1a2462f18707e04f294224053473668820111cf5,PyPi/approximators/regressor.py,Regressor,fit,#Regressor#Any#Any#,24

Before Change

            y (np.array): target;
            fit_params (dict): other parameters.
        
        if x.ndim == 1:
            x = np.expand_dims(x, axis=0)
        elif x.ndim > 2:
            raise ValueError("Training set dimension not suitable for the "
                             "regressor.")

        if self.features:
            x = self.features.fit_transform(x)

        if self.input_scaled:

After Change

            x = np.concatenate((x[0], x[1]), axis=1)

        if isinstance(x, list):
            if self.features:
                x[0] = self.features.fit_transform(x[0])

            if self.input_scaled:
                self.pre_x = preprocessing.StandardScaler()
                x[0] = self.pre_x.fit_transform(x[0])
        else:
            if self.features:
                x = self.features.fit_transform(x)

            if self.input_scaled:
                self.pre_x = preprocessing.StandardScaler()
                x = self.pre_x.fit_transform(x)

        if self.output_scaled:
            self.pre_y = preprocessing.StandardScaler()
            y = self.pre_y.fit_transform(y.reshape(-1, 1))

Instances