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Stanford Parser processes raw text in English, Chinese, German, Arabic, and French, and extracts constituency parse trees.
package edu.stanford.nlp.maxent.iis;
import edu.stanford.nlp.util.logging.Redwood;
import edu.stanford.nlp.io.*;
import edu.stanford.nlp.math.ArrayMath;
import edu.stanford.nlp.maxent.*;
import edu.stanford.nlp.util.MutableDouble;
import java.text.NumberFormat;
import java.io.IOException;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
/**
* This is the main class that does the core computation in IIS.
* (Parts of it still get invoked in the POS tagger, even when not using IIS.)
*
* @author Kristina Toutanova
* @version 1.0
*/
public class LambdaSolve {
/** A logger for this class */
private static Redwood.RedwoodChannels log = Redwood.channels(LambdaSolve.class);
/**
* These are the model parameters that have to be learned.
* This field is used at runtime in all tagger and other IIS/Kristina code.
*/
public double[] lambda;
/** Only allocated and used in the IIS optimization routines. */
protected boolean[] lambda_converged;
/** Only used in the IIS optimization routines. Convergence threshold / allowed "newtonErr" */
protected double eps;
// protected double newtonerr;
/**
* This flag is true if all (x,y) have the same f# in which case the newton equation solving is avoided.
*/
private boolean fixedFnumXY;
protected Problem p;
/**
* Conditional probabilities.
*/
protected double[][] probConds;
/**
* Normalization factors, one for each x. (CDM questions 2008: Are these
* only at training time? Can we not allocate it at test time (unlike
* what LambdaSolveTagger now does)? Is the place where it is set from
* ySize wrong?
*/
protected double[] zlambda;
/**
* This contains the number of features active for each pair (x,y)
*/
protected byte[][] fnumArr;
/**
* This is an array of empirical expectations for the features
*/
protected double[] ftildeArr;
private static final boolean smooth = false;
private static final boolean VERBOSE = false;
/**
* If this is true, assume that active features are binary, and one
* does not have to multiply in a feature value.
*/
private boolean ASSUME_BINARY = false;
private double[] aux; // auxiliary array used by some procedures for computing objective functions and their derivatives
private double[][] sum;// auxiliary array
private double[][] sub;// auxiliary array
public boolean weightRanks = false;
private boolean convertValues = false;
public LambdaSolve(Problem p1, double eps1, double nerr1) {
p = p1;
eps = eps1;
// newtonerr = nerr1;
// lambda = new double[p.fSize];
probConds = new double[p.data.xSize][];
log.info("xSize is " + p.data.xSize);
for (int i = 0; i < p.data.xSize; i++) {
probConds[i] = new double[p.data.numY(i)];
}
fnumArr = new byte[p.data.xSize][];
for (int i = 0; i < p.data.xSize; i++) {
fnumArr[i] = new byte[p.data.numY(i)];
}
zlambda = new double[p.data.xSize];
ftildeArr = new double[p.fSize];
initCondsZlambdaEtc();
if (convertValues) {
transformValues();
}
}
/**
* Reads the lambda parameters from a file.
*
* @param filename File to read from
*/
public LambdaSolve(String filename) {
this.readL(filename);
}
public LambdaSolve() {
}
public void setNonBinary() {
ASSUME_BINARY = false;
}
public void setBinary() {
ASSUME_BINARY = true;
}
/**
* This is a specialized procedure to change the values
* of parses for semantic ranking.
* The highest value is changed to 2/3
* and values of 1 are changed to 1/(3*numones). 0 is unchanged
* this is used to rank higher the ordering for the best parse
* values are in p.data.values
*/
public void transformValues() {
for (int x = 0; x < p.data.values.length; x++) {
double highest = p.data.values[x][0];
double sumhighest = 0;
double sumrest = 0;
for (int y = 0; y < p.data.values[x].length; y++) {
if (p.data.values[x][y] > highest) {
highest = p.data.values[x][y];
}
}
for (int y = 0; y < p.data.values[x].length; y++) {
if (p.data.values[x][y] == highest) {
sumhighest += highest;
} else {
sumrest += p.data.values[x][y];
}
}
if (sumrest == 0) {
continue;
} // do not change , makes no difference
//now change them
for (int y = 0; y < p.data.values[x].length; y++) {
if (p.data.values[x][y] == highest) {
p.data.values[x][y] = .7 * highest / sumhighest;
} else {
p.data.values[x][y] = .3 * p.data.values[x][y] / sumrest;
}
}
}
}
/**
* Initializes the model parameters, empirical expectations of the
* features, and f#(x,y).
