public BatchTrainer()
{
BatchTrainerHelper.GetTypes <TScalar, TTrainer>(out _,
out var svmTrainerType,
out var svmKernelType,
out var sampleType);
this._Parameter = new KernelBaseParameter(svmKernelType, sampleType, 0, 0);
this._SvmTrainerType = svmTrainerType;
this._Bridge = CreateBridge(this._Parameter, this._SvmTrainerType);
var error = NativeMethods.batch_trainer_new(this._Parameter.KernelType.ToNativeKernelType(),
this._Parameter.SampleType.ToNativeMatrixElementType(),
svmTrainerType,
out var ret);
switch (error)
{
case NativeMethods.ErrorType.MatrixElementTypeNotSupport:
throw new ArgumentException($"{sampleType} is not supported.");
case NativeMethods.ErrorType.SvmBatchTrainerNotSupport:
throw new ArgumentException($"{svmTrainerType} is not supported.");
case NativeMethods.ErrorType.SvmKernelNotSupport:
throw new ArgumentException($"{svmKernelType} is not supported.");
}
this.NativePtr = ret;
}
internal BatchTrainer(IntPtr ptr,
bool isEnabledDispose = true) :
base(isEnabledDispose)
{
BatchTrainerHelper.GetTypes <TScalar, TTrainer>(out _,
out var svmTrainerType,
out var svmKernelType,
out var sampleType);
this._Parameter = new KernelBaseParameter(svmKernelType, sampleType, 0, 0);
this._SvmTrainerType = svmTrainerType;
this._Bridge = CreateBridge(this._Parameter, this._SvmTrainerType);
this.NativePtr = ptr;
}
public BatchTrainer(TTrainer trainer,
TScalar minLearningRate,
bool verbose,
bool useCache,
int cacheSize)
{
trainer.ThrowIfDisposed();
BatchTrainerHelper.GetTypes <TScalar, TTrainer>(out _,
out var svmTrainerType,
out var svmKernelType,
out var sampleType);
this._Parameter = new KernelBaseParameter(svmKernelType, sampleType, 0, 0);
this._SvmTrainerType = svmTrainerType;
this._Bridge = CreateBridge(this._Parameter, svmTrainerType);
this.NativePtr = this._Bridge.Create(trainer.NativePtr, minLearningRate, verbose, useCache, cacheSize);
}
public override IntPtr Operator(TTrainer trainer, float minLearningRate, int cacheSize)
{
BatchTrainerHelper.GetTypes <TScalar, TTrainer>(out _,
out var svmTrainerType,
out var svmKernelType,
out var sampleType);
if (!(minLearningRate > 0))
{
throw new ArgumentOutOfRangeException($"{nameof(minLearningRate)} must be greater than 0.");
}
if (!(cacheSize > 0))
{
throw new ArgumentOutOfRangeException($"{nameof(cacheSize)} must be greater than 0.");
}
var error = NativeMethods.verbose_batch_cached_float(svmKernelType.ToNativeKernelType(),
sampleType.ToNativeMatrixElementType(),
svmTrainerType,
trainer.NativePtr,
minLearningRate,
cacheSize,
out var ret);
switch (error)
{
case NativeMethods.ErrorType.MatrixElementTypeNotSupport:
throw new ArgumentException($"{sampleType} is not supported.");
case NativeMethods.ErrorType.SvmBatchTrainerNotSupport:
throw new ArgumentException($"{svmTrainerType} is not supported.");
case NativeMethods.ErrorType.SvmKernelNotSupport:
throw new ArgumentException($"{svmKernelType} is not supported.");
}
return(ret);
}
public static IntPtr Operator(TTrainer trainer, TScalar minLearningRate, int cacheSize)
{
BatchTrainerHelper.GetTypes <TScalar, TTrainer>(out _,
out _,
out _,
out var sampleType);
Bridge <TScalar> bridge;
switch (sampleType)
{
case MatrixElementTypes.Float:
bridge = new FloatBridge() as Bridge <TScalar>;
break;
case MatrixElementTypes.Double:
bridge = new DoubleBridge() as Bridge <TScalar>;
break;
default:
throw new NotSupportedException();
}
return(bridge.Operator(trainer, minLearningRate, cacheSize));
}