summit.multiview_platform.multiview_classifiers.mvml

mvml

classifier_class_name = 'MVMLClassifier'
class MVMLClassifier(random_state=None, lmbda=0.1, eta=0.1, nystrom_param=1, n_loops=50, precision=0.0001, learn_A=0, kernel='rbf', learn_w=0, kernel_params=None)

BaseMultiviewClassifier base of Multiview classifiers

Parameters:

random_state (int seed, RandomState instance, or None (default=None)) – The seed of the pseudo random number multiview_generator to use when shuffling the data.

param_names = ['lmbda', 'eta', 'nystrom_param', 'learn_A', 'learn_w', 'n_loops', 'kernel_params', 'kernel',...
distribs
fit(X, y, train_indices=None, view_indices=None)

Fit the MVML classifier

Parameters:

X

Training multi-view input samples. can be also Kernel where attibute ‘kernel’

is set to precompute “precomputed”

or - Dictionary of {array like} with shape = (n_samples, n_features) for multi-view

for each view.

  • Array of {array like} with shape = (n_samples, n_features) for multi-view for each view.

  • {array like} with (n_samples, nviews * n_features) with ‘views_ind’ diferent to ‘None’

yarray-like, shape = (n_samples,)

Target values (class labels). array of length n_samples containing the classification/regression labels for training data

views_indarray-like (default=[0, n_features//2, n_features])

Paramater specifying how to extract the data views from X:

  • views_ind is a 1-D array of sorted integers, the entries indicate the limits of the slices used to extract the views, where view n is given by X[:, views_ind[n]:views_ind[n+1]] .

    With this convention each view is therefore a view (in the NumPy sense) of X and no copy of the data is done.

Returns:

self – Returns self.

Return type:

object

predict(X, sample_indices=None, view_indices=None)
Parameters:

X (different formats are supported) –

  • Metriclearn_array {array-like, sparse matrix}, shape = (n_samples, n_features) Training multi-view input samples. can be also Kernel where attibute ‘kernel’ is set to precompute “precomputed”

  • Dictionary of {array like} with shape = (n_samples, n_features) for multi-view for each view.

  • Array of {array like} with shape = (n_samples, n_features) for multi-view for each view.

  • {array like} with (n_samples, nviews * n_features) with ‘views_ind’ diferent to ‘None’

Returns:

y – Predicted classes.

Return type:

numpy.ndarray, shape = (n_samples,)