*/
void initCondsZlambdaEtc() {
// init pcond
for (int x = 0; x < p.data.xSize; x++) {
for (int y = 0; y < probConds[x].length; y++) {
probConds[x][y] = 1.0 / probConds[x].length;
}
}
// init zlambda
for (int x = 0; x < p.data.xSize; x++) {
zlambda[x] = probConds[x].length;
}
// init ftildeArr
for (int i = 0; i < p.fSize; i++) {
ftildeArr[i] = p.functions.get(i).ftilde();
p.functions.get(i).setSum();
// if the expectation of a feature is zero make sure we are not
// trying to find a lambda for it
// if (ftildeArr[i] == 0) {
// lambda_converged[i]=true;
// lambda[i]=0;
// }
//dumb smoothing that is not sound and doesn't seem to work
if (smooth) {
double alfa = .015;
for (int j = 0; j < p.fSize; j++) {
ftildeArr[j] = (ftildeArr[j] * p.data.xSize + alfa) / p.data.xSize;
}
}
Feature f = p.functions.get(i);
//collecting f#(x,y)
for (int j = 0; j < f.len(); j++) {
int x = f.getX(j);
int y = f.getY(j);
fnumArr[x][y] += f.getVal(j);
}//j
}//i
int constAll = fnumArr[0][0];
fixedFnumXY = true;
for (int x = 0; x < p.data.xSize; x++) {
for (int y = 0; y < fnumArr[x].length; y++) {
if (fnumArr[x][y] != constAll) {
fixedFnumXY = false;
break;
}
}
}//x
if (VERBOSE) {
log.info(" pcond, zlamda, ftildeArr " + (fixedFnumXY ? "(fixed sum) " : "") + "initialized ");
}
}
/**
* Iterate until convergence. I usually use the other method that
* does a fixed number of iterations.
*/
public void improvedIterative() {
boolean flag;
int iterations = 0;
lambda_converged = new boolean[p.fSize];
int numNConverged = p.fSize;
do {
if (VERBOSE) {
log.info(iterations);
}
flag = false;
iterations++;
for (int i = 0; i < lambda.length; i++) {
if (lambda_converged[i]) {
continue;
}
MutableDouble deltaI = new MutableDouble();
boolean fl = iterate(i, eps, deltaI);
if (fl) {
flag = true;
updateConds(i, deltaI.doubleValue());
// checkCorrectness();
} else {
//lambda_converged[i]=true;
numNConverged--;
}
}
} while ((flag) && (iterations < 1000));
}
/**
* Does a fixed number of IIS iterations.
*
* @param iters Number of iterations to run
*/
public void improvedIterative(int iters) {
int iterations = 0;
lambda_converged = new boolean[p.fSize];
int numNConverged = p.fSize;
//double lOld=logLikelihood();
do {
iterations++;
for (int i = 0; i < lambda.length; i++) {
if (lambda_converged[i]) {
continue;
}
MutableDouble deltaI = new MutableDouble();
boolean fl = iterate(i, eps, deltaI);
if (fl) {
updateConds(i, deltaI.doubleValue());
// checkCorrectness();
} else {
//lambda_converged[i]=true;
numNConverged--;
}
}
/*
double lNew=logLikelihood();
double gain=(lNew-lOld);
if(gain<0) {
log.info(" Likelihood decreased by "+ (-gain));
System.exit(1);
}
if(Math.abs(gain) 200) {
if ((deltaL + lambda[index]) > 200) {
deltaL = 200 - lambda[index];
} else {
deltaL = -lambda[index] - 200;
}
log.info("set delta to smth " + deltaL);
}
lambda[index] = lambda[index] + deltaL;
if (Double.isNaN(deltaL)) {
log.info(" NaN " + index + ' ' + deltaL);
}
ret.set(deltaL);
return (Math.abs(deltaL) >= eps);
}
/*
* Finds the root of an equation by Newton's method.
* This is my implementation. It might be improved
* if we looked at some official library for numerical methods.
