def test_equation_constrained_hebbian_learning_ec_ca3(self):
hpc = HPC([49, 240, 1600, 480, 49],
0.67, 0.25, 0.04, # connection rates: (in_ec, ec_dg, dg_ca3)
0.10, 0.01, 0.04, # firing rates: (ec, dg, ca3)
0.7, 1, 0.1, 0.5, # gamma, epsilon, nu, turnover rate
0.10, 0.95, 0.8, 2.0) # k_m, k_r, a_i, alpha
activation_values_l1 = np.zeros_like(hpc.ec_values.get_value()).astype(np.float32)
activation_values_l1.put([0, 0], 1)
activation_values_l1.put([0, 1], 1)
hpc.set_ec_values(activation_values_l1)
activation_values_l2 = np.zeros_like(hpc.ca3_values.get_value()).astype(np.float32)
activation_values_l2.put([0, 0], 1)
hpc.set_ca3_values(activation_values_l2)
current_weight_element_0 = hpc.ec_ca3_weights.get_value()[0][0]
next_weight_element_0 = current_weight_element_0 + hpc._nu * (1 - current_weight_element_0)
current_weight_element_1 = hpc.ec_ca3_weights.get_value()[1][0]
next_weight_element_1 = current_weight_element_1 + hpc._nu * (1 - current_weight_element_1)
# wire EC to CA3
n_rows = hpc.ec_values.get_value(return_internal_type=True).shape[1]
n_cols = hpc.ca3_values.get_value(return_internal_type=True).shape[1]
u_next_for_elemwise_ops = [hpc.ca3_values.get_value(return_internal_type=True)[0]] * n_rows
u_prev_for_elemwise_ops_transposed = [hpc.ec_values.get_value(return_internal_type=True)[0]] * n_cols
hpc.wire_ec_to_ca3(u_prev_for_elemwise_ops_transposed, u_next_for_elemwise_ops,
hpc.ec_ca3_weights.get_value(return_internal_type=True))
self.assertAlmostEqual(hpc.ec_ca3_weights.get_value()[0][0], next_weight_element_0, places=6,
msg="Weight update did not correspond to the predicted update value of the equation: "
"next_weight_el_0 != w_el : "+str(next_weight_element_0)+" != " +
str(hpc.ec_ca3_weights.get_value()[0][0]))
self.assertAlmostEqual(hpc.ec_ca3_weights.get_value()[1][0], next_weight_element_1, places=6,
msg="Weight update did not correspond to the predicted update value of the equation: "
"next_weight_el_0 != w_el : "+str(next_weight_element_1)+" != " +
str(hpc.ec_ca3_weights.get_value()[1][0]))
# last element in weight matrix:
weight_rows = activation_values_l1.shape[1]
weight_columns = activation_values_l2.shape[1]
# self.assertEqual(weight_columns, 480, msg="weight columns between ec-ca3 wasn't 480")
# self.assertEqual(weight_rows, 240, msg="weight rows between ec-ca3 wasn't 240")
activation_values_l1.put([0, weight_rows-1], 1) # set last element of activation values to 1
activation_values_l2.put([0, weight_columns-1], 1) # set last element of activation values to 1
hpc.set_ec_values(activation_values_l1)
hpc.set_ca3_values(activation_values_l2)
current_weight_element = hpc.ec_ca3_weights.get_value()[weight_rows-1][weight_columns-1]
next_weight_element = current_weight_element + hpc._nu * (1 - current_weight_element)
# wire EC to CA3
n_rows = hpc.ec_values.get_value(return_internal_type=True).shape[1]
n_cols = hpc.ca3_values.get_value(return_internal_type=True).shape[1]
u_next_for_elemwise_ops = [hpc.ca3_values.get_value(return_internal_type=True)[0]] * n_rows
u_prev_for_elemwise_ops_transposed = [hpc.ec_values.get_value(return_internal_type=True)[0]] * n_cols
hpc.wire_ec_to_ca3(u_prev_for_elemwise_ops_transposed, u_next_for_elemwise_ops,
hpc.ec_ca3_weights.get_value(return_internal_type=True))
self.assertAlmostEqual(hpc.ec_ca3_weights.get_value()[weight_rows-1][weight_columns-1], next_weight_element,
places=6, msg="Weight update did not correspond to the predicted update value of the "
"equation: next_weight_el != w_el : "+str(next_weight_element)+" != " +
str(hpc.ec_ca3_weights.get_value()[weight_rows-1][weight_columns-1]))
