AveragedStochasticGradientDescentTKernel, TInput, TLoss Class

public sealed class AveragedStochasticGradientDescent<TKernel, TInput, TLoss> : BaseAveragedStochasticGradientDescent<SupportVectorMachine<TKernel, TInput>, TKernel, TInput, TLoss>
where TKernel : struct, new(), ILinear<TInput>
where TInput : IList, ICloneable
where TLoss : struct, new(), IDifferentiableLoss<bool, double, double>

Public NotInheritable Class AveragedStochasticGradientDescent(Of TKernel As {Structure, New, ILinear(Of TInput)}, TInput As {IList, ICloneable}, TLoss As {Structure, New, IDifferentiableLoss(Of Boolean, Double, Double)})
	Inherits BaseAveragedStochasticGradientDescent(Of SupportVectorMachine(Of TKernel, TInput), TKernel, TInput, TLoss)

// In this example, we will learn a multi-class SVM using the one-vs-one (OvO)
// approach. The OvO approacbh can decompose decision problems involving multiple 
// classes into a series of binary ones, which can then be solved using SVMs.

// Ensure we have reproducible results
Accord.Math.Random.Generator.Seed = 0;

// We will try to learn a classifier
// for the Fisher Iris Flower dataset
var iris = new Iris();
double[][] inputs = iris.Instances; // get the flower characteristics
int[] outputs = iris.ClassLabels;   // get the expected flower classes

// We will use mini-batches of size 32 to learn a SVM using SGD
var batches = MiniBatches.Create(batchSize: 32, maxIterations: 1000,
   shuffle: ShuffleMethod.EveryEpoch, input: inputs, output: outputs);

// Now, we can create a multi-class teaching algorithm for the SVMs
var teacher = new MulticlassSupportVectorLearning<Linear, double[]>
{
    // We will use SGD to learn each of the binary problems in the multi-class problem
    Learner = (p) => new AveragedStochasticGradientDescent<Linear, double[], LogisticLoss>()
    {
        LearningRate = 1e-3,
        MaxIterations = 1 // so the gradient is only updated once after each mini-batch
    }
};

// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
teacher.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)

// Now, we can start training the model on mini-batches:
foreach (var batch in batches)
{
    teacher.Learn(batch.Inputs, batch.Outputs);
}

// Get the final model:
var svm = teacher.Model;

// Now, we should be able to use the model to predict 
// the classes of all flowers in Fisher's Iris dataset:
int[] prediction = svm.Decide(inputs);

// And from those predictions, we can compute the model accuracy:
var cm = new GeneralConfusionMatrix(expected: outputs, predicted: prediction);
double accuracy = cm.Accuracy; // should be approximately 0.973

// In this example, we will learn a multi-class SVM using the one-vs-rest (OvR)
// approach. The OvR approacbh can decompose decision problems involving multiple 
// classes into a series of binary ones, which can then be solved using SVMs.

// Ensure we have reproducible results
Accord.Math.Random.Generator.Seed = 0;

// We will try to learn a classifier
// for the Fisher Iris Flower dataset
var iris = new Iris();
double[][] inputs = iris.Instances; // get the flower characteristics
int[] outputs = iris.ClassLabels;   // get the expected flower classes

// We will use mini-batches of size 32 to learn a SVM using SGD
var batches = MiniBatches.Create(batchSize: 32, maxIterations: 1000,
   shuffle: ShuffleMethod.EveryEpoch, input: inputs, output: outputs);

// Now, we can create a multi-label teaching algorithm for the SVMs
var teacher = new MultilabelSupportVectorLearning<Linear, double[]>
{
    // We will use SGD to learn each of the binary problems in the multi-class problem
    Learner = (p) => new AveragedStochasticGradientDescent<Linear, double[], LogisticLoss>()
    {
        LearningRate = 1e-3,
        MaxIterations = 1 // so the gradient is only updated once after each mini-batch
    }
};

// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
teacher.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)

// Now, we can start training the model on mini-batches:
foreach (var batch in batches)
{
    teacher.Learn(batch.Inputs, batch.Outputs);
}

// Get the final model:
var svm = teacher.Model;

// Now, we should be able to use the model to predict 
// the classes of all flowers in Fisher's Iris dataset:
int[] prediction = svm.ToMulticlass().Decide(inputs);

// And from those predictions, we can compute the model accuracy:
var cm = new GeneralConfusionMatrix(expected: outputs, predicted: prediction);
double accuracy = cm.Accuracy; // should be approximately 0.913

// In this example, we will show how its possible to learn a 
// non-linear SVM using a linear algorithm by using a explicit
// expansion of the kernel function:

// Ensure we have reproducible results
Accord.Math.Random.Generator.Seed = 0;

// We will try to learn a classifier
// for the Fisher Iris Flower dataset
var iris = new WisconsinDiagnosticBreastCancer();
double[][] inputs = iris.Features; // get the flower characteristics
int[] outputs = iris.ClassLabels;   // get the expected flower classes

// We will use mini-batches of size 32 to learn a SVM using SGD
var batches = MiniBatches.Create(batchSize: 32, maxIterations: 1000,
   shuffle: ShuffleMethod.EveryEpoch, input: inputs, output: outputs);

// We will use an explicit Polynomial kernel expansion
var polynomial = new Polynomial(2);

// Now, we can create a multi-class teaching algorithm for the SVMs
var teacher = new MulticlassSupportVectorLearning<Linear, double[]>
{
    // We will use SGD to learn each of the binary problems in the multi-class problem
    Learner = (p) => new AveragedStochasticGradientDescent<Linear, double[], LogisticLoss>()
    {
        LearningRate = 1e-3,
        MaxIterations = 1 // so the gradient is only updated once after each mini-batch
    }
};

// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
teacher.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)

// Now, we can start training the model on mini-batches:
foreach (var batch in batches)
{
    teacher.Learn(polynomial.Transform(batch.Inputs), batch.Outputs);
}

// Get the final model:
var svm = teacher.Model;

// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
svm.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)

// Now, we should be able to use the model to predict 
// the classes of all flowers in Fisher's Iris dataset:
int[] prediction = svm.Decide(polynomial.Transform(inputs));

// And from those predictions, we can compute the model accuracy:
var cm = new GeneralConfusionMatrix(expected: outputs, predicted: prediction);
double accuracy = cm.Accuracy; // should be approximately 0.92