEtRJd8h1E1fNSXpUa45PXEC7GsihLxoxnhSwY3IB36r1K9slsvDD+\n4ypGh6MpmDMcDY9fGhd1TCJZS05iM7QRimruL49ZujoK78OxNPyqIxZerzyNpJhEolaeof3Mjpha\nv+JFSnmcyaov97IgV82Ttf1fbn+UkkPrCTsJVhVwGDC3smTf/gPUK/H73Ll2jcFNm7Lx0iVcPP+C\n3ORbRHZGBi08PTmVkYGxHu/0BfYtWcLd8+cZuXJlqX06rZbJVlZMTU5GYWlZbJ/yyRM2Va1K/5SU\nUs/7vWnT0KnVVNbT1Z29ZAmqqCicfvlF/wWlpkLlSpCQJHUUl4eUp9AuBHbESBoS/2mo8qGuA5xP\nl3Ijbxsv7sfjp69KvinbQPxjOQhBEKoJgnBGEIQYQRC2C4JQ6hcUBMFDEIQjgiDcEAThmiAIr6+J\nqAfKO3dIXLqUwMWL/8ppKFQqOfrBB5ybMoXaU6bw/tOn9Lh2jSYrVhA8ZAiOoaEGjUPXTh2YOnEc\n45u70CbUjPffCeTHy6dp8csvZAwexDsXL2Lp6cnRAQPY4O/PhRkzyDYgDq8Pjq1aEX75Mu6DBnG1\ne3eu9emDSh+HPlJXqsfly5h37MiT5s1JGTwYzdMSqlsyGQwYALdiJQGSju2hZQs4cKB0OatfEGPU\n1phdyabr8WtcOriHoBUz0YQ747t0GqP9XfmoV3cKStRjZ2Vl0bZ1C9zc3DAxMcHdpQLKfBWXvu3M\ngEh/Yp9kEzF5J5PXRaF9ch3N3WNoY08hu3YD46OXMV1zAPMFWxDy1RQO78KHX/diWLtqDF1+WvoA\nUcToSgKKYQsRzkWT93kkmoK4l8ZBdfYB2UtPYftJs2LGQZ2mxOd4HJeruBczDnkZeZxZdpoGQxsV\nMw6iKLJrVRSNOodiZWdO21n7KdBoEEWRD5aeJczckonr13PizBmePksuZRwKCwuZ/P77jP7mm3+N\ncQCwtrPDw9eX2wZovV/Au2pV4g10XsuMjHAKDOSZHoJJcxcXBCMjlI9L9yPYNmxIpoHkt1mzZuQf\nPmyY8NLRESLqg75Sb32o4ALdB8PiGa83/m3jdoyUoP47jANI96NmTdDjqRnCP+ZBCIJwDhgjiuIp\nQRD6A76iKE4pMcYFcBFF8cpzA3IR6CSK4m0D5yzTg7jerx/m/v74Tp78p687NSaGgz174hwWRsMf\nfsD4dVYmz7Fnzx5GDn6fqC9bYVEkNq3TiSw7dJvFxxK5/0iqIBJFkeTz57nz66/EbdyIXXAw/n37\nUql7dxQ2Nq/1eZrcXB7MmkXSsmV4jx2L9+jRyAyEr7SZmWR+9RU5q1ZhM3o0Np9+ikwfTXRBAWzY\nAPP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64TN+PAo9zW1GTk7YTZyI7bhx5O3YQdbixax6GI98vzGnfR25lfmCx0nk+sM0zEyM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\n+EOGYlJwI7gAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if east_timor_available:\n", - " ep = GPy.inference.latent_function_inference.EP()\n", - " kern = GPy.kern.RBF(2, lengthscale=0.6, ARD=True)\n", - " bern = GPy.likelihoods.Bernoulli()\n", - " m_ep = GPy.core.GP(Xeast, Yeast, kernel=kern, inference_method=ep, likelihood=bern)\n", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ep = GPy.inference.latent_function_inference.EP()\n", + "kern = GPy.kern.RBF(2, lengthscale=0.6, ARD=True)\n", + "bern = GPy.likelihoods.Bernoulli()\n", + "m_ep = GPy.core.GP(Xeast, Yeast, kernel=kern, inference_method=ep, likelihood=bern)\n", "\n", "\n", - " fig = plt.figure()\n", - " ax = fig.add_subplot(111)\n", - " for p in polygons:\n", - " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", - " ax.set_xlim(124.,127.5)\n", - " ax.set_ylim(-9.6,-8.1)\n", - " ax.set_xlabel(\"longitude\")\n", - " ax.set_ylabel(\"latitude\")\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "for p in polygons:\n", + " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", + "ax.set_xlim(124.,127.5)\n", + "ax.set_ylim(-9.6,-8.1)\n", + "ax.set_xlabel(\"longitude\")\n", + "ax.set_ylabel(\"latitude\")\n", "\n", - " m_ep.plot(ax=ax)\n" + "m_ep.plot(ax=ax)\n" ] }, { @@ -1707,108 +751,38 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "if east_timor_available:\n", - " m_ep.optimize()\n" + "m_ep.optimize()\n" ] }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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BipoON/A8cjwA5lme2bIXdqHKc95hcPItqGRuN+Df7rAZRRCT0B942g4hBFOn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34ZZ80dxj2Pr0VRNM6fVsTeuk6u+scXZLrsLJpk6FCVlmZw4EAr+e5Eblo8Bmd+Cg+/sImFv1/O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\nMJBOzz3HtrnFhrFu0PqVV/hlwQJy0tJKb3wJ8A0NpW7fvmwrp24ta+RmZLC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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if east_timor_available:\n", - " fig = plt.figure()\n", - " ax = fig.add_subplot(111)\n", - " for p in polygons:\n", - " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", - " ax.set_xlim(124.,127.5)\n", - " ax.set_ylim(-9.6,-8.1)\n", - " ax.set_xlabel(\"longitude\")\n", - " ax.set_ylabel(\"latitude\")\n", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "for p in polygons:\n", + " ax.add_collection(PatchCollection([Polygon(p)],facecolor=\"#F4A460\"))\n", + "ax.set_xlim(124.,127.5)\n", + "ax.set_ylim(-9.6,-8.1)\n", + "ax.set_xlabel(\"longitude\")\n", + "ax.set_ylabel(\"latitude\")\n", "\n", - " m_ep.plot(ax=ax)\n" + "m_ep.plot(ax=ax)\n" ] }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: gp
\n", - "Objective: 31.0040772275
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "
gp. valueconstraintspriors
rbf.variance 2.23903462364 +ve
rbf.lengthscale (2,) +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "if east_timor_available:\n", - " m_ep\n" + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m_ep\n" ] } ], @@ -1829,163 +803,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" - }, - "widgets": { - "state": { - "01ff0618fd22414daaa993b7f03a32d7": { - "views": [] - }, - "04a380d351324581b67d8e09b73496a2": { - "views": [] - }, - "099d951ace4949059037ff993425cbc7": { - "views": [] - }, - "0b6c83c714164c88806966305769d6ff": { - "views": [] - }, - "10ac41e0175647f19a2cdb68d0c61a35": { - "views": [] - }, - "1d510e76d7d3477d83889920a8d3a8ca": { - "views": [] - }, - "1ffe6ded0c23423288b7c711bda4ce33": { - "views": [] - }, - "21183fbd44474501b112e406373ec9e0": { - "views": [] - }, - "356310f7e33b496c9ff8a914b052bbcf": { - "views": [] - }, - "39b3e1034334478c9e0876fb550c4ade": { - "views": [] 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"e640d4bd445741d28163606dc8a8f152": { - "views": [] - }, - "f1197f6a511641b3a17da1c451b75e96": { - "views": [] - }, - "faad52a8a6c04afd8404e82042a8e6a2": { - "views": [] - }, - "fb3b73c8d9c9492087b8e4cfe7977136": { - "views": [] - }, - "fe6bf7438f9a4dfb95b4ba2df8d8d147": { - "views": [] - }, - "ff10b6b99168448084545c6132de1736": { - "views": [] - } - }, - "version": "1.1.1" } }, "nbformat": 4, diff --git a/GPy/Integral_kernel.ipynb b/GPy/Integral_kernel.ipynb index d541d9f..6335457 100644 --- a/GPy/Integral_kernel.ipynb +++ b/GPy/Integral_kernel.ipynb @@ -1,11 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Integral_kernel.ipynb)\n" + ] + }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#imports and helper functions\n", @@ -68,19 +74,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "After 5 seconds the robot was travelling at 5.00 m/s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#the observations\n", "times = np.array([2.0,4.0, 6.0, 8.0])[:,None]\n", @@ -108,44 +104,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 10 0]\n", - " [ 20 10]\n", - " [ 30 20]\n", - " [ 40 30]\n", - " [ 60 40]\n", - " [100 60]]\n" - ] - }, - { - "data": { - "text/plain": [ - "(0, 15)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+aWjbtrQD4rvvwsKF8ZXREjk+M9TMTBgcX+PQiKyLVPUrVb1ZVW9S1a9iLVS1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\nnhdbMI8GAOBBeQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "bins = np.array([19,52,114,45,22,20])[None,:]\n", @@ -188,10 +149,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#generate 100 simulated children heights, weights and ages.\n", @@ -212,45 +171,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Age Range Height Range Average Weight (rounded)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[ 2.5, 3.5, 85. , 90. , 13. ],\n", - " [ 2.5, 3.5, 90. , 95. , 14. ],\n", - " [ 2.5, 3.5, 95. , 100. , 14. ],\n", - " [ 2.5, 3.5, 100. , 105. , 14. ],\n", - " [ 3.5, 4.5, 85. , 90. , 14. ],\n", - " [ 3.5, 4.5, 90. , 95. , 14. ],\n", - " [ 3.5, 4.5, 95. , 100. , 15. ],\n", - " [ 3.5, 4.5, 100. , 105. , 15. ],\n", - " [ 3.5, 4.5, 105. , 110. , 16. ],\n", - " [ 4.5, 5.5, 90. , 95. , 15. ],\n", - " [ 4.5, 5.5, 95. , 100. , 15. ],\n", - " [ 4.5, 5.5, 100. , 105. , 16. ],\n", - " [ 4.5, 5.5, 105. , 110. , 16. ],\n", - " [ 4.5, 5.5, 110. , 115. , 16. ],\n", - " [ 5.5, 6.5, 100. , 105. , 17. ],\n", - " [ 5.5, 6.5, 105. , 110. , 17. ],\n", - " [ 5.5, 6.5, 110. , 115. , 17. ]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\" Age Range Height Range Average Weight (rounded)\")\n", "np.hstack([inputs,np.round(outputs[:,None])])" @@ -265,10 +188,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "outputmean = np.mean(outputs)\n", @@ -277,10 +198,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#we have twice as many dimensions as the actual data as each\n", @@ -294,32 +213,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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lhYDatYMePaITZE3hW/L46iuXUHyTcC2SkZFBRkZGWI8Z8W68XhXWhOI2kFBv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\nB/QA5hSP+RCR+f7jOmLJSiA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "\n", "a = 4.0\n", @@ -348,9 +244,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/MagnificationFactor.ipynb b/GPy/MagnificationFactor.ipynb index 86d2978..3cac298 100644 --- a/GPy/MagnificationFactor.ipynb +++ b/GPy/MagnificationFactor.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Probabilistic metrics for GP-LVM and Bayesian GP-LVMs" + "# Probabilistic metrics for GP-LVM and Bayesian GP-LVMs\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/MagnificationFactor.ipynb)\n" ] }, { @@ -20,20 +22,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/maxz/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", - " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "if not hasattr(np, \"NaN\"):\n", @@ -63,10 +54,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Define dataset \n", @@ -122,53 +111,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GPLVM
\n", - "Objective: 3569.70617678
\n", - "Number of Parameters: 603
\n", - "Number of Optimization Parameters: 603
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GPLVM. valueconstraintspriors
latent_mean (300, 2)
rbf.variance 1.0 +ve
rbf.lengthscale 0.2 +ve
Gaussian_noise.variance 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Q = input_dim\n", "m_gplvm = GPy.models.GPLVM(Y, Q, kernel=GPy.kern.RBF(Q))\n", @@ -181,10 +126,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m_gplvm.optimize(messages=1, max_iters=5e4)" @@ -192,34 +135,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ] [ (1,2) x2,y1 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "figure = GPy.plotting.plotting_library().figure(1, 2)\n", "\n", @@ -231,34 +149,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ] [ (1,2) x2,y1 ] [ (1,3) x3,y1 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "figure = GPy.plotting.plotting_library().figure(1, 3)\n", "\n", @@ -305,10 +198,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "GPy.plotting.change_plotting_library('matplotlib')\n", @@ -317,10 +208,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m.optimize(messages=1, max_iters=5e3)" @@ -328,22 +217,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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BwPqEr4XY8etGXBbblfrbFNyHWZahq6sL1Wq1jvjpNHoTRvz0bh+3xypPr6Vm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\nn1bC+g4sUHtDTBtaJVqPIqS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pUmjJZW5nI+uzRBpiBm4oTPsVudESujOu53oK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\nb5BvRGo9ptsa0q3MUZjUeWvAs705TqMYMKQvpXOyzCZ8Hrm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/aJtFz3smeynET8sV69or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egx9TtYh9Pp9FysEia6RAqcaPkWgYy4WAGP3NFmUf/Y8MoraNyyxScu3vXFiy+ivqDAxz6qshJ4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2GoahddC8DrLyN3EPsqcx+/6FUiVTht1lP6FJRG+Yp4EmpuldyE+DJ/jpeGSkSkXmRM+qS+aP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\neSJpsMn8VeSwNWYRY8o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/w5N3YuyMEg/uX133cZKhBOvN8JaGD1HqNw3WFz05P/oXX1tX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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n", "axes[0].set_title('Latent Space')\n", @@ -369,10 +245,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m_stick = GPy.examples.dimensionality_reduction.stick_bgplvm(plot=False)" @@ -380,64 +254,18 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/maxz/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:1744: UserWarning:\n", - "\n", - "This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", - "\n" - ] - }, - { - "data": { - "image/png": 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93uXX2999x9v28DH1Ng+eZ8cWBfIKv2orsjEDiKMU6YryGQisXivgL2qLA4+q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\nPDHtHxaloEbb5wIx980FPEnmFYBeYqQVdKzkYiXd5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BY9UhIozfOv//V38Pjmd6NXFd8a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\nT+lJ+TMLLaeets3ZKdElUH7J32rtaXKabVZ+q4zWP+0c0n5woK+EWsdqx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MQd4lSCx/HQ\n+hg5Tya0Oi4gcHutrNGkwJx6vZMmdVLCR3m5AFlhhza3rXHRokOLu1wd0PwLx5QekIrlOSiVs+jp\nVgyXeDi65j8sMqvWbsTK9Vvxu7/1yybfY2lL403tKS2Uv/W/HsPTe87C1LMvgpsGAK+zvoXzf1yd\nJMfZTekf+9C9uHrrLqzauh+HTpwP7+Q+U/mUj+baXHDlpbhmnseOPYexY/9RABezuiSfb+n33kOv\nYP+RN3HX4stG6QmJj7WdGHHM5BIgSS+VDcdxvTTW1GbKE9qN6ZwubgykJ3epL4Bq+qU+xei5xE/7\nuAsXfLigRfmloCWVueOYzqE0mJQEiyayKZkm9lhQ+oQkdQeY44vrtg8dxPP73sLLpy7GrFMnRp17\nzb7ShC/Upfho+SO/cCeu3rEPa3fuxQvvXoj4Q/CWa8ByTdCyBO89li26GssWXd35jd/xJH9OAqrR\nblx8DW5asiCp30Lz3mN9/zb8ZMMh7Dk2ExeeNS0roHA81rknjVMOTQpY3OIgJ+jHW8kXPS0JH5Wx\njE+FDO0ak+ql+MbtgTy/RXVKxxotbkfqD0fX5CRY5x43LiV6Sm3QeNqwJ+ZftW4jHn1mO7YdmY7L\nZozsr8WO3H0pbyj/6qc+jMUbt+KH6/Zhz7GZwHQ+mZJslcaPk7GM9dKFV+OG6+djYHA3+ne8McoO\nzn9bfbRky4IrL8HVcy9K6uLalbZ9h49i19FpeP191+OsKQdOy5fcuIuvGy5mWcDJax924fbx5tzo\n3/ZRvjipi3WlftNHdXA2cGOh8UnoucSPTlRr0KEy0j5Hb05goSep9I6/FU115AaVptASvpLxleRj\nvkD/4YrnMPjm3OHfjU0B8PYRkVejWe+GS7w55cXXzsPia4EtO/Zhy9DrI/Rq819CqR0xli6cj2WL\nrk7q1NoupaX4qbOl1/63H3oSK/fPwNQZFw6/stq5QywtcANSSb9ES4HjpTTqC6V9KohINO/tH3PJ\n+Y1ffdWz+5DmvkajcpTWho+wtgfIb2toyaw0FjFyfaMEywKsVGdunVZv8SWUFpe//q0H8cSWk5hy\n9pzh31XjrVF80t7Co8mWtBPKty5biFuWXo/1/YNY239U1Et9IFfH0Tk9mm4AWHLdlVh87TzV50p2\nSXak/LrFPsvcWtM/hMPTlmDqORcMv9jD3OPl+hN0pxIxbUxSddI5pLHXMtapdrTzYx3rsGnJo5YY\np85XTyZ+YVBimuXOpsRnaVNa4Ek0i05JTxtoS5emp402pEVxk3GRFgyS3n967y24cugANuzZhZdx\nKQB+jkk2S4seqU7jpfRUeTgBnDdKb+7e2h4HjZeT1a7HFM0im9OnMPafe+A+XD+wDf/7+YPY8/o5\np+8Q03MTICV43DwuuU6898MLloGhUR8loO1yQZTucwKsFqS0cslv/Di5Nv3gew2S37HEQAtvro+Q\n4lxO/Lbo1OhW+fGCZb6XxmGuTvMl3PFv/eonsGxdPx5ZsR3bj0wHpuX5HwuPRYfUD+rTaP0tS6/H\nzTdcN4JXGxfq96Rx4fRouqmuHD9M5VLt5ui08APAbTdchb2H9mHo1cN44+yrw686RiUr8VgFP6LZ\nZknO4nZiniOvvYVXjjssuHjaabpz8t85cHMmbpsmYrFtqXGL29XmAG1L+rgNV0/1SejJxC9VXxrg\n6CTMDVaxbdagYnH6OZjo+lL6S56OxHKaTloXt3Xd/Mtx3Xxgx55DGNzzM5E3pktOjtalngpZnkpZ\ndKUSoZJ5T6HZY1kEpq5JS1tS/7QxtJRvXXo9bl16PZ4b2I71m48kz2uAlWbF+v5B/OP6/dh9bCYu\nnD5VDFJSWQpwWmCVAn7p651hL73yGevQvrRWUQ4pFpbEvZxr3UKzxNNAB/Ke8mkyHF/AWCSCufNa\n45fqcuipxWQ4Xn7bMtx161L8dP0mrFi3ZVQ93XM6NB4LOHmL/+N4KZ+VptXTtrRN0pvSo9FT7Wm2\nceB45l06B3Mv8dh3eBAHjh2H95eMGt84QeN+4xZAk8L4WOpf2B/52ds4cvJ8vDv7Djg3BO9fGDWm\nXKIYJ2eBR+orJ0/pnKyWdMYbtSWMgfR7Qup3NfRs4pcKSDG/9Y4n1UEDSOqYszNGtwJLqSOdKPoD\ncp+OaMleXE+dDZWJaddedRmuveqyUfOE06HNB6lskbMmgjEP5S9NAiU9ljLXnsTTpN02bNXG+Zal\n1+GWpdeJ4x/DSrPifzz4OJ7Z+77hV047HyWgwYELolxwtJYtW5OnfdaEr/7Grz1Y4yPl544tfkiS\nsdgYHwf9Kd4UPaVP449RErPbmruSHk1/jgylacehfNetN+DOW5aY7ZD2Fh6LjKUs+byYj+s75eVk\nJF3SeEtbjl6L/9bazLFJqpt7yWxcftG54hgDfFLFHUtPvALiZMh7jx0HXsXxC+/B9FkXYIoHTgnt\nBz000YtjTdxmKqFLnZ/cvmtjJv1mktoooecSv/jjLuGESWW6j6HxUH7LMZB3h7GNJNAyAdpCN9uy\n3sXgzmGOPs6hUL5UshfTtUROKlvkqOPS6qX2tHmd2sdIXWMSb8ouTc7SroUWj2FuWdPDncOY3uRa\n+fwnPoTrNw7ix88dwJ7XZwFn2RY20l4rpwKS9vu+nL2WBEqBuKJdWK55a0zM5aM2cHalaCk6oH/8\nJUZO7NViTDfmaUqnVi/VcXRK046lMqVJe82m3D2lafVcOedYspfzUykaJ2/Vp7Vj1cfVae2l/HBc\nl3rCF/uIeP0e+CUbKU+gXXPZuXj5tTU48sZFODV7MYCRTwvjJC62g/v9XGo8adLJPQWUYpflHNMx\n48Y9toEbfw49l/jFJy6m5dzVTO0DL5D3tI+jaf0IaCMQjQXabtuS8EmJWI6+1DnMkY3pVD7WL5W5\ndilvbiJIywHSvLZcAyXtcPUlduWMS0lSbClL54g7TtFzEZ44PjewHWs3nXnlVAoq0t5atmwlT/k4\nmiXp44JfhQ3cNcNdL4FXus5yfA89TvEFfZysRpPkLXUlfBYdTZGjR+LNoVtonJ+Jyzl+yMKTu9f0\nS7wlx9y45NAkn6b5uJR/5uQ5fRZZi6/VeKXzY0mqpDqatMXtxQnQBee+D+f7n+GV157CkTcAP2Ma\na4v1fNA+0TJ9gpc6Z6HM/T5PewVU08mNlYSeTPzCPueupEVvauFoOY5tjPVY+jQZ0MRWbZwkfmt7\nHG8q0ZJ4cujccapM5XITPUt9Di211+y0JocS3aIj1QfruOeWLccpei5iHTffcC1uWrJADLCWvVbW\naE2f9uWWpQBYUYbca1mSAfiEkLZjLXNy4Ti0pdEkeVonyXF8AU0SQStK5rYmI9VxdAstPubKdG+1\nT2qnqU9L2Urpkk/k6qW+SXypcdDas/pqSUbrn4WeozuuC0/wLL9no4gTOdpe7H80u2fPnIbzZox8\nEqe9Lpkag1iO0xHL06dw2u/hpXHV5grXjjT/KXou8ct51TNGoEn7lD6gnd/6jUVw6QZSk1WDlES1\nBSnpS9GsyaJEl24UcHUlfPEcpbSgYzIkfxNBLmfMLee7G6C6aeClZRrELHXSsbRpv+1r+uSvSXCs\n4JFz3QM2n0J1c3tJV0wP5VgfR+PapXwxPUCrt8Rdbc5Z43Yb89aiQ+PR/IiVlvI3JXU5shxKdIdy\nyrdSHs1PUj2SrpS/TcnkyqX6YvGrKV9Mr+2cc6e9Eqn113ouUu1rurUt9dooIH9IxtKP2D7uddQ4\nsbaOec8lfvGgxDTrYpHqsi4mS44l+wO6lQTmXBAWlC54myR8lja1JE1zVpJ+qc0Ub0liJ9WVLtqa\n0lJ7zsYAy/XTtlzqHDQ5N5b5E6NpQsjJ0sBIy9zeWpcKRNqdS2uSp9VZXvWsKIcW72KkbvBo8dPq\nY6g9KT8Q22lNAC31ltisYSzmpLUNiS/lRySadqz5lBSPRmuqj9an2rEcxzTOL0o8lJa7lbQr+XOJ\nZrUxh4972ied41Q/uPNJr+fw5IuLt5xtgU7to4lU3Db9PWHcnnUepOQpj/RxG/rkMtaX+shLTyZ+\nYZ+TtKVkND2A/rSPC1oxT6ovFl6Ljm6htA3uHLQFa5JmTfDaSvq0+SLZVZKsBd1tJIeWPddeyi6p\nLF1fFjk6tiXJnFVGOv9tgtPNBbXcvUTjArK0WX/XR/e5yWHcBrW/ogy517GU4En6YlpOfSgD7Tzp\ny0nyUnOqW7Eqx4YSGale8iMSzep3rDwSTetPjk/T2uPatNZLtnJ8Wn9S/lXzx5x8qj8p3261T6un\nfQ7+O05upFcludgav8aoJXRav0NZs417YhZ4pDHk+hHKnJz0FJAb/zipoz6SvtpJE9/UNQT0cOLH\n0a0BSeMB5EViSj5lqxZYUifyvYTUYnu8kr6SJIOrCzqaJGsWGtVtXfhxTpprr3TRp41Hqh9cWRvX\n3HOTmj8UTRJDKRhzxzQghz0X+Lg6LnimtjYTP4mHtmMJahUypFiUqpN4rX4qJRPKQY6zh9an6Fqf\nNBmtzwFtJoFt+gdLvcWvUFqqnKJJuiW9HE9qb9FLy9SfWOo5O1N8nP+06pT8cxM+DikerZ4mH6Ec\n+KSnUrG/4JKlwMe9Thkfc/U09sb6qB5qM407gTe2PcXHJWhaP7hzFvZxIiiNFd00H9VziR+XIceD\nIJUDLLxWfbQOsCd3Y3F3sRchJSTA6PHl+Cw0jm5NGKSkxiLT7URQouUme9p119b1pdlMx1Qb45I6\n7phDadKnBWbpmAsWYW+tk4JPvEmveTZ53TPFE5ep3RX5yLlOrbyALdGLbYhlQjmWo2Vqv9Qvjh7r\n5+piWGKvNP9SdjWdtxZ5q/+Q6BY/E5eleokvhydnb6FROkejPpCTkXyQxCfRUrwWeyy+W+LL4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gtaCIt5zkj6OVJoCxvfHrNLt378bevXvbORkTBGMdI7/2ta+dvmbuvPNO3HXXXQCaJX3cEz9N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\nUfTaq54VFRU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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n", "wi = [0,3]\n", @@ -457,10 +285,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m_oil = GPy.examples.dimensionality_reduction.bgplvm_oil(N=300, plot=False)" @@ -468,22 +294,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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EHKyfSoecMU5vSZdEa9WpRUBbO9Lao3Vp2q/r0tbx5co0vWLZA5qc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FP9q152GFyj5M+M3n8XwieCjEGRlbU7b5HcY7On4fn7h6Gt6wyKiwpx/GI5d7bPaH52oqlmhlkw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\n2KKTgIeBwwBM7P73qaBPKp/iWeq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6ffS2YcjHYOTso7dPh1k/zwX72JNxpG3koO1j3ekz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\nSUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n", "axes[0].set_title('Latent Space')\n", diff --git a/GPy/ParametricNonParametricInference.ipynb b/GPy/ParametricNonParametricInference.ipynb index cb1a4f5..d0e978e 100644 --- a/GPy/ParametricNonParametricInference.ipynb +++ b/GPy/ParametricNonParametricInference.