Python 机器学习之线性回归详解分析

#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<time.h>
 
double w, q;
int m;
//模型
float Model(float x)
{
	float y;
	y = x * w + q;
	return y;
}
 
//损失函数
double Loss(float *y,float *x)
{
	double L=0;
	//循环参数
	int i, j, k;
	for (i = 0; i < m; i++)
	{
		L += (pow((y[i] - Model(x[i])), 2)) / (2 * m);
	}
	return L;
 
}
 
//梯度下降优化函数
void Gradient_Descent_Optimizer(float *x,float *y,float a)
{
	int j, i;
	double Q = 0, W = 0;
	for (i = 0; i < m; i++)
		W += x[i] * (Model(x[i]) - y[i]);
	W = W / m;
	for (j = 0; j < m; j++)
		Q += Model(x[j]) - y[j];
	Q = Q / m;
	printf("W:%f\nQ:%f\n", W, Q);
	w = w - a * W;
	q = q - a * Q;
}
 
//主函数;训练过程
int main()
{
	//循环标志
	int i, j;
	//训练轮次
	int epoch;
	//损失函数
	double L;
	//学习率
	float a;
	float x[100], y[100];
	//随机数生成
	for (i = 0; i < 100; i++)
	{
		x[i] = 0.1*i;
		y[i] = x[i] * 3 + 5; //+ ((rand() % 11) / 10);
		printf("X:%.2f,Y:%.2f\n", x[i], y[i]);
	}
	//超参数设置
	m = 100;
	a = 0.05;
	epoch = 1000;
	//参数初始化
	w = 2;
	q = 3;
	for (j = 0; j < epoch; j++)
	{
		Gradient_Descent_Optimizer(x, y, a);
		L = Loss(y, x);
		printf("训练轮次:%d,损失:%f,参数w的值:%lf,参数q的值:%lf\n", j+1, L, w, q);
	}
	printf("最终值:\nw:%lf\nq:%lf\n", w, q);
	system("pause");
}

# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
 
np.random.seed(5)
 
x_data = np.linspace(-1,1,100)
y_data = 2 * x_data + 1.0 + np.random.randn(*x_data.shape) * 0.4
 
np.random.randn(10)
 
#x_data.shape
 
#np.random.randn(*x_data.shape)
#np.random.randn(100)
plt.scatter(x_data,y_data)
plt.plot(x_data, 2 * x_data + 1.0, color = 'red',linewidth=3)
 
x = tf.placeholder("float",name="x")
y = tf.placeholder("float",name="y")
 
def model(x,w,b):
    return tf.multiply(x,w)+b
 
w = tf.Variable(1.0,name="w0")
b = tf.Variable(0.0,name="b0")
 
pred = model(x,w,b)
 
train_epochs = 10
learning_rate = 0.05
 
loss_function = tf.reduce_mean(tf.square(y-pred))
 
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
 
sess = tf.Session()
 
init = tf.global_variables_initializer()
sess.run(init)
 
i_ci=0
 
for epoch in range(train_epochs):
    for xs,ys in zip(x_data,y_data):
        _, loss=sess.run([optimizer,loss_function],feed_dict={x:xs,y:ys})
        b0temp=b.eval(session=sess)
        w0temp=w.eval(session=sess)
    plt.plot(x_data,w0temp*x_data+b0temp)
        
print("w:",sess.run(w))
print("b:",sess.run(b))
 
plt.scatter(x_data,y_data,label='Original data')
plt.plot(x_data,x_data*sess.run(w)+sess.run(b),label='Fitted Line',color='r',linewidth=3)
plt.legend(loc=2)