*/
double newton(double lambda0, int index, double err) {
double lambdaN = lambda0;
int i = 0;
if (fixedFnumXY) {
double plambda = fExpected(p.functions.get(index));
return (1 / (double) fnumArr[0][0]) * (Math.log(this.ftildeArr[index]) - Math.log(plambda));
}
do {
i++;
double lambdaP = lambdaN;
double gPrimeVal = gprime(lambdaP, index);
if (Double.isNaN(gPrimeVal)) {
log.info("gPrime of " + lambdaP + " " + index + " is NaN " + gPrimeVal);
//lambda_converged[index]=true;
// System.exit(1);
}
double gVal = g(lambdaP, index);
if (gPrimeVal == 0.0) {
return 0.0;
}
lambdaN = lambdaP - gVal / gPrimeVal;
if (Double.isNaN(lambdaN)) {
log.info("the division of " + gVal + " " + gPrimeVal + " " + index + " is NaN " + lambdaN);
//lambda_converged[index]=true;
return 0;
}
if (Math.abs(lambdaN - lambdaP) < err) {
return lambdaN;
}
if (i > 100) {
if (Math.abs(gVal) > 0.01) {
return 0;
}
return lambdaN;
}
} while (true);
}
/**
* This method updates the conditional probabilities in the model, resulting from the
* update of lambda[index] to lambda[index]+deltaL .
*/
void updateConds(int index, double deltaL) {
// for each x that (x,y)=true / exists y
// recalculate pcond(y,x) for all y
for (int i = 0; i < p.functions.get(index).len(); i++) {
// update for this x
double s = 0;
int x = p.functions.get(index).getX(i);
int y = p.functions.get(index).getY(i);
double val = p.functions.get(index).getVal(i);
double zlambdaX = zlambda[x] + pcond(y, x) * zlambda[x] * (Math.exp(deltaL * val) - 1);
for (int y1 = 0; y1 < probConds[x].length; y1++) {
probConds[x][y1] = (probConds[x][y1] * zlambda[x]) / zlambdaX;
s = s + probConds[x][y1];
}
s = s - probConds[x][y];
probConds[x][y] = probConds[x][y] * Math.exp(deltaL * val);
s = s + probConds[x][y];
zlambda[x] = zlambdaX;
if (Math.abs(s - 1) > 0.001) {
//log.info(x+" index "+i+" deltaL " +deltaL+" tag "+yTag+" zlambda "+zlambda[x]);
}
}
}
public double pcond(int y, int x) {
return probConds[x][y];
}
protected double fnum(int x, int y) {
return fnumArr[x][y];
}
double g(double lambdaP, int index) {
double s = 0.0;
for (int i = 0; i < p.functions.get(index).len(); i++) {
int y = p.functions.get(index).getY(i);
int x = p.functions.get(index).getX(i);
double exponent = Math.exp(lambdaP * fnum(x, y));
s = s + p.data.ptildeX(x) * pcond(y, x) * p.functions.get(index).getVal(i) * exponent;
}
s = s - ftildeArr[index];
return s;
}
double gprime(double lambdaP, int index) {
double s = 0.0;
for (int i = 0; i < p.functions.get(index).len(); i++) {
int y = ((p.functions.get(index))).getY(i);
int x = p.functions.get(index).getX(i);
s = s + p.data.ptildeX(x) * pcond(y, x) * p.functions.get(index).getVal(i) * Math.exp(lambdaP * fnum(x, y)) * fnum(x, y);
}
return s;
}
/**
* Computes the expected value of a feature for the current model.
*
* @param f a feature
* @return The expectation of f according to p(y|x)
*/
double fExpected(Feature f) {
double s = 0.0;
for (int i = 0; i < f.len(); i++) {
int x = f.getX(i);
int y = f.getY(i);
s += p.data.ptildeX(x) * pcond(y, x) * f.getVal(i);
}//for
return s;
}
/**
* Check whether the constraints are satisfied, the probabilities sum to one, etc. Prints out a message
* if there is something wrong.