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/ParametricNonParametricInference.ipynb)\n", + "\n", "Parametric non Parametric inference\n", "===================\n", "\n", @@ -69,15 +71,13 @@ "p(\\mathbf{f}_\\text{n}|\\mathbf{y}) &= \\mathcal{N}(\\mathbf{f}_\\text{n}| \\mathbf{K}_\\mathbf{f}\\mathbf{K}^{-1}\\mathbf{y},\n", "\\mathbf{K}_{\\mathbf{f}}-\\mathbf{K}_{\\mathbf{f}}\\mathbf{K}^{-1}\\mathbf{K}_{\\mathbf{f}}))\n", "\\end{align}\n", - "$$" + "$$\n" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import GPy, numpy as np, pandas as pd\n", @@ -95,10 +95,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(12345)" @@ -106,22 +104,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = np.random.uniform(0, 10, 40)[:,None]\n", "x.sort(0)\n", @@ -141,10 +126,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "k = GPy.kern.Matern32(1, .3)\n", @@ -159,10 +142,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(1234)\n", @@ -180,22 +161,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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qcXHG0NfQocYfWxnyyxBuxt7kZ5+fc8WOho7Mlom8ONA7+a2X1npcBmUkkecR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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.plot(x, f_w)\n", "_ = plt.plot(x, y)\n", @@ -211,10 +179,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "k = (GPy.kern.Bias(1) \n", @@ -229,32 +195,17 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.checkgrad()" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m.optimize()" @@ -262,42 +213,17 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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B7FmoeqhKrRZ1O/sb/DgcDl56N4hrpIeykly+e8VZnUE8HImi0Iwtch1zlLEs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\nplInjS0BlLnrZWq3WfG2hwd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.plot()" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "x_pred = np.linspace(0, 10, 500)[:,None]\n", @@ -309,32 +235,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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BoHWjkbFFJezdI5g2o/Pnh6mnJhz0tYmjC/Hqqbn7SuapQK9kTH7Q\nyw+uOJ7p44uoDkf57ooX2qb1QaqW/cMrLe74mYcnV6pg3xc27kzNnw8kxjF7jtvphjMJx6EgmMll\nWj0zfmSBCvZZogK9klFBv8lNnzueQyYPo64xzneXP8/6jR8uqppUKnnoMYsf3WTy9N9UsM+21jUO\nVv3wLufPW3Ziv7GV/jB+ZAGGkGphVYapQK9knN+bytkfN3csMcvhxw+8xL/f2N52fMZMyQN/jHPj\n9SZrnlVPwWyRUrb16Kt35HH4/M4DvalrA6Ls88RRBbiOQ1KttsuY/n9UlUHJ9Oh845NH8okTp5F0\nJb9e+QaPrXmf1hlWcw6V3POgxTe/4uWlF9XTMBuqwzEaIhZ5AZMP3jU7LX2QcJLkdXPv2WwTQjB5\ndCGxmNoBK1PUK0zJGk0TXHL6HJacfRhCwCOr3uP/nlzXNlNo/gKXu+6xuObzXvbszt1FMwPVxpa0\nzdj8kUQigomTOx6Ite0Ehb3c0CQbdF1j8qh8miJqA/NMUIFeybrTjy3l+ouPxvTorF67nZ88+DIx\nKzXgdszxLh8701GDs1nw7rZUxRHTGsuhh7udrkzWBO2WYe5PPq+HEQU+4qo2TtpUoFf6xNGzx/CD\nK04gP2CyfmMV313+AnWNqd7auecneXKlWtKRae9tTwX6WM0wDu9kIFZKiW+ABflWwwqD6EKqfH2a\nVKBX+syMCcX89AsfZXRJkK17GrjhrufYUdnIkce4NDXCe++o9E2mRGI22/c2YOiCXVuDnc64iVsJ\nivL6b1plVyaOKlD5+jSpQK/0qdElIX569UeZMaGYmoYY37n7ed7ZWs055yV58gnVq8+U93fUISWU\njinif+t1Du0k0CeTSUJprM7ONl3XGF0cVOWN06ACvdLn8oNell5xAkfPHk00nuDm+19i+hF7eeoJ\nXdWvz5D3WvLzo4Nj8Holo0Z1PBDr0bUBv/NTYZ4PjyZx3f6tzZWrVKBX+oXXo3Ptp4/mY0dPxkm6\nPPLyC3h8Cf77snpKZsK722oAkE2jOp0/77oSn5kb/+fjRxYQjXVeYllpX248wsqgpGuCJR8/jHNO\nnErSlchh73LX3eqFnC4rkWRTRRghoH53Qafz5y3bpjDDG4xki6FrlOR7sRNq1WxPqUCv9CshBJd8\nbA4XnjyToinbeGGNn2f/u7O/m5XT3tlag5N0KR1TyDv/83SRn3cJZmB7yb4yoiiEk1C1cHpKBXql\n3wkhuGjRLC4/bxL+4jA//WUlL7+9q7+blbPWb6wE4NApI3jnLY1DO+nR50J+/kBjh4eIqlk4PaIC\nvTJgnF82g9POilGzYTJ3PPoab3ywt7+blJPWb0gVkSs2xjFipKSwsP3rSSnx5kh+fl8hvxdTRw3M\n9kDaj7IQ4nwhxMKWLQPbO35Ly/d2jyvKvn5ww3DiVaOJNZvc+of/8vaW6v5uUk6pCUepqG7C7zVo\n3FvU6fx5K+GQ349lidMxbkS+GpjtgbQCvRBiPoCUcnXLz/PaudoSIcRGYHM651KGhvx8wcfOgOGJ\n+diOy08efIXNLZtn5KJoBPoypbyupST03NLhvP0/vdO0TSLhEPLlTn5+Xx5Dpyho4iQ73hZR+VC6\nPfoLgfqWy1uARe1cZ4mUcpqUck2a51KGiMWfctj73kROPHQccTtV5riyLtJn59+9KzM5aynh7NN8\nXHiOt8/WB7Smuw6fNoL1b2id9ugNTQyIssS9NbIkpOrgdFO6j3IhULfPz+1tHV/cktq5Ls1zKUPE\ncSe4NDUKwv87llnjRxFutrj5/pdojGR/AO7l/2gcO8/HN79sEoumf1/xOAgBd9ya/RLAMcth3YaW\ngdjS0WzcoDFnbseB3jRyN8hDahB/ZFGAuKWCfVcysea80+6PlHIFgBDiFCHEwtY0z76WLl3adrms\nrIyysrIMNEvJVboOf346zg+/b/LqP09i0omvspvN/PShV1j6uePxmtkplVBXC1+7xuSue23+9Q+d\nc0738dvfWZRO6d2g36/v8PDVbyQ4aVGSs0/1Mfcwl1NPz16qYd2GSmzHZcaEYqoqgpSWSvwd7BOf\ncJKEfLlfcqI4309NOM135BxQXl5OeXl5r28vWjeC6PAK7Q+i1kkpnxBCLAOelVKuFkJcAEyWUv7s\ngNu2Xvc6INwa+Pe5juyqDZm2t6aJuCvQtdzu0QwFz63R+NY3PZBfwfAjXuP4ecVcd/HR6FpmpwRK\nCVd81qR0iuS7P0ggJfzhQYPblnm4eZnNWef0LECve13jms+bPP9qHI8H1r+hcdmnvaz8a5yp07Lz\nfP/5w6/y0tu7uOyMOdR/MIt33ta49Y72e7uRmMWkESF83oGx2Ug6IjGLipooAX/fDyw3RWLMHF+M\nluHnY1eEEEgpu33SLiOdlHJFO19PtBx+FChtuTwZeLalEa0TurYAq1oulwCvdbdhigLw0ZNdVr9g\nccIRw3j3iTN5+q9eVvzlTTLdOXjgXoOqSsH1N6ZGToWAz1zq8OAjcZb9yMNN3/Fg9yBD8Os7DK7+\nkoOnJY4ePt/lhu/aLLnES1NTRpsOQFPU4tX39iAEHDtnbEt+vpM3J9cdFEEeIKimW3YprS6tlHId\ngBBiIane+vqWQ6tajq8GFgkhzgdq9jmuKN0WDMHPbxf87DcNVL55CL/54UTuWbk1Y/f/7tuCX/zc\nw2/utjEPmIQy9zDJ356Ns6tCsPjjXnbu6LoT9d47gjfX6Xzy0/sv1b/oM0mOPSHJ179oZnxw9vn1\nFThJl8OmjmB4YYA312udlj4wcngQtj1jh+cTUdMtO5T2o93Sw1+9b0pGSrlgn8tPtHz9PN1zKdmT\ndF0i0TiRSIxYPPU9Eo0PqF7SeWfns+LRavJGVfPTr0/lO99vIt3ZddEIfOlKL9+/2WZSaft/a2Eh\nrHgglb45+1Qfv1tudHreO3/pYckXEvja2Zlv6Y8T1NYIfnNH5vLjUkqeeTX1xrdowSSam2FXhWDG\nrI4fu1wfiD2Q6dHJ9xs4jppu2Z7B9WgrPea6kuZIDIMkk0fnM3NiCdPHFTNzYgmTR+cjXIfm6MDp\nKZ14+FhuvFFj5sef4aknBR9bqPPBe73Pj958U6oWzHmLOw8QQsCSLzg88bc4//irzvlnednwwcHn\n3bpZ8OLzOp+5rP3CW6YJd/3O5qH7DVY/k5mX39r397KzqonifB9HzhrNW29qzD7EbUsbHciyHUKB\n3Jw/35kxw/LVDJwOqEA/hFl2goRtMXVsIeNGFOD17N/L9HoMJowqYNKIEM2R2IDZzu2s46dy4Vkj\nmX7WsySHv8UF55jctsyD1cPZl8+t0ShfrfPDn3Y/OEyZKnnsKYvzP5nkwnN83PEzY7/c/f/9ysMl\nn3MIhTq+j1GjJP93j821X/WydXN6g3hSSlaWfwDAOSdMw2NovLmu8/o2juMQyqFCZt2laYJh+T4s\nWxU9O5AK9ENUNG4RMjWmjivGY3S+X6jfZzJ9XBF23MJJDoxgf8nH5nD8oWMpnPYBhy3+J+vfdDn9\nJB+vvtK9p3Q4DNd/3eRnv7TJz+/ZuTUNPnuZw9Nr4rz1psYZC328sVZjV4XgX0/rfG5J14HmyKNd\nvvGtBEsu8xJp7tn591W+bicbd9aTHzQ55ahJAF0ulELKAbcReKYMLwqSdFQZ4wOpQD8ERWIWw/N9\njBqW1+3b6HrqTSFhWQMib69pgq9ccASzJ5UQlWGKjvoXX/pmjC8uMbnxeg+NjZ3f/qbvmJx2epIT\nPtL7N67RYyT3PmTzlW8kWHKpl89+0ssnL3YoLOre7T9zqcP8I1y+9kWTF5/X2LihZ737usYYDzz9\nFgCXnj4XX8v6gv+t1zrdbMQzyPLzB1LVLQ82uB9x5SDN0TijCv0U57czUtgFTROUji0iOkBy9qZH\n54bPHsP4EXlUVDexru5Fnv53BMcRnHKij2f+2X6v9em/6ax/XeOG76X/EV8I+Pi5SVa9EOO005Nc\ndU3371MI+OEym+IS+M0dHs493cdjD3evpx2zEtzy+//SGLE5dMpwyuaNB6CmGhobBJMmt/9mLKUc\n9IFeVbc8WJcLprLeALVgqs80R+OMLgpQmJfejkJxK8HWvY2Egj1/s8iG6nCUG+56jvqmOCccOo6v\nXbiAV17SueEbJofMdfnBT2xGjExdt6YaTivzc/d9FguO6p80VMJx2bCjjk276qltjOE4LqZHx24o\n5P9unsJnr4jwpa9oeDtIr2zb08CvVr7Otj0NDC8McOs1ZRSEUouF/v4XnUf/YPDgo+33aC07wbCQ\nSWEv3uhzScJJsmlXOOvP0VxZMKUC/RCRqSDfqq4xRk2jhX+AVD/cujvMd1e8QMxyOPv4KVx2xlys\neGp+/KMPG9zwXZsLP53kqstNJpdKvv39vh+wq22I8df/bGLVa9uIWu3nka2mIBuePpmiyTs5fNEG\nxo0IMWZYiIDXg+0k2bwrzLvbapASRpcE+d5lxzGq5MOR38sv9nLGWQ6LP9X+LKJINM6UMQVdjssM\nBpW1zTTbLqYne6UeVKDvbgNUoM+65kic0cWZC/Kttu9twEXDGCBBY/3GKn78wEskXcl5H53OxafO\nRgjBO28Jrv+6iWUJhIC/PRvH24er5V1X8vQrW/j9v97BSqQC8PgReRwyeRgjigKYHh0rkaQmHKOy\nLsL2igQv/vFIfAWNTPzIK2ja/q8PQ9c47ajJXLRo5n7bAO7ZLTitzMcr62IEgu23JRKJMXNie7UH\nBx8pJR/sqCOYxV69CvTdbYAK9FnV3Bxj7LAg+VnYANp1JRt2ZveF1FOvvL2Lnz/yGq4rWXzSDD51\nymwAHAf++HuDo49NMn1G3z3fLNvhtkd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.Mat52), plot_data=False)\n", "plt.plot(x, f_SE)" @@ -342,32 +245,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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YXlGnUMfxC/vhy0c8HLv/VXzoF/4L/rcvWGiYynLpukHhzVedyLpp1i20o4h+ZwoLKJ1x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\nxBkDJMOtKyqXcjfYhNdrIAwY5uboT4G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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.mul), plot_data=False)\n", "plt.plot(x, f_perdom)" @@ -375,32 +255,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "
" - ], - "text/plain": [ - " truth mean std\n", - "0 0.06 0.00 0.00\n", - "1 -2.59 -2.34 0.63\n", - "2 1.90 1.42 0.51\n", - "3 4.36 4.46 0.51" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "np.round(df, 2)" ] diff --git a/GPy/Poisson regression tutorial.ipynb b/GPy/Poisson regression tutorial.ipynb index 8d21ce4..1bd831b 100644 --- a/GPy/Poisson regression tutorial.ipynb +++ b/GPy/Poisson regression tutorial.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Count Data Tutorial" + "# Count Data Tutorial\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Poisson%20regression%20tutorial.ipynb)\n" ] }, { @@ -36,19 +38,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%pylab inline\n", "import numpy as np\n", @@ -66,32 +58,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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3r2dowlBS+6S2u24o+zK4jE0dy/HG42yu3hzaDQshhI1iOjC010zV3YgRZryk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\nx7y46UWOnOwkZe/jxsGvfw0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "K = np.arange(0, 30, 1)[:, None]\n", "rates = np.arange(0, 40, 1)[:, None]\n", @@ -120,32 +89,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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VS/5q9xBCnESW9vTSF7u/YFTCqJOSAhhjutavD4DON6+/TmTnGL47fxDvfPeO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\n5/bt48w1a1hy8CAuSRBeaTe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Hk/R0OF0zUa4YRMB0mBi01guAGqXUM+YjO0hxCeGz99+H886D8s9D91ae3khP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\nTspjwACroxHtuWPMHUQOfI+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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "small_rate = 2\n", "K = np.arange(0, 20, 1)[:, None]\n", @@ -231,50 +144,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/maxz/anaconda/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1590: RuntimeWarning: divide by zero encountered in true_divide\n", - " x = asarray((x-loc)*1.0/scale)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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kDs/leP0hEnrc4IMPzI5GCHPISDOiW9jsNhIvRU6CSOqZxMikkYybdUyqmUTM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\nb4deE4v50kTPHsuiM/5ySR/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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "rates = np.arange(0, 20, 0.01)[:, None]\n", "small_counts = 1\n", @@ -329,42 +201,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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OhxEOh1N+v5AmijkIIUYBeBTAcCnlqqjnpJk2vMTbbwPTpwPTpqlW4k1WrQJG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\nAUd71gAwbx6tOr1iBXDOOcl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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "X = np.linspace(0,23.50,48)[:, None]\n", "intensities = 4 + 3*np.sin(10 + 0.6*X) + np.sin(0.1*X)\n", @@ -388,10 +227,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import GPy\n", @@ -417,10 +254,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "poisson_likelihood = GPy.likelihoods.Poisson()\n", @@ -436,48 +271,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : gp\n", - "Objective : 121.214706928\n", - "Number of Parameters : 2\n", - "Number of Optimization Parameters : 2\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1mgp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mrbf.lengthscale\u001b[0;0m | 1.0 | +ve | \n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Iv+TC5fQNjHHoxCjnv/rN3HnnLUB14rFc9fJU4sZPJn6/g5sym7pmBJPPRkut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\nAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m = GPy.core.GP(X=X, Y=Y, likelihood=poisson_likelihood, inference_method=laplace_inf, kernel=kernel)\n", "print(m)\n", @@ -497,48 +293,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : gp\n", - "Objective : 113.686968138\n", - "Number of Parameters : 2\n", - "Number of Optimization Parameters : 2\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1mgp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.5390223923 | +ve | \n", - " \u001b[1mrbf.lengthscale\u001b[0;0m | 3.61055611736 | +ve | \n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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8GcR2UmkI2xmcGOYmahZ+NTWZSSU6aOOQ17fkT5zJg8S9iIU2uGyEZbt5Bl6P\nh1DEorqyUBJOLI4tEd78XIzpJkglK4Fbb72V9vb2xM3/hhtu4NZbC98Kw7KdnMS3T4XkMde86j63\nh064RlmNK1InpfV7urWCpgICYnXMRSJGtKWzL+Eg9ega0SlgDnIcyVv7OwgW0XedDP6YqGGh68Wz\nSrQceGtfB6Fwca0I4u1cpYjlBAz521qOU9I+yokg1fV7E7Asn4JMNoQQ2LaN10fC3nykrY9yvzfh\nIJ0KSSyB/gh+vy+vYY7pJkgl3yfuvPNO7r33Xm644QbANQdNnz694OagYMTAk4PS17lMHpNS0tET\nZkH5xJYCHwsr1jVMEwLTsoc5gR1HjthaUpE6qV65m4UQW3GVAZR6e8kUkMRMILFZiBmLWujsCeHV\n3UzhoUvXUiTTTmLpkG6CVLLyveqqqwASJqDp06cn3iskkaiNNwc18HOZPCaEIGQUV2SVZcVm+sIN\nPhg667cdiS5K/zrLJyk1lYll/CYj8914vlDJYq2d/YQsxo10iURNIpEIdbXVBINhFsys5mB7kL6+\nEDOn1yGEIBqNsmxu/QRJPvFIKdl9sIvKyuJKzOrvD3PioqIpTDsizUe78fqydQvnnmAoyuJZ1fhH\nanNWAHr6I7T1RAGo8gt6IzYVZQPj1h3ow1/mpyLPjYymcrIYAFLKTZmLVJrEU9bBXRW4IZI65eX+\nxGxlMtimM6W9O0hPyEAvwtjyuP041fLMhcAqULG98Sgr89LRE2Lu9JpCiwJAxLDw6G4EXihsouuD\nR00iEsljisxI1TF8hhBijxDiFSHETUKIT+VbsGJHCAaygqXr6PN4NGqrB2YKpewT6OqL4vf7KfMV\n361M1wRWkWeSFmu9Hl3TCEeLx8lvmDYej46uawQj5oiKXfmFsyNVFfogbpmIfVLKbwGFD7IuMG5U\nUGwlINzU9qEzklJdCERNq7ivvCJv9GOYw23b+SDTRDLDdopm/OJtN3VNIxxbFQwllf4fitFJeS0v\npQwk/XCnfMaw0AQyqa65aTsMrxxd/GaJTOjuCeMrwhVAHE1omEXcaSximOg5iAwaj0wTyXxeD33B\nKHVFUIQvuWuYNoLZR0JRlCuZzKSqBF4VQjwA1Akh7sJtCjOl0YRI5CkKRs7+1GJmiVJTAsGohc9f\nfE7NOLquFXXJjnDEwjMBOQKZJpL5fV4CxaIEkpbTI+ZVSLUSyJZUHcMbkspH7FVZwzEPfNLzEdsb\nxswSgXDBGS2QAAAgAElEQVSYMr+nJJrMOI7EsCW5jMWImjZvNrfTHggzq7GSExc2ZlVuw6NrdPRE\n6OyNAK7VblptGdPrC5O+fbSjj97gQBKWI9NrJFMIopbk9b1tHL+gAb/XQ0tHH7OmVU/IuS3bobs3\nzPT6yoQ5CKC6aniZ8ooKv0oWy5J0zEEbY4Xk9uVToMlE/Mfn/kyH/xCF0LBsh96wgS1lSSiB3v7I\nuL0W0uH5HYf4ye/foCt2wwaoq/az4eOnc85JczI6phCCqiE32XC0cAXSoqZdkDDabBLJKsr96LpO\nOGzi93po7eqfMCUQNSwC/RGm11cO8quNFIRQXgLXVKFJSQkIIS7BbS7TLIRYAnxVSvlEXiWbBCR6\nncKIHaJ0zS03bVoOoYhJY+0EC5gHAsEo/hwkh0kp2fTcbh5pehuABTNrWDq3jj2HuznU1sfdP3+J\nz114Mp/4wPFZnwsoaLSQaTvkIC8swf5jPfzmz3s42NqLR9c4bdkMLjp3CbVVg0102SaSeT06IcOi\n2naIWg6mZed0AjAaoYiJactEHS5Ffkl1JXCrlDJRNiKWPayUQJJjeKQlabzBiSMlZpGGBKZLxLCp\nzMEN7amX9vFI09sIAdd85FQuOncJmubmXvz2L3v48ZNv8NOn3qSy3MfasxZlfb5CNvlxcnRuKSW/\n2ryLR5/dNej93Qe7eOqlZr54yZmsXD478f7MmTMHzfrTLSWhaW45dMO08fm8GObEKIFw1EITomga\nFZU6qY7w0GigKR8dBFDmd3Wopmkj1oHRNS0WbicnTd/bsQhFDEQOEnN2HejkR7/dCcAXL1nBR967\nNLGS0jTBx84/jg0fPx2AH/xmBwdaerI+Z3I54onEtOxEFFk2SCnZ+L87ePTZXWgCPnLeUu68/gN8\n7XPnccqSafQGDe76+Uv85fUjOZB6ANOWhKIm5X4fwUh+i8t1BkKAW25FIghHTRX5MwGkekXvE0L8\nIZYo9jS4zWWEEH+XR9mKnng/Xb/Pg3+UkEnDtJEUb3JQOkQMO+vKl1HT5r5Nr2I7ko++bykXnLlg\nxP3WnbOYNSsXYloO//mrrVmHfArhtlCcaCLRkROc0uXJvzbzh5f34fNo3HLVeXzho6dywoJGVi6f\nxe3XnM+nPng8jiP5z1+9wtsHOnMguYttO4SjJuVlXiJ5TCKTUrL/WADTst06XLqgL2TkrVGRYoBU\nR3gvsDn2vAl4BqjDbTqvGINIvGSwYNKvBoxYVnScTJKRNj23i2OdQebPqOaz604Zc99r/uZUZjVU\ncrC1lyf/ujcr2XXdbaE40YQiFt5xFOd449h8NMBDT74OuCunlctnDfq8pgk+8+GT+Mh5S7EdyX88\n8jKB/mhO5HeAiOkkcmHyRTBs4Cvz0dMfxXEkHl0n0BdRSmACSDVE9Jv5FqRUMSyHygr3JhA1LDxF\nVKY3XQzLQU/ycKabjNTaFeR/n98DwN9/8gy8nrEv8DKfhy989FT+v5/8lV9t3sX7T5tPQ83wMMFU\n8Hp1QhGT2hHCDPNJ1LTxjOMVHmscbdvhe09sx3EkF527hPNPmzfiMYQQfP7iU9h7tJtdB7r4wW92\ncOMVZ2ctv65pRKMmfr9/ULhmrunqjVBbVU5HTwiEhs/rocO0qVeO4bxTfNW/Sg6Bx6MjcaMeKiex\nEkjO3oT0k5EefuYtLNvhg6fPZ/nC1Kp8rjhhFiuXz2LrrhYe37Kbaz92Wkayx1soSiknNK7ccpxx\nC8WNNY5PvbSP5qMBptdV8Nl1J495HI+uccNlK7nhvzbzl9eP8PLpxzj7xNljfmY8dF3HiJWXth23\n1+9IkXDZIKUkYtqUe7xYtsTndycHHo+6PU0EqRaQqxVCLI79f5PqNJY6tdVufLjXoxOMNfNu6ehz\n6+8UOaGIQaA3nHidzUxw37Eent9xGI+uccXak9L67Gc+fBJCwDOv7Kc95jzMhHgCVL7r4iT/fQ0z\n83MFI2YiEugLf/Meyv3j3xRn1FdyZWx8f/TbnZhZ+kF8Xp2KCjf01OfzsvtQF0fae1P6bCr7dfaE\neHN/J1rMZFZe7sfvddVmXXVxJ9SVCukUkFuCmysggMfyJlGJkdxxK2q4F2RbTxiziMsaxOkPG/Ql\ntRsc2iQnORnpyiuv4OGHH+Ghhx4a8VibnnNvZuvOXsyM+vRqsi+cVcv575mHZTs8seWdNL/FABXl\nfvx+X95LSgSjFp09YUIRI3FzG4vRxvHXf3qHvpDBSYsaOSuNGf1F5y1l/oxq2rpDPPVSdrmdQojE\nb9jn9VBVWU4wktoEpqcvOu5kJxgxqa2uSARW+LyexEojn93qFAOkOsp1UsrNQoibpZTXCyEuy6tU\npYqm09sfQSKImjZVhZZnHCJRe9Dsf2h/11STkQ629vLXN47i0TU+8YHjMpJl/YdO4Pmdh3lu2wE+\nvebEYYlRqeLx6G7IY1n+Mk2llPSHTSzbGTVqLJmRxnHV6nX8n4e2AXDVhaekZcLSNcFVF57MHT99\nkU3P7eZDZy7IqRnSlm7U23ilPUzbIWJY+Me4mVu2JIsKIYockOpKQAgh7gS2CSHOwI0MUqRJmd9L\n89EAleX+SdGE3rQd7FimrZRyWGnsgWQkAQiuvvoaZs6cOew4/++FdwFYvXIhjbWZLfHnzahh5fJZ\nGJbDUy81Z3QMiGXBRvJbQsKyJZYDwYiVkv18pHHc1txPOGpx6tLpnLCgIW0ZVpwwi5MWNdIXMvif\nP72b/pcYgzK/j66esc1yjiPRYmUnxmKyR8yVAqkqgQ24CWJ34vYVWJ83iUoYTRP4/D78Pg9WEZc6\njmNLmci0Na3MUvh7+qM8v+MwQsDHzl82/gfGIP7537/YnLESdUMd3e/U1tWflTwjYdkODoJyvzfj\nRvJR0+a3f3ZDYj/5wczKZggh+NxFbgjub/+8h46e8DifSB2PrtE/jknIMC18Xg+Rcf5Opdx9b7KQ\nqhI4XUr5LSllj5TyQWBxPoUqZaor3RDFydCE3rYlUggs28GyM2uE8szW/ZiWw5nHz2R2Y3YGsJMX\nT2Pp3Dp6gwZ/3H4w4+NYtkPUtDjU3pfzLGLDtNB1NyKszJ+ZCWbLtoP0BKMsmVPHqUunZyzL8fMb\nOO+UORiWw+Nbdmd8nJEwbWfMG3goaqHr2pjlOkzLVrWBioAxlYAQ4hIhxKPAD4QQj8YfgMobyJJi\nnwHFu1+59WMsTMtJu5erZTs89aJruvnIe5dmLZMQIrEa+M0LezIeQ8eRdPeE3VII4dyWQgiGTbwe\nT8ZdvWzb4dfPu+abT33w+KzDWT+9+kSEgM1bs4usGoo31nhmNEIRA59XHzMSK2pYaCoZrOCM5xhu\nArYDNwN3MVAvWdUOyhKryJVAvPuVrmkEwyYSmXb25ktvHqWrN8Lc6VWctmxGTuQ675S5/OwPb3Kk\no59t77QOy55NBQn0hAwqK8ro7o9QVZG7Bjlhw8Lr8Wbc1eult47R2hVkdmMl55ycWSntZObPrOH8\n98zj+Z2HeWLLO2z4xOlZHxNijWf6I9RWj5x8Z9kS3StwYNTcjFDUHDebWpF/xlQCUsoeoAfXJ6DI\nIfFIG8eRODL9G2y+iXe/8uiaWzhOgMeTnnnjd7FSDxeftzRnCVoeXePic5fw06fe5Mm/7s1ICei6\njmm6Tttw2Mq6RLKUAwUCTcvBp2fe1et3f3HH7G/etww9R0lZ6z90Ai+8fpjNr+7nkx88Pu0Q3dGI\nmM6oeQimLdG9IDSNqGFR5vfiODIRaABuSQ2Pd/ImT5YKqfYTuBZ3NRBHSikzi/VTACBj/YffOdyN\nadosz7KbVq6JGgMXaNiSCKAijbDtvUfc8gUVfg+rzhi5SFymrF65kF82vc1r77ZxtKOPOWk2O/H7\nvImx1nUP7xzqZnZjJQ0ZtlNs7eynO2gghMiqWNy+owHePtCZ8zGbN6OG80+dx/M7DvP4lt38/SfP\nyMlxPV4Pe4+OXOE1Pg4eXScSdZXAgZYAUWtgBSyERoXqCVNw0okOWiGlXBZ7KAWQJUITBMMGQmiU\nV/iJGIXrfDUSVpLDtLLcT0V5eiaTJ//q+gJWr1yYUqZrOlRX+Hn/6fMB+P1f00+G0jSRqIbq83mp\nrionlEWZ5IhpU1VZTmVFWcIZnE4iXZzfx/wnF6zI/Zitv2A5moBnXz1AW3dufAM+r4fKirIRH/Fx\n8Hp0+mNjazty0D4Vk7iESimR6i9ta8w0pMgRmiZo6eqnrMy1qYbCJjWVE1vcbCxMW45b82Y0Aklh\noReduySncsW5+NwlbN56gOe2HeDKD5+YVZtBIQSmmbmPxoqZPgbJl2ZXr76QwZ92HAbgonNzH3w3\nb0Y15582nz+9diinq4Hx0DSBFRtby5bkzvuiyBWprgQahRBbhRB3xR535lWqKYCuaYQiFrqmuc1n\niih5TEqZVfTSMy/vw7IdVpwwi1lZhoWOxuI5dZy4sJFQ1GLL9kNZHy+bpKWRImBSTaSLs/nVAxim\nzenHzUjbvJUq6y84IWk1EMzLOUYi3iqyuEMhpi6pKoHvA7fgRgvFH1khhLg79v+12R5rMuL16JQn\nRaWYSTfdtu5gQUNIDdPOuIOYZTuJejW5CAsdi4vOc1cZv/9rc9bx/pkWx3McSbbq23Ykf4iZgi4+\nL39jNnd6Ne8/bT624/Z3nihs2yEa76uhKDpSutKllE1DHpvH/9S4XCuEeBe3Yc2UQwhBRdmAEkhO\nqmnp7CdqFK7KaDhqpp0TEOfFN47S3Rdh3vTqrBKdUuHck+dQX13G4fY+Xt/bntWx4klx6WKYFrqW\n3c3t1d0ttHaHmNlQyRnHj75ayAXrP+SuBp7bdpDWrolZDUgEfcFoxhnUivySainprUMer+Tg3NdK\nKY+TUj6bg2NNehwpE6GGEkEoWjhHcShqZVTBsbW1lYf+968AnH/yNL74xS/S0tICSL7znf/ivvu+\nQyrJU6kmWnl0jXVnu/bzJ1/MvJ4QkEiKS5d4ZuxYjPd94l3TLjp3cc7CQkeT4w+/eYz3n+6uBr5x\n/xMp/02yQdMEPcHohDSpV6RPqp3FVsafCyHWAGtycO4GIcRq4Ewp5bdycLxJjYj5BYIRk8oKP+EC\nKgHDtNEzCFd9+Imn6I7UoAubpx69n52vbee2277GySefnIiXN033e42VPJVOotXasxexacsutr59\njLbuUMYx8F6Ph2DYpKIsvYgVNzN27MtorO9zqLWXnXva8Xt1Vq9YmJHsqRKX45OXXwXM51iwglef\n/klKf5Ns8Hh0ensjVFTm9LCKHJH2dE9K2SSE+Gq2J47VIEIIsVYIsTpHJqZJi0fX6Q1G6Q0Z+P1+\nTHMgZDHbblgjfT65Q5Rp2YN8EKblDIt2SQV92slw6BDd+15h3zvbOeGEE9ixYyc7duzkiiuuAEgp\neSqdRKv66jLOO2Uuz+84zB9eauaqC8fuWzwaXo9OfzhKTWV68StR08HnH/tvM9b3ia9gVp2R23LP\nY8vxMxpO/huq5p7GGWuu4uGH7xsmVy7xenSkKK5kSMUAqSaL3ZT0MrW+gGMf71qgS0r5ONCJ27Bm\nmBK4/fbbE89XrVrFqlWrsj110eLz6nT1m4laKskRJ4dbe5gzvSbjRKQDxwIsmlM/6L1jHX3UVfup\nLPfzzsEuPN6Bu34mDrxAX4QXdh4BJH2HXs1Izky5+LylPL/jME1bD3DZ6hPxZ5h0ZzmC/a3pVRYV\nWZhvgmGDLdvcQnhxJ/dE0dv8Z6rmnkpbtBpPeR1WOJC3cwkhqK/JTZayYjhbtmxhy5YtGX8+1ZXA\nPlzDoQCacUtKZ0MzsDX2vBF4ZqSdkpVAqSOEGJQ8YyZlVvaFTQzTpjxDJdDVF2HhkNVA1LTp7I0g\nJXh9HsrTNIMM5amX3LDQUNs7XP7Ji3jzzTfZsWMnp5126iBz0JVXDqwI6uvrRzQ9JCdajbcvwPHz\n61k6t469RwK8sPNwxmaVsizHYDRG+z7TjvsAUdPm1KXTWTCzJi/nHkuOp3bspGruaZy+5nMcX90+\n7jhng3IK54+hE+RvfOMbaX0+VZ/AprSOOv7xNscqlAJ0SClfy+XxSwLNjVbx6BqWIwkbVkbdsGzb\nwXFcO7/fN/DndqQkYth09UYyLnkcxzDtRKOX1WfM4Z/+8SpaWlr5t3/7N77+9a8za9ZMPB4PQgi+\n9KUvAWMnT6WbaCWE4OLzlnDfpm08+de9fOjMBRPaTH48Rvo+F154Mf/6i53u9glaBQyVw7zne7zc\nIekwa/iXz39y3HFWlCYilfjqmAM33ldYAuvzHdUjhJC5rvWeCq2d/YQsCl7QLRwxmNtYQUWZl7cP\ndFFT4WHejNoMjmPyzpEAC6dXUZdUG2fXgU4QAsdxqK7Kbqm+eet+7n9iO4tn1/LtL15QkBuwYdpc\nd/dT9IYM7tjwAZYvzNpqmVe27jrGHT99kRn1Fdz/zx/Oa1TQWHz38Vd59tWDXHDmAr506YqCyDAZ\nMEyLugoPjbXFb9YSQiClTPkHleqd7m5gsZSyATgL1U8g73g9Ov0hA8O00XQt0Q1rJA619iSyfI+0\nDa7uEYq6ES+hpLwDy3aQQlBW5sObQg/csZBS8tukypeFmoH7vDprzloEDNQtKmZ+9xdXxgvPXVIw\nBQBw6QXL0TTBH187xLHO3HdaUxQ/qSqBrnjtICllM6qfQN7xeHT6IybhiJu4NVZzjvZAiGDYoC8Y\npaM3MmhbOGri93kGlaUwYmWUdU2jLEslsHNvOwdaeqmr9nP+qfOyOla2rDtnMZom+MsbR2iZoESo\nTNh3NMCOPW0TEhY6HrMaKrngjAU4juQXf3iroLIoCkOqSqBHCHGjEGJ1LFIof6EEigSm5RCMmvi8\nnlHb9AXDUSrK3eYo3f0R/D7voGxj03Ydwsmfj3e/ygX/74U9AFx0zhK8nsKY0OLJWNPryjnr+Gk4\njuThp3aQzwSobHj4D64vYO3Zi6iu8BZcxstWL8fn0fjLG0d47d1Wsh23sZLjMu24psgfqTqG18du\n/uuBvVLKy/IrlgJA03W6+yLU1lQlyhoM9VV090UoL/MRiUZxJPj9Prp7w8yKFSGzbQePd3CBtHj3\nq2zZc7ib7e+0UubTWXdO4dpOJydjvb3rALJxFS+8fpTot/6Lx3/1cyD3CVCZcqS9j1ff7UA6Dq1v\nPcs9u5/JW5JWqkyvq2BpbT9vd1bwzZ/8kTPrD/PLRx7OWKaxkuMy7bimyB+p5gksBgJSyutjVUQX\nSSn351c0RZnfS29/hFrcqqM9/RHKfIP/ZKGIRXmFF9MBXRN4dI2+SITqWO/cePleB+gPRRFCuJFC\nmWSDDWFTrHn5unMWp51klUuGJmMtet9xOJXz2Ly9JW8JUJnyP396BxDMKg8mFFQxyHj7lz/N335j\nE2Hbx5MvHcxKprGS4zLtuKbIH6naBB4D4tU+m2Kvz8qLRIoEQgga691SzGV+L519BjC4+YnH54Z3\nVpb7iftkNd3DkU63cYg/Fv5ZUe7naFcYcLtpZcuBlh5efusYPo/Gx84vrh5D3p63iVbOo2r+GZjO\n0UKLk6A9EOKP2w+hCZhXESAXBbhyhdejsbSygzd651C7+H2ErGOFFkkxQaTjGN4ObtkIoDt/IimS\niZt/hBCUl/mGPeJFuTRNJCJzfF5PYns8SUfXtMR7mRSHG8rjW94BYM1Zi6gfpdn4RJGcBHXaaafy\n7hsv4422ouk+ntl2bNyOXhPFE1vewXYks6ui/M+vfpZW17F889BDD/HkpoeY4e9D6B5ePOjhhz/K\nTKaxuqpl0nFNkV9SvRv0CCFuxF0FrEU5hqc0R9r7+PPrh/Hogk+8v/CrgOQkqHiS2uc/v4a7fvk6\ndYvP5ez3FT7+/XBbL89s3Y+mCb7w8XM4c9Y/pJwMNxHEz3/ZFZ/mxvueo5XZRGvmZHWskb5fuomA\nivyTUrIYQKxo3EqgWUp5S16lQiWLFTPf/MVLvPjmUdaetWjC2hRmwn89tpU/bj/E+94zl3++4uyC\nynLHT//K1l0trDt7MRs+cXpBZRmP3Qe7uG3jn3Acyf+9+n2cftyMQotUcFSyGCCl/KaU8rKJUACK\n4uXt/R28+OZR/F6dyz60PKfHDmfR7H0kPrP2JHwejT+/foTdBztzeux0eH1vO1t3tVDm83D56tyO\nWT44YUEDn159IgD3/uoVWlQSWUmTalOZWiHE4tj/NwkhFuVXLEUxIqXkx79/A4CPv/84GmvLx/lE\nevT1h7Gs3PVanlZXwcdi5qqHfvd61i0oM8FxJD9+8nUAPrXqeOoK7D9JlU9+8HjOOH4mvSGDf/vx\nX+gNRgstkiJPpLoSeBC33PPduJVEHxt7d8VkYbzkneTtTz7/Ju8e6qamwsvH378sp4k+lmVTV+nD\ntHOnBAA++YHjqKvy886hbpq2Hhi0bSISl/7w8j72Heuhsbacj+a553Iuv4+uCW684iwWz67lWGeQ\nf/3R8/zghyoBrBRJ1TFcF6v8eXMsV0Ali5UI4yXvxLd3dAV4qXUGeCqxWrby3/fvzmmiT9QwmT+9\nmoPtuTU9lPu9XPORU7nnV6/wkydf58zjZyZWMPlOXOoIhPjZU2+6x7z4PYOquOaDXH+fcr+X2z5/\nHrc88Eeaj/XxVlcPXd3/iUeTRZsAJqXkcFsf+1t6ONzWx9GOfgL9UXqDUUJRK7Ea9Ht1qip81FT4\nmFFfyazGSuZOq2LJ3HrqqgqX81IIUv1VCiHEncA2IcQZQF0eZVJMIOMl78S3P7m1lZpFixFGgF0v\n/Y5dL8q0E30syx5UV96ybQzDnfkLJJXlPkTsIh26bza879S5/GnHIbbuauG+x1/l//7t+9A0kdfE\nJceRfPeJbUQMi3NOms173zM3J8eNj4tt20SN5FWTTPo+vwTS//uMRENNOV///Hu5/Ud/pptF/Gl/\nC23bflk0CWBSSg629rFzbxtv7evg7f2d9IZS9C11jlxfalpdOcvm1rNsXj2nLJnGkjmlfbtLVQls\nAC4BNgKX4ZaPUEwR+kw/1QvPRkoHf+erkKFtvT8cpdzvS8yIjajJwplueYt41zRdF9iOQ2egn5nT\n0i+dPRJCCK7/xBn803c2s3NPO79+/l0+9cHjc3Ls0fjfF95l5552aip9XPfx3EQDmZZNfzBCfW0l\n4YjBopk1iQTB7r4woViRQFt40WXunOzzZ9Zwx4b385V7fgc1s5h19ufpM0M5O366OI7k3cNdvPTm\nMV588+iwYoH11WUcN7+e+TOqmTOtmsaaMmoq/VSWe91cGimJmjZ9IYOeYJTWriDHOoMcau2l+WiA\njkCYjkCYF990Ew3LfB5OWFDPiuNncPmHltNQMzn8OqmSau2gZiDeDP7B/ImjmGjG6+L1gx8+xCtH\nfPiqNPzBPbz7+ouDuoWl04lKSIlt28R/dn6vRpl/cPkKj64RiZo0VJdhOw66lptQ3YaaMr546Znc\n8dMXefjpN1kyp47tL/w2rQ5mqbJzTxu/eNqtyPnFS1bkLJnOtCyEcBWwpolBTYZqpeT+e77Pww8/\nwsc+egm11b6cdgp78n8fZc+zP2b5mr+nv6Ke17prufXbj/DvX/k0P/3pj/MyjslYtsMbzR289NZR\nXn7rGN19A9Vyayp8nHnCTE5eMp2TFzUys6Ey45LmtiM52t7HniPd7DrQxZv7Ojja0c+OPe3s2NPO\nx89flquvVDSkWjvoDFxncDfwKG4RuSfyKZhiYhgveceuPwVf1RFmN1Zy8z9cyd3W/kS3sHQTffw+\nHTNWyC5qmEwbwfaqawIcmzkzatjf1k9lee7ssyuXz+bSC05g03O7+Y9HXuYrl6ziH8lt4tLhtl6+\n/cjLOI7kUx88npXLZ+VAchfHkZTFsr09Q3oQ+L0e1qxZg6bBFZdfTnlleU4TseLH+cxn1/OLZ97i\nNy/sZXdXOTfe/xwXrTyPf/gHuOaa3CaARQyL7e+08tJbx3h1VwvBiJnYNq2unHNOmsO5J89h+cLG\nnPVk0DXB/Jk1zJ9ZwwVnumW+u3rD7NjTRktHH3OmVeXkPMVEqp3FtgJrgI1SysuEEFullCvzKphK\nFis4L799jLt+9iKagH/9u/dz0uJpWR1PWiahqEVlZTn9wTAnzG9AG3LxtncH6Q1FWTq3gd0HOqmo\nzG0YquNIvv3Iy7z45lHqqv38+7XvZ06s4mq2tHYFuW3jn+jqjbBy+Sxu+ey5w75fNgSDYSrKPAjd\nizQNFswebKvedaATNI2GSi89ETvrXhFjsXNPG999YhsdAbce1bJ59Vx4zmLOPXkOFRm0QYUBp+6b\n+zrY9k4rO/e0YVgD1W/nTa/mnJNnc+7Jc1gyp25CGxiVcrJYykpASrlSCPFoTAk8LaX8cFaSjn9O\npQQKyNGOfr56/3OEohZXXXgyn/xAdjZ0y7Kp9At6giZlZX6ikSjL5tUP2y8SNYkaNrXVZew/FkD3\n5r75u2Ha/PtP/sIbzR3UVPr4+t++j6Vzs3P+HWjp5d9+/Ge6eiOctKiRr//te3MeDRSNRKit9NMd\nsqgt05nZOHhW+u6hLixHsmR2Lftb+6jI4SpqJAzT5umX9/H4lnfoieUR+Lw6px83g+ULGzlhQQNz\nGquorvANU4ZR06a7N8zh9j4Otvax53A3b+3voDc42JdxwoIGzj5pNuecNDtnyjoTlBIQ4vu4QcBL\ngG3Aknz3FFBKoHCEoyZf+/6fONDSyzknzearnzkn61lXvGdyeyCE1DyU6zLR82A0Ar1h2vuNvMxo\nI4bF3T9/iR172vB5da7/xOl88PT5GX3Pv7x+hO8+7kYCnbSokVs/dx6VGc6Gx8I2DWbUV/Du4QCL\nZ9dQUznY17DvaADDdjh+Xj27DnRRVZXbVdRohKMWL+w8xB+3H+Kt/cMzsz26oLrCj6YJpJREohah\nqDXCkVyn7smLp3HKkmmsXD67aJywpawEUrrTSSk34N7891HCTWUcR9LSFRyzlWMxkk7C13jJPFHD\n4tpUJnYAABFdSURBVI6fvsiBll7mTKviS5euyMmy23EcfF4dn1cnFI5Sn0K2cU1VGZY5/GaRi+Sk\nMp+Hr33uXC44cwGGafOdx17l7l+8NGZbyqHn/d6DD3HnT57n24+8TMSwmFNl8PWr30tlmSfnCVNS\nSjy6wO/1YJjWiIrR6xFouDeBbG3k6Yxxud/D2rMW8+/XfYDvf3UdX7zkTNaetYhFs2upKvdi2ZLu\nvgidPWG6eiOEohYeXTCttpxTl07nI+ct5R8+dQbf/ae1/OCWC/mnT5/Fh89eXDQKoNRJ1TFcCzwD\n/Aq4rlSbyrR1h/i7bzWhaYLpdRXMbKhgVkMl82fUsGh2LYtm1+ZlhpctqSZ8jZfMY1o2d//iJd7c\n10FDTRn/5/PnZWzfHYaUeD06FT4PnXYYfwrlrDVN4PcOzxXIVXKS16PzxUvOZPnCRn785Ou8/NYx\ntu1u4fxT57F6xSKWL2xIhK4OnPe/OdAWoi1azbFgFULvwOfVOb6ujyd/8Z/cX3Y4K5lGwzBtGqp8\n6LqGrgl8I4xLRZkPw3Rt9B5Pdkog0zGeXlfBh1Ys5ENJvZPj4Zjx0GK/z0NVPFxTUXBSNVo+CHwf\nuBlopkSbyvQEo0yrLaOzN0JrV5DWriA7aR+0z8z6Co6b38BJixo5cdE05s+ozqnzLxNSTfgaK5kn\nHDX5j0de4bV326ip9HH7NeczqzG7SAjLtolG3YiO+AiVl3mpTyMjs6bCR2sgPGhm+7nPfz5nyUlC\nCNaetYgzjpvBI01vs2X7QbZsP8SW7Yco83lYOKuGuio/Qgh6jGUsXnczO3vcG7DQ4bxT5nDVhacw\nq6GCOtmSE5kM00r0fAiFo0gpMUybuQ3u6mnaKKuoCr8Hy3bH1qNrBEORYfuUlflSCrvNZQKY36vj\nz3GdKUXuUGUjkjhhQQM/vmUdgbBNd1+Elq4gLZ1BDrb2su9YDwdbemjtDtHaHeKFne6Mr6rcy6nL\nZrDihJmccdzMSVMgLJm27iB3/sw1AVWVe7n9mvOZNyN7J1wkYrBkTi2aEGixWZ/f52HujJqUjzGt\nroLaJKVhWjb7W/uylm2k83zp0hWsv+AEmrYe4KU3j3Kko5/dB7uG7KljhboJtb/DqtPmcNOVn8BV\ncbnzXwV6Q8xorMGyHarLdKbXVwIkGggtmDWyE7vM703kXcybXoPtDDZrOo5k77EeqnMccaWY3Kiy\nESPg8+rMnV7N3OmDb4S27XC4vY9dB7p4a18Hb+7voKs3wl9eP8JfXj8CwNK5dZx5/ExWLJ/Fsrn1\nE7JKGC/ha6ztr73bxr2PvkJv0GDutCpu/dx5OYuF1gQjmn3SNQN4k8pHeD06P/tJ/pKTZjVW8dl1\nJ/PZdScT6I9yuK03EbHywp828/jDD3HZp/4GsWA6Dz/8U+ZOr+Lqq68Z92+QHhLTsrFsm2l1ZYO+\nf6pomkDThn/Om2LAQ26/j6KYUWUj0kDXNRbOqmXhrFrWnbMYKV1H8vZ3Wtm2u5U3mtvZeyTA3iMB\nHntuN3VVflYsn8VZy2dx6rIZw5rE54rxEr5G2r5q9Truf2Ibm2OVNU9bNoMbrziLyvLchWTmKtt3\nKB//6MWUlfm45hr3hpSv7lR1VX7qqqYnXi+dsY7pFVZeO2ZJKaku92JZNo7j4M9xZFS5T8N25LiT\nE9UBbOqQcmexiWYyhohGDYs39nXw6q4Wtu5uSSTSAPg8Gu9ZOp2Vy2ezcvmsnNfiTxXTsmnaeoBN\nz+2muy+CR9e4fPVyPvH+4wY5QXOBYxosnJ37RWNPMEJrIEK5P/c5BIUmaljUlGl09kURwPKFjTk9\nfjhicqgjSHlZ6Y1dPinlENF0MoaTkVLKvDqGJ6MSSEZKyYGWXrbuauGVt4/x7uHuQduXzq1j5fJZ\nrFw+m8Wza/NuNuoNRvnja4f4zQt76OhxldPx8+v54iVnMi8NG32q2I5DmTZ+LkAmOI5kz+EubIcJ\ni4WfKEJhg4UzKtl3rBfdIzh+XkPOz7HrQCeVyi+QFlNeCQw5wRpgTb7bTE52JTCU7r4Ir+52FcKO\nPe0Y5kAZ4KpyL8sXNiYijhbNqslJtml3X4Qde9p4+a1jbN11DMt2x3PBzBo+vXo5Z580J2/KJ2KY\nzKj1U1uZP0d5e3eQ3oidiKQpBfqCYZbPb2DPkW68Ho3FeVhJ5SsTu5RRSmD4SVTZiCyImjav723n\nlbePsf2d1sTMPI4QMKuhkoWzalkws4bG2jLqq8upry6jrsqPz6vT3dXBH/7wFJddfiVR0+LnjzzO\niaeeRdTxsO9YD/uP9XCobSCKRhNw+vEzWbNyEWefOHvUm39raytPPvm7hC34oYce4uKLP8LMmTPT\n+o6hcJRFs6pTygfIFMt2ePdwgKo0FE2uvl++CIX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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.optimize()\n", "print(m)\n", @@ -560,32 +317,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-2, 3)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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8vr+RGocfqzV5hKtwt6DfdSStwqsf7MEfjHDihNK4mAsAKSPYrZlb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\nRU5ErkJggg==\n", 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Fklq/bGqm47XVJL5+fcE0vromsBxHaXKDhGHZiAIGftHZgb6xtwB+5IUPAbj8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\nQv7CQh/HIfSf/wFA7J++kym24riSPz6TtO1fPff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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pred_points = np.linspace(0,24,100)[:, None]\n", "#Predictive GP for log intensity mean and variance\n", diff --git a/GPy/SVI.ipynb b/GPy/SVI.ipynb index 6db7d1c..c441ab9 100644 --- a/GPy/SVI.ipynb +++ b/GPy/SVI.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Stochastic Variational Inference with Gaussian Processes" + "# Stochastic Variational Inference with Gaussian Processes\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/SVI.ipynb)\n" ] }, { @@ -34,22 +36,38 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import GPy\n", "from matplotlib import pyplot as plt\n", "try:\n", + " import collections\n", + " import collections.abc\n", + " import random\n", " import climin\n", + " import climin.util\n", "except ImportError as exc:\n", " raise ImportError(\n", " \"This notebook needs climin, an optional extra that is not a GPy \"\n", " \"dependency. Install it with: pip install climin\"\n", " ) from exc\n", + "if not hasattr(collections, \"Iterable\"):\n", + " collections.Iterable = collections.abc.Iterable # climin 0.1a1, Python 3.10+\n", + "# climin 0.1a1 shuffles a range object (Python 2); list() it for Python 3.\n", + "def _draw_mini_slices(n_samples, batch_size, with_replacement=False):\n", + " slices = climin.util.mini_slices(n_samples, batch_size)\n", + " idxs = list(range(len(slices)))\n", + " if with_replacement:\n", + " yield random.choice(slices)\n", + " else:\n", + " while True:\n", + " random.shuffle(idxs)\n", + " for i in idxs:\n", + " yield slices[i]\n", + "climin.util.draw_mini_slices = _draw_mini_slices\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "#plt.rcParams['figure.figsize'] = 8,8" @@ -64,10 +82,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "N=5000\n", @@ 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}, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig1, axes = plt.subplots(1, 2, figsize=(10,5))\n", "ax = axes[0]\n", @@ -24626,9 +212,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/Space_time_effects.ipynb b/GPy/Space_time_effects.ipynb index 65eb9f2..737a090 100644 --- a/GPy/Space_time_effects.ipynb +++ b/GPy/Space_time_effects.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Space_time_effects.ipynb)\n", + "\n", "Illustration/tutorial by Alan Saul - 07 March 2016\n", "\n", "Looks at modelling a space-time effect, and a multiplier which is a spatial effect. \n", @@ -17,10 +19,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -33,10 +33,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#Determine our inputs\n", @@ -66,10 +64,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# In practice you won't have all of these locations \n", @@ -81,10 +77,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Rearrange data into grids for plotting\n", @@ -103,32 +97,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MxhxS4v/GSVyievXqdy/QTqwFePsmjUj/ZS156WnkJMSRt/NnWjVpZDNf+7aN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5OcHIrZbCA29hxly/rToUMLUlLOYDRq0ekSMZtv0LlzBxSKeDSa8AdtT1K79ks2t/jVq1dHLo8k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QEBNvvPEa/v4BzJ27BIXCAyG0zJxptrWFhYWxY8duNBo7evR4Ch8fH7KyslizZi1xcQk0aFCX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\nfXy80OnMAR1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Overall rate and observations')\n", "plt.contour(space_grid, time_grid, truth_lamda_grid)\n", @@ -149,32 +120,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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u33AVeJK0uSk6XwLDRGSIiOQC1wPvtjjmXeBWABGZBFQrpfbYNZgin2a9TJky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\nE/IN22CUjQKEdj3yCp2Fshpfg+wTIce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ouAw4D+58HnbVwIQK2PxMfOeKYvMz8NB1MHVe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\n3Dnn8tz/A3OHB+00z3BBAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Space-time effect')\n", "plt.contour(space_grid, time_grid, truth_f_grid)\n", @@ -194,32 +142,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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D96vqQVVtAx8DXrlpzM8Bf6GqDwOo6tES1wNkrg+XFnU/ONnCTNpWlks8MUHH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\nHXiOKuFLunywqEcb74TaZRy1iLlFbU6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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Space multiplier effect')\n", "plt.contour(space_grid, time_grid, np.exp(truth_log_e_s_grid))\n", @@ -239,10 +164,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "likelihood = GPy.likelihoods.Poisson()\n", @@ -262,10 +185,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "pred_mean, pred_variance = m._raw_predict(X)\n", @@ -275,32 +196,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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LWutHRkbG3SdYBAFoSXfS4Bh4S1VmFxBMcob1byo5OQMn11s7dFTaYJJ0yah8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\n0oaiMwA+T3w070t+P1oWvwp9AUiK2kibOhEcionG64PlKO3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UKYtGsx8AnS4euEbFihX/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InGlfalPLPCaVzrBbQn/sKgUWPU6cg+vIsXXyi8ZqwCN/RW7O2Ks2VV55n3QikL1Ggt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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Overall predicted rate and observations')\n", "plt.contour(space_grid, time_grid, pred_mean_grid)\n", @@ -321,10 +219,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "pred_mean_space_time, _ = m._raw_predict(X, kern=m.kern.space_time_kern)\n", @@ -334,32 +230,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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X1wDS0zNITc3A1dX/Cq2UXmRlZd3k3BVFcnIGev3VHYXOztVISspg+/ZoLBYb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\nmOIw4A4cOHBQTHEYcAcOHDgopvwfZFqqUm76URwAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Predicted space-time effect and observations')\n", "plt.contour(space_grid, time_grid, pred_mean_space_time_grid)\n", @@ -380,10 +253,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "pred_mean_space_time, _ = m._raw_predict(X, kern=m.kern.space_effect)\n", @@ -393,32 +264,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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QBFRFqGEbnAJDyDaaKoTXyknMW4QOHZqTmvo3ZnMOOTnJWCyn6NatCzpdImlp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\nGkvdSqEMe7O/xYJeISEhzJz6J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eWyR5Gmr1I2wtzwym3gP9u4gff/XyDPC8XTsxKCIKBQ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\nhoSxSmFMAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Predicted space-time effect and observations')\n", "plt.contour(space_grid, time_grid, pred_mean_space_time_grid)\n", diff --git a/GPy/basic_classification.ipynb b/GPy/basic_classification.ipynb index 086b266..1c015e9 100644 --- a/GPy/basic_classification.ipynb +++ b/GPy/basic_classification.ipynb @@ -1,11 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/basic_classification.ipynb)\n" + ] + }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#configure plotting\n", @@ -47,1278 +53,9 @@ }, { 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"outputs": [], "source": [ "m.plot(plot_density=True)\n", "plt.plot(X, p, 'ro', label='Truth')\n", @@ -45968,9 +252,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/basic_gp.ipynb b/GPy/basic_gp.ipynb index 0f30838..71d58be 100644 --- a/GPy/basic_gp.ipynb +++ b/GPy/basic_gp.ipynb @@ -1,11 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/basic_gp.ipynb)\n" + ] + }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import GPy" @@ -20,10 +26,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "GPy.plotting.change_plotting_library('matplotlib')" @@ -45,10 +49,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import numpy as np" @@ -65,10 +67,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "X = np.random.uniform(-3.,3.,(20,1))\n", @@ -86,10 +86,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "kernel = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.)" @@ -104,10 +102,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#type GPy.kern. here:\n", @@ -123,10 +119,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m = GPy.models.GPRegression(X,Y,kernel)" @@ -141,51 +135,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

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GP_regression. valueconstraintspriors
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve
Gaussian_noise.variance 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.display import display\n", "display(m)" @@ -193,34 +145,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig = m.plot()\n", "GPy.plotting.show(fig, filename='basic_gp_regression_notebook')" @@ -237,10 +164,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m.optimize(messages=True)" @@ -255,48 +180,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization restart 1/10, f = -15.1436482683\n", - "Optimization restart 2/10, f = -15.1436482683\n", - "Optimization restart 3/10, f = -15.1436482682\n", - "Optimization restart 4/10, f = -15.1436482682\n", - "Optimization restart 5/10, f = -15.1436482682\n", - "Optimization restart 6/10, f = -15.1436482682\n", - "Optimization restart 7/10, f = -15.1436482683\n", - "Optimization restart 8/10, f = -15.1436482682\n", - "Optimization restart 9/10, f = -15.1436482683\n", - "Optimization restart 10/10, f = -15.1436482683\n" - ] - }, - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.optimize_restarts(num_restarts = 10)" ] @@ -312,73 +198,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
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GP_regression. valueconstraintspriors
rbf.variance 1.35354271667 +ve
rbf.lengthscale 1.94630136743 +ve
Gaussian_noise.variance0.00248112830273 +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(m)\n", "fig = m.plot()\n", @@ -395,73 +217,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
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GP_regression. valueconstraintspriors
rbf.variance 1.35354271667 +ve
rbf.lengthscale 1.94630136743 +ve
Gaussian_noise.variance0.00248112830273 +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(m)\n", "fig = m.plot(plot_density=True)\n", @@ -479,73 +237,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "

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GP_regression. valueconstraintspriors
sum.Mat52.variance 0.361421808902 +ve
sum.Mat52.lengthscale (2,) +ve
sum.white.variance 0.000644606566433 +ve
Gaussian_noise.variance0.000644606566433 +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# sample inputs and outputs\n", "X = np.random.uniform(-3.,3.,(50,2))\n", @@ -583,36 +277,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ]\n", - "[ (2,1) x1,y2 ]\n", - "[ (3,1) x1,y3 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "slices = [-1, 0, 1.5]\n", "figure = GPy.plotting.plotting_library().figure(3, 1)\n", @@ -639,36 +306,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the format of your plot grid:\n", - "[ (1,1) x1,y1 ]\n", - "[ (2,1) x1,y2 ]\n", - "[ (3,1) x1,y3 ]\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "slices = [-1, 0, 1.5]\n", "figure = GPy.plotting.plotting_library().figure(3, 1)\n", diff --git a/GPy/basic_kernels.ipynb b/GPy/basic_kernels.ipynb index e259b58..e2a4a8d 100644 --- a/GPy/basic_kernels.ipynb +++ b/GPy/basic_kernels.ipynb @@ -1,11 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/basic_kernels.ipynb)\n" + ] + }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#configure plotting\n", @@ -29,10 +35,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import GPy\n", @@ -49,10 +53,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "ker1 = GPy.kern.RBF(1) # Equivalent to ker1 = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)\n", @@ -69,901 +71,18 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mrbf. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mvariance \u001b[0;0m | 0.75 | +ve | \n", - " \u001b[1mlengthscale\u001b[0;0m | 2.0 | +ve | \n" - 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} - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "k1 = GPy.kern.PeriodicExponential(input_dim=1, active_dims=[0], period=6, lower=-10, upper=10)# + GPy.kern.Bias(1, variance=0, active_dims=[0])\n", "k2 = GPy.kern.PeriodicExponential(input_dim=1, active_dims=[1], period=8, lower=-10, upper=10)# + GPy.kern.Bias(1, variance=0, active_dims=[0])\n", @@ -12613,9 +221,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/config.ipynb b/GPy/config.ipynb index 8ac2f36..0b6ed49 100644 --- a/GPy/config.ipynb +++ b/GPy/config.ipynb @@ -6,14 +6,13 @@ "source": [ "# GPy Configuration Settings\n", "\n", - "The GPy default configuration settings are stored in a file in the main `GPy` directory called `defaults.cfg`. These settings should not be changed in this file. The file gives you an overview of what the configuration settings are for the install. " + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/config.ipynb)\n", + "The GPy default configuration settings are stored in a file in the main `GPy` directory called `defaults.cfg`. These settings should not be changed in this file. The file gives you an overview of what the configuration settings are for the install. \n" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# This is the default configuration file for GPy\n", "\n", @@ -46,9 +45,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# This is the local installation configuration file for GPy\n", "[parallel]\n", diff --git a/GPy/coregionalized_regression_tutorial.ipynb b/GPy/coregionalized_regression_tutorial.ipynb index ca82d89..45a15aa 100644 --- a/GPy/coregionalized_regression_tutorial.ipynb +++ b/GPy/coregionalized_regression_tutorial.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Coregionalized Regression Model (vector-valued regression)" + "# Coregionalized Regression Model (vector-valued regression)\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/coregionalized_regression_tutorial.ipynb)\n" ] }, { @@ -44,22 +46,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "outputs": [], "source": [ "%pylab inline\n", "import pylab as pb\n", @@ -76,9 +69,8 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -111,35 +103,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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tmUxGpVJJJy5dkkol/U9/8ifKZrNVx/zc9LTe7uvTL/zoR8EOxiQAANY5QjKA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\nm6wAfjxCbTAAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "xlim = (0,100); ylim = (0,50)\n", "fig = pb.figure(figsize=(12,8))\n", @@ -166,9 +136,8 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -243,26 +212,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mB_K. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mrbf.lengthscale\u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mcoregion.W \u001b[0;0m | (2, 1) | | \n", - " \u001b[1mcoregion.kappa \u001b[0;0m | (2,) | +ve | \n" - ] - } - ], + "outputs": [], "source": [ "import GPy\n", "K=GPy.kern.RBF(1)\n", @@ -280,34 +236,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "W matrix\n", - " \u001b[1mindex\u001b[0;0m | B_K.coregion.W | constraints | priors\n", - " \u001b[1m[0 0]\u001b[0;0m | 0.67770340 | | \n", - " \u001b[1m[1 0]\u001b[0;0m | -0.00412505 | | \n", - "\n", - "kappa vector\n", - " \u001b[1mindex\u001b[0;0m | B_K.coregion.kappa | constraints | priors\n", - " \u001b[1m[0] \u001b[0;0m | 0.50000000 | +ve | \n", - " \u001b[1m[1] \u001b[0;0m | 0.50000000 | +ve | \n", - "\n", - "B matrix\n", - "[[ 0.9592819 -0.00279556]\n", - " [-0.00279556 0.50001702]]\n" - ] - } - ], + "outputs": [], "source": [ "#Components of B\n", "print('W matrix\\n',B.W)\n", @@ -324,26 +259,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mICM. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mrbf.lengthscale\u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mB.W \u001b[0;0m | (2, 1) | | \n", - " \u001b[1mB.kappa \u001b[0;0m | (2,) | +ve | \n" - ] - } - ], + "outputs": [], "source": [ "icm = GPy.util.multioutput.ICM(input_dim=1,num_outputs=2,kernel=GPy.kern.RBF(1))\n", "print(icm)" @@ -373,45 +295,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : gp\n", - "Objective : 376.744145175\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 7\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1mgp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mICM.Mat32.variance \u001b[0;0m | 1.0 | fixed +ve | \n", - " \u001b[1mICM.Mat32.lengthscale \u001b[0;0m | 178.415052332 | +ve | \n", - " \u001b[1mICM.B.W \u001b[0;0m | (2, 1) | | \n", - " \u001b[1mICM.B.kappa \u001b[0;0m | (2,) | +ve | \n", - " \u001b[1mmixed_noise.Gaussian_noise_0.variance\u001b[0;0m | 0.352294081182 | +ve | \n", - " \u001b[1mmixed_noise.Gaussian_noise_1.variance\u001b[0;0m | 5.06765701728 | +ve | \n" - ] - }, - { - "data": { - "image/png": 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YpAW3NmzKxs3byTh6Ar3Rn6DQCHRGP4ptF5gpUSmeXzuLuO3LAUhqEMvoVr19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\nIqaEi7d8tONYotEYtVUB6sJVRdvOZFBZf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n1HT7PnUCt7DrWw51ArB4634zh9/67cLkNV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\nSC//H3Myr9+wGl3PAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "K = GPy.kern.Matern32(1)\n", "icm = GPy.util.multioutput.ICM(input_dim=1,num_outputs=2,kernel=K)\n", @@ -439,35 +329,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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43T+F0B2ri+59ctMeDPlpLi/GRgEwr2I9NrfrRHjzBrg4GnBxNJCfm8nsGV+x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Mh4Lr1pPDljJ/DEN9+ywqgjwbdd+YhROAYMrvKaLS8vZ/IH74Ddyo6rribcZqXd/r1kN2lKyFvv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\ndHt8vuNuvLLsG5yVk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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "K = GPy.kern.Matern32(1)\n", "\n", @@ -523,25 +391,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "image/png": 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HABsXLwR31tTj22Yh/J1zueK6PzGsoSFs7hzyIwcX1g37nlyLguIzOX7suDe7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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "kernel = GPy.util.multioutput.ICM(input_dim=1,num_outputs=2,kernel=GPy.kern.Bias(input_dim=1))\n", "m = GPy.models.GPCoregionalizedRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel=kernel)\n", @@ -599,25 +455,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "image/png": 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X/INtm7/jpYoA5Z4SfrF6Ob/JW8ebKTVcMeMvlHp97CnUpyLq0xHLOFDkpbCk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\neJrtiYcV9z0QW26yZ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "K1 = GPy.kern.Bias(1)\n", "K2 = GPy.kern.Linear(1)\n", @@ -640,25 +484,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "image/png": 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JoJGblU5QgA/lypTCy8OMl4cZbw8zXlYzNc8Uh3j37vfXKkOFF1vi5WnBy2oi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EOGY1qvpKXVZ6Flf/yPVCx5n9a5x9k4VnnN4NOnyG3dFuF3T4ZVoqqvo76KVX3NUWJi67OWD5Jv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\nAMAKEJwBAACAFSA4AwAAACvw//3JH4UFrDu9AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "K1 = GPy.kern.Bias(1)\n", "K2 = GPy.kern.Linear(1)\n", @@ -698,31 +530,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[100 1]\n", - " [101 1]\n", - " [102 1]\n", - " [103 1]\n", - " [104 1]\n", - " [105 1]\n", - " [106 1]\n", - " [107 1]\n", - " [108 1]\n", - " [109 1]]\n" - ] - } - ], + "outputs": [], "source": [ "newX = np.arange(100,110)[:,None]\n", "newX = np.hstack([newX,np.ones_like(newX)])\n", @@ -745,9 +559,8 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } @@ -766,43 +579,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[ 61.6493168 ],\n", - " [ 60.97078391],\n", - " [ 60.29634431],\n", - " [ 59.62608188],\n", - " [ 58.96007595],\n", - " [ 58.29840145],\n", - " [ 57.64112904],\n", - " [ 56.98832524],\n", - " [ 56.34005258],\n", - " [ 55.6963697 ]]), array([[ 10.03066081],\n", - " [ 13.9122405 ],\n", - " [ 19.37464407],\n", - " [ 26.60289973],\n", - " [ 35.75497252],\n", - " [ 46.96392513],\n", - " [ 60.33994173],\n", - " [ 75.97221989],\n", - " [ 93.93074111],\n", - " [ 114.26792495]]))" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m.predict(newX,Y_metadata=noise_dict)" ] diff --git a/GPy/heteroscedastic_regression.ipynb b/GPy/heteroscedastic_regression.ipynb index be5ddf4..8d5172c 100644 --- a/GPy/heteroscedastic_regression.ipynb +++ b/GPy/heteroscedastic_regression.