//Coin SVM按鈕事件
private void button8_Click(object sender, EventArgs e)
{
StreamWriter Train_txt = new StreamWriter(@"train.txt");
StreamWriter Test_txt = new StreamWriter(@"test.txt");
for (int i = 0; i < 2000; i++)
{
if (CoinTrainingSet[i, 0] == 1)
{
Train_txt.WriteLine("1" + " 1:" + CoinTrainingSet[i, 1]);
}
else
{
Train_txt.WriteLine("-1" + " 1:" + CoinTrainingSet[i, 1]);
}
}
for (int i = 0; i < 20000; i++)
{
if (i < 10000)
{
Test_txt.WriteLine("1" + " 1:" + RV_XY[i]);
}
else
{
Test_txt.WriteLine("-1" + " 1:" + RV_XY[i]);
}
}
Train_txt.Close();
Test_txt.Close();
SVMProblem problem = SVMProblemHelper.Load(@"train.txt");
SVMProblem testProblem = SVMProblemHelper.Load(@"test.txt");
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 0.0001;
SVMModel model = SVM.Train(problem, parameter);
double[] target = new double[testProblem.Length];
for (int i = 0; i < testProblem.Length; i++)
{
target[i] = SVM.Predict(model, testProblem.X[i]);
}
double accuracy = SVMHelper.EvaluateClassificationProblem(testProblem, target);
label6.Text = accuracy.ToString();
}
//--------------------------------------------------------------------------------------
// private
//---------------------------------------------------------------------------------------
/// <summary>
/// 辞書ファイルを作成する
/// </summary>
/// <param name="input_learing_file"></param>
/// <param name="gammma"></param>
/// <param name="cost"></param>
private void Training(string input_learing_file, float gammma, float cost)
{
//LibSVMのテスト
//学習用のデータの読み込み
SVMProblem problem = SVMProblemHelper.Load(input_learing_file);
//SVMパラメータ
SVMParameter parameter = new SVMParameter();
parameter.Type = LibSVMsharp.SVMType.C_SVC;
parameter.Kernel = LibSVMsharp.SVMKernelType.RBF;
parameter.C = cost;
parameter.Gamma = gammma;
//svmModelが上手く作れていない？ラベルが付けられてない！！
libSVM_model = SVM.Train(problem, parameter);
//辞書ファイルを出力(xmlファイル)
string xml_name = @"model_" + input_learing_file;
xml_name = xml_name.Replace(@".csv", @".xml");
SVM.SaveModel(libSVM_model, xml_name);
//判定結果をファイルに出してみる
SVMProblem testProblem = SVMProblemHelper.Load(input_learing_file);
double[] target = new double[testProblem.Length];
string debug_file_str = @"debug_" + input_learing_file;
using (StreamWriter w = new StreamWriter(debug_file_str))
{
for (int i = 0; i < testProblem.Length; i++)
{
target[i] = SVM.Predict(libSVM_model, testProblem.X[i]);
w.Write(target[i] + "\n");
Console.Out.WriteLine(@"{0} : {1}", i, target[i]);
}
}
//正解率を出す。
double accuracy = SVMHelper.EvaluateClassificationProblem(testProblem, target);
}
static void Main(string[] args)
{
SVMProblem problem = SVMProblemHelper.Load(@"Datasets\wine.txt");
problem = SVMProblemHelper.Normalize(problem, SVMNormType.L2); // Optional
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
// Do 10-fold cross validation
double[] target;
SVM.CrossValidation(problem, parameter, 10, out target);
double crossValidationAccuracy = SVMHelper.EvaluateClassificationProblem(problem, target);
// Train the model
SVMModel model = SVM.Train(problem, parameter);
double correct = 0;
for (int i = 0; i < problem.Length; i++)
{
double y = SVM.Predict(model, problem.X[i]);
if (y == problem.Y[i])
{
correct++;
}
}
double trainingAccuracy = correct / (double)problem.Length;
Console.WriteLine("\nCross validation accuracy: " + crossValidationAccuracy);
Console.WriteLine("\nTraining accuracy: " + trainingAccuracy);
Console.ReadLine();
}
public static double EvaluateRegressionProblem(this SVMProblem testset, double[] target, out double correlation_coef)
{
return(SVMHelper.EvaluateRegressionProblem(testset, target, out correlation_coef));
}
public static double EvaluateClassificationProblem(this SVMProblem testset, double[] target, int[] labels, out int[,] confusionMatrix)
{
return(SVMHelper.EvaluateClassificationProblem(testset, target, labels, out confusionMatrix));
