def test_bias_influence(self):
"""
This method tests the influence of the bias array. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = tools.all_inputs(n)
weight_array = SimulationMajorityLTFArray.normal_weights(
n, k, mu=mu, sigma=sigma, random_instance=RandomState(0xBADA556))
bias_array = SimulationMajorityLTFArray.normal_weights(
1,
k,
mu=mu,
sigma=sigma * 2,
random_instance=RandomState(0xBADAFF1))
biased_ltf_array = SimulationMajorityLTFArray(
weight_array=weight_array,
transform=SimulationMajorityLTFArray.transform_id,
combiner=SimulationMajorityLTFArray.combiner_xor,
sigma_noise=sigma,
random_instance_noise=RandomState(0xCCABAD),
bias=bias_array,
)
ltf_array = SimulationMajorityLTFArray(
weight_array=weight_array,
transform=SimulationMajorityLTFArray.transform_id,
combiner=SimulationMajorityLTFArray.combiner_xor,
sigma_noise=sigma,
random_instance_noise=RandomState(0xCCABAD),
bias=None,
)
self.assertEqual(ltf_array.weight_array.shape,
biased_ltf_array.weight_array.shape)
bias_array = biased_ltf_array.weight_array[:, -1]
bias_array_compared = [
bias == bias_array[0] for bias in bias_array[1:]
]
# the bias values should be different for this test. It is possible that they are all equal but this chance is
# low.
self.assertFalse(array(bias_array_compared).all())
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
# The arithmetic mean of the res
self.assertFalse(array_equal(biased_responses, responses))
def test_bias_influence_array(self):
"""
This method tests the influence of the bias array. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = tools.all_inputs(n)
weight_array = LTFArray.normal_weights(
n, k, mu=mu, sigma=sigma, random_instance=RandomState(0xBADA556))
bias_array = LTFArray.normal_weights(
1,
k,
mu=mu,
sigma=sigma * 2,
random_instance=RandomState(0xBADAFF1))
biased_ltf_array = LTFArray(
weight_array=weight_array,
transform=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
bias=bias_array,
)
ltf_array = LTFArray(
weight_array=weight_array,
transform=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
bias=None,
)
# the second dimension of the weight_array must be the number of elements in biased weight_array
self.assertEqual(
shape(ltf_array.weight_array)[1],
shape(biased_ltf_array.weight_array)[1])
assert_array_equal(biased_ltf_array.weight_array[:, :n],
ltf_array.weight_array[:, :n])
assert_array_equal(biased_ltf_array.weight_array[:, :n], weight_array)
assert_array_equal(biased_ltf_array.weight_array[:, n],
reshape(bias_array, (k, )))
assert_array_equal(ltf_array.weight_array[:, n], zeros((k, )))
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
self.assertFalse(array_equal(biased_responses, responses))
def test_bias_influence_value(self):
"""
This method tests the influence of the bias value. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = array(list(tools.all_inputs(n)))
weight_array = SimulationMajorityLTFArray.normal_weights(n, k, mu=mu, sigma=sigma,
random_instance=RandomState(0xBADA556))
bias_value = 2.5
biased_ltf_array = SimulationMajorityLTFArray(
weight_array=weight_array,
transform=SimulationMajorityLTFArray.transform_id,
combiner=SimulationMajorityLTFArray.combiner_xor,
sigma_noise=sigma,
random_instance_noise=RandomState(0xCCABAD),
bias=bias_value,
)
ltf_array = SimulationMajorityLTFArray(
weight_array=weight_array,
transform=SimulationMajorityLTFArray.transform_id,
combiner=SimulationMajorityLTFArray.combiner_xor,
sigma_noise=sigma,
random_instance_noise=RandomState(0xCCABAD),
bias=None,
)
# the second dimension of the weight_array plus one must be the number of elements in biased weight_array
self.assertEqual(shape(ltf_array.weight_array)[1] + 1, shape(biased_ltf_array.weight_array)[1])
bias_array = biased_ltf_array.weight_array[:, -1]
bias_array_compared = [bias == bias_array[0] for bias in bias_array]
# the bias values should be equal for this tests.
self.assertTrue(array(bias_array_compared).all())
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
# The arithmetic mean of the res
self.assertFalse(array_equal(biased_responses, responses))
def test_bias_influence_value(self):
"""
This method tests the influence of the bias value. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = tools.all_inputs(n)
weight_array = NoisyLTFArray.normal_weights(
n, k, mu=mu, sigma=sigma, random_instance=RandomState(0xBADA556))
bias_value = 2.5
biased_ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=bias_value,
)
ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=None,
)
self.assertEqual(ltf_array.weight_array.shape,
biased_ltf_array.weight_array.shape)
bias_array = biased_ltf_array.weight_array[:, -1]
bias_array_compared = [bias == bias_array[0] for bias in bias_array]
# the bias values should be equal for this test.
self.assertTrue(array(list(bias_array_compared)).all())
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
# The arithmetic mean of the res
self.assertFalse(array_equal(biased_responses, responses))
def test_bias_influence_array(self):
"""
This method tests the influence of the bias array. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = array(list(tools.all_inputs(n)))
weight_array = NoisyLTFArray.normal_weights(n, k, mu=mu, sigma=sigma, random_instance=RandomState(0xBADA556))
bias_array = NoisyLTFArray.normal_weights(1, k, mu=mu, sigma=sigma * 2, random_instance=RandomState(0xBADAFF1))
biased_ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=bias_array,
)
ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=None,
)
# the second dimension of the weight_array plus one must be the number of elements in biased weight_array
self.assertEqual(shape(ltf_array.weight_array)[1] + 1, shape(biased_ltf_array.weight_array)[1])
bias_array = biased_ltf_array.weight_array[:, -1]
bias_array_compared = [bias == bias_array[0] for bias in bias_array[1:]]
# the bias values should be different for this test. It is possible that they are all equal but this chance is
# low.
self.assertFalse(array(bias_array_compared).all())
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
# The arithmetic mean of the res
self.assertFalse(array_equal(biased_responses, responses))
def test_all_inputs(self):
"""This checks the shape and type of the returned multidimensional array."""
n = 8
N = 2**n
arr = all_inputs(n)
self.check_multi_dimensional_array(arr, N, n, BIT_TYPE)
