private static void Train(string prefix)
{
SVMProblem trainingSet = SVMProblemHelper.Load(MnistDataPath + prefix + ".txt");
trainingSet = trainingSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
double[] crossValidationResults;
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
SVMModel model = trainingSet.Train(parameter);
SVM.SaveModel(model, MnistDataPath + "model.txt");
Console.WriteLine("\n\nModel ok!");
}
// for training the face,
public void face_training(SVMProblem f_training)
{
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towp.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towpt.txt");
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.NU_SVC;
parameter.Kernel = SVMKernelType.SIGMOID;
parameter.C = 1;
parameter.Gamma = 1;
parameter.Probability = true;
double[] crossValidationResults;
int nFold = 10;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = trainingSet.Train(parameter);
double[] testResults = testSet.Predict(model);
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
Training_result.Content = "testAccuracy:" + testAccuracy + "\nCross validation accuracy: " + crossValidationAccuracy + "\nCount " + trainingSet.Y.Count;
Training_result.FontSize = 14;
Training_result.FontStyle = FontStyles.Normal;
Training_result.Foreground = Brushes.Red;
Training_result.Background = Brushes.Black;
index++;
}
public void face_training(SVMProblem f_training)
{
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towp.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towpt.txt");
// f_training.Save(@"C:\Users\temp\Desktop\1005f.txt");
// trainingSet.Insert(index, f_training.X[0], 2);
trainingSet.Add(f_training.X[0], 1);
trainingSet.Save(@"C:\Users\temp\Desktop\flag.txt");
// trainingSet.Save(@"C:\Users\temp\Desktop\1005.txt");
// Console.WriteLine();
// SVMNode node = new SVMNode();
// node.Index = Convert.ToInt32(o);
// node.Value = Convert.ToDouble(f_training.X);
// nodes.Add(node);
// trainingSet.Add(nodes.ToArray(), 1);
// int number = randon.Next(0, trainingSet.X.Count);
// int trainingsample = Convert.ToInt32(trainingSet.X.Count * 2 / 3);
// int testingsample = Convert.ToInt32(trainingSet.X.Count / 3);
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.NU_SVC;
parameter.Kernel = SVMKernelType.SIGMOID;
parameter.C = 1;
parameter.Gamma = 1;
parameter.Probability = true;
int nFold = 10;
MainWindow main = new MainWindow();
double[] crossValidationResults; // output labels
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = SVM.Train(trainingSet, parameter);
// SVMModel model = trainingSet.Train(parameter);
SVM.SaveModel(model, @"C:\Users\temp\Desktop\1005.txt");
double[] testResults = testSet.Predict(model);
// Console.WriteLine("");
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
// Console.WriteLine("testAccuracy:" + testAccuracy);
// Console.WriteLine(Convert.ToString(trainingSet.X.Count));
main.Training_result.Content = "testAccuracy:" + testAccuracy + "\nCross validation accuracy: " + crossValidationAccuracy + "\nCount " + trainingSet.X.Count;
main.Training_result.FontSize = 14;
main.Training_result.FontStyle = FontStyles.Normal;
main.Training_result.Foreground = Brushes.Red;
main.Training_result.Background = Brushes.Black;
// Console.WriteLine(trainingSet1.Length);
// trainingSet.Save(@"C:\Users\temp\Desktop\1005.txt");
index++;
}
private static void TestOne(string prefix)
{
SVMModel model = SVM.LoadModel(MnistDataPath + "model.txt");
SVMProblem testSet = SVMProblemHelper.Load(MnistDataPath + prefix + ".txt");
testSet = testSet.Normalize(SVMNormType.L2);
double[] testResults = testSet.Predict(model);
Console.WriteLine("\nTest result: " + testResults[0].ToString());
}
private static void Test(string prefix)
{
SVMModel model = SVM.LoadModel(MnistDataPath + "model.txt");
SVMProblem testSet = SVMProblemHelper.Load(MnistDataPath + prefix + ".txt");
testSet = testSet.Normalize(SVMNormType.L2);
double[] testResults = testSet.Predict(model);
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
Console.WriteLine("\nTest accuracy: " + testAccuracy);
}
//인식 하기
public static int SVM_Classification(SVMModel md)
{
int result = 0;
SVMProblem testSet = SVMProblemHelper.Load("tmp.txt"); //인식데이터셋 열기
testSet = testSet.Normalize(SVMNormType.L2);
double[] testResults = testSet.Predict(md);
result = (int)testResults[0];
return(result);
}
//학습모델 생성
public static SVMModel SVM_GenModel(String dataset)
{
SVMProblem trainingSet = SVMProblemHelper.Load(dataset); //학습데이터셋 열기
trainingSet = trainingSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
double[] crossValidationResults;
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = trainingSet.Train(parameter); // 학습된 모델 생성
SVM.SaveModel(model, "model_" + dataset); // 모델 저장
return(model);
}
static void Main(string[] args)
{
// Load the datasets: In this example I use the same datasets for training and testing which is not suggested
