版权声明：学习交流为主，未经博主同意禁止转载，禁止用于商用。 https://blog.csdn.net/u012965373/article/details/88653647
# -*- codingutf-8 -*-
__author__ = 'yangxin_ryan'
from numpy import *
import matplotlib.pylab as plt
class Regression(object):
def load_data_set(self, file_name):
num_feat = len(open(file_name).readline().split('\t')) - 1
data_mat = []
label_mat = []
fr = open(file_name)
for line in fr.readlines():
line_arr = []
cur_line = line.strip().split("\t")
for i in range(num_feat):
line_arr.append(float(cur_line[i]))
data_mat.append(line_arr)
label_mat.append(float(cur_line[-1]))
return data_mat, label_mat
def stand_regres(self, x_arr, y_arr):
x_mat = mat(x_arr)
y_mat = mat(y_arr).T
xTx = x_mat.T * x_mat
if linalg.det(xTx) == 0.0:
print("This matrix is singular, cannot do inverse")
return
ws = xTx.I * (x_mat.T * y_mat)
return ws
def lwlr(self, test_point, x_arr, y_arr, k=1.0):
x_mat = mat(x_arr)
y_mat = mat(y_arr).T
m = shape(x_mat)[0]
weights = mat(eye((m)))
for j in range(m):
diff_mat = test_point - x_mat[j:]
weights[j, j] = exp(diff_mat * diff_mat / (-2.0 * k ** 2))
xTx = x_mat * (weights * x_mat)
if linalg.det(xTx) == 0.0:
print("This matrix is singular, cannot do inverse")
return
ws = xTx.I * (x_mat.T * (weights * y_mat))
return test_point * ws
def lwlr_test(self, test_arr, x_arr, y_arr, k=1.0):
m = shape(test_arr)[0]
y_mat = zeros(m)
for i in range(m):
y_mat[i] = self.lwlr(test_arr[i], x_arr, y_arr, k)
return y_mat
def lwlr_test_plot(self, x_arr, y_arr, k=1.0):
y_hat = zeros(shape(y_arr))
x_copy = mat(x_arr)
x_copy.sort(0)
for i in range(shape(x_arr)[0]):
y_hat[i] = self.lwlr(x_copy[i], x_arr, y_arr, k)
return y_hat, x_copy
def rss_error(self, y_arr, y_hat_arr):
return ((y_arr - y_hat_arr) ** 2).sum()
def ridge_regres(self, x_mat, y_mat, lam=0.2):
xTx = x_mat.T * x_mat
denom = xTx + eye(shape(x_mat)[1]) * lam
if linalg.det(denom) == 0.0:
print("This matrix is singular, cannot do inverse")
return
ws = denom.I * (x_mat.T * y_mat)
return ws
def ridge_test(self, x_arr, y_arr):
x_mat = mat(x_arr)
y_mat = mat(y_arr).T
y_mean = mean(y_mat, 0)
y_mat = y_mat - y_mean
x_means = mean(x_mat, 0)
x_var = var(x_mat, 0)
x_mat = (x_mat - x_means) / x_var
num_test_pts = 30
w_mat = zeros((num_test_pts, shape(x_mat)[1]))
for i in range(num_test_pts):
# exp() 返回 e^x
ws = self.ridge_regres(x_mat, y_mat, exp(i - 10))
w_mat[i, :] = ws.T
return w_mat
def regularize(self, x_mat): # 按列进行规范化
in_mat = x_mat.copy()
in_means = mean(in_mat, 0) # 计算平均值然后减去它
in_var = var(in_mat, 0) # 计算除以Xi的方差
in_mat = (in_mat - in_means) / in_var
return in_mat
def stageWise(self, x_arr, y_arr, eps=0.01, numIt=100):
x_mat = mat(x_arr)
y_mat = mat(y_arr).T
y_mean = mean(y_mat, 0)
y_mat = y_mat - y_mean # 也可以规则化ys但会得到更小的coef
x_mat = self.regularize(x_mat)
m, n = shape(x_mat)
return_mat = zeros((numIt, n)) # 测试代码删除
ws = zeros((n, 1))
ws_test = ws.copy()
ws_max = ws.copy()
for i in range(numIt):
print(ws.T)
lowest_error = inf
for j in range(n):
for sign in [-1, 1]:
ws_test = ws.copy()
ws_test[j] += eps * sign
y_test = x_mat * ws_test
rss_e = self.rss_error(y_mat.A, y_test.A)
if rss_e < lowest_error:
lowest_error = rss_e
ws_max = ws_test
ws = ws_max.copy()
return_mat[i, :] = ws.T
return return_mat
def regression1(self):
x_arr, y_arr = self.load_data_set("data/8.Regression/data.txt")
x_mat = mat(x_arr)
y_mat = mat(y_arr)
ws = self.stand_regres(x_arr, y_arr)
fig = plt.figure()
ax = fig.add_subplot(111) # add_subplot(349)函数的参数的意思是，将画布分成3行4列图像画在从左到右从上到下第9块
ax.scatter([x_mat[:, 1].flatten()], [y_mat.T[:, 0].flatten().A[0]]) # scatter 的x是xMat中的第二列，y是yMat的第一列
x_copy = x_mat.copy()
x_copy.sort(0)
y_hat = x_copy * ws
ax.plot(x_copy[:, 1], y_hat)
plt.show()
if __name__ == '__main__':
regression = Regression()
regression.regression1()