#%%
if __name__ == "__main__":
print 'This file contains the Neural Network example'
# Parameters and settings
data_lag = 3
N_input_units = data_lag
N_hidden_units = 8
N_output_units = 1
net_fit_repeat = 5
net_max_iter = 10000
net_best_err = 9e9
# Load the Sunspot dataset
year, reading, lag_matrix = get_sunspot(data_lag)
train, test = data_split(lag_matrix)
# Repeat network fit to find the one with the lowest error
net_best = None
for net_fit_count in range(net_fit_repeat):
net = NeuralNetwork(structure=(N_input_units - 1, N_hidden_units,
N_output_units),
train_input=train[:, 1:],
train_target=train[:, 0],
test_input=test[:, 1:],
test_target=test[:, 0],
max_iter=net_max_iter)
net.train()
if net.e_test[-1] < net_best_err:
#%%
if __name__ == "__main__":
print 'This file contains the Gaussian Process example'
# Parameters and settings
N_SIG2 = 20
N_BETA = 20
MAX_BETA = 1
MIN_BETA = 500
MAX_SIG2 = 2
MIN_SIG2 = 0.01
# Load the sunspot dataset
lag = 5
year, reading, lag_matrix = get_sunspot(lag)
empirical_var = np.var(lag_matrix[:, 0]) # Some statics (for later use)
# Split data set into a train/test set
train, test = data_split(lag_matrix)
train_input = train[:, 1:]
train_target = train[:, 0]
test_input = test[:, 1:]
test_target = test[:, 0]
# Construct kernels
K_train_train = kernel(train_input.T, train_input.T)
K_test_train = kernel(test_input.T, train_input.T)
K_test_test = kernel(test_input.T, test_input.T)
"""
Initialize the algorihm.
if __name__ == "__main__":
print 'This file contains the Gaussian Process example'
# Parameters and settings
N_SIG2 = 20
N_BETA = 20
MAX_BETA = 1
MIN_BETA = 500
MAX_SIG2 = 2
MIN_SIG2 = 0.01
# Load the sunspot dataset
lag = 5
year, reading, lag_matrix = get_sunspot(lag)
empirical_var = np.var(lag_matrix[:, 0]) # Some statics (for later use)
# Split data set into a train/test set
train, test = data_split(lag_matrix)
train_input = train[:,1:]
train_target = train[:,0]
test_input = test[:,1:]
test_target = test[:,0]
# Construct kernels
K_train_train = kernel(train_input.T, train_input.T)
K_test_train = kernel(test_input.T, train_input.T)
K_test_test = kernel(test_input.T, test_input.T)
"""
#%%
if __name__ == "__main__":
print 'This file contains the Neural Network example'
# Parameters and settings
data_lag = 3
N_input_units = data_lag
N_hidden_units = 8
N_output_units = 1
net_fit_repeat = 5
net_max_iter = 10000
net_best_err = 9e9
# Load the Sunspot dataset
year, reading, lag_matrix = get_sunspot(data_lag)
train, test = data_split(lag_matrix)
# Repeat network fit to find the one with the lowest error
net_best = None
for net_fit_count in range(net_fit_repeat):
net = NeuralNetwork(structure=(N_input_units-1, N_hidden_units, N_output_units), train_input=train[:, 1:], train_target=train[:, 0], test_input=test[:, 1:], test_target=test[:, 0], max_iter=net_max_iter)
net.train()
if net.e_test[-1] < net_best_err:
net_best = net
net_best_err = net.e_test[-1]
# Train and output error
print 'Minimum test MSE:', str(min(net_best.e_test))