# 机器学习

**Repository Path**: wcscsw/learn

## Basic Information

- **Project Name**: 机器学习
- **Description**: No description available
- **Primary Language**: JavaScript
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 3
- **Forks**: 0
- **Created**: 2020-05-23
- **Last Updated**: 2021-01-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

#第一节 简单线性回归

##  1.1基于线性最小二乘法
````
import numpy as np
import matplotlib.pyplot as plt

# 尺寸
x_train = np.array([[6], [8], [10], [14], [18]]).reshape(-1, 1)
# 价格
price_train = [7, 9, 13, 17.5, 18]

plt.figure()
plt.title('')  # 披萨直径价格散点图
plt.plot(x_train, price_train, 'k.')
plt.axis([0, 25, 0, 25])
plt.grid(True)
plt.show()

# 基于线性最小二乘的方法
from sklearn.linear_model import LinearRegression

# 创建一个估计器实例训练数据拟合模型
model = LinearRegression().fit(x_train, price_train)

test_pizza = np.array([[12]])

predicted_price = model.predict(test_pizza)[0]

print('12寸披萨的价格：', predicted_price)

# 代价函数->残差平方和代价函数

Rss = np.mean((model.predict(x_train) - price_train) ** 2)
print('Rss:%0.2f' % Rss)

# x的均值
x_bar = x_train.mean()
print('x的均值', x_bar)  # 11.2

variamce = ((x_train - x_bar) ** 2).sum() / (x_train.shape[0] - 1)
print('方差:', variamce)

varx = np.var(x_train, ddof=1)
#print(varx)

# 计算price_train的均值
priceArr = np.array(price_train)
price_bar = priceArr.mean()
# 转至成行向量
# covariance = np.multiply((X-x_bar).transpose(),price-price_bar).sum()/(X.shape[0]-1)
covariance = np.cov(x_train.transpose(), priceArr)[0][1]
beta = covariance / variamce
rfa = price_bar - beta * x_bar

#模型评价
x_test = np.array([8,9,11,16,12]).reshape(-1, 1)
prince_test = np.array([11,8.5,15,18,11]).reshape(-1, 1)

model1 = LinearRegression().fit(x_train,price_train)
#R方的方法 R方称为 决定系数 = 皮尔森极差相关系数的平方
r_squared =model1.score(x_test,prince_test)
print('决定系数:%.2f' % r_squared)

````