SVMProblem trainingSet = SVMProblemHelper.Load(@"Dataset\wine.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"Dataset\wine2.txt");
// Normalize the datasets if you want: L2 Norm => x / ||x||
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
// Select the parameter set
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 5;
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
// Train the model, If your parameter set gives good result on cross validation
SVMModel model = trainingSet.Train(parameter);
// Save the model
SVM.SaveModel(model, @"Model\wine_model.txt");
// Predict the instances in the test set
double[] testResults = testSet.Predict(model);
Console.WriteLine("aaa:" + testResults[0] + "\n");
/*
* // Evaluate the test results
* int[,] confusionMatrix;
* double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
*
*
*
*
* // Print the resutls
* Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
* Console.WriteLine("\nTest accuracy: " + testAccuracy);
* Console.WriteLine("\nConfusion matrix:\n");
*
* // Print formatted confusion matrix
* Console.Write(String.Format("{0,6}", ""));
* for (int i = 0; i < model.Labels.Length; i++)
* Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
* Console.WriteLine();
* for (int i = 0; i < confusionMatrix.GetLength(0); i++)
* {
* Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
* for (int j = 0; j < confusionMatrix.GetLength(1); j++)
* Console.Write(String.Format("{0,5}", confusionMatrix[i,j]));
* Console.WriteLine();
* }
*
* Console.WriteLine("\n\nPress any key to quit...");
* Console.ReadLine();*/
}
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);
}
// activity classification
public void SVM_Classification()
{
testSet1 = SVMProblemHelper.Load(@"Dataset\ADLfall_test1.txt");
testSet1 = testSet1.Normalize(SVMNormType.L2);
float sum;
if (testSet1.Length != 0)
{
try
{
//var resut = model.Predict(testSet1.X[testSet1.Length - 1]);
// p = Convert.ToInt16(resut);
//predict the result using model, return result
var result = testSet1.Predict(activity_model);
p = Convert.ToInt16(result[0]);
//put the result into enqueue
myq.Enqueue(p);
switch (p)
{
case 1:
q++;
break;
case 2:
w++;
break;
case 3:
e++;
break;
case 4:
r++;
break;
}
}
catch
{
}
// if the collected data is larger than 30
if (myq.Count > 30)
{
// dequeue the old one
myq.TryDequeue(out p);
switch (p)
{
case 1:
q--;
break;
case 2:
w--;
break;
case 3:
e--;
break;
case 4:
r--;
break;
}
// proportional
sum = q + w + e + r;
// activity.Content = ("Sit down:" + sit_down + "\n" + "Walking" + walkig + "\n" + "Standing" + standing + "\n" + "Fall event" + fallevent);
activity.Content = ("Sit down: " + Math.Round(e / sum, 2) * 100 + "%" + "\n" + "Walking: " + Math.Round(q / sum, 2) * 100 + "%" + "\n" + "Standing: " + Math.Round(w / sum, 2) * 100 + "%" + "\n" + "Fall event: " + Math.Round(r / sum, 2) * 100 + "%");
// activity.Content = ("Sit down:" + Math.Round(h / sum, 2) + "\n" + "Walking" + Math.Round(w / sum, 2) + "\n" + "Standing" + Math.Round(q / sum, 2) + "\n" + "Fall event" + Math.Round(r / sum, 2));
if (e / sum > 0.5)
{
label.Content = ("You have sit down"); label.Foreground = Brushes.Red;
}
else if (q / sum > 0.5)
{
label.Content = "You are walking"; label.Foreground = Brushes.Red;
}
else if (w / sum > 0.5)
{
label.Content = "You are standing"; label.Foreground = Brushes.Red;
}
else if (r / sum > 0.5)
{
label.Content = "You fell down"; label.Foreground = Brushes.Red;
}
activity.FontSize = 20;
activity.FontStyle = FontStyles.Normal;
activity.Foreground = Brushes.Red;
activity.Background = Brushes.Black;
}
}
}
static void wordsSvm()
{//http://www.svm-tutorial.com/2014/10/svm-tutorial-classify-text-csharp/
var index = 11;
var rooms11 = File.ReadAllText(string.Format("../../room{0}.txt", index));
var rooms22 = File.ReadAllText(string.Format("../../room{0}.txt", ++index));
var rooms1 = rooms11.Split(';');
var rooms2 = rooms22.Split(';');
var termFreqWeight = new PanGuTermFreqWeight();
termFreqWeight.computerFW(rooms1, rooms2);
var problem = new SVMProblem(); //SVM.SetPrintStringFunction(mySVMPrintFunction);
var lableFeaturesBuilder = new LableFeaturesBuilder();
var segment = new PanGuSegment();
foreach (var room in rooms2)
{
var words = termFreqWeight.TermsByDoc[1, room];
words = words.Where(t => t.Length > 1 && termFreqWeight.TermFWByGlobal.ContainsKey(t) && termFreqWeight.TermFWByType.ContainsKey(0, t)).ToArray();
//lableFeaturesBuilder.AddToProblem(problem, room, words.Select(t => new KeyValuePair<string, double>(t,termFreqWeight.TermFWByDoc[1, room, t].Freq*GetW(t))));
lableFeaturesBuilder.AddToProblem(problem, room, words.Select(t => new KeyValuePair <string, double>(t, Math.Max(termFreqWeight.TermFWByType[0, t].Freq, termFreqWeight.TermFWByType[1, t].Freq))));
}
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.LINEAR;
parameter.C = 1;
parameter.Probability = true;
//parameter.
