static public int [] IterativeLeastSquares(double[][] input1, int[] output1, string fName)
{
double[] labels = System.Array.ConvertAll <int, double>(output1, x => x);
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
// Gets or sets the tolerance value used to determine whether the algorithm has converged.
Tolerance = 1e-4, // Let's set some convergence parameters
MaxIterations = 10,
//MaxIterations = 100, // maximum number of iterations to perform
Regularization = 0
};
// Now, we can use the learner to finally estimate our model:
LogisticRegression regression = learner.Learn(input1, output1);
double [] coefficients = learner.Solution;
double[] scores = regression.Probability(input1);
regression.Save(fName.Replace(".csv", ".IRLS.save"), compression: SerializerCompression.None);
// Finally, if we would like to arrive at a conclusion regarding
// each sample, we can use the Decide method, which will transform
// the probabilities (from 0 to 1) into actual true/false values:
return(Funcs.Utility.BoolToInt(regression.Decide(input1)));
// mean(double(p == y)) * 100);
}
/// <summary>
/// Classify our data using Logistic Regression classifer and save the model.
/// </summary>
/// <param name="train_data">Frame objects that we will use to train classifers.</param>
/// <param name="test_data">Frame objects that we will use to test classifers.</param>
/// <param name="train_label">Labels of the train data.</param>
/// <param name="test_label">Labels of the test data.</param>
/// <param name="Classifier_Path">Path where we want to save the classifer on the disk.</param>
/// <param name="Classifier_Name">Name of the classifer we wnat to save.</param>
/// <returns></returns>
public void LogisticRegression(double[][] train_data, double[][] test_data, int[] train_label, int[] test_label, String Classifier_Path, String Classifier_Name)
{
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4,
MaxIterations = 100,
Regularization = 0
};
LogisticRegression regression = learner.Learn(train_data, train_label);
double ageOdds = regression.GetOddsRatio(0);
double smokeOdds = regression.GetOddsRatio(1);
double[] scores = regression.Probability(test_data);
//bool[] pre = regression.Decide(test_data);
var cm = GeneralConfusionMatrix.Estimate(regression, test_data, test_label);
double error = cm.Error;
Console.WriteLine(error);
regression.Save(Path.Combine(Classifier_Path, Classifier_Name));
}
public void Classification_Train(double[,] train_docrule, int[] label, string algorithm)
{
string classmodelpath;
int attrSize = eclatlitems.Count;
int attrSizeTest = eclatlitems.Count;
// Specify the input variables
DecisionVariable[] variables = new DecisionVariable[attrSize];
for (int i = 0; i < attrSize; i++)
{
variables[i] = new DecisionVariable((i + 1).ToString(), DecisionVariableKind.Discrete);
}
if (algorithm == "Tree")
{
classmodelpath = algorithm + ".model";
//RandomForest tree2 = new RandomForest(2, variables);
DecisionTree tree = new DecisionTree(variables, 2);
C45Learning teacher = new C45Learning(tree);
var model = teacher.Learn(train_docrule.ToJagged(), label);
//save model
teacher.Save(Path.Combine("", classmodelpath));
}
if (algorithm == "SVM")
{
classmodelpath = algorithm + ".model";
var learn = new SequentialMinimalOptimization()
{
UseComplexityHeuristic = true,
UseKernelEstimation = false
};
SupportVectorMachine teacher = learn.Learn(train_docrule.ToJagged(), label);
//save model
teacher.Save(Path.Combine("", classmodelpath));
}
if (algorithm == "Logistic")
{
classmodelpath = algorithm + ".model";
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 1, // maximum number of iterations to perform
Regularization = 0
};
LogisticRegression teacher = learner.Learn(train_docrule.ToJagged(), label);
teacher.Save(Path.Combine("", classmodelpath));
}
if (algorithm == "GA")
{
weights_ga_matlab();
}
}
