public static PAPNetworkContainer LoadPAPContainer(string pathName, string fileName)
{
throw new NotImplementedException();
//Load the container
PAPNetworkContainer returnItem = (PAPNetworkContainer)SerializeObject.Load(pathName + fileName + ".PAPc");
//Load the network in as well
returnItem.playerNetworkPool = new NNThreadSafeNetworkPool(NNLoadSave.loadNetwork(fileName + ".eNN", pathName), returnItem.playerId.ToString(), NNThreadSafeNetworkPool.DefaultListLength);
return(returnItem);
}
public void SavePAPContainer(string pathName, string fileName)
{
if (playerNetworkPool == null)
{
throw new Exception("Network must be set before the PAP container can be saved out.");
}
//Save out the PAPContainer (without the network)
SerializeObject.Save(pathName + fileName + ".PAPc", this);
//Save out the network
NNLoadSave.saveNetwork(playerNetworkPool.BaseNetwork, fileName + ".eNN", pathName);
}
public void Go3()
{
Console.WriteLine("Let's get this show on the road!!");
long[] allPlayerIds = new long[0];
//Todo - Add players to allPlayerIds
//Get all of the playerActions
Console.WriteLine("Testing new network accuracy for {0} players.", allPlayerIds.Length);
for (int i = 0; i < allPlayerIds.Length; i++)
{
try
{
PokerPlayerNNModelv1 playerPrediction = new PokerPlayerNNModelv1();
playerPrediction.populatePlayActions(allPlayerIds[i], 10000, 0);
playerPrediction.SuffleDataSource();
//playerPrediction.createNetwork();
//playerPrediction.createTrainingSets();
//playerPrediction.trainNetwork();
playerPrediction.Network = NNLoadSave.loadNetwork("generalPlayer.eNN", "");
Console.WriteLine("... data loaded for player index {0} ({1}). {2} actions added.", i, allPlayerIds[i], playerPrediction.NeuralNetDataSource.Count);
//playerPrediction.SaveNNDataSource("allPlayerPAPData.csv");
playerPrediction.createTestingSets();
decimal accuracy = playerPrediction.getNetworkAccuracy();
Console.WriteLine("PlayerId:{0} Predicted {1}% Of Actions Correctly.", allPlayerIds[i], Math.Round(accuracy, 1));
using (System.IO.StreamWriter sw = new System.IO.StreamWriter("accuracy.csv", true))
sw.WriteLine(allPlayerIds[i] + ", " + Math.Round(accuracy, 1));
}
catch (Exception ex)
{
Console.WriteLine("Meh - Error on playerId {0}.", allPlayerIds[i]);
using (System.IO.StreamWriter sw = new System.IO.StreamWriter("errors.txt", true))
sw.WriteLine(ex.ToString());
}
}
//NNLoadSave.saveNetwork(playerPrediction.Network, "generalPlayer.eNN", "");
Console.WriteLine("... completed.");
Console.ReadKey();
}
public playerActionPredictionNetworkManager()
{
if (InfoProviderBase.CurrentJob == null)
{
defaultNetworkPool = new NNThreadSafeNetworkPool(NNLoadSave.loadNetwork("generalPlayer.eNN", PAP_STORE), "default", NNThreadSafeNetworkPool.DefaultListLength);
}
else
{
// defaultNetworkPool = new NNThreadSafeNetworkPool(NNLoadSave.loadNetwork(InfoProviders.InfoProviderBase.CurrentJob.JobData.NeuralNetworkBytes("PAP_generalPlayer.eNN")), "default", NNThreadSafeNetworkPool.DefaultListLength);
}
if (backgroundWorkerThread != null)
{
throw new Exception("The network manager worker thread is already running!");
}
//Start the monitor thread
//backgroundWorkerThread = new Thread(networkBackgroundWorker);
//backgroundWorkerThread.Name = "PAP_BackgroundWorker";
//backgroundWorkerThread.Priority = ThreadPriority.BelowNormal;
//We are not going to start the worker thread at the moment.
