lda.LDA()

Alias for sklearn.discriminant_analysis.LinearDiscriminantAnalysis.

Methods

__init__(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001) [source]

decision_function(X) [source]

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters:	X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.
Returns:	array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) : Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

fit(X, y, store_covariance=None, tol=None) [source]

Fit LinearDiscriminantAnalysis model according to the given

training data and parameters.

Changed in version 0.17: Deprecated store_covariance have been moved to main constructor.

Changed in version 0.17: Deprecated tol have been moved to main constructor.

Parameters:	X : array-like, shape (n_samples, n_features) Training data. y : array, shape (n_samples,) Target values.

fit_transform(X, y=None, **fit_params) [source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

predict(X) [source]

Predict class labels for samples in X.

Parameters:	X : {array-like, sparse matrix}, shape = [n_samples, n_features] Samples.
Returns:	C : array, shape = [n_samples] Predicted class label per sample.

predict_log_proba(X) [source]

Estimate log probability.

Parameters:	X : array-like, shape (n_samples, n_features) Input data.
Returns:	C : array, shape (n_samples, n_classes) Estimated log probabilities.

predict_proba(X) [source]

Estimate probability.

Parameters:	X : array-like, shape (n_samples, n_features) Input data.
Returns:	C : array, shape (n_samples, n_classes) Estimated probabilities.

score(X, y, sample_weight=None) [source]

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float Mean accuracy of self.predict(X) wrt. y.

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it?s possible to update each component of a nested object.

Returns:	self :

transform(X) [source]

Project data to maximize class separation.

Parameters:	X : array-like, shape (n_samples, n_features) Input data.
Returns:	X_new : array, shape (n_samples, n_components) Transformed data.