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Heteroscedastic Regression" + "# Heteroscedastic Regression\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/heteroscedastic_regression.ipynb)\n" ] }, { @@ -48,19 +50,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "import pylab as pb\n", @@ -77,22 +69,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def f(X):\n", " return 10. + .1*X + 2*np.sin(X)/X\n", @@ -111,22 +90,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "X = np.random.uniform(-10,20, 50)\n", "X = X[~np.logical_and(X>-2,X<3)] #Remove points between -2 and 3 (just for illustration) \n", @@ -148,10 +114,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "kern = GPy.kern.MLP(1) + GPy.kern.Bias(1)" @@ -167,39 +131,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " /home/maxz/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:2651: MatplotlibDeprecationWarning:Use of None object as fmt keyword argument to suppress plotting of data values is deprecated since 1.4; use the string \"none\" instead.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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a8H6YBOrkI2h+hIQSYaIxxlUJEAN+Is/rgffp2rB77OstCDCge3gySrNGorQi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\nTJ/UBPKKKth76Cwntv2L/P1ZTJ/0E3q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rX/6M1EQrlZUO3B7Zf42xEybX08dDrVioqkpE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\nUYtiAicialH/B1ABXIGvN6xaAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m = GPy.models.GPHeteroscedasticRegression(X[:,None],Y[:,None],kern)\n", "m['.*het_Gauss.variance'] = abs(error)[:,None] #Set the noise parameters to the error in Y\n", @@ -221,10 +155,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def noise_effect(noise):\n", @@ -248,32 +180,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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U1up+/JkXMVvsdIrXkZIUfBhmjTBBqtU9qTTQGzZsqLF/yZIlQRnvaLxHWor2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\nxei4qF9gpW7KzVb6d4tDra6ryiXFG8k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mpHjNfUx8FLyoJcoqLqWPmaqiWq1hYSYXa/IG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\nlFJM4EREKcUETkSUUkzgREQp9T9Rk+JiTAcBjQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from ipywidgets import interact\n", "interact(noise_effect, noise=(0.1,2.))" @@ -288,51 +197,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Kernel parameters (optimized) in the heteroscedastic model\n", - " \u001b[1msum. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mmlp.variance \u001b[0;0m | 6.74905510673 | +ve | \n", - " \u001b[1mmlp.weight_variance\u001b[0;0m | 4.51460469888 | +ve | \n", - " \u001b[1mmlp.bias_variance \u001b[0;0m | 11.4561726893 | +ve | \n", - " \u001b[1mbias.variance \u001b[0;0m | 116.301919585 | +ve | \n", - "\n", - "Kernel parameters (optimized) in the homoscedastic model\n", - " \u001b[1msum. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1mmlp.variance \u001b[0;0m | 6.74905510673 | +ve | \n", - " \u001b[1mmlp.weight_variance\u001b[0;0m | 4.51460469888 | +ve | \n", - " \u001b[1mmlp.bias_variance \u001b[0;0m | 11.4561726893 | +ve | \n", - " \u001b[1mbias.variance \u001b[0;0m | 116.301919585 | +ve | \n" - ] - }, - { - "data": { - "image/png": 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T3SLgQt04boBZQcBffP0aAO+4pY/33L4TgG/98EJNxzcNE9eTolZCDtcLuHh1ktcvj+P4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\neocNTsbxMC2Tc4O5Jgv9Oyp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8sZVXzujrq6MqIeQKLzUTM6ksw2MpYrEo8djCieJv/uAsf/71H+MHIfv72vnU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\nPvK+a4FcqODATok8ErYfcsf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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#Heteroscedastic model\n", "m1 = GPy.models.GPHeteroscedasticRegression(X[:,None],Y[:,None],kern)\n", @@ -368,10 +235,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "kern = GPy.kern.MLP(1) + GPy.kern.Bias(1)" @@ -379,10 +244,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m = GPy.models.GPHeteroscedasticRegression(X[:,None],Y[:,None],kern)\n", @@ -391,30 +254,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " /home/maxz/Documents/gpy/GPy/likelihoods/gaussian.py:101: RuntimeWarning:invalid value encountered in sqrt\n", - " /home/maxz/Documents/gpy/GPy/likelihoods/gaussian.py:359: RuntimeWarning:invalid value encountered in sqrt\n" - ] - }, - { - "data": { - "image/png": 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V1/7+UT30N184LWyVfP4B2XNr9JnIsU3fVXY5vPTSS2rYsyfuM4pL1as5eeY3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\nFAAAJBo9oAAAAACApEMABWKgNwNOQS3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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(1,1,figsize=(13,5))\n", "m.plot_f(ax=ax)\n", @@ -433,10 +275,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "mu, var = m._raw_predict(m.X)" @@ -452,22 +292,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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8krx0nVOSrfasdXqsr12/V3Wl0jxD+ubfPaotf/WVM8JW0ZcflD2vUl9wjm3i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\nHlAAAAAAQNwhgAIR0JsBt6CW4RbUMty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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(1,1,figsize=(13,5))\n", "m.plot_f(ax=ax, plot_density=True)\n", @@ -480,9 +307,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/index.ipynb b/GPy/index.ipynb index d910d72..280dc8a 100644 --- a/GPy/index.ipynb +++ b/GPy/index.ipynb @@ -5,10 +5,13 @@ "metadata": {}, "source": [ "# Introduction\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/index.ipynb)\n", + "\n", "Here are a few notebooks outlining basic functionality of GPy\n", - "* [Handling models](./models_basic.ipynb)\n", - "* [Basic GP regression](./basic_gp.ipynb)\n", - "* [Working with kernels](./basic_kernels.ipynb)" + "* [Handling models](./models_basic.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/models_basic.ipynb)\n", + "* [Basic GP regression](./basic_gp.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/basic_gp.ipynb)\n", + "* [Working with kernels](./basic_kernels.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/basic_kernels.ipynb)\n" ] }, { @@ -20,28 +23,28 @@ "This set of examples shows some of the functionality of the GPy Gaussian process framework in python. The framework is BSD licensed and we welcome collaborators to develop new functionality.\n", "\n", "### Multiple Output Gaussian Processes\n", - "* [Coregionalization with Gaussian Processes](./coregionalized_regression_tutorial.ipynb) This tutorial shows the use of a coregionalized model within GPy. In particular such models can be used for multi-task or multi-output learning.\n", - "* [Coregionalization on Marathon Data](./multiple%20outputs.ipynb) This tutorial runs the multioutput regression on a higher level, introducing stacked hierarchical multitask regression.\n", + "* [Coregionalization with Gaussian Processes](./coregionalized_regression_tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/coregionalized_regression_tutorial.ipynb) This tutorial shows the use of a coregionalized model within GPy. In particular such models can be used for multi-task or multi-output learning.\n", + "* [Coregionalization on Marathon Data](./multiple%20outputs.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/multiple%20outputs.ipynb) This tutorial runs the multioutput regression on a higher level, introducing stacked hierarchical multitask regression.\n", "\n", "### Different Noise Models\n", - "* [GP classification](./basic_classification.ipynb) A very simple turorial on GP classification.\n", - "* [Count Data with GPy](./Poisson%20regression%20tutorial.ipynb) This tutorial gives an example of Poisson regression using GPy.\n", - "* [Heteroschedastic Gaussian Processes](./heteroscedastic_regression.ipynb) This tutorial shows how heteroschedastic Gaussian processes can be fit using GPy (with an interactive widget!).\n", + "* [GP classification](./basic_classification.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/basic_classification.ipynb) A very simple turorial on GP classification.\n", + "* [Count Data with GPy](./Poisson%20regression%20tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Poisson%20regression%20tutorial.ipynb) This tutorial gives an example of Poisson regression using GPy.\n", + "* [Heteroschedastic Gaussian Processes](./heteroscedastic_regression.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/heteroscedastic_regression.ipynb) This tutorial shows how heteroschedastic Gaussian processes can be fit using GPy (with an interactive widget!).\n", "\n", "### Approximations\n", - "* [Sparse Gaussian Processes](./sparse_gp_regression.ipynb) This tutorial gives a quick overview of the variational approximation used to fit sparse Gaussian processes. \n", + "* [Sparse Gaussian Processes](./sparse_gp_regression.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/sparse_gp_regression.ipynb) This tutorial gives a quick overview of the variational approximation used to fit sparse Gaussian processes. \n", "\n", "### SVI\n", - "* [Stochastic Variational Inference for GP Regression](./SVI.ipynb) A simple demonstration of using SVI to fit a regression model (requires the climin library)\n", + "* [Stochastic Variational Inference for GP Regression](./SVI.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/SVI.ipynb) A simple demonstration of using SVI to fit a regression model (requires the climin library)\n", "\n", "### Partially parametric models\n", - "* [Parametric non-parametric Gaussian Process Regression](./ParametricNonParametricInference.ipynb) The steps from Linear regression to non-linear regression and finally non-parametric (better: infinite parametric) regression.\n", + "* [Parametric non-parametric Gaussian Process Regression](./ParametricNonParametricInference.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/ParametricNonParametricInference.ipynb) The steps from Linear regression to non-linear regression and finally non-parametric (better: infinite parametric) regression.\n", "\n", "### MCMC\n", - "* [Using Hybrid Monte Carlo to infer posterior distributions on kernel parameters](./sampling_hmc.ipynb)\n", + "* [Using Hybrid Monte Carlo to infer posterior distributions on kernel parameters](./sampling_hmc.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/sampling_hmc.ipynb)\n", "\n", "### Other apllications\n", - "* [Probabilistic metrics for GP-LVM and Bayesian GP-LVMs](./MagnificationFactor.ipynb) Introduction to the probabilistic geometries approach for latent variable models, with a demonstration of the plotting tool for the visualization of magnification factors with different data sets." + "* [Probabilistic metrics for GP-LVM and Bayesian GP-LVMs](./MagnificationFactor.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/MagnificationFactor.ipynb) Introduction to the probabilistic geometries approach for latent variable models, with a demonstration of the plotting tool for the visualization of magnification factors with different data sets." ] }, { @@ -49,8 +52,8 @@ "metadata": {}, "source": [ "# Developer and Own modules\n", - "* [Implementing Optimizers](./optimizer-implementation.ipynb)\n", - "* [Integral Kernels](./Integral_kernel.ipynb)" + "* [Implementing Optimizers](./optimizer-implementation.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/optimizer-implementation.ipynb)\n", + "* [Integral Kernels](./Integral_kernel.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Integral_kernel.ipynb)" ] }, { @@ -58,15 +61,13 @@ "metadata": {}, "source": [ "# GPy Configuration Files\n", - "* [Configuration Files](./config.ipynb) Setting up your local GPy configuration." + "* [Configuration Files](./config.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/config.ipynb) Setting up your local GPy configuration." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/models_basic.ipynb b/GPy/models_basic.ipynb index 5ba430b..fe36b7a 100644 --- a/GPy/models_basic.ipynb +++ b/GPy/models_basic.ipynb @@ -1,11 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/models_basic.ipynb)\n" + ] + }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#import necessary modules, set up the plotting\n", @@ -33,10 +39,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m = GPy.examples.regression.sparse_GP_regression_1D(plot=False, optimize=False)" @@ -57,53 +61,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: sparse gp
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\n", - "Updates: True
\n", - "

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sparse_gp. valueconstraintspriors
inducing inputs (5, 1)
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve
Gaussian_noise.variance 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m" ] @@ -119,36 +79,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "
rbf. valueconstraintspriors
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lengthscale 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.rbf" ] @@ -162,58 +95,17 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "\u001b[1msparse_gp.inducing_inputs\u001b[0;0m:\n", - "Param([[ 0.1745099 ],\n", - " [ 2.19478599],\n", - " [ 1.0945254 ],\n", - " [-0.42446266],\n", - " [ 0.45784368]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.inducing_inputs" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m.inducing_inputs[0] = 1" @@ -230,30 +122,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : sparse gp\n", - "Objective : 590.608017128\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 0.2 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 1.0 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.rbf.lengthscale = 0.2\n", "print(m)" @@ -277,22 +148,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mindex\u001b[0;0m | sparse_gp.rbf.variance | constraints | priors\n", - " \u001b[1m[0] \u001b[0;0m | 1.00000000 | +ve | \n", - " \u001b[1m-----\u001b[0;0m | sparse_gp.Gaussian_noise.variance | ----------- | ------\n", - " \u001b[1m[0] \u001b[0;0m | 1.00000000 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(m['.*var'])\n", "#print \"variances as a np.array:\", m['.*var'].values()\n", @@ -308,42 +166,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : sparse gp\n", - "Objective : 694.201181379\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 2.0 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 0.2 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 2.0 | +ve | \n", - "\n", - "Name : sparse gp\n", - "Objective : 714.484895561\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 2.0 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 0.2 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 3.0 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m['.*var'] = 2.\n", "print(m)\n", @@ -360,30 +185,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mindex\u001b[0;0m | sparse_gp.inducing_inputs | constraints | priors\n", - " \u001b[1m[0 0]\u001b[0;0m | 1.00000000 | | \n", - " \u001b[1m[1 0]\u001b[0;0m | 2.19478599 | | \n", - " \u001b[1m[2 0]\u001b[0;0m | 1.09452540 | | \n", - " \u001b[1m[3 0]\u001b[0;0m | -0.42446266 | | \n", - " \u001b[1m[4 0]\u001b[0;0m | 0.45784368 | | \n", - " \u001b[1m-----\u001b[0;0m | sparse_gp.rbf.variance | ----------- | ------\n", - " \u001b[1m[0] \u001b[0;0m | 2.00000000 | +ve | \n", - " \u001b[1m-----\u001b[0;0m | sparse_gp.rbf.lengthscale | ----------- | ------\n", - " \u001b[1m[0] \u001b[0;0m | 0.20000000 | +ve | \n", - " \u001b[1m-----\u001b[0;0m | sparse_gp.Gaussian_noise.variance | ----------- | ------\n", - " \u001b[1m[0] \u001b[0;0m | 3.00000000 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(m[''])" ] @@ -399,31 +203,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-4. -2. 0. 2. 4. 0.1 2. 0.7]\n", - "\n", - "Name : sparse gp\n", - "Objective : 322.317452127\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 0.1 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.0 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.7 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "new_params = np.r_[[-4,-2,0,2,4], [.1,2], [.7]]\n", "print(new_params)\n", @@ -441,24 +223,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1mindex\u001b[0;0m | sparse_gp.inducing_inputs | constraints | priors\n", - " \u001b[1m[0 0]\u001b[0;0m | -4.00000000 | | \n", - " \u001b[1m[1 0]\u001b[0;0m | -2.00000000 | | \n", - " \u001b[1m[2 0]\u001b[0;0m | 1.00000000 | | \n", - " \u001b[1m[3 0]\u001b[0;0m | 3.00000000 | | \n", - " \u001b[1m[4 0]\u001b[0;0m | 5.00000000 | | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.inducing_inputs[2:, 0] = [1,3,5]\n", "print(m.inducing_inputs)" @@ -473,19 +240,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1.42857143]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "precision = 1./m.Gaussian_noise.variance\n", "print(precision)" @@ -502,24 +259,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "all gradients of the model:\n", - "[ 2.01697286 3.69837406 1.1975515 -0.38669436 -0.31342694\n", - " 99.38364871 -12.37834911 -268.18547317]\n", - "\n", - " gradients of the rbf kernel:\n", - "[ 99.38364871 -12.37834911]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"all gradients of the model:\\n\", m.gradient)\n", "print(\"\\n gradients of the rbf kernel:\\n\", m.rbf.gradient)" @@ -534,20 +276,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ -9.07579744e-04 1.55292147e-03 2.70479755e-04 8.65120340e-04\n", - " 9.78466589e-04 4.66719022e-04 2.65199506e-04 -3.76904571e-01]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.optimize()\n", "print(m.gradient)" @@ -566,30 +297,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : sparse gp\n", - "Objective : -590.884678521\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.37750834107 | | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.47448694644 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.00267772602954 | +ve | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.rbf.variance.unconstrain()\n", "print(m)" @@ -597,30 +307,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : sparse gp\n", - "Objective : -590.884678521\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.37750834107 | | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.47448694644 | | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.00267772602954 | | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.unconstrain()\n", "print(m)" @@ -635,42 +324,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : sparse gp\n", - "Objective : -590.884678521\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 7\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | {fixed} | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.37750834107 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.47448694644 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.00267772602954 | | \n", - "\n", - "Name : sparse gp\n", - "Objective : -590.884678521\n", - "Number of Parameters : 8\n", - "Number of Optimization Parameters : 8\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1msparse_gp. \u001b[0;0m | value | constraints | priors\n", - " \u001b[1minducing inputs \u001b[0;0m | (5, 1) | | \n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.37750834107 | +ve | \n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 2.47448694644 | +ve | \n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.00267772602954 | | \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.inducing_inputs[0].fix()\n", "m.rbf.constrain_positive()\n", @@ -699,19 +355,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: reconstraining parameters sparse_gp.rbf\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.Gaussian_noise.constrain_positive()\n", "m.rbf.constrain_positive()\n", @@ -743,1789 +389,25 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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} - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "GPy.plotting.change_plotting_library('matplotlib')\n", "fig = m.plot(plot_density=True)\n", @@ -2535,9 +417,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/GPy/multiple outputs.ipynb b/GPy/multiple outputs.ipynb index fbf796c..0e11068 100644 --- a/GPy/multiple outputs.ipynb +++ b/GPy/multiple outputs.ipynb @@ -6,6 +6,7 @@ "source": [ "# Multiple Outputs Gaussian Processes\n", "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/multiple%20outputs.ipynb)\n", "# Gaussian Process Summer School, Melbourne, Australia\n", "### 25th-27th February 2015\n", "### Neil D. Lawrence\n", @@ -14,15 +15,13 @@ "\n", "## Getting started: Modelling Multiple Outputs\n", "\n", - "Just as in the first lab, we firstly specify to include plots in the notebook and to import relevant libraries." + "Just as in the first lab, we firstly specify to include plots in the notebook and to import relevant libraries.\n" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -49,19 +48,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning. Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "data = pods.datasets.olympic_sprints()\n", "X = data['X']\n", @@ -78,19 +67,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(data['citation'])" ] @@ -104,19 +83,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: '100m Men', 1: '100m Women', 2: '200m Men', 3: '200m Women', 4: '400m Men', 5: '400m Women'}\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(data['output_info'])" ] @@ -130,24 +99,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First column of X contains the olympic years.\n", - "[ 1896. 1900. 1904. 1906. 1908. 1912. 1920. 1924. 1928. 1932.\n", - " 1936. 1948. 1952. 1956. 1960. 1964. 1968. 1972. 1976. 1980.\n", - " 1984. 1988. 1992. 1996. 2000. 2004. 2008.]\n", - "Second column of X contains the event index.\n", - "[ 0. 1. 2. 3. 4. 5.]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('First column of X contains the olympic years.')\n", "print((np.unique(data['X'][:, 0])))\n", @@ -164,32 +118,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "markers = ['bo', 'ro', 'bx', 'rx', 'bs', 'rs']\n", "for i in range(6):\n", @@ -213,10 +144,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "GPy.kern.Coregionalize?" @@ -231,37 +160,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
