// static ArrayList<Double> error_prob=new ArrayList<>() ;
double CalculateThreshold(
double[] xvalues,
int[] yvalues,
double[] probabilities,
double[] goodnessweight,
double[] threshold,
int q,
ArrayList<ArrayList<Double>> fvalues,
double bound,
char[] c) {
ArrayList<Double> error_prob = new ArrayList<>();
double ht_error = 0;
double normalizationfac = 0;
// char c;
int arraysize = yvalues.length;
double[] normalizedprobabilities = new double[arraysize];
int[] correctness = new int[yvalues.length];
int[] h1 = new int[arraysize];
int aindex;
int bindex;
ArrayList<Integer> errors = new ArrayList<>();
ArrayList<Integer> indexes = new ArrayList<>();
double[] prenormalised_array = new double[arraysize];
ArrayList<Double> fval = new ArrayList<>();
ArrayList<ArrayList<Integer>> final_wrong = new ArrayList<ArrayList<Integer>>();
int errorsize = 0;
// Special Case when bindex=0
bindex = 0;
int count = 0;
double zeroerror = 0;
ArrayList<Integer> zerowrong = new ArrayList<>();
for (int j = bindex; j < yvalues.length; j++) {
if (yvalues[j] != yvalues[0]) {
count++;
zeroerror += probabilities[j];
zerowrong.add(j);
// System.out.println("b prob "+probabilities[j]);
}
}
error_prob.add(zeroerror);
indexes.add(0);
final_wrong.add(zerowrong);
for (int i = 1; i < yvalues.length; i++) {
int acount = 0;
int bcount = 0;
int a = yvalues[i - 1];
int b = yvalues[i];
double error = 0;
ArrayList<Integer> wrong = new ArrayList<>();
if (a != b) {
aindex = i - 1;
bindex = i;
// System.out.println("Index of a "+(i-1));
// System.out.println("Index of b "+(i));
for (int j = 0; j < aindex; j++) {
if (yvalues[j] != a) {
acount++;
error += probabilities[j];
wrong.add(j);
}
}
for (int j = bindex; j < yvalues.length; j++) {
if (yvalues[j] != b) {
bcount++;
error += probabilities[j];
wrong.add(j);
}
}
errors.add(acount + bcount);
indexes.add(aindex);
error_prob.add(error);
// for(int k=0;k<wrong.size();k++)
// {
// System.out.println("Wrong values "+wrong.get(k));
// }
final_wrong.add(wrong);
}
}
double min = error_prob.get(0);
int minindex = indexes.get(0);
int valindex = 0;
for (int k = 0; k < error_prob.size(); k++) {
// System.out.println("Error is "+error_prob.get(k));
// System.out.println("Index of a is "+indexes.get(k));
if (error_prob.get(k) < min) {
min = error_prob.get(k);
minindex = indexes.get(k);
valindex = k;
}
}
int v = minindex;
int w = minindex + 1;
// Special Case: When valindex = 0
/** ************************************************************************ */
// if(valindex==0)
// {
// double r=error_prob.get(0);
// double s=error_prob.get(1);
// System.out.println("Before 0 "+error_prob.get(0));
// System.out.println("Between 0 and 1 "+error_prob.get(1));
// if(r<s)
// {
// threshold[q]=0;
// c='>';
//
// }
// else
// {
// threshold[q]=(xvalues[v]+xvalues[w])/2;
// if(yvalues[minindex]>0)
// {
// c='<';
// }
// else
// {
// c='>';
// }
// }
//
// }
// else
/** ****************************************************************** */
{
// System.out.println("Min error is "+min);
// System.out.println("Min index is "+v);
threshold[q] = (xvalues[v] + xvalues[w]) / 2;
if (yvalues[minindex] > 0) {
c[q] = '<';
} else {
c[q] = '>';
}
}
System.out.println(
"The selected weak classifier h_" + (q + 1) + ": I(x " + c[q] + " " + threshold[q] + ")");
ht_error = error_prob.get(valindex);
System.out.println("The error e of h_" + (q + 1) + ": " + ht_error);
goodnessweight[q] = CalculateWeight(ht_error);
System.out.println("The weight of h_" + (q + 1) + ": " + goodnessweight[q]);
ArrayList<Integer> wrongarray = final_wrong.get(valindex);
// for(int i=0;i<wrongarray.size();i++)
// {
// System.out.println("Wrong indexes are "+wrongarray.get(i));
// }
int g = 0;
for (int h = 0; h < yvalues.length; h++) {
correctness[h] = 1;
}
for (int i = 0; i < wrongarray.size(); i++) {
correctness[wrongarray.get(i)] = 0;
}
