// 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);
}
public void ClassifiGesture(string pathModel, string pathTest, string pathResult)
{
testProblem = SVMProblemHelper.Load(pathTest);
model = SVM.LoadModel(pathModel);
double[] testResults = testProblem.Predict(model);
using (StreamWriter file = new StreamWriter(pathResult, true))
{
foreach (double element in testResults)
{
file.Write(element.ToString()+"\n");
file.Write(Environment.NewLine);
}
}
}//end ClassifyGesture
public static int predictSVM()
{
double[] results = { 99 };
//Variables.model = getExistingModel();
if (!Variables.newdata.Contains("null"))
{
SVMProblem newData = SVMProblemHelper.Load(Constants.NEWDATA_PATH);
Console.Write("Predicted command:\n");
results = newData.Predict(Variables.model);
/*foreach (var item in results)
* {
* Console.WriteLine(item.ToString());
* }*/
Console.WriteLine(results[0]);
}
else
{
Console.WriteLine("invalid new data");
}
return((int)results[0]);
}
private void listBox2_SelectedIndexChanged(object sender, EventArgs e)
{
File.Create(parameter["hog_test_file"]).Dispose();
System.Diagnostics.Stopwatch sw = new System.Diagnostics.Stopwatch();
if (listBox2.SelectedIndex < 0)
{
return;
}
else
{
try
{
List <Mat> ListImage = new List <Mat>();
Mat img_display = new Mat();
string dir = listBox2.SelectedItem.ToString();
img_display = Cv2.ImRead(dir);
pictureBox2.Image = img_display.ToBitmap();
// Process predict
sw.Start();
Cv2.Resize(img_display, img_display, sizes2);
ListImage.Add(img_display);
FeatureExtraction.compute_hog_test(ListImage, sizes2, 100, parameter["hog_test_file"]);
SVMProblem Test = SVMProblemHelper.Load(parameter["hog_test_file"]);
double[] Target = Test.Predict(model_load);
sw.Stop();
label13.Text = MAPPING[(int)(Target[0])].ToString();
label10.Text = sw.ElapsedMilliseconds.ToString() + " (ms)";
string time = sw.ElapsedMilliseconds.ToString();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
}
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 static void Main(string[] args)
{
var report = new MakeReport();
List <string> consoleOutput = new List <string>();
consoleOutput.Add($"{System.DateTime.Now} - Loading");
var train = ConvertHelper.CSVToDataConstrutor(File.ReadAllLines("train.csv"), ',');
var test = ConvertHelper.CSVToDataConstrutor(File.ReadAllLines("test.csv"), ',');
var fs = new DataProcessing(train, test, "SalePrice", "Id");
fs.GetFeature("SalePrice").Transform((value) => Math.Log(1 + value));
fs.SetInactive(new List <string> {
"Id", "PoolArea", "LandContour", "PoolQC", "LotConfig", "Utilities", "Alley",
"Street", "BsmtHalfBath", "LowQualFinSF", "3SsnPorch", "LandSlope", "YrSold", "Condition1", "BsmtFinType2", "RoofMatl",
"MiscVal", "MiscFeature", "BsmtFinSF2", "Condition2", "BldgType", "ScreenPorch", "MoSold", "Functional"
});
fs.SetInactive(new List <string> {
"BsmtCond", "BsmtUnfSF", "GarageCars", "PavedDrive", "SaleType", "SaleCondition",
"BsmtExposure", "GarageCond", "Fence", "Heating", "BsmtQual",
});
//fs.SetInactive(new List<string> { "EnclosedPorch" });