indexsparse_gp.inducing_inputsconstraintspriors
[0 0] 0.17450990
[1 0] 2.19478599
[2 0] 1.09452540
[3 0] -0.42446266
[4 0] 0.45784368
\n", - "\n", - "\n", - "\n", - "\n", - "
mul. valueconstraintspriors
rbf.variance 1.0 +ve
rbf.lengthscale 80.0 +ve
coregion.W (6, 5)
coregion.kappa (6,) +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "kern = GPy.kern.RBF(1, lengthscale=80)**GPy.kern.Coregionalize(1,output_dim=6, rank=5)\n", "display(kern)" @@ -276,10 +177,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "model = GPy.models.GPRegression(X, y, kern)\n", @@ -295,32 +194,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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abNieYuOONBt2pNiwI8XmnV2lxKkimiY4pbEYwFHNxFG1PfJ7jgbP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\nIWxbzaR7GcANN5g6Q+3t7QDU1JjrlW0v+3K9rc1cT/7+rbfeZv7+jTd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "for i in range(6):\n", @@ -338,10 +214,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "kern1 = GPy.kern.RBF(1, lengthscale=80)**GPy.kern.Coregionalize(1,output_dim=6, rank=1)\n", @@ -351,10 +225,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "model = GPy.models.GPRegression(X, y, kern)\n", @@ -363,32 +235,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - 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2Ky+8vptCqwAsQ2Pm1NGc+7bxzJo6CiHAdlwsXVBVFqUyGVVGQDGkkFLS3pmn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\nQDzPE0CUUqKUEkA8z5f6xozsaGiWmpqx4nm+bNi8TdZvrBP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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "for i in range(6):\n", @@ -406,10 +255,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m.plot?" @@ -417,38 +264,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
product. valueconstraintspriors
rbf_plus_bias.rbf.variance 1.0 +ve
rbf_plus_bias.rbf.lengthscale 80.0 +ve
rbf_plus_bias.bias.variance 1.0 +ve
coregion.W (6, 5)
coregion.kappa (6,) +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "kern1 = GPy.kern.RBF(1, lengthscale=80) + GPy.kern.Bias(1)\n", "kern1.name = 'rbf_plus_bias'\n", @@ -460,10 +278,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "model = GPy.models.GPRegression(X, y, kern)\n", @@ -472,32 +288,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Ewxb+JFBLkMP/TQKhBmiTUmYOZUAld9FMKeV9PY59E3hGSvlcKYA8v+f50jVK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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "for i in range(6):\n", @@ -517,10 +310,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Question 1 answer here" @@ -537,10 +328,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# set up the inputs to index sex and event separately.\n", @@ -552,10 +341,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "X2mean = X2[:,0].mean()\n", @@ -566,10 +353,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "kern = ((GPy.kern.RBF(1, variance=1, lengthscale=70)+\n", @@ -583,10 +368,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "#display(kern)\n", @@ -599,51 +382,18 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1962.41269841\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(X2mean)" ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pU8lyNOLaSr05j3xccC+QOhHOtkkmnIMWlHEC4g/v+yV/fuolZi84iVzo0j/j\n11hCke/cy8LGCiqcIr//9U/5yPsv4MMf+lB0xYFXBgf8nEGIH4ao6LdkCT2btaMGSUlHR6AkXbtk\nfjscJhJSaiUehlKbBP0QP5QEUf6KUv0BBRJdWdeyRKmj3+HGTBT+5yul7ivb/xrw60gRnA6crJS6\nbtA9asveboIwRIYSJRWWRam9og1YtiDh2iRsnY3q2DrxyLa0bXe8P0oVx6lHP4AglHiBdjIWA/3D\nCJUqlUpQ6PfQxdUOfrbkByGbd3XwzNb9/GPrfl7Y0TbEidhQk+akBfWcuLCOExfUc2xTVelHOJZo\novLPWvQCwjDqnzuoqXrKdMU6IF40O+zJF/ECpU0ASumyGRNcJZT//+WLAd+55U7mLn4t+7pDtuzq\nYPf+3iH3uLbFornVpdXBHx65n/vXrwPUuIT/aIRSC0wpZan7HSgsQamDniV0GKuFfhZCj8+yRSmU\n10JfLwQjdt2L5ZBUSk/MynJI4nySMIwmeUrnlCgotXWViv5n9PsQlUzXCs06Ypv7zEThvwbYSiTk\nhRA3AjcqpZ6KhP9ypdRVg+5RLe09pBMOyYSL68y8frpx05a8F5ArePiBVgza2XPwTVv8IOTFnXpl\n8PyONl7c0T4ktDSuT3Tiwjp2vPA4P/7hzbzvwvOAocv+MX2mqKl6EISIqH+ujjsXtO/fx0/vu4cv\nXv0FbNuastpEhyux46+nr0je0zPMUCosyyLhHrw9uK/gs21PJ5t39zuVm9uHro4tJGnbZ9/OF3nd\na5bwnnNOY/7sSmbXZqdd6IVST9xiQQ6U+m7H8mbwiGIpJISIVu7xalwrC6tsf6yMZ2J0ODPjon3K\n7PzLI2EPY5iOzK4dJqRjBmHbFmnbIp1yqasaaI7xowJvvXmfoueNSRAM/oLese52zjnnnaw4/RWA\nDvVbf/+veMXSt/LCjnb+9uzL9HnwzNZWntnaClRyzNs/y8YdHRS79/LW8y7nlLeeS1/BJzvGgnW2\nZWEnrAEJSKBnYff+7FfceMudNHd5KCn54R3r6PEdPnb5ZaQSxmZs2xbZdGJAbL9SikIxoCdfjCYI\nsYM5SuQZh0LIptzIH9SfsdyT86KQ0w7+saWZ519uJR/Y9IVJsnNfw/NtcM26PwM6Silteyw96Vjm\nNFTwwjOPs3zZqZy0ZD5VU9QzwbYsmAbryWjCfbRIqqlC52voCVXRl6XyKV6gJ45+5FfQylGvVkor\nGKkVZhivB187AAAgAElEQVSdU0r3Mo9NcVZk4ShtC4EQE5vAT4nwj2b97ZHZpw1YjHb0xumVtdHx\nIXz5y18ubS9btoxly5ZNxRCnhLhMQlVZsxelFPmiT0+fR97zSkkelm2RTLhjCvVb9/3vsnq1/nM9\n94u7+Mhln2Dpm87ihe3tenWwfT9OphYnU8vLOfjvWx4DYG5DBUvm1bBkXg0Lm6pZ2Fg1aphpOUII\n1nzkQ/R0d5bG9eGPfIiPf+ITgKCvGNKZK5RsxgJtonMcq+RnSEY2Y/23mXkrualACEE65ZIuU77D\nKgSpsCwbN/r7jJXKTIJ/OW42/3LcbN77thMB6M177NzXw6593exo6YmylLtp7y7gBw6/e2pXdHcF\nT/7oGeAZMkmHuqokYb6DN578KhprM2z662OcsezfOf7YeVRlk7S27pvw7PlgZ94HK9wvvfRSOjo6\nBvhD4tcqxw+k7rRXiKLCvIB8wSdfDMgVAwrFINrWxwpl2/ragIIf4JUE/ch9KiaL7j3/pGfvcwf1\nGlM1898KPB5t1wMPR/unABuARdGxIZQL/yMBIQSZlM7QjJFSkSt4dOc8Llp9EXv2tHDHHXdh2faQ\nL+hwX+DL13wQELzxlXO57bZb+fXD3+PdF15Cb5Bk0z93MP/4f6U3SJT68P7+6V2l16vOJlk4J+5+\nVs3Cpmrmz66ckK1/cP2awRSlpK/XJ5RaQShVZi9Gz2bsqEb+4ehYHA/jUQgiWimORyFUpBOcFFWm\nLacvX+S6b93Mb//4FE6mlsUnvpaqhnm0tPWRKwbkWgMgwU8ffSl+Jf5219+Bv+PYAlcEdO7v44/b\nf4gjQl587p9s6Uhy+rI3U5lJ4Bf6+Nuf/8Cq911IKuGw/u4fcu47tYA+2Jn3cPcrBZe8/4N4fsC7\nz1/F3v093HP/LxF2gnMu+BAnveEsfrtpeyTIfbb11VF70juwnCT/6Grk/978e3JFLbBjAT4VwloX\n87NJOFGCVxR2qwMvbGzbinyURL4TMew+MKCGlozMafKERqRaVvJX3r7pJ+Me45Q6fKPNRUqpb0TH\nYj/AiKGeMynvYDooZex+81vYToo1H/tPPvO5L5TMRcmkwzdvGDnUb6TZUV39LHbu62bTP7fz2BPP\nka6Zw/bm7mFLHlsCZtdmmTergrmzKpkX9Uye11DBj++5k+9+d+RQwsngQI5Fu3yZGzVkTw76EU1n\n8bipJnbK9+Q8cgUfL0oeEpaFbU8kyGD4aCClop4J7b3cuu4e/vrU8zipauYvOoGq+ibauvL05kdu\nqHMgLKHIppMkXZvenm56erpQSlJbXc2cOY3azGgJwiBgf9t+GhubkErR0rKPquoabFuXKZZS0bp/\nPz29eYTt4CbSIKxSldnJwrYE6aRDOulGz4MeCZd0Kt52tBKPtjPR8VTCiYS7rR3e0+hnmYk2//uG\nORYL/A1T9b6HG9dccw033HBDWb3/rzO/sZqrr76afNHnq1+7nrU3reWS938Qy3a486711NbWloRv\nY2PjAEFcvr14bg2/e+jH/P4eLbzrK2D9ww9y3soPcvyr36C7oDV3s2d/j26a0t7HEy+0DBhfOtHA\na//jC9jHHMvs2izvuriR4177BvZ19FFflR51hj6WpX/J5zAGfKnI5wJCqaLVhOxfUQhRUhblkWFJ\n18F1rKiZzcxWFEIIUkl3SKXPoh/Ql/fpzWvTYRDofAR7lHyE4QoLxt+fqmySqmyC2ak+urf+AYB3\n/GsVn/3UakBQ9EM6uvN87/u385tH/4Ttpll6yr/xmqWvp6fPoyfn0Z0rsnvvPnr6Clh2AstNIpXQ\n5YgBcHGzumhiXwibd3UOGqFL28uxBdihozA4uimBE/lTJIDSGc0JxyYMPPp6u6mqyGAJyf6WvRy7\nYB6vOGEJ6aSLCou8vPUl3vKmfyOTdPnjH37Hm//9jcxtmq2FdiTIEzP8OzEcnh+1eS3q4okTwaT8\nHWIuueQSgFKJ5lmzZnHJJZeUzEWXffgiMgnJ1VdfjR+ENFS5nLX8DAqFIkEosSw9GxzJ/DKc2eiz\nn7yYct+7H4TsbetjT2uPLk1Reu4lV/DJU24zzvL0ff8A/oFtCWoqEgi/l3951XE01mZ47u+Ps+zN\nb2DJwrnUV6Um3elmWYKENbavrVSKvqKkK1cklBIiJ5qgzIlmCeyyFUXsn5hppqdk1LijPMggDCW5\ngq4jU/C9AfkIlq1NR+ec806AkvKtra0tHYMDK4eka/PLn63nF/fcVnb+e7x2nuDTlw7KM/il/n9d\ntWoVn/zUp8kXQ+648y7Wrfsh577rXSjgwV/+mgtXXMjZ55yLDBWBDLnv3nvZsGEDKMXy5aezevWq\nUojoAz+7n7vuupP/OPccLCG57557uPyyj5RyHPonFitKn+Wcc5YN8im8obT1tqWXTNr/x3SiA0kC\ncgVfJ6VFyWeu4+A4Lu4EFdeMK+8wk8Yz0xloMw4GOJMTrhvNBieeBKSUoqu3yO7WXprbe2npyLGv\nI8e+jj72deQO2E4xJptyUX4fbS07CYu9nHTcAs45c1mp7n5tZYrq7KHPQtaJQSGhlDriQioEugSE\nHdlfnciZnZrhTuw46iyX93W1VKmVQgjYljaVuY4uoT3aymy08wfKMB591Te20hVHeqhmOX5UwLCv\nXNDTX5tqOIIw4NRXNM6cOP+JYIT/wSNlHF2k487X3nwLN918M6tXr8a2BHffPXk2e88Pae3s48Zb\n7uD3f34KJ13D4hNfw6w5C2nvztPWXRi2DPJghIDKdILqiiQ1FSmqK5LRtn5UV6TKtpOHtNZ97J8I\npU4AbG5u4aFf/ZJLL/0AtiW45fs3897zzmPRsfO1gnDtGRMGK6VuN5mL6hz50WQhfkyknefBCOjR\nSlMc2Sg8PyRX8Cl4oRb0Ub9tHUhhIcaYqDdR4W/MPkcYliUGxJ1//EMrqHCKfOazn6MnXySTSnD6\naafT15cv1S1JuM6EktISrs28WZXUJvL07toEwCte38hnL9NmJaUUN93yA26/4x5Of8e5eNLhiaef\n4zVL30hD0wLauvO0d+XpyevIp+6cDlccjXTCprYqXVIGscKoqUhSUxkrCv08WhTTeIVXv39C/3Qe\n/d0Gbr31+xQKOvFKV93McPElHyAMvSh2W5dOiE1NthDYtiCddEklHJzIeT3VCsKyyv0JQ0uGlOLR\nPd3v2Qt83cozjjCRKnK29keiVNXUsfqi95fMTeMR3KOZpMaKjpWPMoOjcZYfF4Airuci9DaAGlgG\nRpVtCAstfMsSzOJKAuP9f/Kj2kG5ghdV/4zGFZWRcR0H12aUNkmTj5n5H8WUmwcKflgqS6uitPgD\nLTVjRpu9jc1s8D0ueN9F+NLmoUce5ax3vpvXnvxGunqLPP7Us7y0bRcNjcfgK5tiIBDW2Gf+mZRb\nWjWUFEOZgvj9bx/i7jt+wIXvPRdLjH/8EzWrlcxMoTyggki6DqmErgU/U/wQYSgJpESGUUOeUEUl\nWRR+WQkGReSkhVLCUky/oI22ygRq+V8uPlzK/KV/Pz4f+21EZJ6zbatUUiIW1kJoYT4gS3iENONy\nZRJGnzNU2nTmhyHafRSVnJDatxT4Ei/UvyEvkFiWQCkRCXndjOhgSoofiCmZ+QshqkZ7AaVU93je\n0DBzGC4pDfoLm+UKPvmir23G0Y9AAZYdR87Yo87eDhSNFN+nHdJ3AJHw/PRFxL/M95/9qki4fgPQ\nDsWPfuw/6ewp0tlbpKu3SGdvobTd0VvQzz36OVfwyRV89gxTI0dTxby3fYo/tkHo5Tjx7CvZFs7l\n+rv/Rm1Vii3P/51HN/yWl/flSFohP15/57CfY7yUHNcHmO5JpegpSjpyfhTjLbGie61IOQznrJ6q\nooQxdqyEXEZt63mkUF79M24qJIiKSsZ+NluQjFa8RV9n9AoJoZL4ns7qtS1rUmqCTQajmX1eRsfl\nj8QidBKX4QjCsS2cQSULYmLTQFwff1ZdLRetvoh8vkgoFReuWIVlWRS9YMx24/EghM5wrcwkmT+K\nWVlKRV/BKymKzt5CtF2I9ot09hTY1dyGJy3sRIZ8CM9u3V/2Khka/uW9PNOl9xYsv4rf7U7wzP/+\nhrqqFPv37uDvm3Zx2ns/SsIKuOdnPydbVcdHP/zBMf3AD7SyEEKUFPSB6A9/9UplAojCX8tLAcTh\nr64d+yKsssJnYkasKqaLuPLn4GKOXhBG1T4p+UKUVCjRX6hO2+RdHMAZo+5TStv4C0WfXDHQVUaj\nyZTt2CQcZ9prMI0m/G9WSn1hpJNCiGsneTyGGU4sjMozlsuJ2y4WvaBkN46zE+MyugrtWLQswQ/v\nWMedd949bKghHDgUcTQsS1CZSR5QUdx226387qHvsmrVKnxlcf/Pf817LljFG960jPbuPO3dBf7y\nxDPs2deJnarESVWW/BMv7+0CktQc91Y2RwuLOf++hoe2wm//+2fUVqWpTNnku/dzytJXUleV4u9P\n/JnT3noqxx07l9rKyQmFHW/4ay5QhEV/QPhrbKqJimEOX7UTIDKh2LZVioIqmV0GVe4sN7XoW8uE\n20iV3egv/qbLrpfZ8ssqfkq0+UmhorDRuLw7A6p+Dq74SVnlTxEJ9IFl3F0sW5cmmkyHqBBClzpJ\nOFRX9h8PQkmh6NOX9/CKEj/UZiJ3nBneE+GAny8W/EKIanRdnnbgo8A9SqmXD6QYDEcnpbaLBygq\nJ6UqrSDOf9c7CAp9XPbRywmVIpVMcdrbTyeXyyMlvG3Z6RR9yfvffwmObVNdUzMhp+BIDDZbNdZX\nc845Zw3wSTz589inUeTOO7/Nhy/7OMvPPq8U0RQrCe3A1vu5YkBLex86ZS7B7t9vjt6xgifW69o6\nQkB1tolXnvM5fv3P7YTFHt5y3sdY+NrTeHrzPuqrUtRVpckc4G85Xoe1EFEzlwmUBC/Hl4qi6k+y\niwV0LHAHO1mJ92MGu/YG6AVtOhGRkV8gSsI6rvgJWukJYkXlaOUTvcbhUpjcsS0qMkkqMv0NnDw/\nag9Z8PAiH4o7qLXrZDAmh68QYj1wE3Ah2gx0oVLq9ZM6EozD1zCU2NYaV0X0I1tq7HDTdU/6Z4NW\nNAO1RH+NoIOxr05UOOaLQaQU8vzwRz/hr5uexU5VsfC4k6ibPY/27gKdPYUxlSpIJRzqq1PUV6Wp\nixRCfXWa+qoUj/3uYe5adxsrz38XYgIO64N12B9KZvLYJpMgCOnLe/RGTeERgkTCLSnwqQ71rFFK\nbRBCfEEpdbkQYsV4P4DBMBHilcTgcgfDEdtxg1BXViz6AZ4fIANFGJ0Py5p/xGF8ljWyohjNYT0S\n6aSj6yPNqmB2qpful/8EwNkn1/HZj2uHdhhKbrrldu740U9YdsbZeNLhqWdf5KTXvo7ahnm0defZ\n35Wn4AXsbtUZ10Op4phln+YPrRLp53jF2Z9juzyG//nR36iuSLL5+Wd57NHH2NxcJGGFPPjznxBI\nWPPhsVW+HM0sNZUC+GCreh4pysFxbKor01RX6vBczw/o7i2SLxaj6KqJTW7GKvyFEOIaYJMQYilQ\nM6F3MximEG37tsdUoTSuuR63+husKEKp47G1KaPfR+HY9rgKyR3IZ2HbFue/+xzSrhokoN5eElC3\n3nor31t7K//x3hV40uHRPz3Bv7/ldBYsOUmbmbrz7GruILBs7GQFuRD+vqW1bARp6l91Ls9F6RPz\n3vKf/HILbPj/7ieTdsmmXLo75zJr6UpkUGBzbz0//PU/yaZcsmmXE153Ju+4oMh9v3wEJX3eveL9\nnP3uFXT2FEgmbH7xi1/w3e8OL4APNnt4oiWbY+X+wM9/wU1rb6a1rQsF3Pfjn5IPBBdcsDLKW5AD\nfFFxPkOpxWNZh7/y4+XGif6GM2U+EeIQ1f7j2txmlUxupYdjDdwvPUbOJ0i4Dg21Tul73NM3eqb9\ncIzV7LMYOB9YC6xgmP67k4Ex+xhmImEYRYT4OsSv4AU69js6F0bJT3HXKdvuD7c8+NnngfMIYrPN\n+1atwpc2P3vwYc47fyX/9qZldPXp8NfH/rKJ7buasRJZqmpnoazkkFahB4NAEvpFlJKkU0mqqipx\nLIuenm462tuoralGCEVbaytz5jbR1NgEQHNzM3v27mX2LN2oZl/rfubMmcPs2bNLr71nzx72t7Uj\nhE1NbS21tXWEceixVPT09FLwfISwcBwXy7Z1puwRQMK1Sbk2iYRNynVIJnQ4b9wjIxkdd12LL3/w\n1EmP83+vUurHSqmtQNxv9+bB58f/sQyGw4c4rj2ZcKg8wHW6FabOkSj6IUXfp6GultWrLiKf06Gw\nF6xYjW1ZFIp+NPM7ONfkYId1U0PVEIf14w/ENv0Cd975dT7+8Y9z0cXvp6/gs2NXMxs2PsrbTjuT\nvoLPht/+nkXHnYiwk/QVfP75/Ets3baDprnzCJWgvbOb6pp6EskMxUgZKmVhuZFJQsL+znz8lyNR\nOZu+UO8la+fTnof2UhVPl1TtArqjopSp2vl0FKBjR3vZJ0yRrJ4LQC6E3JB8DQc7coRKQEaC34oy\ncmUYEPgeSklSyQSVlRU6R8Lqj1TSORPWgOOlzlm2GHTcQoj+3LRyJ/fgY7G/Oz4XRmGlOikuaigf\nTSwCGe+r0jHP183nyU3wyzEKB5z5CyE2AzcycrriZUqp4yZtMGbmbzjCKU8WKvoBBU/XCArKzA8S\nLbjiUNgbv/e9Cde/mepuWrfeeivfu3EtF6xYiULwk5/cz/s/8AHe894LkFJy2w9u58FfPoSwLJaf\neSYXXrgCEfd4FIr169fzyCOPgIIzlp/B+1aujM4JHvzFz7nvvh9z1pnLEULxywcfZNWqlVx4wQXY\nlqCjvY3f/mYDF120GssS3PnDOzjn7LOZ09SEZYnDunaQlEqbIz39PSl6IYUohLro9x8veDpL/4oL\nTp50h+9VDA3KKseEehoM42AsDuw4FNbzA957rg6FXfPRywilIpVKs2zZ6fT25qO4+qlxWI/1/ne+\n850IUR4qW8U555xNY0MFt912K/evv71M+P6AY2dnynI4bh1ULvo2jp2dLp2vO/8d1KXD0mvPn5XV\nr12v+3zPrs1y4pJ+53TsxI6ZrNpBhwLLEqQSukkMJA94bSglV0zgPaa8to8Q4kql1HXR9vnoXr6m\nk5fBcJDEZqZ+h3XY74uQKirHUe6PGJjQNJEiZePhYB2+hrEx0U5eUyr8hRBnAJ9XSp0phDgZ3dLx\nvqid4xCnsRH+BsPko5S2I5cc11G+RFykTEbZs1JRaqMZZ8uqKGO3P4plUKXLUl7F1CoSw8jMuDaO\nEeWSfCXwULS9FTgDmPSIIYPBMJCx1ggaDh3uqEMdZRiHQvZXuoy3AxmiJCVHZ1zVM94tf44ZbaJ3\nIGUyHilX/i7DOWZL2clxeQjRX/1TWP11jya7TtWhZsqEvxBiaZwYFh2qRpeHiDEF4QyGGY6OiImU\nxtFRwLMUvhuGuoSzX8os9/ub30TZ5SWfywSV66FkTMI/Suy6B+gA1gNbxhDiWTfcS41veAaDwTC9\n6NBewLVHLVmtHfNh1Ezdxw8lYbQqsqKOaJNdk2eyGOuobgZOAdYqpa4TQjwOjCj841n/oMOd9CuE\nWqANg8FgOIyJzWmDy58rpSh6Ab153U/CCyR+IBGWpctpzwCFMOYRKKU6y2xw7Qe6FlgcZQXXA3XR\nyuFHaAWyAd0H4OHxD9dgMBhmPkKUt8zsRysEj96cRzHQDdodxyaVcKfdYT5W4f+EEOJGoEYI8TX0\nLH5ElFL3AURRPdX6kHpKCHGKEOJ0oFMp9dRw9375y18ubS9btoxly5aNcYgGg8Ews4lr+tdXZwDt\nUO/J6zIcRV/ihxJ7DMrgT489yp8e+z0wuuN8JMYc6imE+ChwMvDEcDH6k4EJ9TQYDEczJWXQU6To\nhwRS1/JPJkaep09pnH9ktllJfzVPpZT62HjeaEyDMcLfYDAYSgShpKu3QHefp/sGWzappDug5eNU\nx/nfDFyDNvfoJj0Gg8FgmFIc26K+OlMyE+ULPm3dOfL5kEAqksnEhHv/jlX4t8d2fIPBYDAcGtIp\nl2NS1YAOM+3oydPV503otcZq9rkSWI7OzAVj9jEYDIYZgxBiysw+l6EreHZF+0ZCGwwGw2HMWIX/\nJmP2MRgMhiOHsZp9HkbP9jdFh5RS6upJH4wx+xgMBsO4mUqzz9cmMB6DwWAwzFBG6+EbN2JZPszp\nwbV7DAaDwXCYMNrMP47ueRzj5DUYDIYjhtEauK9XSq2YtsEYm7/BYDCMm4nY/EdrTbP4IMZjMBgM\nhhnKaDP/duAmhjZhMdE+BoPBMEOYimifdvrt/gaDwWA4QhhN+HdOVflmg8FgMBw6RrP5Pz4tozAY\nDAbDtDLmZi7TgbH5GwwGw/iZimgfg8FgMByBGOFvMBgMRyFTJvyFEBcIIU6PGr/Hx86Pjq2Zqvc1\nGAwGw+hMifAXQpwOnK6U2gAsFkIsFUKcDBAdi/sCGwwGg+EQMCXCXym1oazTV51S6kl0A/iO6NhW\n4IypeG+DwWAwjM5YSzqPGyFENfBRdON3gGp00lhM/VS9t8FgMBgOzJQJf6VUF3CdEOLXQoi4CczE\n2swbDAaDYVKZEuEf2fdVZO7ZBFwAdAJ10SW1QNtw937pv/4vtmVhWYJly5axbNmyqRiiwWAwHLZs\n3LiRjRs3HtRrTEmSlxDiSnTf3w1RtM+vgW3AKUqpm6PzDyulnhp0n3p+RzuhVFgobFtgCYFjWyQd\ni3TKJZVwcB0byzKLCIPBYICpbeM4XtYCK4QQi4EOpdSPAYQQp0SRQJ2DBX9MJp0c9gXzoaSnq0go\n80gpsQVYloVtCRxbkErYZJIuCdch4dpT9LEMBoPhyGDGlXfY0do3oXv9ICQIQ2QoUVJh2QJbCFxb\nYNuCdNIlndSrBtcxysFgMBw5zKSZ/7RzIKEulaKjL2B/TxEZSgRgWUKvGiyB69qkEg6phG2Ug8Fg\nOCo4YoT/gRBCkEw4jPRxfanI9fqEsjCscrAsUVIOCdf4HAwGw+HPjBP+3/vJk6STzsBHwiVVtp9J\nOqQiM07CsRDi4ASxZR1YOSil6ClKOnI+Uqoyn4NWELbQpqWk65BM2CRdB8e2sG1TOslgMMxMZpzw\nf/hvL4/ressSZJIOFekE2bRLNu1SkdLbFdF+NpWItvuPV6RdMqkE9hhm8EKIUc1BMlIQnfmAMMyj\npEKgEJaOWLLjlUSkJBKuRcKxcRwbxygJg8Ewzcw4h+/aB54h7wXki/pRiJ71MX/IMT+QB/We6aRD\nNu1SmU5QkU5QkXGpSCeozGiFUZFJUJGKntMJKjIJKtMuCdee0IpDSkUQhoRSoiRIJUEprGglESsK\nSwgcx8K1LZIJG9e2sW0Lx7aMyclgMAxgIg7fGSf8xxvt4weSXMGnr+DRl/fpzfvRs0dvwScXb0fH\n+wo+fdF+rugz0Y/vOpZWBmmXyky8ooiVQ5niSCeojBRKRdolnXTHLLz7FYVCSYlSCqkUFiAsUTI9\nWSJ6WODaNq5rk3BiRWEUhsFwpHNECP9Hn9kzwESSSrgkXRvX0YJsMpFSkS/69OS1QujJ9SuGnrxH\nb87TiiOnFUhPTp/rzXsTXnFYArJlyqEyUhgV6f4VR7a0HSsNfX40H4JSilAqwlAilUSGCtAKQ0Tv\nLYSIFAZaYUSDSkQ+Cte2cMoUh20J47swGGY4R4Tw/9tL/VUfFIogkIRhWBJgjt3vYE0l3FJ45sE6\nfcdL0Q8HKIeefLzdryBKSqNsO18MJvyemaTTb34qrTTcAeYobaZKkElp5/hYHeOhlJEzWw1QHEoB\nSiEE+lFaaUSKhFihgGMNVCC2ZWHZsSnLrD4MhqniiBP+B0IpRRDK0izXgtKKwXVsUkmntGKYSfXk\nglBq5ZDz6Cvo1Ua50uiJFUp0Tb/JykMexH+VZQnSif6IqVL0VOmYO0yUVaw84jyI6DGMMolNUrEC\nCaX2Zeiv18DVB4ysRIQAd9AqREdVDXyeLHbu3Mm6deu4+uqrAbjmmmu45JJLmD9//qS9h8Ew1RwR\nSV69fXk9wwQQAqGfAKLtgZ/Pot+cARBKhV/06O4tECqJklLPPIU2I7mOpYWX6+DYonRfrAP1RFdF\nz/0nlNLn4mtENLZ4cJYQkfDSZhUROW4Hj9exLWoqktRUDF/GYiTKTVSDzVGDzVQ9Oa/kMI8fQSi1\nv6Pgj+t9R8ISlBRBrBTSSZ0LUX5cK5ChyiM96LpUQitrYQkKUiIDiZSRY7z0nzNIiYgy85U1VIG4\nro6kcsoc5Y49UGmtW7eOL33pS7S2tgJwww03APDFL35xUv5ORrkYZiozTvh7gSQTzUQzKS08JtPm\nHEqJ54coKfECGZkr+jN9M/HM1nWGnWGqaDYbz3JVPNuNVyKxzV0q/DBASkBqZSIH3a9U/75SoES/\nEgFKyiRWKKmkQybl0lSXHffn9gNJwfOHKIU4aqoUQRVFVMURV4ViQMELKHihfo72vUCSKwbkDsKM\nNRwl5VFSDPYg5dL/nErYA5VPmaJxHYuEY+E6FkL0K3IVKY/YhLXi/Zezc18va2//ATKUXP7Jz/Hh\nyz9JZ3ce17XZu3cPd991J1+8+iqEEOMW3lOtXAyGiTLjhP+Xbnp0yLGka5NOOWQi00Qm6Ub7WkmU\nQjUHhGn2O1LL4+htyyKdHF6ZeFLS1+Mhu4qEMiwVj3NsPYuPTSQ6mmby4/Njc4lUChkOVShhKAlk\nOKJCUUpF+5RMLv2WIj1LziZtsimnfxVVWl1FKxb6TTIH8hEEoaToBeS9sExBRI9iqBWKN1R55Adc\nO/jesPSA4qT9XROORSqhFWf8ncim+n0m+2mi8aS3If0ChcRs/rmjk3RCTz7uvvNnrF37Q3a3F0Ep\n7rzrTnqCBB/58IexLUFLSzM/u/+nfOqTnyDpOHzzm//DxRet5tiFC7Bti6uvvprW1taS0L/iiitK\nq4VJIAYAACAASURBVACD4VAy44T/iQvqyBX07DMXzUSLfkjRD+nsmZhASEdJYBWZ4eP5q7JJqrP6\nOd7OpBNDXqenGNKRy+vicVFsvm1bpSzfpGtH9nC7ZHIYD5YlsKwokcyd0EcdlvLViYw0QryvzSqU\nFIxCK55AhkQpCKXXAK1s4n3XAidlUZl0kbgQK5tRTGVCCL1ftm0JgVLgBSFe9P9d9EOK8YrDC0sr\nlHIlU/CCQYomLDuvz3mBxAs8unMetA8XSlxP/avOBeDZbvj8d/snILbVwAlnf4E/NXci/TxvfO/n\nCWefxF2/eZHKTJJnnvwrjzz0Z/Z0WziW5IH7f0JRVHDJJRcDepVRIEtlw0KkDCnIBDtbuki6ehLR\n2rKXH919N1dd9Xkc2+JrX/uaMQsZgDjUWxKEIX6gLRZ+KAkCWfot62sm5gyccQ7fwXH+Sin9wy/6\n5ApaGeSKAfmCfs4V9PHYUdpTHnETHZuIozTp2pEySFAdPZcrh8Hb6aRDKBVBEJaEqFA6wzd2RNtR\n4lbStSNndH/i1kQ5HGzKA01dQxVQKURVxtsyUkBS/9/F5jHKfDLxSkeVKabS/7O2++t/FH5ISYnE\n36M476O5tZ1/PP8SCxefQF/eY/O2naSy1RR87Zj3/HDcn9cS6IlFNkG+p4Nd27eyaMEcXCvkmaee\n4O1vPZV3LD+NbMrl4V/9nHX/7zbOf/e7AMndd9/N5Zd9lA9/6FIsS9DS0sIDP7ufj11+OQnX4jvf\n/l8uWvU+Fi6YX/ruTHekm2H8xIJcSkkQapOz74d4QYiSipD+34M2J+uJliVEtBK3oonm0ICHUEoW\nNVYe/tE+Ey3pPBJSKvJeUOYM7Y+u6clpZdHd59HdV6Srr1jaHm8cv2NbZQoiQXUmWdouPWeSOtEr\n5ZBO2lhC0NLczK9++Ss++MH3IwT84LZbede73sW8OXOwLKJSEDpqybatUjhlzFe/+lW+9KUvccUV\nVwDapvyVr3zlqLUpx6ucckWio8L6o8P8UA4wk8W+mzAyl8loxWIJwe3r7uAH6+7kne86j1BZ/PZ3\nj3HGWe/ktSe/gZ4+j+5ckaeeeY69+zqw3DTpihoCNf6qsEqGSD9PZSbB4gVzqczqVem2l57jr3/+\nA68/+V9wLMnvfvMI5593Lu9bcT7ZlMP+1lYe+lX59+c2zj33XObOacISgubmZn7205/wqU/9J65t\n8c1v3sBFF61iwYL52FEex6FSHqNNXGbSxEZHFfZHsukZuU7CDKLvFjL6PkXfIakUSg78PoGIwp+t\nktXgYP/+ExX+M87sM9lYliCb0jbeRsbmKNWrjYCuSBFoxdC/3d3nDVAU3X1FCl5Ie3eB9u7CmMeW\nSbkQFuho9Xni2vtwLcnmF3awM/8H3v7WU3W9opRLNuWQSTtUpFxAIJTCsrTJ5MJLYofl7SgpufxT\nV3Lpmv+ktaNvUhK2DvYHON0/YBHlgNgHWZU7nql9+H3vIhn28ulPfwovDPlWpeQ/3v1WGhubUMDN\nN3+fP937XS5afTGKHPfccyNrLruM916wkt5CQC7KKu/N+3TnPHr6itGzV9rf39lLaNnYyQpyITy7\nbX/ZSNLUnriczdGcqPH1F/OH3fCH/9kAgC0UxVySP11zP44l2burkxe6/8jr/vXVVGQS/H3TX3nk\n139lZ8+NWELy8EMP00MVF69aQcK1ad23j4d+9Ss+8IFIefzgB5x7zjk0zWlCCGhubuHnD/yMj350\nDQLB2rU38p7z3sPceXOxhUBY2o9WCte19KrLEoLdu3dz9913c+XnPocQcN1132D1Rav5/9s77zgp\niuyBf6t7ZnaXnJe0CBjOU0/PcJ6ip0g0nZGgkj0FFUQMBM87FRV/oN4piAiid8oiCKinCIIEMYEB\nBEwoAktaYIFNbN6Z6X6/P7om7EoQFBjZ+n5cp7uqp7vonn5V9d6r99KaNwcFL7+SzoMPPUjWzt0o\nBePGPYvjwvDhwxGkQj3A+PHjCTswbNhQRGBrZibTp0/n3nvvRUR46l//pnv3bjRt0gwX2Lo1k9df\nf4OBgwbiusJzEyZw9dXX0rhxY88mBnufTVLJbubpLEHFvMmUsqJeZt56Fhu8//itBIRPOOEfv9go\nqiLQyuSInjjmihlTKiv9v8pdX+VOtfJER+I3VOTDe8A1k21qplSnWf3qKMu7QMQQalkqGlpBKUV5\nyNlvR1FY4s02CvRosagkSElZCLBJqtOMPO2BWavlefyQAz/8b6+JzvBbLnVqVqN2zZRox5DpptH8\n7Gtww+XkWU15/6vtnt9+wEdpcT7Lln5E75tuwLYtpqanc+mlnWmc2sj7d8T9oK04nbxS3mzmhf+8\nyujRo9melQ0KJjznvYDDRwzTL3gmr059lb//fe/C/bfq7WJZioBlc8LxrXj4wVhbRz00osJxt/fr\nTg1fMNq5pdVP4sabrqZJk/qEtK42FHII6iRDkZmGo0eFL788hcnTJ9O1W3cc5efd+Yu4+rquXHhR\ne4pKvd/RR0s/58eMzdj+FBo1SaNW3QZRN1/HBV9KbYodwIFqqb9nSwFs+WidbmF1Gv7xetYUentN\nzr+FjzLhoycXArrzKE1h6ejZ2Mole1cZ3+R9xsknHk/Ab7N+3Q+sXrWZtXlTsRBWfrmGrGA92l58\nET6fRUlRAatWLufyyy7DthTz3p3LBRdcQIMG9Zkzex7Tps9l2x7vLXv7rbcopgY9etyEAq7t0Z/t\n+SGmv/E6ALfd80+uuelW1m3Lhwr1bwAw4O5/cvVN/fl+Sx4i8NqMuaS/+g7bCgBRzHl3HkWqNldd\ndQ0iwtuzFzLr9YVsK/QGPIsWLaGQOnTqfGl0duhqeRNRO7oSKY+N8ivXR5wxHNeNHvuT70TO43jP\nuvK5Iuf5NTjUsySc2icru9DrTSOGVD0tivSysZ43Inh/6vt/qFTUJce7YkrclE8qed9ovbR+sQXi\nOq64c0b+jZZ2PURRXh6mqCzES/9N5/0Pl2L5Uzj73Db88ZzzoiqF9Rsz2ZWTT0qNuoTlly1Ys3AJ\nlZdQq0YKqQ3rR/3yU5L8+rOiK2VSwCbZbzN3ztu8v2ghrhvi8s6duOVv/bTNQjF16lRemDSJbl27\nAjD9tencfttt9Lu5X7Sl/37qKdLT0wHo1bOn13F4wyiysrJ4++3/MeiOO1CW4rnx4+natQstmjeP\nrg72vJC8T+LcYSN1v4SjrVrYunUrr0xJ576hQ3Eclyee/BfXXd+VhqmNCYddJk6axLhxz9KrV29Q\niukzZnD7bXfQq09vz5gccnl+4ou8u2ARli+Zi9q2p237TtH1IIUlQb7+9nsyd+zE8iVRp15DUqrX\nirr5Go4NVkzumTg6f6XUrXrzeBEZocuuB/KB1iIyeS/fkUTqjH5N4uPuRHSGobDL08+M48mnnuLm\nm/8GKNKnvsrgu+6i7803eyNFF8Y9O47XZ80Cy+a6rjdwY88+2ggeJmtXLsu/XMUZZ/2JotIQK1d/\nS8PUpjjY0ZXB2bkFBB1Q1q8/IbUtBeIQLCtB3DC1alSjSeNGBHw2SQEvwFzGhvVs2rgBcUKc/LsT\naHPenwn4bQJ+mxXLP+OD9xfT5rw/o5Tw0QdLuOqvV/DXK6/AZ1sU7Mll6UcfccMN3fD5FDOmTefS\nSzvRsFGj6MwvsigwOgaIzGQi27ou6m1ExM0VJk18nqeeeJK//a0fCmHixIn84x8PMGzofd7MZvs2\npk+bxogRw7GUYsyY0UdUL72v8zdr1pyw4zJ69BgeHjmS2+8YhKB46b8vc+99QxnQfwAuMHnyZMaP\nn0CPHj1BKWbMnMmAAbfRu09vAMqCDs9PmszceQuwbD/tOnTm8iv/SlnQ1R4mYd7/4CO+/uY7lO3j\ndyefwimn/oFgyKU87BAKO2zI2Mzu7FxQFvXq1aNe/QZ6kOSSn19AcUkpyrIIJCXj9yd5o22JrIdx\nozG7ItsR1eTe6m3bIuD3R3NplJWWUFpaCuJSvXo16tWtE1v0Zymyd+8mNzcHxKVhg/qkpTXHUnq1\neDT0SEx9E9mOnD8WmoSosTWax0OXR1SqFc+lsKKr0iuVKxX9zq+B47pc1aZ1Yuj8dZL2RSKyUSk1\nU+/nAojIYqVUa6XUmSKy6nBcPxFReoVxZc+eATf3oJrfjakNUmvRq1d30prXA7xOI8kpIDtzLUpZ\nBILZ/LF1fW8lc8ghdGIDrmhzUtT164ZLTqzoMeDCs+Of5fXXZ6FsP9dc342bevamPOhSrr0NyuN8\n8CN++JEFXj9uyGDz1u00aNQYRywKi0qpVqMmlu2nPORFHAULO6kGACUObNDT9hg1qNHsDAC2lcKs\nJWvj6qpT/7SrWFvk7TU6+0Y+2w6fvfBx3DF1+eCphXq7Posnr6BackAbwr0OJqDXXnidioXf53lU\neW63Vqwucqz+7hkXX8cNBT7mvP8+ImH6Dn6QS67uwVcZOfhsxRuzZvPSS9PYkR9ERJgxYyaF4RT6\n9vWE58svz+S58ZPZuqsYENLT0ykK+bitf39Qil07s3jrf//jzsED8dk248Y+w0033kCLtDQsy1Ob\nTZu270VkaWlpFVRk8dsBy6Zf3174bKK/n+aN69Gr102kNakDwNWdL2LFJwt57l8jAdjw7Wdc3fE8\nTmxWB8d1GTP6CV58eiR33DEQQTH5+cdIqxnmtjtux3Vh4qSJzJsylt69+oBSTJ06mtMGD+aOm2/2\nZrqu8MzTH/HlJ6+hlOKC7jcw+JarQcGUV9J54dVJdOvWDaUUM2fOpH//AfTq1ROlFDt37eK9996j\nV8+eAKRPncqlnTuTmpoKwM5dO5k/fwG9evaoUN+oUSMApqSnM3nSC3Tt5s06Z86cya39+9OrVy8A\npqans2SOd32v/hXOvbU/vXv3Yl9E1PuRbfDUwPEq5Yh7MgLKitTHNBExD52DNeZKnBopsr7H8yCs\n6KQQe7dFhFD44D3S4PDp/Fvrv8lAht7uCETe4AygA1BlhP++2N/LDd5Ib+zYZyp48zRJbfizdeaP\nP/44E//9CIPvugsRGP/0wzSpCXcNuTuquor/YXkarJiqKmtHI+a++y59+lyDCEyZMoXOl15Co9RU\nFMKU9Ff5z8tTuPra6xBRzJu/gOu6dKVj50sJh4XdObks/3Il57e5kGDIYdmnn9P6hJPwJ6UQCrkE\nw2FWf/0tmzdnomwfjZs2p3GTZlF//1DYJS+/gPJgGGX7UJYPVxRFpSEo/XVCVUATUv/kCZivC2Do\ncx/G1dWnVaf7+SLfQdwwJ19xP6uLajB80ic6JMWp/Lnr3/lo4xbECXFJj7+T3PJMpi9ZTyBgs3rl\ncj7+8BsyCl7EwuXjDz8lx6nLX6+4jIDf5p3Z7zA1/Q225gRR4jJj5msUhpPp169vTJDExz+C6MzG\nti1yCkopDSnyC0qxbEXIgbJg2LtfSrFw0WIWLlzEvffeC8DChQtp27Yt5513Hj7bonOn9nzy8QeM\ne+YpANb98A1/vewSmjeqDcDtN99EDb8TG5w0qlFhcPL4448z8ZnHYr/Ppx8lrUEyf//737mm8/ms\n+ORdnh3zICLCuq+XcWX7czmxeV1EhOOb1ub8M06M3umHhw2q8FT84T2kSBGtm3htSZFi0hqk0Lyp\nt39rj2uoRjHDhw8HoGndAD17XkuLNK9tA3pdR3VVwv33e3aatPpJ9Op1PWlp9ff7a4hoH+LXuFQw\nCkdskHHq4ZCeBYUcl3DIIRx0KdVl4bBLWPvji+viCIRdz+bjaPWwDhKDZVvejMSy8Pt9cbMY27NF\nome0cficQ1PfHRbhX0mlcxYwAzgbiI/atv8nYACIjmIiL1/Dhg2jZYfy/cap3vfr1Ur5Wd8/oVld\nLjzn99H9/3vw7gqLxm656SpSpIh777sX1xWa1hK6du1Ek6aNtb9+Iy5vc1LUa+LyP7Wo4GUxefJk\nPp4+np49ewJhXp3+OAMHDqTvzX0Bb3QzbtxYZsydhVLQrVt3+t92O2FHCDkuoZAQDIcJO+L5Toe9\nDiOiVgs5kTLxFo+Fnah/dTDksn5DBhs3byW1cVNcFLl5e6hbrwHJ1WoQDEUWnIVRlo2ybEIC2fml\nle5SNaqlngxAVhm8s3RDXF116p96OeuiM5sb+HwHfP7iUl1fj+Pa3cPKIk+YnHTFP1mel8LX4z8k\nyW9jKaGoII/j0pqTFLDZunkjrVq2oG7tmgR8Nt98vYpPl/3Aj/kvYOHy6dLlZAXrctllnUnyW7S5\nvCc3ZztMfetNnHCQAUP+yXU9BrB2ay4KeOu9T/hs9ToG3vNPQFj25fe8/d5HpLb4HQrYkV1MUchm\na9YeUFAcstiZW4KdUogFnHXuX2jf+UpG/OMRLEvx9Zp1/On8i8gvKGXue4tZ/MHHDLlnKACL3/+Q\nCy5cwulnnPWTEfHeBsjpU6cxcuTD7NLOAs899xyuwH333QdAMOTguFBW7g0CQg6UloUpLPEWg9au\n25CBd95NflE5IkL/O7wB0K68Ir2AMWKAj60nAaKeQPELGiODoXi7oD60QmiWyBoTpSx8fptAklb/\n7Mc+FR3pR8LC6N9wSAetdEJ6HUD0vfOi6KI8v3+RQwsvf1gNvkqps4CuInK/UmoiMElEVmk1UMeI\nLSDu+GNW52/YOwfSmcevYxCBsWPH8uhjjzFsmOcOKBH1llRcrRzRK0cWi0Vd9iq5+O3YnsWcuXPo\n1+9mBOG//32Zyy67nEaNGiF4qovJL06mS9duiLJ4a/YcevTozZVXX0Uw6PLO3HeZM3ceF17UFkHx\n+RdfcuFFF3P6H88mGPLUaV+uXE3Gpi0oO0DTZs1JbdJcr14OUx5y2FNYTNgRlHUknO+Eakl+AjrX\ndHLAJjd7F7t3ZSFOiLRmTfjDaadEA+19993XLP/iC844/TQUsGrll/zlLxdwQZsLsC1YuvQTFi9a\nxAVtzgeEjz/+iCsuu5TOnTtjWfDGG6+zeOEiRIROHTvQ/Ybu2k0ScnNyWfbpMq7665UIindmz+Yv\nF11Ig/r1PTWLeKqduXPnAsKVV15B3z59tZCFN958k2nTpnHllVcAMOedOfToeRNdru8CQE5uDh8s\nWUKXLl1RSvH66zO55JJ2NGrYMGrziXj2WZV7n7j9A3kQVtzff6em9lO5v+vEZgAWisjKXm9gU1Ye\n5uzfpSaOwRdAKTVURJ7U26OBhVrn3wVoFamLO14eeuih6H7btm1p27btYWufIfFJBG+cKVPSGTZ8\nOK4rjHniCW644UaaNm2K4wpbt2YyY+YsBg4aRNhxef7557n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S/GTeT5jz2BwASr9fypwrjXaQkcYi22I5tkDmDz0bMXyRf2fX5OHWrolEjrS1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\nUnofu3YhYH5XUpq+RDjSELMCY+f/hJqJAl6p3OOsriagBMv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WUnoone8MiLp1plWv6JAW7qj8Cf2e5NvhP/75P1hpr6Tuj3U651EFhUQBtVSR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plotlim = (-100, 100)\n", "fig, ax = plt.subplots()\n", @@ -748,58 +470,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
\n", - "Objective: 100.923305908
\n", - "Number of Parameters: 16
\n", - "Number of Optimization Parameters: 16
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
mul.sum.rbf.variance 0.0132120051848 +ve
mul.sum.rbf.lengthscale 36.4508323102 +ve
mul.sum.linear.variances1.84224719324e-06 +ve
mul.sum.white.variance 0.000531301474393 +ve
mul.sum.bias.variance 9.98742548332 +ve
mul.gender.W (2, 1)
mul.gender.kappa (2,) +ve
mul.event.W (3, 1)
mul.event.kappa (3,) +ve
Gaussian_noise.variance 0.0117504562955 +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(model)" ] @@ -815,10 +488,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "X = np.sort(np.random.rand(50,1)*12,0)\n", @@ -837,32 +508,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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T3svGvQ1MqCxAkrP3x27bwVZiipGJHScpHwZb1/vjsJlB01gwpRSDIrH/eDu+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\nDtZ3p/3npMMbYuPoWQBcPal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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model = GPy.models.GPRegression(X,y)\n", "model.optimize()\n", @@ -892,10 +540,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Exercise 2 answer here" @@ -912,52 +558,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: sparse gp
\n", - "Objective: 70.0551347892
\n", - "Number of Parameters: 6
\n", - "Number of Optimization Parameters: 6
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
sparse_gp. valueconstraintspriors
inducing inputs (3, 1)
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve
Gaussian_noise.variance 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Z = np.random.rand(3,1)*12\n", "model = GPy.models.SparseGPRegression(X,y,Z=Z)\n", @@ -974,32 +577,9 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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fMEv6TPlOth7r7FoH6ezXxb+tsC9d+Is+/KfVk/Q/K+zL0kVEtv+QP2L7n+XvQ52FYBol\n2HfKb5c923+9sv4qvgcfEZ9Jul92t2x/e+2dQJWxEfwd239aaWc6cH6bDZ39MKX6fJZpi9+PfdEh\nlXLe5FhnX+DYX3V/lm3sZ+/NrC+ocKETACTFkn0AkBQBDwBJEfAAkBQBDwBJEfAAkBQBDwBJEfAA\nkBQBDwBJ/R8DgMXM9txBaAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "mu, var = model._raw_predict(Z) \n", "plt.vlines(Z[:,0], mu[:,0]-2.*np.sqrt(var[:,0]), mu[:,0]+2.*np.sqrt(var[:,0]),color='r')" @@ -1018,10 +598,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Exercise 3 answer" @@ -1056,10 +634,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Exercise 5 answer" diff --git a/GPy/optimizer-implementation.ipynb b/GPy/optimizer-implementation.ipynb index 4263f18..19bf82d 100644 --- a/GPy/optimizer-implementation.ipynb +++ b/GPy/optimizer-implementation.ipynb @@ -1,11 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/optimizer-implementation.ipynb)\n" + ] + }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import GPy\n", @@ -22,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "try:\n", @@ -49,10 +53,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Get the parameters for Rprop of climin:\n", @@ -61,10 +63,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "class RProp(Optimizer):\n", @@ -107,10 +107,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "m = GPy.examples.regression.toy_rbf_1d_50(optimize=False, plot=False)" @@ -125,94 +123,26 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "Model: GP regression
\n", - "Objective: 71.7157240818
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve
Gaussian_noise.variance 1.0 +ve
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "Model: GP regression
\n", - "Objective: -15.5366629083
\n", - "Number of Parameters: 3
\n", - "Number of Optimization Parameters: 3
\n", - "Updates: True
\n", - "

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
GP_regression. valueconstraintspriors
rbf.variance 2.09912029816 +ve
rbf.lengthscale 0.275423851372 +ve
Gaussian_noise.variance0.0106782996505 +ve
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WHTnnmcth5QvjTuCME0exq64Zd0EhDz74IACXXPJVAPY3NPLmplrsg45j0BlT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\npo7Haun8bwkEglQMcffZk6C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b2FTTiM//2YCjYCjKpp0NJAxGLGYTWmse/ev7JDRMPnME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\nCCH6CUnoQgjRT/x/o6J9X8THF7YAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m.plot()" ] diff --git a/GPy/sampling_hmc.ipynb b/GPy/sampling_hmc.ipynb index e65b598..ac726fd 100644 --- a/GPy/sampling_hmc.ipynb +++ b/GPy/sampling_hmc.ipynb @@ -6,15 +6,14 @@ "source": [ "#A Brief Guide of Using Hybrid Monte Carlo for Gaussian Process Models\n", "\n", - "### Zhenwen Dai and Mu Niu, November 2014" + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/sampling_hmc.ipynb)\n", + "### Zhenwen Dai and Mu Niu, November 2014\n" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -40,22 +39,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's make some synthetic data\n", "x = np.linspace(0.,2*np.pi,100)[:,None]\n", @@ -72,31 +58,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: reconstraining parameters GP_regression.rbf.lengthscale\n", - "WARNING: reconstraining parameters GP_regression.rbf.variance\n", - "WARNING: reconstraining parameters GP_regression.Gaussian_noise.variance\n" - ] - }, - { - "data": { - "image/png": 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RV1Xm5d+vuYDHN33Ab556mwOxcnYdivHpq/45o/cyVz4bIntkYmgxKA0NDaxY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\nj/w/1eY1Z4TzGkIAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Make a GP regression model\n", "m = GPy.models.GPRegression(x,y)\n", @@ -123,10 +87,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "hmc = GPy.inference.mcmc.HMC(m,stepsize=5e-2)\n", @@ -143,34 +105,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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FDzyzNLuAS5J7BaetWOAeD7BrV/NcjxGNio7TTEGI+UyazeFCYUZBdXXq57Cib19g8GC6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\n9VUqN3QDPqHz9xsZMqyM1KumG5Jw4ObnMf6M8Yl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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "labels = ['kern variance', 'kern lengthscale','noise variance']\n", "samples = s[300:] # cut out the burn-in period\n", @@ -222,32 +146,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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mYFaUm58fTicyOmunS/pEjYH5eTnO9bV1atzpwdFTpwaDpQsL7rOctpmgtDM+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\nqw1bRkoa6uZk1N0xt2H2X6qzjHWF1js9w/TjMZ0bM/VsdDR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rpRCSH4zMEkLygvqEEJIH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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig = figure(figsize=(14,4))\n", "ax = fig.add_subplot(131)\n", @@ -270,39 +171,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : GP regression\n", - "Objective : 39.2970574179\n", - "Number of Parameters : 3\n", - "Number of Optimization Parameters : 3\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1mGP_regression. \u001b[0;0m | value | constraints | priors \n", - " \u001b[1mrbf.variance \u001b[0;0m | 1.41818926221 | +ve | Ga(0.1, 0.1)\n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 1.53693651461 | +ve | Ga(0.1, 0.1)\n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.0920819762721 | +ve | Ga(0.1, 0.1)\n" - ] - }, - { - "data": { - "image/png": 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thqpSJ7sPeJAVtd8Byu6u3Q03LGb137/i1Y92cc+zG7nzyjOYND79Hi6SJGG3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\ndditBox6Xa9BNFVViURlAqE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QjhKOyISiMcIRmWAkRjgSIxSRickKsqwgKyoxWSEcibGp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\nOoSiDQAd8htvMnWB29pPTgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Set the model parameters as the posterior mean\n", "m.kern.variance[:] = samples[:,0].mean()\n", @@ -335,10 +206,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "y_new = np.array([1.5])[:,None]" @@ -353,47 +222,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : inferenceX\n", - "Objective : -12.192425512\n", - "Number of Parameters : 1\n", - "Number of Optimization Parameters : 1\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1minferenceX.\u001b[0;0m | value | constraints | priors\n", - " \u001b[1mlatent mean\u001b[0;0m | [ 2.11646619] | | \n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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LljBjxgx27PiIHTs+Oiog+IuHn2GXtwhJkqi0tPOPP/2W2lJtD4E4NqBos9nS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\nQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x_new,mi = m.infer_newX(y_new)\n", "print(mi)\n", @@ -410,10 +241,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "hmc_new = GPy.inference.mcmc.HMC(mi,stepsize=2e-1)\n", @@ -429,22 +258,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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HkRdJHteiVeSEkKEAMJRSOgkABhJCNpHv51coV9dKRwHrph5CAK65BuDoo+O1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\n4ab5IGRx4XzgKOnfNMnKqLL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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "_ = plot(s_new[:,:])" ] @@ -458,22 +274,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from scipy import stats\n", "samples_new = s_new[:]\n", @@ -501,41 +304,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: reconstraining parameters GP_regression.rbf.variance\n", - "WARNING: reconstraining parameters GP_regression.rbf.lengthscale\n", - "\n", - "Name : GP regression\n", - "Objective : 15.5939533439\n", - "Number of Parameters : 3\n", - "Number of Optimization Parameters : 3\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1mGP_regression. \u001b[0;0m | value | constraints | priors \n", - " \u001b[1mrbf.variance \u001b[0;0m | 25.3995048123 | +ve | Ga(4.2, 0.17)\n", - " \u001b[1mrbf.lengthscale \u001b[0;0m | 152.045313268 | +ve | Ga(7.2, 0.06)\n", - " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.0485064747583 | +ve | \n" - ] - }, - { - "data": { - "image/png": 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KHh7f+C6xpAnArDNGc/38s5g+IVSUMvQkkUyhHYfKgIdRtUEZ3i9KUmt7kqOR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\ncbsNNj6/nksvuYT6USP44zN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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "m = GPy.examples.regression.olympic_marathon_men()\n", "#\n", @@ -561,22 +332,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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2+CJ0nPXOvTtDcce8dp+eX7p5qecc3z986GgVRA5/xZYVRmFmw3I0vDVEUHw2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\nbEiJ+KaOZVm9Sk7n4SbQHGEWEpls99139XLSV17xnhMHFf3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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# initialise hmc\n", "hmc = GPy.inference.mcmc.HMC(m,stepsize=2e-1)\n", @@ -592,30 +350,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 2.34246741e+01 1.25455721e+02 5.32988933e-02]\n", - "[ 1.06955975e+01 3.11140579e+01 1.72613018e-02]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(t.mean(axis=0))\n", "print(t.std(axis=0))\n", @@ -632,10 +369,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import seaborn as sns, pandas as pd\n", @@ -644,22 +379,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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hq9XO2by8PHbs2AHA4sWLGTp0aNO2zZs38+CDD2K32xk9ejQzZswAYPny5bz3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\nl0QilBK6BVl1UBzAkCGNAPzzn9ZMhLE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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = pd.DataFrame(t, columns=m.parameter_names_flat())\n", "var_col = next(c for c in df.columns if c.endswith('rbf.variance'))\n", @@ -670,22 +392,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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YLeEtzjkcDrjhepAMK7GfaoJxWtChpIZjsfjYUbv7aDzR3acKsv22ojCmbQvk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OELYpJIJFZ6CfYdXMH2uRZaDR+y0JCcKJkWVmKs9FJcfcUE4jTHcjcxkGoOzxFQpr7UbnoEFlAa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\nSYkyHWpk1JAr4ymSEWEYX79+rUZPV0MJebwQEpdrpwo0nEXBN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"#### 29th September 2014 Neil Lawrence (added sub-titles, notes and some references)." + "#### 29th September 2014 Neil Lawrence (added sub-titles, notes and some references).\n" ] }, { @@ -19,9 +20,8 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "collapsed": false, "slideshow": { "slide_type": "slide" } @@ -46,10 +46,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "N = 50\n", @@ -71,1478 +69,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Name : GP regression\n", - "Objective : 50.0860723468\n", - "Number of Parameters : 3\n", - "Number of Optimization Parameters : 3\n", - "Updates : True\n", - "Parameters:\n", - " \u001b[1mGP_regression. \u001b[0;0m | value | constraints | priors\n", - 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"metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] } diff --git a/README.md b/README.md index 9ea454d..7fe3a3b 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ We have split the notebooks into groups. [Computational Biology Notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/compbio/index.ipynb) Give some examples of work completed in different projects and implemented in GPy. -[GPy Tutorial Notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb) These notebooks give short tutorials on various features of GPy. [![GPy tutorials](https://github.com/SheffieldML/notebook/actions/workflows/gpy-tutorials.yml/badge.svg)](https://github.com/SheffieldML/notebook/actions/workflows/gpy-tutorials.yml) +[GPy Tutorial Notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb) These notebooks give short tutorials on various features of GPy. [![GPy tutorials](https://github.com/SheffieldML/notebook/actions/workflows/gpy-tutorials.yml/badge.svg)](https://github.com/SheffieldML/notebook/actions/workflows/gpy-tutorials.yml) Each of these tutorials has an Open in Colab badge. Their executed outputs are stripped on commit (`pre-commit install` after [pre-commit](https://pre-commit.com/) is available). [Lab Classes](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/lab_classes/index.ipynb) Gives a summary of notebooks that have been used in lab classes for teaching using our software. They include notebooks for undergraduate, postgraduate and summer school courses on Gaussian processes and machine learning. diff --git a/backlog/documentation/2026-10-24_gpy-colab-badges.md b/backlog/documentation/2026-10-24_gpy-colab-badges.md new file mode 100644 index 0000000..fa7e18e --- /dev/null +++ b/backlog/documentation/2026-10-24_gpy-colab-badges.md @@ -0,0 +1,48 @@ +--- +id: "2026-10-24_gpy-colab-badges" +title: "Add Open in Colab badges to GPy tutorials" +status: "Completed" +priority: "Medium" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "documentation" +related_cips: ["0004"] +owner: "Neil Lawrence" +dependencies: [] +tags: +- backlog +- gpy +- colab +--- + +# Task: Add Open in Colab badges to GPy tutorials + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +Add an Open in Colab badge at the top of each GPy tutorial in this repository +(`GPy/*.ipynb`), opening that file from SheffieldML/notebook on branch `main`. +Also place a badge next to each tutorial link in `GPy/index.ipynb`. + +## Acceptance Criteria + +- [x] Every `GPy/*.ipynb` contains a Colab badge +- [x] `GPy/index.ipynb` lists a Colab badge beside each tutorial link +- [x] Filenames with spaces use `%20` in the Colab URL + +## Implementation Notes + +Use the default branch `main`. Do not invent new titles for code-first +notebooks; insert a leading markdown cell with the badge only. + +## Related + +- CIP: 0004 + +## Progress Updates + +### 2026-10-24 +Task created with CIP-0004. +Badges added on each GPy tutorial and beside index links. diff --git a/backlog/infrastructure/2026-10-24_gpy-nbstripout-pre-commit.md b/backlog/infrastructure/2026-10-24_gpy-nbstripout-pre-commit.md new file mode 100644 index 0000000..a01598e --- /dev/null +++ b/backlog/infrastructure/2026-10-24_gpy-nbstripout-pre-commit.md @@ -0,0 +1,47 @@ +--- +id: "2026-10-24_gpy-nbstripout-pre-commit" +title: "Strip GPy tutorial outputs with pre-commit nbstripout" +status: "Completed" +priority: "High" +created: "2026-10-24" +last_updated: "2026-10-24" +category: "infrastructure" +related_cips: ["0004"] +owner: "Neil Lawrence" +dependencies: [] +tags: +- backlog +- gpy +- pre-commit +--- + +# Task: Strip GPy tutorial outputs with pre-commit nbstripout + +> **Note**: Backlog tasks are DOING the work defined in CIPs (HOW). +> Use `related_cips` to link to CIPs. Don't link directly to requirements (bottom-up pattern). + +## Description + +Add `.pre-commit-config.yaml` running nbstripout 0.9.1 on `GPy/*.ipynb` in +this repository (the GPy tutorials), and strip outputs already present in +those files. + +## Acceptance Criteria + +- [x] `.pre-commit-config.yaml` exists and targets `^GPy/.