public void TrainAndTest(IEnumerable <ItemRating> trainSet, IEnumerable <ItemRating> testSet)
{
var problem = new SvmProblem()
{
X = trainSet.Select(ir => FeatureBuilder.GetSvmNode(ir)).ToArray(),
Y = trainSet.Select(ir => LabelSelector(ir)).ToArray()
};
Parameters.Check(problem);
Console.WriteLine("Writing training samples...");
WriteSvmFile(problem, "train.libsvm");
Console.WriteLine("LibSvm training...");
LibSvm.SvmModel model = Svm.Train(problem, Parameters);
var predictedClasses = new List <float>();
Console.WriteLine("LibSvm testing...");
foreach (var ir in testSet)
{
ir.PredictedRating = (float)model.Predict(FeatureBuilder.GetSvmNode(ir));
predictedClasses.Add(ir.PredictedRating);
}
Console.WriteLine("Writing output...");
File.WriteAllLines("output.libsvm", predictedClasses.Select(i => i.ToString()));
}
public SvmTracker(SpellFigure figure) : base(0)
{
if (_svm == null)
{
_svm = new Svm();
}
_figure = figure;
}
public static void Run()
{
Console.WriteLine("OneClassDemo");
var trainData = DemoHelper.GenerateClass(0, 0.5, 0.5, 100);
var parameters = new SvmParameter
{
SvmType = SvmType.ONE_CLASS,
KernelType = KernelType.Rbf,
Gamma = 0.5,
Nu = 0.5,
CacheSize = 128,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => 1.0).ToArray(),
X = trainData.Select(p => p.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
var x = new Point(0.9, 0.9).ToSvmNodes();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.5, 0.5).ToSvmNodes();
var resy = model.Predict(y);
Console.WriteLine(resy);
var z = new Point(0.45, 0.45).ToSvmNodes();
var resz = model.Predict(z);
Console.WriteLine(resz);
}
public IEnumerable <Label> Label(double[] input)
{
var labels = Svm
.Classify(input)
.Select(c => new Label(Labels[c.Number], c.Score));
foreach (var filter in MangledName.Filters)
{
if (filter.IsPercent)
{
labels = labels.Where(l => l.Score >= filter.Value / 100d);
}
else
{
labels = labels.Take(filter.Value);
}
}
return(labels);
}
public static void Run()
{
Console.WriteLine("EpsSVRDemo");
var rnd = new Random();
var trainData = DemoHelper.Range(-10.0, 10.01, 0.1).Select(val => new { X = val, Y = DemoHelper.Sinc(val) + (rnd.NextDouble() - 0.5) / 4 });
var parameters = new SvmParameter
{
SvmType = SvmType.EPSILON_SVR,
KernelType = KernelType.Rbf,
Gamma = 0.5,
CacheSize = 128,
C = 1,
Eps = 1e-3,
P = 0.1,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => p.Y).ToArray(),
X = trainData.Select(p => p.X.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
foreach (var item in DemoHelper.Range(-1.0, 1.01, 0.1))
{
var x = item.ToSvmNodes();
var yPred = model.Predict(x);
var yReal = DemoHelper.Sinc(item);
Console.WriteLine("x: {0}", item);
Console.WriteLine("y_real: {0}", yReal);
Console.WriteLine("y_pred: {0}", yPred);
Console.WriteLine();
}
}
public static void Run()
{
Console.WriteLine("CSVMDemo");
var class1 = DemoHelper.GenerateClass(0, 0.1, 0.1, 50);
var class2 = DemoHelper.GenerateClass(1, 0.8, 0.8, 50);
var trainData = class1.Concat(class2);
var parameters = new SvmParameter
{
SvmType = SvmType.C_SVC,
KernelType = KernelType.Rbf,
Gamma = 0.5,
CacheSize = 128,
C = 1,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => (double)p.Label).ToArray(),
X = trainData.Select(p => p.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
var x = new Point(0.9, 0.9).ToSvmNodes();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.1, 0.1).ToSvmNodes();
var resy = model.Predict(y);
Console.WriteLine(resy);
}
private static void Main()
{
// ランダムにデータを作成
var r = new Random();
const int trainCount = 500;
const int testCount = 100;
var temp = Path.GetTempPath();
var trainDic = new StringBuilder();
var testDic = new StringBuilder();
var testDicAns = new List <int>();
for (var l = 0; l < 10; l++)
{
for (var i = 0; i < trainCount; i++)
{
// 0 以上 10未満の小数を生成
var v = r.NextDouble() + l;
trainDic.AppendLine($"{l} 1:{v}");
}
for (var i = 0; i < testCount; i++)
{
// 0 以上 10未満の小数を生成
var v = r.NextDouble() + l;
testDic.AppendLine($"{l} 1:{v}");
testDicAns.Add(l);
}
}
var tempTrainPath = Path.Combine(temp, "train");
var tempTestPath = Path.Combine(temp, "test");
using (var fs = new FileStream(tempTrainPath, FileMode.Create, FileAccess.Write, FileShare.Write))
using (var sw = new StreamWriter(fs, Encoding.ASCII))
sw.Write(trainDic.ToString());
using (var fs = new FileStream(tempTestPath, FileMode.Create, FileAccess.Write, FileShare.Write))
using (var sw = new StreamWriter(fs, Encoding.ASCII))
sw.Write(testDic.ToString());
// データセットの読み込み
var train = Svm.ReadProblem(tempTrainPath);
var test = Svm.ReadProblem(tempTestPath);
// パラメータの設定
var param = new Parameter();
param.SvmType = SvmType.C_SVC;
param.KernelType = KernelType.RBF;
param.Gamma = 0.05d;
param.C = 5d;
param.CacheSize = 100;
param.Degree = 3;
param.Coef0 = 0;
param.Nu = 0.5;
param.Eps = 1e-3;
param.p = 0.1;
param.Shrinking = 1;
param.Probability = 0;
param.WeightLabel = new int[0];
param.Weight = new double[0];
var message = Svm.CheckParameter(train, param);
if (!string.IsNullOrWhiteSpace(message))
{
Console.WriteLine($"Error: {message} for train problem");
return;
}
message = Svm.CheckParameter(test, param);
if (!string.IsNullOrWhiteSpace(message))
{
Console.WriteLine($"Error: {message} for test problem");
return;
}
// 学習データとパラメータから学習
var model = Svm.Train(train, param);
var correct = 0;
var total = 0;
for (var i = 0; i < test.Count; i++)
{
// 学習データのベクトル
var x = test.x[i];
// 分類結果 (ラベルが返ってくる)
var ret1 = (int)Svm.Predict(model, x);
if (ret1 == testDicAns[i])
{
correct++;
}
total++;
}
Console.WriteLine($"Accuracy: {correct / (double)total * 100}%");
}