//backgroundWorkerThread.Start();
}
public void Go2()
{
Console.WriteLine("Let's get this show on the road!!");
//Get all of the playerActions
for (int i = 0; i < 20; i++)
{
PokerPlayerNNModelv1 playerPrediction = new PokerPlayerNNModelv1();
playerPrediction.LoadNNDatasource("allPlayerPAPData.csv", PokerPlayerNNModelv1.Input_Neurons, PokerPlayerNNModelv1.Output_Neurons);
playerPrediction.SuffleDataSource();
playerPrediction.createNetwork();
playerPrediction.createTrainingSets();
playerPrediction.trainNetwork();
playerPrediction.createTestingSets();
decimal accuracy = playerPrediction.getNetworkAccuracy();
Console.WriteLine("Achieved {0}% accuracy.", accuracy);
NNLoadSave.saveNetwork(playerPrediction.Network, accuracy.ToString() + "_generalPlayer.eNN", "");
}
Console.WriteLine("... completed.");
Console.ReadKey();
}
protected double[] getPlayerNetworkPrediction(string aiConfigStr, double[] networkInputs)
{
try
{
lock (locker)
{
//Check to see if the network has already been retreived
if (threadSafeNetworkDict.ContainsKey(aiConfigStr))
{
return(threadSafeNetworkDict[aiConfigStr].getNetworkPrediction(networkInputs));
}
else
{
BasicNetwork network = null;
//We need to load the network
if (InfoProviders.InfoProviderBase.CurrentJob == null)
{
network = NNLoadSave.loadNetwork(aiConfigStr, AI_FILES_STORE);
}
else
{
//network = NNLoadSave.loadNetwork(InfoProviders.InfoProviderBase.CurrentJob.JobData.NeuralNetworkBytes(aiConfigStr));
/*
* string[] directories = aiConfigStr.Split('\\');
* //Check to see if the network needs to be created
* if (!File.Exists(FileLocations.ConvertWinFileReferenceToLocal("LocalNetworkStore\\" + aiConfigStr)))
* {
* string networkStr = InfoProviders.InfoProviderBase.CurrentJob.JobData.NeuralNetwork(aiConfigStr);
*
* if (!Directory.Exists("LocalNetworkStore"))
* Directory.CreateDirectory("LocalNetworkStore");
*
* //Make sure all of the necessary directories exist for the network
* //string[] directories = aiConfigStr.Split('\\');
* string currentDir = "LocalNetworkStore";
* for (int i = 0; i < directories.Length - 1; i++)
* {
* if (!Directory.Exists(Path.Combine(currentDir, directories[i])))
* Directory.CreateDirectory(Path.Combine(currentDir, directories[i]));
*
* currentDir = Path.Combine(currentDir, directories[i]);
* }
*
* File.WriteAllBytes(FileLocations.ConvertWinFileReferenceToLocal("LocalNetworkStore\\" + aiConfigStr), (from current in networkStr.Split('-') select Convert.ToByte(current, 16)).ToArray());
* }
*
* if (InfoProviders.InfoProviderBase.CurrentJob.JobData.ContainsNeuralNetwork(aiConfigStr))
* //We can now delete the entry in the job file otherwise it just takes up alot of memory
* InfoProviders.InfoProviderBase.CurrentJob.JobData.RemoveNeuralNetwork(aiConfigStr);
*
* //Console.WriteLine("Loading network from LocalNetworkStore combine with {0}", FileLocations.ConvertWinFileReferenceToLocal(aiConfigStr));
* network = NNLoadSave.loadNetwork(FileLocations.ConvertWinFileReferenceToLocal(aiConfigStr), "LocalNetworkStore");
*/
}
if (!threadSafeNetworkDict.ContainsKey(aiConfigStr))
{