fs.SetTrainRowsInactiveByIds(new List <string> {
"1299", "186", "198", "636", "1032", "1183", "1153", "1174"
});
//fs.SetTrainRowsInactiveByIds(new List<string> { "130", "188", "199", "268", "305", "497", "524", "530", "692", "770", "884", "1025", "1231", "1371", "1387", "1424", "1441" });
consoleOutput.Add($"{System.DateTime.Now} - Transforming model");
fs.GetFeature("LotFrontage").ReplaceValues("NA", "0");
fs.GetFeature("MasVnrArea").ReplaceValues("NA", "-1");
fs.GetFeature("GarageYrBlt").ReplaceValues("NA", "-1");
fs.Features.Where(f => !f.IsNumeric() && !f.IsClass).All(n => { n.TransformEnumToInt(); return(true); });
//fs.Features.Select(f => new OutlierLine { FeatureName = f.Name, Outliers = string.Join(", ", f.GetOutliers()) }).ToList().ForEach(o => consoleOutput.Add($"{o.FeatureName} => {o.Outliers}"));
//TODO: To jakoś nie polepszyło, a nawet pogorszyło, trzeba by dodać taką funkcję zamiast wyliczać to tutaj i ująć zmienną GarageYrBlt
//var oldestYearBuild = fs.GetFeature("YearBuilt").Values.Min(v => double.Parse(v.NewValue));
//fs.GetFeature("YearBuilt").Transform((value) => (double.Parse(value) - oldestYearBuild).ToString());
//fs.Features.ToList().ForEach(f => report.AddScatterPlot(f.Name, f.Values.Where(v => !v.IsTest).Select(v => new Point() { X = double.Parse(v.NewValue), Y = double.Parse(fs.GetClassForTrainId(v.RowId)) }).ToList()));
//report.CreatePDF("Report.pdf");
//var oldestYearRemodAdd = fs.GetFeature("YearRemodAdd").Values.Min(v => double.Parse(v.NewValue));
//fs.GetFeature("YearRemodAdd").Transform((value) => (double.Parse(value) - oldestYearRemodAdd).ToString());
//OUTLIERS
//foreach (var feature in fs.Features.Where(f => f.IsActive && !f.IsClass && !f.IsId && new List<string> { "EnclosedPorch", "BsmtFinSF2", "GarageYrBlt", "OpenPorchSF", "ScreenPorch", "MasVnrArea", "LotArea", "Condition1", "MSSubClass", "MiscVal" }.IndexOf(f.Name) < 0))
//{
// feature.MarkOutliers();
//}
//fs.PrintOutliersAmount();
consoleOutput.Add($"{System.DateTime.Now} - Gathering data");
var dataModel = fs.GetDataModel();
File.WriteAllLines("output-train.csv", ConvertHelper.DataSetToCSV(dataModel.HeadersWithClass, dataModel.OutputTrain, ","));
File.WriteAllLines("output-test.csv", ConvertHelper.DataSetToCSV(dataModel.HeadersWithoutClass, dataModel.OutputTest, ","));
consoleOutput.Add($"{System.DateTime.Now} - Preparing SVM problem");
//if (false)
{
//SVM.SetPrintStringFunction(null);
SVMProblem testSet = SVMLoadHelper.Load(dataModel.OutputTest, isWithClass: false);
SVMProblem trainingSet = SVMLoadHelper.Load(dataModel.OutputTrain, isWithClass: true);
SVMParameter parameter = new SVMParameter()
{
Type = SVMType.EPSILON_SVR,
Kernel = SVMKernelType.RBF,
//C = 10,
Gamma = 0.01,
CacheSize = 2000,
Eps = 0.1,// * Math.Pow(10, i);
//parameter.Probability = true;
};
//List<Tuple<string, double>> rmseAfterRemoveFeature = new List<Tuple<string, double>>();
//var activeFeatures = fs.Features.Where(f => f.IsActive || !f.IsClass);
//foreach (var feature in activeFeatures)
//{
// feature.IsActive = false;
