def _is_redundant(self, matrix, cutoff=None):
        """Identify rdeundant rows in a matrix that can be removed."""

        cutoff = 1.0 - self.feasibility_tol

        # Avoid zero variances
        extra_col = matrix[:, 0] + 1

        # Avoid zero rows being correlated with constant rows
        extra_col[matrix.sum(axis=1) == 0] = 2
        corr = np.corrcoef(np.c_[matrix, extra_col])
        corr = np.tril(corr, -1)

        return (np.abs(corr) > cutoff).any(axis=1)
def _is_redundant(self, matrix, cutoff=None):
        """Identify rdeundant rows in a matrix that can be removed."""

        cutoff = 1.0 - self.feasibility_tol

        # Avoid zero variances
        extra_col = matrix[:, 0] + 1

        # Avoid zero rows being correlated with constant rows
        extra_col[matrix.sum(axis=1) == 0] = 2
        corr = np.corrcoef(np.c_[matrix, extra_col])
        corr = np.tril(corr, -1)

        return (np.abs(corr) > cutoff).any(axis=1)
def _is_redundant ( self , matrix , cutoff = None ) : cutoff = 1.0 - self . feasibility_tol # Avoid zero variances extra_col = matrix [ : , 0 ] + 1 # Avoid zero rows being correlated with constant rows extra_col [ matrix . sum ( axis = 1 ) == 0 ] = 2 corr = np . corrcoef ( np . c_ [ matrix , extra_col ] ) corr = np . tril ( corr , - 1 ) return ( np . abs ( corr ) > cutoff ) . any ( axis = 1 )
Identify rdeundant rows in a matrix that can be removed.
Identify rdeundant rows in a matrix that can be removed .