k近鄰法 kNN分類

模型原型

sklearn.neighbors.kneighborsclassifier(n_neighbors=5,weights=』uniform』,algorithm=』auto』,leaf_size=30,p=2, metric=』minkowski』,metric_params=none,n_jobs=1,**kwargs)

引數

algorithm:指定計算最近鄰的演算法

leaf_size:指定balltree,kdtree葉節點規模

p:指定在』minkowski』度量上的指數

n_jobs

方法

import numpy as np

import matplotlib.pyplot as plt
from sklearn import neighbors,datasets,cross_validation

def

load_classification_data
(): digits=datasets.load_digits()
x_train=digits.data
y_train=digits.target
return cross_validation.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
clf=neighbors.kneighborsclassifier()
clf.fit(x_train,y_train)
print('training score:%f'%clf.score(x_train,y_train))
print('testing score:%f'%clf.score(x_test,y_test))
x_train,x_test,y_train,y_test=load_classification_data()
test_kneighborsclassifier(x_train,x_test,y_train,y_test)

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')
weights=['uniform','distance']
#繪圖fig=plt.figure()
ax=fig.add_subplot(1,1,1)
for weight in weights:
training_scores=
testing_scores=
for k in ks:
clf=neighbors.kneighborsclassifier(weights=weight,
n_neighbors=k)
clf.fit(x_train,y_train)
ax.plot(ks,testing_scores,label='testing score:weight=%s'%weight)
ax.plot(ks,training_scores,label='training 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')
plt.show()
`x_train,x_test,y_train,y_test=load_classification_data()
test_kneighborsclassifier_k_w(x_train,x_test,y_train,y_test)

def

test_kneighborsclassifier_k_p
(*data):
x_train,x_test,y_train,y_test=data
ks=np.linspace(1,y_train.size,endpoint=false,dtype='int')
ps=[1,2,10]
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
for p in ps:
training_scores=
testing_scores=
for k in ks:
clf=neighbors.kneighborsclassifier(p=p,n_neighbors=k)
clf.fit(x_train,y_train)
ax.plot(ks,testing_scores,label='testing score:p=%d'%p)
ax.plot(ks,training_scores,label='training score:p=%d'%p)
ax.legend(loc='best')
ax.set_xlabel("k")
ax.set_ylabel('score')
ax.set_ylim(0,1.05)
ax.set_title('kneighborsclassifier')
plt.show()
x_train,x_test,y_train,y_test=load_classification_data()
test_kneighborsclassifier_k_p(x_train,x_test,y_train,y_test)

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