private void SafeUpdateThresholdsUpward(FeedbackLevel level, FeedbackLevel next, int input, Dictionary <FeedbackLevel, FeedbackLevelData> updatedLevels, float factor)
{
FeedbackLevel realLevel;
this.GetExistingLevel(level, out realLevel);
FeedbackLevelData values, hierLevelValues;
if (!this.UpdateLevelValues(realLevel, input, factor, out values, out hierLevelValues, updatedLevels))
{
return;
}
if (level == next)
{
return;
}
var upDownDiff = hierLevelValues.UpperBound - values.UpperBound;
if (MinimalLevelSize > upDownDiff)
{
this.UpdateThresholdsInt(values.UpperBound + MinimalLevelSize, next, 1.0f, ref updatedLevels);
}
}
public DFAFeedback(FeedbackLevel level, HashSet<char> alphabet, double utility, CharSetSolver solver)
{
this.alphabet = alphabet;
this.level = level;
this.solver = solver;
this.utility = Math.Round(Math.Max(1-utility,0)*100);
}
public ServerBunch(byte priority, float reserveRatio, FeedbackLevel takeFromReserveLevel = FeedbackLevel.Highest)
{
this.reserveRatio = reserveRatio;
this.Priority = priority;
this.takeFromReserveLoadLevel = (int)takeFromReserveLevel;
this.serverList = new SortedDictionary <TServer, ServerStateData <TServer> >();
}
public DFAFeedback(FeedbackLevel level, HashSet <char> alphabet, double utility, CharSetSolver solver)
{
this.alphabet = alphabet;
this.level = level;
this.solver = solver;
this.utility = Math.Round(Math.Max(1 - utility, 0) * 100);
}
private void UpdateServerState(FeedbackLevel workload, int peerCount, ServerState state)
{
Dictionary <byte, int[]> predictionData = null;
if (this.loadStatsCollector != null)
{
this.loadStatsCollector.UpdatePrediction(peerCount, workload, out predictionData);
}
if (!this.IsRegistered)
{
return;
}
var contract = new UpdateServerEvent
{
LoadIndex = (byte)workload,
PeerCount = peerCount,
State = (int)state,
LoadLevelsCount = (int)FeedbackLevel.LEVELS_COUNT,
PredictionData = predictionData,
};
var eventData = new EventData((byte)ServerEventCode.UpdateServer, contract);
this.SendEvent(eventData, new SendParameters());
}
/// <summary>
/// Attempts to add a server instance.
/// </summary>
/// <param name = "server">The server instance to add.</param>
/// <param name = "loadLevel">The current workload of the server instance.</param>
/// <returns>
/// True if the server instance was added successfully. If the server instance already exists,
/// this method returns false.
/// </returns>
public bool TryAddServer(TServer server, FeedbackLevel loadLevel)
{
lock (this.serverList)
{
// check if the server instance was already added
if (this.serverList.ContainsKey(server))
{
log.WarnFormat("LoadBalancer already contains server {0}", server);
return(false);
}
var serverState = new ServerState(server)
{
LoadLevel = loadLevel,
Weight = this.GetLoadLevelWeight(loadLevel)
};
this.serverList.Add(server, serverState);
if (serverState.Weight > 0)
{
this.AddToAvailableServers(serverState);
}
this.UpdateTotalWorkload(FeedbackLevel.Lowest, loadLevel);
if (log.IsDebugEnabled)
{
log.DebugFormat("Added server: workload={0}", loadLevel);
}
return(true);
}
}
public ServerStateData <TServer> TryAddServer(TServer server, FeedbackLevel loadLevel, int loadLevelWeight)
{
// check if the server instance was already added
if (this.serverList.ContainsKey(server))
{
log.WarnFormat("LoadBalancer already contains server {0}", server);
return(null);
}
var serverState = new ServerStateData <TServer>(server)
{
LoadLevel = loadLevel,
Weight = loadLevelWeight,
Priority = this.Priority
};
this.serverList.Add(server, serverState);
// we add new server to reserve only when it should be increased by 1
// let's say we had 9 server and ratio is 0.2, then reserve will contain 1 server
// we add new server. 10*0.2 == 2. 2 - resevedServersCount(1) == 1. and -1 will gives us almost 0. so we increase amount of reserved servers
if (Math.Abs(this.reserveRatio * this.serverList.Count - this.reservedServersCount - 1) < 0.0001)
{
serverState.MarkReserved();
++this.reservedServersCount;
}
this.UpdateServerReturnThreshold();
return(serverState);
}
public FeedbackLevel SetInput(FeedbackName key, int input, out FeedbackLevel level)
{
level = this.Output;
// Controllers are optional, we don't need to configure them all.
FeedbackController controller;
if (this.values.TryGetValue(key, out controller))
{
if (controller.SetInput(input))
{
level = controller.Output;
if (controller.Output > this.Output)
{
return(this.Output = controller.Output);
}
if (controller.Output < this.Output)
{
return(this.CalculateOutput());
}
}
level = controller.Output;
}
return(this.Output);
}
public FeedbackController(
FeedbackName feedbackName, Dictionary <FeedbackLevel, int> thresholdValues, int initialInput, FeedbackLevel initalFeedbackLevel)
{
this.thresholdValues = thresholdValues;
this.feedbackName = feedbackName;
this.currentFeedbackLevel = initalFeedbackLevel;
this.currentInput = initialInput;
}
public FeedbackController(
FeedbackName feedbackName, Dictionary<FeedbackLevel, int> thresholdValues, int initialInput, FeedbackLevel initalFeedbackLevel)
{
this.thresholdValues = thresholdValues;
this.feedbackName = feedbackName;
this.currentFeedbackLevel = initalFeedbackLevel;
this.currentInput = initialInput;
}
/// <summary>
/// Initializes a new instance of the <see cref = "LoadBalancer{TServer}" /> class.
