def plot_sample(nonreg=True, k = 0):
(X_train_raw, y_train, X_test_raw, y_test) = get_data()
X_train = transform(X_train_raw)
X_test = transform(X_test_raw)
N = X_train.shape[0]
if nonreg is True:
w = linear_regression(X_train, y_train)
else:
w = weight_decay_regression(X_train, y_train, 10.0**k)
def plot_decision_fn(X):
X_trans = transform(X)
return np.sign(np.dot(X_trans, w))
(cont_x, cont_y, cont_z) = decision_boundary_2D(-1, 1, 0.0025, -1, 1, 0.0025,
plot_decision_fn)
print("E_in :", linear_error(X_train, y_train, w))
print("E_out:", linear_error(X_test, y_test, w))
x_plot = X_test_raw[:,0]
y_plot = X_test_raw[:,1]
c = np.where(y_test==1, 'r', 'b')
plt.scatter(x_plot,y_plot, c=c)
plt.contour(cont_x, cont_y, cont_z, [0], colors='g')
plt.xlim([-1, 1])
plt.ylim([-1, 1])
plt.grid()
plt.show()
def fit(self, X, y, mus=None):
self.mus = mus
if mus is None:
self.mus = cluster_centers(X, self.k, self.bounds)
matrix = rbf_matrix(self.gamma, X, self.mus)
if self.bias is True:
matrix = np.hstack([np.ones((X.shape[0], 1)), matrix])
self.weights = linear_regression(matrix, y)
def answers():
k_list = np.array([-3, -2, -1, 0, 1, 2, 3])
(X_train_raw, y_train, X_test_raw, y_test) = get_data()
X_train = transform(X_train_raw)
X_test = transform(X_test_raw)
assert X_train.shape[0] == y_train.shape[0]
assert X_test.shape[0] == y_test.shape[0]
w_nonreg = linear_regression(X_train, y_train)
E_in_nonreg = linear_error(X_train, y_train, w_nonreg)
E_out_nonreg = linear_error(X_test, y_test, w_nonreg)
print("Number of train points:", X_train.shape[0])
print("Number of test points: ", X_test.shape[0])
print("\nNon-regularized stats: ")
print(" E_in: ", E_in_nonreg)
print(" E_out: ", E_out_nonreg)
print(" w^2sum: ", np.power(w_nonreg, 2).sum())
print("Regularized stats: ")
E_in_reg = np.zeros(len(k_list))
E_out_reg = np.zeros(len(k_list))
for i, k in enumerate(k_list):
decay = 10.0**k
w_reg = weight_decay_regression(X_train, y_train, decay)
E_in_reg[i] = linear_error(X_train, y_train, w_reg)
E_out_reg[i] = linear_error(X_test, y_test, w_reg)
print(" k =", k, "constant =", decay)
print(" E_in: ", E_in_reg[i])
print(" E_out: ", E_out_reg[i])
print(" w^2sum: ", np.power(w_reg, 2).sum())
def train_and_eval(X_train, y_train, X_val, y_val, X_test, y_test):
w = linear_regression(X_train, y_train)
E_val = linear_error(X_val, y_val, w)
E_out = linear_error(X_test, y_test, w)
return E_val, E_out