import numpy as np
import tensorflow as tf
import unittest
hungarian_module = tf.load_op_library("hungarian.so")


class HungarianTests(unittest.TestCase):

  def test_min_weighted_bp_cover_1(self):
    W = np.array([[3, 2, 2], [1, 2, 0], [2, 2, 1]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([2, 1, 1])
    c_1_t = np.array([1, 1, 0])
    M_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

    pass

  def test_min_weighted_bp_cover_2(self):
    W = np.array([[5, 0, 4, 0], [0, 4, 6, 8], [4, 0, 5, 7]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([5, 6, 5])
    c_1_t = np.array([0, 0, 0, 2])
    M_t = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

  def test_min_weighted_bp_cover_3(self):
    W = np.array([[5, 0, 2], [3, 1, 0], [0, 5, 0]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([2, 0, 4])
    c_1_t = np.array([3, 1, 0])
    M_t = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

  def test_min_weighted_bp_cover_4(self):
    W = np.array([[[5, 0, 2], [3, 1, 0], [0, 5, 0]], [[3, 2, 2], [1, 2, 0],
                                                      [2, 2, 1]]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([[2, 0, 4], [2, 1, 1]])
    c_1_t = np.array([[3, 1, 0], [1, 1, 0]])
    M_t = np.array([[[0, 0, 1], [1, 0, 0], [0, 1, 0]], [[1, 0, 0], [0, 1, 0],
                                                        [0, 0, 1]]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

  def test_real_values_1(self):
    # Test the while loop terminates with real values.
    W = np.array(
        [[0.90, 0.70, 0.30, 0.20, 0.40, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.80, 0.75, 0.92, 0.10, 0.15, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.78, 0.85, 0.66, 0.29, 0.21, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.42, 0.55, 0.23, 0.43, 0.33, 0.002, 0.001, 0.001, 0.001, 0.001],
         [0.64, 0.44, 0.33, 0.33, 0.34, 0.001, 0.002, 0.001, 0.001, 0.001],
         [0.22, 0.55, 0.43, 0.43, 0.14, 0.001, 0.001, 0.002, 0.001, 0.001],
         [0.43, 0.33, 0.34, 0.22, 0.14, 0.001, 0.001, 0.001, 0.002, 0.001],
         [0.33, 0.42, 0.23, 0.13, 0.43, 0.001, 0.001, 0.001, 0.001, 0.002],
         [0.39, 0.24, 0.53, 0.56, 0.89, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.12, 0.34, 0.82, 0.82, 0.77, 0.001, 0.001, 0.001, 0.001, 0.001]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
    M_t = np.array(
        [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
         [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]])
    self.assertTrue((M == M_t).all())

