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[patch:docs] Fix type specifiers in documentation #131

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Dec 31, 2020
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[patch:docs] Fix type specifiers in documentation #131

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881a980
Merge branch 'dev' of https://github.com/eonu/sequentia into dev
eonu Dec 31, 2020
238991e
Fix type specifier in documentation
eonu Dec 31, 2020
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32 changes: 16 additions & 16 deletions lib/sequentia/classifiers/hmm/gmmhmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,45 +10,45 @@ class GMMHMM:
Parameters
----------
label : str or numeric
label: str or numeric
A label for the model, corresponding to the class being represented.
n_states : int > 0
n_states: int > 0
The number of states for the model.
n_components : int > 0
n_components: int > 0
The number of mixture components used in the emission distribution for each state.
covariance_type : {'spherical', 'diag', 'full', 'tied'}
covariance_type: {'spherical', 'diag', 'full', 'tied'}
The covariance matrix type for emission distributions.
topology : {'ergodic', 'left-right', 'linear'}
topology: {'ergodic', 'left-right', 'linear'}
The topology for the model.
random_state : numpy.random.RandomState, int, optional
random_state: numpy.random.RandomState, int, optional
A random state object or seed for reproducible randomness.
Attributes
----------
label : str or numeric
label: str or numeric
The label for the model.
n_states : int
n_states: int
The number of states for the model.
n_components : int
n_components: int
The number of mixture components used in the emission distribution for each state.
covariance_type : str
covariance_type: str
The covariance matrix type for emission distributions.
n_seqs : int
n_seqs: int
The number of observation sequences use to train the model.
initial : numpy.ndarray (float)
initial: numpy.ndarray (float)
The initial state distribution of the model.
transitions : numpy.ndarray (float)
transitions: numpy.ndarray (float)
The transition matrix of the model.
"""

Expand Down Expand Up @@ -98,7 +98,7 @@ def fit(self, X):
Parameters
----------
X : list of numpy.ndarray (float)
X: list of numpy.ndarray (float)
Collection of multivariate observation sequences, each of shape :math:`(T \\times D)` where
:math:`T` may vary per observation sequence.
"""
Expand Down Expand Up @@ -133,14 +133,14 @@ def forward(self, x):
Parameters
----------
x : numpy.ndarray (float)
x: numpy.ndarray (float)
An individual sequence of observations of size :math:`(T \\times D)` where
:math:`T` is the number of time frames (or observations) and
:math:`D` is the number of features.
Returns
-------
log-likelihood : float
log-likelihood: float
The log-likelihood of the model generating the observation sequence.
"""
try:
Expand Down
36 changes: 18 additions & 18 deletions lib/sequentia/classifiers/hmm/hmm_classifier.py
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Expand Up @@ -9,13 +9,13 @@ class HMMClassifier:
Attributes
----------
models : list of GMMHMM
models: list of GMMHMM
A collection of the :class:`~GMMHMM` objects to use for classification.
encoder : sklearn.preprocessing.LabelEncoder
encoder: sklearn.preprocessing.LabelEncoder
The label encoder fitted on the set of ``classes`` provided during instantiation.
classes : numpy.ndarray (str/numeric)
classes: numpy.ndarray (str/numeric)
The complete set of possible classes/labels.
"""

Expand All @@ -27,7 +27,7 @@ def fit(self, models):
Parameters
----------
models : array-like of GMMHMM
models: array-like of GMMHMM
A collection of :class:`~GMMHMM` objects to use for classification.
"""

