135bed105e13d5ceaf97207e20c5b2e3b6c19ef6,scanpy/preprocessing/_utils.py,,_get_mean_var,#Any#,8

Before Change

    // - using X.multiply is slower
    if not STANDARD_SCALER_FIXED:
        mean = X.mean(axis=0)
        if issparse(X):
            mean_sq = X.multiply(X).mean(axis=0)
            mean = mean.A1
            mean_sq = mean_sq.A1
        else:
            mean_sq = np.multiply(X, X).mean(axis=0)
        // enforece R convention (unbiased estimator) for variance
        var = (mean_sq - mean ** 2) * (X.shape[0] / (X.shape[0] - 1))
    else:
        from sklearn.preprocessing import StandardScaler

After Change

def _get_mean_var(X):
    if sparse.issparse(X):
        mean, var = sparse_mean_variance_axis(X, axis=0)
    else:
        if STANDARD_SCALER_FIXED:
            from sklearn.preprocessing import StandardScaler

Instances