for j in range(n):
for sign in [-1, 1]:
w_test = w.copy()
w_test[j, 0] += eps * sign
test_error = rss(X, y, w_test)
if test_error < min_error:
min_error = test_error
w = w_test
all_ws[i, :] = w.T
return all_ws
if '__main__' == __name__:
X, y = load_data('abalone.txt')
X, y = standarize(X), standarize(y)
epsilon = 0.005
niter = 1000
all_ws = stagewise_regression(X, y, eps=epsilon, niter=niter)
w = all_ws[-1, :]
y_prime = X * w.T
# 计算相关系数
corrcoef = get_corrcoef(np.array(y.reshape(1, -1)),
np.array(y_prime.reshape(1, -1)))
print('Correlation coefficient: {}'.format(corrcoef))
# 绘制逐步线性回归回归系数变化轨迹
return w
def lasso_traj(X, y, ntest=30):
''' 获取回归系数轨迹矩阵
'''
_, n = X.shape
ws = np.zeros((ntest, n))
for i in range(ntest):
w = lasso_regression(X, y, lambd=exp(i-10))
ws[i, :] = w.T
print('lambda = e^({}), w = {}'.format(i-10, w.T[0, :]))
return ws
if '__main__' == __name__:
X, y = load_data('abalone.txt')
X, y = standarize(X), standarize(y)
# w = lasso_regression(X, y, lambd=10)
#
# y_prime = X*w
# # 计算相关系数
# corrcoef = get_corrcoef(np.array(y.reshape(1, -1)),
# np.array(y_prime.reshape(1, -1)))
# print('Correlation coefficient: {}'.format(corrcoef))
ntest = 30
# 绘制轨迹
ws = lasso_traj(X, y, ntest)
fig = plt.figure()
ax = fig.add_subplot(111)