DEV: ADD KNN PYTHON CODE AND DATA FILE

src/python/02.kNN/kNN.py

@@ -1,24 +1,28 @@
+#!/usr/bin/env python
+# encoding: utf-8
 '''
 导入科学计算包numpy和运算符模块operator
 '''
 from numpy import *
 import operator
+from os import listdir
 
-'''
- 创建数据集和标签
 
- 调用方式
- import kNN
- group, labels = kNN.createDataSet()
-'''
 def createDataSet():
-    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
-    labels = ['A','A','B','B']
+    """
+    创建数据集和标签
+
+     调用方式
+     import kNN
+     group, labels = kNN.createDataSet()
+    """
+    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
+    labels = ['A', 'A', 'B', 'B']
     return group, labels
 
 
 def classify0(inX, dataSet, labels, k):
-    '''
+    """
     inX: 用于分类的输入向量
     dataSet: 输入的训练样本集
     labels: 标签向量
@@ -27,19 +31,155 @@ def classify0(inX, dataSet, labels, k):
 
     预测数据所在分类可在输入下列命令
     kNN.classify0([0,0], group, labels, 3)
-    '''
+    """
     # 1. 距离计算
     dataSetSize = dataSet.shape[0]
-    diffMat = tile(inX, (dataSetSize,1)) - dataSet
-    sqDiffMat = diffMat**2
+    # tile生成和训练样本对应的矩阵，并与训练样本求差
+    diffMat = tile(inX, (dataSetSize, 1)) - dataSet
+    # 取平方
+    sqDiffMat = diffMat ** 2
+    # 将矩阵的每一行相加
     sqDistances = sqDiffMat.sum(axis=1)
-    distances = sqDistances**0.5
+    # 开方
+    distances = sqDistances ** 0.5
+    # 距离排序
     sortedDistIndicies = distances.argsort()
+
     # 2. 选择距离最小的k个点
     classCount = {}
     for i in range(k):
+        # 找到该样本的类型
         voteIlabel = labels[sortedDistIndicies[i]]
-        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
-    # 3. 排序
+        # 在字典中将该类型加一
+        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
+    # 3. 排序并返回出现最多的那个类型
     sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
-    return sortedClassCount[0][0]
+    return sortedClassCount[0][0]
+
+
+def test1():
+    """
+    第一个例子演示
+    """
+    group, labels = createDataSet()
+    print str(group)
+    print str(labels)
+    print classify0([0, 0], group, labels, 3)
+
+
+# ----------------------------------------------------------------------------------------
+def file2matrix(filename):
+    """
+    导入训练数据
+    :param filename: 数据文件路径
+    :return: 数据矩阵returnMat和对应的类别classLabelVector
+    """
+    fr = open(filename)
+    numberOfLines = len(fr.readlines())  # get the number of lines in the file
+    # 生成对应的空矩阵
+    returnMat = zeros((numberOfLines, 3))  # prepare matrix to return
+    classLabelVector = []  # prepare labels return
+    fr = open(filename)
+    index = 0
+    for line in fr.readlines():
+        line = line.strip()
+        listFromLine = line.split('\t')
+        # 每列的属性数据
+        returnMat[index, :] = listFromLine[0:3]
+        # 每列的类别数据
+        classLabelVector.append(int(listFromLine[-1]))
+        index += 1
+    # 返回数据矩阵returnMat和对应的类别classLabelVector
+    return returnMat, classLabelVector
+
+
+def autoNorm(dataSet):
+    """
+    归一化特征值，消除属性之间量级不同导致的影响
+    :param dataSet: 数据集
+    :return: 归一化后的数据集normDataSet,ranges和minVals即最小值与范围，并没有用到
+    """
+    # 计算每种属性的最大值、最小值、范围
+    minVals = dataSet.min(0)
+    maxVals = dataSet.max(0)
+    ranges = maxVals - minVals
+    normDataSet = zeros(shape(dataSet))
+    m = dataSet.shape[0]
+    # 生成与最小值之差组成的矩阵
+    normDataSet = dataSet - tile(minVals, (m, 1))
+    # 将最小值之差除以范围组成矩阵
+    normDataSet = normDataSet / tile(ranges, (m, 1))  # element wise divide
+    return normDataSet, ranges, minVals
+
+
+def datingClassTest():
+    """
+    对约会网站的测试方法
+    :return: 错误数
+    """
+    hoRatio = 0.9  # 测试范围,一部分测试一部分作为样本
+    # 从文件中加载数据
+    datingDataMat, datingLabels = file2matrix('../../../testData/datingTestSet2.txt')  # load data setfrom file
+    # 归一化数据
+    normMat, ranges, minVals = autoNorm(datingDataMat)
+    m = normMat.shape[0]
+    # 测试的数据
+    numTestVecs = int(m * hoRatio)
+    errorCount = 0.0
+    for i in range(numTestVecs):
+        # 对数据测试，
+        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
+        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])
+        if (classifierResult != datingLabels[i]): errorCount += 1.0
+    print "the total error rate is: %f" % (errorCount / float(numTestVecs))
+    print errorCount
+
+
+def img2vector(filename):
+    """
+    将图像数据转换为向量
+    :param filename: 图片文件
+    :return: 一纬矩阵
+    """
+    returnVect = zeros((1, 1024))
+    fr = open(filename)
+    for i in range(32):
+        lineStr = fr.readline()
+        for j in range(32):
+            returnVect[0, 32 * i + j] = int(lineStr[j])
+    return returnVect
+
+
+def handwritingClassTest():
+    # 1. 导入数据
+    hwLabels = []
+    trainingFileList = listdir('../../../testData/trainingDigits')  # load the training set
+    m = len(trainingFileList)
+    trainingMat = zeros((m, 1024))
+    for i in range(m):
+        fileNameStr = trainingFileList[i]
+        fileStr = fileNameStr.split('.')[0]  # take off .txt
+        classNumStr = int(fileStr.split('_')[0])
+        hwLabels.append(classNumStr)
+        trainingMat[i, :] = img2vector('../../../testData/trainingDigits/%s' % fileNameStr)
+
+    # 2. 导入测试数据
+    testFileList = listdir('../../../testData/testDigits')  # iterate through the test set
+    errorCount = 0.0
+    mTest = len(testFileList)
+    for i in range(mTest):
+        fileNameStr = testFileList[i]
+        fileStr = fileNameStr.split('.')[0]  # take off .txt
+        classNumStr = int(fileStr.split('_')[0])
+        vectorUnderTest = img2vector('../../../testData/testDigits/%s' % fileNameStr)
+        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
+        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
+        if (classifierResult != classNumStr): errorCount += 1.0
+    print "\nthe total number of errors is: %d" % errorCount
+    print "\nthe total error rate is: %f" % (errorCount / float(mTest))
+
+
+if __name__ == '__main__':
+    # test1()
+    # datingClassTest()
+    handwritingClassTest()

src/python/08.Predictive numerical data regression/regression.py

@@ -223,7 +223,7 @@ def crossValidation(xArr,yArr,numVal=10):
 
 
     #test for xianxinghuigui
-    def regression1():
+def regression1():
     xArr, yArr = loadDataSet("ex0.txt")
     xMat = mat(xArr)
     yMat = mat(yArr)
@@ -242,7 +242,7 @@ if __name__ == "__main__":
 
 
     #test for jiaquanhuigui
-    def regression1():
+def regression1():
     xArr, yArr = loadDataSet("ex0.txt")
     yHat = lwlrTest(xArr, xArr, yArr, 0.003)
     xMat = mat(xArr)