// var outputTrain = fs.GetTransfomedTrain();
// SVMProblem trainingSet = SVMLoadHelper.Load(outputTrain, isWithClass: true);
// consoleOutput.Add("=====================");
// //SVMModel model = trainingSet.Train(parameter);
// //double[] trainingResults = trainingSet.Predict(model);
// double[] crossvalidationResults;
// trainingSet.CrossValidation(parameter, 3, out crossvalidationResults);
// //consoleOutput.Add(parameter.GetOutput());
// var rmselog = EvaulationHelper.RMSELog(trainingSet.Y.ToArray(), crossvalidationResults);
// rmseAfterRemoveFeature.Add(new Tuple<string, double>(feature.Name, rmselog));
// consoleOutput.Add($"{System.DateTime.Now} - {feature.Name} - {rmselog}");
// feature.IsActive = true;
//}
//consoleOutput.Add($"{System.DateTime.Now} - {bestToRemove.Item1} - {bestToRemove.Item2}");
consoleOutput.Add("=====================");
consoleOutput.Add("Ordered");
//consoleOutput.AddRange(rmseAfterRemoveFeature.OrderBy(t => t.Item2).Select(t => $"{t.Item1} - {t.Item2}"));
SVMModel model = trainingSet.Train(parameter);
double[] trainResults = trainingSet.Predict(model);
double[] testResults = testSet.Predict(model);
//double meanSquaredErr = testSet.EvaluateRegressionProblem(testResults, out correlationCoef);
trainingSet.Y
.Select((y, index) => Math.Abs(y - trainResults[index]))
.Select((v, index) => new { index, v })
.OrderByDescending(v => v.v)
.Take(15)
.ToList()
.ForEach(e => consoleOutput.Add($"{e.index}:{e.v}"));
var rmselog = EvaulationHelper.RMSELog(trainingSet.Y.ToArray(), trainResults);
consoleOutput.Add($"{System.DateTime.Now} - {rmselog}");
File.WriteAllLines("submission_fs.csv", ConvertHelper.ResultToCSV("Id,SalePrice", testResults.Select(v => Math.Exp(v) - 1).ToArray(), dataModel.TestIds));
consoleOutput.Add($"{System.DateTime.Now} -I had finish");
SVM.SaveModel(model, "model.txt");
}
//report.CreatePDF("Report.pdf");
consoleOutput.ForEach(s => System.Console.WriteLine(s));
File.WriteAllLines("consoleOutput.txt", consoleOutput);
System.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);
}
private void btnTrain_Click(object sender, EventArgs e)
{
System.Diagnostics.Stopwatch time = new System.Diagnostics.Stopwatch();
parameter = load_json_file(parameter_file);
try
{
File.Create(parameter["path_model"] + textBox3.Text + ".txt").Dispose();
File.Create(parameter["result_file"]).Dispose();
if (textBox3.Text == "")
{
MessageBox.Show("create model name");
}
//if(SHOWRESULT.ContainsKey(txtModelName.Text))
//{
// MessageBox.Show("Model name already exits");
//}
else
{
time.Start();
#region Creat file Model
// Creat param SVM
SVMProblem FileTrain = SVMProblemHelper.Load(parameter["hog_train_file"]);
SVMParameter param = new SVMParameter();
param.Type = SVMType.C_SVC;
if (parameter["kernel_svm"] == "RBF")
{
param.Kernel = SVMKernelType.RBF;
}
if (parameter["kernel_svm"] == "Linear")
{
param.Kernel = SVMKernelType.LINEAR;
}
if (parameter["kernel_svm"] == "Poly")
{
param.Kernel = SVMKernelType.POLY;
}
if (parameter["kernel_svm"] == "Sigmoid")
{
param.Kernel = SVMKernelType.SIGMOID;
}
