public int Predict(ValueSet val, WriteToCMDLine write)
{
Stonk st = new Stonk();
StonkContext ctxt = new StonkContext(Datatype.AAPL);
var comps = Comparison.GenerateComparisons(val);
double[] Results = st.Forward(comps, ctxt);
Results = Network.Forward(Results);
return(Results.ToList().IndexOf(Results.Max()));
}
public double[] Forward(List <Comparison> vals, StonkContext ctxt, StonkMem sm)
{
Parallel.For(0, vals.Count(), j =>
{
sm.LocationOutputs[j].Add(vals[j].Values.Duplicate());
for (int i = 0; i < Network.Layers.Count(); i++)
{
sm.LocationOutputs[j].Add(Network.Layers[i].Output(sm.LocationOutputs[j].Last()));
}
ctxt.Contextualize(vals[j], j, sm);
});
return(sm.Multiply());
}
public void Propogate
(ValueSet val, WriteToCMDLine write)
{
Stonk stk = new Stonk();
StonkContext ctxt = new StonkContext(datatype);
var vals = val.ReadValues(Datatypes.Datatype.AAPL, 24);
NetworkMem AAPLMem = new NetworkMem(Network);
NetworkMem StkMem = new NetworkMem(stk.Network);
NetworkMem CtxtMem = new NetworkMem(ctxt.Network);
double e = 0;
Parallel.For(0, vals.Count(), j =>
{
try
{
List <Comparison> comps = Comparison.GenerateComparisons(vals[j]);
if (j == 0 || j == 1)
{
write("Comparisons : " + comps.Count());
}
StonkMem sm = new StonkMem(comps.Count());
var MktOutput = stk.Forward(comps, ctxt, sm);
var F = Network.Forward(MktOutput, dropout, write);
var output = new double[2];
int opnumb = vals[j].Increase ? 1 : 0;
output[opnumb] = 1;
var Error = CategoricalCrossEntropy.Forward(F.Last().GetRank(0), output);
e += Error.Max();
var D = Network.Backward(F, output, AAPLMem, write);
stk.Backward(D, ctxt, sm, StkMem, CtxtMem);
}
catch { }
});
write("Samples : " + vals.Count());
write("Loss : " + e);
AAPLMem.Update(vals.Count(), 1e-4, Network);
StkMem.Update(vals.Count(), 1e-4, stk.Network);
CtxtMem.Update(vals.Count(), 1e-4, ctxt.Network);
Network.Save();
stk.Network.Save();
ctxt.Save();
}
public double[] Forward(List <Comparison> vals, StonkContext context)
{
double[] ctxt = new double[vals.Count()];
double[,] loc = new double[vals.Count(), MktSize];
Parallel.For(0, vals.Count(), j =>
{
double[] a = vals[j].Values.Duplicate();
for (int i = 0; i < Network.Layers.Count(); i++)
{
a = Network.Layers[i].Output(a);
}
loc.SetRank(a, j);
ctxt[j] = context.Contextualize(vals[j]);
});
return(loc.Multiply(Activations.SoftMax(ctxt)));
}
public void Backward(double[] DValues, StonkContext context, StonkMem sm, NetworkMem mem, NetworkMem CtxtMem)
{
var LocDValues = sm.DLocation(DValues);
DValues = sm.DGlobalContext(DValues);
DValues = Activations.InverseSoftMax(DValues, sm.GlobalOutputs.ToArray());
context.Backward(DValues, sm.LocationOutputs.Count(), sm, CtxtMem);
Parallel.For(0, sm.GlobalOutputs.Count(), j =>
{
var ldv = LocDValues[j];
for (int i = Network.Layers.Count() - 1; i >= 0; i--)
{
ldv = mem.Layers[i].DActivation(ldv, sm.LocationOutputs[j][i + 1]);
mem.Layers[i].DBiases(ldv, Network.Layers[i], sm.GlobalOutputs.Count());
mem.Layers[i].DWeights(ldv, sm.LocationOutputs[j][i], Network.Layers[i], sm.GlobalOutputs.Count());
ldv = mem.Layers[i].DInputs(ldv, Network.Layers[i]);
}
});
}