第1节：感知机perceptron原理与numpy复现

# -*- coding:utf-8 -*-
# /usr/bin/python
'''
-------------------------------------------------------------------------
   @File Name      :  perception.py
   @Description    :  感知机numpy实现版本
   @Run Script     :  python  perception.py 
   @Envs           :  pip install 
   @Change Activity:
        1.  2021/12/1  下午4:18  : build
-------------------------------------------------------------------------
   @CodeStyle      :  standard, simple, readable, maintainable, and portable!
   @Author         :  Yan Errol 13075851954
   @Email          :  260187357@qq.com
   @Copyright      :  "Copyright 2021, Yan Errol"
-------------------------------------------------------------------------
'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#  类封装
class perception_model():

    def __init__(self,XTrain):
        '''参数定义'''
        self.learning_rate = 0.5
        self.b = 0
        self.w = np.ones(len(XTrain[0]),dtype=np.float32)

    def sign(self,x,w,b):
        '''误点判段'''
        y = np.dot(x, w) + b
        return y

    def fit(self,xTrain,yTrain):
        '''训练'''
        isWrong = False
        while not isWrong:
            wrongNums = 0
            # 错误点判段
            ytest = self.sign(xTrain,self.w,self.b)
            wrongPointIndex = np.where(yTrain*ytest <= 3)
            xWrong,yWrong = xTrain[wrongPointIndex],yTrain[wrongPointIndex]
            wrongNums = len(yWrong)
            if wrongNums == 0: #终止条件判段
                break
            else:
                i = np.random.randint(0,wrongNums)
                x,y = xWrong[i],yWrong[i]
                self.w = self.w + self.learning_rate * np.dot(y, x)
                self.b = self.b + self.learning_rate * y
        return 'Perception train is Done!'

    def predict(self,x):
        '''预测'''
        y = np.dot(x, self.w) + self.b
        print("y",y)
        if y > 0:
            y = 1
        else:
            y = 0
        return y

# 数据加载
data = pd.read_csv("dataset.csv")
print(data.label.value_counts())
print("data\n",data)

# 数据可视化 验证线性可分性
plt.scatter(data[:50]['sepal length'], data[:50]['sepal width'], label='0')
plt.scatter(data[50:100]['sepal length'], data[50:100]['sepal width'], label='1')
plt.xlabel('sepal length')
plt.ylabel('sepal width')
plt.legend()
plt.show()
plt.savefig("show.png")

# 数据集拆分
data = np.array(data.iloc[:100,[0,1,-1]])
XTrain,Ytrain = data[:-1,:-1],data[:-1,-1]
Xtest,Ytest = data[-1, : -1], data[-1, -1]

# 训练
perception = perception_model(XTrain)
perception.fit(XTrain,Ytrain)
yPredict = perception.predict(Xtest)
print(yPredict,Ytest)