internal void Register(GpuMatrix matrix)
{
if (_allocationLayer.Any())
{
var layer = _allocationLayer.Peek();
if (layer != null)
{
layer.Add(matrix);
}
}
}
public IVector FindDistances(IReadOnlyList <IVector> data, DistanceMetric distance)
{
Debug.Assert(IsValid && data.All(v => v.IsValid));
if (distance == DistanceMetric.Cosine)
{
var norm = DotProduct(this);
var dataNorm = data.Select(d => d.DotProduct(d)).ToList();
var ret = new float[data.Count];
for (var i = 0; i < data.Count; i++)
{
ret[i] = Convert.ToSingle(1d - DotProduct(data[i]) / Math.Sqrt(norm * dataNorm[i]));
}
return(new GpuVector(_cuda, data.Count, i => ret[i]));
}
else if (distance == DistanceMetric.Euclidean)
{
var ptrArray = data.Cast <GpuVector>().Select(d => d._data.DevicePointer).ToArray();
var ret = _cuda.MultiEuclideanDistance(_data, ptrArray, _size);
using (var matrix = new GpuMatrix(_cuda, _size, data.Count, ret)) {
using (var temp = matrix.ColumnSums())
return(temp.Sqrt());
}
}
else if (distance == DistanceMetric.Manhattan)
{
var ptrArray = data.Cast <GpuVector>().Select(d => d._data.DevicePointer).ToArray();
var ret = _cuda.MultiManhattanDistance(_data, ptrArray, _size);
using (var matrix = new GpuMatrix(_cuda, _size, data.Count, ret)) {
return(matrix.ColumnSums());
}
}
else
{
var distanceFunc = _GetDistanceFunc(distance);
var ret = new float[data.Count];
for (var i = 0; i < data.Count; i++)
{
ret[i] = distanceFunc(data[i]);
}
return(new GpuVector(_cuda, data.Count, i => ret[i]));
}
}
public IMatrix SoftmaxActivation()
{
Debug.Assert(IsValid);
var rowOutput = new List <GpuVector>();
for (var i = 0; i < _rows; i++)
{
using (var row = Row(i))
rowOutput.Add(row.Softmax() as GpuVector);
}
var ret = _cuda.Allocate(_rows * _columns);
for (var i = 0; i < _rows; i++)
{
using (var row = rowOutput[i])
ret.DeviceVariable.CopyToDevice(row.CudaDeviceVariable, 0, _columns * i * sizeof(float), _columns * sizeof(float));
}
using (var temp = new GpuMatrix(_cuda, _columns, _rows, ret))
return(temp.Transpose());
}
public IMatrix SoftmaxActivation()
{
Debug.Assert(IsValid);
var rowOutput = new List <GpuVector>();
for (var i = 0; i < _rows; i++)
{
using (var row = Row(i))
rowOutput.Add(row.Softmax() as GpuVector);
}
var ret = new CudaDeviceVariable <float>(_rows * _columns);
for (var i = 0; i < _rows; i++)
{
using (var row = rowOutput[i]) {
ret.CopyToDevice(row.CudaDeviceVariable, 0, _columns * i * sizeof(float), _columns * sizeof(float));
//CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle, _columns * sizeof(float), row.CudaDeviceVariable.DevicePointer, sizeof(float), ret.DevicePointer, _rows * sizeof(float));
}
}
//return new GpuMatrix(_cuda, _rows, _columns, ret);
using (var temp = new GpuMatrix(_cuda, _columns, _rows, ret))
return(temp.Transpose());
}
public void Add(GpuMatrix matrix)
{
_matrix.Add(matrix);
}
