public void Regression_logistic_gd_check_accuracy_model_jason_input()
{
initData_Jason();
BuildLogisticSGD lm = new BuildLogisticSGD();
Dasmic.MLLib.Algorithms.Regression.ModelBase mb = (Dasmic.MLLib.Algorithms.Regression.ModelBase)
lm.BuildModel(_trainingData,
_attributeHeaders, _indexTargetAttribute);
double [] validateData = new double[2];
double crisp;
for (int idx = 0; idx < _trainingData[0].Length; idx++)
{
validateData[0] = _trainingData[0][idx];
validateData[1] = _trainingData[1][idx];
double value = mb.RunModelForSingleData(
validateData);
if (value < .5)
{
crisp = 0;
}
else
{
crisp = 1;
}
Assert.AreEqual(crisp, _trainingData[_indexTargetAttribute][idx]);
}
}
public void Regression_logistic_gd_check_model_jason_input()
{
initData_Jason();
BuildLogisticSGD lm = new BuildLogisticSGD();
Dasmic.MLLib.Algorithms.Regression.ModelBase mb = (Dasmic.MLLib.Algorithms.Regression.ModelBase)
lm.BuildModel(_trainingData,
_attributeHeaders, _indexTargetAttribute);
double[] validateData = { 1.465489372, 2.362125076 };
double value = mb.RunModelForSingleData(
validateData);
Assert.IsTrue(value > .13 && value < .15);
validateData[0] = 7.673756466;
validateData[1] = 3.508563011;
value = mb.RunModelForSingleData(
validateData);
Assert.IsTrue(value > .8 && value < .99);
}