/// </summary>
/// <remarks>
/// This overload is used for unit testing to provide a fixed seed for the
/// random number generator.
/// </remarks>
public LoadBalancer(int[] loadLevelWeights, int seed,
FeedbackLevel priorityUpThreshold = FeedbackLevel.Highest, FeedbackLevel priorityDownThreshold = FeedbackLevel.Highest)
: this(loadLevelWeights)
{
this.PriorityUpThreshold = priorityUpThreshold;
this.priorityDownThreshold = priorityDownThreshold;
this.random = new Random(seed);
}
/// <summary>
/// Gets either 'level' if it is exist or first existing level below 'level'
/// </summary>
/// <param name="level"></param>
/// <param name="realLevel"></param>
private void GetExistingLevel(FeedbackLevel level, out FeedbackLevel realLevel)
{
while (!this.DoesLevelExists(level))
{
level = this.GetNextExistingLowerLevel(level);
}
realLevel = level;
}
private void InitializeFromConfig(string configFilePath)
{
string message;
LoadBalancerSection section;
int[] weights = null;
if (!ConfigurationLoader.TryLoadFromFile(configFilePath, out section, out message))
{
log.WarnFormat(
"Could not initialize LoadBalancer from configuration: Invalid configuration file {0}. Using default settings... ({1})",
configFilePath,
message);
}
if (section != null)
{
this.ReserveRatio = section.ReserveRatio;
this.priorityDownThreshold = section.ValueDown;
if (section.ValueDown == FeedbackLevel.Highest)
{
this.priorityDownThreshold = section.ValueUp == 0 ? section.ValueUp : section.ValueUp - 1;
}
this.PriorityUpThreshold = section.ValueUp;
// load weights from config file & sort:
var dict = new SortedDictionary <int, int>();
foreach (LoadBalancerWeight weight in section.LoadBalancerWeights)
{
dict.Add((int)weight.Level, weight.Value);
}
if (dict.Count == (int)FeedbackLevel.Highest + 1)
{
weights = new int[dict.Count];
dict.Values.CopyTo(weights, 0);
log.InfoFormat("Initialized Load Balancer from configuration file: {0}", configFilePath);
}
else
{
log.WarnFormat(
"Could not initialize LoadBalancer from configuration: {0} is invalid - expected {1} entries, but found {2}. Using default settings...",
configFilePath,
(int)FeedbackLevel.Highest + 1,
dict.Count);
}
}
if (weights == null)
{
weights = DefaultConfiguration.GetDefaultWeights();
}
this.loadLevelWeights = weights;
}
private bool UpdateLevelValues(FeedbackLevel level, int input, float factor, out FeedbackLevelData upDownValues, out FeedbackLevelData higherLevelValues, Dictionary <FeedbackLevel, FeedbackLevelData> updatedLevels)
{
higherLevelValues = new FeedbackLevelData(-1, -1);
if (!this.thresholdValues.TryGetValue(level, out upDownValues))
{
log.WarnFormat("Can not find values for level {0}", level);
return(false);
}
if (input < 0)
{
input = 0;
}
if (level == FeedbackLevel.Highest)
{
if (log.IsDebugEnabled)
{
log.Debug("We do not update upper bound for 'Highest' level");
}
//upDownValues.UpperBound = (int)(factor * (input - upDownValues.UpperBound)) + upDownValues.UpperBound;
//this.thresholdValues[level] = upDownValues;
//updatedLevels[level] = upDownValues;
return(true);
}
var nextLevel = this.GetNextExistingHigherLevel(level);
if (!this.thresholdValues.TryGetValue(nextLevel, out higherLevelValues))
{
log.WarnFormat("Can not find values for level {0}", nextLevel);
return(false);
}
var upDownDiff = upDownValues.UpperBound - higherLevelValues.LowerBound;
upDownValues.UpperBound = (int)(factor * (input - upDownValues.UpperBound)) + upDownValues.UpperBound;
higherLevelValues.LowerBound = upDownValues.UpperBound - upDownDiff;
if (higherLevelValues.LowerBound > upDownValues.UpperBound)
{
higherLevelValues.LowerBound = upDownValues.UpperBound;
}
this.thresholdValues[level] = upDownValues;
this.thresholdValues[nextLevel] = higherLevelValues;
updatedLevels[level] = upDownValues;
updatedLevels[nextLevel] = higherLevelValues;
if (log.IsDebugEnabled)
{
log.DebugFormat("Level updated. Input:{0}, level:{1}, next:{2}, {1}'s data:{3}, {2}'s data:{4}",
input, level, nextLevel, upDownValues, higherLevelValues);
}
return(true);
}
public DFAEDFeedback(Automaton <BDD> dfaGoal, Automaton <BDD> dfaAttempt, FeedbackLevel level, HashSet <char> alphabet, DFAEditScript script, double utility, CharSetSolver solver)
: base(level, alphabet, utility, solver)
{
var positiveDifference = dfaGoal.Minus(dfaAttempt, solver).Determinize(solver).Minimize(solver);
var negativeDifference = dfaAttempt.Minus(dfaGoal, solver).Determinize(solver).Minimize(solver);
this.counterexample = DFAUtilities.GenerateShortTerm(positiveDifference.IsEmpty ? negativeDifference : positiveDifference, solver);
this.type = FeedbackType.DFAED;
this.script = script;
}
public NFANotMinimalFeedback (
FeedbackLevel level, HashSet<char> alphabet,
int stateDiff, int transitionDiff,
NFAEditScript script,
CharSetSolver solver)