*/
public boolean checkCorrectness() {
boolean flag = true;
for (int f = 0; f < lambda.length; f++) {
if (Math.abs(lambda[f]) > 100) {
log.info("lambda " + f + " too big " + lambda[f]);
log.info("empirical " + ftildeArr[f] + " expected " + fExpected(p.functions.get(f)));
}
}
log.info(" x size" + p.data.xSize + " " + " ysize " + p.data.ySize);
double summAllExp = 0;
for (int i = 0; i < ftildeArr.length; i++) {
double exp = Math.abs(ftildeArr[i] - fExpected(p.functions.get(i)));
summAllExp += ftildeArr[i];
if (exp > 0.001)
//if(true)
{
flag = false;
log.info("Constraint not satisfied " + i + " " + fExpected(p.functions.get(i)) + " " + ftildeArr[i] + " lambda " + lambda[i]);
}
}
log.info(" The sum of all empirical expectations is " + summAllExp);
for (int x = 0; x < p.data.xSize; x++) {
double s = 0.0;
for (int y = 0; y < probConds[x].length; y++) {
s = s + probConds[x][y];
}
if (Math.abs(s - 1) > 0.0001) {
for (int y = 0; y < probConds[x].length; y++)
//log.info(y+" : "+ probConds[x][y]);
{
log.info("probabilities do not sum to one " + x + " " + (float) s);
}
}
}
return flag;
}
double ZAlfa(double alfa, Feature f, int x) {
double s = 0.0;
for (int y = 0; y < probConds[x].length; y++) {
s = s + pcond(y, x) * Math.exp(alfa * f.getVal(x, y));
}
return s;
}
double GSF(double alfa, Feature f, int index) {
double s = 0.0;
for (int x = 0; x < p.data.xSize; x++) {
s = s - p.data.ptildeX(x) * Math.log(ZAlfa(alfa, f, x));
}
return s + alfa * ftildeArr[index];
}
double GSF(double alfa, Feature f) {
double s = 0.0;
for (int x = 0; x < p.data.xSize; x++) {
s = s - p.data.ptildeX(x) * Math.log(ZAlfa(alfa, f, x));
}
return s + alfa * f.ftilde();
}
double pcondFAlfa(double alfa, int x, int y, Feature f) {
double s;
s = (1 / ZAlfa(alfa, f, x)) * pcond(y, x) * Math.exp(alfa * f.getVal(x, y));
return s;
}
double GSFPrime(double alfa, Feature f, int index) {
double s = 0.0;
s = s + ftildeArr[index];
for (int x1 = 0; x1 < f.indexedValues.length; x1++) {
double s1 = 0.0;
int x = f.getX(x1);
int y = f.getY(x1);
s1 = s1 + pcondFAlfa(alfa, x, y, f) * f.getVal(x1);
s = s - p.data.ptildeX(x) * s1;
}
return s;
}
double GSFPrime(double alfa, Feature f) {
double s = 0.0;
s = s + f.ftilde();
for (int x1 = 0; x1 < f.indexedValues.length; x1++) {
double s1 = 0.0;
int x = f.getX(x1);
int y = f.getY(x1);
s1 = s1 + pcondFAlfa(alfa, x, y, f) * f.getVal(x1);
s = s - p.data.ptildeX(x) * s1;
}
return s;
}
double GSFSecond(double alfa, Feature f) {
double s = 0.0;
for (int x = 0; x < p.data.xSize; x++) {
double s1 = 0.0;
double psff = 0.0;
for (int y1 = 0; y1 < p.data.ySize; y1++) {
psff = psff + pcondFAlfa(alfa, x, y1, f) * f.getVal(x, y1);
}
for (int y = 0; y < probConds[x].length; y++) {
s1 = s1 + pcondFAlfa(alfa, x, y, f) * (f.getVal(x, y) - psff) * (f.getVal(x, y) - psff);
}
s = s - s1 * p.data.ptildeX(x);
}
return s;
}
/**
* Computes the gain from a feature. Used for feature selection.
*/
public double GainCompute(Feature f, double errorGain) {
double r = (f.ftilde() > fExpected(f) ? 1.0 : -1.0);
f.initHashVals();
int iterations = 0;
double alfa = 0.0;
GSF(alfa, f);
double gsfValNew = 0.0;
while (iterations < 30) {
iterations++;
double alfanext = alfa + r * Math.log(1 - r * GSFPrime(alfa, f) / GSFSecond(alfa, f));
gsfValNew = GSF(alfanext, f);
if (Math.abs(alfanext - alfa) < errorGain) {
return gsfValNew;
}
alfa = alfanext;
}
return gsfValNew;
}
/**
* Print out p(y|x) for all pairs to the standard output.
*/
public void print() {
for (int i = 0; i < p.data.xSize; i++) {
for (int j = 0; j < probConds[i].length; j++) {
System.out.println("P(" + j + " | " + i + ") = " + pcond(j, i));
}
}
}
/**
* Writes the lambda feature weights to the file.
* Can be read later with readL.
* This method opens a new file and closes it after writing it.
*
* @param filename The file to write the weights to.
*/
public void save_lambdas(String filename) {
try {
DataOutputStream rf = IOUtils.getDataOutputStream(filename);
save_lambdas(rf, lambda);
rf.close();
} catch (IOException e) {
e.printStackTrace();
}
}
/**
* Writes the lambdas to a stream.