threadSafeNetworkDict.Add(aiConfigStr, new NNThreadSafeNetworkPool(network, aiConfigStr, NNThreadSafeNetworkPool.DefaultListLength));
}
return(threadSafeNetworkDict[aiConfigStr].getNetworkPrediction(networkInputs));
}
}
}
catch (Exception ex)
{
throw new Exception("aiConfigStr was not formatted correctly for the current AI type.", ex);
}
}
public void Go()
{
double[][] networkPredictedOutputs;
byte idealColumn;
double outputMaxValue1st = -1;
byte outputColumn = byte.MaxValue;
int correctPredictions = 0;
Console.WriteLine("Let's get this show on the road!!");
long[] allPlayerIds = new long[0];
//Todo - Add players to allPlayerIds
//Before we do PAP we need select specific players based on agression
//int numHandsCounted=0;
//double RFreq_PreFlop=0, RFreq_PostFlop=0, CFreq_PreFlop=0, CFreq_PostFlop=0, PreFlopPlayFreq=0;
//using (System.IO.StreamWriter sw = new System.IO.StreamWriter("playerAggression.csv", false))
//{
// for (int i = 0; i < allPlayerIds.Length; i++)
// {
// //Break the stats down for the top player to find out what is the needed number of hands for a good answer
// //for (int j = 0; j < 353; j++)
// //{
// numHandsCounted = 0; RFreq_PreFlop = 0; RFreq_PostFlop = 0; CFreq_PreFlop = 0; CFreq_PostFlop = 0; PreFlopPlayFreq = 0;
// databaseQueries.PlayerAgressionMetrics(allPlayerIds[i],10000, 1, 1, ref numHandsCounted, ref RFreq_PreFlop, ref RFreq_PostFlop, ref CFreq_PreFlop, ref CFreq_PostFlop, ref PreFlopPlayFreq);
// //sw.WriteLine("{0},{1},{2},{3},{4},{5},{6}", allPlayerIds[i], numHandsCounted, RFreq_PreFlop, RFreq_PostFlop, CFreq_PreFlop, CFreq_PostFlop, PreFlopPlayFreq);
// sw.WriteLine("{0},{1},{2},{3},{4},{5}", allPlayerIds[i], RFreq_PreFlop, RFreq_PostFlop, CFreq_PreFlop, CFreq_PostFlop, PreFlopPlayFreq);
// //Console.WriteLine(j);
// //}
// sw.Flush();
// Console.WriteLine(i);
// }
//}
//return;
PokerPlayerNNModelv1 playerPrediction = new PokerPlayerNNModelv1();
//Get all of the playerActions
if (true)
{
Console.WriteLine("Loading PAP data for {0} players.", allPlayerIds.Length);
for (int i = 0; i < allPlayerIds.Length; i++)
{
try
{
playerPrediction.populatePlayActions(allPlayerIds[i], 10000, 0);
Console.WriteLine("... data loaded for player index {0} ({1}). {2} actions added.", i, allPlayerIds[i], playerPrediction.NeuralNetDataSource.Count);
playerPrediction.SaveNNDataSource("allPlayerPAPData.csv");
}
catch (Exception ex)
{
Console.WriteLine("Meh - Error on playerId {0}.", allPlayerIds[i]);
using (System.IO.StreamWriter sw = new System.IO.StreamWriter("errors.txt", true))
sw.WriteLine(ex.ToString());
}
}
}
else
{
Console.WriteLine("Loading NNData...");
playerPrediction.LoadNNDatasource("allPlayerPAPData.csv", PokerPlayerNNModelv1.Input_Neurons, PokerPlayerNNModelv1.Output_Neurons);
Console.WriteLine("... complete.");
}
Console.WriteLine("Shuffling NNData...");
playerPrediction.SuffleDataSource();
Console.WriteLine("... complete.");
Console.WriteLine("Initiating training ...");
//playerPrediction.createNetwork();
playerPrediction.Network = NNLoadSave.loadNetwork("generalPlayer.eNN", "");
//neuralPokerAI.getBotAiNN(false);