// param.C = Convert.ToDouble(parameter["c"]);
param.C = double.Parse(parameter["c"], CultureInfo.InvariantCulture);
param.P = double.Parse(parameter["p"], CultureInfo.InvariantCulture);
param.Gamma = double.Parse(parameter["gamma"], CultureInfo.InvariantCulture);
param.Degree = Convert.ToInt16(parameter["degree"]);
param.Nu = double.Parse(parameter["nu"], CultureInfo.InvariantCulture);
param.Coef0 = double.Parse(parameter["coef0"], CultureInfo.InvariantCulture);
param.Eps = double.Parse(parameter["eps"], CultureInfo.InvariantCulture);
//Train model
model = LibSVMsharp.SVM.Train(FileTrain, param);
LibSVMsharp.SVM.SaveModel(model, parameter["path_model"] + textBox3.Text + ".txt");
time.Stop();
double train_time = time.ElapsedMilliseconds;
#endregion
#region Validation data
SVMProblem Validation = SVMProblemHelper.Load(parameter["hog_val_file"]);
double[] Target_validation = Validation.Predict(model);
StreamWriter sw = new StreamWriter(parameter["result_file"], true, Encoding.UTF8);
for (int i = 0; i < Target_validation.Length; i++)
{
string lines = Target_validation[i].ToString();
sw.WriteLine(lines);
}
sw.Close();
Accuracy = SVMHelper.EvaluateClassificationProblem(Validation, Target_validation);
Accuracy = Math.Round(Accuracy, 3);
// show result training
textBox4.Text = (train_time / 1000).ToString();
textBox5.Text = Accuracy.ToString();
MessageBox.Show("Trainning sucessful");
#endregion
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
// 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 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();
}
static void Main(string[] args)
{
SVMProblem testSet = SVMProblemHelper.Load(@"Dataset\wine.txt"); // Same as the training set
SVMModel model = SVM.LoadModel(@"Model\wine_model.txt");
Console.WriteLine("Feature count in one instance: " + model.SV[0].Length + "\n\n");
// Test 1: Predict instances with SVMProblem's Predict extension method.
sw.Start();
double[] target = testSet.Predict(model);
sw.Stop();
double elapsedTimeInTest1 = (double)sw.ElapsedMilliseconds / (double)testSet.Length;
Console.WriteLine("> Test 1: \nPredict instances with SVMProblem's Predict extension method.\n");
Console.WriteLine("\tAverage elapsed time of one prediction: " + elapsedTimeInTest1 + " ms\n");
// Test 2: Predict instances with RapidPreditor class which is an explicit implementation of the method used in Test 1.
using (RapidPredictor predictor = new RapidPredictor(model)) // It needs to be Disposed
{
sw.Start();
target = new double[testSet.Length];
for (int i = 0; i < testSet.Length; i++)
{
target[i] = predictor.Predict(testSet.X[i]);
}
sw.Stop();
}
double elapsedTimeInTest2 = (double)sw.ElapsedMilliseconds / (double)testSet.Length;
Console.WriteLine("> Test 2: \nPredict instances with RapidPreditor class which is an explicit implementation of the method used in Test 1.\n");
Console.WriteLine("\tAverage elapsed time of one prediction: " + elapsedTimeInTest2 + " ms\n");
// Test 3: Predict instances with standard SVM.Predict method or SVMNode[]'s predict extension method.
