def propagationConstraint(self, network: Network, input_data,
output_data, anneal_inputs=False,
lagrange = 1):
# training data
# TODO: pass training data by argument and process function in loop
values = Array(input_data)
expected = Array(output_data)
# forward propagation
for layer_num, layer in enumerate(network.layers[:-1]):
# if you want to ensure input layer will not be annealed
if layer_num == 0 and not anneal_inputs:
values = values.dot(network.weights[layer_num])
continue
qubit_values = []
nodes = {self.get_label(layer_num, i) for i in range(layer)}
# TODO: delete zeros from matrices to speed up computation
for node in nodes:
# making sure the variable is in final equation
if node not in self.H_vars:
x_var = Binary(node)
self.H_vars.add(x_var)
else:
x_var = H_vars[node]
qubit_values.append(x_var)
print(self.H_vars)
values *= Array(qubit_values)
values = values.dot(network.weights[layer_num])
# values is output array at this point
self.H += sum((values - expected)*(values - expected))
def test_array_dot(self):
# inner product of 1-D arrays
array_a = Array([Binary('a'), Binary('b')])
array_b = Array([Binary('c'), Binary('d')])
expected = ((Binary('a') * Binary('c')) + (Binary('b') * Binary('d')))
self.assertTrue(expected == array_a.dot(array_b))
# dot product of 1-D array and N-D array
array_a = Array([[Binary('a'), Binary('b')],
[Binary('c'), Binary('d')]])
array_b = Array([Binary('e'), Binary('f')])
expected = Array([
((Binary('a') * Binary('e')) + (Binary('b') * Binary('f'))),
((Binary('c') * Binary('e')) + (Binary('d') * Binary('f')))
])
self.assertTrue(expected == array_a.dot(array_b))
# both are 2-D arrays
array_a = Array([[Binary('a'), Binary('b')],
[Binary('c'), Binary('d')]])
array_b = Array([[Binary('e'), Binary('f')],
[Binary('g'), Binary('h')]])
expected = Array(
[[((Binary('a') * Binary('e')) + (Binary('b') * Binary('g'))),
((Binary('a') * Binary('f')) + (Binary('b') * Binary('h')))],
[((Binary('c') * Binary('e')) + (Binary('d') * Binary('g'))),
((Binary('c') * Binary('f')) + (Binary('d') * Binary('h')))]])
self.assertTrue(expected == array_a.dot(array_b))
# array_a is a N-D array and array_b is an M-D array (where N, M>=2)
array_a = Array.create('a', shape=(3, 2, 4), vartype='BINARY')
array_b = Array.create('b', shape=(5, 4, 3), vartype='BINARY')
i, j, k, m = (1, 1, 3, 2)
self.assertTrue(
array_a.dot(array_b)[i, j, k, m] == sum(array_a[i, j, :] *
array_b[k, :, m]))
# array_a is 1-D array and array_b is a list
array_a = Array([Binary('a'), Binary('b')])
array_b = [3, 4]
self.assertTrue((Binary('a') * 3 +
(Binary('b') * 4)) == array_a.dot(array_b))
# test validation
self.assertRaises(TypeError, lambda: array_a.dot(1))
