8000 MAINT deprecate HMMs · mohitsingh1007/scikit-learn@c77255f · GitHub
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MAINT deprecate HMMs
Fixes scikit-learn#2731.
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‎doc/modules/hmm.rst

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@@ -6,6 +6,14 @@ Hidden Markov Models
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.. currentmodule:: sklearn.hmm
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.. warning::
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The `sklearn.hmm` module has now been deprecated due to it no longer matching
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the scope and the API of the project. It is scheduled for removal in the
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0.17 release of the project. Discussions are still underway regarding
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moving this module to a seperate repository.
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`sklearn.hmm` implements the Hidden Markov Models (HMMs).
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The HMM is a generative probabilistic model, in which a sequence of observable
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:math:`\mathbf{X}` variable is generated by a sequence of internal hidden

‎doc/whats_new.rst

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Changelog
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---------
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- :mod:`sklearn.hmm` is deprecated. Its removal is planned
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for the 0.17 release.
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- Use of :class:`covariance.EllipticEnvelop` has now been removed after
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deprecation.
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Please use :class:`covariance.EllipticEnvelope` instead.

‎examples/applications/plot_hmm_stock_analysis.py

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It uses stock price data, which can be obtained from yahoo finance.
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For more information on how to get stock prices with matplotlib, please refer
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to date_demo1.py of matplotlib.
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.. warning::
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The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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"""
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from __future__ import print_function

‎examples/plot_hmm_sampling.py

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between them. We can see that, as specified by our transition matrix,
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there are no transition between component 1 and 3.
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.. warning::
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The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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"""
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print(__doc__)
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‎sklearn/hmm.py

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The :mod:`sklearn.hmm` module implements hidden Markov models.
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**Warning:** :mod:`sklearn.hmm` is orphaned, undocumented and has known
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numerical stability issues. If nobody volunteers to write documentation and
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make it more stable, this module will be removed in version 0.11.
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numerical stability issues. This module will be removed in version 0.17.
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"""
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import string
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decoder_algorithms = ("viterbi", "map")
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@deprecated("WARNING: The HMM module and its functions will be removed in 0.17"
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"as it no longer falls within the project's scope and API.")
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def normalize(A, axis=None):
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""" Normalize the input array so that it sums to 1.
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WARNING: The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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Parameters
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----------
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A: array, shape (n_samples, n_features)
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return A / Asum
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@deprecated("WARNING: The HMM module and its function will be removed in 0.17"
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"as it no longer falls within the project's scope and API.")
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class _BaseHMM(BaseEstimator):
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"""Hidden Markov Model base class.
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See the instance documentation for details specific to a
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particular object.
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.. warning::
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The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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Attributes
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----------
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n_components : int
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self._algorithm = algorithm
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self.random_state = random_state
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@deprecated("HMM.eval was renamed to HMM.score_samples in 0.14 and will be"
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" removed in 0.16.")
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def eval(self, X):
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return self.score_samples(X)
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This class allows for easy evaluation of, sampling from, and
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maximum-likelihood estimation of the parameters of a HMM.
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.. warning::
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The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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Parameters
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----------
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n_components : int
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class MultinomialHMM(_BaseHMM):
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"""Hidden Markov Model with multinomial (discrete) emissions
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.. warning::
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The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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Attributes
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n_components : int
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class GMMHMM(_BaseHMM):
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"""Hidden Markov Model with Gaussin mixture emissions
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.. warning::
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The HMM module and its functions will be removed in 0.17
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as it no longer falls within the project's scope and API.
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Attributes
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----------
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init_params : string, optional

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