adjacency_matrix.py

import unittest

class TestExtract(unittest.TestCase):

    def test_adjacency_matrix(self):
        X = np.array([
            [1, 8, 3],
            [5, 0, 0],
            [0, 4, 2]])
        tested = adjacency_matrix(X)
        expected = np.array([
            [(0,), (1,), (2,)],
            [(1,), (0,), (0,)],
            [(2,), (0,), (0,)]], dtype=[('weight', '<i8')])
        test = np.array_equal(expected, tested)
        msg = '\nExpected:\n{}\nGot:\n{}'.format(expected, tested)
        self.assertTrue(test, msg=msg)


def adjacency_matrix(mat):
    '''
    Returns adjacency matrix from a bipartite graph weights matrix
    '''
    n_samples = mat.shape[0]
    res = np.ndarray((n_samples, n_samples), dtype=np.dtype([("weight", int)]))
    i = 0
    while i < n_samples:
        j = 0
        while j < n_samples:
            if i == j:
                res[i][j] = (0,)
            else:
                res[i][j] = (np.logical_and(mat[i], mat[j]).sum(),)
            j += 1
        i += 1
    return res