// for(int h=0;h<yvalues.length;h++)
// {
// System.out.print(correctness[h]+" ");
// }
// System.out.println(" ");
CalculateQ(correctness, goodnessweight[q], arraysize, probabilities, prenormalised_array);
for (int i = 0; i < arraysize; i++) {
// System.out.print(prenormalised_array[i]+" ");
normalizationfac += prenormalised_array[i];
}
System.out.println("Normalization Factor Zt :" + normalizationfac);
bound *= normalizationfac;
CalculateNormalizedProb(prenormalised_array, normalizationfac, normalizedprobabilities);
System.out.print("Normalized Probabilities :");
for (int i = 0; i < arraysize; i++) {
System.out.print(normalizedprobabilities[i] + " ");
}
System.out.println(" ");
if (q == 0) {
System.out.println(
"The boosted classifier:f_"
+ (q + 1)
+ "(x)= "
+ goodnessweight[q]
+ " I(x "
+ c[q]
+ " "
+ threshold[q]
+ ")");
} else {
System.out.print("The boosted classifier:f_" + (q + 1) + "(x)= ");
for (int p = 0; p <= q; p++) {
if (p < q) {
System.out.print(goodnessweight[p] + " I(x " + c[p] + " " + threshold[p] + ")+");
} else {
System.out.print(goodnessweight[p] + " I(x " + c[p] + " " + threshold[p] + ")");
}
}
System.out.println(" ");
}
System.arraycopy(normalizedprobabilities, 0, probabilities, 0, probabilities.length);
/** *****Special Case: Valindex=0********* */
// if(valindex==0)
// {
// v=0;
// w=0;
//
// }
/** *************************************** */
int a1 = yvalues[v];
int b1 = yvalues[w];
for (int j = 0; j <= v; j++) {
h1[j] = a1;
}
for (int j = w; j < yvalues.length; j++) {
h1[j] = b1;
}
//
// Temp F array multiplied with alpha
for (int y = 0; y < h1.length; y++) {
fval.add(h1[y] * goodnessweight[q]);
}
fvalues.add(fval);
// System.out.println("Bound: "+errorbound);
return bound;
}
/**
* Normalizes the copy number signal, allele A signal and allele B signal chromosome by
* chromosome.
*/
public void localNormalization() {
double chrMean = 0;
double[] chrCopyNumber = null;
String chr = chrID[0] + "";
int chrStartPos = 0;
for (int i = 0; i < copyNumber.length; i++) {
if (!chr.equals(chrID[i])) {
chrCopyNumber = new double[i - chrStartPos];
System.arraycopy(copyNumber, chrStartPos, chrCopyNumber, 0, i - chrStartPos);
chrMean = robustMu(chrCopyNumber);
// Local normalization is done only when the difference is small, otherwise, it may result
// in some artifial
if (abs(2 - chrMean) < 0.05) {
for (int j = chrStartPos; j < i; j++) {
copyNumber[j] = copyNumber[j] / chrMean * 2;
}
}
chrStartPos = i;
chr = chrID[i] + "";
}
}
chrCopyNumber = new double[copyNumber.length - chrStartPos];
System.arraycopy(copyNumber, chrStartPos, chrCopyNumber, 0, copyNumber.length - chrStartPos);
chrMean = robustMu(chrCopyNumber);
if (abs(2 - chrMean) < 0.075) {
for (int j = chrStartPos; j < copyNumber.length; j++) {
copyNumber[j] = copyNumber[j] / chrMean * 2;
}
}
// double chrMean = 0;
// double[] chrCopyNumber = null;
// String chr = chrID[0] + "";
// int chrStartPos = 0;
// int chrLength = 0;
// for (int i = 0; i < copyNumber.length; i ++) {
// if(!chr.equals(chrID[i])) {
// chrCopyNumber = new double[chrLength];
// int k = 0;
// for (int j = chrStartPos; j < i; j ++) {
// if (!isOutlier[j]) {
// chrCopyNumber[k] = copyNumber[j];
// k ++;
// }
// }
// chrMean = robustMu(chrCopyNumber);
// if (abs(2 - chrMean) < 0.1) {
// for (int j = chrStartPos; j < i; j ++) {
// copyNumber[j] = copyNumber[j] / chrMean * 2;
// }
// }
// chrStartPos = i;
// chrLength = 0;
// chr = chrID[i] + "";
// }
// if (!isOutlier[i]) {
// chrLength ++;
// }
// }
// chrCopyNumber = new double[chrLength];
// int k = 0;
// for (int j = chrStartPos; j < copyNumber.length; j ++) {
// if (!isOutlier[j]) {
// chrCopyNumber[k] = copyNumber[j];
// k ++;
// }
// }
// chrMean = robustMu(chrCopyNumber);
// if (abs(2 - chrMean) < 0.1) {
// for (int j = chrStartPos; j < copyNumber.length; j ++) {
// copyNumber[j] = copyNumber[j] / chrMean * 2;
// }
// }
}
