b5264000f20db429a3bd371ec66178c9fb49e5ce,Orange/distance/distance.py,SpearmanModel,compute_correlation,#SpearmanModel#Any#Any#,434

Before Change

        if isinstance(rho, np.float):
            return np.array([[rho]])
        slc = x1.shape[1 - self.axis]
        return rho[:slc, slc:]


class CorrelationDistance(Distance):
    supports_missing = False

After Change

    def compute_correlation(self, x1, x2):
        n1 = x1.shape[1 - self.axis]
        n2 = x2.shape[1 - self.axis] if x2 is not None else 0
        if x2 is None:
            if n1 == 2:
                // Special case to properly fill degenerate self correlations
                // (nan, inf on the diagonals)
                rho = stats.spearmanr(x1, x1, axis=self.axis)[0]
                assert rho.shape == (4, 4)
                rho = rho[:2, :2].copy()
            else:
                // scalar if n1 == 1
                rho = stats.spearmanr(x1, axis=self.axis)[0]
            return np.atleast_2d(rho)
        else:
            // this computes too much (most of it is thrown away)
            rho = stats.spearmanr(x1, x2, axis=self.axis)[0]
            if np.isscalar(rho):
                // scalar if n1 + n2 <= 2
                assert n1 + n2 <= 2
                return np.atleast_2d(rho)
            else:
                assert rho.shape == (n1 + n2, n1 + n2)
                return rho[:n1, n1:].copy()


class CorrelationDistance(Distance):
    supports_missing = False

Instances