public Data(List <Entry> r1, List <Entry> r2, Random r, int epochs, double learning_rate, double margin, double c, bool logistic_regression, double tradeoff)
{
C = c;
Tradeoff = tradeoff;
Training_Data = r1;
Test_Data = r2;
AccuracyWeightB = new Dictionary <int, AccuracyWB>();
perceptron = new Perceptron(Training_Data, Test_Data, learning_rate, margin, C, logistic_regression, Tradeoff, r);
Dictionary <int, double> w = new Dictionary <int, double>();
double b = (r.NextDouble() * (0.01 + 0.01) - 0.01);
WeightBias wb = new WeightBias(w, b, 0);
for (int i = 0; i < epochs; i++)
{
wb = perceptron.CalculateWB(wb);
AccuracyWeightB.Add(i + 1, new AccuracyWB(perceptron.GetAccuracy(Test_Data, wb), wb));
perceptron.ShuffleTraining_Data(r);
}
AccuracyWB bestAccuracy = AccuracyWeightB.OrderByDescending(x => x.Value.Accuracy).ThenByDescending(y => y.Key).Select(z => z.Value).First();
Train_Accuracy = perceptron.GetAccuracy(Training_Data, bestAccuracy.Weight_Bias); //Train Accuracy
Test_Accuracy = bestAccuracy.Accuracy; //Test Accuracy
BestWeightBias = bestAccuracy.Weight_Bias;
Learning_Rate = learning_rate;
}
public Data(List <Entry> train, List <Entry> test, int epochs, double learning_rate, Random r, bool DymanicLearningRate, double margin, bool Average, bool Aggressive,
double c, bool svm, double tradeoff, bool logistic_regression, int forestSize)
{
C = c;
SVM = svm;
Tradeoff = tradeoff;
Logistic_Regression = logistic_regression;
ForestSize = forestSize;
double[] w_average = new double[ForestSize];
double b_average;
WeightBias wb_average = null;
if (Average)
{
for (int i = 0; i < ForestSize; i++)
{
double randomNumber = (r.NextDouble() * (0.01 + 0.01) - 0.01);
w_average[i] = randomNumber;
}
b_average = (r.NextDouble() * (0.01 + 0.01) - 0.01);
wb_average = new WeightBias(w_average, b_average, 0);
}
data_1 = train;
data_2 = test;
AccuracyWeightB = new Dictionary <int, AccuracyWB>();
perceptron = new Perceptron(data_1, data_2, learning_rate, DymanicLearningRate, margin, wb_average, Aggressive, C, SVM, Tradeoff, Logistic_Regression, ForestSize);
double[] w = new double[ForestSize];
double b = (r.NextDouble() * (0.01 + 0.01) - 0.01);
for (int i = 0; i < ForestSize; i++)
{
double randomNumber = (r.NextDouble() * (0.01 + 0.01) - 0.01);
w[i] = randomNumber;
}
WeightBias wb = new WeightBias(w, b, 0);
for (int i = 0; i < epochs; i++)
{
wb = perceptron.CalculateWB(wb);
if (Average)
{
perceptron.WeightBias_Average.Updates = wb.Updates;
AccuracyWeightB.Add(i + 1, new AccuracyWB(perceptron.GetAccuracy(data_2, perceptron.WeightBias_Average), perceptron.WeightBias_Average));
}
else
{
AccuracyWeightB.Add(i + 1, new AccuracyWB(perceptron.GetAccuracy(data_2, wb), wb));
}
perceptron.ShuffleTraining_Data(r);
}
//foreach (var item in AccuracyWeightB)
//{
// Console.WriteLine(item.Value.Accuracy);
//}
AccuracyWB bestAccuracy = AccuracyWeightB.OrderByDescending(x => x.Value.Accuracy).ThenByDescending(y => y.Key).Select(z => z.Value).First();
Test_Accuracy = bestAccuracy.Accuracy;
BestWeightBias = bestAccuracy.Weight_Bias;
Learning_Rate = learning_rate;
//Console.WriteLine("\n" + Accuracy);
}