Expand All @@ -48,10 +48,10 @@ def predict(self, X, prior='frequency', return_scores=False, original_labels=Tru
Parameters
----------
X : numpy.ndarray (float) or list of numpy.ndarray (float)
X: numpy.ndarray (float) or list of numpy.ndarray (float)
An individual observation sequence or a list of multiple observation sequences.
prior : {'frequency', 'uniform'} or array-like of float
prior: {'frequency', 'uniform'} or array-like of float
How the prior probability for each model is calculated to perform MAP estimation by scoring with
the joint probability (or un-normalized posterior) :math:`\\mathbb{P}(O, \\lambda_c)=\\mathbb{P}(O|\\lambda_c)\\mathbb{P}(\\lambda_c)`.
Expand All @@ -60,20 +60,20 @@ def predict(self, X, prior='frequency', return_scores=False, original_labels=Tru
Alternatively, class prior probabilities can be specified in an iterable of floats, e.g. `[0.1, 0.3, 0.6]`.
return_scores : bool
return_scores: bool
Whether to return the scores of each model on the observation sequence(s).
original_labels : bool
original_labels: bool
Whether to inverse-transform the labels to their original encoding.
Returns
-------
prediction(s) : str/numeric or :class:`numpy:numpy.ndarray` (str/numeric)
prediction(s): str/numeric or :class:`numpy:numpy.ndarray` (str/numeric)
The predicted label(s) for the observation sequence(s).
If ``original_labels`` is true, then the returned labels are inverse-transformed into their original encoding.
scores : :class:`numpy:numpy.ndarray` (float)
scores: :class:`numpy:numpy.ndarray` (float)
An :math:`N\\times M` matrix of scores (log un-normalized posteriors), for each of the :math:`N` observation sequences,
for each of the :math:`M` HMMs. Only returned if ``return_scores`` is true.
"""
Expand Down Expand Up @@ -122,13 +122,13 @@ def evaluate(self, X, y, prior='frequency'):
Parameters
----------
X : list of numpy.ndarray (float)
X: list of numpy.ndarray (float)
A list of multiple observation sequences.
y : array-like of str/numeric
y: array-like of str/numeric
An iterable of labels for the observation sequences.
prior : {'frequency', 'uniform'} or array-like of float
prior: {'frequency', 'uniform'} or array-like of float
How the prior probability for each model is calculated to perform MAP estimation by scoring with
the joint probability (or un-normalized posterior) :math:`\\mathbb{P}(O, \\lambda_c)=\\mathbb{P}(O|\\lambda_c)\\mathbb{P}(\\lambda_c)`.
Expand All @@ -139,10 +139,10 @@ def evaluate(self, X, y, prior='frequency'):
Returns
-------
accuracy : float
accuracy: float
The categorical accuracy of the classifier on the observation sequences.
confusion : :class:`numpy:numpy.ndarray` (int)
confusion: :class:`numpy:numpy.ndarray` (int)
The confusion matrix representing the discrepancy between predicted and actual labels.
"""
X, y = self._val.observation_sequences_and_labels(X, y)
Expand All @@ -155,7 +155,7 @@ def save(self, path):
Parameters
----------
path : str
path: str
File path (usually with `.pkl` extension) to store the serialized :class:`HMMClassifier` object.
"""
try:
Expand All @@ -172,12 +172,12 @@ def load(cls, path):
Parameters
----------
path : str
path: str
File path of the serialized data generated by the :meth:`save` method.
Returns
-------
deserialized : :class:`HMMClassifier`
deserialized: :class:`HMMClassifier`
The deserialized HMM classifier object.
"""
with open(path, 'rb') as file:
Expand Down
10 changes: 5 additions & 5 deletions lib/sequentia/classifiers/hmm/topologies/ergodic.py
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Expand Up @@ -7,10 +7,10 @@ class _ErgodicTopology(_Topology):
Parameters
----------
n_states : int
n_states: int
Number of states in the HMM.
random_state : numpy.random.RandomState
random_state: numpy.random.RandomState
A random state object for reproducible randomness.
"""

Expand All @@ -20,7 +20,7 @@ def uniform_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The uniform transition matrix of shape `(n_states, n_states)`.
"""
return np.ones((self._n_states, self._n_states)) / self._n_states
Expand All @@ -32,7 +32,7 @@ def random_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The random transition matrix of shape `(n_states, n_states)`.
"""
return self._random_state.dirichlet(np.ones(self._n_states), size=self._n_states)
Expand All @@ -42,7 +42,7 @@ def validate_transitions(self, transitions: np.ndarray) -> None:
Parameters
----------
transitions : numpy.ndarray (float)
transitions: numpy.ndarray (float)
The transition matrix to validate.
"""
super().validate_transitions(transitions)
Expand Down
10 changes: 5 additions & 5 deletions lib/sequentia/classifiers/hmm/topologies/left_right.py
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Expand Up @@ -6,10 +6,10 @@ class _LeftRightTopology(_Topology):
Parameters
----------
n_states : int
n_states: int
Number of states in the HMM.
random_state : numpy.random.RandomState
random_state: numpy.random.RandomState
A random state object for reproducible randomness.
"""

