public void Should_be_able_to_read_valid_dataset()
{
// Dataset extracted from first five rows of mpg.ds file.
var dataset = new List <List <double> >
{
new List <double> {
18, 8, 307.0, 130.0, 3504.0, 12.0, 70, 1
},
new List <double> {
15, 8, 350.0, 165.0, 3693.0, 11.5, 70, 1
},
new List <double> {
18, 8, 318.0, 150.0, 3436.0, 11.0, 70, 1
},
new List <double> {
16, 8, 304.0, 150.0, 3433.0, 12.0, 70, 1
},
new List <double> {
17, 8, 302.0, 140.0, 3449.0, 10.5, 70, 1
}
};
svm_problem prob = ProblemHelper.ReadAndScaleProblem(dataset);
// Data count
int dataCount = dataset.Count();
Assert.AreEqual(prob.l, dataCount, 0.01);
Assert.AreEqual(prob.x.Count(), dataCount, 0.01);
Assert.AreEqual(prob.y.Count(), dataCount, 0.01);
// Scale
Assert.AreEqual(prob.x.Max(v => v.Max(n => n.value)), 1.0, 0.1);
Assert.AreEqual(prob.x.Min(v => v.Max(n => n.value)), 0.0, 0.1);
}
static void Main(string[] args)
{
var path = Environment.CurrentDirectory;
string DvCPath = System.IO.Path.Combine(path, DvC_TEST_FILE);
string DvHPath = System.IO.Path.Combine(path, DvH_TEST_FILE);
string HvCPath = System.IO.Path.Combine(path, HvC_TEST_FILE);
DvC_prob = ProblemHelper.ReadAndScaleProblem(DvCPath);
DvH_prob = ProblemHelper.ReadAndScaleProblem(DvHPath);
HvC_prob = ProblemHelper.ReadAndScaleProblem(HvCPath);
var DvCsvm = new C_SVC(DvC_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
var DvHsvm = new C_SVC(DvH_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
var HvCsvm = new C_SVC(HvC_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
var DvCcva = DvCsvm.GetCrossValidationAccuracy(5);
var DvHcva = DvHsvm.GetCrossValidationAccuracy(2);
var HvCcva = HvCsvm.GetCrossValidationAccuracy(5);
DvCsvm.Export(System.IO.Path.Combine(path, DvC_MODEL_FILE));
DvHsvm.Export(System.IO.Path.Combine(path, DvH_MODEL_FILE));
HvCsvm.Export(System.IO.Path.Combine(path, HvC_MODEL_FILE));
Console.WriteLine(String.Format("--------------------------"));
Console.WriteLine(String.Format("DvC Result: {0}%", (Math.Round(DvCcva * 100, 2)).ToString()));
Console.WriteLine(String.Format("DvH Result: {0}%", (Math.Round(DvHcva * 100, 2)).ToString()));
Console.WriteLine(String.Format("HvC Result: {0}%", (Math.Round(HvCcva * 100, 2)).ToString()));
Console.WriteLine(String.Format("--------------------------"));
Console.ReadKey();
}
private void buttonSVM_Click(object sender, EventArgs e)
{
List <double> values = new List <double>();
foreach (var column in checkedListBoxVariableRellenar.SelectedItems)
{
for (int i = 0; i < dt.Rows.Count; i++)
{
values.Add(double.Parse(dt.Rows[i][column.ToString()].ToString()));
}
var dataTraining = ProblemHelper.ReadAndScaleProblem(new List <List <double> >()
{
values
});
var svm = new Epsilon_SVR(DataProblem, KernelHelper.RadialBasisFunctionKernel(Gamma), C, Elipson);
double mse = svm.GetMeanSquaredError();
var prediction = svm.Predict(dataTraining.x[0]);
}
// 1. primero se debe armar una subtabla con los atributos que se van a utilizar.
// que serian los que estan en el checkbox.
