public void XORTest()
{
var range = Enumerable.Range(-10, 16);
var log2gammas = range.Select(i => Math.Pow(2, i));
var log2Cs = range.Select(i => Math.Pow(2, i + 1));
var log2Rs = range.Select(i => Math.Pow(2, i + 1));
var prob = ProblemHelper.ReadProblem(XOR_TRAINING_FILE);
//Assert.IsTrue(prob.l == 4);
Tuple <double, double, double, int> best = Tuple.Create(0.0, 0.0, 0.0, prob.l);
foreach (var g in log2gammas)
{
foreach (var c in log2Cs)
{
foreach (var r in log2Rs)
{
var svm = new C_SVC(prob, KernelHelper.SigmoidKernel(g, r), c);
var errorCout = 0;
for (int i = 0; i < prob.l; i++)
{
//var x = (prob.x[i].FirstOrDefault(xi => xi.index == 1) == null) ? 0.0 : prob.x[i].FirstOrDefault(xi => xi.index == 1).value;
//var y = (prob.x[i].FirstOrDefault(xi => xi.index == 2) == null) ? 0.0 : prob.x[i].FirstOrDefault(xi => xi.index == 2).value;
var z = svm.Predict(prob.x[i]);
var probabilities = svm.PredictProbabilities(prob.x[i]);
if (z != prob.y[i])
{
errorCout++;
}
//Debug.WriteLine(String.Format("x={0} & y={1} => z={2} -- {3}", x, y, z, z == prob.y[i]));
}
if (errorCout < best.Item4)
{
best = Tuple.Create(g, c, r, errorCout);
}
//Debug.WriteLine(String.Format("g={0} && C={1} && C={2} => Error rate = {3}%", g, c, r, (double)errorCout / prob.l * 100));
}
}
}
Debug.WriteLine(String.Format("BEST :: g={0} && C={1} && R={2} => Error rate = {3}%", best.Item1, best.Item2, best.Item3, (double)best.Item4 / (double)prob.l * 100));
}
static void Main(string[] args)
{
bool kernelparam = false;
int numberofArgs = args.Length;
string inputmatrix;
string path = Directory.GetCurrentDirectory();
string save_model_name;
string kerneltype;
string testfile;
/* SVM specific initializations
*/
int degree = 3; // default for none specified
int r = 1;
// C and gamma come from using grid.py on the training set resume.mat 982 x 7768
double C = 2.0;
double gamma = 0.001953125; // used for Radial Basis Function Kernel (RBF)
C_SVC svm; // setup the default variable for the SVM
/*
* Three parameters are required, kernel selection, training file and test file
*/
if (args.Length != 3)
{
Console.WriteLine(MyStrings.usage);
System.Environment.Exit(1);
}
if (kernelparam = Int32.TryParse(args[0], out int kernelchoice) && kernelchoice <= 3)
{
//Legal value for kernelchoice are 0-3
//kernelchoice = 1;
}
else
{
// Not a legal kernel selection
Console.WriteLine(MyStrings.usage);
System.Environment.Exit(1);
}
inputmatrix = args[1];
testfile = args[2];
if (!HelperFunctions.CheckFormat(inputmatrix))
{
Console.WriteLine(MyStrings.TrainingFileFormat, inputmatrix);
System.Environment.Exit(1);
}
if (!File.Exists(testfile))
{
Console.WriteLine(MyStrings.File_error, inputmatrix);
System.Environment.Exit(1);
}
// Train the SVM
switch (kernelchoice)
{
case 0:
svm = new C_SVC(inputmatrix, KernelHelper.LinearKernel(), C);
kerneltype = MyStrings.Linear;
break;
case 1:
svm = new C_SVC(inputmatrix, KernelHelper.PolynomialKernel(degree, gamma, r), C);
kerneltype = MyStrings.Polynomial;
break;
case 2:
svm = new C_SVC(inputmatrix, KernelHelper.RadialBasisFunctionKernel(gamma), C);
kerneltype = MyStrings.RBF;
break;
case 3:
svm = new C_SVC(inputmatrix, KernelHelper.SigmoidKernel(gamma, r), C);
kerneltype = MyStrings.Sigmoid;
break;
default:
svm = new C_SVC(inputmatrix, KernelHelper.LinearKernel(), C);
kerneltype = MyStrings.Linear;
break;
}
// var accuracy = svm.GetCrossValidationAccuracy(5);
save_model_name = String.Concat(inputmatrix, ".model");
svm.Export(save_model_name);
var predfile = ProblemHelper.ReadProblem(testfile);
double result = HelperFunctions.PredictTestSet(testfile, svm);
Console.WriteLine(MyStrings.Accuracy, Math.Round(result * 100, 2));
Console.Write("SVM kernel type {0} ", kerneltype);
Console.WriteLine(MyStrings.Parameters, C, gamma, degree, r);
}
static void Main(string[] args)
{
List <double[]> continuousTrainData = DataWrangler.LoadContinuousDataAsync(TrainingCsv, _indexToIgnore).Result;
List <double[]> continuousTestData = DataWrangler.LoadContinuousDataAsync(TestingCsv, _indexToIgnore).Result;
// Print continuous columns for calculating elbows in external tool(https://bl.ocks.org/rpgove/0060ff3b656618e9136b)
foreach (int i in _continuousIndexes)
{
using (StreamWriter sw = new StreamWriter($"{i}.txt"))
{
sw.WriteLine(string.Join(",", continuousTrainData.Select(array => array[i])));
}
}
// Convert continuous to discrete
Dictionary <int, GaussianClusterCollection> indexClusterMapping = DataWrangler.GetIndexClustersMap(continuousTrainData, _indexElbowMap);
List <int[]> discreteTrainData = DataWrangler.ConvertContinuesToDiscrete(continuousTrainData, indexClusterMapping);
List <int[]> discreteTestData = DataWrangler.ConvertContinuesToDiscrete(continuousTestData, indexClusterMapping);
var problem = ProblemHelper.ReadProblem(discreteTrainData.Select(arr =>
{
// Move class to front as it is expected by libsvm.