Expand All @@ -19,7 +19,7 @@ def uniform_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The uniform transition matrix of shape `(n_states, n_states)`.
"""
upper_ones = np.triu(np.ones((self._n_states, self._n_states)))
Expand All @@ -34,7 +34,7 @@ def random_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The random transition matrix of shape `(n_states, n_states)`.
"""
transitions = self._random_state.dirichlet(np.ones(self._n_states), size=self._n_states)
Expand All @@ -49,7 +49,7 @@ def validate_transitions(self, transitions: np.ndarray) -> None:
Parameters
----------
transitions : numpy.ndarray (float)
transitions: numpy.ndarray (float)
The transition matrix to validate.
"""
super().validate_transitions(transitions)
Expand Down
10 changes: 5 additions & 5 deletions lib/sequentia/classifiers/hmm/topologies/linear.py
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Expand Up @@ -6,10 +6,10 @@ class _LinearTopology(_Topology):
Parameters
----------
n_states : int
n_states: int
Number of states in the HMM.
random_state : numpy.random.RandomState
random_state: numpy.random.RandomState
A random state object for reproducible randomness.
"""

Expand All @@ -19,7 +19,7 @@ def uniform_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The uniform transition matrix of shape `(n_states, n_states)`.
"""
transitions = np.zeros((self._n_states, self._n_states))
Expand All @@ -35,7 +35,7 @@ def random_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The random transition matrix of shape `(n_states, n_states)`.
"""
transitions = np.zeros((self._n_states, self._n_states))
Expand All @@ -49,7 +49,7 @@ def validate_transitions(self, transitions: np.ndarray) -> None:
Parameters
----------
transitions : numpy.ndarray (float)
transitions: numpy.ndarray (float)
The transition matrix to validate.
"""
super().validate_transitions(transitions)
Expand Down
16 changes: 8 additions & 8 deletions lib/sequentia/classifiers/hmm/topologies/topology.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,10 @@ class _Topology:
Parameters
----------
n_states : int
n_states: int
Number of states in the HMM.
random_state : numpy.random.RandomState
random_state: numpy.random.RandomState
A random state object for reproducible randomness.
"""

Expand All @@ -21,7 +21,7 @@ def uniform_initial(self) -> np.ndarray:
Returns
-------
initial : :class:`numpy:numpy.ndarray` (float)
initial: :class:`numpy:numpy.ndarray` (float)
The initial state distribution of shape `(n_states,)`.
"""
return np.ones(self._n_states) / self._n_states
Expand All @@ -31,7 +31,7 @@ def random_initial(self) -> np.ndarray:
Returns
-------
initial : :class:`numpy:numpy.ndarray` (float)
initial: :class:`numpy:numpy.ndarray` (float)
The initial state distribution of shape `(n_states,)`.
"""
return self._random_state.dirichlet(np.ones(self._n_states), size=1).flatten()
Expand All @@ -42,7 +42,7 @@ def uniform_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The uniform transition matrix of shape `(n_states, n_states)`.
"""
raise NotImplementedError
Expand All @@ -54,7 +54,7 @@ def random_transitions(self) -> np.ndarray:
Returns
-------
transitions : :class:`numpy:numpy.ndarray` (float)
transitions: :class:`numpy:numpy.ndarray` (float)
The random transition matrix of shape `(n_states, n_states)`.
"""
raise NotImplementedError
Expand All @@ -64,7 +64,7 @@ def validate_initial(self, initial: np.ndarray) -> None:
Parameters
----------
initial : numpy.ndarray (float)
initial: numpy.ndarray (float)
The initial state distribution to validate.
"""
if not isinstance(initial, np.ndarray):
Expand All @@ -79,7 +79,7 @@ def validate_transitions(self, transitions: np.ndarray) -> None:
Parameters
----------
transitions : numpy.ndarray (float)
transitions: numpy.ndarray (float)
The transition matrix to validate.
"""
if not isinstance(transitions, np.ndarray):
Expand Down
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