static void spiral ()
{
Dataset train = Dataset.GenerateSpiral(0, 0, 1, 1, 0.1f, 1000, 10, 100);
train.WriteCSV("./data/spiral_train.csv", true);
Dataset test = Dataset.CloneInputSet(train);
DecisionForest df = new DecisionForest(train, 10, 30, 20);
for (int row = 0; row < test.Inputs.Count; row++)
{
List<float> outputs = new List<float>();
outputs.Add(df.Classify(test.Inputs[row]));
test.Outputs.Add(outputs);
}
test.WriteCSV("./data/spiral_test.csv", true);
}
static void ocr ()
{
OCRDataset train = new OCRDataset();
train.ReadTrainCSV("./data/ocr_train.csv");
OCRDataset test = new OCRDataset();
test.ReadTestCSV("./data/ocr_test.csv");
DecisionForest df = new DecisionForest(train, 20000, 1000, 10000);
for (int row = 0; row < test.Inputs.Count; row++)
{
Console.WriteLine("i: " + row + "/" + test.Inputs.Count);
List<float> outputs = new List<float>();
outputs.Add(df.Classify(test.Inputs[row]));
test.Outputs.Add(outputs);
}
test.WriteTestCSV("./data/ocr_est.csv");
}
static void fin()
{
Dataset train = new Dataset();
train.ReadCSV("./data/validate.win", 2000, false);
Dataset test = new Dataset();
test.Inputs.Add(train.Inputs[train.Inputs.Count - 1]);
DecisionForest df = new DecisionForest(train, 10, 500, 500);
for (int row = 0; row < test.Inputs.Count; row++)
{
Console.WriteLine("i: " + row + "/" + test.Inputs.Count);
List<float> outputs = new List<float>();
outputs.Add(df.Classify(test.Inputs[row]));
test.Outputs.Add(outputs);
}
test.WriteCSV("./data/est.win", false);
}
public static float Fitness (DecisionForest DecisionForest, Dataset Validation)
{
int correct = 0;
for (int row = 0; row < Validation.Inputs.Count; row++)
{
if (Validation.Outputs[row][0] == DecisionForest.Classify(Validation.Inputs[row]))
{
correct++;
}
}
return ((float)correct) / ((float)Validation.Inputs.Count);
}