Accord.Math.Random.Generator.Seed = 0;
double[][] inputs =
{
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
};
int[] xor =
{
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
};
var gscv = GridSearch<double[], int>.CrossValidate(
ranges: new
{
Complexity = GridSearch.Values(0.00000001, 5.20, 0.30, 0.50),
Degree = GridSearch.Values(1, 10, 2, 3, 4, 5),
Constant = GridSearch.Values(0, 1, 2),
},
learner: (p, ss) => new SequentialMinimalOptimization<Polynomial>
{
Complexity = p.Complexity,
Kernel = new Polynomial(p.Degree, p.Constant)
},
fit: (teacher, x, y, w) => teacher.Learn(x, y, w),
loss: (actual, expected, r) => new ZeroOneLoss(expected).Loss(actual),
folds: 3
);
gscv.ParallelOptions.MaxDegreeOfParallelism = 1;
var result = gscv.Learn(inputs, xor);
var crossValidation = result.BestModel;
double bestError = result.BestModelError;
double trainError = result.BestModel.Training.Mean;
double trainErrorVar = result.BestModel.Training.Variance;
double valError = result.BestModel.Validation.Mean;
double valErrorVar = result.BestModel.Validation.Variance;
double bestC = result.BestParameters.Complexity;
double bestDegree = result.BestParameters.Degree;
double bestConstant = result.BestParameters.Constant;
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