public void Setup()
{
monitorOutputLogMock = Mock.Of <IOutputLog>();
Mock.Get(monitorOutputLogMock).Setup(x => x.LogMessage(It.IsAny <string>()));
config = new ConfigProvider("127.0.0.1", 4321, 1);
var m = new MonitorProject.Monitor(config, monitorOutputLogMock);
monitorServiceThread = Task.Run(() => m.Start());
}
/// <summary>
/// Runs State Machine demo. This is the main function.
/// For each demo case defined in xml file function calls PerformDemoCase.
/// </summary>
/// <param name="log">Into this interface demo writes output to be displayed</param>
/// <param name="demoSettingsXmlFile">Xml file containing definitions of demo cases to be prformed</param>
public static void RunDemo(IOutputLog log, string demoSettingsXmlFile)
{
log.Write("State Machine demo started", false);
//Instantiate demo settings from the xml file
DemoSettings demoSettings = new DemoSettings(demoSettingsXmlFile);
//Loop through all demo cases
foreach (DemoSettings.CaseSettings demoCaseParams in demoSettings.CaseCfgCollection)
{
//Execute the demo case
PerformDemoCase(log, demoCaseParams);
}
log.Write("State Machine demo finished", false);
log.Write(string.Empty);
return;
}
/// <summary>
/// Runs State Machine demo. This is the main function.
/// For each demo case defined in xml file function calls PerformDemoCase.
/// </summary>
/// <param name="log">Into this interface demo writes output to be displayed</param>
/// <param name="demoSettingsXmlFile">Xml file containing definitions of demo cases to be prformed</param>
public static void RunDemo(IOutputLog log, string demoSettingsXmlFile)
{
log.Write("State Machine demo started");
//Instantiate demo settings from the xml file
DemoSettings demoSettings = new DemoSettings(demoSettingsXmlFile);
//Loop through all demo cases
Stopwatch sw = new Stopwatch();
foreach (DemoSettings.CaseSettings demoCaseParams in demoSettings.CaseCfgCollection)
{
sw.Reset();
sw.Start();
//Execute the demo case
PerformDemoCase(log, demoCaseParams);
sw.Stop();
TimeSpan ts = sw.Elapsed;
log.Write("Run time of demo case: " + String.Format("{0:00}:{1:00}:{2:00}.{3:00}", ts.Hours, ts.Minutes, ts.Seconds, ts.Milliseconds / 10));
log.Write(string.Empty);
log.Write(string.Empty);
}
log.Write("State Machine demo finished");
log.Write(string.Empty);
return;
}
/// <summary>
/// Performs specified demo case.
/// Loads and prepares sample data, trains State Machine and displayes results
/// </summary>
/// <param name="log">Into this interface are written output messages</param>
/// <param name="demoCaseParams">An instance of DemoSettings.CaseSettings to be performed</param>
public static void PerformDemoCase(IOutputLog log, DemoSettings.CaseSettings demoCaseParams)
{
//For demo purposes is allowed only the normalization range (-1, 1)
Interval normRange = new Interval(-1, 1);
log.Write(" Performing demo case " + demoCaseParams.Name, false);
//Bundle normalizer object
BundleNormalizer bundleNormalizer = null;
//Prediction input vector (relevant only for time series prediction task)
double[] predictionInputVector = null;
//Instantiate an State Machine
StateMachine stateMachine = new StateMachine(demoCaseParams.stateMachineCfg, normRange);
//Prepare regression stage input object
log.Write(" ", false);
StateMachine.RegressionStageInput rsi = null;
if (demoCaseParams.stateMachineCfg.TaskType == CommonEnums.TaskType.Prediction)
{
//Time series prediction task
//Load data bundle from csv file
TimeSeriesBundle data = TimeSeriesDataLoader.Load(demoCaseParams.FileName,
demoCaseParams.stateMachineCfg.InputFieldNameCollection,
demoCaseParams.stateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection,
normRange,
demoCaseParams.NormalizerReserveRatio,
true,
demoCaseParams.SingleNormalizer,
out bundleNormalizer,
out predictionInputVector
);
rsi = stateMachine.PrepareRegressionStageInput(data, demoCaseParams.NumOfBootSamples, PredictorsCollectionCallback, log);
}
else
{
//Classification or hybrid task
//Load data bundle from csv file
