public override void Train(IForecastingDataSets datasets)
{
Parameter svmpara = mParameter as Parameter;
OnStartRunning(new ComponentRunEventArgs(datasets));
double[] yvalue = null;
int maxIndex = 0;
Node[][] nodes = CreateNodes(datasets, out yvalue, out maxIndex);
Problem problem = new Problem(nodes.Length, yvalue, nodes, maxIndex);
mRange = Scaling.DetermineRange(problem);
problem = Scaling.Scale(mRange, problem);
TrainedModel = Training.Train(problem, svmpara as Parameter);
datasets.ForecastedData = new double[datasets.InputData.Length][];
for (int i = 0; i < datasets.InputData.Length; i++)
{
datasets.ForecastedData[i] = new double[1];
datasets.ForecastedData[i][0] = Forecast(datasets.InputData[i]);
OnRunningEpoch(new ComponentRunEpochEventArgs(i));
}
svmpara.Count = TrainedModel.SupportVectorCount;
svmpara.Percentage = TrainedModel.SupportVectorCount / (double)problem.Count;
OnFinishRunning(new ComponentRunEventArgs(datasets));
}
public void svmproject(Dictionary <int, Characteristic> Characteristics)
{
Dictionary <int, Characteristic> _characteristics = new Dictionary <int, Characteristic>();
_characteristics = Characteristics;
Model model;
RangeTransform range;
double C;
double gamma;
// default values
Parameter parameters = new Parameter();
//parameters.SvmType = SvmType.C_SVC;
//parameters.KernelType = KernelType.RBF;
//parameters.Degree = 3;
//parameters.Gamma = 0;
//parameters.Coefficient0 = 0;
//parameters.Nu = 0.5;
//parameters.CacheSize = 40;
//parameters.C = 1000;
//parameters.EPS = 1e-3;
//parameters.P = 0.1;
//parameters.Shrinking = true;
//parameters.WeightCount = 0;
//parameters.WeightLabels = new int[0];
//parameters.Weights = new double[0];
//parameters.C = 5;
//parameters.Gamma = 1;
string str = null;
for (int i = 1; i < _characteristics.Count(); i++)
{
if (_characteristics[i].IsQualifiedColony == false && _characteristics[i].IsInvalidColony == true)
{
str = str + "0";
str = str + " 1:" + _characteristics[i].Area.ToString() + " 2:" + _characteristics[i].MajToMinAxisRatio.ToString() +
" 3:" + _characteristics[i].CentreAcerageColor.R.ToString() + " 4:" + _characteristics[i].CentreAcerageColor.G.ToString()
+ " 5:" + _characteristics[i].CentreAcerageColor.B.ToString() + "\r\n";
}
if (_characteristics[i].IsQualifiedColony == true && _characteristics[i].IsInvalidColony == false)
{
str = str + "1";
str = str + " 1:" + _characteristics[i].Area.ToString() + " 2:" + _characteristics[i].MajToMinAxisRatio.ToString() +
" 3:" + _characteristics[i].CentreAcerageColor.R.ToString() + " 4:" + _characteristics[i].CentreAcerageColor.G.ToString()
+ " 5:" + _characteristics[i].CentreAcerageColor.B.ToString() + "\r\n";
}
}
if (str != null)
{
byte[] array = Encoding.ASCII.GetBytes(str);
MemoryStream stream = new MemoryStream(array); //convert stream 2 string
Problem train = new Problem();
train = Problem.Read(stream);
range = Scaling.DetermineRange(train);
train = Scaling.Scale(train, range);
//String outfile001="D:\\parameters.txt";
ParameterSelection.Grid(train, parameters, @"D:\\parameters.txt", out C, out gamma);
parameters.C = C;
parameters.Gamma = gamma;
model = Training.Train(train, parameters);
//MessageBox.Show("学习完毕");
//stream.Dispose();
stream.Close();
}
else
{
MessageBox.Show("无学习数据");
model = null;
range = null;
}
string str1 = null;
for (int i = 1; i < _characteristics.Count(); i++)
{
str1 = str1 + "0";
str1 = str1 + " 1:" + _characteristics[i].Area.ToString() + " 2:" + _characteristics[i].MajToMinAxisRatio.ToString() +
" 3:" + _characteristics[i].CentreAcerageColor.R.ToString() + " 4:" + _characteristics[i].CentreAcerageColor.G.ToString()
+ " 5:" + _characteristics[i].CentreAcerageColor.B.ToString() + "\r\n";
}
if (str1 != null)
{
byte[] array = Encoding.ASCII.GetBytes(str1);
MemoryStream stream = new MemoryStream(array); //convert stream 2 string
Problem pre = new Problem();
pre = Problem.Read(stream);
pre = Scaling.Scale(pre, range);
Prediction.Predict(pre, @"D:\result.txt", model, false);
MessageBox.Show("筛选完毕");
//stream.Dispose();
stream.Close();
}
else
{
MessageBox.Show("无筛选数据");
}
//svm_problem prob = new svm_problem();
//prob.l = point_list.Count;
//prob.y = new double[prob.l];
// if(param.svm_type == svm_parameter.EPSILON_SVR ||
// param.svm_type == svm_parameter.NU_SVR)
//{
// if(param.gamma == 0) param.gamma = 1;
// prob.x = new svm_node[prob.l][];
// for(int i=0;i<prob.l;i++)
// {
// point p = (point)point_list[i];
// prob.x[i][0] = new svm_node();
// prob.x[i][0].index = 1;
// prob.x[i][0].value_Renamed = p.x;
// prob.y[i] = p.y;
// }
// svm_model model = svm.svm_train(prob, param);
// svm_node[] x = new svm_node[1];
// x[0] = new svm_node();
// x[0].index = 1;
// int[] j = new int[XLEN];
//C = Convert.ToInt16(numericUpDown8.Value);
//gamma = Convert.ToInt16(numericUpDown9.Value);
//StudyAlgorithm study = new StudyAlgorithm();
//study.GetModel(AllColony, C, gamma, out model, out range);
//ScreenAlgorithm screenAlgorithm = new ScreenAlgorithm();
//screenAlgorithm.ScreenTheColony(CharacteristicsValue, model, range);
}