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linear_models | ||
decomposition | ||
metrics | ||
neighbors | ||
datasets | ||
utils |
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Neighbors | ||
========= | ||
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.. automodule:: skmatter.neighbors | ||
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.. _sparse-kde-api: | ||
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Sparse Kernel Density Estimation | ||
-------------------------------- | ||
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.. autoclass:: skmatter.neighbors.SparseKDE |
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examples/selection/index | ||
examples/regression/index | ||
examples/reconstruction/index | ||
examples/neighbors/index |
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Neighbors | ||
========= |
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#!/usr/bin/env python | ||
# coding: utf-8 | ||
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""" | ||
Sparse KDE examples | ||
==================================== | ||
We start by importing our modules | ||
""" | ||
# %% | ||
# | ||
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import numpy as np | ||
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from skmatter.datasets import load_roy_dataset # TODO add a dataset | ||
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# %% | ||
# | ||
# After importing we start with the actual calculations and examples. | ||
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roy_data = load_roy_dataset() | ||
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a = np.array(2) | ||
print(a) |
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from ._sparsekde import SparseKDE, covariance, effdim, oas | ||
""" | ||
Some introduction what the neighbors directory is about. | ||
Get some inspiration form sklearn. | ||
__all__ = ["SparseKDE", "covariance", "effdim", "oas"] | ||
* :ref:`sparse-kde-api` TODO | ||
""" | ||
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from ._sparsekde import SparseKDE | ||
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__all__ = ["SparseKDE"] |
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