【数据挖掘】分类之kNN

1.算法简介

kNN的思想很简单：计算待分类的数据点与训练集所有样本点，取距离最近的k个样本；统计这k个样本的类别数量；根据多数表决方案，取数量最多的那一类作为待测样本的类别。距离度量可采用Euclidean distance，Manhattan distance和cosine。

用Iris数据集作为测试，代码参考[1]

import numpy as np
import scipy.spatial.distance as ssd

def read_data(fn):
    """ read dataset and separate into characteristics data
        and label data
    """
 
    # read dataset file
    with open(fn) as f:
        raw_data = np.loadtxt(f, delimiter= ',', dtype="float", 
            skiprows=1, usecols=None)

    #initialize
    charac=[]; label=[]
    #obtain input characrisitics and label
    for row in raw_data:
        charac.append(row[:-1])
        label.append(int (row[-1]))
    return np.array(charac),np.array(label)

def knn(k,dtrain,dtest,dtr_label):
    """k-nearest neighbors algorithm"""

    pred_label=[]
    #for each instance in test dataset, calculate
    #distance in respect to train dataset
    for di in dtest:
        distances=[]
        for ij,dj in enumerate(dtrain):
            distances.append((ssd.euclidean(di,dj),ij))

        #sort the distances to get k-neighbors
        k_nn=sorted(distances)[:k]

        #classify accroding to the maxmium label
        dlabel=[]
        for dis,idtr in k_nn:
            dlabel.append(dtr_label[idtr])
        pred_label.append(np.argmax(np.bincount(dlabel)))

    return pred_label

def evaluate(result):
    """evaluate the predicited label"""

    eval_result=np.zeros(2,int)
    for x in result:
        #pred_label==dte_label
        if x==0:
            eval_result[0]+=1
        #pred_label!=dte_label
        else:
            eval_result[1]+=1

    return eval_result


dtrain,dtr_label=read_data('iris-train.csv')
dtest,dte_label=read_data('iris-test.csv')

K=[1,3,7,11]

print "knn classification result for iris data set:\n"
print "k    | number of correct/wrong classified test records"

for k in K:
    pred_label=knn(k,dtrain,dtest,dtr_label)
    eval_result=evaluate(pred_label-dte_label)

    #print the evaluted result into screen
    print k,"   | ", eval_result[0], "/", eval_result[1]

print

2. Referrence

[1] M. Saad Nurul Ishlah, Python: Simple K Nearest Neighbours Classifier.