IPFP（iterative proportional fitting procedure）算法实现

如下是我原创的IPFP算法实现，可以和大家交流，希望有高手批评指正

package IPFProcedure;import java.util.ArrayList;import java.util.Arrays;import java.util.Comparator;import java.util.HashMap;import java.util.Iterator;import java.util.LinkedHashMap;import java.util.Map;import java.util.Set;public class IPFProcedureFuntion {/** *  算法描述如下： iterative proportional fitting procedure *  (1) 初始值： Q_0(X) , R = {R(Y^1),R(Y^2),R(Y^3)} *  (2) for k = 1 ,重复以下迭代过程，直到收敛 *      2.1  i = k mod m; *      2.2  if Q_k-1(Y^i)!= 0  Q_k(X) = Q_k-1(X) * R(Y^i) / Q_k-1(Y^i)  *           else Q_k(X) = 0 *      2.3  k = k + 1   *//* * 如下是各个参数 */public double c  = 0;//c是一个参数值 伊普西龙 ε     public ArrayList<HashMap<String, Double>> R;//约束集    public ArrayList<Boolean[]> ConstraitVariableList;    public int m = 0;//约束集个数 等于 R.size()    public String[] X;//随机变量标识    public int variableNum;    public LinkedHashMap<String,Double> Q ;//存放迭代结果    public LinkedHashMap<String,Double> lastQ ;//上次迭代结果，当一致时收敛    public int iterativeCount = 1;//实验迭代次数    public HashMap<String,Boolean> convergenceMap ;//收敛情况    public boolean isConvergenced = false;//是否已经收敛    /*     * 初始化工作，给各个field variable赋值     */    public  void init(String[] X, ArrayList<HashMap<String,Double>> R, HashMap<String,Double> Q){        this.X = new String[X.length];    for(int i = 0; i < X.length; i++)    this.X[i] = X[i];    this.variableNum = X.length;        this.R = new ArrayList<HashMap<String,Double>>();//    for(HashMap<String, Float> r : R)//    this.R.add(r);    ConstraitVariableList = new ArrayList<Boolean[]>();    for(int i = 0; i < R.size(); i++){    this.R.add(R.get(i));    Boolean[] e = new Boolean[variableNum];    Iterator<String> ir = R.get(i).keySet().iterator();    if(ir.hasNext()){    String s = ir.next();    for(int j = 0; j < s.length(); j++){    char c = s.charAt(j);    if(c == 'T' || c == 'F')    e[j] = true;    else if(c == 'o')    e[j] = false;    else    System.out.println("constrait set data error!");    }    }        ConstraitVariableList.add(e);    }        this.m = R.size();            this.Q = new LinkedHashMap<String,Double>();    this.Q.putAll(Q);    convergenceMap = new HashMap<String, Boolean>();    Iterator<String> i = Q.keySet().iterator();    while(i.hasNext()){    convergenceMap.put(i.next(), false);    }    lastQ = new LinkedHashMap<String, Double>();    Set<String> set = this.Q.keySet();    Iterator<String> ir = set.iterator();    while(ir.hasNext()){    lastQ.put(ir.next(), 0d);    }        }        /*     * 对于迭代收敛的判断还是不正确，所以从新写了这个方法如下     */    public void iterativeFunctionFittingProcedureConvergenceModify(){    this.isConvergenced = judgeIsConvergenced();    if(this.isConvergenced){    System.out.println("联合概率分布已经收敛！结果如下：");    printResult();    return;    }else if(iterativeCount == 100){    System.out.println("迭代次数超过100！");    return;    }else{    Iterator<String> QKeysIterator1 = this.Q.keySet().iterator();    while(QKeysIterator1.hasNext()){    String s = QKeysIterator1.next();    this.lastQ.put(s, this.Q.get(s));    }    Iterator<String> QKeysIterator2 = this.Q.keySet().iterator();    int i = iterativeCount % m;    while(QKeysIterator2.hasNext()){    String s = QKeysIterator2.next();        Double f ;Double q1 = this.lastQ.get(s);char[] c = new char[variableNum];for(int k = 0; k < c.length; k++){if(ConstraitVariableList.get(i)[k] == false)c[k] = 'o';elsec[k] = s.charAt(k);}Double r = R.get(i).get(String.valueOf(c));Double q2 = accMarginalProbabilityFunction(String.valueOf(c));if(q2 == 0d){f = 0d;}elsef =  q1 * r / q2;this.Q.put(s, f);    }    System.out.println("the IPFP Step " + iterativeCount + " has completed");    System.out.println("收敛情况： " + isConvergenced);    printResult();        System.out.println();        iterativeCount++;        iterativeFunctionFittingProcedureConvergenceModify();            }    }    public void iterativeFunctionFittingProcedure(){    if(isConvergenced == true){    System.out.println("联合概率分布已经收敛！结果如下：");    printResult();    return;    }else if(iterativeCount == 100){    System.out.println("迭代次数超过100！");    return;}    Iterator<String> QStringIterator = this.Q.keySet().iterator();    while(QStringIterator.hasNext()){    String s = QStringIterator.next();    if(convergenceMap.get(s) == false){    if(this.Q.get(s).equals((this.lastQ.get(s))))//用等号判断 还是equals()方法    convergenceMap.put(s, true);    else{//迭代计算    this.lastQ.put(s, this.Q.get(s));    int i = iterativeCount % m;//得到i，公式2.1    Double f ;    Double q1 = this.lastQ.get(s);    char[] c = new char[variableNum];    for(int k = 0; k < c.length; k++){    if(ConstraitVariableList.get(i)[k] == false)    c[k] = 'o';    else    c[k] = s.charAt(k);    }    Double r = R.get(i).get(String.valueOf(c));    Double q2 = accMarginalProbabilityFunction(String.valueOf(c));    if(q2 == 0d){    f = 0d;    convergenceMap.put(s, true);    }    else    f =  q1 * r / q2;    this.Q.put(s, f);    }        }    }    System.out.println("the IPFP Step " + iterativeCount + " has completed");        printResult();    isConvergenced = judgeConvergence();    System.out.println("收敛情况： " + isConvergenced);    printQConvergence();    System.out.println();    iterativeCount++;    iterativeFunctionFittingProcedure();    }    /*     * 根据查询String从联合概率分布Q中计算概率Q_k-1(Y^i)     */    public Double accMarginalProbabilityFunction(String query){    Iterator<String> i = this.lastQ.keySet().iterator();    Double result = 0d;    while(i.hasNext()){    String key = i.next();    if(judgeQueryStringIsMatched(key, query))    result += this.lastQ.get(key);    }    return result;    }    /*     * 根据查询的String查找联合概率分布Q     */    public boolean judgeQueryStringIsMatched(String key,String query){    if(key.length() != variableNum || query.length() != variableNum)    System.out.println("概率查询出错，请检查数据输入初始化是否正确");    for(int i = 0; i < query.length(); i++){    if(query.charAt(i) == 'T' && key.charAt(i) == 'F'|| query.charAt(i) == 'F' && key.charAt(i) == 'T')    return false;    }    return true;    }    /*     * 根据联合概率表 Q和lastQ是否一致来判断是否收敛     */    public boolean judgeIsConvergenced(){    Iterator<String> iq = Q.keySet().iterator();    while(iq.hasNext()){    String s = iq.next();    if(!lastQ.get(s).equals(Q.get(s)))    return false;    }    return true;    }    /*     * 判断收敛情况，ConvergenceMap存储各个X=x时的各个收敛情况，但是有疑问     */    public boolean judgeConvergence(){    Iterator<String> i = convergenceMap.keySet().iterator();    while(i.hasNext()){    if(convergenceMap.get(i.next()) == false)    return false;    }    return true;    }        public void printResult(){//也可以写到csv中去    if(isConvergenced){    int num = iterativeCount - 1;    System.out.println( num + " Step, " + "最后收敛的联合概率分布如下： ");}    else    System.out.println("第 "+ iterativeCount +" 次迭代概率分布如下： ");//    Iterator<String> si = Q.keySet().iterator();//    while(si.hasNext()){//    String s = si.next();//    printVariable(s);//    }    String[] keys =  Q.keySet().toArray(new String[Q.keySet().size()]);    Comparator<String> c = new Comparator<String>() {public int compare(String s1,String s2) {// TODO Auto-generated method stubfor(int i = 0; i<s1.length(); i++){if(s1.charAt(i)> s2.charAt(i))return -1;else if(s1.charAt(i) < s2.charAt(i))return 1;}return 0;}    };    Arrays.sort(keys, c);    for(int i = 0 ; i < keys.length; i++)    printVariable(keys[i]);        System.out.println("联合概率Q打印完毕！");    }        public void printVariable(String s){    StringBuilder sb = new StringBuilder("");    for(int i = 0; i < s.length(); i++){    if(s.charAt(i) == 'T')    sb.append(X[i] + " = " + "T");    else if(s.charAt(i) == 'F')    sb.append(X[i] + " = " + "F");    sb.append(",");    }    sb.append("probality is " + Q.get(s));    System.out.println(sb);    }        public void printQConvergence(){    Iterator<String> ir = convergenceMap.keySet().iterator();    while(ir.hasNext()){    String s = ir.next();    StringBuilder sb = new StringBuilder("");        for(int i = 0; i < s.length(); i++){        if(s.charAt(i) == 'T')        sb.append(X[i] + " = " + "T");        else if(s.charAt(i) == 'F')        sb.append(X[i] + " = " + "F");        sb.append(",");        }        sb.append("convergenced is " + convergenceMap.get(s));        System.out.println(sb);            }    }    public static void main(String[] args) {// TODO Auto-generated method stub    IPFProcedureFuntion ipfp = new IPFProcedureFuntion();    ipfp.setC(4/20d);    Double C = 4 / 20d;    String[] X = {"X","Y","Z"};        HashMap<String,Double> Q = new HashMap<String,Double>();    Q.put("TTT", 0.125d);    Q.put("TTF", 0.125d);    Q.put("TFT", 0.125d);    Q.put("TFF", 0.125d);    Q.put("FTT", 0.125d);    Q.put("FTF", 0.125d);    Q.put("FFT", 0.125d);    Q.put("FFF", 0.125d);        ArrayList<HashMap<String,Double>> R = new ArrayList<HashMap<String,Double>>();        HashMap<String,Double> R2 = new HashMap<String, Double>();    R2.put("TTo", 1/2d - C);    R2.put("TFo", C);    R2.put("FTo", C);    R2.put("FFo", 1/2d - C);    R.add(R2);        HashMap<String,Double> R3 = new HashMap<String, Double>();    R3.put("oTT", 1/2d - C);    R3.put("oTF", C);    R3.put("oFT", C);    R3.put("oFF", 1/2d - C);    R.add(R3);        HashMap<String,Double> R1 = new HashMap<String, Double>();    R1.put("ToT", C);    R1.put("ToF", 1/2d - C);    R1.put("FoT", 1/2d - C);    R1.put("FoF", C);    R.add(R1);        ipfp.init(X, R, Q);    ipfp.iterativeFunctionFittingProcedureConvergenceModify();    ipfp.printResult();    }public double getC() {return c;}public void setC(double c) {this.c = c;}}