: base(level, alphabet, solver)
{
this.transitionDiff = transitionDiff;
this.stateDiff = stateDiff;
this.script = script;
}
public void UpdateTotalWorkload(ServerStateData <TServer> server, FeedbackLevel oldLoadLevel, FeedbackLevel newLoadLevel)
{
this.totalWorkload -= (int)oldLoadLevel;
this.totalWorkload += (int)newLoadLevel;
if (!server.IsInReserve)
{
this.serversInUseWorkload -= (int)oldLoadLevel;
this.serversInUseWorkload += (int)newLoadLevel;
}
}
public NFANotMinimalFeedback(
FeedbackLevel level, HashSet <char> alphabet,
int stateDiff, int transitionDiff,
NFAEditScript script,
CharSetSolver solver)
: base(level, alphabet, solver)
{
this.transitionDiff = transitionDiff;
this.stateDiff = stateDiff;
this.script = script;
}
public void UpdateServerState(FeedbackLevel workload, int peerCount, ServerState state)
{
if (!this.IsRegistered)
{
return;
}
var contract = new UpdateServerEvent { LoadIndex = (byte)workload, PeerCount = peerCount, State = (int)state };
var eventData = new EventData((byte)ServerEventCode.UpdateServer, contract);
this.SendEvent(eventData, new SendParameters());
}
private bool GetLowerThreshold(FeedbackLevel level, out int result)
{
FeedbackLevelData values;
if (this.thresholdValues.TryGetValue(level, out values))
{
result = values.LowerBound;
return(true);
}
result = 0;
return(false);
}
internal void UpdateThresholds(SortedDictionary <FeedbackLevel, FeedbackLevelData> dict)
{
foreach (var feedbackLevelData in dict)
{
this.thresholdValues[feedbackLevelData.Key] = feedbackLevelData.Value;
}
var t = this.currentInput;
this.currentInput = 0;
this.currentFeedbackLevel = FeedbackLevel.Lowest;
this.SetInput(t);
}
public NFAEDFeedback(Automaton <BDD> nfaGoal, Automaton <BDD> nfaAttempt,
FeedbackLevel level, HashSet <char> alphabet,
NFAEditScript script, CharSetSolver solver)
: base(level, alphabet, solver)
{
//TODO might have to determinize to do this operations
var positiveDifference = nfaGoal.Minus(nfaAttempt, solver).Determinize(solver).Minimize(solver);
var negativeDifference = nfaAttempt.Minus(nfaGoal, solver).Determinize(solver).Minimize(solver);
this.counterexample = DFAUtilities.GenerateShortTerm(positiveDifference.IsEmpty ? negativeDifference : positiveDifference, solver);
this.script = script;
}
public void UpdateServerState(FeedbackLevel workload, int peerCount, ServerState state)
{
if (!this.IsRegistered)
{
return;
}
var contract = new UpdateServerEvent {
LoadIndex = (byte)workload, PeerCount = peerCount, State = (int)state
};
var eventData = new EventData((byte)ServerEventCode.UpdateServer, contract);
this.SendEvent(eventData, new SendParameters());
}
private bool UpdateThresholdsInt(int input, FeedbackLevel level, float factor,
ref Dictionary <FeedbackLevel, FeedbackLevelData> updatedLevels)
{
var expectedLevel = FeedbackLevel.Lowest;
foreach (var thresholdValue in this.thresholdValues)
{
if (thresholdValue.Value.UpperBound >= input)
{
expectedLevel = thresholdValue.Key;
break;
}
if (thresholdValue.Key == FeedbackLevel.Highest)
{
expectedLevel = FeedbackLevel.Highest;
break;
}
}
if (expectedLevel == level)
{
return(false);
}
if (log.IsDebugEnabled)
{
log.DebugFormat("Level updating. input:{0}, level:{1}, factor:{2}", input, level, factor);
}
if (updatedLevels == null)
{
updatedLevels = new Dictionary <FeedbackLevel, FeedbackLevelData>();
}
if (expectedLevel < level)
{
var next = this.GetNextExistingLowerLevel(level);
this.SafeUpdateThresholdsDownward(level, next, input, factor, ref updatedLevels);
}
else
{
var next = this.GetNextExistingHigherLevel(level);
this.SafeUpdateThresholdsUpward(level, next, input, updatedLevels, factor);
}
return(true);
}
public void SendMessage(FeedbackLevel level, LocalizedString message)
{
switch (level)
{
case FeedbackLevel.Info:
Logger.Information(message.ToString());
return;
case FeedbackLevel.Warn:
Logger.Information(message.ToString());
return;
case FeedbackLevel.Error:
Logger.Information(message.ToString());
return;
}
}
private FeedbackLevel GetNextExistingLowerLevel(FeedbackLevel level)
{
var next = level;
while (next != FeedbackLevel.Lowest)
{
next = GetNextLower(next);
if (this.DoesLevelExists(next))
{
return(next);
}
}
return(FeedbackLevel.Lowest);
}
public FeedbackLevel GetNextLowerThreshold(FeedbackLevel level, out int result)
{
FeedbackLevel next = level;
while (next != FeedbackLevel.Lowest)
{
next = FeedbackLevelOrder.GetNextLower(next);
if (this.thresholdValues.TryGetValue(next, out result))
{
return next;
}
}
this.thresholdValues.TryGetValue(level, out result);
return level;
}
private bool DoesLevelExists(FeedbackLevel level)
{
FeedbackLevelData values;
if (this.thresholdValues.TryGetValue(level, out values))
{
return(true);
}
if (level == FeedbackLevel.Highest)
{
return(true);
}
return(false);
}
