public NNFetch Predict(Matrix <float> X, Vector <float> y = null, params object[] parameters)
{
float[][] preds = nn.Forward(X, true);
NNFetch nnFetch = new NNFetch(X.RowCount);
nnFetch.AddSampleToPreds(preds[0].Select(x => (double)x).ToArray());
if (nn.ComputeSDOutput == true)
{
nnFetch.AddSampleToSigmas(preds[1].Select(x => Global.ToSigma(Global.TrimPhi(x))).ToArray());
}
nnFetch.SetCost(0);
return(nnFetch);
}
public NNFetch Predict(Matrix <float> X, Vector <float> y = null, params object[] parameters)
{
if (X.RowCount > Global.TFMAXBATCHSIZE)
{
throw new Exception();
}
int minSample = 1;
if (parameters != null && parameters.Length != 0)
{
minSample = (int)parameters[0];
}
int iter = minSample % numSamples == 0 ? minSample / numSamples : minSample / numSamples + 1;
NNFetch nnFetch = new NNFetch(X.RowCount);
List <float> costs = new List <float>();
for (int i = 0; i < iter; i++)
{
var runner = _session.GetRunner();
runner.AddInput(_input, X.ToArray());
runner.AddInput(_decay, 0F);
if (y != null)
{
runner.AddInput(_output, y.ToArray());
}
foreach (TFOutput pred in _preds)
{
runner.Fetch(pred);
}
if (y != null)
{
runner.Fetch(_cost);
}
var result = runner.Run();
if (y != null)
{
costs.Add((float)result.Last().GetValue());
}
for (int j = 0; j < _preds.Length; j++)
{
nnFetch.AddSampleToPreds(((float[])result[j].GetValue()).Select(x => (double)x).ToArray());
}
}
if (y != null)
{
nnFetch.SetCost(costs.Average());
}
return(nnFetch);
}
public override int H(IState state, bool verbose)
{
int h;
bool cached = hCache.TryGetValue(state.GetHashCodeLong(), out h);
if (cached == false)
{
List <TrainingInstance> trainingInstances = new List <TrainingInstance>()
{
new TrainingInstance(state, 0)
};
var data = representationSolve.BuildData(trainingInstances);
NNFetch nnFetch = nnSolve.Predict(data.Item1);
double pred = nnFetch.Preds[0][0];
int hUnadj = 0;
if (verbose == true)
{
hUnadj = (int)Math.Round(representationSolve.ResponseFuncInv(pred), 0);;
}
if (confLevel == null)
{
pred = representationSolve.ResponseFuncInv(pred);
}
else
{
if (l2Loss == true)
{
pred = MathNet.Numerics.Distributions.Normal.InvCDF(nnFetch.Preds[0][0], nnFetch.Sigmas[0][0], (double)confLevel);
}
else
{
pred = Global.InvLaplace(nnFetch.Preds[0][0], nnFetch.Sigmas[0][0], (double)confLevel);
}
pred = representationSolve.ResponseFuncInv(pred);
}
h = (int)Math.Round(pred, 0);
if (verbose == true)
{
Console.WriteLine("h: " + h + " hUnadj: " + hUnadj);
}
if (representationSolve.MultHeuristic == null)
{
if (h < 0)
{
h = 0;
}
}
else
{
h = Math.Max(h, representationSolve.MultHeuristic.HArr.Max());
}
if (hCache.Count > Global.HCAHCEMAXRECORDS)
{
ClearCache();
}
hCache[state.GetHashCodeLong()] = h;
}
return(h);
}
