剛開始學習機器學習不久，這也是我第一次寫部落格。就先寫個簡單的，利用sklearn包中的kNN分類器進行分類，分類的資料集為皮馬印第安人糖尿病資料集（pima-indians-diabetes.data.csv）。廢話不多說，直接上程式碼，寫的不完善的地方，望大家指正。

#coding:utf-8
''''' 
Create by Ma Chao
August 4th
kNN
'''''
from sklearn.neighbors import NearestNeighbors, KNeighborsClassifier
from sklearn import datasets
import numpy as np

 

#載入資料集
def loadDataSet(fileName):
    input = []
    output = []
    with open(fileName) as fr:
        for line in fr.readlines():
            lineArr = line.strip().split(',')
            input.append([float(lineArr[0]), float(lineArr[1]), float(lineArr[2]), float(lineArr[3]), float(lineArr[4]),
                          float(lineArr[5]), float(lineArr[6]), float(lineArr[7])])
            output.append(int(lineArr[8]))
    return input, output

if __name__ == "__main__":
    input, output = loadDataSet('pima-indians-diabetes.data.csv')
    np.random.seed(0)
    indices = np.random.permutation(len(input))
    input_train = input[:-10]
    output_train = output[:-10]
    input_test = input[-10:]
    output_test = output[-10:]
    kNN = KNeighborsClassifier()
    kNN.fit(input_train, output_train)
    output_predict = kNN.predict(input_test)
    prob = kNN.predict_proba(input_test)
    score = kNN.score(input_test, output_test, sample_weight=None)
    kNeighbor = kNN.kneighbors(input_test[:-1], 4)#找出與測試集中最後一個元素距離最近的4個元素

    print 'output_test:', output_test
    print 'output_predic:', output_predict
    print '****************'
    print 'Prob:', prob
    print '****************'
    print 'Score:', score