public SonarDataTrainer(List <string[]> csvData)
{
Vector initWeights = new Vector(60);
perceptron = new Perceptron(initWeights, 0, 1);
sonarData = new List <Vector>();
sonarClassification = new List <bool>();
csvData.ForEach(dataset =>
{
double[] data = new double[60];
for (int i = 0; i < 60; i++)
{
data[i] = double.Parse(dataset[i]);
}
sonarData.Add(new Vector(data));
sonarClassification.Add(dataset[60] == "M");
});
}
public void Init(int seed, double[] initWeight, double initBias, double learningRate, List <Vector> positives, List <Vector> negatives)
{
this.Seed = seed;
this.Runs = 0;
this.InitWeight = initWeight;
this.InitBias = initBias;
this.LearningRate = learningRate;
this.Perceptron = new Perceptron(new Vector(initWeight), initBias, learningRate);
this.Datapoints = new List <PerceptronTrainerDatapoint>();
positives.ForEach(p => this.Datapoints.Add(new PerceptronTrainerDatapoint()
{
Datapoint = p, IsPositive = true
}));
negatives.ForEach(n => this.Datapoints.Add(new PerceptronTrainerDatapoint()
{
Datapoint = n, IsPositive = false
}));
Datapoints.Shuffle(seed);
this.BiasedLearning = false;
State = PerceptronTrainerState.Initialized;
}
