/// <summary>
/// Initializes a new instance of the <see cref="BaseSupportVectorLearning"/> class.
/// </summary>
///
/// <param name="machine">The machine to be learned.</param>
/// <param name="inputs">The input data.</param>
/// <param name="outputs">The corresponding output data.</param>
///
protected BaseSupportVectorLearning(SupportVectorMachine machine, double[][] inputs, int[] outputs)
{
// Initial argument checking
SupportVectorLearningHelper.CheckArgs(machine, inputs, outputs);
// Machine
this.machine = machine;
// Kernel (if applicable)
KernelSupportVectorMachine ksvm = machine as KernelSupportVectorMachine;
if (ksvm == null)
{
isLinear = true;
Linear linear = new Linear(0);
kernel = linear;
}
else
{
Linear linear = ksvm.Kernel as Linear;
isLinear = linear != null;
kernel = ksvm.Kernel;
}
// Learning data
this.inputs = inputs;
this.outputs = outputs;
}
//protected virtual TModel Create(int inputs, TKernel kernel)
//{
// return SupportVectorLearningHelper.Create<TModel, TInput, TKernel>(inputs, kernel);
//}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair.</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public TModel Learn(TInput[] x, double[] y, double[] weights = null)
{
if (Model == null)
{
Model = Create(SupportVectorLearningHelper.GetNumberOfInputs(x), kernel);
}
Model.Kernel = kernel;
// Initial argument checking
SupportVectorLearningHelper.CheckArgs(Model, x, y);
// Learning data
this.inputs = x;
this.outputs = y;
// Initialization heuristics
if (useComplexityHeuristic)
{
complexity = kernel.EstimateComplexity(inputs);
}
C = new double[inputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
C[i] = complexity;
}
if (sampleWeights != null)
{
for (int i = 0; i < C.Length; i++)
{
C[i] *= sampleWeights[i];
}
}
try
{
InnerRun();
}
finally
{
if (machine != null)
{
// TODO: This block is only necessary to offer compatibility
// to code written using previous versions of the framework,
// and should be removed after a few releases.
machine.SupportVectors = Model.SupportVectors;
machine.Weights = Model.Weights;
machine.Threshold = Model.Threshold;
machine.Kernel = Model.Kernel;
machine.IsProbabilistic = Model.IsProbabilistic;
}
}
return(Model);
}
/// <summary>
/// Initializes a new instance of a Sequential Minimal Optimization (SMO) algorithm.
/// </summary>
///
/// <param name="machine">A Support Vector Machine.</param>
/// <param name="inputs">The input data points as row vectors.</param>
/// <param name="outputs">The output label for each input point. Values must be either -1 or +1.</param>
///
public SequentialMinimalOptimization(SupportVectorMachine machine,
double[][] inputs, int[] outputs)
{
// Initial argument checking
SupportVectorLearningHelper.CheckArgs(machine, inputs, outputs);
// Machine
this.machine = machine;
// Kernel (if applicable)
KernelSupportVectorMachine ksvm = machine as KernelSupportVectorMachine;
if (ksvm == null)
{
isLinear = true;
Linear linear = new Linear();
kernel = linear;
}
else
{
Linear linear = ksvm.Kernel as Linear;
isLinear = linear != null;
kernel = ksvm.Kernel;
}
// Learning data
this.inputs = inputs;
this.outputs = outputs;
int samples = inputs.Length;
int dimension = inputs[0].Length;
// Lagrange multipliers
this.alpha = new double[inputs.Length];
if (isLinear) // Hyperplane weights
{
this.weights = new double[dimension];
}
// Error cache
this.errors = new double[samples];
// Kernel cache
this.cacheSize = samples;
// Index sets
activeExamples = new HashSet <int>();
nonBoundExamples = new HashSet <int>();
atBoundsExamples = new HashSet <int>();
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public TModel Learn(TInput[] x, bool[] y, double[] weights = null)
{
SupportVectorLearningHelper.CheckArgs(x, y);
if (machine == null)
{
this.machine = Create(SupportVectorLearningHelper.GetNumberOfInputs(kernel, x), Kernel);
}
InnerRun();
return(machine);
}
/// <summary>
/// Constructs a new Least Squares SVM (LS-SVM) learning algorithm.