sw.Start();
target = new double[testSet.Length];
for (int i = 0; i < testSet.Length; i++)
{
target[i] = SVM.Predict(model, testSet.X[i]);
}
sw.Stop();
double elapsedTimeInTest3 = (double)sw.ElapsedMilliseconds / (double)testSet.Length;
Console.WriteLine("> Test 3: \nPredict instances with standard SVM.Predict method or SVMNode[]'s Predict extension method.\n");
Console.WriteLine("\tAverage elapsed time of one prediction: " + elapsedTimeInTest3 + " ms\n");
// Print the results
Console.WriteLine("\nExplanation:\n");
Console.WriteLine(
"In standard SVM.Predict method, the SVMModel object is allocated and deallocated every time when the method called. " +
"Also the SVMNode[]'s Predict extension methods directly calls the SVM.Predict. " +
"However, the model is allocated once and is used to predict whole instances with its pointer in SVMProblem's " +
"Predict extension method as implemented in the RapidPredictor class. You can take or modify this class in order " +
"to use in your applications, if you have performance considerations. " +
"I am not suggesting that SVMProblem's Predict extension method is used in real-time, because the model is allocated" +
"in every method call.");
Console.WriteLine("\n\nPress any key to quit...");
Console.ReadLine();
}
public static bool trainProblem()
{
if (checkExistingDataset())
{
SVMProblem problem = SVMProblemHelper.Load(Constants.DATA_PATH);
SVMProblem randdata = SVMProblemHelper.Load(Constants.RAND_PATH);
List <string> resultsstring = new List <string>();
List <SVMClass.SVMResult> ResultsList = new List <SVMClass.SVMResult>();
double C, gammasq;
double Cmin = 1, Cmax = 10000, Cstep = 10;
double gmin = 0.0001, gmax = 1000, gstep = 10;
bool satisfied = false;
while (!satisfied)
{
for (C = Cmin; C <= Cmax; C = C * Cstep)
{
for (gammasq = gmin; gammasq <= gmax; gammasq = gammasq * gstep)
{
SVMParameter tempparameter = new SVMParameter();
tempparameter.Type = SVMType.C_SVC;
tempparameter.Kernel = SVMKernelType.RBF;
tempparameter.C = C;
tempparameter.Gamma = gammasq;
SVMModel tempmodel = SVM.Train(problem, tempparameter);
SVMProblem testData = SVMProblemHelper.Load(Constants.RAND_PATH);
double[] results = testData.Predict(tempmodel);
int[,] confusionMatrix;
double testAccuracy = testData.EvaluateClassificationProblem(results, tempmodel.Labels, out confusionMatrix);
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 10;
problem.CrossValidation(tempparameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = problem.EvaluateClassificationProblem(crossValidationResults);
SVMClass.SVMResult compiled = new SVMClass.SVMResult();
compiled.C = C;
compiled.gamma = gammasq;
compiled.testAcc = testAccuracy;
compiled.crossValidAcc = crossValidationAccuracy;
ResultsList.Add(compiled);
}
}
// Evaluate the test results
double maxTestAcc = ResultsList.Max(resultdata => resultdata.testAcc);
//int maxTestAccIndex = ResultsList.FindIndex(resultdata => resultdata.testAcc.Equals(maxTestAcc));
double maxValidAcc = ResultsList.Max(resultdata => resultdata.crossValidAcc);
//int maxValidAccIndex = ResultsList.FindIndex(resultdata => resultdata.crossValidAcc.Equals(maxValidAcc));
if (maxTestAcc < 95 || maxValidAcc < 95)
{
satisfied = false;
Cstep--;
gstep--;
}
else
{
satisfied = true;
List <SVMClass.SVMResult> topResults = ResultsList.FindAll(resultdata => resultdata.testAcc.Equals(maxTestAcc));
List <SVMClass.SVMResult> topValid = ResultsList.FindAll(resultdata => resultdata.crossValidAcc.Equals(maxValidAcc));
while (topResults.Count > topValid.Count)
{
topResults.RemoveAt(ResultsList.FindIndex(resultsdata => resultsdata.crossValidAcc.Equals(ResultsList.Min(resultdata => resultdata.crossValidAcc))));
}
double maxC = topResults.Max(resultdata => resultdata.C);
int maxCIndex = topResults.FindIndex(resultdata => resultdata.C.Equals(maxC));
double bestgamma = topResults[maxCIndex].gamma;
// maxC or not???