PatternBundle data = PatternDataLoader.Load(demoCaseParams.stateMachineCfg.TaskType == CommonEnums.TaskType.Classification,
demoCaseParams.FileName,
demoCaseParams.stateMachineCfg.InputFieldNameCollection,
demoCaseParams.stateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection,
normRange,
demoCaseParams.NormalizerReserveRatio,
true,
out bundleNormalizer
);
rsi = stateMachine.PrepareRegressionStageInput(data, PredictorsCollectionCallback, log);
}
//Report reservoirs statistics
ReportReservoirsStatistics(rsi.ReservoirStatCollection, log);
//Regression stage
log.Write(" Regression stage", false);
//Training - State Machine regression stage
ValidationBundle vb = stateMachine.RegressionStage(rsi, RegressionControl, log);
//Perform prediction if the task type is Prediction
double[] predictionOutputVector = null;
if (demoCaseParams.stateMachineCfg.TaskType == CommonEnums.TaskType.Prediction)
{
predictionOutputVector = stateMachine.Compute(predictionInputVector);
//Values are normalized so they have to be denormalized
bundleNormalizer.NaturalizeOutputVector(predictionOutputVector);
}
//Display results
//Report training (regression) results and prediction
log.Write(" Results", false);
List <ReadoutLayer.ClusterErrStatistics> clusterErrStatisticsCollection = stateMachine.ClusterErrStatisticsCollection;
//Classification results
for (int outputIdx = 0; outputIdx < demoCaseParams.stateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection.Count; outputIdx++)
{
ReadoutLayer.ClusterErrStatistics ces = clusterErrStatisticsCollection[outputIdx];
if (demoCaseParams.stateMachineCfg.TaskType == CommonEnums.TaskType.Classification)
{
//Classification task report
log.Write(" OutputField: " + demoCaseParams.stateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection[outputIdx], false);
log.Write(" Num of bin 0 samples: " + ces.BinaryErrStat.BinValErrStat[0].NumOfSamples.ToString(), false);
log.Write(" Bad bin 0 classif.: " + ces.BinaryErrStat.BinValErrStat[0].Sum.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 0 error rate: " + ces.BinaryErrStat.BinValErrStat[0].ArithAvg.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 0 accuracy: " + (1 - ces.BinaryErrStat.BinValErrStat[0].ArithAvg).ToString(CultureInfo.InvariantCulture), false);
log.Write(" Num of bin 1 samples: " + ces.BinaryErrStat.BinValErrStat[1].NumOfSamples.ToString(), false);
log.Write(" Bad bin 1 classif.: " + ces.BinaryErrStat.BinValErrStat[1].Sum.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 1 error rate: " + ces.BinaryErrStat.BinValErrStat[1].ArithAvg.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 1 accuracy: " + (1 - ces.BinaryErrStat.BinValErrStat[1].ArithAvg).ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total num of samples: " + ces.BinaryErrStat.TotalErrStat.NumOfSamples.ToString(), false);
log.Write(" Total bad classif.: " + ces.BinaryErrStat.TotalErrStat.Sum.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total error rate: " + ces.BinaryErrStat.TotalErrStat.ArithAvg.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total accuracy: " + (1 - ces.BinaryErrStat.TotalErrStat.ArithAvg).ToString(CultureInfo.InvariantCulture), false);
}
else
{
//Prediction task report
log.Write(" OutputField: " + demoCaseParams.stateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection[outputIdx], false);
log.Write(" Predicted next value: " + predictionOutputVector[outputIdx].ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total num of samples: " + ces.PrecissionErrStat.NumOfSamples.ToString(), false);
log.Write(" Total Max Real Err: " + (bundleNormalizer.OutputFieldNormalizerRefCollection[outputIdx].ComputeNaturalSpan(ces.PrecissionErrStat.Max)).ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total Avg Real Err: " + (bundleNormalizer.OutputFieldNormalizerRefCollection[outputIdx].ComputeNaturalSpan(ces.PrecissionErrStat.ArithAvg)).ToString(CultureInfo.InvariantCulture), false);
}
log.Write(" ", false);
}
log.Write(" ", false);
return;
}
/// <summary>
/// Function simply displays important statistics of the State Machine's reservoirs.
