感知器实现鸢尾花的分类

import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
class perceptron:
 '''
 感知器类实现
 '''
 def __init__(self,eta,epoch):
 '''
 init perceptron

 parameters
 --------
 eta: float
 learning rate
 epoch: int
 learning count
 '''
 self.eta=eta
 self.epoch=epoch

 def step(self,z):
 '''
 come true step function

 Parameters
 ---------
 z: array_like or scalar
 net input

 Returns
 -----
 step:array_like or scalar
 output classification
 '''
 return np.where(z>=0,1,0)

 def fit(self,X,y):
 '''
 fit weight

 Parameters
 --------
 X: array_like
 Input data
 y: array_like
 Input data's label

 Returns
 ------
 fit:array_like
 perceptron's weight
 '''

 X,y=np.asarray(X),np.asarray(y)
 self.w_=np.zeros(X.shape[1]+1)
 self.errors_=[]
 for i in range(self.epoch):
 errors=0
 for xi,target in zip(X,y):
 update=self.eta*(target-self.predict(xi))
 self.w_[1:]+=update*xi
 self.w_[0]+=update
 errors+=int(update!=0.0)
 # if errors==0:
 # break
 self.errors_.append(errors)
 # print(self.errors_)
 return self.w_
 def predict(self,X):
 '''
 return y_hat

 Parameters
 --------
 X:array_like
 Input data
 y:array_like or scalar
 Input data's label
 Returns
 -------
 predict:array_like or scalar
 output y_hat
 '''

 z = np.dot(X, self.w_[1:])+self.w_[0]
 y_hat = self.step(z)
 return y_hat

if __name__=='__main__':
 '''
 p=perceptron(0.1,10)
 x=((0,0),(0,1),(1,0),(1,1))
 y=(0,0,0,1)
 x,y=np.asarray(x),np.asarray(y)
 # p.predict(x)
 k=p.fit(x,y)
 print(k)
 '''
 X,y=load_iris(True)
 x=np.concatenate((X,y.reshape(-1,1)),1)
 data=pd.DataFrame(x)
 # print(data)
 data=data.drop_duplicates()
 data=data[data[4]!=2]
 x1,y1=data[[1,2,3]],data[4]
 x1,y1=x1.values,y1.values
 lenght=int(len(data)*0.8)
 train_x,train_label=x1[:lenght,:],y1[:lenght]
 test_x,test_label=x1[lenght:,:],y1[lenght:]
 p = perceptron(0.1, 10)
 w=p.fit(train_x,train_label)
 # out=p.predict(train_x)
 print(w)
 print()
 fi=np.dot(test_x,w[1:])+w[0]
 out=p.step(fi)
 right=np.sum(out==test_label)
 # print(out)
 # print(right)
 print(str(right*100/len(test_label))+'%')

 mpl.rcParams['font.family']='SimHei'
 mpl.rcParams['axes.unicode_minus']=False
 # plt.plot(out,'go',ms=15,label='真实值')
 # plt.plot(test_label,'rx',ms=15,label='预测值')
 plt.plot(p.errors_,'o-')
 plt.title('感知器实现鸢尾花的二分类')
 # plt.legend()
 plt.show()