def main():
# Number of possible degrees to be tested
K = 20
data_path = 'data_linreg.json'
# Load the data
f = open(data_path, 'r')
data = json.load(f)
for k, v in data.items():
data[k] = np.array(v).reshape((len(v), 1))
# Init vectors storing MSE (Mean square error) values of each sets at each degrees
mse_train = np.zeros(K)
mse_val = np.zeros(K)
mse_test = np.zeros(K)
theta_list = np.zeros(K, dtype=object)
n_centers = np.arange(K) + 1
# Compute the MSE values
for i in range(K):
theta_list[i], mse_train[i], mse_val[i], mse_test[
i] = rbf.train_and_test(data, n_centers[i])
i_best = np.argmin(mse_val)
print(mse_test[i_best])
#
# TODO END
######################
# Plot the training error as a function of the degrees
plot_errors(i_best, n_centers, mse_train, mse_val, mse_test)
plot_rbf(data, n_centers[i_best], theta_list[i_best])
plt.show()
def main():
# Number of possible degrees to be tested
K = 40
data_path = 'data_linreg.json'
# Load the data
f = open(data_path, 'r')
data = json.load(f)
for k, v in data.items():
data[k] = np.array(v).reshape((len(v), 1))
######################
#
# TODO
#
# Compute the arrays containing the Means Square Errors in all cases
#
# Find the degree that minimizes the validation error
# Store it in the variable i_best for plotting the results
#
# TIP:
# - You are invited to adapt the code you did for the polynomial case
# - use the argmin function of numpy
#
# Init vectors storing MSE (Mean square error) values of each sets at each degrees
mse_train = np.zeros(K)
mse_val = np.zeros(K)
mse_test = np.zeros(K)
theta_list = np.zeros(K, dtype=object)
n_centers = np.arange(K) + 1
# Compute the MSE values
for i in range(K):
theta_list[i], mse_train[i], mse_val[i], mse_test[
i] = rbf.train_and_test(data, n_centers[i])
i_best = np.argmin(mse_val)
#i_best = np.argmin(mse_test)
#i_best = np.argmin(mse_train)
#
# TODO END
######################
# Plot the training error as a function of the degrees
plot_errors(i_best, n_centers, mse_train, mse_val, mse_test)
plot_rbf(data, n_centers[i_best], theta_list[i_best])
plt.show()
def main():
# Set the n_centers of the polynomial expansion
n_centers = 5
data_path = 'data_linreg.json'
# Load the data
f = open(data_path, 'r')
data = json.load(f)
for k, v in data.items():
data[k] = np.array(v).reshape((len(v), 1))
# Print the training and testing errors
theta, err_train, err_val, err_test = rbf.train_and_test(data, n_centers)
print(
'Training error {} \t Validation error {} \t Testing error {} '.format(
err_train, err_val, err_test))
plot_rbf(data, n_centers, theta)
plt.show()