/// </summary>
/// <param name="statisticsCollection">Collection of reservoir's statistics</param>
/// <param name="log">Output log object</param>
private static void ReportReservoirsStatistics(List <ReservoirStat> statisticsCollection, IOutputLog log)
{
log.Write(" Reservoir(s) info:", false);
foreach (ReservoirStat resStat in statisticsCollection)
{
log.Write($" Statistics of reservoir instance: {resStat.ReservoirInstanceName} ({resStat.ReservoirSettingsName})", false);
foreach (ReservoirStat.PoolStat poolStat in resStat.PoolStatCollection)
{
log.Write($" Statistics of pool: {poolStat.PoolName}", false);
foreach (ReservoirStat.PoolStat.NeuronGroupStat groupStat in poolStat.NeuronGroupStatCollection)
{
log.Write($" Statistics of group: {groupStat.GroupName}", false);
log.Write($" Neurons stimulation (all components)", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + groupStat.AvgTStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgTStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgTStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgTStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + groupStat.MaxTStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxTStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxTStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxTStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MIN Avg, Max, Min, SDdev: " + groupStat.MinTStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinTStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinTStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinTStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" SPAN Avg, Max, Min, SDdev: " + groupStat.TStimuliSpansStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.TStimuliSpansStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.TStimuliSpansStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.TStimuliSpansStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Neurons stimulation (component coming from connected reservoir's neurons)", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + groupStat.AvgRStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgRStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgRStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgRStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + groupStat.MaxRStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxRStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxRStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxRStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MIN Avg, Max, Min, SDdev: " + groupStat.MinRStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinRStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinRStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinRStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" SPAN Avg, Max, Min, SDdev: " + groupStat.RStimuliSpansStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.RStimuliSpansStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.RStimuliSpansStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.RStimuliSpansStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Synapses efficacy", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + groupStat.AvgSynEfficacyStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgSynEfficacyStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgSynEfficacyStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgSynEfficacyStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + groupStat.MaxSynEfficacyStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxSynEfficacyStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxSynEfficacyStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxSynEfficacyStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MIN Avg, Max, Min, SDdev: " + groupStat.MinSynEfficacyStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinSynEfficacyStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinSynEfficacyStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinSynEfficacyStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" SPAN Avg, Max, Min, SDdev: " + groupStat.SynEfficacySpansStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.SynEfficacySpansStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.SynEfficacySpansStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.SynEfficacySpansStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Activations state", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + groupStat.AvgActivationStatesStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgActivationStatesStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgActivationStatesStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgActivationStatesStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + groupStat.MaxActivationStatesStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxActivationStatesStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxActivationStatesStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxActivationStatesStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" SPAN Avg, Max, Min, SDdev: " + groupStat.ActivationStateSpansStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.ActivationStateSpansStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.ActivationStateSpansStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.ActivationStateSpansStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Neurons output signal", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + groupStat.AvgOutputSignalStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgOutputSignalStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgOutputSignalStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.AvgOutputSignalStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + groupStat.MaxOutputSignalStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxOutputSignalStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxOutputSignalStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MaxOutputSignalStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MIN Avg, Max, Min, SDdev: " + groupStat.MinOutputSignalStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinOutputSignalStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinOutputSignalStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ groupStat.MinOutputSignalStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
}
log.Write($" Weights statistics", false);
log.Write(" Input Avg, Max, Min, SDdev: " + poolStat.InputWeightsStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InputWeightsStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InputWeightsStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InputWeightsStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" Internal Avg, Max, Min, SDdev: " + poolStat.InternalWeightsStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InternalWeightsStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InternalWeightsStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InternalWeightsStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
}
}
log.Write(" ", false);
//Console.ReadLine();
return;
}
/// <summary>
/// Performs one demo case.
