第52天
主要任务：
1.重新实现 computeNearests, 仅需要扫描一遍训练集, 即可获得 k kk 个邻居. 提示: 现代码与插入排序思想相结合.
2.增加 setDistanceMeasure() 方法.
3.增加 setNumNeighors() 方法.
代码分别如下：
/**
* @Description: 计算最近的k个邻居。在每一轮扫描中选择一个邻居
* @Param: [paraIndex]
* @return: int[]
*/
public int[] computeNearests(int paraCurrent) {
int[] resultNearests = new int[numNeighbors];
boolean[] tempSelected = new boolean[trainingSet.length];
double tempDistance;
double tempMinimalDistance;
int tempMinimalIndex = 0;
/*//选择最近的k个索引
for (int i = 0; i < numNeighbors; i++) {
tempMinimalDistance = Double.MAX_VALUE;
for (int j = 0; j < trainingSet.length; j++) {
if (tempSelected[j]) {
continue;
}
tempDistance = distance(paraCurrent, trainingSet[j]);
if (tempDistance < tempMinimalDistance) {
tempMinimalDistance = tempDistance;
tempMinimalIndex = j;
}
}
resultNearests[i] = trainingSet[tempMinimalIndex];
tempSelected[tempMinimalIndex] = true;
}*/
//使用直接插入排序
//创建一个临时二维数组去存储距离
double[][] tempDistanceArray = new double[trainingSet.length][2];
tempDistanceArray[0][0] = 0;
tempDistanceArray[0][1] = distance(paraCurrent, trainingSet[0]);
int j;
for (int i = 1; i < trainingSet.length; i++) {
tempDistance = distance(paraCurrent, trainingSet[i]);
for (j = i - 1; j >= 0; j--) {
if (tempDistance < tempDistanceArray[j][1]) {
tempDistanceArray[j + 1] = tempDistanceArray[j];
} else {
break;
}
}
tempDistanceArray[j + 1][0] = i;
tempDistanceArray[j + 1][1] = tempDistance;
}
for (int i = 0; i < numNeighbors; i++) {
resultNearests[i] = trainingSet[(int)tempDistanceArray[i][0]];
}
System.out.println("The nearest of " + paraCurrent + " are: " + Arrays.toString(resultNearests));
return resultNearests;
}
/**
* @Description: 选择距离计算方式
* @Param: [paraType：0 or 1]
* @return: void
*/
public void setDistanceMeasure(int paraType) {
if (paraType == 0) {
distanceMeasure = MANHATTAN;
} else if (paraType == 1) {
distanceMeasure = EUCLIDEAN;
} else {
System.out.println("Wrong Distance Measure!!!");
}
}
public static void main(String[] args) {
KnnClassification tempClassifier = new KnnClassification("D:\\JAVA\\学习\\data_set\\iris.arff");
tempClassifier.setDistanceMeasure(1);
tempClassifier.splitTrainingTesting(0.8);
tempClassifier.predict();
System.out.println("The accuracy of the classifier is: " + tempClassifier.getAccuracy());
}
/**
* @Description: 设置邻居数量
* @Param: [paraNumNeighbors]
* @return: void
*/
public void setNumNeighbors(int paraNumNeighbors) {
if (paraNumNeighbors > dataset.numInstances()) {
System.out.println("The number of neighbors is bigger than the number of dataset!!!");
return;
}
numNeighbors = paraNumNeighbors;
}
public static void main(String[] args) {
KnnClassification tempClassifier = new KnnClassification(":\\JAVA\\学习\\data_set\\iris.arff"");
tempClassifier.setDistanceMeasure(1);
tempClassifier.setNumNeighbors(8);
tempClassifier.splitTrainingTesting(0.8);
tempClassifier.predict();
System.out.println("The accuracy of the classifier is: " + tempClassifier.getAccuracy());
}
运行结果?
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第53天?
主要任务：
- 增加 weightedVoting() 方法, 距离越短话语权越大. 支持两种以上的加权方式.
- 实现 leave-one-out 测试
?代码分别如下：
/**
* @Description: 距离权重投票
* 距离越短，话语权越大，直接设置个权值根据顺序来增强其关联性
* @Param: [paraCurrent, paraNeighbors]
* @return: int
*/
public int weightedVoting(int paraCurrent, int[] paraNeighbors) {
//numClasses?? 计算每一个类型出现的次数
double[] tempVotes = new double[dataset.numClasses()];
double tempDistance;
int a = 1, b = 1;
//这样距离越短则值就越大
for (int i = 0; i < paraNeighbors.length; i++) {
tempDistance = distance(paraCurrent, paraNeighbors[i]);
tempVotes[(int) dataset.instance(paraNeighbors[i]).classValue()]
+= getWeightedNum(a, b, tempDistance);
}
int tempMaximalVotingIndex = 0;
double tempMaximalVoting = 0;
for (int i = 0; i < dataset.numClasses(); i++) {
if (tempVotes[i] > tempMaximalVoting) {
tempMaximalVoting = tempVotes[i];
tempMaximalVotingIndex = i;
}
}
return tempMaximalVotingIndex;
}
/**
* @Description: 获取权重，利用反函数
* @Param: [a, b, paraDistance]
* @return: double
*/
public double getWeightedNum(int a, int b, double paraDistance) {
return b / (paraDistance + a);
}
/**
* @Description: 留一交叉验证
* 留一法交叉验证是一种用来训练和测试分类器的方法，会用到图像数据集里所有的数据，假定数据集有N个样本（N1、N2、...Nn），
* 将这个样本分为两份，第一份N-1个样本用来训练分类器，另一份1个样本用来测试，
* 如此从N1到Nn迭代N次，所有的样本里所有对象都经历了测试和训练。
* @Param: []
* @return: void
*/
public void leave_one_out() {
int tempSize = dataset.numInstances();
int[] tempIndices = getRandomIndices(tempSize);
double tempCorrect = 0;
for (int i = 0; i < tempSize; i++) {
trainingSet = new int[tempSize - 1];
testingSet = new int[1];
int tempIndex = 0;
for (int j = 0; j < tempSize; j++) {
if (j == i) {
continue;
}
trainingSet[tempIndex++] = tempIndices[j];
}
testingSet[0] = tempIndices[i];
this.predict();
if (predictions[0] == dataset.instance(testingSet[0]).classValue()) {
tempCorrect++;
}
}
System.out.println("正确率为：" + tempCorrect / tempSize);
}
public static void main(String[] args) {
KnnClassification tempClassifier = new KnnClassification(":\\JAVA\\学习\\data_set\\iris.arff"");
tempClassifier.setDistanceMeasure(1);
tempClassifier.setNumNeighbors(8);
tempClassifier.splitTrainingTesting(0.8);
tempClassifier.predict();
System.out.println("The accuracy of the classifier is: " + tempClassifier.getAccuracy());
System.out.println("\r\n-------leave_one_out-------");
tempClassifier.leave_one_out();
}
?运行结果如下:
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