def cross_validate(network_shape, epochs_num, learn_rate, _groups_x,
_groups_y):
k = _groups_x.shape[0]
_sum = 0
results = np.zeros(k)
for i in range(k):
train_x = None
train_y = None
valid_x = np.copy(
_groups_x[i]) # the validation set for th i'th iteration.
valid_y = np.copy(_groups_y[i])
net = NeuralNetwork(network_shape, epochs_num, learn_rate)
for j in range(k):
if j != i:
# arrange the train set for the i'th iteration.
if train_x is None:
train_x = np.copy(_groups_x[j])
train_y = np.copy(_groups_y[j])
else:
train_x = np.concatenate((train_x, _groups_x[j]), axis=0)
train_y = np.concatenate((train_y, _groups_y[j]), axis=0)
old_mins, denoms = norm.minmax_params(train_x)
train_x = norm.minmax(train_x, 0, 1)
valid_x = norm.minmax(valid_x, 0, 1, old_mins, denoms)
net.train(train_x, train_y)
results[i] = net.accuracy(valid_x, valid_y)
old_mins, denoms = norm.minmax_params(train_x)
train_x = norm.minmax(train_x, 0, 1)
valid_x = norm.minmax(valid_x, 0, 1, old_mins, denoms)
print(results)
return np.average(results)
