extreme_learning_machines.py 文件源码

def _decision_scores(self, X):
        """Predict using the ELM model

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y_pred : array-like, shape (n_samples,) or (n_samples, n_outputs)
            The predicted values.
        """
        X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])

        if self.batch_size is None:
            hidden_activations = self._compute_hidden_activations(X)
            y_pred = safe_sparse_dot(hidden_activations, self.coef_output_)
        else:
            n_samples = X.shape[0]
            batches = gen_batches(n_samples, self.batch_size)

            y_pred = np.zeros((n_samples, self.n_outputs_))
            for batch in batches:
                h_batch = self._compute_hidden_activations(X[batch])
                y_pred[batch] = safe_sparse_dot(h_batch, self.coef_output_)

        return y_pred