public List <IProbScoreProxy> Predicting(IResults results, int scanNum)
{
List <IScore> scores = results.GetResult(scanNum);
ISpectrum spectrum = results.GetSpectrum(scanNum);
List <double[]> estimationList = new List <double[]>();
List <IProbScoreProxy> probabilities = new List <IProbScoreProxy>();
foreach (IScore score in scores)
{
List <SVMNode> X = new List <SVMNode>();
// store score value in X
int idx = 0;
foreach (MassType type in types)
{
SVMNode node = new SVMNode();
node.Index = idx;
node.Value = score.GetScore(type);
X.Add(node);
idx++;
}
testingProblem.X.Add(X.ToArray());
}
// prediction
double[] target = testingProblem.PredictProbability(model, out estimationList);
testingProblem.X.Clear();
// create new scores
for (int i = 0; i < scores.Count; i++)
{
probabilities.Add(new ProbScoreProxy(scores[i], spectrum, estimationList[i][1]));
}
return(probabilities);
}
public Tuple <List <double[]>, double[]> Predicting(List <IScore> scores)
{
List <double[]> estimationList = new List <double[]>();
foreach (IScore score in scores)
{
List <SVMNode> X = new List <SVMNode>();
// store score value in X
int idx = 0;
foreach (MassType type in types)
{
SVMNode node = new SVMNode();
node.Index = idx;
node.Value = score.GetScore(type);
X.Add(node);
idx++;
}
testingProblem.X.Add(X.ToArray());
}
double [] target = testingProblem.PredictProbability(model, out estimationList);
testingProblem.X.Clear();
return(Tuple.Create(estimationList, target));
}
public int SVM_face_recognition()
{
SVMProblem face_data = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\Face_feature.txt");
face_data = face_data.Normalize(SVMNormType.L2);
//using Libsvm package which has api to calculate the probabilty
face_data.PredictProbability(face_recognition_model, out prolist);
var ok = prolist.ToArray();
var v = ok[0];
// we have 13 person
int maxconfidenceindex = 0;
double maxconfidence = v[maxconfidenceindex];
double threshold = 0.25;
for (int i = 0; i < v.Count(); i++)
{
if (v[i] > maxconfidence)
{
maxconfidenceindex = i;
maxconfidence = v[i];
}
}
if (threshold < maxconfidence)
{
f1 = v[0];
f2 = v[1];
f3 = v[2];
f4 = v[3];
f5 = v[4];
/*
* f6 = v[5];
* f7 = v[6];
* f8 = v[7];
* f9 = v[8];
* f10 = v[9];
* f11 = v[10];
* f12 = v[11];
* f13 = v[12];
*/
double[] faceresult = face_data.Predict(face_recognition_model);
facename = Convert.ToInt16(faceresult[0]);
// facename =facemodel.Labels[maxconfidenceindex];
faceshow++;
}
int labelnum = face_recognition_model.Labels[maxconfidenceindex];
if (threshold > maxconfidence)
{
// Console.WriteLine("Unknow");
facename = 0;
display.Content = "Unknow";
facefail++;
}
return(facename);
}
public static SvmResult TrainAndTestSvm(SVMProblem trainingSet, SVMProblem testSet)
{
// find the ratio of malignant:benign cases:
double mbTrainRatio = trainingSet.Y.Where(x => x == 0).ToArray().Length *1F / trainingSet.Y.Count;
Console.WriteLine($"MB TRAIN RATIO: {mbTrainRatio}");
double mbTestRatio = testSet.Y.Where(x => x == 0).ToArray().Length * 1F / testSet.Y.Count;
Console.WriteLine($"MB TEST RATIO: {mbTestRatio}");
SVMParameter parameter = new SVMParameter
{
Type = SVMType.C_SVC,
Kernel = SVMKernelType.RBF,
C = double.Parse(Configuration.Get("C")),
Gamma = double.Parse(Configuration.Get("Gamma")),
Probability = true,
WeightLabels = new[] { 0, 1 },
Weights = new[] { (1 - mbTrainRatio) / mbTrainRatio, 1 }
};
//parameter = TrainingHelper.FindBestHyperparameters(trainingSet, parameter);
Console.WriteLine($"Found best parameters: c={parameter.C},gamma={parameter.Gamma}");
SVMModel model = trainingSet.Train(parameter);
SVM.SaveModel(model, Configuration.Get("ModelLocation"));
// The following evaluation has code from:
// https://csharp.hotexamples.com/examples/LibSVMsharp/SVMParameter/-/php-svmparameter-class-examples.html
// Predict the instances in the test set
double[] testResults = testSet.Predict(model);
// Evaluate the test results
double testAccuracy =