//double bestC = topResults[topResults.Count - 2].C; //topResults[maxCIndex].C;
//double bestgamma = topResults[topResults.Count - 2].gamma;//topResults[maxCIndex].gamma;
Console.WriteLine("Best C: " + maxC + " Best gammasq: " + bestgamma);
Constants.C = maxC;
Constants.gammasq = bestgamma;
foreach (SVMClass.SVMResult resultdata in topResults)
{
Console.WriteLine(resultdata.C.ToString() + " " + resultdata.gamma.ToString());
}
}
}
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = Constants.C;
parameter.Gamma = Constants.gammasq;
Variables.model = SVM.Train(problem, parameter);
//File.WriteAllText(Constants.MODEL_PATH, String.Empty);
//SVM.SaveModel(Variables.model, Constants.MODEL_PATH);
Console.WriteLine("Trained and saved model.\n");
//return Variables.model;
return(true);
}
else
{
MessageBox.Show("Invalid training data!");
return(false);
}
}
private void testSVM()
{
if (!holdCommandListener)
{
holdCommandListener = true;
}
string parentpath = System.AppDomain.CurrentDomain.BaseDirectory;
string DATA_PATH = parentpath + "Datasets\\dataset - Copy (2).txt";
string MODEL_PATH = parentpath + "Model\\testmodel.txt";
string NEWDATA_PATH = parentpath + "Datasets\\testdata.txt";
string RESULTS_PATH = parentpath + "Datasets\\results.txt";
List <string> resultsstring = new List <string>();
SVMProblem testSet = SVMProblemHelper.Load(NEWDATA_PATH);
SVMParameter testparameter = new SVMParameter();
testparameter.Type = SVMType.C_SVC;
testparameter.Kernel = SVMKernelType.RBF;
testparameter.C = 0.1; //Constants.C;
testparameter.Gamma = 0.001; // Constants.gammasq;
List <SVMClass.SVMResult> ResultsList = new List <SVMClass.SVMResult>();
SVMProblem problem = SVMProblemHelper.Load(DATA_PATH);
double C = 0.001;
double gammasq = 0.001;
for (C = 1; C <= 1000; C = C * 10)
{
for (gammasq = 0.001; gammasq <= 1000; gammasq = gammasq * 10)
{
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = C;
parameter.Gamma = gammasq;
SVMModel model = SVM.Train(problem, parameter);
//File.WriteAllText(MODEL_PATH, String.Empty);
//SVM.SaveModel(model, MODEL_PATH);
//Console.WriteLine("Trained and saved model.\n");
//model = SVM.LoadModel(MODEL_PATH);
SVMProblem newData = SVMProblemHelper.Load(NEWDATA_PATH);
//Console.Write("Predicted Result:\n");
double[] results = newData.Predict(model);
//Console.Write(results[0]);
int[,] confusionMatrix;
double testAccuracy = newData.EvaluateClassificationProblem(results, model.Labels, out confusionMatrix);
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 10;
problem.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
double crossValidationAccuracy = problem.EvaluateClassificationProblem(crossValidationResults);
//Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
string temp = "";
string resultstring = "Predict accuracy: " + testAccuracy + " C: " + C + " gamma: " + gammasq + " Cross validation accuracy: " + crossValidationAccuracy;
resultsstring.Add(resultstring);
if (parameter.C == testparameter.C && parameter.Gamma == testparameter.Gamma)
{
resultsstring.Add("This one is same as separate test.");
}
foreach (double res in results)
{
temp += res.ToString() + " ";
}
resultsstring.Add(temp);
SVMClass.SVMResult compiled = new SVMClass.SVMResult();
compiled.C = C;
compiled.gamma = gammasq;
compiled.testAcc = testAccuracy;
compiled.crossValidAcc = crossValidationAccuracy;
ResultsList.Add(compiled);
}
}
File.WriteAllLines(RESULTS_PATH, resultsstring);
