/// <summary>
///
/// </summary>
/// <param name="times"></param>
/// <returns>The cost of each iteration</returns>
public List <BinaryClassificationTrainResult> Train(int times)
{
List <BinaryClassificationTrainResult> results = new List <BinaryClassificationTrainResult>();
//double[] costs = new double[times];
sampleCount = samples.Count;
x = new double[sampleCount, featureCount_plus_1];
y = new double[sampleCount];
Theta = MLMath.GetRandomVector(featureCount_plus_1, 0, 1);
for (int i = 0; i < sampleCount; i++)
{
x[i, 0] = 1;
for (int j = 0; j < featureCount_plus_1 - 1; j++)
{
x[i, j + 1] = samples[i].X[j];
}
y[i] = samples[i].Y;
}
for (int i = 0; i < times; i++)
{
Tuple <double, double[]> costFunction = CostFunction();
Theta = MLMath.Add(Theta, costFunction.Item2);
BinaryClassificationTrainResult result = new BinaryClassificationTrainResult();
result.Cost = costFunction.Item1;
result.Theta = Theta;
results.Add(result);
}
return(results);
}
public LogisticClassifier(int featureNumber, double alpha = 0.1, double lambda = 0)
{
this.Alpha = alpha;
this.Lambda = lambda;
featureCount_plus_1 = featureNumber + 1;
Theta = MLMath.GetRandomVector(featureCount_plus_1, 0, 1);
}
public void GetRandomVectorTest()
{
double[] r = MLMath.GetRandomVector(10, 0, 1);
foreach (double d in r)
{
Debug.Print(d.ToString());
}
Assert.AreNotEqual(r[0], r[1]);
Assert.AreNotEqual(r[2], r[3]);
Assert.AreNotEqual(r[3], r[1]);
Assert.AreNotEqual(r[5], r[1]);
}