public static void copyInto(StatePredictor original, StatePredictor destination)
{
original.table.CopyTo(destination.table, 0);
destination.iterationCount = original.iterationCount;
destination.state = original.state;
destination.stateSize = original.stateSize;
destination.inputSymbolSize = original.inputSymbolSize;
destination.tableStride = original.tableStride;
destination.trainingDepth = original.trainingDepth;
original.stateMetrics.CopyTo(destination.stateMetrics, 0);
}
public Couppy(int[] layerStateSizes, int[] layerOutputSizes, Translator translator)
{
int layerCount = layerStateSizes.Length;
this.layerStateSizes = layerStateSizes;
if (layerOutputSizes.Length != layerCount)
{
throw new Exception("Invalid Couppy parameters, layer definitions must have same size.");
}
this.layerOutputSizes = layerOutputSizes;
this.translator = translator;
int symbolSize = translator.getSymbolCount();
if (symbolSize != layerOutputSizes[layerCount - 1])
{
throw new Exception("Invalid Couppy parameters, final layer output doesn't match translator symbol count.");
}
if (symbolSize > 256)
{
throw new NotImplementedException("Invalid symbol size, must be between 1 and 256");
}
for (int i = 0; i < layerStateSizes.Length - 1; i++)
{
if (layerStateSizes[i] > 256)
{
throw new NotImplementedException("Invalid state size, must be between 1 and 256");
}
}
if (layerStateSizes[layerStateSizes.Length - 1] > ushort.MaxValue)
{
throw new NotImplementedException("Invalid state size, must be between 1 and 256");
}
if (layerCount == 1)
{
outputLayer = new RestrictedStatePredictor(symbolSize, symbolSize, layerStateSizes[layerStateSizes.Length - 1]);
}
else
{
deepLayers = new StatePredictor[layerStateSizes.Length - 1];
deepLayers[0] = new StatePredictor(symbolSize, layerOutputSizes[0], layerStateSizes[0]);
for (int i = 1; i < layerStateSizes.Length - 1; i++)
{
deepLayers[i] = new StatePredictor(layerOutputSizes[i - 1], layerOutputSizes[i], layerStateSizes[i]);
}
outputLayer = new RestrictedStatePredictor(layerOutputSizes[layerOutputSizes.Length - 2], layerOutputSizes[layerOutputSizes.Length - 1], layerStateSizes[layerStateSizes.Length - 1]);
}
}
/*
* public void printCascade()
* {
* Console.WriteLine("Printing cascade");
*
* int topLayer;
* int states;
* if (deepLayers != null)
* {
* topLayer = deepLayers.Length;
* states = outputLayer.stateSize;
*
* //Set maximum states to all possible combinations between layers
* for (int k = 0; k < deepLayers.Length; k++)
* states *= deepLayers[k].stateSize;
* }
* else
* {
* topLayer = 0;
* states = outputLayer.stateSize;
* }
*
* byte single;
* byte feed;
* int iState;
* for (int i = 0; i < states; i++)
* {
* List<byte> output = new List<byte>();
*
* //Set Cascading States
* outputLayer.state = (ushort)(i % outputLayer.stateSize);
* iState = i / outputLayer.stateSize;
* if (deepLayers != null)
* {
* for (int k = 0; k < deepLayers.Length; k++)
* {
* deepLayers[k].state = (byte)(iState % deepLayers[k].stateSize);
* iState = iState / deepLayers[k].stateSize;
* }
* }
*
*
* if (deepLayers != null)
* {
* feed = deepLayers[deepLayers.Length - 1].state; //Use last state on last layer
* for (int k = deepLayers.Length - 1; k >= 0; k--)
* {
* deepLayers[k].state = feed;
* if (k != 0)
* feed = deepLayers[k].predict(deepLayers[k - 1].state);
* else
* feed = deepLayers[k].predict(outputLayer.state);
* }
* }
* else
* feed = outputLayer.state;
*
* outputLayer.state = feed;
* single = outputLayer.process(0);
*
* if (single == 0)
* continue;
*
* output.Add(single);
*
* for (int j = 1; j < 128; j++)
* {
* if (deepLayers != null)
* {
* feed = deepLayers[deepLayers.Length - 1].state; //Use last state on last layer
* for (int k = deepLayers.Length - 1; k >= 0; k--)
* {
* deepLayers[k].state = feed;
* if (k != 0)
* feed = deepLayers[k].predict(deepLayers[k - 1].state);
* else
* feed = deepLayers[k].predict(outputLayer.state);
* }
* }
* else
* feed = outputLayer.state;
*
* outputLayer.state = feed;
* single = outputLayer.process(output[j - 1]);
*
* //Break if AI prints voidChar
* if (single == 0)
* break;
*
* output.Add(single);
* }
*
*
* string stateString = "";
* iState = i;
* stateString += iState % outputLayer.stateSize;
* iState = i / outputLayer.stateSize;
* if (deepLayers != null)
* {
* for (int k = 0; k < deepLayers.Length; k++)
* {
* stateString = iState % deepLayers[k].stateSize + " , " + stateString;
* iState = iState / deepLayers[k].stateSize;
* }
* }
* Console.WriteLine(stateString + " : " + translator.symbolToText(output.ToArray()));
* }
* }
*/
public static void copyInto(Couppy original, Couppy destination)
{
RestrictedStatePredictor.copyInto(original.outputLayer, destination.outputLayer);
if (original.deepLayers != null)
{
for (int i = 0; i < original.deepLayers.Length; i++)
{
StatePredictor.copyInto(original.deepLayers[i], destination.deepLayers[i]);
}
}
else
{
destination.deepLayers = null;
}
original.layerStateSizes.CopyTo(destination.layerStateSizes, 0);
original.layerOutputSizes.CopyTo(destination.layerOutputSizes, 0);
destination.name = original.name;
destination.trainingDepth = original.trainingDepth;
destination.translator = original.translator;
}