*/
public static void save_lambdas(DataOutputStream rf, double[] lambdas) {
try {
ObjectOutputStream oos = new ObjectOutputStream(rf);
oos.writeObject(lambdas);
oos.flush();
} catch (IOException e) {
e.printStackTrace();
}
}
/**
* Read the lambdas from the file.
* The file contains the number of lambda weights (int) followed by
* the weights.
* Historical note: The file does not contain
* xSize and ySize as for the method read(String).
*
* @param filename The file to read from
*/
public void readL(String filename) {
try {
DataInputStream rf = IOUtils.getDataInputStream(filename);
lambda = read_lambdas(rf);
rf.close();
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Read the lambdas from the file.
*
* @param modelFilename A filename. It will be read and closed
* @return An array of lambda values read from the file.
*/
static double[] read_lambdas(String modelFilename) {
try {
DataInputStream rf = IOUtils.getDataInputStream(modelFilename);
double[] lamb = read_lambdas(rf);
rf.close();
return lamb;
} catch (IOException e) {
e.printStackTrace();
}
return null;
}
/**
* Read the lambdas from the stream.
*
* @param rf Stream to read from.
* @return An array of lambda values read from the stream.
*/
public static double[] read_lambdas(DataInputStream rf) {
if (VERBOSE) {
log.info("Entering read_lambdas");
}
try {
ObjectInputStream ois = new ObjectInputStream(rf);
Object o = ois.readObject();
if (o instanceof double[]) {
return (double[]) o;
}
throw new RuntimeIOException("Failed to read lambdas from given input stream");
} catch (IOException | ClassNotFoundException e) {
throw new RuntimeIOException(e);
}
}
/**
* This method writes the problem data into a file, which is good for reading
* with MatLab. It could also have other applications,
* like reducing the memory requirements
*/
void save_problem(String filename) {
try {
PrintFile pf = new PrintFile(filename);
int N = p.data.xSize;
int M = p.data.ySize;
int F = p.fSize;
// byte[] nl = "\n".getBytes();
// byte[] dotsp = ". ".getBytes();
// int space = (int) ' ';
// write the sizes of X, Y, and F( number of features );
pf.println(N);
pf.println(M);
pf.println(F);
// save the objective vector like 1.c0, ... ,N*M. cN*M-1
for (int i = 0; i < N * M; i++) {
pf.print(i + 1);
pf.print(". ");
pf.println(p.data.ptildeX(i / M));
}// for i
// save the constraints matrix B
// for each feature , save its row
for (int i = 0; i < p.fSize; i++) {
int[] values = p.functions.get(i).indexedValues;
for (int value : values) {
pf.print(i + 1);
pf.print(". ");
pf.print(value);
pf.print(" ");
pf.println(1);
}// k
}// i
// save the constraints vector
// for each feature, save its empirical expectation
for (int i = 0; i < p.fSize; i++) {
pf.print(i + 1);
pf.print(". ");
pf.println(ftildeArr[i]);
}// end
pf.close();
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* @return The loglikelihood of the empirical distribution as predicted by the model p.
*/
public double logLikelihood() {
//L=sumx,y log(p(y|x))*#x,y
double sum = 0.0;
int sz = p.data.size();
for (int index = 0; index < sz; index++) {
int[] example = p.data.get(index);
sum += Math.log(pcond(example[1], example[0]));
}// index
return sum / sz;
}
/**
* Given a numerator and denominator in log form, this calculates
* the conditional model probabilities.
*
* @return Math.exp(first)/Math.exp(second);
*/
public static double divide(double first, double second) {
return Math.exp(first - second); // cpu samples #3,#14: 5.3%
}
/**
* With arguments, this will print out the lambda parameters of a
* bunch of .lam files (which are assumed to all be the same size).
* (Without arguments, it does some creaky old self-test.)