/// Loads and prepares sample data, trains State Machine and displayes results
/// </summary>
/// <param name="log">Into this interface are written output messages</param>
/// <param name="demoCaseParams">An instance of DemoSettings.CaseSettings to be performed</param>
public static void PerformDemoCase(IOutputLog log, DemoSettings.CaseSettings demoCaseParams)
{
log.Write(" Performing demo case " + demoCaseParams.Name, false);
//Bundle normalizer object
BundleNormalizer bundleNormalizer = null;
//Prediction input vector (relevant only for input continuous feeding)
double[] predictionInputVector = null;
//Instantiate the State Machine
StateMachine stateMachine = new StateMachine(demoCaseParams.StateMachineCfg);
//Prepare input object for regression stage
log.Write(" ", false);
StateMachine.RegressionStageInput rsi = null;
List <string> outputFieldNameCollection = (from rus in demoCaseParams.StateMachineCfg.ReadoutLayerConfig.ReadoutUnitCfgCollection select rus.Name).ToList();
List <CommonEnums.TaskType> outputFieldTaskCollection = (from rus in demoCaseParams.StateMachineCfg.ReadoutLayerConfig.ReadoutUnitCfgCollection select rus.TaskType).ToList();
if (demoCaseParams.StateMachineCfg.InputConfig.FeedingType == CommonEnums.InputFeedingType.Continuous)
{
//Continuous input feeding
//Load data bundle from csv file
TimeSeriesBundle data = TimeSeriesBundle.LoadFromCsv(demoCaseParams.FileName,
demoCaseParams.StateMachineCfg.InputConfig.ExternalFieldNameCollection(),
outputFieldNameCollection,
outputFieldTaskCollection,
StateMachine.DataRange,
demoCaseParams.NormalizerReserveRatio,
true,
out bundleNormalizer,
out predictionInputVector
);
rsi = stateMachine.PrepareRegressionStageInput(data, PredictorsCollectionCallback, log);
}
else
{
//Patterned input feeding
//Load data bundle from csv file
PatternBundle data = PatternBundle.LoadFromCsv(demoCaseParams.FileName,
demoCaseParams.StateMachineCfg.InputConfig.ExternalFieldNameCollection(),
outputFieldNameCollection,
outputFieldTaskCollection,
StateMachine.DataRange,
demoCaseParams.NormalizerReserveRatio,
true,
out bundleNormalizer
);
rsi = stateMachine.PrepareRegressionStageInput(data, PredictorsCollectionCallback, log);
}
//Report statistics of the State Machine's reservoirs
ReportReservoirsStatistics(rsi.ReservoirStatCollection, log);
//Regression stage
log.Write(" Regression stage", false);
//Perform the regression
ValidationBundle vb = stateMachine.RegressionStage(rsi, RegressionControl, log);
//Perform prediction if the input feeding is continuous (we know the input but we don't know the ideal output)
double[] predictionOutputVector = null;
if (demoCaseParams.StateMachineCfg.InputConfig.FeedingType == CommonEnums.InputFeedingType.Continuous)
{
predictionOutputVector = stateMachine.Compute(predictionInputVector);
//Values are normalized so they have to be denormalized
bundleNormalizer.NaturalizeOutputVector(predictionOutputVector);
}
//Display results
//Report training (regression) results and prediction
log.Write(" Results", false);
List <ReadoutLayer.ClusterErrStatistics> clusterErrStatisticsCollection = stateMachine.ClusterErrStatisticsCollection;
//Results
for (int outputIdx = 0; outputIdx < demoCaseParams.StateMachineCfg.ReadoutLayerConfig.ReadoutUnitCfgCollection.Count; outputIdx++)
{
ReadoutLayer.ClusterErrStatistics ces = clusterErrStatisticsCollection[outputIdx];
if (demoCaseParams.StateMachineCfg.ReadoutLayerConfig.ReadoutUnitCfgCollection[outputIdx].TaskType == CommonEnums.TaskType.Classification)
{
//Classification task report
log.Write(" OutputField: " + demoCaseParams.StateMachineCfg.ReadoutLayerConfig.ReadoutUnitCfgCollection[outputIdx].Name, false);
log.Write(" Num of bin 0 samples: " + ces.BinaryErrStat.BinValErrStat[0].NumOfSamples.ToString(), false);
log.Write(" Bad bin 0 classif.: " + ces.BinaryErrStat.BinValErrStat[0].Sum.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 0 error rate: " + ces.BinaryErrStat.BinValErrStat[0].ArithAvg.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 0 accuracy: " + (1 - ces.BinaryErrStat.BinValErrStat[0].ArithAvg).ToString(CultureInfo.InvariantCulture), false);
log.Write(" Num of bin 1 samples: " + ces.BinaryErrStat.BinValErrStat[1].NumOfSamples.ToString(), false);
log.Write(" Bad bin 1 classif.: " + ces.BinaryErrStat.BinValErrStat[1].Sum.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 1 error rate: " + ces.BinaryErrStat.BinValErrStat[1].ArithAvg.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Bin 1 accuracy: " + (1 - ces.BinaryErrStat.BinValErrStat[1].ArithAvg).ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total num of samples: " + ces.BinaryErrStat.TotalErrStat.NumOfSamples.ToString(), false);
log.Write(" Total bad classif.: " + ces.BinaryErrStat.TotalErrStat.Sum.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total error rate: " + ces.BinaryErrStat.TotalErrStat.ArithAvg.ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total accuracy: " + (1 - ces.BinaryErrStat.TotalErrStat.ArithAvg).ToString(CultureInfo.InvariantCulture), false);
}
else
{
//Forecast task report
log.Write(" OutputField: " + demoCaseParams.StateMachineCfg.ReadoutLayerConfig.ReadoutUnitCfgCollection[outputIdx].Name, false);