/// </summary>
///
/// <param name="machine">A support vector machine.</param>
/// <param name="inputs">The input data points as row vectors.</param>
/// <param name="outputs">The output label for each input point. Values must be either -1 or +1.</param>
///
public LeastSquaresLearning(SupportVectorMachine machine, double[][] inputs, int[] outputs)
{
SupportVectorLearningHelper.CheckArgs(machine, inputs, outputs);
// Set the machine
this.machine = machine;
// Grab the machine kernel
KernelSupportVectorMachine ksvm = machine as KernelSupportVectorMachine;
this.kernel = (ksvm == null) ? new Linear() : ksvm.Kernel;
// Kernel cache
this.cacheSize = inputs.Length;
// Get learning data
this.inputs = inputs;
this.outputs = outputs;
this.ones = Matrix.Vector(outputs.Length, 1);
}
//protected virtual TModel Create(int inputs, TKernel kernel)
//{
// return SupportVectorLearningHelper.Create<TModel, TInput, TKernel>(inputs, kernel);
//}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair.</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public TModel Learn(TInput[] x, double[] y, double[] weights)
{
// Initial argument checking
SupportVectorLearningHelper.CheckArgs(machine, inputs, outputs);
if (machine == null)
{
int numberOfInputs = SupportVectorLearningHelper.GetNumberOfInputs(x);
this.machine = Create(numberOfInputs, Kernel);
}
// Learning data
this.inputs = x;
this.outputs = y;
// Initialization heuristics
if (useComplexityHeuristic)
{
complexity = kernel.EstimateComplexity(inputs);
}
C = new double[inputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
C[i] = complexity;
}
if (sampleWeights != null)
{
for (int i = 0; i < C.Length; i++)
{
C[i] *= sampleWeights[i];
}
}
InnerRun();
// Compute error if required.
return((TModel)machine);
}
//protected virtual TModel Create(int inputs, TKernel kernel)
//{
// return SupportVectorLearningHelper.Create<TModel, TInput, TKernel>(inputs, kernel);
//}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair.</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public override TModel Learn(TInput[] x, bool[] y, double[] weights = null)
{
bool initialized = false;
SupportVectorLearningHelper.CheckArgs(x, y);
if (kernel == null)
{
kernel = SupportVectorLearningHelper.CreateKernel <TKernel, TInput>(x);
initialized = true;
}
if (!initialized && useKernelEstimation)
{
kernel = SupportVectorLearningHelper.EstimateKernel(kernel, x);
}
if (Model == null)
{
Model = Create(SupportVectorLearningHelper.GetNumberOfInputs(kernel, x), kernel);
}
Model.Kernel = kernel;
// Initial argument checking
SupportVectorLearningHelper.CheckArgs(Model, x, y);
// Count class prevalence
int positives, negatives;
Classes.GetRatio(y, out positives, out negatives);
// If all examples are positive or negative, terminate
// learning early by directly setting the threshold.