*
* @param args command line arguments
*/
public static void main(String[] args) {
if (args.length > 0) {
NumberFormat nf = NumberFormat.getNumberInstance();
nf.setMaximumFractionDigits(6);
nf.setMinimumFractionDigits(6);
LambdaSolve[] lambdas = new LambdaSolve[args.length];
System.out.print(" ");
for (int i = 0; i < args.length; i++) {
lambdas[i] = new LambdaSolve();
lambdas[i].readL(args[i]);
System.out.print(" " + args[i]);
}
System.out.println();
int numLambda = lambdas[0].lambda.length;
for (int j = 0; j < numLambda; j++) {
System.out.print("lambda[" + j + "] = ");
for (int i = 0; i < args.length; i++) {
System.out.print(nf.format(lambdas[i].lambda[j]) + " ");
}
System.out.println();
}
} else {
LambdaSolve prob = new LambdaSolve("trainhuge.txt.holder.prob");
prob.save_lambdas("trainhuge.txt.holder.prob");
prob.readL("trainhuge.txt.holder.prob");
}
}
/**
* Calculate the log-likelihood from scratch, hashing the conditional
* probabilities in pcond, which we will use later. This is for
* a different model, in which all features effectively get negative weights
* this model is easier to use for heauristic search
* p(ti|s)=exp(sum_j{-(e^lambda_j)*f_j(ti)})
*
* @return The negative log likelihood of the data
*/
public double logLikelihoodNeg() {
// zero all the variables
double s = 0;
for (int i = 0; i < probConds.length; i++) {
for (int j = 0; j < probConds[i].length; j++) {
probConds[i][j] = 0;
}
zlambda[i] = 0;
}
//add up in pcond y|x the unnormalized scores
for (int fNo = 0, fSize = p.fSize; fNo < fSize; fNo++) {
// add for all occurences of the function the values to probConds
Feature f = p.functions.get(fNo);
double fLambda = -Math.exp(lambda[fNo]);
double sum = ftildeArr[fNo];
//if(sum==0){continue;}
sum *= p.data.getNumber();
s -= sum * fLambda;
if (Math.abs(fLambda) > 200) { // was 50
log.info("lambda " + fNo + " too big: " + fLambda);
}
for (int i = 0, length = f.len(); i < length; i++) {
int x = f.getX(i);
int y = f.getY(i);
if (ASSUME_BINARY) {
probConds[x][y] += fLambda;
} else {
double val = f.getVal(i);
probConds[x][y] += (val * fLambda);
}
} //for
} //for fNo
for (int x = 0; x < probConds.length; x++) {
//again
zlambda[x] = ArrayMath.logSum(probConds[x]); // cpu samples #4,#15: 4.5%
//log.info("zlambda "+x+" "+zlambda[x]);
s += zlambda[x] * p.data.ptildeX(x) * p.data.getNumber();
for (int y = 0; y < probConds[x].length; y++) {
probConds[x][y] = divide(probConds[x][y], zlambda[x]); // cpu samples #13: 1.6%
//log.info("prob "+x+" "+y+" "+probConds[x][y]);
} //y
}//x
if (s < 0) {
throw new IllegalStateException("neg log lik smaller than 0: " + s);
}
return s;
}
// -- stuff for CG version below -------
/**
* calculate the log likelihood from scratch, hashing the conditional
* probabilities in pcond which we will use for the derivative later.
*
* @return The log likelihood of the data
*/
public double logLikelihoodScratch() {
// zero all the variables
double s = 0;
for (int i = 0; i < probConds.length; i++) {
for (int j = 0; j < probConds[i].length; j++) {
probConds[i][j] = 0;
}
zlambda[i] = 0;
}
//add up in pcond y|x the unnormalized scores
Experiments exp = p.data;
for (int fNo = 0, fSize = p.fSize; fNo < fSize; fNo++) {
// add for all occurences of the function the values to probConds
Feature f = p.functions.get(fNo);
double fLambda = lambda[fNo];
double sum = ftildeArr[fNo];
//if(sum==0){continue;}
sum *= exp.getNumber();
s -= sum * fLambda;
if (Math.abs(fLambda) > 200) { // was 50
log.info("lambda " + fNo + " too big: " + fLambda);
}
for (int i = 0, length = f.len(); i < length; i++) {
int x = f.getX(i);
int y = f.getY(i);
if (ASSUME_BINARY) {
probConds[x][y] += fLambda;
} else {
double val = f.getVal(i);
probConds[x][y] += (val * fLambda);
}
} //for
} //for fNo
for (int x = 0; x < probConds.length; x++) {
//again
zlambda[x] = ArrayMath.logSum(probConds[x]); // cpu samples #4,#15: 4.5%
//log.info("zlambda "+x+" "+zlambda[x]);
s += zlambda[x] * exp.ptildeX(x) * exp.getNumber();
for (int y = 0; y < probConds[x].length; y++) {
probConds[x][y] = divide(probConds[x][y], zlambda[x]); // cpu samples #13: 1.6%
//log.info("prob "+x+" "+y+" "+probConds[x][y]);
} //y
}//x
if (s < 0) {
throw new IllegalStateException("neg log lik smaller than 0: " + s);
}
return s;
}
/**
* assuming we have the lambdas in the array and we need only the
* derivatives now.
