def test_ideal_2(self):
tf.compat.v1.disable_eager_execution()
linguistic_scale_size = 5
first_tuple = Model2Tuple(term_index=3,
alpha=0.2,
linguistic_scale_size=linguistic_scale_size,
weight=None)
second_tuple = Model2Tuple(term_index=2,
alpha=0,
linguistic_scale_size=linguistic_scale_size,
weight=None)
aggregate_and_check(first_tuple, second_tuple)
def test_ideal_negative_weights_1(self):
tf.compat.v1.disable_eager_execution()
linguistic_scale_size = 5
first_tuple = Model2Tuple(term_index=4,
alpha=0.2,
linguistic_scale_size=linguistic_scale_size,
weight=0.5)
second_tuple = Model2Tuple(term_index=3,
alpha=-0.1,
linguistic_scale_size=linguistic_scale_size,
weight=0.5)
aggregate_and_check(first_tuple, second_tuple)
def main():
print('Converting 2-tuple to TPR')
tf.compat.v1.disable_eager_execution()
linguistic_scale_size = 5
first_tuple = Model2Tuple(term_index=4,
alpha=0.2,
linguistic_scale_size=linguistic_scale_size)
second_tuple = Model2Tuple(term_index=2,
alpha=-0.1,
linguistic_scale_size=linguistic_scale_size)
aggregate_and_check(first_tuple, second_tuple)
print('Converting 2-tuple to TPR and back works with no information loss!')
mta_result_encoded, _ = encode_model_2_tuple(first_tuple)