private double trainingStats(EvalParameters evalParams)
{
int numCorrect = 0;
for (int ei = 0; ei < numUniqueEvents; ei++)
{
for (int ni = 0; ni < this.numTimesEventsSeen[ei]; ni++)
{
double[] modelDistribution = new double[numOutcomes];
if (values != null)
{
PerceptronModel.eval(contexts[ei], values[ei], modelDistribution, evalParams, false);
}
else
{
PerceptronModel.eval(contexts[ei], null, modelDistribution, evalParams, false);
}
int max = maxIndex(modelDistribution);
if (max == outcomeList[ei])
{
numCorrect++;
}
}
}
double trainingAccuracy = (double)numCorrect / numEvents;
display("Stats: (" + numCorrect + "/" + numEvents + ") " + trainingAccuracy + "\n");
return(trainingAccuracy);
}
private MutableContext[] findParameters(int iterations, bool useAverage)
{
display("Performing " + iterations + " iterations.\n");
int[] allOutcomesPattern = new int[numOutcomes];
for (int oi = 0; oi < numOutcomes; oi++)
{
allOutcomesPattern[oi] = oi;
}
/// <summary>
/// Stores the estimated parameter value of each predicate during iteration. </summary>
MutableContext[] parameters = new MutableContext[numPreds];
for (int pi = 0; pi < numPreds; pi++)
{
parameters[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
for (int aoi = 0; aoi < numOutcomes; aoi++)
{
parameters[pi].setParameter(aoi, 0.0);
}
}
EvalParameters evalParams = new EvalParameters(parameters, numOutcomes);
/// <summary>
/// Stores the sum of parameter values of each predicate over many iterations. </summary>
MutableContext[] summedParams = new MutableContext[numPreds];
if (useAverage)
{
for (int pi = 0; pi < numPreds; pi++)
{
summedParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
for (int aoi = 0; aoi < numOutcomes; aoi++)
{
summedParams[pi].setParameter(aoi, 0.0);
}
}
}
// Keep track of the previous three accuracies. The difference of
// the mean of these and the current training set accuracy is used
// with tolerance to decide whether to stop.
double prevAccuracy1 = 0.0;
double prevAccuracy2 = 0.0;
double prevAccuracy3 = 0.0;
// A counter for the denominator for averaging.
int numTimesSummed = 0;
double stepsize = 1;
for (int i = 1; i <= iterations; i++)
{
// Decrease the stepsize by a small amount.
if (stepSizeDecrease != null)
{
stepsize *= 1 - stepSizeDecrease.GetValueOrDefault();
}
displayIteration(i);
int numCorrect = 0;
for (int ei = 0; ei < numUniqueEvents; ei++)
{
int targetOutcome = outcomeList[ei];
for (int ni = 0; ni < this.numTimesEventsSeen[ei]; ni++)
{
// Compute the model's prediction according to the current parameters.
double[] modelDistribution = new double[numOutcomes];
if (values != null)
{
PerceptronModel.eval(contexts[ei], values[ei], modelDistribution, evalParams, false);
}
else
{
PerceptronModel.eval(contexts[ei], null, modelDistribution, evalParams, false);
}
int maxOutcome = maxIndex(modelDistribution);
// If the predicted outcome is different from the target
// outcome, do the standard update: boost the parameters
// associated with the target and reduce those associated
// with the incorrect predicted outcome.
if (maxOutcome != targetOutcome)
{
for (int ci = 0; ci < contexts[ei].Length; ci++)
{
int pi = contexts[ei][ci];
if (values == null)
{
parameters[pi].updateParameter(targetOutcome, stepsize);
parameters[pi].updateParameter(maxOutcome, -stepsize);
}
else
{
parameters[pi].updateParameter(targetOutcome, stepsize * values[ei][ci]);
parameters[pi].updateParameter(maxOutcome, -stepsize * values[ei][ci]);
}
}
}
// Update the counts for accuracy.
if (maxOutcome == targetOutcome)
{
numCorrect++;
}
}
}
// Calculate the training accuracy and display.
double trainingAccuracy = (double)numCorrect / numEvents;
if (i < 10 || (i % 10) == 0)
{
display(". (" + numCorrect + "/" + numEvents + ") " + trainingAccuracy + "\n");
}
// TODO: Make averaging configurable !!!
bool doAveraging;
if (useAverage && useSkippedlAveraging && (i < 20 || isPerfectSquare(i)))
{
doAveraging = true;
}
else if (useAverage)
{
doAveraging = true;
}
else
{
doAveraging = false;
}
if (doAveraging)
{
numTimesSummed++;
for (int pi = 0; pi < numPreds; pi++)
{
for (int aoi = 0; aoi < numOutcomes; aoi++)
{
summedParams[pi].updateParameter(aoi, parameters[pi].Parameters[aoi]);
}
}
}
// If the tolerance is greater than the difference between the
// current training accuracy and all of the previous three
// training accuracies, stop training.
if (Math.Abs(prevAccuracy1 - trainingAccuracy) < tolerance &&
Math.Abs(prevAccuracy2 - trainingAccuracy) < tolerance &&
Math.Abs(prevAccuracy3 - trainingAccuracy) < tolerance)
{
display("Stopping: change in training set accuracy less than " + tolerance + "\n");
break;
}
// Update the previous training accuracies.
prevAccuracy1 = prevAccuracy2;
prevAccuracy2 = prevAccuracy3;
prevAccuracy3 = trainingAccuracy;
}
// Output the final training stats.
trainingStats(evalParams);
// Create averaged parameters
if (useAverage)
{
for (int pi = 0; pi < numPreds; pi++)
{
for (int aoi = 0; aoi < numOutcomes; aoi++)
{
summedParams[pi].setParameter(aoi, summedParams[pi].Parameters[aoi] / numTimesSummed);
}
}
return(summedParams);
}
else
{
return(parameters);
}
}