def test_think_1():
training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
neuron = Neuron(3, 1, seed)
expected = array([ [0.2689864], [0.3262757], [0.23762817], [0.36375058]])
result = neuron.think(training_set_inputs)
testing.assert_allclose(expected, result)
def test_think_2():
training_set_inputs = array([[0, 0, 1, 0], [1, 1, 1, 0], [1, 0, 1, 0], [0, 1, 1, 0]])
neuron = Neuron(4, 2, seed)
expected = array([[0.33037528, 0.3067574 ], [0.13328558, 0.31647723], [0.29474597, 0.40741215], [0.15364951, 0.22958471]])
result = neuron.think(training_set_inputs)
testing.assert_allclose(expected, result)
def test_convert_to_between_minus_one_and_one_3():
neuron = Neuron(1, 1, seed)
expected = -0.5
result = neuron.convert_to_between_minus_one_and_one(0.25)
assert expected == result
def test___get_synaptic_stating_weights_3_1():
neuron = Neuron(3, 1, seed)
expected = array([[-0.16595599],[ 0.44064899], [-0.99977125]])
result = neuron.synaptic_weights
testing.assert_allclose(expected, result)
def test_convert_to_between_minus_one_and_one_1():
neuron = Neuron(1, 1, seed)
expected = 1
result = neuron.convert_to_between_minus_one_and_one(1)
assert expected == result
def test_sigmoid_3():
neuron = Neuron(1, 1, seed)
expected = 0.9525741268224334
result = neuron.sigmoid(3)
assert expected == result
def test_sigmoid_2():
neuron = Neuron(1, 1, seed)
expected = 0.8807970779778823
result = neuron.sigmoid(2)
assert expected == result
def test_sigmoid_1():
neuron = Neuron(1, 1, seed)
expected = 0.7310585786300049
result = neuron.sigmoid(1)
assert expected == result
def test___get_synaptic_stating_weights_4_2():
neuron = Neuron(4, 2, seed)
expected = array([[-0.16595599, 0.44064899], [-0.99977125, -0.39533485], [-0.70648822, -0.81532281], [-0.62747958, -0.30887855]])
result = neuron.synaptic_weights
testing.assert_allclose(expected, result)