*\.ipynb$` +- [x] `GPy/*.ipynb` code cells have empty outputs and null execution_count +- [x] README GPy tutorials section mentions `pre-commit install` + +## Implementation Notes + +Does not strip `lab_classes/`, `compbio/`, or `GPy-phil/`. + +## Related + +- CIP: 0004 + +## Progress Updates + +### 2026-10-24 +Task created with CIP-0004. +nbstripout 0.9.1 hook targets `GPy/*.ipynb`; tutorial outputs stripped. diff --git a/cip/cip0001.md b/cip/cip0001.md index befcaf5..951fa08 100644 --- a/cip/cip0001.md +++ b/cip/cip0001.md @@ -97,7 +97,8 @@ the same vintage; they are not this CIP. 1. **Mechanical port first.** Do not rewrite the teaching narrative in the same pass. 2. **Clear outputs** after a successful execute so git diffs stay about source - cells, not huge Plotly blobs. + cells, not huge Plotly blobs. CIP-0004 makes this a pre-commit hook + (`nbstripout` on `GPy/*.ipynb`). 3. **`climin` notebooks** (`SVI.ipynb`, `optimizer-implementation.ipynb`): make the import fail clearly (short markdown + an early cell that states the extra). Do not add climin as a hard dependency of GPy. Skip policy is diff --git a/cip/cip0003.md b/cip/cip0003.md index 83a8465..92c2d5e 100644 --- a/cip/cip0003.md +++ b/cip/cip0003.md @@ -74,8 +74,12 @@ ahead of a green run. (`gpy==1.15.0` once published; until then the `release/1.15.0` branch or #1235 SHA). Do not float on GPy `devel` for the release-gate run. - **Python:** one version GPy 1.15 supports and Actions provides (3.12). -- **Extras:** `gpy[plotting]` plus Jupyter (`nbclient`, `ipykernel`). Optional - `climin` for the two SVI/optimizer notebooks. +- **Extras:** `gpy[plotting]` plus Jupyter (`nbclient`, `ipykernel`), `pods`, + and `climin` so SVI/optimizer notebooks are required. East Timor in + `GPyCrashCourse` is loaded from `lawrennd/datasets_mirror` until + `pods.datasets.east_timor` is on PyPI. A second Actions job installs current + PyPI GPy; it is informational (`continue-on-error`) and is not the 1.15 + release gate. - **Where:** GitHub Actions **in this repository** (the files under test live here). GPy may later add a complementary job that checks out a **pinned SHA of this repo**; that is GPy's CIP, not this one. diff --git a/cip/cip0004.md b/cip/cip0004.md new file mode 100644 index 0000000..912068a --- /dev/null +++ b/cip/cip0004.md @@ -0,0 +1,94 @@ +--- +author: Neil Lawrence +created: "2026-10-24" +id: "0004" +last_updated: "2026-10-24" +status: Implemented +compressed: false +related_requirements: [] +related_cips: +- "0001" +- "0003" +tags: +- cip +- notebooks +- pre-commit +title: Strip GPy tutorial outputs and add Colab badges +--- + +# CIP-0004: Strip GPy tutorial outputs and add Colab badges + +> **Note**: CIPs describe HOW to achieve requirements (WHAT). +> Use `related_requirements` to link to the requirements this CIP implements. + +## Status + +- [x] Proposed - Initial idea documented +- [x] Accepted - Approved, ready to start work +- [x] In Progress - Actively being implemented +- [x] Implemented - Work complete, awaiting verification +- [ ] Closed - Verified and complete +- [ ] Rejected - Will not be implemented +- [ ] Deferred - Postponed + +## Summary + +In **this** repository (`SheffieldML/notebook`), keep the GPy tutorial +notebooks under `GPy/` as source cells: strip executed outputs on commit, and +add an Open in Colab badge on each of those tutorials. This is not a change to +the GPy library. Lab classes, computational biology, and other trees in this +repo are unchanged. + +## Motivation + +Stored outputs hide source-cell diffs when we use these tutorials as a test +surface for a GPy release. Colab badges let a reader open a tutorial from this +repository in the browser. + +## Detailed Description + +**Scope:** `GPy/*.ipynb` in SheffieldML/notebook (the tutorial set). Not +`lab_classes/`, `compbio/`, `GPy-phil/`, or the GPy library repo. + +**nbstripout via pre-commit.** `.pre-commit-config.yaml` runs +[nbstripout](https://github.com/kynan/nbstripout) 0.9.1 on `GPy/*.ipynb`. +Contributors run `pre-commit install` once. + +**Colab badges.** Each tutorial in `GPy/` gets a badge that opens that file +from SheffieldML/notebook on the default branch `main`. `GPy/index.ipynb` +also has a badge next to each tutorial link. + +## Implementation Plan + +1. Add `.pre-commit-config.yaml` with nbstripout on `GPy/*.ipynb`. +2. Strip existing outputs in those tutorials. +3. Insert Colab badges in each `GPy/` tutorial and in `GPy/index.ipynb`. +4. Note the hook and Colab in the README GPy tutorials section. + +## Backward Compatibility + +GitHub / nbviewer views of the tutorials lose stored plots until the reader +runs the notebook. Colab opens `main`, so badges on this feature branch show +published `main` until merge. + +## Testing Strategy + +- After strip, `GPy/*.ipynb` code cells have empty outputs. +- Badge links open SheffieldML/notebook, path `GPy/.ipynb`, branch + `main`. + +## Related Requirements + +None yet. + +## Implementation Status + +- [x] `.pre-commit-config.yaml` with nbstripout on `GPy/*.ipynb` +- [x] Strip current `GPy/*.ipynb` outputs +- [x] Colab badges on each GPy tutorial and in `GPy/index.ipynb` +- [x] README note + +## References + +- Tutorial index: `GPy/index.ipynb` +- nbstripout: https://github.com/kynan/nbstripout diff --git a/scripts/run_gpy_tutorials.py b/scripts/run_gpy_tutorials.py index bdb25c1..2a30d46 100755 --- a/scripts/run_gpy_tutorials.py +++ b/scripts/run_gpy_tutorials.py @@ -76,11 +76,25 @@ def main() -> int: return 1 have_climin = climin_available() + # Import GPy with the notebook directory as cwd so this folder does not shadow the library. + import subprocess + + try: + gpy_info = subprocess.check_output( + [ + sys.executable, + "-c", + "import GPy; print(getattr(GPy, '__version__', '?'), GPy.__file__)", + ], + cwd=str(TUTORIAL_DIR), + text=True, + ).strip() + except Exception as exc: # pragma: no cover - environment misconfigured + gpy_info = f"unavailable ({exc})" + print(f"python: {sys.executable}", flush=True) + print(f"GPy: {gpy_info}", flush=True) print(f"climin installed: {have_climin}", flush=True) - print( - f"GPy pin check happens in the environment; executing {len(notebooks)} notebooks", - flush=True, - ) + print(f"executing {len(notebooks)} notebooks", flush=True) failed: list[str] = [] skipped: list[str] = [] diff --git a/scripts/run_gpy_tutorials.sh b/scripts/run_gpy_tutorials.sh index 5674203..3996e14 100755 --- a/scripts/run_gpy_tutorials.sh +++ b/scripts/run_gpy_tutorials.sh @@ -9,4 +9,6 @@ ROOT="$(cd "$(dirname "$0")/.." && pwd)" export MPLBACKEND="${MPLBACKEND:-Agg}" export PYTHONUNBUFFERED="${PYTHONUNBUFFERED:-1}" -exec python3 "$ROOT/scripts/run_gpy_tutorials.py" "$@" +# Prefer the caller's interpreter so a conda/venv env is used locally. +PYTHON="${PYTHON:-python3}" +exec "$PYTHON" "$ROOT/scripts/run_gpy_tutorials.py" "$@" From 3d6a65863bf689a2a213ae83d9c4a9df979d6643 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 18:02:54 +0100 Subject: [PATCH 7/9] CIP-0003: Run tutorial CI on Python 3.11 and skip pods download prompts. climin 0.1a1 still imports imp, which 3.12 removed. nbclient has no stdin, so the runner authorizes pods dataset downloads before each notebook. --- .github/workflows/gpy-tutorials.yml | 11 +++++++---- cip/cip0003.md | 3 ++- scripts/run_gpy_tutorials.py | 14 +++++++++++++- 3 files changed, 22 insertions(+), 6 deletions(-) diff --git a/.github/workflows/gpy-tutorials.yml b/.github/workflows/gpy-tutorials.yml index 576fed6..eeee5a2 100644 --- a/.github/workflows/gpy-tutorials.yml +++ b/.github/workflows/gpy-tutorials.yml @@ -7,6 +7,9 @@ # East Timor in GPyCrashCourse is loaded from datasets_mirror (PyPI pods 0.1.18 # does not yet ship pods.datasets.east_timor). # +# Python 3.11: climin 0.1a1 still imports the removed stdlib module `imp`. +# pods first-time downloads are authorized in the runner (no stdin in nbclient). +# # climin is installed so SVI.ipynb and optimizer-implementation.ipynb are # required (CIP-0003 skip only applies when the extra is absent). # @@ -48,14 +51,14 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: "3.12" + python-version: "3.11" - name: Install GPy 1.15.0 candidate, plotting, Jupyter run: | python -m pip install --upgrade pip python -m pip install "gpy[plotting] @ git+https://github.com/SheffieldML/GPy.git@refs/pull/1235/head" python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas climin - python -c "import GPy; print('GPy', getattr(GPy, '__version__', '?'))" + python -c "import GPy, climin; print('GPy', getattr(GPy, '__version__', '?')); print('climin', climin.__file__)" - name: Register kernel run: python -m ipykernel install --user --name python3 @@ -88,14 +91,14 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: "3.12" + python-version: "3.11" - name: Install latest PyPI GPy, plotting, Jupyter run: | python -m pip install --upgrade pip python -m pip install "gpy[plotting]" python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas climin - python -c "import GPy; print('GPy', getattr(GPy, '__version__', '?'))" + python -c "import GPy, climin; print('GPy', getattr(GPy, '__version__', '?')); print('climin', climin.__file__)" - name: Register kernel run: python -m ipykernel install --user --name python3 diff --git a/cip/cip0003.md b/cip/cip0003.md index 92c2d5e..4e0e417 100644 --- a/cip/cip0003.md +++ b/cip/cip0003.md @@ -73,7 +73,8 @@ ahead of a green run. - **GPy pin:** install GPy from a version or git SHA recorded in the workflow (`gpy==1.15.0` once published; until then the `release/1.15.0` branch or #1235 SHA). Do not float on GPy `devel` for the release-gate run. -- **Python:** one version GPy 1.15 supports and Actions provides (3.12). +- **Python:** 3.11 (GPy 1.15 supports it; climin 0.1a1 still `import imp`, + which Python 3.12 removed). - **Extras:** `gpy[plotting]` plus Jupyter (`nbclient`, `ipykernel`), `pods`, and `climin` so SVI/optimizer notebooks are required. East Timor in `GPyCrashCourse` is loaded from `lawrennd/datasets_mirror` until diff --git a/scripts/run_gpy_tutorials.py b/scripts/run_gpy_tutorials.py index 2a30d46..64160e4 100755 --- a/scripts/run_gpy_tutorials.py +++ b/scripts/run_gpy_tutorials.py @@ -18,6 +18,16 @@ import nbformat from nbclient import NotebookClient from nbclient.exceptions import CellExecutionError +from nbformat.v4 import new_code_cell + +# pods prompts on stdin for first-time dataset downloads. nbclient has no stdin. +PODS_CI_PREAMBLE = """\ +try: + import pods.access as _pods_access + _pods_access.overide_manual_authorize = True +except ImportError: + pass +""" ROOT = Path(__file__).resolve().parents[1] TUTORIAL_DIR = ROOT / "GPy" @@ -28,13 +38,15 @@ def climin_available() -> bool: try: import climin # noqa: F401 - except ImportError: + except Exception as exc: + print(f"climin import failed: {type(exc).__name__}: {exc}", flush=True) return False return True def execute_notebook(path: Path, timeout: int, output_dir: Path | None) -> None: nb = nbformat.read(path, as_version=4) + nb.cells.insert(0, new_code_cell(PODS_CI_PREAMBLE)) client = NotebookClient( nb, timeout=timeout, From d61530dc3ac20b4ccca0b6fcf21bfb3c9b34dd3b Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 18:28:57 +0100 Subject: [PATCH 8/9] CIP-0001: Use .item() for NumPy 2 when labelling Poisson K. int(K[k]) fails on ndarray values in the 1.15 CI environment. --- GPy/Poisson regression tutorial.ipynb | 776 +++++++++++++------------- 1 file changed, 388 insertions(+), 388 deletions(-) diff --git a/GPy/Poisson regression tutorial.ipynb b/GPy/Poisson regression tutorial.ipynb index 1bd831b..7ed664d 100644 --- a/GPy/Poisson regression tutorial.ipynb +++ b/GPy/Poisson regression tutorial.ipynb @@ -1,390 +1,390 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Count Data Tutorial\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Poisson%20regression%20tutorial.ipynb)\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Count Data Tutorial\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SheffieldML/notebook/blob/main/GPy/Poisson%20regression%20tutorial.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 28th May 2014" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### by Alan Saul, with edits by Neil Lawrence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will look at using a Gaussian Process with a Poisson likelihood to model a response variable that is count data, i.e $ Y \\in \\mathbb{N}^{0} = \\{0, 1, 2, 3, ..\\}$.\n", + "\n", + "This allows us to incorporate an approximate Poisson Process regression model into our Gaussian Process framework, indeed as the Poisson rate parameter, $\\lambda$, becomes large the Poisson Process asymptotically approaches a Gaussian Process. This is important as if your counts are large, the rate, $\\lambda$ at which events is happening is likely to be large, and it may not be required to use the approximation routine used here. Instead it may be more appropriate simply use a Gaussian Process, this will drastically reduce the computation required without a significant loss in accuracy.\n", + "\n", + "The approximation we will use here is known as the Laplace approximation. The Laplace approximation was applied to Gaussian processes for classification by [Williams and Barber, 1999](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=735807&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D735807). More details are given in [Rasmussen and Williams, 2006](http://www.gaussianprocess.org/gpml/), Section 3.4 and Section 5.5.1. It has also been suggested for Gaussian random fields by [Rue, Martino and Chopin, 2009](http://hachamam-inla.googlecode.com/hg/r-inla.org/papers/inla-rss.pdf). The Laplace approximation is a local approximation. It proceeds by finding the mode of the posterior distribution and computing the curvature to find the fit.\n", + "\n", + "First we perform some setup." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "%pylab inline\n", + "import numpy as np\n", + "import scipy as sp\n", + "from scipy import stats\n", + "import pylab as pb" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we show the Poisson distribution for several different rate parameters." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "K = np.arange(0, 30, 1)[:, None]\n", + "rates = np.arange(0, 40, 1)[:, None]\n", + "\n", + "#Make a matrix with PMF for (rate x count) combinations\n", + "rates_count = np.array([sp.stats.poisson(r).pmf(K) for r in rates]).reshape(rates.shape[0], K.shape[0])\n", + "\n", + "rates_plot = [1,3,8,20]\n", + "\n", + "#Plot each rate as a function of counts K\n", + "for r in rates_plot:\n", + " pb.plot(K, rates_count[r, :], label='rate = {}'.format(float(rates[r].item())))\n", + "\n", + "pb.title('Poisson probability mass distribution for different rates')\n", + "pb.ylabel('PMF')\n", + "pb.xlabel('Counts')\n", + "pb.legend()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the rate increases, the Poisson distribution over discrete values begins to take on the form of a Gaussian density, over continuous values, as can be seen below." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "Ks = [1,5,10,25]\n", + "pb.figure()\n", + "for k in Ks:\n", + " pb.plot(rates, rates_count[:, k], label='K = {}'.format(int(K[k].item())))\n", + "\n", + "pb.xlabel('rate')\n", + "pb.ylabel('PMF')\n", + "pb.legend()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variance of the Poisson distribution is equal to its mean. The Poisson only has support for non-negative integers, whereas a Gaussian has support over all real numbers (including negatives). This can complicate matters because if we place a Gaussian process prior directly over the rate of the Poisson, then the implication is that the rate can go negative. In practice we normally consider a Gaussian process over the logarithm of the rate. This is known as a log Gaussian process. It is formed by combining a logarithmic link function with a Gaussian model. " + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "small_rate = 2\n", + "K = np.arange(0, 20, 1)[:, None]\n", + "Kcont = np.linspace(0, 20, 100)[:, None]\n", + "gauss20 = sp.stats.norm(loc=small_rate, scale=np.sqrt(small_rate)).pdf(Kcont)\n", + "poisson20 = sp.stats.poisson(small_rate).pmf(K)\n", + "pb.plot(K, poisson20, label='Poisson')\n", + "pb.plot(Kcont, gauss20, label='Gaussian')\n", + "pb.title('Gaussian and Poisson small rate')\n", + "pb.ylabel('PDF/PMF')\n", + "pb.xlabel('Counts')\n", + "pb.legend()\n", + "\n", + "pb.figure()\n", + "large_rate = 40\n", + "K = np.arange(0, 80, 1)[:, None]\n", + "Kcont = np.linspace(0, 80, 200)[:, None]\n", + "gauss40 = sp.stats.norm(loc=large_rate, scale=np.sqrt(large_rate)).pdf(Kcont)\n", + "poisson40 = sp.stats.poisson(large_rate).pmf(K)\n", + "pb.plot(K, poisson40, label='Poisson')\n", + "pb.plot(Kcont, gauss40, label='Gaussian')\n", + "pb.title('Gaussian and Poisson large rate')\n", + "pb.ylabel('PDF/PMF')\n", + "pb.xlabel('Counts')\n", + "pb.legend()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "rates = np.arange(0, 20, 0.01)[:, None]\n", + "small_counts = 1\n", + "gauss3 = sp.stats.norm(loc=rates, scale=np.sqrt(rates)).pdf(small_counts)\n", + "#gauss3 = np.array([sp.stats.norm(loc=r, scale=np.sqrt(r)).pdf(small_counts) for r in rates])\n", + "poisson3 = sp.stats.poisson(rates).pmf(small_counts)\n", + "pb.plot(rates, poisson3, label='Poisson')\n", + "pb.plot(rates, gauss3, label='Gaussian')\n", + "pb.title('Gaussian and Poisson small count (K = {})'.format(small_counts))\n", + "pb.ylabel('PDF/PMF')\n", + "pb.xlabel('Rate')\n", + "pb.legend()\n", + "\n", + "pb.figure()\n", + "rates = np.arange(0, 100, 0.01)[:, None]\n", + "large_counts = 40\n", + "gauss40 = sp.stats.norm(loc=rates, scale=np.sqrt(rates)).pdf(large_counts)\n", + "poisson40 = sp.stats.poisson(rates).pmf(large_counts)\n", + "pb.plot(rates, poisson40, label='Poisson')\n", + "pb.plot(rates, gauss40, label='Gaussian')\n", + "pb.title('Gaussian and Poisson large count (K = {})'.format(large_counts))\n", + "pb.ylabel('PDF/PMF')\n", + "pb.xlabel('Rate')\n", + "pb.legend()\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the counts are believed to follow a Poisson distribution and are very large, then the underlying rate is also likely to be large and we might reasonably approximate the Poisson likelihood with a Gaussian likelihood by matching the mean and variance to the observed counts. \n", + "\n", + "If, however, counts are relatively small, and we wish to fit Poisson process regression into a Gaussian Process framework, we may need a more involved approximation to the posterior.\n", + "\n", + "The approximation used in this tutorial is called the Laplace approximation, it uses the location of the mode of the marginal distribution and the curvature of this mode (the hessian) to find a more appropriate Gaussian posterior approximation. As we shall see, in practice this is quite effective, and future tutorials will show us how it can be used to make Gaussian Process approximations to a range of different likelihoods, rather than just the Poisson, with relative ease.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Approximate Poisson Process Regression\n", + "======================================\n", + "\n", + "Poisson process regression, contrasting to the above example, allows the rate to change, for example over time. That is the intensity of the counts can change, initially perhaps the data suggests there were few counts due to a low number of counts, but later this intensity is assumed to rise as we start observing larger more frequent counts. \n", + "\n", + "An example of data that might follow this change in 'intensity' is the times at which buses come throughout the day, we will choose this as our toy modelling example.\n", + "\n", + "First we will generate some toy data, we will say that there are very few buses overnight, thus low counts, then during the morning rush-hour the number of observed counts rises, it then reduces and plateus until the evening rush-hour, and then finally as the evening draws on we observe again a reduced number " + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "X = np.linspace(0,23.50,48)[:, None]\n", + "intensities = 4 + 3*np.sin(10 + 0.6*X) + np.sin(0.1*X)\n", + "pb.plot(X, intensities)\n", + "pb.title('Real underlying intensities (rate at which buses are issued)')\n", + "\n", + "Y = np.random.poisson(intensities.ravel())[:, None]\n", + "pb.figure()\n", + "pb.bar(X.ravel(), Y.ravel(), width=0.5)\n", + "pb.xlabel('Time')\n", + "pb.ylabel('Buses in half an hour')\n", + "pb.title('Observed (noisey) counts of buses throughout day')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now fit a Gaussian Process to this data using a Poisson likelihood and a Laplace approximation using GPy, as we shall see this is relatively simple, however to use Gaussian Processes we must make some assumptions about the intensity, we do this by defining a kernel." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "import GPy\n", + "kernel = GPy.kern.RBF(1, variance=1.0, lengthscale=1.0)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By defining a radial basis function (RBF) kernel (also known as a exponentiated quadratic, squared exponential or Gaussian kernel), we are making *a priori* a smoothness assumption about the intensity parameter throughout time in this case. This is a very general assumption that is required in many learning algorithms, that data 'close' to one another shares similar properties. In this case we are saying that times that are close to one another have a similar number of buses arriving. \n", + "\n", + "The measure of closeness is defined by the lengthscale (or timescale) parameter, $\\ell$, initially we set this to be 2.0, roughly speaking this says that as we have moved two time-steps (1 hour in this case) the intensity shares almost nothing with the intensity a hour previous.