  def test_real_values_2(self):
    W = np.array([[
        0.00604139, 0.0126045, 0.0117373, 0.01245, 0.00808836, 0.0162662,
        0.0137996, 0.00403898, 0.0123786, 1e-05
    ], [
        0.00604229, 0.0126071, 0.0117400, 0.0124528, 0.00808971, 0.0162703,
        0.0138028, 0.00403935, 0.0123812, 1e-05
    ], [
        0.00604234, 0.0126073, 0.0117402, 0.012453, 0.00808980, 0.0162706,
        0.0138030, 0.00403937, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_3(self):
    W = np.array([[
        0.00302646, 0.00321431, 0.0217552, 0.00836773, 0.0256353, 0.0177026,
        0.0289461, 0.0214768, 0.0101898, 1e-05
    ], [
        0.00302875, 0.003217, 0.0217628, 0.00836405, 0.0256229, 0.0177137,
        0.0289468, 0.0214719, 0.0101904, 1e-05
    ], [
        0.00302897, 0.00321726, 0.0217636, 0.00836369, 0.0256217, 0.0177148,
        0.0289468, 0.0214714, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.0177149,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.0177149,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_4(self):
    W = np.array([[
        1e-05, 0.0634311, 1e-05, 4.76687e-05, 1.00079e-05, 1.00378e-05, 1e-05,
        1e-05, 1e-05, 3.9034e-05
    ], [
        1e-05, 3.42696e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1.0122e-05,
        3.43236e-05, 1e-05
    ], [
        1e-05, 0.0426792, 0.031155, 1.0008e-05, 0.00483961, 0.0228187, 1e-05,
        1e-05, 1e-05, 0.102463
    ], [
        1e-05, 1e-05, 1e-05, 1.07065e-05, 1e-05, 1.00185e-05, 1e-05, 1e-05,
        1e-05, 1.00007e-05
    ], [
        1e-05, 4.22947e-05, 0.00062168, 0.623917, 1.03468e-05, 0.00588984,
        1.00004e-05, 1.44433e-05, 1.00014e-05, 0.000213425
    ], [
        1e-05, 1.01764e-05, 1e-05, 0.000667249, 1e-05, 0.000485082, 1e-05,
        1e-05, 1.00002e-05, 1e-05
    ], [
        1e-05, 1e-05, 1.50331e-05, 1e-05, 0.11269, 1e-05, 1e-05, 1e-05, 1e-05,
        1.13251e-05
    ], [
        1.0001e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.0246974, 1e-05, 1e-05,
        1e-05
    ], [
        1e-05, 2.89144e-05, 1e-05, 1.05147e-05, 1e-05, 0.000894762, 1.03587e-05,
        0.150301, 1e-05, 1.00045e-05
    ], [
        1e-05, 3.97901e-05, 1e-05, 1.11641e-05, 1e-05, 2.34249e-05, 1.0007e-05,
        2.42828e-05, 1e-05, 1.10529e-05
    ]])

    p = 1e6
    W = np.round(W * p) / p
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_5(self):
    W = np.array([[
        1.4e-05, 1e-05, 1e-05, 0.053306, 0.044139, 1e-05, 1.2e-05, 1e-05, 1e-05,
        1e-05
    ], [
        0.001234, 1e-05, 1e-05, 2.1e-05, 1e-05, 0.001535, 0.019553, 1e-05,
        1e-05, 1e-05
    ], [
        0.002148, 1e-05, 1e-05, 1.6e-05, 0.651536, 2e-05, 7.4e-05, 0.002359,
        1e-05, 1e-05
    ], [
        3.8e-05, 1e-05, 0.000592, 4.7e-05, 0.09173, 1e-05, 1e-05, 1e-05, 1e-05,
        1e-05
    ], [
        1e-05, 1e-05, 1e-05, 0.213736, 1e-05, 4.5e-05, 0.000768, 1e-05, 1e-05,
        1e-05
    ], [
        1e-05, 1e-05, 1e-05, 0.317609, 1e-05, 1e-05, 0.002151, 1e-05, 1e-05,
        1e-05
    ], [
        0.002802, 1e-05, 1.2e-05, 1e-05, 1e-05, 0.002999, 4.8e-05, 1.1e-05,
        0.000919, 1e-05
    ], [
        1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.028816, 1e-05
    ], [
        1e-05, 1e-05, 0.047335, 1e-05, 1.2e-05, 1e-05, 1e-05, 1e-05, 1e-05,
        1e-05
    ], [1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05]])

    p = 1e6
    W = np.round(W * p) / p
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_6(self):
    W = np.array([[
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ]])

    p = 1e6
    W = np.round(W * p) / p
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()


if __name__ == '__main__':
  suite = unittest.TestLoader().loadTestsFromTestCase(HungarianTests)
  unittest.TextTestRunner(verbosity=2).run(suite)

import numpy as np
import tensorflow as tf
import unittest
hungarian_module = tf.load_op_library("hungarian.so")


class HungarianTests(unittest.TestCase):

  def test_min_weighted_bp_cover_1(self):
    W = np.array([[3, 2, 2], [1, 2, 0], [2, 2, 1]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([2, 1, 1])
    c_1_t = np.array([1, 1, 0])
    M_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

    pass

  def test_min_weighted_bp_cover_2(self):
    W = np.array([[5, 0, 4, 0], [0, 4, 6, 8], [4, 0, 5, 7]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([5, 6, 5])
    c_1_t = np.array([0, 0, 0, 2])
    M_t = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