try
{
if (positives == 0 || negatives == 0)
{
Model.SupportVectors = new TInput[0];
Model.Weights = new double[0];
Model.Threshold = (positives == 0) ? -1 : +1;
return(Model);
}
// Initialization heuristics
if (useComplexityHeuristic)
{
complexity = kernel.EstimateComplexity(x);
}
if (useClassLabelProportion)
{
WeightRatio = positives / (double)negatives;
}
// Create per sample complexity
Cpositive = complexity * positiveWeight;
Cnegative = complexity * negativeWeight;
Inputs = x;
C = new double[y.Length];
for (int i = 0; i < y.Length; i++)
{
C[i] = y[i] ? Cpositive : Cnegative;
}
Outputs = new int[y.Length];
for (int i = 0; i < y.Length; i++)
{
Outputs[i] = y[i] ? 1 : -1;
}
if (weights != null)
{
for (int i = 0; i < C.Length; i++)
{
C[i] *= weights[i];
}
}
InnerRun();
SupportVectorLearningHelper.CheckOutput(Model);
return(Model);
}
finally
{
if (machine != null)
{
// TODO: This block is only necessary to offer compatibility
// to code written using previous versions of the framework,
// and should be removed after a few releases.
machine.SupportVectors = Model.SupportVectors;
machine.Weights = Model.Weights;
machine.Threshold = Model.Threshold;
machine.Kernel = Model.Kernel;
machine.IsProbabilistic = Model.IsProbabilistic;
}
}
}
/// <summary>
/// Learns a model that can map the given inputs to the desired outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="weights">The weight of importance for each input sample.</param>
/// <returns>
/// A model that has learned how to produce suitable outputs
/// given the input data <paramref name="x" />.
/// </returns>
public TModel Learn(TInput[] x, double[] weights = null)
{
bool initialized = false;
SupportVectorLearningHelper.CheckArgs(x);
if (kernel == null)
{
kernel = SupportVectorLearningHelper.CreateKernel <TKernel, TInput>(x);
initialized = true;
}
if (!initialized && useKernelEstimation)
{
kernel = SupportVectorLearningHelper.EstimateKernel(kernel, x);
}
if (Model == null)
{
Model = Create(SupportVectorLearningHelper.GetNumberOfInputs(kernel, x), kernel);
}
Model.Kernel = kernel;
try
{
this.inputs = x;
double[] zeros = new double[inputs.Length];
int[] ones = Vector.Ones <int>(inputs.Length);
this.alpha = Vector.Ones <double>(inputs.Length);
int l = inputs.Length;
int n = (int)(nu * l); // # of alpha's at upper bound
for (int i = 0; i < n; i++)
{
alpha[i] = 1;
}
if (n < inputs.Length)
{
alpha[n] = nu * l - n;
}
for (int i = n + 1; i < l; i++)
{
alpha[i] = 0;
}
Func <int, int[], int, double[], double[]> Q = (int i, int[] indices, int length, double[] row) =>
{
for (int j = 0; j < length; j++)
{
row[j] = Kernel.Function(x[i], x[indices[j]]);
}
return(row);
};
var s = new FanChenLinQuadraticOptimization(alpha.Length, Q, zeros, ones)
{
Tolerance = eps,
Shrinking = this.shrinking,
Solution = alpha,
Token = Token
};
bool success = s.Minimize();
int sv = 0;
for (int i = 0; i < alpha.Length; i++)
{
if (alpha[i] > 0)
{
sv++;
}
}
Model.SupportVectors = new TInput[sv];
Model.Weights = new double[sv];
for (int i = 0, j = 0; i < alpha.Length; i++)
{
if (alpha[i] > 0)
{
Model.SupportVectors[j] = inputs[i];
Model.Weights[j] = alpha[i];
j++;
}
}
Model.Threshold = s.Rho;
if (success == false)
{
throw new ConvergenceException("Convergence could not be attained. " +
"Please reduce the cost of misclassification errors by reducing " +
"the complexity parameter C or try a different kernel function.");
}
}
finally
{
if (machine != null)
{
// TODO: This block is only necessary to offer compatibility
// to code written using previous versions of the framework,
// and should be removed after a few releases.
machine.SupportVectors = Model.SupportVectors;
machine.Weights = Model.Weights;
machine.Threshold = Model.Threshold;
machine.Kernel = Model.Kernel;
machine.IsProbabilistic = Model.IsProbabilistic;
}
}
return(Model);
}