public void Test_SVM_XOR_Classification()
{
var xor = new[]
{
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
};
var d = Descriptor.New("XOR")
.With("a").As(typeof(bool))
.With("b").As(typeof(bool))
.Learn("c").As(typeof(bool));
var generator = new numl.Supervised.SVM.SVMGenerator {
Descriptor = d, C = 0.1,
KernelFunction = new Math.Kernels.PolyKernel(3)
};
var model = Learner.Learn(xor, 1.0, 10, generator).Model;
Matrix x = new[, ]
{
{ -1, -1 }, // false, false -> -
{ -1, 1 }, // false, true -> +
{ 1, -1 }, // true, false -> +
{ 1, 1 }
}; // true, true -> -
Vector actual = new int[] { -1, 1, 1, -1 };
Vector y = new[] { 0, 0, 0, 0 };
for (int i = 0; i < x.Rows; i++)
{
y[i] = model.Predict(x[i, VectorType.Row]);
}
var score = numl.Supervised.Score.ScorePredictions(y, actual);
Console.WriteLine($"SVM Model\n: { score }");
}
public void Test_SVM_XOR_Classification()
{
var xor = new[]
{
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
};
var d = Descriptor.New("XOR")
.With("a").As(typeof(bool))
.With("b").As(typeof(bool))
.Learn("c").As(typeof(bool));
var generator = new numl.Supervised.SVM.SVMGenerator {
Descriptor = d, C = 0.1,
KernelFunction = new Math.Kernels.PolyKernel(3)
};
var model = Learner.Learn(xor, 1.0, 10, generator).Model;
Matrix x = new[,]
{{ -1, -1 }, // false, false -> -
{ -1, 1 }, // false, true -> +
{ 1, -1 }, // true, false -> +
{ 1, 1 }}; // true, true -> -
Vector actual = new int[] { -1, 1, 1, -1 };
Vector y = new[] { 0, 0, 0, 0 };
for (int i = 0; i < x.Rows; i++)
y[i] = model.Predict(x[i, VectorType.Row]);
var score = numl.Supervised.Score.ScorePredictions(y, actual);
Console.WriteLine($"SVM Model\n: { score }");
}
