def fit(self, X, y):
<EOL>self.hidden_activations_ = self.hidden_layer.fit_transform(X)<EOL>self._fit_regression(as_float_array(y, copy=True))<EOL>return self<EOL>
Fit the model using X, y as training data.

Parameters
----------
X : {array-like, sparse matrix} of shape [n_samples, n_features]
    Training vectors, where n_samples is the number of samples
    and n_features is the number of features.

y : array-like of shape [n_samples, n_outputs]
    Target values (class labels in classification, real numbers in
    regression)

Returns
-------
self : object

    Returns an instance of self.