import?numpy?as?np

class?Perceptron(object):
????def?__init__(self,?eta=0.01,?n_iter=10):
????????self.eta=eta
????????self.n_iter=n_iter
????????
????????pass
????def?fit(self,X,y):
????????self.W=np.zeros(1+X.shape[1])
????????self.errors=[]
????????for?_?in?range(self.n_iter):
????????????error=0
????????????dw=np.zeros(1+X.shape[1])
????????????for?xi,target?in?zip(X,y):????????????????
????????????????update=self.eta*(target-self.predict(xi))
????????????????dw[1:]+=update*xi
????????????????dw[0]+=update
????????????????error+=int(update!=0)
????????????????pass
????????????self.W+=dw
????????????self.errors.append(error)
????????????
????????????pass
????????
????????pass
????def?net_input(self,xi):
????????return?np.dot(self.W[1:],xi)+self.W[0]
????def?predict(self,xi):
????????return?np.where(self.net_input(xi)>0,1,-1)
????
???????
????????pass

#文件讀取
file="D:/python/nn/data.csv"
import?pandas?as?pd
df=pd.read_csv(file,header=None)
df.head(10)		

#顯示原始數(shù)據(jù)
import?matplotlib.pyplot?as?plt
import?numpy?as?np

y=df.loc[0:99,4].values
y=np.where(y=='Iris-setosa',-1,1)
X=df.iloc[0:100,[0,2]].values

plt.scatter(X[:50,0],X[:50,1],color='red',marker='o',label='setosa')
plt.scatter(X[50:100,0],X[50:100,1],color='blue',marker='x',label='versicolor'?)
plt.xlabel(u'花瓣長度')
plt.ylabel(u'花徑長度')
plt.legend(loc='upper?left')
#plt.show()

#訓(xùn)練并打印錯(cuò)誤曲線
ppn=Perceptron(0.1,20)
ppn.fit(X,y)
print?(ppn.W)
plt.scatter(range(1,len(ppn.errors)+1),ppn.errors,color='red',marker='o')

#plt.show()

#定義打印分類器邊界函數(shù)
from?matplotlib.colors?import?ListedColormap
def?plot_decision_regions(X,y,classifier,resolution=0.02):
????markers=('s','x','o','v')
????colors=('lightgreen','gray','cyan','red','blue')
????cmap=ListedColormap(colors[:len(np.unique(y))])
????
????x1_min,x1_max=X[:,0].min()-1,X[:,0].max()
????x2_min,x2_max=X[:,1].min()-1,X[:,1].max()
????
????print(x1_min,x1_max)
????print(x2_min,x2_max)
????
????xx1,xx2=np.meshgrid(np.arange(x1_min,x1_max,resolution),
??????????????????????np.arange(x2_min,x2_max,resolution)?)
????#print(xx2.shape)
????#print(xx2)
????
????z=classifier.predict(np.array([xx1.ravel(),xx2.ravel()]))
????#print(xx1.ravel())
????#print(xx2.ravel())
????#print(z)
????z=z.reshape(xx1.shape)
????plt.contourf(xx1,xx2,z,alpha=0.8,cmap=cmap)
????plt.xlim(xx1.min(),xx1.max())
????plt.ylim(xx2.min(),xx2.max())
????
????for?idx,cl?in?enumerate(np.unique(y)):
????????plt.scatter(x=X[y==cl,0],y=X[y==cl,1],alpha=0.8,c=cmap(idx),marker=markers[idx],label=cl)

#打印邊界及原始數(shù)據(jù)		
plot_decision_regions(X,y,ppn)
plt.xlabel(u'花瓣長度')
plt.ylabel(u'花徑長度')
plt.legend(loc='upper?left')
plt.show()

給你，每個(gè)模塊自己復(fù)制分開下