log.Write(" Predicted next value: " + predictionOutputVector[outputIdx].ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total num of samples: " + ces.PrecissionErrStat.NumOfSamples.ToString(), false);
log.Write(" Total Max Real Err: " + (bundleNormalizer.OutputFieldNormalizerRefCollection[outputIdx].ComputeNaturalSpan(ces.PrecissionErrStat.Max)).ToString(CultureInfo.InvariantCulture), false);
log.Write(" Total Avg Real Err: " + (bundleNormalizer.OutputFieldNormalizerRefCollection[outputIdx].ComputeNaturalSpan(ces.PrecissionErrStat.ArithAvg)).ToString(CultureInfo.InvariantCulture), false);
}
log.Write(" ", false);
}
log.Write(" ", false);
return;
}
public Monitor(IConfigProvider configProvider, IOutputLog outputLog)
{
_configProvider = configProvider;
_outputLog = outputLog;
}
//Constructor
protected ExampleBase()
{
_log = new ConsoleLog();
return;
}
/// <summary>
/// Performs one demo case.
/// Loads and prepares sample data, trains State Machine and displayes results
/// </summary>
/// <param name="log">Into this interface are written output messages</param>
/// <param name="demoCaseParams">An instance of DemoSettings.CaseSettings to be performed</param>
public static void PerformDemoCase(IOutputLog log, DemoSettings.CaseSettings demoCaseParams)
{
log.Write(" Performing demo case " + demoCaseParams.Name, false);
log.Write(" ", false);
//Instantiate the State Machine
StateMachine stateMachine = new StateMachine(demoCaseParams.StateMachineCfg);
//Prepare input object for regression stage
StateMachine.RegressionInput rsi = null;
//Prediction input vector (relevant only for input continuous feeding)
double[] predictionInputVector = null;
if (demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.FeedingType == NeuralPreprocessor.InputFeedingType.Continuous)
{
//Continuous input feeding
//Load data bundle from csv file
VectorBundle data = VectorBundle.LoadFromCsv(demoCaseParams.FileName,
demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.ExternalFieldNameCollection(),
demoCaseParams.StateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection,
out predictionInputVector
);
rsi = stateMachine.PrepareRegressionData(data, PredictorsCollectionCallback, log);
}
else
{
//Patterned input feeding
//Load data bundle from csv file
PatternBundle data = PatternBundle.LoadFromCsv(demoCaseParams.FileName,
demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.ExternalFieldNameCollection(),
demoCaseParams.StateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection
);
rsi = stateMachine.PrepareRegressionData(data, PredictorsCollectionCallback, log);
}
//Report key statistics of the State Machine's reservoirs
string statisticsReport = rsi.CreateReport(4);
log.Write(statisticsReport);
log.Write(string.Empty);
//Regression stage - building of trained readout layer
log.Write(" Regression stage (training of readout layer)", false);
//Perform the regression
ResultBundle rcb = stateMachine.BuildReadoutLayer(rsi, RegressionControl, log);
log.Write(string.Empty);
//Report training (regression) results
log.Write(" Training results", false);
string trainingReport = stateMachine.RL.GetTrainingResultsReport(6);
log.Write(trainingReport);
log.Write(string.Empty);
//Perform prediction if the input feeding is continuous (we know the input but we don't know the ideal output)
if (demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.FeedingType == NeuralPreprocessor.InputFeedingType.Continuous)
{
double[] predictionOutputVector = stateMachine.Compute(predictionInputVector);
string predictionReport = stateMachine.RL.GetForecastReport(predictionOutputVector, 6);
log.Write(" Forecasts", false);
log.Write(predictionReport);
log.Write(string.Empty);
}
return;
}
//Constructor
public SMDemo(IOutputLog log)
{
_log = log;
return;
}
void CmdUnlock(string command, ITMGame game, ITMPlayer caller, ITMPlayer player, IOutputLog log)
{
OnLockpickSwing(Items.Lockpick, player.RightHand);
}
public Sensor(IMetricsProvider metricPovider, ITCPSender client, IOutputLog outputLog)
{
_client = client;
_metricPovider = metricPovider;
_outputLog = outputLog;
}
private void ConsoleCommand(string str, ITMGame game, ITMPlayer player1, ITMPlayer player2, IOutputLog log)
{
str = str.ToLower();
string[] commands = str.Split(' ');
if (commands.Length > 1)
{
switch (commands[1])
{
case "help": log.WriteLine(QuickGatherHelp(commands)); break;
case "toggleveinmine": log.WriteLine(ToggleVeinMining()); break;
case "togglelumberjack": log.WriteLine(ToggleLumberJack()); break;
case "toggleharvest": log.WriteLine(ToggleHarvesting()); break;
case "saveconfig": log.WriteLine(SaveConfigs()); break;
case "ver": log.WriteLine(GetVersion()); break;
case "version": log.WriteLine(GetVersion()); break;
default: log.WriteLine("Unknown Command use 'qg help' for more info"); break;
}
}
else
{
log.WriteLine("Unknown Command use 'qg help' for help");
}
}
public TcpClientWrap(TcpClient client, IOutputLog output)
{
_client = client;
_stream = _client.GetStream();
_output = output;
}
public TCPSender(TcpClient client, IOutputLog output)
: base(client, output)
{
_output = output;
}
/// <summary>
/// Function displays reservoirs statistics.
