python之实战----KNN之手写数字位图

K近邻法分类和回归

#-*- coding=utf-8 -*-
import numpy  as np
from sklearn import datasets,neighbors
from sklearn.model_selection  import train_test_split
import matplotlib.pyplot as plt

def load_data():
digits=datasets.load_digits()
X_train=digits.data
y_train=digits.target
return train_test_split(X_train,y_train, test_size=0.25,random_state=0,stratify=y_train)
def creat_regression_data(n):
'''
在sin(x)基础上添加噪声
'''
X=5*np.random.rand(n,1)
y=np.sin(X).ravel()
y[::5]+=1*(0.5*np.random.rand(int(n/5)))
return train_test_split(X_train,y_train, test_size=0.25,random_state=0,stratify=y_train)

def test_KNeighborsClassifier(*data):
X_train,X_test,y_train,y_test=data
cls=neighbors.KNeighborsClassifier()
cls.fit(X_train,y_train)
print('KNeighborsClassifier Train score :%.2f'%cls.score(X_train,y_train))
print('KNeighborsClassifier Test score :%.2f'%cls.score(X_test,y_test))
def test_KNeighborsRegressor(*data):
X_train,X_test,y_train,y_test=data
cls=neighbors.KNeighborsRegressor()
cls.fit(X_train,y_train)
print('KNeighborsRegressor Train score :%.2f'%cls.score(X_train,y_train))
print('KNeighborsRegressor Test score :%.2f'%cls.score(X_test,y_test))

if __name__=='__main__':
X_train,X_test,y_train,y_test=load_data()
test_KNeighborsClassifier(X_train,X_test,y_train,y_test)
X_train,X_test,y_train,y_test=creat_regression_data(1000)
test_KNeighborsRegressor(X_train,X_test,y_train,y_test)

PS E:\p> python test.py
KNeighborsClassifier Train score :0.99
KNeighborsClassifier Test score :0.98
KNeighborsRegressor Train score :0.97
KNeighborsRegressor Test score :0.94

#-*- coding=utf-8 -*-
import numpy  as np
from sklearn import datasets,neighbors
from sklearn.model_selection  import train_test_split
import matplotlib.pyplot as plt

def load_data():
digits=datasets.load_digits()
X_train=digits.data
y_train=digits.target
return train_test_split(X_train,y_train, test_size=0.25,random_state=0,stratify=y_train)

def test_KNeighborsClassifier_k_w(*data):
X_train,X_test,y_train,y_test=data
Ks=np.linspace(1,y_train.size,num=100,endpoint=False,dtype='int')#arange数据类型的size
#linspace最初是从MATLAB中学来的，用此来创建等差数列
#endpoint : bool, optional
#If True, stop is the last sample. Otherwise, it is not included. Default is True.
weights=['uniform','distance']
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
for weight in weights:
Train_scores=[]
Test_scores=[]
for K in Ks:
clf=neighbors.KNeighborsClassifier(weights=weight,n_neighbors=K)
clf.fit(X_train,y_train)
Train_scores.append(clf.score(X_train,y_train))
Test_scores.append(clf.score(X_test,y_test))
ax.plot(Ks,Test_scores,label="test score:weight=%s"%weight)
ax.plot(Ks,Train_scores,label="train score:weight=%s"%weight)
ax.legend(loc='best')
ax.set_xlabel("K")
ax.set_ylabel("Score")
ax.set_ylim(0,1.05)
ax.set_title("KNeighborsClassifier_k_w")
plt.show()
if __name__=='__main__':
X_train,X_test,y_train,y_test=load_data()
test_KNeighborsClassifier_k_w( X_train,X_test,y_train,y_test)

#-*- coding=utf-8 -*-
import numpy  as np
from sklearn import datasets,neighbors
from sklearn.model_selection  import train_test_split
import matplotlib.pyplot as plt

def load_data():
digits=datasets.load_digits()
X_train=digits.data
y_train=digits.target
return train_test_split(X_train,y_train, test_size=0.25,random_state=0,stratify=y_train)

def test_KNeighborsClassifier_k_p(*data):
X_train,X_test,y_train,y_test=data
Ks=np.linspace(1,y_train.size,num=100,endpoint=False,dtype='int')#arange数据类型的size
#linspace最初是从MATLAB中学来的，用此来创建等差数列
#endpoint : bool, optional
#If True, stop is the last sample. Otherwise, it is not included. Default is True.
ps=[1,2,10]
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
for p in ps:
Train_scores=[]
Test_scores=[]
for K in Ks:
clf=neighbors.KNeighborsClassifier(p=p,n_neighbors=K)
clf.fit(X_train,y_train)
Train_scores.append(clf.score(X_train,y_train))
Test_scores.append(clf.score(X_test,y_test))
ax.plot(Ks,Test_scores,label="test score:Minkowski-p=%s"%p)#闵可夫斯基
ax.plot(Ks,Train_scores,label="train score:Minkowski-p=%s"%p)
ax.legend(loc='best')
ax.set_xlabel("P")
ax.set_ylabel("Score")
ax.set_ylim(0,1.05)
ax.set_title("KNeighborsClassifier_k_p")
plt.show()
if __name__=='__main__':
X_train,X_test,y_train,y_test=load_data()
test_KNeighborsClassifier_k_p( X_train,X_test,y_train,y_test)