日撸java_day58-59
文章目录
- 第 58 -59天: 符号型数据与数值型数据的 NB 算法
-
- 代码
- 运行结果
第 58 -59天: 符号型数据与数值型数据的 NB 算法
代码
package machineLearning.nb;
import weka.core.Instance;
import weka.core.Instances;
import java.io.FileReader;
import java.util.Arrays;
/**
* ClassName: NaiveBayes
* Package: machineLearning.nb
* Description:The Naive Bayes algorithm.
*
* @Author: luv_x_c
* @Create: 2023/8/4 15:05
*/
public class NaiveBayes {
/**
* An inner class to store parameters.
*/
private static class GaussianParameters {
double mu;
double sigma;
public GaussianParameters(double paraMu, double paraSigma) {
mu = paraMu;
sigma = paraSigma;
}// OF the constructor
@Override
public String toString() {
return "GaussianParameters{}";
}// Of toString
}// OF class GaussianParameters
/**
* The data.
*/
Instances dataset;
/**
* The number of classes. For binary classification it is 2.
*/
int numClasses;
/**
* The number of instances.
*/
int numInstances;
/**
* The number of conditional attributes.
*/
int numConditions;
/**
* The prediction, including queried and predicted labels.
*/
int[] predicts;
/**
* Class distribution.
*/
double[] classDistribution;
/**
* Class distribution with Laplacian smooth.
*/
double[] classDistributionLaplacian;
/**
* To calculate the conditional probabilities for all classes over all attributes on all values.
*/
double[][][] conditionalCounts;
/**
* The conditional probabilities with Laplacian smooth.
*/
double[][][] conditionalProbabilitiesLaplacian;
/**
* The Gaussian parameters.
*/
GaussianParameters[][] gaussianParameters;
/**
* Data type.
*/
int dataType;
/**
* Nominal.
*/
public static final int NOMINAL = 0;
/**
* Numerical.
*/
public static final int NUMERICAL = 1;
/**
* The constructor.
*
* @param paraFileName The given file.
*/
public NaiveBayes(String paraFileName) {
dataset = null;
try {
FileReader fileReader = new FileReader(paraFileName);
dataset = new Instances(fileReader);
fileReader.close();
} catch (Exception ee) {
System.out.println("Cannot read the file: " + paraFileName + "\r\t" + ee);
System.exit(0);
}// Of try
dataset.setClassIndex(dataset.numAttributes() - 1);
numConditions = dataset.numAttributes() - 1;
numInstances = dataset.numInstances();
numClasses = dataset.attribute(numConditions).numValues();
}// Of the constructor
/**
* Set the data type.
*/
public void setDataType(int paraDataType) {
dataType = paraDataType;
}// Of setDataType
/**
* Calculate the class distribution with Laplacian smooth.
*/
public void calculateClassDistribution() {
classDistribution = new double[numClasses];
classDistributionLaplacian = new double[numClasses];
double[] tempCounts = new double[numClasses];
for (int i = 0; i < numInstances; i++) {
int tempClassValue = (int) dataset.instance(i).classValue();
tempCounts[tempClassValue]++;
}// OF for i
for (int i = 0; i < numClasses; i++) {
classDistribution[i] = tempCounts[i] / numInstances;
classDistributionLaplacian[i] = (tempCounts[i] + 1) / (numInstances + numClasses);
}// OF for i
System.out.println("Class distribution: " + Arrays.toString(classDistribution));
System.out.println("Class distribution Laplacian: " + Arrays.toString(classDistributionLaplacian));
}//Of calculateClassDistribution
public void calculateConditionalProbabilities() {
conditionalCounts = new double[numConditions][numConditions][];
conditionalProbabilitiesLaplacian = new double[numClasses][numConditions][];
//Allocate space.
for (int i = 0; i < numClasses; i++) {
for (int j = 0; j < numConditions; j++) {
int tempNumValues = (int) dataset.attribute(j).numValues();
conditionalCounts[i][j] = new double[tempNumValues];
conditionalProbabilitiesLaplacian[i][j] = new double[tempNumValues];
}// Of for j
}// Of for i
//Count the numbers
int[] tempClassCounts = new int[numClasses];
for (int i = 0; i < numInstances; i++) {
int tempClass = (int) dataset.instance(i).classValue();
tempClassCounts[tempClass]++;
for (int j = 0; j < numConditions; j++) {
int tempValue = (int) dataset.instance(i).value(j);
conditionalCounts[tempClass][j][tempValue]++;
}//Of for j
}// OF for i
// Now for the real probability with Laplacian
for (int i = 0; i < numClasses; i++) {
for (int j = 0; j < numConditions; j++) {
int tempNumValue = (int) dataset.attribute(j).numValues();
for (int k = 0; k < tempNumValue; k++) {
conditionalProbabilitiesLaplacian[i][j][k] =
(conditionalCounts[i][j][k] + 1) / (tempClassCounts[i] + tempNumValue);
}// OF for k
}// OF for j
}// OF for i
System.out.println("Conditional probabilities: " + Arrays.deepToString(conditionalCounts));
}// OF calculateConditionalProbabilities
/**
* Calculate the conditional probabilities with Laplacian smooth.
