def idd_estrank(eps, A):
 A = np.asfortranarray(A)
 (m, n) = A.shape
 (n2, w) = idd_frmi(m)
 ra = np.empty(((n * n2) + ((n + 1) * (n2 + 1))), order='F')
 (k, ra) = _id.idd_estrank(eps, A, w, ra)
 return k

def idd_estrank ( eps , A ) : A = np . asfortranarray ( A ) ( m , n ) = A . shape ( n2 , w ) = idd_frmi ( m ) ra = np . empty ( ( ( n * n2 ) + ( ( n + 1 ) * ( n2 + 1 ) ) ) , order = 'F' ) ( k , ra ) = _id . idd_estrank ( eps , A , w , ra ) return k
estimate rank of a real matrix to a specified relative precision using random sampling .