def __init__(self):
"""Initialize data set and all test cases for assignment.
Notes:
* All sizes requested are transposed to account for column matrix as default vector in matlab.
a4_rand(..) then returns a transposed matrix to have the correct shape.
"""
super(A4Run, self).__init__()
data = loadmat(os.path.join(os.getcwd(), 'Data/data.mat'))
self.data_sets = data['data']
self.test_rbm_w = self.a4_rand([256, 100], 0) * 2 - 1
self.small_test_rbm_w = self.a4_rand([256, 10], 0) * 2 - 1
self.data_1_case = self.sample_bernoulli(self.extract_mini_batch(
self.data_sets['training'], 0, 1)['inputs'],
report_calls=False)
self.data_10_cases = self.sample_bernoulli(self.extract_mini_batch(
self.data_sets['training'], 99, 10)['inputs'],
report_calls=False)
self.data_37_cases = self.sample_bernoulli(self.extract_mini_batch(
self.data_sets['training'], 199, 37)['inputs'],
report_calls=False)
self.test_hidden_state_1_case = self.sample_bernoulli(
self.a4_rand([1, 100], 0), report_calls=False)
self.test_hidden_state_10_cases = self.sample_bernoulli(
self.a4_rand([10, 100], 1), report_calls=False)
self.test_hidden_state_37_cases = self.sample_bernoulli(
self.a4_rand([37, 100], 2), report_calls=False)
def __init__(self):
"""
Notes:
* All sizes requested are transposed to account for column matrix as default vector in matlab.
a4_rand(..) then returns a transposed matrix to have the correct shape.
"""
a4_randomness_source = loadmat(os.path.join(os.getcwd(), 'Data/a4_randomness_source.mat'))
self.randomness_source = a4_randomness_source['randomness_source']
def __init__(self):
"""
Notes:
* All sizes requested are transposed to account for column matrix as default vector in matlab.
a4_rand(..) then returns a transposed matrix to have the correct shape.
"""
a4_randomness_source = loadmat(
os.path.join(os.getcwd(), 'Data/a4_randomness_source.mat'))
self.randomness_source = a4_randomness_source['randomness_source']
def __init__(self):
"""Initialize data set and all test cases for assignment.
Notes:
* All sizes requested are transposed to account for column matrix as default vector in matlab.
a4_rand(..) then returns a transposed matrix to have the correct shape.
"""
super(A4Run, self).__init__()
data = loadmat(os.path.join(os.getcwd(), 'Data/data.mat'))
self.data_sets = data['data']
self.test_rbm_w = self.a4_rand([256, 100], 0) * 2 - 1
self.small_test_rbm_w = self.a4_rand([256, 10], 0) * 2 - 1
self.data_1_case = self.sample_bernoulli(self.extract_mini_batch(self.data_sets['training'], 0, 1)['inputs'],
report_calls=False)
self.data_10_cases = self.sample_bernoulli(self.extract_mini_batch(self.data_sets['training'], 99,
10)['inputs'], report_calls=False)
self.data_37_cases = self.sample_bernoulli(self.extract_mini_batch(self.data_sets['training'], 199,
37)['inputs'], report_calls=False)
self.test_hidden_state_1_case = self.sample_bernoulli(self.a4_rand([1, 100], 0), report_calls=False)
self.test_hidden_state_10_cases = self.sample_bernoulli(self.a4_rand([10, 100], 1), report_calls=False)
self.test_hidden_state_37_cases = self.sample_bernoulli(self.a4_rand([37, 100], 2), report_calls=False)
def __init__(self):
"""Initialize data set and all test cases for assignment.
"""
data = loadmat(os.path.join(os.getcwd(), 'Data/data.mat'))
self.data_sets = data['data']
def __init__(self):
"""Initialize data set and all test cases for assignment.
"""
data = loadmat(os.path.join(os.getcwd(), 'Data/data.mat'))
self.data_sets = data['data']