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)