  def test_min_weighted_bp_cover_3(self):
    W = np.array([[5, 0, 2], [3, 1, 0], [0, 5, 0]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([2, 0, 4])
    c_1_t = np.array([3, 1, 0])
    M_t = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

  def test_min_weighted_bp_cover_4(self):
    W = np.array([[[5, 0, 2], [3, 1, 0], [0, 5, 0]], [[3, 2, 2], [1, 2, 0],
                                                      [2, 2, 1]]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
      c_0 = c_0.eval()
      c_1 = c_1.eval()
    c_0_t = np.array([[2, 0, 4], [2, 1, 1]])
    c_1_t = np.array([[3, 1, 0], [1, 1, 0]])
    M_t = np.array([[[0, 0, 1], [1, 0, 0], [0, 1, 0]], [[1, 0, 0], [0, 1, 0],
                                                        [0, 0, 1]]])
    self.assertTrue((c_0.flatten() == c_0_t.flatten()).all())
    self.assertTrue((c_1.flatten() == c_1_t.flatten()).all())
    self.assertTrue((M == M_t).all())

  def test_real_values_1(self):
    # Test the while loop terminates with real values.
    W = np.array(
        [[0.90, 0.70, 0.30, 0.20, 0.40, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.80, 0.75, 0.92, 0.10, 0.15, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.78, 0.85, 0.66, 0.29, 0.21, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.42, 0.55, 0.23, 0.43, 0.33, 0.002, 0.001, 0.001, 0.001, 0.001],
         [0.64, 0.44, 0.33, 0.33, 0.34, 0.001, 0.002, 0.001, 0.001, 0.001],
         [0.22, 0.55, 0.43, 0.43, 0.14, 0.001, 0.001, 0.002, 0.001, 0.001],
         [0.43, 0.33, 0.34, 0.22, 0.14, 0.001, 0.001, 0.001, 0.002, 0.001],
         [0.33, 0.42, 0.23, 0.13, 0.43, 0.001, 0.001, 0.001, 0.001, 0.002],
         [0.39, 0.24, 0.53, 0.56, 0.89, 0.001, 0.001, 0.001, 0.001, 0.001],
         [0.12, 0.34, 0.82, 0.82, 0.77, 0.001, 0.001, 0.001, 0.001, 0.001]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()
    M_t = np.array(
        [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
         [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]])
    self.assertTrue((M == M_t).all())

