def fit(self, data):
    '\n         Apply standard scale for input data\n         Parameters\n         ----------\n         data: data_instance, input data\n\n         Returns\n         ----------\n         data:data_instance, data after scale\n         mean: list, each column mean value\n         std: list, each column standard deviation\n         '
    (self.column_min_value, self.column_max_value) = self._get_min_max_value(data)
    self.scale_column_idx = self._get_scale_column_idx(data)
    self.header = self._get_header(data)
    self.data_shape = self._get_data_shape(data)
    data = self.fit_feature_range(data)
    if ((not self.with_mean) and (not self.with_std)):
        self.mean = [0 for _ in range(self.data_shape)]
        self.std = [1 for _ in range(self.data_shape)]
    else:
        self.summary_obj = MultivariateStatisticalSummary(data, (- 1))
        if self.with_mean:
            self.mean = self.summary_obj.get_mean()
            self.mean = [self.mean[key] for key in self.header]
        else:
            self.mean = [0 for _ in range(self.data_shape)]
        if self.with_std:
            self.std = self.summary_obj.get_std_variance()
            self.std = [self.std[key] for key in self.header]
            for (i, value) in enumerate(self.std):
                if (np.abs((value - 0)) < 1e-06):
                    self.std[i] = 1
        else:
            self.std = [1 for _ in range(self.data_shape)]
    f = functools.partial(self.__scale, mean=self.mean, std=self.std, process_cols_list=self.scale_column_idx)
    fit_data = data.mapValues(f)
    return fit_data
Apply standard scale for input data
Parameters
----------
data: data_instance, input data

Returns
----------
data:data_instance, data after scale
mean: list, each column mean value
std: list, each column standard deviation
def fit(self, data):
    '\n         Apply standard scale for input data\n         Parameters\n         ----------\n         data: data_instance, input data\n\n         Returns\n         ----------\n         data:data_instance, data after scale\n         mean: list, each column mean value\n         std: list, each column standard deviation\n         '
    (self.column_min_value, self.column_max_value) = self._get_min_max_value(data)
    self.scale_column_idx = self._get_scale_column_idx(data)
    self.header = self._get_header(data)
    self.data_shape = self._get_data_shape(data)
    data = self.fit_feature_range(data)
    if ((not self.with_mean) and (not self.with_std)):
        self.mean = [0 for _ in range(self.data_shape)]
        self.std = [1 for _ in range(self.data_shape)]
    else:
        self.summary_obj = MultivariateStatisticalSummary(data, (- 1))
        if self.with_mean:
            self.mean = self.summary_obj.get_mean()
            self.mean = [self.mean[key] for key in self.header]
        else:
            self.mean = [0 for _ in range(self.data_shape)]
        if self.with_std:
            self.std = self.summary_obj.get_std_variance()
            self.std = [self.std[key] for key in self.header]
            for (i, value) in enumerate(self.std):
                if (np.abs((value - 0)) < 1e-06):
                    self.std[i] = 1
        else:
            self.std = [1 for _ in range(self.data_shape)]
    f = functools.partial(self.__scale, mean=self.mean, std=self.std, process_cols_list=self.scale_column_idx)
    fit_data = data.mapValues(f)
    return fit_data