public void TestLibsvmClassify()
{
var advancedClassify = new AdvancedClassify();
var numericalset = advancedClassify.LoadNumerical();
var result = advancedClassify.ScaleData(numericalset);
var scaledSet = result.Item1;
var scalef = result.Item2;
var prob = new SVMProblem();
foreach (var matchRow in scaledSet)
{
prob.Add(matchRow.NumData.Select((v, i) => new SVMNode(i + 1, v)).ToArray(), matchRow.Match);
}
var param = new SVMParameter()
{
Kernel = SVMKernelType.RBF
};
var m = prob.Train(param);
m.SaveModel("trainModel");
Func <double[], SVMNode[]> makeInput = ma => scalef(ma).Select((v, i) => new SVMNode(i + 1, v)).ToArray();
var newrow = new[] { 28, -1, -1, 26, -1, 1, 2, 0.8 };//男士不想要小孩，而女士想要
TestOutput(m.Predict(makeInput(newrow)));
newrow = new[] { 28, -1, 1, 26, -1, 1, 2, 0.8 };//双方都想要小孩
TestOutput(m.Predict(makeInput(newrow)));
}
public void TestLoadNumerical()
{
var advancedClassify = new AdvancedClassify();
var numericalset = advancedClassify.LoadNumerical();
var dataRow = numericalset[0].Data;
_output.WriteLine(JsonConvert.SerializeObject(dataRow));
}
public void TestScaledLinearTrain()
{
var advancedClassify = new AdvancedClassify();
var numericalset = advancedClassify.LoadNumerical();
var result = advancedClassify.ScaleData(numericalset);
var scaledSet = result.Item1;
var scalef = result.Item2;
var avgs = advancedClassify.LinearTrain(scaledSet);
_output.WriteLine(JsonConvert.SerializeObject(numericalset[0].NumData));
_output.WriteLine(numericalset[0].Match.ToString());
_output.WriteLine(advancedClassify.DpClassify(scalef(numericalset[0].NumData), avgs).ToString());
_output.WriteLine(numericalset[11].Match.ToString());
_output.WriteLine(advancedClassify.DpClassify(scalef(numericalset[11].NumData), avgs).ToString());
}
public void TestNlClassifyMore()
{
var advancedClassify = new AdvancedClassify();
var numericalset = advancedClassify.LoadNumerical();
var result = advancedClassify.ScaleData(numericalset);
var scaledSet = result.Item1;
var scalef = result.Item2;
var ssoffset = advancedClassify.GetOffset(scaledSet);
TestOutput(numericalset[0].Match);
TestOutput(advancedClassify.NlClassify(scalef(numericalset[0].NumData), scaledSet, ssoffset));
TestOutput(numericalset[1].Match);
TestOutput(advancedClassify.NlClassify(scalef(numericalset[1].NumData), scaledSet, ssoffset));
TestOutput(numericalset[2].Match);
TestOutput(advancedClassify.NlClassify(scalef(numericalset[2].NumData), scaledSet, ssoffset));
var newrow = new[] { 28, -1, -1, 26, -1, 1, 2, 0.8 };//男士不想要小孩，而女士想要
TestOutput(advancedClassify.NlClassify(scalef(newrow), scaledSet, ssoffset));
newrow = new[] { 28, -1, 1, 26, -1, 1, 2, 0.8 };//双方都想要小孩
TestOutput(advancedClassify.NlClassify(scalef(newrow), scaledSet, ssoffset));
}