\n", + "\n", + "The variance parameter describes our prior assumptions about the scale of the function being learnt. Both of these parameters although initialised here are usually optimized to find the most appropriate parameters, as such we set them to something that seems sensible but do not need to worry about having them absolutely correct.\n", + "\n", + "There are a variety of likelihoods implemented in GPy, type 'GPy.likelihoods.' then press tab to list them.\n", + "\n", + "There is also a number of alternative inference methods implemented, a subject for another tutorial, however it is worth noting that not all likelihoods are yet compatible with all inference methods. \n", + "\n", + "We now choose the type of likelihood and inference method we would like to use, in this case the Poisson likelihood and Laplace inference method. " + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "poisson_likelihood = GPy.likelihoods.Poisson()\n", + "laplace_inf = GPy.inference.latent_function_inference.Laplace()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we are ready to create our model Gaussian Process model" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "m = GPy.core.GP(X=X, Y=Y, likelihood=poisson_likelihood, inference_method=laplace_inf, kernel=kernel)\n", + "print(m)\n", + "\n", + "#Predictions of counts of buses, including noise arising from Poisson noise\n", + "m.plot()\n", + "pb.xlabel('Time')\n", + "pb.ylabel('Buses counts per 30 minutes')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the model as it stands is not all that great, the lengthscale parameter is soo small. We noted before that the kernels hyperparameters we chose were not the correct ones, only an initial guess. Next we try maximizing the likelihood of the model with respect to the parameters. Optimization within GPy is straightforward." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "m.optimize()\n", + "print(m)\n", + "\n", + "#Predictions of counts of buses, including noise arising from Poisson noise\n", + "m.plot()\n", + "pb.xlabel('Time')\n", + "pb.ylabel('Buses counts per 30 minutes')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we should have a clearly better fit to the data, we have made predictions of the number of counts we would see throughout the day with our model and it seems to fit the training data we have provided quite well.\n", + "\n", + "To get a better indication of the intensity we have infered we can plot the latent function $f$ of the model, this is the models prediction, before we have added Poisson noise to it. In fact the model is modelling the log intensity, this is what is displayed below" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "#Posterior distribution of the intensity of buses, and true intensity in red\n", + "m.plot_f()\n", + "pb.plot(X, np.log(intensities), '--r', linewidth=2)\n", + "pb.title('Real log intensity vs posterior belief')\n", + "pb.xlabel('Time')\n", + "pb.ylabel('Log intensity')\n", + "plt.ylim(-2,3)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the predictions from the model is straightforward with the plot functionality." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "pred_points = np.linspace(0,24,100)[:, None]\n", + "#Predictive GP for log intensity mean and variance\n", + "f_mean, f_var = m._raw_predict(pred_points)\n", + "f_upper, f_lower = f_mean + 2*np.sqrt(f_var), f_mean - 2.*np.sqrt(f_var)\n", + "pb.plot(X, intensities, '--r', linewidth=2, label='true intensity')\n", + "#Plotting Y on an exponential scale as we are now looking at intensity rather than log intensity\n", + "from GPy.plotting import Tango\n", + "Tango.reset()\n", + "b = Tango.nextMedium()\n", + "pb.plot(pred_points, np.exp(f_mean), color=b, lw=2)\n", + "pb.fill_between(pred_points[:,0], np.exp(f_lower[:,0]), np.exp(f_upper[:,0]), color=b, alpha=.1)\n", + "pb.title('Real intensity vs posterior belief')\n", + "pb.xlabel('Time')\n", + "pb.ylabel('intensity')\n", + "pb.legend()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 28th May 2014" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### by Alan Saul, with edits by Neil Lawrence" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we will look at using a Gaussian Process with a Poisson likelihood to model a response variable that is count data, i.e $ Y \\in \\mathbb{N}^{0} = \\{0, 1, 2, 3, ..\\}$.\n", - "\n", - "This allows us to incorporate an approximate Poisson Process regression model into our Gaussian Process framework, indeed as the Poisson rate parameter, $\\lambda$, becomes large the Poisson Process asymptotically approaches a Gaussian Process. This is important as if your counts are large, the rate, $\\lambda$ at which events is happening is likely to be large, and it may not be required to use the approximation routine used here. Instead it may be more appropriate simply use a Gaussian Process, this will drastically reduce the computation required without a significant loss in accuracy.\n", - "\n", - "The approximation we will use here is known as the Laplace approximation. The Laplace approximation was applied to Gaussian processes for classification by [Williams and Barber, 1999](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=735807&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D735807). More details are given in [Rasmussen and Williams, 2006](http://www.gaussianprocess.org/gpml/), Section 3.4 and Section 5.5.1. It has also been suggested for Gaussian random fields by [Rue, Martino and Chopin, 2009](http://hachamam-inla.googlecode.com/hg/r-inla.org/papers/inla-rss.pdf). The Laplace approximation is a local approximation. It proceeds by finding the mode of the posterior distribution and computing the curvature to find the fit.\n", - "\n", - "First we perform some setup." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pylab inline\n", - "import numpy as np\n", - "import scipy as sp\n", - "from scipy import stats\n", - "import pylab as pb" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we show the Poisson distribution for several different rate parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "K = np.arange(0, 30, 1)[:, None]\n", - "rates = np.arange(0, 40, 1)[:, None]\n", - "\n", - "#Make a matrix with PMF for (rate x count) combinations\n", - "rates_count = np.array([sp.stats.poisson(r).pmf(K) for r in rates]).reshape(rates.shape[0], K.shape[0])\n", - "\n", - "rates_plot = [1,3,8,20]\n", - "\n", - "#Plot each rate as a function of counts K\n", - "for r in rates_plot:\n", - " pb.plot(K, rates_count[r, :], label='rate = {}'.format(float(rates[r].item())))\n", - "\n", - "pb.title('Poisson probability mass distribution for different rates')\n", - "pb.ylabel('PMF')\n", - "pb.xlabel('Counts')\n", - "pb.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As the rate increases, the Poisson distribution over discrete values begins to take on the form of a Gaussian density, over continuous values, as can be seen below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Ks = [1,5,10,25]\n", - "pb.figure()\n", - "for k in Ks:\n", - " pb.plot(rates, rates_count[:, k], label='K = {}'.format(int(K[k])))\n", - "\n", - "pb.xlabel('rate')\n", - "pb.ylabel('PMF')\n", - "pb.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The variance of the Poisson distribution is equal to its mean. The Poisson only has support for non-negative integers, whereas a Gaussian has support over all real numbers (including negatives). This can complicate matters because if we place a Gaussian process prior directly over the rate of the Poisson, then the implication is that the rate can go negative. In practice we normally consider a Gaussian process over the logarithm of the rate. This is known as a log Gaussian process. It is formed by combining a logarithmic link function with a Gaussian model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "small_rate = 2\n", - "K = np.arange(0, 20, 1)[:, None]\n", - "Kcont = np.linspace(0, 20, 100)[:, None]\n", - "gauss20 = sp.stats.norm(loc=small_rate, scale=np.sqrt(small_rate)).pdf(Kcont)\n", - "poisson20 = sp.stats.poisson(small_rate).pmf(K)\n", - "pb.plot(K, poisson20, label='Poisson')\n", - "pb.plot(Kcont, gauss20, label='Gaussian')\n", - "pb.title('Gaussian and Poisson small rate')\n", - "pb.ylabel('PDF/PMF')\n", - "pb.xlabel('Counts')\n", - "pb.legend()\n", - "\n", - "pb.figure()\n", - "large_rate = 40\n", - "K = np.arange(0, 80, 1)[:, None]\n", - "Kcont = np.linspace(0, 80, 200)[:, None]\n", - "gauss40 = sp.stats.norm(loc=large_rate, scale=np.sqrt(large_rate)).pdf(Kcont)\n", - "poisson40 = sp.stats.poisson(large_rate).pmf(K)\n", - "pb.plot(K, poisson40, label='Poisson')\n", - "pb.plot(Kcont, gauss40, label='Gaussian')\n", - "pb.title('Gaussian and Poisson large rate')\n", - "pb.ylabel('PDF/PMF')\n", - "pb.xlabel('Counts')\n", - "pb.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rates = np.arange(0, 20, 0.01)[:, None]\n", - "small_counts = 1\n", - "gauss3 = sp.stats.norm(loc=rates, scale=np.sqrt(rates)).pdf(small_counts)\n", - "#gauss3 = np.array([sp.stats.norm(loc=r, scale=np.sqrt(r)).pdf(small_counts) for r in rates])\n", - "poisson3 = sp.stats.poisson(rates).pmf(small_counts)\n", - "pb.plot(rates, poisson3, label='Poisson')\n", - "pb.plot(rates, gauss3, label='Gaussian')\n", - "pb.title('Gaussian and Poisson small count (K = {})'.format(small_counts))\n", - "pb.ylabel('PDF/PMF')\n", - "pb.xlabel('Rate')\n", - "pb.legend()\n", - "\n", - "pb.figure()\n", - "rates = np.arange(0, 100, 0.01)[:, None]\n", - "large_counts = 40\n", - "gauss40 = sp.stats.norm(loc=rates, scale=np.sqrt(rates)).pdf(large_counts)\n", - "poisson40 = sp.stats.poisson(rates).pmf(large_counts)\n", - "pb.plot(rates, poisson40, label='Poisson')\n", - "pb.plot(rates, gauss40, label='Gaussian')\n", - "pb.title('Gaussian and Poisson large count (K = {})'.format(large_counts))\n", - "pb.ylabel('PDF/PMF')\n", - "pb.xlabel('Rate')\n", - "pb.legend()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the counts are believed to follow a Poisson distribution and are very large, then the underlying rate is also likely to be large and we might reasonably approximate the Poisson likelihood with a Gaussian likelihood by matching the mean and variance to the observed counts. \n", - "\n", - "If, however, counts are relatively small, and we wish to fit Poisson process regression into a Gaussian Process framework, we may need a more involved approximation to the posterior.\n", - "\n", - "The approximation used in this tutorial is called the Laplace approximation, it uses the location of the mode of the marginal distribution and the curvature of this mode (the hessian) to find a more appropriate Gaussian posterior approximation. As we shall see, in practice this is quite effective, and future tutorials will show us how it can be used to make Gaussian Process approximations to a range of different likelihoods, rather than just the Poisson, with relative ease.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Approximate Poisson Process Regression\n", - "======================================\n", - "\n", - "Poisson process regression, contrasting to the above example, allows the rate to change, for example over time. That is the intensity of the counts can change, initially perhaps the data suggests there were few counts due to a low number of counts, but later this intensity is assumed to rise as we start observing larger more frequent counts. \n", - "\n", - "An example of data that might follow this change in 'intensity' is the times at which buses come throughout the day, we will choose this as our toy modelling example.\n", - "\n", - "First we will generate some toy data, we will say that there are very few buses overnight, thus low counts, then during the morning rush-hour the number of observed counts rises, it then reduces and plateus until the evening rush-hour, and then finally as the evening draws on we observe again a reduced number " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X = np.linspace(0,23.50,48)[:, None]\n", - "intensities = 4 + 3*np.sin(10 + 0.6*X) + np.sin(0.1*X)\n", - "pb.plot(X, intensities)\n", - "pb.title('Real underlying intensities (rate at which buses are issued)')\n", - "\n", - "Y = np.random.poisson(intensities.ravel())[:, None]\n", - "pb.figure()\n", - "pb.bar(X.ravel(), Y.ravel(), width=0.5)\n", - "pb.xlabel('Time')\n", - "pb.ylabel('Buses in half an hour')\n", - "pb.title('Observed (noisey) counts of buses throughout day')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now fit a Gaussian Process to this data using a Poisson likelihood and a Laplace approximation using GPy, as we shall see this is relatively simple, however to use Gaussian Processes we must make some assumptions about the intensity, we do this by defining a kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import GPy\n", - "kernel = GPy.kern.RBF(1, variance=1.0, lengthscale=1.0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By defining a radial basis function (RBF) kernel (also known as a exponentiated quadratic, squared exponential or Gaussian kernel), we are making *a priori* a smoothness assumption about the intensity parameter throughout time in this case. This is a very general assumption that is required in many learning algorithms, that data 'close' to one another shares similar properties. In this case we are saying that times that are close to one another have a similar number of buses arriving. \n", - "\n", - "The measure of closeness is defined by the lengthscale (or timescale) parameter, $\\ell$, initially we set this to be 2.0, roughly speaking this says that as we have moved two time-steps (1 hour in this case) the intensity shares almost nothing with the intensity a hour previous.\n", - "\n", - "The variance parameter describes our prior assumptions about the scale of the function being learnt. Both of these parameters although initialised here are usually optimized to find the most appropriate parameters, as such we set them to something that seems sensible but do not need to worry about having them absolutely correct.\n", - "\n", - "There are a variety of likelihoods implemented in GPy, type 'GPy.likelihoods.' then press tab to list them.\n", - "\n", - "There is also a number of alternative inference methods implemented, a subject for another tutorial, however it is worth noting that not all likelihoods are yet compatible with all inference methods. \n", - "\n", - "We now choose the type of likelihood and inference method we would like to use, in this case the Poisson likelihood and Laplace inference method. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "poisson_likelihood = GPy.likelihoods.Poisson()\n", - "laplace_inf = GPy.inference.latent_function_inference.Laplace()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally we are ready to create our model Gaussian Process model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = GPy.core.GP(X=X, Y=Y, likelihood=poisson_likelihood, inference_method=laplace_inf, kernel=kernel)\n", - "print(m)\n", - "\n", - "#Predictions of counts of buses, including noise arising from Poisson noise\n", - "m.plot()\n", - "pb.xlabel('Time')\n", - "pb.ylabel('Buses counts per 30 minutes')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the model as it stands is not all that great, the lengthscale parameter is soo small. We noted before that the kernels hyperparameters we chose were not the correct ones, only an initial guess. Next we try maximizing the likelihood of the model with respect to the parameters. Optimization within GPy is straightforward." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.optimize()\n", - "print(m)\n", - "\n", - "#Predictions of counts of buses, including noise arising from Poisson noise\n", - "m.plot()\n", - "pb.xlabel('Time')\n", - "pb.ylabel('Buses counts per 30 minutes')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we should have a clearly better fit to the data, we have made predictions of the number of counts we would see throughout the day with our model and it seems to fit the training data we have provided quite well.\n", - "\n", - "To get a better indication of the intensity we have infered we can plot the latent function $f$ of the model, this is the models prediction, before we have added Poisson noise to it. In fact the model is modelling the log intensity, this is what is displayed below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Posterior distribution of the intensity of buses, and true intensity in red\n", - "m.plot_f()\n", - "pb.plot(X, np.log(intensities), '--r', linewidth=2)\n", - "pb.title('Real log intensity vs posterior belief')\n", - "pb.xlabel('Time')\n", - "pb.ylabel('Log intensity')\n", - "plt.ylim(-2,3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get the predictions from the model is straightforward with the plot functionality." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred_points = np.linspace(0,24,100)[:, None]\n", - "#Predictive GP for log intensity mean and variance\n", - "f_mean, f_var = m._raw_predict(pred_points)\n", - "f_upper, f_lower = f_mean + 2*np.sqrt(f_var), f_mean - 2.*np.sqrt(f_var)\n", - "pb.plot(X, intensities, '--r', linewidth=2, label='true intensity')\n", - "#Plotting Y on an exponential scale as we are now looking at intensity rather than log intensity\n", - "from GPy.plotting import Tango\n", - "Tango.reset()\n", - "b = Tango.nextMedium()\n", - "pb.plot(pred_points, np.exp(f_mean), color=b, lw=2)\n", - "pb.fill_between(pred_points[:,0], np.exp(f_lower[:,0]), np.exp(f_upper[:,0]), color=b, alpha=.1)\n", - "pb.title('Real intensity vs posterior belief')\n", - "pb.xlabel('Time')\n", - "pb.ylabel('intensity')\n", - "pb.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 72e8165a9568680faa259643da171096d73252bc Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sat, 24 Oct 2026 18:42:16 +0100 Subject: [PATCH 9/9] CIP-0003: Drop execute-latest until GPy 1.15 is on PyPI. The 1.14.2 Plotly matplotlib path cannot pass these tutorials, and continue-on-error still shows as a failed GitHub check. --- .github/workflows/gpy-tutorials.yml | 45 ++--------------------------- cip/cip0003.md | 8 +++-- 2 files changed, 8 insertions(+), 45 deletions(-) diff --git a/.github/workflows/gpy-tutorials.yml b/.github/workflows/gpy-tutorials.yml index eeee5a2..8cd204c 100644 --- a/.github/workflows/gpy-tutorials.yml +++ b/.github/workflows/gpy-tutorials.yml @@ -13,8 +13,9 @@ # climin is installed so SVI.ipynb and optimizer-implementation.ipynb are # required (CIP-0003 skip only applies when the extra is absent). # -# execute-latest is a second job against current PyPI GPy. It is not the 1.15 -# release gate; keep that pin in execute. +# Do not add a "latest PyPI GPy" job until 1.15.0 is published. Current PyPI +# is 1.14.2; these notebooks target 1.15, and GitHub still paints +# continue-on-error jobs as failing checks. name: GPy tutorials on: @@ -73,43 +74,3 @@ jobs: name: executed-gpy-notebooks path: executed-gpy/ if-no-files-found: ignore - - execute-latest: - # Informational: current PyPI GPy, not the 1.15 release gate. - # continue-on-error keeps a latest-GPy regression from blocking 1.15. - continue-on-error: true - runs-on: ubuntu-latest - timeout-minutes: 90 - env: - MPLBACKEND: Agg - MPLCONFIGDIR: ${{ github.workspace }}/.mplconfig - PYTHONUNBUFFERED: "1" - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Install latest PyPI GPy, plotting, Jupyter - run: | - python -m pip install --upgrade pip - python -m pip install "gpy[plotting]" - python -m pip install nbclient ipykernel jupyter_client ipywidgets pods seaborn pandas climin - python -c "import GPy, climin; print('GPy', getattr(GPy, '__version__', '?')); print('climin', climin.__file__)" - - - name: Register kernel - run: python -m ipykernel install --user --name python3 - - - name: Execute GPy tutorials - run: bash scripts/run_gpy_tutorials.sh --output-dir executed-gpy-latest - - - name: Upload executed notebooks - if: failure() - uses: actions/upload-artifact@v4 - with: - name: executed-gpy-latest-notebooks - path: executed-gpy-latest/ - if-no-files-found: ignore diff --git a/cip/cip0003.md b/cip/cip0003.md index 4e0e417..3abe508 100644 --- a/cip/cip0003.md +++ b/cip/cip0003.md @@ -78,9 +78,11 @@ ahead of a green run. - **Extras:** `gpy[plotting]` plus Jupyter (`nbclient`, `ipykernel`), `pods`, and `climin` so SVI/optimizer notebooks are required. East Timor in `GPyCrashCourse` is loaded from `lawrennd/datasets_mirror` until - `pods.datasets.east_timor` is on PyPI. A second Actions job installs current - PyPI GPy; it is informational (`continue-on-error`) and is not the 1.15 - release gate. + `pods.datasets.east_timor` is on PyPI. Do not run a second job against + current PyPI GPy until 1.15.0 is published: 1.14.2 still uses the Plotly + matplotlib path these tutorials left, and `continue-on-error` still shows + as a failed GitHub check. After the release, switch the pin to + `gpy==1.15.0` (or add a latest job that is the same install). - **Where:** GitHub Actions **in this repository** (the files under test live here). GPy may later add a complementary job that checks out a **pinned SHA of this repo**; that is GPy's CIP, not this one.