/// <summary>Generate model based on a set of examples.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X);
Vector w = Vector.Zeros(X.Cols);
Vector a = Vector.Zeros(X.Cols);
double wb = 0;
double ab = 0;
int n = 1;
if (Normalize)
X.Normalize(VectorType.Row);
// repeat 10 times for *convergence*
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < X.Rows; j++)
{
var x = X[j];
var yi = y[j];
// perceptron update
if (yi * (w.Dot(x) + wb) <= 0)
{
w = w + yi * x;
wb += yi;
a = (a + yi * x) + n;
ab += yi * n;
}
n += 1;
}
}
return new PerceptronModel
{
W = w - (a / n),
B = wb - (ab / n),
Normalized = Normalize,
Descriptor = Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties
};
}
public override IModel Generate(Matrix X, Vector Y)
{
Vector w = Vector.Zeros(X.Cols);
Vector a = Vector.Zeros(X.Cols);
double wb = 0;
double ab = 0;
int n = 1;
if (Normalize)
X.Normalize(VectorType.Row);
// repeat 10 times for *convergence*
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < X.Rows; j++)
{
var x = X[j];
var y = Y[j];
// perceptron update
if (y * (w.Dot(x) + wb) <= 0)
{
w = w + y * x;
wb += y;
a = (a + y * x) + n;
ab += y * n;
}
n += 1;
}
}
return new PerceptronModel
{
W = w - (a / n),
B = wb - (ab / n),
Normalized = Normalize,
Descriptor = Descriptor
};
}
