public static extern ErrorType batch_cached_double(SvmKernelType kernelType,
MatrixElementType type,
SvmBatchTrainerType trainerType,
IntPtr trainer,
double minLearningRate,
int cacheSize,
out IntPtr ret);
public static extern ErrorType verbose_batch_cached_float(SvmKernelType kernelType,
MatrixElementType type,
SvmBatchTrainerType trainerType,
IntPtr trainer,
float minLearningRate,
int cacheSize,
out IntPtr ret);
public static extern ErrorType batch_trainer_train(SvmKernelType kernel_type,
MatrixElementType type,
SvmBatchTrainerType trainer_type,
IntPtr trainer,
IntPtr x,
IntPtr y,
out IntPtr ret);
internal static NativeMethods.SvmKernelType ToNativeKernelType(this SvmKernelType kernelType)
{
switch (kernelType)
{
case SvmKernelType.HistogramIntersection:
return(NativeMethods.SvmKernelType.Histogramintersection);
case SvmKernelType.Linear:
return(NativeMethods.SvmKernelType.Linear);
case SvmKernelType.Offset:
return(NativeMethods.SvmKernelType.Offset);
case SvmKernelType.Polynomial:
return(NativeMethods.SvmKernelType.Polynomial);
case SvmKernelType.RadialBasis:
return(NativeMethods.SvmKernelType.RadialBasis);
case SvmKernelType.Sigmoid:
return(NativeMethods.SvmKernelType.Sigmoid);
default:
throw new ArgumentOutOfRangeException(nameof(kernelType), kernelType, null);
}
}
public static extern ErrorType kcentroid_operator_float(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr obj,
IntPtr sample,
out float ret);
public static extern ErrorType kkmeans_operator(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kkmeans,
IntPtr sample,
out uint ret);
public static void GetTypes <TScalar, TTrainer>(out Type trainerType,
out NativeMethods.SvmBatchTrainerType svmTrainerType,
out SvmKernelType svmKernelType,
out MatrixElementTypes sampleType)
where TScalar : struct
where TTrainer : Trainer <TScalar>
{
trainerType = typeof(TTrainer);
var svmTrainer = trainerType.GetGenericTypeDefinition();
if (!BatchTrainerTypesRepository.Types.TryGetValue(svmTrainer, out svmTrainerType))
{
throw new ArgumentException();
}
var kernelType = trainerType.GenericTypeArguments[1].GetGenericTypeDefinition();
if (!KernelTypesRepository.KernelTypes.TryGetValue(kernelType, out svmKernelType))
{
throw new ArgumentException();
}
var elementType = trainerType.GenericTypeArguments[0];
if (!KernelTypesRepository.ElementTypes.TryGetValue(elementType, out sampleType))
{
throw new ArgumentException();
}
}
public static extern ErrorType krls_train_double(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr obj,
IntPtr x,
double y);
public static extern ErrorType deserialize_krls(byte[] filName,
SvmKernelType kernelType,
MatrixElementType matrixElementType,
int templateRows,
int templateColumns,
IntPtr obj,
out IntPtr errorMessage);
public static extern ErrorType normalized_function_set_function(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
SvmFunctionType function_type,
IntPtr function,
IntPtr sub_function);
public static extern ErrorType kkmeans_get_kcentroid(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kkmeans,
uint i,
out IntPtr kcentroid);
public static extern ErrorType decision_function_operator_float(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr function,
IntPtr sample,
out float ret);
public static extern ErrorType krls_operator_double(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr obj,
IntPtr sample,
out double ret);
public static extern ErrorType normalized_function_get_normalizer(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
SvmFunctionType function_type,
IntPtr function,
out IntPtr ret);
public static extern ErrorType deserialize_decision_function(byte[] filName,
SvmKernelType kernelType,
MatrixElementType matrixElementType,
int templateRows,
int templateColumns,
out IntPtr function,
out IntPtr errorMessage);
public static extern ErrorType svm_pegasos_new2_double(SvmKernelType kernelType,