*/
public double[] getDerivatives() {
double[] drvs = new double[lambda.length];
Experiments exp = p.data;
for (int fNo = 0; fNo < drvs.length; fNo++) { // cpu samples #2,#10,#12: 27.3%
Feature f = p.functions.get(fNo);
double sum = ftildeArr[fNo] * exp.getNumber();
drvs[fNo] = -sum;
for (int index = 0, length = f.len(); index < length; index++) {
int x = f.getX(index);
int y = f.getY(index);
if (ASSUME_BINARY) {
drvs[fNo] += probConds[x][y] * exp.ptildeX(x) * exp.getNumber();
} else {
double val = f.getVal(index);
drvs[fNo] += probConds[x][y] * val * exp.ptildeX(x) * exp.getNumber();
}
}//for
//if(sum==0){drvs[fNo]=0;}
}
return drvs;
}
/**
* assuming we have the lambdas in the array and we need only the
* derivatives now.
* this is for the case where the model is parameterezied such that all weights are negative
* see also logLikelihoodNeg
*/
public double[] getDerivativesNeg() {
double[] drvs = new double[lambda.length];
Experiments exp = p.data;
for (int fNo = 0; fNo < drvs.length; fNo++) { // cpu samples #2,#10,#12: 27.3%
Feature f = p.functions.get(fNo);
double sum = ftildeArr[fNo] * exp.getNumber();
double lam = -Math.exp(lambda[fNo]);
drvs[fNo] = -sum * lam;
for (int index = 0, length = f.len(); index < length; index++) {
int x = f.getX(index);
int y = f.getY(index);
if (ASSUME_BINARY) {
drvs[fNo] += probConds[x][y] * exp.ptildeX(x) * exp.getNumber() * lam;
} else {
double val = f.getVal(index);
drvs[fNo] += probConds[x][y] * val * exp.ptildeX(x) * exp.getNumber() * lam;
}
}//for
//if(sum==0){drvs[fNo]=0;}
}
return drvs;
}
/**
* Each pair x,y has a value in p.data.values[x][y]
*
* @return - expected value of corpus -sum_xy (ptilde(x,y)*value(x,y)*pcond(x,y))
*/
public double expectedValue() {
// zero all the variables
double s = 0;
aux = new double[probConds.length];
for (int i = 0; i < probConds.length; i++) {
for (int j = 0; j < probConds[i].length; j++) {
probConds[i][j] = 0;
}
zlambda[i] = 0;
}
//add up in pcond y|x the unnormalized scores
for (int fNo = 0, fSize = p.fSize; fNo < fSize; fNo++) {
// add for all occurrences of the function the values to probConds
Feature f = p.functions.get(fNo);
double fLambda = lambda[fNo];
if (Math.abs(fLambda) > 200) { // was 50
log.info("lambda " + fNo + " too big: " + fLambda);
}
for (int i = 0, length = f.len(); i < length; i++) {
int x = f.getX(i);
int y = f.getY(i);
if (ASSUME_BINARY) {
probConds[x][y] += fLambda;
} else {
double val = f.getVal(i);
probConds[x][y] += (val * fLambda);
}
} //for
} //for fNo
Experiments exp = p.data;
for (int x = 0; x < probConds.length; x++) {
//again
zlambda[x] = ArrayMath.logSum(probConds[x]); // cpu samples #4,#15: 4.5%
//log.info("zlambda "+x+" "+zlambda[x]);
for (int y = 0; y < probConds[x].length; y++) {
probConds[x][y] = divide(probConds[x][y], zlambda[x]); // cpu samples #13: 1.6%
//log.info("prob "+x+" "+y+" "+probConds[x][y]);
s -= exp.values[x][y] * probConds[x][y] * exp.ptildeX(x) * exp.getNumber();
aux[x] += exp.values[x][y] * probConds[x][y];
}
}//x
return s;
}
/**
* assuming we have the probConds[x][y] , compute the derivatives for the expectedValue function
*
* @return The derivatives of the expected
*/
public double[] getDerivativesExpectedValue() {
double[] drvs = new double[lambda.length];
Experiments exp = p.data;
for (int fNo = 0; fNo < drvs.length; fNo++) { // cpu samples #2,#10,#12: 27.3%
Feature f = p.functions.get(fNo);
//double sum = ftildeArr[fNo] * exp.getNumber();
//drvs[fNo] = -sum;
for (int index = 0, length = f.len(); index < length; index++) {
