6.10.2. scikits.learn.pca.ProbabilisticPCA

class scikits.learn.pca.ProbabilisticPCA(n_components=None, copy=True, whiten=False)

Methods

fit(X[, homoscedastic])	Additionally to PCA.fit, learns a covariance model
score(X)	Return a score associated to new data
transform(X)	Apply the dimension reduction learned on the train data.

__init__(n_components=None, copy=True, whiten=False)

fit(X, homoscedastic=True)

Additionally to PCA.fit, learns a covariance model

Parameters :

X: array of shape(n_samples, n_dim) :

The data to fit

homoscedastic: bool, optional, :

If True, average variance across remaining dimensions

score(X)

Return a score associated to new data

Parameters :

X: array of shape(n_samples, n_dim) :

The data to test

Returns :

ll: array of shape (n_samples), :

log-likelihood of each row of X under the current model

transform(X)

Apply the dimension reduction learned on the train data.