Java学习笔记（day51-53）
一、学习内容
主题：kNN分类器
今日任务：
今天先把代码抄完并运行, 明后天有修改程序的工作. 要求熟练掌握.
具体内容：
1、两种距离度量.
2、数据随机分割方式.
3、间址的灵活使用: trainingSet 和 testingSet 都是整数数组, 表示下标.
4、arff 文件的读取. 需要 weka.jar 包.
5、求邻居.
6、投票.
二、具体代码
package machinelearning.knn;
import java.io.FileReader;
import java.util.Arrays;
import java.util.Random;
import weka.core.*;
/**
* kNN classification.
*
* @author WeiZe [email protected].
*/
public class KnnClassification {
/**
* Manhattan distance.一种计算距离的方法
*/
public static final int MANHATTAN = 0;
/**
* Euclidean distance.另一种计算距离的方法
*/
public static final int EUCLIDEAN = 1;
/**
* The distance measure.距离测量
*/
public int distanceMeasure = EUCLIDEAN;
/**
* A random instance;随机实例
*/
public static final Random random = new Random();
/**
* The number of neighbors.临近数
*/
int numNeighbors = 7;
/**
* The whole dataset.整个数据集
*/
Instances dataset;
/**
* The training set. Represented by the indices of the data.训练集
*/
int[] trainingSet;
/**
* The testing set. Represented by the indices of the data.测试集
*/
int[] testingSet;
/**
* The predictions.预测
*/
int[] predictions;
/**
*********************
* The first constructor.
*
* @param paraFilename
* The arff filename.
*********************
*/
public KnnClassification(String paraFilename) {
try {
FileReader fileReader = new FileReader(paraFilename);
dataset = new Instances(fileReader);
// The last attribute is the decision class.
dataset.setClassIndex(dataset.numAttributes() - 1);
fileReader.close();
} catch (Exception ee) {
System.out.println("Error occurred while trying to read \'" + paraFilename
+ "\' in KnnClassification constructor.\r\n" + ee);
System.exit(0);
} // Of try
}// Of the first constructor
/**
*********************
* Get a random indices for data randomization.
*
* @param paraLength
* The length of the sequence.
* @return An array of indices, e.g., {4, 3, 1, 5, 0, 2} with length 6.
*********************
*/
public static int[] getRandomIndices(int paraLength) {
int[] resultIndices = new int[paraLength];
// Step 1. Initialize.
for (int i = 0; i < paraLength; i++) {
resultIndices[i] = i;
} // Of for i
// Step 2. Randomly swap.
int tempFirst, tempSecond, tempValue;
for (int i = 0; i < paraLength; i++) {
// Generate two random indices.
tempFirst = random.nextInt(paraLength);
tempSecond = random.nextInt(paraLength);
// Swap.
tempValue = resultIndices[tempFirst];
resultIndices[tempFirst] = resultIndices[tempSecond];
resultIndices[tempSecond] = tempValue;
} // Of for i
return resultIndices;
}// Of getRandomIndices
/**
*********************
* Split the data into training and testing parts.
*
* @param paraTrainingFraction
* The fraction of the training set.
*********************
*/
public void splitTrainingTesting(double paraTrainingFraction) {
int tempSize = dataset.numInstances();
int[] tempIndices = getRandomIndices(tempSize);
int tempTrainingSize = (int) (tempSize * paraTrainingFraction);
trainingSet = new int[tempTrainingSize];
testingSet = new int[tempSize - tempTrainingSize];
for (int i = 0; i < tempTrainingSize; i++) {
trainingSet[i] = tempIndices[i];
} // Of for i
for (int i = 0; i < tempSize - tempTrainingSize; i++) {
testingSet[i] = tempIndices[tempTrainingSize + i];
} // Of for i
}// Of splitTrainingTesting
/**
*********************
* Predict for the whole testing set. The results are stored in predictions.
* #see predictions.
*********************
*/
public void predict() {
predictions = new int[testingSet.length];
for (int i = 0; i < predictions.length; i++) {
predictions[i] = predict(testingSet[i]);
} // Of for i
}// Of predict
/**
*********************
* Predict for given instance.
*
* @return The prediction.
*********************
*/
public int predict(int paraIndex) {
int[] tempNeighbors = computeNearests(paraIndex);
int resultPrediction = simpleVoting(tempNeighbors);
return resultPrediction;
}// Of predict
/**
*********************
* The distance between two instances.