int x = f.getX(index);
int y = f.getY(index);
double val = f.getVal(index);
double mult = val * probConds[x][y] * exp.ptildeX(x) * exp.getNumber();
drvs[fNo] -= mult * exp.values[x][y];
drvs[fNo] += mult * aux[x];
}//for
//if(sum==0){drvs[fNo]=0;}
}
return drvs;
}
/**
* calculate the loss for Dom ranking
* using the numbers in p.data.values to determine domination relationships in the graphs
* if values[x][y]> values[x][y'] then there is an edge (x,y)->(x,y')
*
* @return The loss
*/
public double lossDomination() {
// zero all the variables
double s = 0;
for (int i = 0; i < probConds.length; i++) {
for (int j = 0; j < probConds[i].length; j++) {
probConds[i][j] = 0;
}
zlambda[i] = 0;
}
//add up in pcond y|x the unnormalized scores
for (int fNo = 0, fSize = p.fSize; fNo < fSize; fNo++) {
// add for all occurrences of the function the values to probConds
Feature f = p.functions.get(fNo);
double fLambda = lambda[fNo];
//if(sum==0){continue;}
if (Math.abs(fLambda) > 200) { // was 50
log.info("lambda " + fNo + " too big: " + fLambda);
}
for (int i = 0, length = f.len(); i < length; i++) {
int x = f.getX(i);
int y = f.getY(i);
if (ASSUME_BINARY) {
probConds[x][y] += fLambda;
} else {
double val = f.getVal(i);
probConds[x][y] += (val * fLambda);
}
} //for
} //for fNo
//will use zlambda[x] for the number of domination graphs for x
// keeping track of other arrays as well - sum[x][y], and sub[x][y]
//now two double loops over (x,y) to collect zlambda[x], sum[x][y], and sub[x][y];
sum = new double[probConds.length][];
sub = new double[probConds.length][];
for (int x = 0; x < probConds.length; x++) {
sum[x] = new double[probConds[x].length];
sub[x] = new double[probConds[x].length];
double localloss = 0;
for (int u = 0; u < sum[x].length; u++) {
boolean hasgraph = false;
for (int v = 0; v < sum[x].length; v++) {
//see if u dominates v
if (p.data.values[x][u] > p.data.values[x][v]) {
hasgraph = true;
sum[x][u] += Math.exp(probConds[x][v] - probConds[x][u]);
}
}
sum[x][u] += 1;
double weight = 1;
if (weightRanks) {
weight = p.data.values[x][u];
}
if (hasgraph) {
zlambda[x] += weight;
}
localloss += weight * Math.log(sum[x][u]);
}
//another loop to get the sub[x][y]
for (int u = 0; u < sum[x].length; u++) {
for (int v = 0; v < sum[x].length; v++) {
//see if u dominates v
if (p.data.values[x][u] > p.data.values[x][v]) {
double weight = 1;
if (weightRanks) {
weight = p.data.values[x][u];
}
sub[x][v] += weight * Math.exp(probConds[x][v] - probConds[x][u]) / sum[x][u];
}
}
}
log.info(" for x " + x + " number graphs " + zlambda[x]);
if (zlambda[x] > 0) {
localloss /= zlambda[x];
s += p.data.ptildeX(x) * p.data.getNumber() * localloss;
}
}//x
return s;
}
/**
* Using the arrays calculated when computing the loss, it should not be
* too hard to get the derivatives.
*
* @return The derivative of the loss
*/
public double[] getDerivativesLossDomination() {
double[] drvs = new double[lambda.length];
for (int fNo = 0; fNo < drvs.length; fNo++) { // cpu samples #2,#10,#12: 27.3%
Feature f = p.functions.get(fNo);
for (int index = 0, length = f.len(); index < length; index++) {
int x = f.getX(index);
int y = f.getY(index);
double val = f.getVal(index);
//add the sub and sum components
if (zlambda[x] == 0) {
continue;
}
double mult = val * p.data.ptildeX(x) * p.data.getNumber() * (1 / zlambda[x]);
double weight = 1;
if (weightRanks) {
weight = p.data.values[x][y];
}
drvs[fNo] += mult * sub[x][y];
drvs[fNo] -= mult * weight * (sum[x][y] - 1) / sum[x][y];
}//for
//if(sum==0){drvs[fNo]=0;}
}
return drvs;
}
}