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 byX[:, 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,)