MatrixElementType type,
IntPtr kernel,
double lambda,
double tolerance,
uint max_num_sv,
out IntPtr ret);
public static TKernel Create <TKernel, TScalar, TSample>(IntPtr ptr,
SvmKernelType kernelType,
int templateRow,
int templateColumn,
bool isEnabledDispose = true)
where TKernel : KernelBase
where TScalar : struct
where TSample : Matrix <TScalar>, new()
{
switch (kernelType)
{
case SvmKernelType.HistogramIntersection:
return(new HistogramIntersectionKernel <TScalar, TSample>(ptr, templateRow, templateColumn, isEnabledDispose) as TKernel);
case SvmKernelType.Linear:
return(new LinearKernel <TScalar, TSample>(ptr, templateRow, templateColumn, isEnabledDispose) as TKernel);
case SvmKernelType.Polynomial:
return(new PolynomialKernel <TScalar, TSample>(ptr, templateRow, templateColumn, isEnabledDispose) as TKernel);
case SvmKernelType.RadialBasis:
return(new RadialBasisKernel <TScalar, TSample>(ptr, templateRow, templateColumn, isEnabledDispose) as TKernel);
case SvmKernelType.Sigmoid:
return(new SigmoidKernel <TScalar, TSample>(ptr, templateRow, templateColumn, isEnabledDispose) as TKernel);
default:
throw new ArgumentOutOfRangeException(nameof(kernelType), kernelType, null);
}
}
public KernelBaseParameter(SvmKernelType kernelType, MatrixElementTypes sampleType, int templateRows, int templateColumns)
{
this.KernelType = kernelType;
this.SampleType = sampleType;
this.TemplateRows = templateRows;
this.TemplateColumns = templateColumns;
}
public static extern ErrorType rank_features(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kcentroid,
IntPtr samples,
IntPtr labels,
out IntPtr ret);
public static extern ErrorType krls_new(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kernel,
double tolerance,
uint maxDictionarySize,
out IntPtr ret);
public static extern ErrorType spectral_cluster(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kernel,
IntPtr samples,
uint numClusters,
out IntPtr ret);
public static extern ErrorType kkmeans_train(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kkmeans,
IntPtr samples,
IntPtr centers,
uint max_iter);
public static extern ErrorType serialize_krls(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr obj,
byte[] fileName,
int fileNameLength,
out IntPtr errorMessage);
public static extern ErrorType serialize_probabilistic_decision_function(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr function,
byte[] fileName,
int fileNameLength,
out IntPtr errorMessage);
public static extern ErrorType normalized_function_operator_float(SvmKernelType kernel_type,
MatrixElementType type,
int templateRows,
int templateColumns,
SvmFunctionType function_type,
IntPtr function,
IntPtr sample,
out float ret);
public static extern ErrorType batch_trainer_new2_float(SvmKernelType kernelType,
MatrixElementType type,
SvmBatchTrainerType trainerType,
IntPtr trainer,
float minLearningRate,
bool verbose,
bool useCache,
int cacheSize,
out IntPtr ret);
public static extern ErrorType reduced2(SvmKernelType kernel_type,
MatrixElementType type,
int templateRows,
int templateColumns,
SvmTrainerType trainer_type,
IntPtr trainer,
uint num_bv,
double eps,
out IntPtr ret);
public static extern ErrorType kcentroid_new(SvmKernelType kernelType,
MatrixElementType type,
int templateRows,
int templateColumns,
IntPtr kernel,
double tolerance,
uint maxDictionarySize,
bool removeOldestFirst,
out IntPtr ret);
public static extern ErrorType normalized_function_serialize(SvmKernelType kernel_type,
MatrixElementType type,
int templateRows,
int templateColumns,
SvmFunctionType function_type,
IntPtr function,
byte[] file_name,
int file_name_length,
out IntPtr error_message);
public static extern ErrorType pick_initial_centers(SvmKernelType kernelType,
MatrixElementType elementType,
int templateRows,
int templateColumns,
long num_centers,
IntPtr centers,
IntPtr samples,
IntPtr k,
double percentile);
