private void RunTestPCAKNN(int _short)
{
Console.WriteLine("\nTesting PCA/k-NN classifier");
Console.WriteLine("Mode=" + _short);
int numClasses = 10;
int k = 10;
int numPatternsPerClass = 100;
int numPatterns = (int)Math.Round(0.9 * numClasses * numPatternsPerClass);
int numTests = numClasses * numPatternsPerClass - numPatterns;
int numSVDSamples = (int)Math.Round(0.1 * numPatterns);
int keep = 1;
KNNClassifier pca_knn = KNNClassifier.GetBuilder()
.K(k)
.NumSVDSamples(numSVDSamples)
.NumSVDDims((int)KnnMode.ADAPTIVE)
.Build();
KNNClassifier knn = KNNClassifier.GetBuilder()
.K(k)
.Build();
Console.WriteLine("Training PCA k-NN");
double[][] trainData = knnData.GetPcaKNNShortData()[TRAIN].GetDataArray();
int[] trainClass = knnData.GetPcaKNNShortData()[TRAIN].GetClassArray();
for (int i = 0; i < numPatterns; i++)
{
knn.Learn(trainData[i], trainClass[i]);
pca_knn.Learn(trainData[i], trainClass[i]);
}
Console.WriteLine("Testing PCA k-NN");
int numWinnerFailures = 0;
int numInferenceFailures = 0;
int numDistFailures = 0;
int numAbsErrors = 0;
double[][] testData = knnData.GetPcaKNNShortData()[TEST].GetDataArray();
int[] testClass = knnData.GetPcaKNNShortData()[TEST].GetClassArray();
for (int i = 0; i < numTests; i++)
{
var result = knn.Infer(testData[i]);
var winner = (int)result.Get(0);
var inference = (double[])result.Get(1);
var dist = (double[])result.Get(2);
var categoryDist = result.Get(3);
var pcaResult = pca_knn.Infer(testData[i]);
var pcawinner = (int)pcaResult.Get(0);
var pcainference = (double[])pcaResult.Get(1);
var pcadist = (double[])pcaResult.Get(2);
var pcacategoryDist = pcaResult.Get(3);
if (winner != testClass[i])
{
numAbsErrors += 1;
}
if (pcawinner != winner)
{
numWinnerFailures += 1;
}
if (ArrayUtils.Abs(ArrayUtils.Sub(pcainference, inference)).Any(n => n > 1e-4))
{
numInferenceFailures += 1;
}
if (ArrayUtils.Abs(ArrayUtils.Sub(pcadist, dist)).Any(n => n > 1e-4))
{
numDistFailures += 1;
}
}
double s0 = 100.0 * (numTests - numAbsErrors) / numTests;
double s1 = 100.0 * (numTests - numWinnerFailures) / numTests;
double s2 = 100.0 * (numTests - numInferenceFailures) / numTests;
double s3 = 100.0 * (numTests - numDistFailures) / numTests;
Console.WriteLine("PCA/k-NN success rate = {0}%", s0);
Console.WriteLine("Winner success = {0}%", s1);
Console.WriteLine("Inference success = {0}%", s2);
Console.WriteLine("Distance success = {0}%", s3);
}