*
* @param paraI
* The index of the first instance.
* @param paraJ
* The index of the second instance.
* @return The distance.
*********************
*/
public double distance(int paraI, int paraJ) {
int resultDistance = 0;
double tempDifference;
switch (distanceMeasure) {
case MANHATTAN:
for (int i = 0; i < dataset.numAttributes() - 1; i++) {
tempDifference = dataset.instance(paraI).value(i) - dataset.instance(paraJ).value(i);
if (tempDifference < 0) {
resultDistance -= tempDifference;
} else {
resultDistance += tempDifference;
} // Of if
} // Of for i
break;
case EUCLIDEAN:
for (int i = 0; i < dataset.numAttributes() - 1; i++) {
tempDifference = dataset.instance(paraI).value(i) - dataset.instance(paraJ).value(i);
resultDistance += tempDifference * tempDifference;
} // Of for i
break;
default:
System.out.println("Unsupported distance measure: " + distanceMeasure);
}// Of switch
return resultDistance;
}// Of distance
/**
*********************
* Get the accuracy of the classifier.
*
* @return The accuracy.
*********************
*/
public double getAccuracy() {
// A double divides an int gets another double.
double tempCorrect = 0;
for (int i = 0; i < predictions.length; i++) {
if (predictions[i] == dataset.instance(testingSet[i]).classValue()) {
tempCorrect++;
} // Of if
} // Of for i
return tempCorrect / testingSet.length;
}// Of getAccuracy
/**
************************************
* Compute the nearest k neighbors. Select one neighbor in each scan. In
* fact we can scan only once. You may implement it by yourself.
*
* @param paraK
* the k value for kNN.
* @param paraCurrent
* current instance. We are comparing it with all others.
* @return the indices of the nearest instances.
************************************
*/
public int[] computeNearests(int paraCurrent) {
int[] resultNearests = new int[numNeighbors];
boolean[] tempSelected = new boolean[trainingSet.length];
double tempDistance;
double tempMinimalDistance;
int tempMinimalIndex = 0;
// Select the nearest paraK indices.
for (int i = 0; i < numNeighbors; i++) {
tempMinimalDistance = Double.MAX_VALUE;
for (int j = 0; j < trainingSet.length; j++) {
if (tempSelected[j]) {
continue;
} // Of if
tempDistance = distance(paraCurrent, trainingSet[j]);
if (tempDistance < tempMinimalDistance) {
tempMinimalDistance = tempDistance;
tempMinimalIndex = j;
} // Of if
} // Of for j
resultNearests[i] = trainingSet[tempMinimalIndex];
tempSelected[tempMinimalIndex] = true;
} // Of for i
System.out.println("The nearest of " + paraCurrent + " are: " + Arrays.toString(resultNearests));
return resultNearests;
}// Of computeNearests
/**
************************************
* Voting using the instances.
*
* @param paraNeighbors
* The indices of the neighbors.
* @return The predicted label.
************************************
*/
public int simpleVoting(int[] paraNeighbors) {
int[] tempVotes = new int[dataset.numClasses()];
for (int i = 0; i < paraNeighbors.length; i++) {
tempVotes[(int) dataset.instance(paraNeighbors[i]).classValue()]++;
} // Of for i
int tempMaximalVotingIndex = 0;
int tempMaximalVoting = 0;
for (int i = 0; i < dataset.numClasses(); i++) {
if (tempVotes[i] > tempMaximalVoting) {
tempMaximalVoting = tempVotes[i];
tempMaximalVotingIndex = i;
} // Of if
} // Of for i
return tempMaximalVotingIndex;
}// Of simpleVoting
/**
*********************
* The entrance of the program.
*
* @param args
* Not used now.
*********************
*/
public static void main(String args[]) {
KnnClassification tempClassifier = new KnnClassification("D:/data/iris.arff");
tempClassifier.splitTrainingTesting(0.8);
tempClassifier.predict();
System.out.println("The accuracy of the classifier is: " + tempClassifier.getAccuracy());
}// Of main
}// Of class KnnClassificationDAY52
1、重新实现 computeNearests, 仅需要扫描一遍训练集, 即可获得 k 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.
DAY53
1、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.
2、实现 leave-one-out 测试.