e729606d6f2ae2e91574857878a87f40c600f88d,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,MultiTargetRegressionHoeffdingTree,normalize_sample,#MultiTargetRegressionHoeffdingTree#Any#,475

Before Change

        mean = self.sum_of_attribute_values / self.examples_seen

        sd = np.sqrt((self.sum_of_attribute_squares -
                      (self.sum_of_attribute_values ** 2) /
                      self.examples_seen) / self.examples_seen)

        normalized_sample = np.zeros(X.shape[0] + 1, dtype=np.float64)
        np.divide(X - mean, sd, where=sd != 0, out=normalized_sample[:-1])
        // Augments sample with the bias input signal (or y intercept for

After Change

            return np.zeros((c + 1), dtype=np.float64)

        mean = self.sum_of_attribute_values / self.examples_seen
        variance = (self.sum_of_attribute_squares -
                    (self.sum_of_attribute_values ** 2) /
                    self.examples_seen) / (self.examples_seen - 1)

        sd = np.sqrt(variance, out=np.zeros_like(variance),
                     where=variance >= 0.0)

        normalized_sample = np.zeros(X.shape[0] + 1, dtype=np.float64)
        np.divide(X - mean, sd, where=sd != 0, out=normalized_sample[:-1])
        // Augments sample with the bias input signal (or y intercept for
        // each target)

Instances