def test_consensus_score_issue2445():
 a_rows = np.array([[True, True, False, False], [False, False, True, True], [False, False, False, True]])
 a_cols = np.array([[True, True, False, False], [False, False, True, True], [False, False, False, True]])
 idx = [0, 2]
 s = consensus_score((a_rows, a_cols), (a_rows[idx], a_cols[idx]))
 assert_almost_equal(s, (2.0 / 3.0))

def test_consensus_score_issue2445 ( ) : a_rows = np . array ( [ [ True , True , False , False ] , [ False , False , True , True ] , [ False , False , False , True ] ] ) a_cols = np . array ( [ [ True , True , False , False ] , [ False , False , True , True ] , [ False , False , False , True ] ] ) idx = [ 0 , 2 ] s = consensus_score ( ( a_rows , a_cols ) , ( a_rows [ idx ] , a_cols [ idx ] ) ) assert_almost_equal ( s , ( 2.0 / 3.0 ) )
different number of biclusters in a and b .