(X)
return x_StdS
Standardize features by removing the mean and scaling to unit variance.
    
    The transformation is given by::

        x_StdS = (X - X.mean(axis=0)) / X.var(axis=0)

    Parameters
    ----------
    X: array-like
        Data matrix with [n_samples, n_features]
    
    Returns
    -------
    x_StdS: array-like
        index of training set, shape like [n_split_count, n_training_indexes]
Standardize features by removing the mean and scaling to unit variance.
    
    The transformation is given by::
Standardize features by removing the mean and scaling to unit variance . The transformation is given by ::
def StandardScale(X):
    '''
        Standardize features by removing the mean and scaling to unit variance.
    
    The transformation is given by::

        x_StdS = (X - X.mean(axis=0)) / X.var(axis=0)

    Parameters
    ----------
    X: array-like
        Data matrix with [n_samples, n_features]
    
    Returns
    -------
    x_StdS: array-like
        index of training set, shape like [n_split_count, n_training_indexes]


    '''
    if sparse.issparse(X):
            raise TypeError("MinMaxScaler does not support sparse input. "
                            "You may consider to use MaxAbsScaler instead.")
    if not isinstance(X, np.ndarray):
        X = np.asarray(X)

    if not(hasattr(X, 'mean') and hasattr(X, 'var')):
        raise TypeError("X array does not has mean or var methods")
    
    data_mean = X.mean(axis=0)
    data_var = X.var(axis=0)
    x_StdS = (X - data_mean) / data_var

    return x_StdS
def StandardScale ( X ) : if sparse . issparse ( X ) : raise TypeError ( "MinMaxScaler does not support sparse input. " "You may consider to use MaxAbsScaler instead." ) if not isinstance ( X , np . ndarray ) : X = np . asarray ( X ) if not ( hasattr ( X , 'mean' ) and hasattr ( X , 'var' ) ) : raise TypeError ( "X array does not has mean or var methods" ) data_mean = X . mean ( axis = 0 ) data_var = X . var ( axis = 0 ) x_StdS = ( X - data_mean ) / data_var return x_StdS