b39db6e2bf0c806554578b5c9895d9b7ef6bd48c,Orange/statistics/util.py,,_count_nans_per_row_sparse,#Any#Any#,13

Before Change

    counts -= nnz_per_row
    if weights is not None:
        counts *= weights
    return np.sum(counts)


def bincount(X, max_val=None, weights=None, minlength=None):
    Return counts of values in array X.

After Change

def _count_nans_per_row_sparse(X, weights):
     Count the number of nans (undefined) values per row. 
    counts = np.fromiter((np.isnan(row.data).sum() for row in X), dtype=np.float)

    if weights is not None:
        counts *= weights

    return counts


def bincount(X, max_val=None, weights=None, minlength=None):
    Return counts of values in array X.

Instances