// 2. elegir la columna sobre la que se quiere rellenar valores
// 3. se quitan los registros que contengan datos faltantes de las variables predictoras, para este caso son los que tengan valor de -200
// 4. Aplicar el algoritmo de VSM
// 5. Generar Vista con valores resultado
// 6. Generar resumen de resultados: en tal fila, cambie tal por tal.
}
public void TestInitialize()
{
var path = Environment.CurrentDirectory;
var pos = path.IndexOf("libsvm.net");
var basePath = path.Substring(0, pos + 10);
training_prob = ProblemHelper.ReadAndScaleProblem(System.IO.Path.Combine(basePath, TRAINING_FILE));
test_prob = ProblemHelper.ReadAndScaleProblem(System.IO.Path.Combine(basePath, TEST_FILE));
}
/// <summary>
///Test pour ReadAndScaleProblem
///</summary>
//[TestMethod()]
public void ReadAndScaleProblemTest()
{
string full_path = System.IO.Path.Combine(base_path, TEST_FILE);
var prob = ProblemHelper.ReadAndScaleProblem(full_path);
Assert.IsNotNull(prob);
Assert.IsTrue(prob.x.Max(v => v.Max(n => n.value)) == 1.0);
Assert.IsTrue(prob.x.Min(v => v.Min(n => n.value)) == -1.0);
}
public static void LibSVM(List <string> inputData, List <string> testData)
{
var inputFilePath = @"D:\新西兰学习生活\大学上课\乐谱数据\input.txt";
var testFilePath = @"D:\新西兰学习生活\大学上课\乐谱数据\test.txt";
PrepareDataLibSvm(inputData, inputFilePath);
PrepareDataLibSvm(testData, testFilePath);
var _prob = ProblemHelper.ReadAndScaleProblem(inputFilePath);
var svm = new C_SVC(_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
}
public void TestInitialize()
{
var path = Environment.CurrentDirectory;
var pos = path.IndexOf("libsvm.net");
var basePath = path.Substring(0, pos + 10);
string fullPath = System.IO.Path.Combine(basePath, LEU_TEST_FILE);
// get data from file
// Note that you should always scale your data
_prob = ProblemHelper.ReadAndScaleProblem(fullPath);
}
public bool buildSVMCorpus(string filename)
{
string trainDataPath = filename + "SimpleScaledTrainSVM.txt";
if (File.Exists(trainDataPath))
{
_prob = ProblemHelper.ReadAndScaleProblem(trainDataPath);
svm = new C_SVC(_prob, KernelHelper.LinearKernel(), C);
fileExistance = true;
var reader = new StreamReader(File.OpenRead(filename + "MinMax.txt"));
string[] minMax = reader.ReadLine().Split(',');
scale.min = Convert.ToDouble(minMax[0]);
scale.max = Convert.ToDouble(minMax[1]);
}
return(fileExistance);
}
public void Should_be_able_to_detect_invalid_feature_count_in_dataset()
{
var dataset = new List <List <double> >
{
new List <double> {
18, 8, 307.0, 130.0, 3504.0, 12.0, 70, 1
},
new List <double> {
15, 8, 350.0, 165.0, 3693.0, 11.5, 70, 1
},
new List <double> {
18, 8, 318.0, 150.0, 11.0, 70, 1
}, // miss the 4th feature column.
new List <double> {
16, 8, 304.0, 150.0, 3433.0, 12.0, 70, 1
},
new List <double> {
17, 8, 302.0, 140.0, 3449.0, 10.5, 70, 1
}
};
ProblemHelper.ReadAndScaleProblem(dataset);
}
public void Should_be_able_to_detect_void_dataset()
{
ProblemHelper.ReadAndScaleProblem(new List <List <double> >());
}
public void Should_be_able_to_detect_null_dataset()
{
ProblemHelper.ReadAndScaleProblem((List <List <double> >)null);
}
