(self, feat_array, mean_variance_file)
return 0, new_feat_array
normalization feat using mean variance
        args:
            feat_array: input feat array
            mean_variance_file: input mean variance file
        return:
            -1, array: failed
            0, array: success and return the new feat array
normalization feat using mean variance
        args:
            feat_array: input feat array
            mean_variance_file: input mean variance file
        return:
            -1, array: failed
            0, array: success and return the new feat array
normalization feat using mean variance args : feat_array : input feat array mean_variance_file : input mean variance file return : - 1 array : failed 0 array : success and return the new feat array
def normalization_feat_by_mean_variance(self, feat_array, mean_variance_file):
    """
        normalization feat using mean variance
        args:
            feat_array: input feat array
            mean_variance_file: input mean variance file
        return:
            -1, array: failed
            0, array: success and return the new feat array
        """
    if not self._mean_variance_statu:
      if self._read_mean_variance(mean_variance_file, feat_array.shape[1]) < 0:
        print("[ERROR] _read_mean_variance failed with shape %s %s" %
              (feat_array.shape[0], feat_array.shape[1]))
        return -1, np.array([])

    mean_array = np.array([self._mean_array] * feat_array.shape[0])
    variance_array = np.array([self._variance_array] * feat_array.shape[0])

    new_feat_array = (feat_array - mean_array) / variance_array
    return 0, new_feat_array
def normalization_feat_by_mean_variance ( self , feat_array , mean_variance_file ) : if not self . _mean_variance_statu : if self . _read_mean_variance ( mean_variance_file , feat_array . shape [ 1 ] ) < 0 : print ( "[ERROR] _read_mean_variance failed with shape %s %s" % ( feat_array . shape [ 0 ] , feat_array . shape [ 1 ] ) ) return - 1 , np . array ( [ ] ) mean_array = np . array ( [ self . _mean_array ] * feat_array . shape [ 0 ] ) variance_array = np . array ( [ self . _variance_array ] * feat_array . shape [ 0 ] ) new_feat_array = ( feat_array - mean_array ) / variance_array return 0 , new_feat_array