1d2bee04f27bb519fb6e9561b2808b4e9ec86def,Orange/statistics/util.py,,bincount,#Any#Any#Any#Any#,23

Before Change

        nans = 0. if X.ndim == 1 else np.zeros(X.shape[1], dtype=float)
    if minlength is None and max_val is not None:
        minlength = max_val + 1
    bc = np.array([]) if minlength is not None and minlength <= 0 else \
        np.bincount(X.astype(np.int32, copy=False),
                    weights=weights, minlength=minlength).astype(float)
    return bc, nans


def countnans(X, weights=None, axis=None, dtype=None, keepdims=False):

After Change

    if minlength is None and max_val is not None:
        minlength = max_val + 1

    if minlength is not None and minlength <= 0:
        bc = np.array([])
    else:
        bc = np.bincount(
            X.astype(np.int32, copy=False), weights=weights, minlength=minlength
        ).astype(float)
        // Since `csr_matrix.values` only contain non-zero values, we must count
        // those separately and set the appropriate bin
        if sp.issparse(X_):
            bc[0] = np.prod(X_.shape) - X_.nnz

    return bc, nans


def countnans(X, weights=None, axis=None, dtype=None, keepdims=False):

Instances