private FeedbackLevel GetNextLowerThreshold(FeedbackLevel level, out int result)
{
var next = level;
while (next != FeedbackLevel.Lowest)
{
next = this.GetNextLower(next);
if (this.GetLowerThreshold(next, out result))
{
return(next);
}
}
this.GetLowerThreshold(next, out result);
return(level);
}
public FeedbackLevel GetNextLowerThreshold(FeedbackLevel level, out int result)
{
FeedbackLevel next = level;
while (next != FeedbackLevel.Lowest)
{
next = FeedbackLevelOrder.GetNextLower(next);
if (this.thresholdValues.TryGetValue(next, out result))
{
return(next);
}
}
this.thresholdValues.TryGetValue(level, out result);
return(level);
}
private FeedbackLevel GetNextExistingHigherLevel(FeedbackLevel level)
{
var next = level;
while (next != FeedbackLevel.Highest)
{
next = GetNextHigher(next);
if (this.DoesLevelExists(next))
{
return(next);
}
}
this.DoesLevelExists(level);
return(level);
}
public void SendMessage(FeedbackLevel level, LocalizedString message)
{
switch (level)
{
case FeedbackLevel.Info:
Logger.Information(message.ToString());
return;
case FeedbackLevel.Warn:
Logger.Information(message.ToString());
return;
case FeedbackLevel.Error:
Logger.Information(message.ToString());
return;
}
}
private void SafeUpdateThresholdsDownward(FeedbackLevel level, FeedbackLevel next, int input,
float factor, ref Dictionary <FeedbackLevel, FeedbackLevelData> updatedLevels)
{
FeedbackLevelData values, higherLevelValues;
if (!this.UpdateLevelValues(next, input - 1, factor, out values, out higherLevelValues, updatedLevels))
{
return;
}
var upDownDiff = values.UpperBound - values.LowerBound;
if (upDownDiff < MinimalLevelSize)
{
this.UpdateThresholdsInt(values.UpperBound - MinimalLevelSize, next, 1.0f, ref updatedLevels);
}
}
/// <summary>
/// Tries to update a server instance.
/// </summary>
/// <param name = "server">The server to update.</param>
/// <param name = "newLoadLevel">The current workload of the server instance.</param>
/// <returns>
/// True if the server instance was updated successfully.
/// If the server instance does not exists, this method returns false.
/// </returns>
public bool TryUpdateServer(TServer server, FeedbackLevel newLoadLevel)
{
lock (this.serverList)
{
// check if server instance exits
ServerState serverState;
if (this.serverList.TryGetValue(server, out serverState) == false)
{
return(false);
}
// check if load level has changed
if (serverState.LoadLevel == newLoadLevel)
{
return(true);
}
if (log.IsDebugEnabled)
{
log.DebugFormat("Updating server: oldWorkload={0}, newWorkload={1}", serverState.LoadLevel, newLoadLevel);
}
// apply new state
this.UpdateTotalWorkload(serverState.LoadLevel, newLoadLevel);
serverState.LoadLevel = newLoadLevel;
var newWeight = this.GetLoadLevelWeight(newLoadLevel);
// check if the weight for the server instance has changes
// if it has not changed we don't have to update the list of available servers
if (newWeight == serverState.Weight)
{
return(true);
}
this.RemoveFromAvailableServers(serverState);
serverState.Weight = newWeight;
if (serverState.Weight > 0)
{
this.AddToAvailableServers(serverState);
}
return(true);
}
}
public DensityFeedback(FeedbackLevel level, HashSet <char> alphabet, Automaton <BDD> dfaGoal, Automaton <BDD> dfaAttempt, double utility, CharSetSolver solver)
: base(level, alphabet, utility, solver)
{
BDD pred = solver.False;
foreach (var el in alphabet)
{
pred = solver.MkOr(pred, solver.MkCharConstraint(false, el));
}
var dfaAll = Automaton <BDD> .Create(0, new int[] { 0 }, new Move <BDD>[] { new Move <BDD>(0, 0, pred) });
this.type = FeedbackType.Density;
this.positiveDifference = dfaGoal.Minus(dfaAttempt, solver).Determinize(solver).Minimize(solver);
this.negativeDifference = dfaAttempt.Minus(dfaGoal, solver).Determinize(solver).Minimize(solver);
this.symmetricDifference = dfaAll.Minus(dfaAll.Minus(positiveDifference, solver).Intersect(dfaAll.Minus(negativeDifference, solver), solver), solver).Determinize(solver).Minimize(solver);
this.solver = solver;
}
// automata come already determinized
public NFACounterexampleFeedback(
FeedbackLevel level, HashSet <char> alphabet,
Automaton <BDD> solutionDFA, Automaton <BDD> attemptDFA,
CharSetSolver solver)
: base(level, alphabet, solver)
{
BDD pred = solver.False;
foreach (var el in alphabet)
{
pred = solver.MkOr(pred, solver.MkCharConstraint(false, el));
}
var dfaAll = Automaton <BDD> .Create(0, new int[] { 0 }, new Move <BDD>[] { new Move <BDD>(0, 0, pred) });
this.positiveDifference = solutionDFA.Minus(attemptDFA, solver).Determinize(solver).Minimize(solver);
this.negativeDifference = attemptDFA.Minus(solutionDFA, solver).Determinize(solver).Minimize(solver);
this.solver = solver;
}
public FeedbackLevel SetInput(FeedbackName key, int input)
{
// Controllers are optional, we don't need to configure them all.
