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DOC Cleaning parameter docstrings in discriminant_analysis (#15761) (#…
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@lopusz
lopusz committed Jan 30, 2020
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Showing 1 changed file with 55 additions and 55 deletions.
110 changes: 55 additions & 55 deletions sklearn/discriminant_analysis.py
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Expand Up @@ -34,18 +34,18 @@ def _cov(X, shrinkage=None):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : string or float, optional
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter, possible values:
- None or 'empirical': no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Returns
-------
s : array, shape (n_features, n_features)
s : ndarray of shape (n_features, n_features)
Estimated covariance matrix.
"""
shrinkage = "empirical" if shrinkage is None else shrinkage
Expand Down Expand Up @@ -74,15 +74,15 @@ def _class_means(X, y):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Returns
-------
means : array-like, shape (n_classes, n_features)
means : array-like of shape (n_classes, n_features)
Class means.
"""
classes, y = np.unique(y, return_inverse=True)
Expand All @@ -98,24 +98,24 @@ def _class_cov(X, y, priors, shrinkage=None):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Input data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
priors : array-like, shape (n_classes,)
priors : array-like of shape (n_classes,)
Class priors.
shrinkage : string or float, optional
shrinkage : 'auto' or float, default=None
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Returns
-------
cov : array-like, shape (n_features, n_features)
cov : array-like of shape (n_features, n_features)
Class covariance matrix.
"""
classes = np.unique(y)
Expand Down Expand Up @@ -146,71 +146,71 @@ class LinearDiscriminantAnalysis(BaseEstimator, LinearClassifierMixin,
Parameters
----------
solver : string, optional
solver : {'svd', 'lsqr', 'eigen'}, default='svd'
Solver to use, possible values:
- 'svd': Singular value decomposition (default).
Does not compute the covariance matrix, therefore this solver is
recommended for data with a large number of features.
- 'lsqr': Least squares solution, can be combined with shrinkage.
- 'eigen': Eigenvalue decomposition, can be combined with shrinkage.
shrinkage : string or float, optional
shrinkage : 'auto' or float, default=None
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Note that shrinkage works only with 'lsqr' and 'eigen' solvers.
priors : array, optional, shape (n_classes,)
priors : array-like of shape (n_classes,), default=None
Class priors.
n_components : int, optional (default=None)
n_components : int, default=None
Number of components (<= min(n_classes - 1, n_features)) for
dimensionality reduction. If None, will be set to
min(n_classes - 1, n_features).
store_covariance : bool, optional
store_covariance : bool, default=False
Additionally compute class covariance matrix (default False), used
only in 'svd' solver.
.. versionadded:: 0.17
tol : float, optional, (default 1.0e-4)
tol : float, default=1.0e-4
Threshold used for rank estimation in SVD solver.
.. versionadded:: 0.17
Attributes
----------
coef_ : array, shape (n_features,) or (n_classes, n_features)
coef_ : ndarray of shape (n_features,) or (n_classes, n_features)
Weight vector(s).
intercept_ : array, shape (n_classes,)
intercept_ : ndarray of shape (n_classes,)
Intercept term.
covariance_ : array-like, shape (n_features, n_features)
covariance_ : array-like of shape (n_features, n_features)
Covariance matrix (shared by all classes).
explained_variance_ratio_ : array, shape (n_components,)
explained_variance_ratio_ : ndarray of shape (n_components,)
Percentage of variance explained by each of the selected components.
If ``n_components`` is not set then all components are stored and the
sum of explained variances is equal to 1.0. Only available when eigen
or svd solver is used.
means_ : array-like, shape (n_classes, n_features)
means_ : array-like of shape (n_classes, n_features)
Class means.
priors_ : array-like, shape (n_classes,)
priors_ : array-like of shape (n_classes,)
Class priors (sum to 1).
scalings_ : array-like, shape (rank, n_classes - 1)
scalings_ : array-like of shape (rank, n_classes - 1)
Scaling of the features in the space spanned by the class centroids.
xbar_ : array-like, shape (n_features,)
xbar_ : array-like of shape (n_features,)
Overall mean.
classes_ : array-like, shape (n_classes,)
classes_ : array-like of shape (n_classes,)
Unique class labels.
