import numpy as np
from linear_regression_train import load_data
def lwlr(feature, label, k):
'''局部加权线性回归
input: feature(mat):特征
label(mat):标签
k(int):核函数的系数
output: predict(mat):最终的结果
'''
m = np.shape(feature)[0]
predict = np.zeros(m)
weights = np.mat(np.eye(m))
for i in xrange(m):
for j in xrange(m):
diff = feature[i, ] - feature[j, ]
weights[j,j] = np.exp(diff * diff.T / (-2.0 * k ** 2))
xTx = feature.T * (weights * feature)
ws = xTx.I * (feature.T * (weights * label))
predict[i] = feature[i, ] * ws
return predict
if __name__ == "__main__":
# 1、导入数据集
feature, label = load_data("data.txt")
predict = lwlr(feature, label, 0.002)
m = np.shape(predict)[0]
for i in xrange(m):
print feature[i, 1], predict[i]
from linear_regression_train import load_data
def lwlr(feature, label, k):
'''局部加权线性回归
input: feature(mat):特征
label(mat):标签
k(int):核函数的系数
output: predict(mat):最终的结果
'''
m = np.shape(feature)[0]
predict = np.zeros(m)
weights = np.mat(np.eye(m))
for i in range(m):
for j in range(m):
diff = feature[i, ] - feature[j, ]
weights[j, j] = np.exp(diff * diff.T / (-2.0 * k**2))
xTx = feature.T * (weights * feature)
ws = xTx.I * (feature.T * (weights * label))
predict[i] = feature[i, ] * ws
return predict
if __name__ == "__main__":
# 1、导入数据集
feature, label = load_data("data.txt")
predict = lwlr(feature, label, 0.002)
m = np.shape(predict)[0]
for i in range(m):
print feature[i, 1], predict[i]
from linear_regression_train import load_data
def lwlr(feature, label, k):
'''局部加权线性回归
input: feature(mat):特征
label(mat):标签
k(int):核函数的系数
output: predict(mat):最终的结果
'''
m = np.shape(feature)[0]
predict = np.zeros(m)
weights = np.mat(np.eye(m))
for i in range(m):
for j in range(m):
diff = feature[i, ] - feature[j, ]
weights[j, j] = np.exp(diff * diff.T / (-2.0 * k**2))
xTx = feature.T * (weights * feature)
ws = xTx.I * (feature.T * (weights * label))
predict[i] = feature[i, ] * ws
return predict
if __name__ == "__main__":
# 1、导入数据集
feature, label = load_data("train_data.txt")
predict = lwlr(feature, label, 0.002)
m = np.shape(predict)[0]
for i in range(m):
print(feature[i, 1], predict[i])