FeedbackController controller;
if (this.values.TryGetValue(key, out controller))
{
if (controller.SetInput(input))
{
if (controller.Output > this.Output)
{
return this.Output = controller.Output;
}
if (controller.Output < this.Output)
{
return this.CalculateOutput();
}
}
}
return this.Output;
}
/// <summary>
/// Computes the grade for attempt using all the possible metrics
/// </summary>
/// <param name="dfaGoal">minimal correct dfa</param>
/// <param name="dfaAttempt">dfa to be graded</param>
/// <param name="al">input alphabet</param>
/// <param name="solver">SMT solver for char set</param>
/// <param name="timeout">timeout for the PDL enumeration (suggested > 1000)</param>
/// <param name="maxGrade">Max grade for the homework</param>
/// <param name="enableDFAED">true to enable DFA edit distance</param>
/// <param name="enablePDLED">true to enable PDL edit distance</param>
/// <param name="enableDensity">true to enable density distance</param>
/// <returns>Grade for dfa2</returns>
public static Pair<int, IEnumerable<DFAFeedback>> GetGrade(
Automaton<BDD> dfaGoal, Automaton<BDD> dfaAttempt, HashSet<char> al,
CharSetSolver solver, long timeout,
int maxGrade, FeedbackLevel level,
bool enableDFAED, bool enablePDLED, bool enableDensity)
{
PDLEnumerator pdlEnumerator = new PDLEnumerator();
var feedList = new List<DFAFeedback>();
DFAFeedback defaultFeedback = new StringFeedback(level, StringFeedbackType.Wrong, al, solver);
#region Accessory and initial vars
//Compute minimized version of DFAs
var dfaGoalMin = dfaGoal.Determinize(solver).Minimize(solver);
var dfaAttemptMin = dfaAttempt.Determinize(solver).Minimize(solver);
//Initialize distances at high values in case they are not used
// they only produce positive grade if between 0 and 1
double pdlEditDistanceScaled = 2;
double densityRatio = 2;
double dfaED = 2;
#endregion
#region deductions on the grade based on the size of the dfa
//Deduction if DFA is smaller than it should be: used only for PDL ed and for density
var smallerDFADeduction = 0.2 * Math.Sqrt(
Math.Max(0.0, dfaGoalMin.StateCount - dfaAttemptMin.StateCount) /
((double)dfaGoalMin.StateCount));
#endregion
#region check whether the attempt is equivalent to the solution
if (dfaGoal.IsEquivalentWith(dfaAttempt, solver))
{
Console.WriteLine("Correct");
feedList.Add(new StringFeedback(level, StringFeedbackType.Correct, al, solver));
return new Pair<int, IEnumerable<DFAFeedback>>(maxGrade, feedList);
}
#endregion
#region metrics computation
Stopwatch swPDLed = new Stopwatch();
swPDLed.Start();
#region PDL edit distance
Transformation feedbackTransformation = null;
if (enablePDLED)
{
var trpair = PDLEditDistance.GetMinimalFormulaEditDistanceTransformation(dfaGoalMin, dfaAttemptMin, al, solver, timeout, pdlEnumerator);
if (trpair != null)
{
var transformationGrade = trpair.First;
feedbackTransformation = trpair.Second;
var scaling = 1.0;
pdlEditDistanceScaled = transformationGrade.totalCost / (transformationGrade.minSizeForTreeA * scaling) + smallerDFADeduction;
}
}
#endregion
swPDLed.Stop();
Stopwatch swDensity = new Stopwatch();
swDensity.Start();
#region density distance
if (enableDensity)
{
densityRatio = DFADensity.GetDFADifferenceRatio(dfaGoalMin, dfaAttemptMin, al, solver);
densityRatio += smallerDFADeduction;
}
#endregion
swDensity.Stop();
Stopwatch swDFAed = new Stopwatch();
swDFAed.Start();
#region DFA edit distance
DFAEditScript dfaEditScript = null;
if (enableDFAED)
{
//limit the depth of the DFA edit distance search
var maxMoves = Math.Max(1, 6 - (int)Math.Sqrt(dfaAttempt.MoveCount + dfaAttempt.StateCount));
dfaEditScript = DFAEditDistance.GetDFAOptimalEdit(dfaGoal, dfaAttempt, al, solver, timeout, new StringBuilder());
if (dfaEditScript != null)
dfaED = ((double)(dfaEditScript.GetCost())) / ((double)((dfaGoalMin.StateCount + 1) * al.Count));
}
#endregion
swDFAed.Stop();
#endregion
#region metrics scaling
var scalingSquarePDLED = 1.005;
var scalingSquareDensity = 1; var multv2 = 0.5;
var scalingSquareDFAED = 1.03;
var scaledPdlED = (0.9 * (scalingSquarePDLED + pdlEditDistanceScaled) * (scalingSquarePDLED + pdlEditDistanceScaled)) - scalingSquarePDLED * scalingSquarePDLED;
var scaledDensityRatio = (scalingSquareDensity + (multv2 * densityRatio)) * (scalingSquareDensity + (multv2 * densityRatio)) - scalingSquareDensity * scalingSquareDensity;
var scaledDfaED = (scalingSquareDFAED + dfaED) * (scalingSquareDFAED + dfaED) - scalingSquareDFAED * scalingSquareDFAED;
//Select dominating Feedback based on grade
double unscaledGrade = Math.Min(Math.Min(scaledPdlED, scaledDensityRatio), scaledDfaED);
var pdledwins = scaledPdlED <= Math.Min(scaledDensityRatio, scaledDfaED);
var dfaedwins = scaledDfaED <= Math.Min(scaledDensityRatio, scaledPdlED);
var densitywins = scaledDensityRatio <= Math.Min(scaledDfaED, scaledPdlED);
#endregion
#region Feedback Selection
if (pdledwins && feedbackTransformation != null && feedbackTransformation.pdlB.GetFormulaSize()<10)