}
public static double EvaluateClassificationProblem(this SVMProblem testset, double[] target)
{
return(SVMHelper.EvaluateClassificationProblem(testset, target));
}
//学習ファイルの作成
public void TrainingExec(List <FaceFeature.FeatureValue> FeatureList)
{
//特徴量をMatに移し替える 2個で一つ
//2個のfloat * LISTの大きさの配列
double[] feature_array = new double[2 * FeatureList.Count];
//特徴量をSVMで扱えるように配列に置き換える
SetFeatureListToArray(FeatureList, ref feature_array);
CvPoint2D32f[] feature_points = new CvPoint2D32f[feature_array.Length / 2];
int id = 0;
for (int i = 0; i < feature_array.Length / 2; i++)
{
feature_points[id].X = (float)feature_array[i * 2];
feature_points[id].Y = (float)feature_array[i * 2 + 1];
id++;
}
CvMat dataMat = new CvMat(feature_points.Length, 2, MatrixType.F32C1, feature_points, true);
//これがラベル番号
int[] id_array = new int[FeatureList.Count];
for (int i = 0; i < id_array.Length; i++)
{
id_array[i] = FeatureList[i].ID;
}
CvMat resMat = new CvMat(id_array.Length, 1, MatrixType.S32C1, id_array, true);
// dataとresponsesの様子を描画
CvPoint2D32f[] points = new CvPoint2D32f[id_array.Length];
int idx = 0;
for (int i = 0; i < id_array.Length; i++)
{
points[idx].X = (float)feature_array[i * 2];
points[idx].Y = (float)feature_array[i * 2 + 1];
idx++;
}
//学習データを図にする
Debug_DrawInputFeature(points, id_array);
//デバッグ用 学習させる特徴量を出力する
OutPut_FeatureAndID(points, id_array);
//LibSVMのテスト
//学習用のデータの読み込み
SVMProblem problem = SVMProblemHelper.Load(@"wine.txt");
SVMProblem testProblem = SVMProblemHelper.Load(@"wine.txt");
SVMParameter parameter = new SVMParameter();
parameter.Type = LibSVMsharp.SVMType.C_SVC;
parameter.Kernel = LibSVMsharp.SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
SVMModel model = SVM.Train(problem, parameter);
double[] target = new double[testProblem.Length];
for (int i = 0; i < testProblem.Length; i++)
{
target[i] = SVM.Predict(model, testProblem.X[i]);
}
double accuracy = SVMHelper.EvaluateClassificationProblem(testProblem, target);
//SVMの用意
CvTermCriteria criteria = new CvTermCriteria(1000, 0.000001);
CvSVMParams param = new CvSVMParams(
OpenCvSharp.CPlusPlus.SVMType.CSvc,
OpenCvSharp.CPlusPlus.SVMKernelType.Rbf,
10.0, // degree
100.0, // gamma 調整
1.0, // coeff0
10.0, // c 調整
0.5, // nu
0.1, // p
null,
criteria);
//学習実行
svm.Train(dataMat, resMat, null, null, param);
Debug_DispPredict();
}
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);
}
}
private void btnSVM_Click(object sender, EventArgs e)
{
SVMProblem problem = SVMProblemHelper.Load(@"train.txt"); //訓練樣本
SVMProblem testProblem = SVMProblemHelper.Load(@"train.txt"); //測試樣本
Image <Bgr, Byte> img = new Image <Bgr, Byte>(testProblem.Length, 128); //建立圖片
img.SetValue(255); //設為全白
double res = 0.0;
//SVM的模板
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.LINEAR;
//SVM模型
SVMModel model = SVM.Train(problem, parameter);
double[] target = new double[testProblem.Length];
//上色
for (int i = img.Height - 1, k = 0; i > 0; --i)
{
for (int j = 0; j < img.Width; ++j)
{
if (k >= testProblem.Length)
{
break;
}
//SVM預測
res = SVM.Predict(model, testProblem.X[k]);
target[k] = res;
k++;
if (res == 1)
{
for (int count = 100; count > 0; count--)
{
img[i - count, j] = new Bgr(255, 0, 0);
}
}
else
if (res == 2)
{
for (int count = 100; count > 0; count--)
{
img[i - count, j] = new Bgr(0, 255, 0);
}
}
else
if (res == 3)
{
for (int count = 100; count > 0; count--)
{
img[i - count, j] = new Bgr(0, 0, 255);
}
}
else
if (res == 4)
{
for (int count = 100; count > 0; count--)
{
img[i - count, j] = new Bgr(0, 0, 0);
}
}
else
if (res == 0)
{
for (int count = 100; count > 0; count--)
{
img[i - count, j] = new Bgr(127, 127, 127);
}
}
}
}
//輸出圖片
ImageViewer output = new ImageViewer(img, "123");
output.Show();
//算出準確率
double accuracy = SVMHelper.EvaluateClassificationProblem(testProblem, target);
lblAccuracy.Text = "準確率： " + accuracy;
}
