static void Main(string[] args)
{
// SOME BASIC EXAMPLES OF USING THIS LIBRARY
// begin of data loading
Datarow[] testdata = Datarow.GetDatarowsFromCSV(@"C:\Users\Marián Trpkoš\source\repos\DecisionTreeClassifier\DecisionTreeClassifierV2\testdata.csv");
Datarow[] training = Datarow.GetDatarowsFromStringArray(new string[]
{
"Green;3;Apple",
"Yellow;3;Apple",
"Red;1;Grape",
"Red;1;Grape",
"Yellow;3;Lemon",
});
// end of data loading
foreach (object val in DecisionTree.FindUniqueValues(testdata))
{
Console.WriteLine(val.ToString()); // finding unique values in last column
}
Console.WriteLine("----------------");
foreach (Tuple <object, int> tuple in DecisionTree.GetClassCounts(testdata))
{
Console.WriteLine($"{tuple.Item1.ToString()}: {tuple.Item2}"); // getting counts of unique values
}
Console.WriteLine("----------------");
Tuple <Datarow[], Datarow[]> truefalse_rows = DecisionTree.Partition(testdata, new Question(1, testdata[1].values[1])); // partition data with one basic question - Is <something> orange?
Console.WriteLine("TRUE: ");
foreach (Datarow row in truefalse_rows.Item1)
{
Console.WriteLine(row.ToString()); // printing all true rows
}
Console.WriteLine("FALSE: ");
foreach (Datarow row in truefalse_rows.Item2)
{
Console.WriteLine(row.ToString()); // printing all false rows
}
Console.WriteLine("----------------");
Datarow[] lots_of_mixing = Datarow.GetDatarowsFromStringArray(new string[] { "Apple", "Orange", "Grape", "Grapefruit", "Blueberry" });
Console.WriteLine(DecisionTree.Gini(lots_of_mixing)); // just a basic test of gini - value which tells how many different kinds of things there are (uncertainty)
Console.WriteLine("----------------");
double uncertainty = DecisionTree.Gini(training); // getting uncertainty for training dataset
Console.WriteLine(uncertainty); // pritning this uncertainty
Tuple <Datarow[], Datarow[]> truefalse_training = DecisionTree.Partition(training, new Question(0, "Red")); // splitting data with question Is <something> red?
Console.WriteLine(DecisionTree.InfoGain(truefalse_training.Item1, truefalse_training.Item2, uncertainty)); // getting info (numeric value) how efficient this question is (higher = better)
Console.WriteLine("----------------");
var bestdata = DecisionTree.FindBestQuestion(training); // finding best question to ask for training data
Console.WriteLine(bestdata.Item1); // printing gain
Console.WriteLine(bestdata.Item2.ToString()); // printing question
Console.WriteLine("----------------");
DecisionTree trainingDataTree = new DecisionTree(training); // creating decision tree classifier with training dataset
trainingDataTree.BuildTree(); // building tree
trainingDataTree.PrintTree(); // tree visualized
Console.WriteLine(); // spacing lel
// classifying data
Console.WriteLine((trainingDataTree.Classify(new Datarow("Yellow;5")).ToString())); // should output: can be both apple and lemon
Console.WriteLine((trainingDataTree.Classify(new Datarow("Red;2")).ToString())); // should output: can be only grape
Console.WriteLine((trainingDataTree.Classify(new Datarow("Green;10")).ToString())); // should ouput: can be only apple
// --> save tree to .dat file trainingDataTree.SaveToDat(path);
// --> load tree from .dat file DecisionTree trainingDataTree = DecisionTree.ReadFromDat(path);
Console.ReadKey();
}