*/
public void calculateGaussianParameters() {
gaussianParameters = new GaussianParameters[numClasses][numConditions];
double[] tempValueArray = new double[numInstances];
int tempNumValues = 0;
double temSum = 0;
for (int i = 0; i < numClasses; i++) {
for (int j = 0; j < numConditions; j++) {
temSum = 0;
//Obtain values for this class
tempNumValues = 0;
for (int k = 0; k < numInstances; k++) {
if ((int) dataset.instance(k).classValue() != i) {
continue;
}// OF if
tempValueArray[tempNumValues] = dataset.instance(k).value(j);
temSum += tempValueArray[tempNumValues];
tempNumValues++;
}// Of for k
// Obtain parameters.
double tempMu = temSum / tempNumValues;
double tempSigma = 0;
for (int k = 0; k < tempNumValues; k++) {
tempSigma += (tempValueArray[k] - tempMu) * (tempValueArray[k] - tempMu);
}// OF for k
tempSigma /= tempNumValues;
tempSigma = Math.sqrt(tempSigma);
gaussianParameters[i][j] = new GaussianParameters(tempMu, tempSigma);
}//Of for j
}// OF for i
System.out.println(Arrays.deepToString(gaussianParameters));
}// OF calculateGaussianParameters
/**
* Classify all instances, the results are stored in predicts[].
*/
public void classify() {
predicts = new int[numInstances];
for (int i = 0; i < numInstances; i++) {
predicts[i] = classify(dataset.instance(i));
}// OF for i
}// of classify
/**
* Classify an instances.
*/
public int classify(Instance paraInstance) {
if (dataType == NOMINAL) {
return classifyNominal(paraInstance);
} else if (dataType == NUMERICAL) {
return classifyNumerical(paraInstance);
}// OF if
return -1;
}//OF classify
/**
* Classify an instances with nominal data.
*/
public int classifyNominal(Instance paraInstance) {
// Find the biggest one.
double tempBiggest = -10000;
int resultBestIndex = 0;
for (int i = 0; i < numClasses; i++) {
double tempPseudoProbability = Math.log(classDistributionLaplacian[i]);
for (int j = 0; j < numConditions; j++) {
int tempAttributeValue = (int) paraInstance.value(j);
tempPseudoProbability += Math.log(conditionalProbabilitiesLaplacian[i][j][tempAttributeValue]);
}// of for j
if (tempBiggest < tempPseudoProbability) {
tempBiggest = tempPseudoProbability;
resultBestIndex = i;
}// OF if
}// Of for i
return resultBestIndex;
}// OF classifyNominal
/**
* Classify an instances with numerical data.
*/
public int classifyNumerical(Instance paraInstance) {
//Find the biggest one
double tempBiggest = -10000;
int resultBestIndex = 0;
for (int i = 0; i < numClasses; i++) {
double tempPseudoProbability = Math.log(classDistributionLaplacian[i]);
for (int j = 0; j < numConditions; j++) {
double tempAttributeValue = paraInstance.value(j);
double tempSigma = gaussianParameters[i][j].sigma;
double tempMu = gaussianParameters[i][j].mu;
tempPseudoProbability += -Math.log(tempSigma) - (tempAttributeValue - tempMu) * (tempAttributeValue - tempMu) / (2 * tempSigma * tempSigma);
}// Of for j
if (tempBiggest < tempPseudoProbability) {
tempBiggest = tempPseudoProbability;
resultBestIndex = i;
}// Of if
}// OF for i
return resultBestIndex;
}// OF classifyNumerical
/**
* Compute accuracy.
*/
public double computeAccuracy() {
double tempCorrect = 0;
for (int i = 0; i < numInstances; i++) {
if (predicts[i] == (int) dataset.instance(i).classValue()) {
tempCorrect++;
}//Of if
}// oF for i
return tempCorrect / numInstances;
}//OF computeAccuracy
/**
* Test nominal data.
*/
public static void testNominal() {
System.out.println("Hello, Naive Bayes. I only want to test the nominal data. ");
String tempFileName = "E:\\java_code\\data\\sampledata\\mushroom.arff";
NaiveBayes tempLearner = new NaiveBayes(tempFileName);
tempLearner.setDataType(NOMINAL);
tempLearner.calculateClassDistribution();
tempLearner.calculateConditionalProbabilities();
tempLearner.classify();
System.out.println("The accuracy is: " + tempLearner.computeAccuracy());
}//Of testNominal
/**
* Test numerical data.
*/
public static void testNumerical() {
System.out.println("Hello, Naive Bayes. I only want to test the numerical data with Gaussian assumption.");
String tempFileName = "E:/java_code/data/sampledata\\iris.arff";
NaiveBayes tempLearner = new NaiveBayes(tempFileName);
tempLearner.setDataType(NUMERICAL);
tempLearner.calculateClassDistribution();
tempLearner.calculateGaussianParameters();
tempLearner.classify();
System.out.println("The accuracy is: " + tempLearner.computeAccuracy());
}// Of testNumerical
/**
* The entrance of the program.
*
* @param args Not used now.
*/
public static void main(String[] args) {
testNominal();
testNumerical();
}// OF main
}//OF class NaiveBayes
运行结果
