def predict(self, X):
if not self.fitted_:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>self.hidden_activations_ = self.hidden_layer.transform(X)<EOL>predictions = self._get_predictions()<EOL>return predictions<EOL>
Predict values using the model

Parameters
----------
X : {array-like, sparse matrix} of shape [n_samples, n_features]

Returns
-------
C : numpy array of shape [n_samples, n_outputs]
    Predicted values.