feedList.Add(new PDLEDFeedback(level, al, feedbackTransformation, scaledPdlED, solver));
if ((dfaedwins || feedList.Count == 0) && dfaEditScript != null && !dfaEditScript.IsComplex())
{
feedList = new List<DFAFeedback>();
feedList.Add(new DFAEDFeedback(dfaGoal, dfaAttempt, level, al, dfaEditScript, scaledDfaED, solver));
}
if (densitywins || feedList.Count == 0)
{
feedList = new List<DFAFeedback>();
feedList.Add(new DensityFeedback(level, al, dfaGoal, dfaAttempt, scaledDensityRatio, solver));
}
if (feedList.Count == 0)
{
Console.WriteLine("Why no feedback!!");
feedList.Add(defaultFeedback);
}
#endregion
#region normalize grade
var scaledGrade = maxGrade - (int)Math.Round(unscaledGrade * (double)(maxGrade));
//If rounding yields maxgrade deduct 1 point by default
if (scaledGrade == maxGrade)
scaledGrade = maxGrade - 1;
//Remove possible deduction
scaledGrade = scaledGrade < 0 ? 0 : scaledGrade;
return new Pair<int, IEnumerable<DFAFeedback>>(scaledGrade, feedList);
#endregion
}
public NFAEDFeedback(Automaton<BDD> nfaGoal, Automaton<BDD> nfaAttempt,
FeedbackLevel level, HashSet<char> alphabet,
NFAEditScript script, CharSetSolver solver)
: base(level, alphabet, solver)
{
//TODO might have to determinize to do this operations
var positiveDifference = nfaGoal.Minus(nfaAttempt, solver).Determinize(solver).Minimize(solver);
var negativeDifference = nfaAttempt.Minus(nfaGoal, solver).Determinize(solver).Minimize(solver);
this.counterexample = DFAUtilities.GenerateShortTerm(positiveDifference.IsEmpty ? negativeDifference : positiveDifference, solver);
this.script = script;
}
private void TryAddServer(Server server, FeedbackLevel loadLevel, bool expectedResult = true)
{
var result = this.balancer.TryAddServer(server, loadLevel);
Assert.AreEqual(result, expectedResult);
}
public DFAEDFeedback(Automaton<BDD> dfaGoal, Automaton<BDD> dfaAttempt, FeedbackLevel level, HashSet<char> alphabet, DFAEditScript script, double utility, CharSetSolver solver)
: base(level, alphabet, utility, solver)
{
var positiveDifference = dfaGoal.Minus(dfaAttempt, solver).Determinize(solver).Minimize(solver);
var negativeDifference = dfaAttempt.Minus(dfaGoal, solver).Determinize(solver).Minimize(solver);
this.counterexample = DFAUtilities.GenerateShortTerm(positiveDifference.IsEmpty ? negativeDifference : positiveDifference, solver);
this.type = FeedbackType.DFAED;
this.script = script;
}
public StringFeedback(FeedbackLevel level, StringFeedbackType sfType, HashSet<char> alphabet, CharSetSolver solver)
: base(level, alphabet, 1, solver)
{
this.type = FeedbackType.String;
this.sfType = sfType;
}
public NFAFeedback(FeedbackLevel level, HashSet<char> alphabet, CharSetSolver solver)
{
this.alphabet = alphabet;
this.level = level;
this.solver = solver;
}
private DFAFeedback() {
this.level = FeedbackLevel.Minimal;
}
public DensityFeedback(FeedbackLevel level, HashSet<char> alphabet, Automaton<BDD> dfaGoal, Automaton<BDD> dfaAttempt, double utility, CharSetSolver solver)
: base(level, alphabet, utility,solver)
{
BDD pred = solver.False;
foreach (var el in alphabet)
pred=solver.MkOr(pred,solver.MkCharConstraint(false,el));
var dfaAll = Automaton<BDD>.Create(0,new int[]{0},new Move<BDD>[]{new Move<BDD>(0,0,pred)});
this.type = FeedbackType.Density;
this.positiveDifference = dfaGoal.Minus(dfaAttempt, solver).Determinize(solver).Minimize(solver);
this.negativeDifference = dfaAttempt.Minus(dfaGoal, solver).Determinize(solver).Minimize(solver);
this.symmetricDifference = dfaAll.Minus(dfaAll.Minus(positiveDifference,solver).Intersect(dfaAll.Minus(negativeDifference,solver),solver),solver).Determinize(solver).Minimize(solver);
this.solver = solver;
}
public PDLEDFeedback(FeedbackLevel level, HashSet<char> alphabet, Transformation transformation, double utility, CharSetSolver solver)
: base(level, alphabet, utility,solver)
{
this.transformation = transformation;
this.type = FeedbackType.PDLED;
}
public static FeedbackLevel GetNextHigher(FeedbackLevel level)
{
return ascending[level];
}
public static FeedbackLevel GetNextLower(FeedbackLevel level)
{
return descending[level];
}
public FeedbackLevel CalculateOutput()
{
return this.Output = this.values.Values.Max(controller => controller.Output);
}
// automata come already determinized
public NFACounterexampleFeedback(
FeedbackLevel level, HashSet<char> alphabet,
Automaton<BDD> solutionDFA, Automaton<BDD> attemptDFA,
CharSetSolver solver)
: base(level, alphabet, solver)
{
BDD pred = solver.False;
foreach (var el in alphabet)
pred = solver.MkOr(pred, solver.MkCharConstraint(false, el));
var dfaAll = Automaton<BDD>.Create(0, new int[] { 0 }, new Move<BDD>[] { new Move<BDD>(0, 0, pred) });
this.positiveDifference = solutionDFA.Minus(attemptDFA, solver).Determinize(solver).Minimize(solver);
this.negativeDifference = attemptDFA.Minus(solutionDFA, solver).Determinize(solver).Minimize(solver);
this.solver = solver;
}
/// <summary>
/// Clears the data stored in the model.
