def test_transform(self):
testing_object = neurone.Neurone(
b=3, bias=2, weights=[-5, 17, -4.67, 9, 0, -27, -13, 11, 99.2, 37])
transformation = testing_object.transform_to_object()
del testing_object
testing_object_new = neurone.Neurone()
testing_object_new.transform_from_object(transformation)
del transformation
self.assertEqual(testing_object_new.get_size(), 10)
self.assertEqual(testing_object_new.get_weights_size(), 10)
self.assertEqual(testing_object_new.get_bias(), 2)
self.assertEqual(testing_object_new.get_b(), 3)
self.assertEqual(testing_object_new.get_weights(),
[-5, 17, -4.67, 9, 0, -27, -13, 11, 99.2, 37])
def test_get_output2(self):
testing_object = neurone.Neurone(
b=2, bias=2, weights=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
self.assertEqual(
testing_object.get_output([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
0.9820137900379085)
"""Trzy pierwsze testy back propagation sprawdzają czy ustawianie teaching_ratio i teaching_variable daje odpowiednie efekty"""
def random_generate(self):
if (self.__size_of_input is None or self.__count_of_neurons is None
or len(self.__array_of_neurons) != 0):
raise Exception("Hidden Layer is actualy generate")
for i in range(self.__count_of_neurons):
new_neurone = Neurone.Neurone(size=self.__size_of_input,
teaching=True)
new_neurone.random_generate()
self.__array_of_neurons.append(new_neurone)
def test_sets(self):
testing_object = neurone.Neurone(
b=3, bias=2, weights=[-5, 17, -4.67, 9, 0, -27, -13, 11, 99.2, 37])
self.assertEqual(testing_object.get_b(), 3)
self.assertEqual(testing_object.get_bias(), 2)
self.assertEqual(testing_object.get_weights(),
[-5, 17, -4.67, 9, 0, -27, -13, 11, 99.2, 37])
testing_object.set_weight(4, 17)
self.assertEqual(testing_object.get_weights(),
[-5, 17, -4.67, 9, 17, -27, -13, 11, 99.2, 37])
def transform_from_object(self, obj):
if len(self.__array_of_neurons) != 0:
raise Exception("You cannot create")
self.__count_of_neurons = obj['countOfNeurons']
self.__size_of_input = obj['sizeOfInput']
self.__array_of_neurons = []
for i in obj['arrayOfNeurons']:
new_neurone = Neurone.Neurone()
new_neurone.transform_from_object(i)
self.__array_of_neurons.append(new_neurone)
def test_back_propagation5(self):
testing_object = neurone.Neurone(
b=2, bias=2, weights=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
self.assertEqual(
testing_object.get_output(
[1, 2, 7, 0, 19, 7.5, 4.32, 9, 181, -1083]),
0.9820137900379085)
testing_object.back_propagation(77, 0)
self.assertEqual(
testing_object.get_output(
[1, 2, 7, 0, 19, 7.5, 4.32, 9, 181, -1083]),
0.9820137900379085)
def test_get_output(self):
testing_object = neurone.Neurone(b=2,
bias=2,
weights=[1, 2, 3, 4, 5, 6])
self.assertEqual(testing_object.get_output([6, 7, 4, 19, 7, 1]), 1.0)