由于連接方在一段時間內沒有正確答復或連接的主機沒有反映，連接嘗試失敗，請問這個連接是失效了嘛？

#?coding=utf-8
import?numpy?as?np

#分類器代碼
class?Perceptron(object):
????"""
????eta:學習率
????n_iter:權重向量的訓練次數
????w_:神經分叉權重向量
????errors_:用于記錄神經元判斷出錯次數
????"""
????def?__init__(self,eta?=?0.01,n_iter?=?10):
????????self.eta?=?eta;
????????self.n_iter?=?n_iter
????????pass
????def?fit(self,X,y):
????????"""
????????輸入訓練數據，培訓神經元
????????:param?X:?輸入樣本向量
????????:param?y:?對應樣本分類
????????
????????X:shape[n_samples,?n_features]
????????X:[[1,2,3],[4,5,6]]
????????n_samples?:2
????????n_features:3
????????
????????y:[1,-1]
????????"""

????????"""
????????初始化向量為0
????????加一是因為步調函數閾值
????????"""
????????self.w_?=np.zeros(1+X.shape[1]);
????????self.errors_=[]

????????for?_?in?range(self.n_iter):
????????????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)
????????????????self.errors_.append(errors)
????????????????pass


????????????pass

????def?net_input(self,X):
????????return?np.dot(X,self.w_[1:]+self.w_[0])
????????pass
????def?predict(self,X):
????????return?np.where(self.net_input(X)>=0.0,1,-1)
????????pass

import?pandas?as?pd

file?=?'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'

df?=?pd.read_csv(file,header=None)


import?matplotlib.pyplot?as?plt


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

#根據整數位置選取單列或單行數據
X?=?df.loc[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('huabanchangdu')
plt.ylabel('huajingchangdu')
plt.legend(loc='upper?left')


ppn?=Perceptron(eta=0.1,n_iter=10)
ppn.fit(X,y)

from?matplotlib.colors?import?ListedColormap
def?plot_decision_region(X,y,classifier,resolution=0.02):
????markers=('s','x','o','v')
????colors=('red','blue','lightgreen','gray','cyan')
????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()
????xx1,xx2?=?np.meshgrid(np.arange(x1_min,x1_max,resolution),
??????????????????????np.arange(x2_min,?x2_max,?resolution))
????Z?=classifier.predict(np.array([xx1.ravel(),xx2.ravel()]).T)
????print?(xx1.ravel())
????print(xx2.ravel())
????print?Z
????Z=Z.reshape(xx1.shape)
????plt.contourf(xx1,xx2,Z,alpha?=0.4,?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)

plot_decision_region(X,y,ppn,resolution=0.02)
plt.xlabel('huajingchang')
plt.ylabel('huabanchang')
plt.legend(loc='upper?left')
plt.show()

#?coding=utf-8
import?numpy?as?np
import?matplotlib.pyplot?as?plt
#ada代碼
class?AdalineGd(object):
????'''
????eta:?float
????學習效率，處于0和1之間
????
????n_iter：int
????對訓練數據進行學習，改進次數
????
????w_：一維向量
????存儲權重數值
????
????error_：
????一維向量
????存儲每次迭代改進時，神經網絡對數據進行錯誤判斷的次數
????'''
????def?__init__(self,eta?=?0.01,n_iter=50):
????????self.eta=eta
????????self.n_iter?=n_iter

????def?fit(self,X,y):
????????'''
????????
????????:param?X:?二維數組[n_samples,?n_features]
????????n_samples?表示X中含有訓練數據條目數
????????n_features含有4個數據的一維向量，用于表示一條訓練條目
????????:param?y:?一維向量
????????用于存儲每一訓練條目對應的正確分類
????????:return:?
????????'''
????????self.w_?=np.zeros(1+X.shape[1])#權重初始化為零
????????self.cost_?=[]

????????for?i?in?range(self.n_iter):
????????????output?=?self.net_input(X)
????????????errors?=?(y?-?output)#向量
????????????self.w_[1:]+=self.eta?*X.T.dot(errors)
????????????self.w_[0]?+=self.eta?*errors.sum()
????????????cost??=(errors?**2).sum()/2
????????????self.cost_.append(cost)
????????return?self


????def?net_input(self,X):
????????return??np.dot(X,self.w_[1:]+self.w_[0])

????def?activation(self,X):
????????return?self.net_input(X)

????def?predict(self,X):
????????return?np.where(self.activation(X)>=0,1,-1)


import?pandas?as?pd

file?=?'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'

df?=?pd.read_csv(file,header=None)
y?=?df.loc[0:100,4].values
y=np.where(y=='Iris-setosa',-1,1)
#根據整數位置選取單列或單行數據
X?=?df.loc[0:100,[0,2]].values

from?matplotlib.colors?import?ListedColormap
def?plot_decision_region(X,y,classifier,resolution=0.02):
????markers=('s','x','o','v')
????colors=('red','blue','lightgreen','gray','cyan')
????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()
????xx1,xx2?=?np.meshgrid(np.arange(x1_min,x1_max,resolution),
??????????????????????np.arange(x2_min,?x2_max,?resolution))
????Z?=classifier.predict(np.array([xx1.ravel(),xx2.ravel()]).T)
????print?(xx1.ravel())
????print(xx2.ravel())
????print?Z
????Z=Z.reshape(xx1.shape)
????plt.contourf(xx1,xx2,Z,alpha?=0.4,?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)

ada?=?AdalineGd(eta=0.0001,n_iter=100)
ada.fit(X,y)
plot_decision_region(X,y,classifier=ada)
plt.xlabel('huajingchang')
plt.ylabel('huabanchang')
plt.legend(loc='upper?left')
plt.show()


plt.plot(range(1,len(ada.cost_)+1),ada.cost_,marker?='o')
plt.xlabel('epochs')
plt.ylabel('sum-squard-error')
plt.show()