/// </summary>
/// <param name="statisticsCollection">Collection of reservoir statistics</param>
/// <param name="log">Output log object</param>
private static void ReportReservoirsStatistics(List <ReservoirStat> statisticsCollection, IOutputLog log)
{
log.Write(" Reservoir(s) info:", false);
foreach (ReservoirStat resStat in statisticsCollection)
{
log.Write($" Statistics of reservoir instance: {resStat.ReservoirInstanceName} ({resStat.ReservoirSettingsName})", false);
foreach (ReservoirStat.PoolStat poolStat in resStat.PoolStatCollection)
{
log.Write($" Statistics of pool instance: {poolStat.PoolInstanceName}", false);
log.Write($" Neurons states", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + poolStat.NeuronsAvgStatesStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgStatesStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgStatesStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgStatesStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + poolStat.NeuronsMaxStatesStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxStatesStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxStatesStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxStatesStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" SPAN Avg, Max, Min, SDdev: " + poolStat.NeuronsStateSpansStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsStateSpansStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsStateSpansStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsStateSpansStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Neurons stimuli", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + poolStat.NeuronsAvgStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + poolStat.NeuronsMaxStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MIN Avg, Max, Min, SDdev: " + poolStat.NeuronsMinStimuliStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMinStimuliStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMinStimuliStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMinStimuliStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" SPAN Avg, Max, Min, SDdev: " + poolStat.NeuronsStimuliSpansStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsStimuliSpansStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsStimuliSpansStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsStimuliSpansStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Neurons transmission signals", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + poolStat.NeuronsAvgTransmissionSignalStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgTransmissionSignalStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgTransmissionSignalStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgTransmissionSignalStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MAX Avg, Max, Min, SDdev: " + poolStat.NeuronsMaxTransmissionSignalStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxTransmissionSignalStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxTransmissionSignalStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMaxTransmissionSignalStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" MIN Avg, Max, Min, SDdev: " + poolStat.NeuronsMinTransmissionSignalStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMinTransmissionSignalStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMinTransmissionSignalStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsMinTransmissionSignalStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Neurons transmission frequencies", false);
log.Write(" AVG Avg, Max, Min, SDdev: " + poolStat.NeuronsAvgTransmissionFreqStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgTransmissionFreqStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgTransmissionFreqStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.NeuronsAvgTransmissionFreqStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write($" Weights statistics", false);
log.Write(" Input Avg, Max, Min, SDdev: " + poolStat.InputWeightsStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InputWeightsStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InputWeightsStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InputWeightsStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
log.Write(" Internal Avg, Max, Min, SDdev: " + poolStat.InternalWeightsStat.ArithAvg.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InternalWeightsStat.Max.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InternalWeightsStat.Min.ToString("N4", CultureInfo.InvariantCulture) + ", "
+ poolStat.InternalWeightsStat.StdDev.ToString("N4", CultureInfo.InvariantCulture), false);
}
}
log.Write(" ", false);
//Console.ReadLine();
return;
}
public TCPReciever(TcpClient client, IOutputLog output)
: base(client, output)
{
_output = output;
}
public OSAPI(IOutputLog log)
{
this.log = log;
}
