请问老师~代码可不要上传到git上
由于连接方在一段时间内没有正确答复或连接的主机没有反映，连接尝试失败，请问这个连接是失效了嘛？
# coding=utf-8
import numpy as np
#分类器代码
class Perceptron(object):
"""
eta:学习率
n_iter:权重向量的训练次数
w_:神经分叉权重向量
errors_:用于记录神经元判断出错次数
"""
def __init__(self,eta = 0.01,n_iter = 10):
self.eta = eta;
self.n_iter = n_iter
pass
def fit(self,X,y):
"""
输入训练数据，培训神经元
:param X: 输入样本向量
:param y: 对应样本分类
X:shape[n_samples, n_features]
X:[[1,2,3],[4,5,6]]
n_samples :2
n_features:3
y:[1,-1]
"""
"""
初始化向量为0
加一是因为步调函数阈值
"""
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)
#根据整数位置选取单列或单行数据
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代码
class AdalineGd(object):
'''
eta: float
学习效率，处于0和1之间
n_iter：int
对训练数据进行学习，改进次数
w_：一维向量
存储权重数值
error_：
一维向量
存储每次迭代改进时，神经网络对数据进行错误判断的次数
'''
def __init__(self,eta = 0.01,n_iter=50):
self.eta=eta
self.n_iter =n_iter
def fit(self,X,y):
'''
:param X: 二维数组[n_samples, n_features]
n_samples 表示X中含有训练数据条目数
n_features含有4个数据的一维向量，用于表示一条训练条目
:param y: 一维向量
用于存储每一训练条目对应的正确分类
:return:
'''
self.w_ =np.zeros(1+X.shape[1])#权重初始化为零
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)
#根据整数位置选取单列或单行数据
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()