本文之编写程序涉及到API介绍，程序的完整实现，具体算法原理请查看之前所写的KNN算法介绍
一、基础准备
1、python 基础
2、numpy 基础
np.argmax
返回数组的最大值
>>> a = np.arange(6).reshape(2,3)
>>> a.argmax()
5
>>> a.argmax(0)
array([1, 1, 1])
>>> a.argmax(1)
array([2, 2])
3、tensorflow 基础
tf.arg_max
求数组最大值的下标,如axis=0，代表第一维度，8<2:0,1>3:1,2>4:1 =[0,1,1],如axis=1，代表第二维度，[8,1,2]:0,[2,3,4]:2 = [0 2]
data = tf.constant([[8,1,2],[2,3,4]])
sess = tf.Session()
print(sess.run(tf.arg_max(data,0)))
# >> [0 1 1]
print(sess.run(tf.arg_max(data,1)))
# >>[0 2]
tf.arg_min
与arg_min相反
二、完整程序
import tensorflow as tf
import numpy as np
def file2Mat(testFileName, parammterNumber):
fr = open(testFileName)
lines = fr.readlines()
lineNums = len(lines)
resultMat = np.zeros((lineNums, parammterNumber))
classLabelVector = []
for i in range(lineNums):
line = lines[i].strip()
itemMat = line.split('\t')
resultMat[i, :] = itemMat[0:parammterNumber]
classLabelVector.append(itemMat[-1])
fr.close()
return resultMat, classLabelVector;
# 为了防止某个属性对结果产生很大的影响，所以有了这个优化，比如:10000,4.5,6.8 10000就对结果基本起了决定作用
def autoNorm(dataSet):
minVals = dataSet.min(0)
maxVals = dataSet.max(0)
ranges = maxVals - minVals
normMat = np.zeros(np.shape(dataSet))
size = normMat.shape[0]
normMat = dataSet - np.tile(minVals, (size, 1))
normMat = normMat / np.tile(ranges, (size, 1))
return normMat, minVals, ranges
if __name__=='__main__':
trainigSetFileName = 'data\\datingTrainingSet.txt'
testFileName = 'data\\datingTestSet.txt'
# 读取训练数据
trianingMat, classLabel = file2Mat(trainigSetFileName, 3)
# 都数据进行归一化的处理
autoNormTrianingMat, minVals, ranges = autoNorm(trianingMat)
# 读取测试数据
testMat, testLabel = file2Mat(testFileName, 3)
autoNormTestMat = []
for i in range(len(testLabel)):
autoNormTestMat.append((testMat[i] - minVals) / ranges)
# 循环迭代计算每一个测试数据的预测值，并且和真正的值进行对比，并计算精确度。该算法比较经典的是不需要提前训练，直接在测试阶段进行识别。
traindata_tensor=tf.placeholder('float',[None,3])
testdata_tensor=tf.placeholder('float',[3])
distance = tf.sqrt(tf.reduce_sum(tf.pow(tf.add(traindata_tensor, tf.negative(testdata_tensor)), 2), reduction_indices=1))
pred = tf.arg_min(distance,0)
test_num=1
accuracy=0
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(test_num):
print(sess.run(distance,feed_dict={traindata_tensor:autoNormTrianingMat,testdata_tensor:autoNormTestMat[i]}))
idx=sess.run(pred,feed_dict={traindata_tensor:autoNormTrianingMat,testdata_tensor:autoNormTestMat[i]})
print(idx)
print('test No.%d,the real label %d, the predict label %d'%(i,np.argmax(testLabel[i]),np.argmax(classLabel[idx])))
if np.argmax(testLabel[i])==np.argmax(classLabel[idx]):
accuracy+=1
print("result:%f"%(1.0*accuracy/test_num))