//parameter.WeightLabels = lableFeaturesBuilder.CreateWeightFeatures("大", "双", "套", "大床", "双床").ToArray();
//parameter.Weights = new double[] { 1.90, 1.90, 1.90, 1.99, 1.99 };
//parameter.WeightLabels = lableFeaturesBuilder.CreateWeightLables<int>("行政湖景双床房").ToArray();
//parameter.Weights = new double[] { 1.90, 1.90, 1.90, 1.99, 1.99 };
problem = problem.Normalize(SVMNormType.L1);
problem.CheckParameter(parameter);
var model2 = LibSVMsharp.SVM.Train(problem, parameter);
model2.SaveModel("roomMatching.model");
var model = LibSVMsharp.SVM.LoadModel("roomMatching.model");
foreach (var room in rooms1)
{
var words = termFreqWeight.TermsByDoc[0, room];
words = words.Where(t => t.Length > 1 && termFreqWeight.TermFWByGlobal.ContainsKey(t) && termFreqWeight.TermFWByType.ContainsKey(1, t)).ToArray();
//var nodes = lableFeaturesBuilder.CreateNodes(words.Select(t => new KeyValuePair<string, double>(t, termFreqWeight.TermFWByDoc[0, room, t].Freq * GetW(t))));
var nodes = lableFeaturesBuilder.CreateNodes(words.Select(t => new KeyValuePair <string, double>(t, Math.Max(termFreqWeight.TermFWByType[0, t].Freq, termFreqWeight.TermFWByType[1, t].Freq))));
if (nodes.Length > 0)
{
nodes = nodes.Normalize(SVMNormType.L1);
double predictedY = 0;
predictedY = LibSVMsharp.SVM.Predict(model, nodes);
double[] values = null; double probabilityValue = 0;
probabilityValue = LibSVMsharp.SVM.PredictValues(model, nodes, out values);
double[] est = null; double probability = 0;
probability = LibSVMsharp.SVM.PredictProbability(model, nodes, out est);
Console.WriteLine("{0,22}\t{1},{2},{3},{4},{5}", room, lableFeaturesBuilder.GetLable(predictedY), predictedY, probabilityValue, probability, string.Empty);
}
}
Console.WriteLine(new string('=', 80));
}
static void Main(string[] args)
{
// Load the datasets: In this example I use the same datasets for training and testing which is not suggested
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_train.txt");
// SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_test.txt");
SVMProblem testSet1 = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_test1.txt");
// SVMProblem testSet1 = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\result.txt");
// Normalize the datasets if you want: L2 Norm => x / ||x||
trainingSet = trainingSet.Normalize(SVMNormType.L2);
// testSet = testSet.Normalize(SVMNormType.L2);
testSet1 = testSet1.Normalize(SVMNormType.L2);
// Select the parameter set
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 32768.0;
parameter.Gamma = 8.0;
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 5;
// trainingSet1.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
// double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
// Train the model, If your parameter set gives good result on cross validation
// SVMModel model = trainingSet.Train(parameter);
// Save the model
// SVM.SaveModel(model, @"Model\activity_recognition.txt");
SVMModel model = SVM.LoadModel(@"Model\activity_recognition.txt");
int p, q, w, e, r, ok = 0;
double sum;
q = 0;
w = 0;
e = 0;
r = 0;
// Predict the instances in the test set
double[] testResults = testSet1.Predict(model);
while (ok < testSet1.Length)
{
var resut = model.Predict(testSet1.X[ok]);
// Console.WriteLine("resut111:" + resut);
p = Convert.ToInt16(resut);
switch (p)
{
case 1:
q++;
break;
case 2:
w++;
break;
case 3:
e++;
break;
case 4:
r++;
break;
}
ok++;
}
sum = q + w + e + r;
Console.WriteLine("result:" + Math.Round(q / sum, 2) + "," + Math.Round(w / sum, 2) + "," + Math.Round(e / sum, 2) + "," + Math.Round(r / sum, 2));
// Evaluate the test results
int[,] confusionMatrix;
double testAccuracy = testSet1.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Print the resutls
// Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
Console.WriteLine("\nTest accuracy: " + testAccuracy);
Console.WriteLine("\nConfusion matrix:\n");
// Print formatted confusion matrix
Console.Write(String.Format("{0,6}", ""));
for (int i = 0; i < model.Labels.Length; i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
}
Console.WriteLine();
for (int i = 0; i < confusionMatrix.GetLength(0); i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
for (int j = 0; j < confusionMatrix.GetLength(1); j++)
{
Console.Write(String.Format("{0,5}", confusionMatrix[i, j]));
}
Console.WriteLine();
}
Console.WriteLine("\n\nPress any key to quit...");
Console.ReadLine();
}
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);
}