4638cbc8d106450f1e821750b4af0ea88408a6ee,models/hbos.py,Hbos,fit,#Hbos#Any#,23

Before Change

            // very important to do scaling. Not necessary to use min max
            density_norm = (
                hist[:, i].reshape(-1, 1))
            out_score = np.log(1 / (density_norm + self.alpha))

            for j in range(self.n):
                // out sample left
                if bin_ind[j] == 0:

After Change

            bin_ind = np.digitize(X[:, i], bin_edges[:, i], right=False)

            // very important to do scaling. Not necessary to use min max
            out_score = np.max(hist[:, i]) - hist[:, i]
            out_score = MinMaxScaler().fit_transform(out_score.reshape(-1, 1))

            for j in range(self.n):
                // out sample left

Instances