  def test_real_values_2(self):
    W = np.array([[
        0.00604139, 0.0126045, 0.0117373, 0.01245, 0.00808836, 0.0162662,
        0.0137996, 0.00403898, 0.0123786, 1e-05
    ], [
        0.00604229, 0.0126071, 0.0117400, 0.0124528, 0.00808971, 0.0162703,
        0.0138028, 0.00403935, 0.0123812, 1e-05
    ], [
        0.00604234, 0.0126073, 0.0117402, 0.012453, 0.00808980, 0.0162706,
        0.0138030, 0.00403937, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ], [
        0.00604235, 0.0126073, 0.0117402, 0.012453, 0.00808981, 0.0162706,
        0.0138030, 0.00403938, 0.0123814, 1e-05
    ]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_3(self):
    W = np.array([[
        0.00302646, 0.00321431, 0.0217552, 0.00836773, 0.0256353, 0.0177026,
        0.0289461, 0.0214768, 0.0101898, 1e-05
    ], [
        0.00302875, 0.003217, 0.0217628, 0.00836405, 0.0256229, 0.0177137,
        0.0289468, 0.0214719, 0.0101904, 1e-05
    ], [
        0.00302897, 0.00321726, 0.0217636, 0.00836369, 0.0256217, 0.0177148,
        0.0289468, 0.0214714, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.0177149,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.0177149,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ], [
        0.003029, 0.0032173, 0.0217637, 0.00836364, 0.0256216, 0.017715,
        0.0289468, 0.0214713, 0.0101905, 1e-05
    ]])
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_4(self):
    W = np.array([[
        1e-05, 0.0634311, 1e-05, 4.76687e-05, 1.00079e-05, 1.00378e-05, 1e-05,
        1e-05, 1e-05, 3.9034e-05
    ], [
        1e-05, 3.42696e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1.0122e-05,
        3.43236e-05, 1e-05
    ], [
        1e-05, 0.0426792, 0.031155, 1.0008e-05, 0.00483961, 0.0228187, 1e-05,
        1e-05, 1e-05, 0.102463
    ], [
        1e-05, 1e-05, 1e-05, 1.07065e-05, 1e-05, 1.00185e-05, 1e-05, 1e-05,
        1e-05, 1.00007e-05
    ], [
        1e-05, 4.22947e-05, 0.00062168, 0.623917, 1.03468e-05, 0.00588984,
        1.00004e-05, 1.44433e-05, 1.00014e-05, 0.000213425
    ], [
        1e-05, 1.01764e-05, 1e-05, 0.000667249, 1e-05, 0.000485082, 1e-05,
        1e-05, 1.00002e-05, 1e-05
    ], [
        1e-05, 1e-05, 1.50331e-05, 1e-05, 0.11269, 1e-05, 1e-05, 1e-05, 1e-05,
        1.13251e-05
    ], [
        1.0001e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.0246974, 1e-05, 1e-05,
        1e-05
    ], [
        1e-05, 2.89144e-05, 1e-05, 1.05147e-05, 1e-05, 0.000894762, 1.03587e-05,
        0.150301, 1e-05, 1.00045e-05
    ], [
        1e-05, 3.97901e-05, 1e-05, 1.11641e-05, 1e-05, 2.34249e-05, 1.0007e-05,
        2.42828e-05, 1e-05, 1.10529e-05
    ]])

    p = 1e6
    W = np.round(W * p) / p
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_5(self):
    W = np.array([[
        1.4e-05, 1e-05, 1e-05, 0.053306, 0.044139, 1e-05, 1.2e-05, 1e-05, 1e-05,
        1e-05
    ], [
        0.001234, 1e-05, 1e-05, 2.1e-05, 1e-05, 0.001535, 0.019553, 1e-05,
        1e-05, 1e-05
    ], [
        0.002148, 1e-05, 1e-05, 1.6e-05, 0.651536, 2e-05, 7.4e-05, 0.002359,
        1e-05, 1e-05
    ], [
        3.8e-05, 1e-05, 0.000592, 4.7e-05, 0.09173, 1e-05, 1e-05, 1e-05, 1e-05,
        1e-05
    ], [
        1e-05, 1e-05, 1e-05, 0.213736, 1e-05, 4.5e-05, 0.000768, 1e-05, 1e-05,
        1e-05
    ], [
        1e-05, 1e-05, 1e-05, 0.317609, 1e-05, 1e-05, 0.002151, 1e-05, 1e-05,
        1e-05
    ], [
        0.002802, 1e-05, 1.2e-05, 1e-05, 1e-05, 0.002999, 4.8e-05, 1.1e-05,
        0.000919, 1e-05
    ], [
        1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.028816, 1e-05
    ], [
        1e-05, 1e-05, 0.047335, 1e-05, 1.2e-05, 1e-05, 1e-05, 1e-05, 1e-05,
        1e-05
    ], [1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05]])

    p = 1e6
    W = np.round(W * p) / p
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()

  def test_real_values_6(self):
    W = np.array([[
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ], [
        0.003408, 0.010531, 0.002795, 1e-05, 0.019786, 0.010435, 0.002743,
        0.023617, 0.010436, 0.003116
    ]])

    p = 1e6
    W = np.round(W * p) / p
    M, c_0, c_1 = hungarian_module.hungarian(W)
    with tf.Session() as sess:
      M = M.eval()


if __name__ == '__main__':
  suite = unittest.TestLoader().loadTestsFromTestCase(HungarianTests)
  unittest.TextTestRunner(verbosity=2).run(suite)

# Test the while loop terminates with real values.