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Relief算法程序.txt ~ 6KB

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package com.relief.algorithm;

import java.util.Random;

import java.util.Set;

import java.util.ArrayList;

import java.util.Collections;

import java.util.HashMap;

import java.util.List;

import java.util.Map;

public class Relief {

public Relief(){

}

public double[][] matrix;

public int length;

public int width;

public double[] weight;

//样本抽样次数

public int m;

//样本数量

public int k;

public int n_vars;

/**

* relief算法

*/

public void relief(){

matrix = new double[length][width];

//将样本数据赋值到matrix中

//权重值全部置为0

weight = new double[n_vars];

for(int i = 0; i < n_vars; i++){

weight[i] = 0.0;

}

//属性的最大值和最小值

double[] max = new double[n_vars];

double[] min = new double[n_vars];

for(int i = 0; i < width; i++){

for(int j = 0; j < length; j++){

double d = matrix[j][i];

if(d > max[i]){

max[i] = d;

}

if(d < min[i]){

min[i] = d;

}

}

}

//随机抽样m次

for(int i = 0; i < m; i++){

//随机抽取样本R

Random random = new Random();

int R_index = random.nextInt(width);

double[] R = new double[width];

for(int index = 0; index < width; index++){

R[index] = matrix[R_index][index];

}

//计算出距离样本R最近的样本和最远的样本

double maxvalue = 0.0;

double minvalue = 0.0;

int maxrow = 0;

int minrow = 0;

double distince = 0.0;

for(int len = 0; len < length; len++){

if(len != R_index){

for(int wid = 0; wid < width; wid++){

distince += Math.pow(R[wid]-matrix[len][wid], 2);

}

distince = Math.sqrt(distince);

if(len == 0){

maxvalue = distince;

minvalue = distince;

}

if(distince > maxvalue){

maxvalue = distince;

maxrow = len;

}

if(distince < minvalue){

minvalue = distince;

minrow = len;

}

}

}

int H_index = minrow;

double[] H = new double[width];

for (int index = 0; index < width; index++) {

H[index] = matrix[H_index][index];

}

int M_index = maxrow;

double[] M = new double[width];

for(int index = 0; index < width; index++){

M[index] = matrix[M_index][index];

}

//relief计算权重

for(int j = 0; j < n_vars; j++){

weight[j] = weight[j]-(Math.abs(R[j]-H[j])/(max[j]-min[j]))/m + (Math.abs(R[j]-M[j])/(max[j]-min[j]))/m;

}

}

for(int i = 0; i < width; i++){

System.out.println(weight[i]);

}

}

//有t个不同类

public int t;

/**

* reliefF算法

*/

public void reliefF(){

matrix = new double[length][width];

//将样本数据赋值到matrix中

//权重值全部置为0

weight = new double[n_vars];

for(int i = 0; i < n_vars; i++){

weight[i] = 0.0;

}

//属性的最大值和最小值

double[] max = new double[n_vars];

double[] min = new double[n_vars];

for(int i = 0; i < width; i++){

for(int j = 0; j < length; j++){

double d = matrix[j][i];

if(d > max[i]){

max[i] = d;

}

if(d < min[i]){

min[i] = d;

}

}

}

//随机抽样m次

Map map = new HashMap();

double[][] H = new double[k][width];

double[][] M = new double[k][width];

List lstM = new ArrayList();

double[] R = new double[width];

for(int i = 0; i < m; i++){

//随机抽取样本R

Random random = new Random();

int R_index = random.nextInt(width);

for(int index = 0; index < width; index++){

R[index] = matrix[R_index][index];

}

//计算所有数据到样本的距离

for(int len = 0; len < length; len++){

double distince = 0.0;

int row = 0;

if(len != R_index){

for(int wid = 0; wid < width; wid++){

distince += Math.pow(R[wid]-matrix[len][wid], 2);

}

distince = Math.sqrt(distince);

row = len;

map.put(distince, row);

}

}

//对样本距离排序

Set set = map.keySet();

List lst = new ArrayList(set);

Collections.sort(lst);

//获取距离最近的k个样本作为H

for(int a = 0; a < k; a++){

double d = lst.get(a);

int r = map.get(d);

for(int b = 0; b < width; b++){

H[a][b] = matrix[r][b];

}

}

//假设有t个不同分类，在每个不同类中获取k个样本

//在数据中随机获取k个样本作为M(C)

for(int a = 0; a < t; a++){

double[] RD = new double[width];

//随机抽取样本

Random rd = new Random();

int id = rd.nextInt(width);

while(true){

if(id != R_index){

break;

}

else{

id = rd.nextInt(width);

}

}

for(int index = 0; index < width; index++){

R[index] = matrix[R_index][index];

}

//计算所有数据到样本的距离

Map maprd = new HashMap();

for(int len = 0; len < length; len++){

double distince = 0.0;

int row = 0;

if(len != R_index){

for(int wid = 0; wid < width; wid++){

distince += Math.pow(R[wid]-matrix[len][wid], 2);

}

distince = Math.sqrt(distince);

row = len;

maprd.put(distince, row);

}

}

//对样本距离排序

Set setrd = map.keySet();

List lstrd = new ArrayList(set);

Collections.sort(lst);

//获取不同类距离最近的k个样本作为

for(int q = 0; q < k; q++){

double d = lst.get(q);

int r = map.get(d);

for(int p = 0; p < width; p++){

M[q][p] = matrix[r][p];

}

}

lstM.add(M);

}

}

//计算H的diff

double[] tmpval1 = new double[n_vars];

double[] tmpval2 = new double[n_vars];

for(int i = 0; i < n_vars; i++){

for(int j = 0; j < k; j++){

tmpval1[i] += Math.abs(R[i]-H[j][i])/(max[i]-min[i]);

}

}

//计算不同类的diff

double[] value = new double[n_vars];

for(double[][] d : lstM){

for(int i = 0; i < n_vars; i++){

for(int j = 0; j < k; j++){

value[i] += Math.abs(R[i]-M[j][i])/(max[i]-min[i]);

}

}

}

for(int i = 0; i < n_vars; i++){

tmpval2[i] += value[i]/t;

}

//计算权重

for(int j = 0; j < n_vars; j++){

weight[j] = weight[j]- tmpval1[j]/(m*k) + tmpval2[j]/(m*k);

}

for(int i = 0; i < width; i++){

System.out.println(weight[i]);

}

}

}