/// </summary>
public void Clear()
{
Level = FeedbackLevel.None;
Description = string.Empty;
}
/// <summary>
/// Computes the grade for attempt using all the possible metrics
/// </summary>
/// <param name="solutionNFA">correct nfa</param>
/// <param name="attemptNFA">nfa to be graded</param>
/// <param name="alpahbet">input alphabet</param>
/// <param name="solver">SMT solver for char set</param>
/// <param name="timeout">timeout for the PDL enumeration (suggested > 1000)</param>
/// <param name="maxGrade">Max grade for the homework</param>
/// <param name="level">Feedback level</param>
/// <returns>Grade for nfa2</returns>
public static Pair<int, IEnumerable<NFAFeedback>> GetGrade(
Automaton<BDD> solutionNFA, Automaton<BDD> attemptNFA, HashSet<char> alphabet,
CharSetSolver solver, long timeout, int maxGrade, FeedbackLevel level)
{
var feedbacks = new List<NFAFeedback>();
int deadStateDeduction = 0;
int tooBigDeduction = 0;
int incorrectDeduction = 0;
// Remove at most a percentage of max grade when NFA is big
double maxDeductionForTooBig = ((double)maxGrade * 0.3);
double maxDeductionForDeadStates = ((double)maxGrade * 0.1);
double solutionStateCount = solutionNFA.StateCount;
double attemptStateCount = attemptNFA.StateCount;
double solutionTransCount = solutionNFA.MoveCount;
double attemptTransCount = attemptNFA.MoveCount;
NFAEditDistanceProvider nfaedp = new NFAEditDistanceProvider(solutionNFA, alphabet, solver, timeout);
//Check if using epsilon and nondeterminism
if (solutionNFA.IsEpsilonFree)
solutionNFA.CheckDeterminism(solver);
if (attemptNFA.IsEpsilonFree)
attemptNFA.CheckDeterminism(solver);
bool shouldUseEpsilon = !solutionNFA.IsEpsilonFree && attemptNFA.IsEpsilonFree;
bool shouldUseNonDet = !shouldUseEpsilon && !solutionNFA.isDeterministic && attemptNFA.isDeterministic;
//Check if solution has dead states and remove if it does
var statesBeforeDeadStatesElimination = attemptNFA.StateCount;
attemptNFA.EliminateDeadStates();
var solutionHasDeadStates = attemptNFA.StateCount < statesBeforeDeadStatesElimination;
//Start checking equiv
bool areEquivalent = solutionNFA.IsEquivalentWith(attemptNFA, solver);
if (areEquivalent)
{
// prompt nfa is correct
feedbacks.Insert(0, new NFAStringFeedback(level, alphabet, solver, "Your NFA accepts the CORRECT language."));
#region Check number of states and decrease grade if too big
int stateDiff = (int)(attemptStateCount - solutionStateCount);
int transDiff = (int)(attemptTransCount - solutionTransCount);
//If is not minimal apply deduction and compute edit
if (stateDiff > 0 || transDiff > 0)
{
#region Try to collapse for feedback
// Try to find a way to collaps states or remove states and transitions to make the NFA smaller
NFAEditScript collapseScript = null;
var edit = nfaedp.NFACollapseSearch(attemptNFA);
if (edit != null)
{
collapseScript = new NFAEditScript();
collapseScript.script.Insert(0, edit);
}
feedbacks.Add(new NFANotMinimalFeedback(level, alphabet, stateDiff, transDiff, collapseScript, solver));
#endregion
#region Compute tooBigDeduction
if (stateDiff > 0)
{
// ((att/sol)^2)-1
var stateRatio = attemptStateCount / solutionStateCount;
var stateRatioSqM1 = Math.Pow(stateRatio, 2) - 1;
var sclaedStateRatio = stateRatioSqM1 * maxDeductionForTooBig / 2;
tooBigDeduction = (int)Math.Round(Math.Min(sclaedStateRatio, maxDeductionForTooBig));
}
else
{
if (transDiff > 0)
{
// ((att/sol)^2)-1
var transRatio = attemptTransCount / solutionTransCount;
var transRatioSqM1 = Math.Pow(transRatio, 2) - 1;
var sclaedTransRatio = transRatioSqM1 * maxDeductionForTooBig / 2;
tooBigDeduction = (int)Math.Round(Math.Min(sclaedTransRatio, maxDeductionForTooBig));
}
}
//Make sure deduction is positive
tooBigDeduction = Math.Max(tooBigDeduction, 0);
#endregion
}
#endregion
}
else
{
// prompt nfa is incorrect
feedbacks.Add(new NFAStringFeedback(level, alphabet, solver, "Your NFA does NOT accept the correct langauge."));
//inequivalent, try using grading metrics and based on winning metric give feedback
int remainingGrade = maxGrade - tooBigDeduction;
#region metric computation
//compute deterministic versions
var solutionNFAdet = solutionNFA.RemoveEpsilons(solver.MkOr).Determinize(solver).MakeTotal(solver).Minimize(solver);
var attemptNFAdet = attemptNFA.RemoveEpsilons(solver.MkOr).Determinize(solver).MakeTotal(solver).Minimize(solver);
solutionNFAdet.EliminateDeadStates();
attemptNFAdet.EliminateDeadStates();
//compute density
double densityRatio = DFADensity.GetDFADifferenceRatio(solutionNFAdet, attemptNFAdet, alphabet, solver);
//compute edit distance
double nfaED = 2;
var editScript = nfaedp.GetNFAOptimalEdit(attemptNFA);
if (editScript != null)
nfaED = ((double)(editScript.GetCost())) / ((double)((solutionNFA.StateCount + 1) * alphabet.Count));
#endregion
#region metrics scaling
var scalingSquareDensity = 1; var multv2 = 0.5;
var scalingSquareDFAED = 1.03;
var scaledDensityRatio = (scalingSquareDensity + (multv2 * densityRatio)) * (scalingSquareDensity + (multv2 * densityRatio)) - scalingSquareDensity * scalingSquareDensity;
var scaledNfaED = (scalingSquareDFAED + nfaED) * (scalingSquareDFAED + nfaED) - scalingSquareDFAED * scalingSquareDFAED;
//If the edit script was not computed make sure it loses.
