/*************************************************************************
Serializer: unserialization
-- ALGLIB --
Copyright 14.03.2011 by Bochkanov Sergey
*************************************************************************/
public static void mlpunserialize(alglib.serializer s,
multilayerperceptron network)
{
int i0 = 0;
int i1 = 0;
int i = 0;
int j = 0;
int k = 0;
int fkind = 0;
double threshold = 0;
double v0 = 0;
double v1 = 0;
int nin = 0;
int nout = 0;
bool issoftmax = new bool();
int[] layersizes = new int[0];
//
// check correctness of header
//
i0 = s.unserialize_int();
alglib.ap.assert(i0 == scodes.getmlpserializationcode(), "MLPUnserialize: stream header corrupted");
i1 = s.unserialize_int();
alglib.ap.assert(i1 == mlpfirstversion, "MLPUnserialize: stream header corrupted");
//
// Create network
//
issoftmax = s.unserialize_bool();
apserv.unserializeintegerarray(s, ref layersizes);
alglib.ap.assert((alglib.ap.len(layersizes) == 2 || alglib.ap.len(layersizes) == 3) || alglib.ap.len(layersizes) == 4, "MLPUnserialize: too many hidden layers!");
nin = layersizes[0];
nout = layersizes[alglib.ap.len(layersizes) - 1];
if (alglib.ap.len(layersizes) == 2)
{
if (issoftmax)
{
mlpcreatec0(layersizes[0], layersizes[1], network);
}
else
{
mlpcreate0(layersizes[0], layersizes[1], network);
}
}
if (alglib.ap.len(layersizes) == 3)
{
if (issoftmax)
{
mlpcreatec1(layersizes[0], layersizes[1], layersizes[2], network);
}
else
{
mlpcreate1(layersizes[0], layersizes[1], layersizes[2], network);
}
}
if (alglib.ap.len(layersizes) == 4)
{
if (issoftmax)
{
mlpcreatec2(layersizes[0], layersizes[1], layersizes[2], layersizes[3], network);
}
else
{
mlpcreate2(layersizes[0], layersizes[1], layersizes[2], layersizes[3], network);
}
}
//
// Load neurons and weights
//
for (i = 1; i <= alglib.ap.len(layersizes) - 1; i++)
{
for (j = 0; j <= layersizes[i] - 1; j++)
{
fkind = s.unserialize_int();
threshold = s.unserialize_double();
mlpsetneuroninfo(network, i, j, fkind, threshold);
for (k = 0; k <= layersizes[i - 1] - 1; k++)
{
v0 = s.unserialize_double();
mlpsetweight(network, i - 1, k, i, j, v0);
}
}
}
//
// Load standartizator
//
for (j = 0; j <= nin - 1; j++)
{
v0 = s.unserialize_double();
v1 = s.unserialize_double();
mlpsetinputscaling(network, j, v0, v1);
}
for (j = 0; j <= nout - 1; j++)
{
v0 = s.unserialize_double();
v1 = s.unserialize_double();
mlpsetoutputscaling(network, j, v0, v1);
}
}