playerPrediction.createTrainingSets();
playerPrediction.trainNetwork();
playerPrediction.createTestingSets();
networkPredictedOutputs = new double[playerPrediction.NetworkInput.Length][];
;
//Create the output array
for (int i = 0; i < networkPredictedOutputs.Length; i++)
{
networkPredictedOutputs[i] = new double[PokerPlayerNNModelv1.Output_Neurons];
}
Stopwatch timer = new Stopwatch();
timer.Start();
playerPrediction.testNetwork(networkPredictedOutputs);
timer.Stop();
for (int i = 0; i < networkPredictedOutputs.GetLength(0); i++)
{
outputMaxValue1st = -1;
//Determine if the output match the ideal
if (playerPrediction.NetworkIdealOutput[i][0] == 1)
{
idealColumn = 0;
}
else if (playerPrediction.NetworkIdealOutput[i][1] == 1)
{
idealColumn = 1;
}
else if (playerPrediction.NetworkIdealOutput[i][2] == 1)
{
idealColumn = 2;
}
else
{
throw new Exception("This should never happen");
}
if (networkPredictedOutputs[i][0] > outputMaxValue1st)
{
outputMaxValue1st = networkPredictedOutputs[i][0];
outputColumn = 0;
}
if (networkPredictedOutputs[i][1] > outputMaxValue1st)
{
outputMaxValue1st = networkPredictedOutputs[i][1];
outputColumn = 1;
}
if (networkPredictedOutputs[i][2] > outputMaxValue1st)
{
outputMaxValue1st = networkPredictedOutputs[i][2];
outputColumn = 2;
}
if (outputColumn == byte.MaxValue)
{
throw new Exception("This should not happen!");
}
if (outputColumn != idealColumn)
{
Console.WriteLine("**** Actual-{0},Predicted-{1} - [{2}, {3}, {4}]", idealColumn, outputColumn, String.Format("{0:0.00}", networkPredictedOutputs[i][0]), String.Format("{0:0.00}", networkPredictedOutputs[i][1]), String.Format("{0:0.00}", networkPredictedOutputs[i][2]));
}
else
{
//if (idealColumn == 4)
//Console.WriteLine("Actual-{0}, Predicted-{1} - [{2}, {3}, {4}, {5}, {6}]", idealColumn, outputColumn, String.Format("{0:0.00}", networkPredictedOutputs[i][0]), String.Format("{0:0.00}", networkPredictedOutputs[i][1]), String.Format("{0:0.00}", networkPredictedOutputs[i][2]), String.Format("{0:0.00}", networkPredictedOutputs[i][3]), String.Format("{0:0.00}", networkPredictedOutputs[i][4]));
//Console.WriteLine("Actual-{0},Predicted-{1} - [{2}, {3}, {4}]", idealColumn, outputColumn, String.Format("{0:0.00}", networkPredictedOutputs[i][0]), String.Format("{0:0.00}", networkPredictedOutputs[i][1]), String.Format("{0:0.00}", networkPredictedOutputs[i][2]));
correctPredictions++;
}
}
Console.WriteLine("Predicted {0}% Of Actions Correctly.", ((double)correctPredictions / (double)(int)(playerPrediction.DataSourceCount - (int)(playerPrediction.DataSourceCount * playerPrediction.Train_Data_Percent))) * 100);
Console.WriteLine("Per network compute - {0}ms.", (double)timer.ElapsedMilliseconds / ((double)(playerPrediction.DataSourceCount - (int)(playerPrediction.DataSourceCount * playerPrediction.Train_Data_Percent))));
//Console.WriteLine("Predicted {0}% Of Actions Correctly.", ((double)correctPredictions / (double)(int)(playerPrediction.DataSourceCount)) * 100);
NNLoadSave.saveNetwork(playerPrediction.Network, "generalPlayerNew.eNN", "");
Console.ReadKey();
}