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upgrade version by xadupre · Pull Request #54 · sdpython/mlstatpy · GitHub

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2 changes: 1 addition & 1 deletion .github/workflows/documentation.yml
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Expand Up @@ -21,7 +21,7 @@ jobs:

- uses: actions/setup-python@v4
with:
python-version: '3.11'
python-version: '3.12'

- uses: tlylt/install-graphviz@v1

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30 changes: 0 additions & 30 deletions .local.jenkins.lin.yml

This file was deleted.

1 change: 0 additions & 1 deletion MANIFEST.in
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
recursive-include onnx_extended *.c *.cpp *.h *.pyx *.pxd *.pxi *.py
include pyproject.toml
include MANIFEST.in
include setup.cfg
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8 changes: 6 additions & 2 deletions _doc/conf.py
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Expand Up @@ -24,7 +24,6 @@
"sphinx_runpython.epkg",
"sphinx_runpython.gdot",
"sphinx_runpython.runpython",
"sphinxcontrib.blockdiag",
"matplotlib.sphinxext.plot_directive",
]

Expand Down Expand Up @@ -73,7 +72,7 @@
"""

# The following is used by sphinx.ext.linkcode to provide links to github
linkcode_resolve = make_linkcode_resolve(
_linkcode_resolve = make_linkcode_resolve(
"mlstatpy",
(
"https://github.com/sdpython/mlstatpy/"
Expand All @@ -82,6 +81,11 @@
),
)


def linkcode_resolve(domain, info):
return _linkcode_resolve(domain, info)


latex_elements = {
"papersize": "a4",
"pointsize": "10pt",
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3 changes: 2 additions & 1 deletion _doc/index.rst
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Expand Up @@ -10,7 +10,7 @@ Les maths d'abord, la programmation ensuite
Le livre `The Elements of Statistical Learning <http://statweb.stanford.edu/~tibs/ElemStatLearn/>`_
est considéré comme la bible en matière de machine learning. Ce site aborde des sujets connexes.
Le site est aussi disponible (format brut de fonderie) sur
`GitHub/mlstatpy <https://github.com/sdpython/mlstatpy>`_ |gitlogo|.
`github/mlstatpy <https://github.com/sdpython/mlstatpy>`_ |gitlogo|.

.. toctree::
:maxdepth: 1
Expand Down Expand Up @@ -78,4 +78,5 @@ Xavier Dupré
Older versions
++++++++++++++

* `0.5.0 <../v0.5.0/index.html>`_
* `0.4.0 <../v0.4.0/index.html>`_
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Expand Up @@ -940,7 +940,7 @@
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": null,
"metadata": {
"scrolled": false
},
Expand All @@ -962,7 +962,7 @@
"\n",
"def pairplot_cross_val(data, model=None, ax=None, **params):\n",
" if ax is None:\n",
" fig, ax = plt.subplots(\n",
" _fig, ax = plt.subplots(\n",
" data.shape[1], data.shape[1], figsize=params.get(\"figsize\", (10, 10))\n",
" )\n",
" if \"figsize\" in params:\n",
Expand Down Expand Up @@ -3040,4 +3040,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
2 changes: 1 addition & 1 deletion _doc/notebooks/dsgarden/discret_gradient.ipynb
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Expand Up @@ -3910,4 +3910,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
2 changes: 1 addition & 1 deletion _doc/notebooks/dsgarden/quantization_f8.ipynb
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Expand Up @@ -833,4 +833,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
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Expand Up @@ -2385,4 +2385,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
2 changes: 1 addition & 1 deletion _doc/notebooks/dsgarden/split_train_test.ipynb
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Expand Up @@ -1348,4 +1348,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
2 changes: 1 addition & 1 deletion _doc/notebooks/image/segment_detection.ipynb
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Expand Up @@ -470,4 +470,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
4 changes: 2 additions & 2 deletions _doc/notebooks/metric/pvalues_examples.ipynb
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Expand Up @@ -1045,7 +1045,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1087,7 +1087,7 @@
" if ax is None:\n",
" import matplotlib.pyplot as plt\n",
"\n",
" fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
" _fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
"\n",
" smarker = {\n",
" (True, True): \"o-\",\n",
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6 changes: 3 additions & 3 deletions _doc/notebooks/ml/logreg_voronoi.ipynb
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Expand Up @@ -167,7 +167,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -233,7 +233,7 @@
" cmap = plt.cm.tab20\n",
" Z = Z.reshape(xx.shape)\n",
" if ax is None:\n",
" fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3))\n",
" _fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3))\n",
" ax.pcolormesh(xx, yy, Z, cmap=cmap)\n",
"\n",
" # Plot also the training points\n",
Expand Down Expand Up @@ -1963,4 +1963,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
2 changes: 1 addition & 1 deletion _doc/notebooks/ml/neural_tree.ipynb
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Expand Up @@ -1826,4 +1826,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}
6 changes: 3 additions & 3 deletions _doc/notebooks/ml/neural_tree_cost.ipynb
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Expand Up @@ -351,7 +351,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"id": "ecef383a",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -773,7 +773,7 @@
],
"source": [
"from tqdm import tqdm\n",
"from onnx_array_api.ext_test_case import measure_time\n",
"from mlstatpy.ext_test_case import measure_time\n",
"\n",
"data = []\n",
"for d in tqdm(range(2, 10)):\n",
Expand Down Expand Up @@ -870,4 +870,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
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