testSet.EvaluateClassificationProblem(testResults, model.Labels, out var confusionMatrix);
// Print the resutls
Console.WriteLine("\nTest accuracy: " + testAccuracy);
Console.WriteLine("\nConfusion matrix:\n");
// Print formatted confusion matrix
Console.Write($"{"",6}");
for (int i = 0; i < model.Labels.Length; i++)
{
Console.Write($"{"(" + model.Labels[i] + ")",5}");
}
Console.WriteLine();
for (int i = 0; i < confusionMatrix.GetLength(0); i++)
{
Console.Write($"{"(" + model.Labels[i] + ")",5}");
for (int j = 0; j < confusionMatrix.GetLength(1); j++)
{
Console.Write($"{confusionMatrix[i, j],5}");
}
Console.WriteLine();
}
double sensitivity = confusionMatrix[0, 0] * 1.0 /
(confusionMatrix[0, 1] + confusionMatrix[0, 0]);
double specificity = confusionMatrix[1, 1] * 1.0 /
(confusionMatrix[1, 1] + confusionMatrix[1, 0]);
double[] results = testSet.PredictProbability(model, out var probabilities);
for (int i = 0; i < probabilities.Count; i++)
{
// ReSharper disable once CompareOfFloatsByEqualityOperator
String x = results[i] != testSet.Y[i] ? "MISPREDICTION" :"";
Console.WriteLine($"{results[i]} | {probabilities[i][0]} | {probabilities[i][1]} | {testSet.Y[i]} | {x}");
}
return(new SvmResult()
{
C = parameter.C, Gamma = parameter.Gamma, TestAccuracy = testAccuracy, Sensitivity = sensitivity,
Specificity = specificity
});
}
private async Task PerformAnalysis(String path, Rectangle rectangle)
{
UShortArrayAsImage image = null;
double[] pcaComponents = null;
int tasksComplete = 0;
UpdateStatus(path, startingImageStatusStr);
List <Task> tasks = new List <Task>()
{
new Task(() =>
{
var file = db.FileStorage.FindById($"images/{path}");
var ms = new MemoryStream();
file.CopyTo(ms);
ms.Seek(0, 0);
image = DicomFile.Open(ms).GetUshortImageInfo();
UpdateStatus(path, loadedImageStatusStr);
}),
new Task(() =>
{
image = Normalization.GetNormalizedImage(image, rectangle,
int.Parse(Configuration.Get("sizeImageToAnalyze")));
db.FileStorage.Upload($"images/{path}-cropped", $"{path}-cropped",
image.GetPngAsMemoryStream());
UpdateStatus(path, croppedImageStatusStr);
}),
new Task(() =>
{
image = Contrast.ApplyHistogramEqualization(image);
db.FileStorage.Upload($"images/{path}-croppedContrast", $"{path}-croppedContrast",
image.GetPngAsMemoryStream());
UpdateStatus(path, contrastImageStatusStr);
}),
new Task(() =>
{
//PCA
PCA pca = PCA.LoadModelFromFile(Configuration.Get("PcaModelLocation"));
if (!int.TryParse(Configuration.Get("componentsToUse"), out int components))
{
components = pca.Eigenvalues.Length;
}
pcaComponents = pca.GetComponentsFromImage(image, components);
UpdateStatus(path, pcaImageStatusStr);
}),
new Task(() =>
{
//SVM
SVMProblem svmProblem = new SVMProblem();
// add all the components to an SVMNode[]
SVMNode[] nodes = new SVMNode[pcaComponents.Length];
for (int i = 0; i < pcaComponents.Length; i++)
{
nodes[i] = new SVMNode(i + 1, pcaComponents[i]);
}
svmProblem.Add(nodes, 0);
svmProblem = svmProblem.Normalize(SVMNormType.L2);
SVMModel svmModel = SVM.LoadModel(Configuration.Get("ModelLocation"));
double[] results = svmProblem.PredictProbability(svmModel, out var probabilities);
var analysis = db.GetCollection <Analysis>("analysis");
Analysis currentAnalysis = analysis.FindOne(x => x.Id.ToString().Equals(path));
currentAnalysis.Certainty = results[0] == 0 ? probabilities[0][1] * 100 : probabilities[0][0] * 100;
currentAnalysis.Diagnosis = results[0] == 0
? DdsmImage.Pathologies.Benign
: DdsmImage.Pathologies
.Malignant;
analysis.Update(currentAnalysis);
UpdateStatus(path, svmImageStatusStr);
})
};
foreach (Task task in tasks)
{
task.Start();
await task;
// lets set percentage done:
var analysis = db.GetCollection <Analysis>("analysis");
Analysis currentAnalysis = analysis.FindOne(x => x.Id.ToString().Equals(path));
currentAnalysis.PercentageDone = (++tasksComplete * 100) / tasks.Count;
analysis.Update(currentAnalysis);
}
UpdateStatus(path, doneStatusStr);
}