int temp = arr[0];
arr[SVMSupportedClassIndex] = arr[OriginalClassIndex];
arr[OriginalClassIndex] = temp;
return(arr.Select(i => (double)i).ToList());
}).ToList());
var test = ProblemHelper.ReadProblem(discreteTestData.Select(arr =>
{
// Move class to front as it is expected by libsvm.
int temp = arr[0];
arr[SVMSupportedClassIndex] = arr[OriginalClassIndex];
arr[OriginalClassIndex] = temp;
return(arr.Select(i => (double)i).ToList());
}).ToList());
// defaults taken from documentation http://weka.sourceforge.net/doc.stable/weka/classifiers/functions/LibSVM.html
double c = 1; // default C is 1
double gamma = 1.0 / problem.l; // default gamma is 1/k
double r = 0; // default coef0 is 0
int degree = 3; // default degree is 3
Dictionary <string, Kernel> nameKernelMap = new Dictionary <string, Kernel>(StringComparer.OrdinalIgnoreCase)
{
{ "Linear", KernelHelper.LinearKernel() },
{ "Polynomial", KernelHelper.PolynomialKernel(degree, gamma, r) },
{ "Radial", KernelHelper.RadialBasisFunctionKernel(gamma) },
{ "Sigmoid", KernelHelper.SigmoidKernel(gamma, r) },
};
// Get accuracies for base comparison
// DON'T DO PARALLEL. We don't know if the underlying implementation is MT safe or not.
//Parallel.ForEach(nameKernelMap.Keys, (kernelName) =>
foreach (string kernelName in nameKernelMap.Keys)
{
Console.WriteLine($"{kernelName}: {GetSVMAccuracy(problem, test, nameKernelMap[kernelName], c)}");
}
;
// Get accuracy of with Naive Bayes
double[] classWeightPrior = new[] { 1.0, 1.0 };
double[] classPriorProbability = new[] { 0.5, 0.5 };
NaiveBayesClassifier naiveBayes = NaiveBayesClassifier.Load(discreteTrainData, SVMSupportedClassIndex, classWeightPrior, classPriorProbability);
Console.WriteLine($"Naive Bayes: {naiveBayes.GetPredictionAccuracy(discreteTestData, SVMSupportedClassIndex)}");
// Calculate SVMs Bias and Variance
List <List <int[]> > samples = Sampler.SampleData(discreteTrainData, BiasVarianceNumOfSamples);
ConcurrentDictionary <string, ConcurrentDictionary <int, ConcurrentDictionary <int, int> > > kernelInstanceClassifierPredictionsMappings = new ConcurrentDictionary <string, ConcurrentDictionary <int, ConcurrentDictionary <int, int> > >(StringComparer.OrdinalIgnoreCase);
foreach (string kernelName in nameKernelMap.Keys)
{
ConcurrentDictionary <int, ConcurrentDictionary <int, int> > instanceClassifierPredictionMappings = kernelInstanceClassifierPredictionsMappings.GetOrAdd(kernelName, new ConcurrentDictionary <int, ConcurrentDictionary <int, int> >());
for (int classifierIndex = 0; classifierIndex < BiasVarianceNumOfSamples; classifierIndex++)
{
problem = ProblemHelper.ReadProblem(samples[classifierIndex].Select(arr => arr.Select(i => (double)i).ToList()).ToList());
var svm = new C_SVC(problem, nameKernelMap[kernelName], c);
for (int instanceIndex = 0; instanceIndex < discreteTestData.Count; instanceIndex++)
{
ConcurrentDictionary <int, int> classifierPredictionMappings = instanceClassifierPredictionMappings.GetOrAdd(instanceIndex, new ConcurrentDictionary <int, int>());
test = ProblemHelper.ReadProblem(new List <List <double> > {
discreteTestData[instanceIndex].Select(i => (double)i).ToList()
});
for (int i = 0; i < test.l; i++)
{
var x = test.x[i];
var y = test.y[i];
classifierPredictionMappings.GetOrAdd(classifierIndex, (int)svm.Predict(x));
}
}
}
}
Console.WriteLine("Kernel, Bias, Variance, Accuracy");
foreach (string kernelName in nameKernelMap.Keys)
{
ConcurrentDictionary <int, ConcurrentDictionary <int, int> > instanceClassifierPredictionMappings = kernelInstanceClassifierPredictionsMappings.GetOrAdd(kernelName, new ConcurrentDictionary <int, ConcurrentDictionary <int, int> >());
Tuple <double, double, double> biasVarianceAccuracy = BiasVarianceHelper.GetBiasVarianceAccuracy(discreteTestData, SVMSupportedClassIndex, instanceClassifierPredictionMappings);
Console.WriteLine($"{kernelName}, {biasVarianceAccuracy.Item1}, {biasVarianceAccuracy.Item2}, {biasVarianceAccuracy.Item3}");
}
Console.WriteLine("Press ENTER to continue...");
Console.ReadLine();
}