public SVM()
{
// CvSVM
// SVMを利用して2次元ベクトルの3クラス分類問題を解く
const int S = 1000;
const int SIZE = 400;
CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks);
// (1)画像領域の確保と初期化
using (IplImage img = new IplImage(SIZE, SIZE, BitDepth.U8, 3))
{
img.Zero();
// (2)学習データの生成
CvPoint[] pts = new CvPoint[S];
int[] res = new int[S];
for (int i = 0; i < S; i++)
{
pts[i].X = (int)(rng.RandInt() % SIZE);
pts[i].Y = (int)(rng.RandInt() % SIZE);
if (pts[i].Y > 50 * Math.Cos(pts[i].X * Cv.PI / 100) + 200)
{
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), new CvColor(255, 0, 0));
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), new CvColor(255, 0, 0));
res[i] = 1;
}
else
{
if (pts[i].X > 200)
{
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), new CvColor(0, 255, 0));
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), new CvColor(0, 255, 0));
res[i] = 2;
}
else
{
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), new CvColor(0, 0, 255));
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), new CvColor(0, 0, 255));
res[i] = 3;
}
}
}
// (3)学習データの表示
Cv.NamedWindow("SVM", WindowMode.AutoSize);
Cv.ShowImage("SVM", img);
Cv.WaitKey(0);
// (4)学習パラメータの生成
float[] data = new float[S * 2];
for (int i = 0; i < S; i++)
{
data[i * 2] = ((float)pts[i].X) / SIZE;
data[i * 2 + 1] = ((float)pts[i].Y) / SIZE;
}
// (5)SVMの学習
using (CvSVM svm = new CvSVM())
{
CvMat data_mat = new CvMat(S, 2, MatrixType.F32C1, data);
CvMat res_mat = new CvMat(S, 1, MatrixType.S32C1, res);
CvTermCriteria criteria = new CvTermCriteria(1000, float.Epsilon);
CvSVMParams param = new CvSVMParams(SVMType.CSvc, SVMKernelType.Rbf, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, null, criteria);
svm.Train(data_mat, res_mat, null, null, param);
// (6)学習結果の描画
for (int i = 0; i < SIZE; i++)
{
for (int j = 0; j < SIZE; j++)
{
float[] a = { (float)j / SIZE, (float)i / SIZE };
CvMat m = new CvMat(1, 2, MatrixType.F32C1, a);
float ret = svm.Predict(m);
CvColor color = new CvColor();
switch ((int)ret)
{
case 1:
color = new CvColor(100, 0, 0); break;
case 2:
color = new CvColor(0, 100, 0); break;
case 3:
color = new CvColor(0, 0, 100); break;
}
img[i, j] = color;
}
}
// (7)トレーニングデータの再描画
for (int i = 0; i < S; i++)
{
CvColor color = new CvColor();
switch (res[i])
{
case 1:
color = new CvColor(255, 0, 0); break;
case 2:
color = new CvColor(0, 255, 0); break;
case 3:
color = new CvColor(0, 0, 255); break;
}
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), color);
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), color);
}
// (8)サポートベクターの描画
int sv_num = svm.GetSupportVectorCount();
for (int i = 0; i < sv_num; i++)
{
var support = svm.GetSupportVector(i);
img.Circle(new CvPoint((int)(support[0] * SIZE), (int)(support[1] * SIZE)), 5, new CvColor(200, 200, 200));
}
// (9)画像の表示
Cv.NamedWindow("SVM", WindowMode.AutoSize);
Cv.ShowImage("SVM", img);
Cv.WaitKey(0);
Cv.DestroyWindow("SVM");
}
}
}
/// <summary>
/// SVM
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildSvmClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
//C_SVCのパラメータ
const float SvmC = 1000;
//RBFカーネルのパラメータ
const float SvmGamma = 0.1f;
CvMat data = null;
CvMat responses = null;
CvMat sampleIdx = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvSVM svm = new CvSVM();
CvTermCriteria criteria = new CvTermCriteria(100, 0.001);
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.2);
// Create or load Random Trees classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
svm.Load(filenameToLoad);
ntrainSamples = 0;
if (svm.GetSupportVectorCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// create classifier by using <data> and <responses>
Console.Write("Training the classifier ...");
// 2. create sample_idx
sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1);
{
CvMat mat;
Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples);
mat.Set(CvScalar.RealScalar(1));
Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll);
mat.SetZero();
}
// 3. train classifier
// 方法、カーネルにより使わないパラメータは0で良く、
// 重みについてもNULLで良い
svm.Train(data, responses, null, sampleIdx, new CvSVMParams(CvSVM.C_SVC, CvSVM.RBF, 0, SvmGamma, 0, SvmC, 0, 0, null, criteria));
Console.WriteLine();
}
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
double r;
CvMat sample;
Cv.GetRow(data, out sample, i);
r = svm.Predict(sample);
// compare results
Console.WriteLine(
"predict: {0}, responses: {1}, {2}",
(char)r,
(char)responses.DataArraySingle[i],
Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? "Good!" : "Bad!"
);
r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0;
if (i < ntrainSamples)
trainHr += r;
else
testHr += r;
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Gamma={0:F5}, C={1:F5}", SvmGamma, SvmC);
if (filenameToLoad != null)
{
Console.WriteLine("Recognition rate: test = {0:F1}%", testHr * 100.0);
}
else
{
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
}
Console.WriteLine("Number of Support Vector: {0}", svm.GetSupportVectorCount());
// Save SVM classifier to file if needed
if (filenameToSave != null)
{
svm.Save(filenameToSave);
}
Console.Read();
if (sampleIdx != null) sampleIdx.Dispose();
data.Dispose();
responses.Dispose();
svm.Dispose();
}