SVMModel testmodel = SVM.Train(problem, testparameter);
// Predict the instances in the test set
double[] testResults = testSet.Predict(testmodel);
foreach (double result in testResults)
{
Console.WriteLine(result);
}
// Evaluate the test results
double maxTestAcc = ResultsList.Max(resultdata => resultdata.testAcc);
int maxTestAccIndex = ResultsList.FindIndex(resultdata => resultdata.testAcc.Equals(maxTestAcc));
//double maxValidAcc = ResultsList.Max(resultdata => resultdata.crossValidAcc);
//int maxValidAccIndex = ResultsList.FindIndex(resultdata => resultdata.crossValidAcc.Equals(maxValidAcc));
List <SVMClass.SVMResult> topResults = ResultsList.FindAll(resultdata => resultdata.testAcc.Equals(maxTestAcc));
double maxC = topResults.Max(resultdata => resultdata.C);
int maxCIndex = topResults.FindIndex(resultdata => resultdata.C.Equals(maxC));
double bestC = topResults[topResults.Count - 2].C; //topResults[maxCIndex].C;
double bestgamma = topResults[topResults.Count - 2].gamma; //topResults[maxCIndex].gamma;
Console.WriteLine("Best C: " + bestC + " Best gammasq: " + bestgamma);
foreach (SVMClass.SVMResult resultdata in topResults)
{
Console.WriteLine(resultdata.C.ToString() + " " + resultdata.gamma.ToString());
}
//int[,] confusionMatrix;
//double testAccuracy = testSet.EvaluateClassificationProblem(testResults, testmodel.Labels, out confusionMatrix);
//Console.WriteLine("\n\nTest accuracy: " + testAccuracy);
}
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();*/
}
private void button2_Click(object sender, EventArgs e)
{
SVMProblem trainingSet = new SVMProblem();
SVMProblem testSet = trainingSet;
foreach (DataInfo info in mList)
{
SVMNode[] node = new SVMNode[2];
node[0] = new SVMNode(1, info.X / mWidth);
node[1] = new SVMNode(2, info.Y / mHeight);
trainingSet.Add(node, info.Group);
}
// Normalize the datasets if you want: L2 Norm => x / ||x||
//trainingSet = trainingSet.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 = 4;
parameter.Coef0 = hScrollBar1.Value;
parameter.Degree = 3;
// 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, FILE_MODEL);
// Predict the instances in the test set
double[] testResults = testSet.Predict(model);
// 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();
}
Pen[] pen = new Pen[4];
pen[0] = new Pen(Color.Black, 1);
pen[1] = new Pen(Color.Red, 1);
pen[2] = new Pen(Color.LightGreen, 1);
pen[3] = new Pen(Color.Blue, 1);
Pen[] pen2 = new Pen[4];
pen2[0] = new Pen(Color.LightGray, 1);
pen2[1] = new Pen(Color.DarkRed, 1);
pen2[2] = new Pen(Color.DarkGreen, 1);
pen2[3] = new Pen(Color.DarkBlue, 1);
Bitmap canvas = new Bitmap(pictureBox1.ClientSize.Width, pictureBox1.ClientSize.Height);
using (Graphics g = Graphics.FromImage(canvas))
{
for (int i = 0; i < pictureBox1.ClientSize.Width; i++)
{
for (int j = 0; j < pictureBox1.ClientSize.Height; j++)
{
SVMNode[] node = new SVMNode[2];
node[0] = new SVMNode(1, (double)i / (double)mWidth);
node[1] = new SVMNode(2, (double)j / (double)mHeight);
double result = SVM.Predict(model, node);
g.DrawRectangle(pen2[(int)result], i, j, 1, 1);
}
}
foreach (DataInfo info in mList)
{
g.DrawEllipse(pen[(int)info.Group], (float)info.X - 5, (float)info.Y - 5, 5, 5);
}
}
Bitmap image = new Bitmap(pictureBox1.ClientSize.Width, pictureBox1.ClientSize.Height);
pictureBox1.BackgroundImage = canvas; // 設置為背景層
pictureBox1.Refresh();
pictureBox1.CreateGraphics().DrawImage(canvas, 0, 0);
}