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Mh stephen boyd et al #15

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merged 3 commits into from
Oct 20, 2019
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Mh stephen boyd et al #15

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db55a25
Update metropolis_hastings_unit_test.py
FranciscoKloganB Oct 20, 2019
eac2cb7
Update metropolis_hastings_unit_test.py
FranciscoKloganB Oct 20, 2019
a12e485
Update metropolis_hastings_unit_test.py
FranciscoKloganB Oct 20, 2019
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85 changes: 81 additions & 4 deletions hive/app/tests/metropolis_hastings_unit_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,12 +45,83 @@ def construct_rejection_matrix_div_by_zero_error_exist_test():
return print_test("construct_rejection_matrix_div_by_zero_error_exist_test", "no excpetion", "ZeroDivisionError", False)


def arbitrary_matrix_converges_to_ddv():
target = [0.2, 0.3, 0.5, 0.0]
def arbitrary_matrix_converges_to_ddv_1():
target = [0.2, 0.3, 0.5, 0]
adj = np.asarray([[1, 1, 0, 0], [1, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
return print_test("metropols_algorithm_test", target, mh_pow[:, 0], np.allclose(target, mh_pow[:, 0]))
for j in range(mh_pow.shape[1]):
if not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_converges_to_ddv_1", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_converges_to_ddv_1", target, mh_pow[:, 0], True)


def arbitrary_matrix_converges_to_ddv_2():
target = [0.2, 0.3, 0.2, 0.3]
adj = np.asarray([[1, 1, 0, 0], [1, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
for j in range(mh_pow.shape[1]):
if not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_converges_to_ddv_2", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_converges_to_ddv_2", target, mh_pow[:, 0], True)


def arbitrary_matrix_converges_to_ddv_3():
target = [0.2, 0.3, 0.5, 0]
adj = np.asarray([[1, 1, 1, 1], [1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 1, 1]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
for j in range(mh_pow.shape[1]):
if not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_converges_to_ddv_3", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_converges_to_ddv_3", target, mh_pow[:, 0], True)


def arbitrary_matrix_converges_to_ddv_4():
target = [0.0, 0.1, 0.1, 0.8]
adj = np.asarray([[1, 1, 0, 0], [1, 0, 0, 1], [0, 1, 1, 1], [0, 1, 1, 1]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
for j in range(mh_pow.shape[1]):
if not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_converges_to_ddv_4", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_converges_to_ddv_4", target, mh_pow[:, 0], True)


def arbitrary_matrix_converges_to_ddv_5():
target = [0.2, 0.3, 0.5, 0.0]
adj = np.asarray([[1, 1, 0, 0], [1, 0, 1, 0], [0, 1, 1, 0], [0, 1, 1, 0]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
for j in range(mh_pow.shape[1]):
if not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_converges_to_ddv_5", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_converges_to_ddv_5", target, mh_pow[:, 0], True)


def arbitrary_matrix_converges_to_ddv_6():
target = [1, 0, 0, 0]
adj = np.asarray([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
for j in range(mh_pow.shape[1]):
if not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_converges_to_ddv_6", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_converges_to_ddv_6", target, mh_pow[:, 0], True)


def arbitrary_matrix_does_not_converges_to_ddv_1():
target = [1, 0, 0, 0]
adj = np.asarray([[0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0]])
mh = mH.metropolis_algorithm(adj, ddv=target, column_major_in=False, column_major_out=True)
mh_pow = np.linalg.matrix_power(mh, 1000)
for j in range(mh_pow.shape[1]):
if j == 1 and np.allclose(target, mh_pow[:, 1]):
return print_test("arbitrary_matrix_does_not_converges_to_ddv_1", [0, 1, 0, 0], mh_pow[:, 1], False)
elif j != 1 and not np.allclose(target, mh_pow[:, j]):
return print_test("arbitrary_matrix_does_not_converges_to_ddv_1", target, mh_pow[:, j], False)
return print_test("arbitrary_matrix_does_not_converges_to_ddv_1", target, mh_pow[:, 0], True)


if __name__ == "__main__":
Expand All @@ -62,7 +133,13 @@ def arbitrary_matrix_converges_to_ddv():
matrix_converges_to_known_ddv_test,
construct_random_walk_test,
construct_rejection_matrix_div_by_zero_error_exist_test,
arbitrary_matrix_converges_to_ddv,
arbitrary_matrix_converges_to_ddv_1,
arbitrary_matrix_converges_to_ddv_2,
arbitrary_matrix_converges_to_ddv_3,
arbitrary_matrix_converges_to_ddv_4,
arbitrary_matrix_converges_to_ddv_5,
arbitrary_matrix_converges_to_ddv_6,
arbitrary_matrix_does_not_converges_to_ddv_1
]

passed = 0
Expand Down