See also
Expand Down Expand Up @@ -267,15 +267,15 @@ def _solve_lsqr(self, X, y, shrinkage):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_classes)
y : array-like of shape (n_samples,) or (n_samples, n_classes)
Target values.
shrinkage : string or float, optional
shrinkage : 'auto', float or None
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- None: no shrinkage.
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Expand Down Expand Up @@ -305,15 +305,15 @@ class scatter). This solver supports both classification and
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
shrinkage : string or float, optional
shrinkage : 'auto', float or None
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- None: no shrinkage.
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage constant.
Expand Down Expand Up @@ -349,10 +349,10 @@ def _solve_svd(self, X, y):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
"""
n_samples, n_features = X.shape
Expand Down Expand Up @@ -417,10 +417,10 @@ def fit(self, X, y):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training data.
y : array, shape (n_samples,)
y : array-like of shape (n_samples,)
Target values.
"""
X, y = check_X_y(X, y, ensure_min_samples=2, estimator=self,
Expand Down Expand Up @@ -483,12 +483,12 @@ def transform(self, X):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
X_new : array, shape (n_samples, n_components)
X_new : ndarray of shape (n_samples, n_components)
Transformed data.
"""
if self.solver == 'lsqr':
Expand All @@ -509,12 +509,12 @@ def predict_proba(self, X):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
C : array, shape (n_samples, n_classes)
C : ndarray of shape (n_samples, n_classes)
Estimated probabilities.
"""
check_is_fitted(self)
Expand All @@ -531,12 +531,12 @@ def predict_log_proba(self, X):
Parameters
----------
X : array-like, shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
C : array, shape (n_samples, n_classes)
C : ndarray of shape (n_samples, n_classes)
Estimated log probabilities.
"""
return np.log(self.predict_proba(X))
Expand All @@ -558,20 +558,20 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):
Parameters
----------
priors : array, optional, shape = [n_classes]
priors : ndarray of shape (n_classes,), default=None
Priors on classes
reg_param : float, optional
reg_param : float, default=0.0
Regularizes the covariance estimate as
``(1-reg_param)*Sigma + reg_param*np.eye(n_features)``
store_covariance : boolean
store_covariance : bool, default=False
If True the covariance matrices are computed and stored in the
`self.covariance_` attribute.
.. versionadded:: 0.17
tol : float, optional, default 1.0e-4
tol : float, default=1.0e-4
Threshold used for rank estimation.
.. versionadded:: 0.17
Expand All @@ -584,21 +584,21 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):
means_ : array-like of shape (n_classes, n_features)
Class means.
priors_ : array-like of shape (n_classes)
priors_ : array-like of shape (n_classes,)
Class priors (sum to 1).
rotations_ : list of arrays
For each class k an array of shape [n_features, n_k], with
rotations_ : list of ndarrays
For each class k an array of shape (n_features, n_k), with
``n_k = min(n_features, number of elements in class k)``
It is the rotation of the Gaussian distribution, i.e. its
principal axis.
scalings_ : list of arrays
For each class k an array of shape [n_k]. It contains the scaling
scalings_ : list of ndarrays
For each class k an array of shape (n_k,). It contains the scaling
of the Gaussian distributions along its principal axes, i.e. the
variance in the rotated coordinate system.
classes_ : array-like, shape (n_classes,)
classes_ : array-like of shape (n_classes,)
Unique class labels.
Examples
Expand Down Expand Up @@ -642,7 +642,7 @@ def fit(self, X, y):
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array, shape = [n_samples]
y : array-like of shape (n_samples,)
Target values (integers)
"""
X, y = check_X_y(X, y)
Expand Down

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