def fit(self, X, y):
        # fit random hidden layer and compute the hidden layer activations
        self.hidden_activations_ = self.hidden_layer.fit_transform(X)

        # solve the regression from hidden activations to outputs
        self._fit_regression(as_float_array(y, copy=True))

        return self
Fit the model using X, y as training data.
dataset/ETHPy150Open dclambert/Python-ELM/elm.py/GenELMRegressor.fit