if (editScript == null)
scaledNfaED = Double.MaxValue;
//Select dominating Feedback based on grade
double unscaledGrade = Math.Min(scaledDensityRatio, scaledNfaED);
var dfaedwins = scaledNfaED <= scaledDensityRatio;
var densitywins = scaledDensityRatio <= scaledNfaED;
incorrectDeduction = (int)Math.Round(unscaledGrade * (double)(maxGrade));
#endregion
//If edit distance search works, provides feedback based upon result
//Otherwise, gives counterexample feedback
if (level != FeedbackLevel.Minimal)
{
if (dfaedwins)
feedbacks.Add(new NFAEDFeedback(solutionNFA, attemptNFA, level, alphabet, editScript, solver));
else
feedbacks.Add(new NFACounterexampleFeedback(level, alphabet, solutionNFAdet, attemptNFAdet, solver));
}
}
// Feedback related to nondeterminism and epislon
if (shouldUseEpsilon)
feedbacks.Add(new NFAStringFeedback(level, alphabet, solver, "You should try using epsilon transitions."));
if (shouldUseNonDet)
feedbacks.Add(new NFAStringFeedback(level, alphabet, solver, "You should try using nondeterminism."));
// Deduct points and prompt feedback is solution has dead states
if (solutionHasDeadStates)
{
deadStateDeduction = (int)maxDeductionForDeadStates;
feedbacks.Add(new NFAStringFeedback(level, alphabet, solver, "Your NFA has some dead states."));
}
//Grade computation
int grade = Math.Max(maxGrade - deadStateDeduction - tooBigDeduction - incorrectDeduction, 0);
return new Pair<int, IEnumerable<NFAFeedback>>(grade, feedbacks);
}
private void TryUpdateServer(Server server, FeedbackLevel newLoadLevel, bool expectedResult = true)
{
var result = this.balancer.TryUpdateServer(server, newLoadLevel);
Assert.AreEqual(expectedResult, result, "Unexpected update server result.");
}
/// <summary>
/// Computes the grade for attempt using all the possible metrics
/// </summary>
/// <param name="solution">correct dfa</param>
/// <param name="attempt">dfa to be graded</param>
/// <param name="alpahbet">input alphabet</param>
/// <param name="solver">SMT solver for char set</param>
/// <param name="timeout">timeout for the PDL enumeration (suggested > 1000)</param>
/// <param name="maxGrade">Max grade for the homework</param>
/// <param name="level">Feedback level</param>
/// <returns>Grade for dfa2</returns>
public static Pair<int, IEnumerable<DFAFeedback>> GetGrade(
Automaton<BDD> solution, Automaton<BDD> attempt, HashSet<char> alpahbet,
CharSetSolver solver, long timeout, int maxGrade, FeedbackLevel level)
{
return GetGrade(solution, attempt, alpahbet, solver, timeout, maxGrade, level, true, true, true);
}
public NFAStringFeedback(FeedbackLevel level, HashSet<char> alphabet, CharSetSolver solver, string message)
: base(level, alphabet, solver)
{
this.message = message;
}
private void UpdateServer(Server server, FeedbackLevel newLoadLevel)
{
bool result = this.balancer.TryUpdateServer(server, newLoadLevel);
Assert.IsTrue(result, "Failed to update {0} to the load level '{1}'", server.Name, newLoadLevel);
}
public bool SetInput(int input)
{
if (log.IsDebugEnabled)
{
log.DebugFormat("SetInput: {0} value {1}", this.FeedbackName, input);
}
if (input > this.currentInput)
{
int threshold;
FeedbackLevel last = this.currentFeedbackLevel;
FeedbackLevel next = this.GetNextHigherThreshold(last, out threshold);
while (next != last)
{
if (input >= threshold)
{
last = next;
next = this.GetNextHigherThreshold(last, out threshold);
}
else
{
next = last;
}
}
this.currentInput = input;
if (next != this.currentFeedbackLevel)
{
if (log.IsInfoEnabled)
{
log.InfoFormat("Transit {0} from {1} to {2} with input {3}", this.FeedbackName, this.currentFeedbackLevel, next, input);
}
this.currentFeedbackLevel = next;
return true;
}
}
else if (input < this.currentInput)
{
int threshold;
FeedbackLevel last = this.currentFeedbackLevel;
FeedbackLevel next = this.GetNextLowerThreshold(last, out threshold);
while (next != last)
{
if (input <= threshold)
{
last = next;
next = this.GetNextLowerThreshold(last, out threshold);
}
else
{
next = last;
}
}
this.currentInput = input;
if (next != this.currentFeedbackLevel)
{
if (log.IsInfoEnabled)
{
log.InfoFormat("Transit {0} from {1} to {2} with input {3}", this.FeedbackName, this.currentFeedbackLevel, next, input);
}
this.currentFeedbackLevel = next;
return true;
}
}
return false;
}
