def iddp_aid(eps, A):
 A = np.asfortranarray(A)
 (m, n) = A.shape
 (n2, w) = idd_frmi(m)
 proj = np.empty((((n * ((2 * n2) + 1)) + n2) + 1), order='F')
 (k, idx, proj) = _id.iddp_aid(eps, A, w, proj)
 proj = proj[:(k * (n - k))].reshape((k, (n - k)), order='F')
 return (k, idx, proj)

def iddp_aid ( eps , A ) : A = np . asfortranarray ( A ) ( m , n ) = A . shape ( n2 , w ) = idd_frmi ( m ) proj = np . empty ( ( ( ( n * ( ( 2 * n2 ) + 1 ) ) + n2 ) + 1 ) , order = 'F' ) ( k , idx , proj ) = _id . iddp_aid ( eps , A , w , proj ) proj = proj [ : ( k * ( n - k ) ) ] . reshape ( ( k , ( n - k ) ) , order = 'F' ) return ( k , idx , proj )
compute id of a real matrix to a specified relative precision using random sampling .