在多元线性回归中会用到梯度下降来计算参数值。这里我用python实现一个梯度下降版本。
这里多元线性方程为 y = A0+A1*x1+...+An* xn
数据输入格式，y表示
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y \t x1 \t x2 \t .... xn |
代码如下：
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import
osimport
systheta =
[]training_data =
[]h_value =
[]alpha =
0.0000009def load(path): f =
open(path,‘r‘) for
x in
f: x =
x.strip(‘\r\n‘) field =
x.split(‘\t‘) v_list =
[] for
v in
field: v_list.append(int(v)) training_data.append(v_list) f.close() for
x in
training_data: h_value.append(0.0)def
init(path,theta_num): for
x in
range(theta_num): theta.append(1.0) load(path);def
gradient(): i =
0 loss =
100.0 theta_num =
len(theta) data_num =
len(training_data) while
i < 3000
and loss > 0.0001: #compute hvalue for
index in
range(data_num): hv =
theta[0] for
k in
range(1,theta_num): hv +=
theta[k]*training_data[index][k] h_value[index] =
hv #update theta for
index in
range(theta_num): s =
0.0 for
k in
range(data_num): if
index ==
0: s +=
(h_value[k] -
training_data[k][0])*1 else: s +=
(h_value[k] -
training_data[k][0])*training_data[k][index] theta[index] =
theta[index] -
alpha *
1/data_num *
(s) #compute loss loss =
0.0 for
index in
range(data_num): hv =
theta[0] /
(2*data_num) for
k in
range(1,theta_num): hv +=
theta[k]*training_data[index][k] loss +=
pow((hv -
training_data[index][0]),2)/(2*data_num) print
loss i +=
1 for
x in
theta: print
x,if __name__==‘__main__‘: path =
sys.argv[1] init(path,int(sys.argv[2])) gradient() sys.exit(0) |
原文：http://www.cnblogs.com/clyskyblue/p/3594991.html