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