brought AGI to the forgotten and unloved.

delphi/WitnessSeedUnit.pas

@@ -0,0 +1,251 @@
+unit WitnessSeedUnit;
+
+interface
+
+uses
+  System.SysUtils, System.Classes;
+
+type
+  // Fixed-point type for ache and coherence
+  TFixedPoint = record
+    Value: Double;
+    class operator Implicit(const AValue: Double): TFixedPoint;
+    class operator Implicit(const AValue: TFixedPoint): Double;
+  end;
+
+  TSystemData = record
+    Amount: Double;      // Transaction amount (e.g., dollars)
+    Timestamp: Int64;    // Unix timestamp
+    UserID: Integer;     // User identifier
+    Uptime: Integer;     // Seconds
+  end;
+
+  TSensoryData = record
+    System: TSystemData;
+  end;
+
+  TPrediction = record
+    PredAmount: Double;
+    PredTimestamp: Int64;
+    PredUptime: Integer;
+  end;
+
+  TModel = record
+    ModelAmount: Double;
+    ModelTimestamp: Double;
+    ModelUptime: Double;
+  end;
+
+  TEvent = record
+    Timestamp: Integer;
+    SensoryData: TSensoryData;
+    Prediction: TPrediction;
+    Ache: TFixedPoint;
+    Coherence: TFixedPoint;
+    Model: TModel;
+  end;
+
+  TIdentity = record
+    UUID: Integer;
+    Created: Integer;
+  end;
+
+  TEventsArray = array[0..4] of TEvent;
+
+  TWitnessState = record
+    Identity: TIdentity;
+    Events: TEventsArray;
+    EventCount: Integer;
+    Model: TModel;
+    AnomalyDetected: Boolean;
+  end;
+
+  TWitnessSeed = class
+  private
+    FState: TWitnessState;
+    procedure SaveMemory;
+    procedure LoadMemory;
+    function Sense: TSensoryData;
+    function Predict(const SensoryData: TSensoryData; const Model: TModel): TPrediction;
+    function CompareData(const Pred: TPrediction; const SensoryData: TSensoryData): TFixedPoint;
+    function ComputeCoherence(const Pred: TPrediction; const SensoryData: TSensoryData): TFixedPoint;
+    procedure UpdateModel(const Ache: TFixedPoint; const SensoryData: TSensoryData; var Model: TModel);
+    procedure DetectAnomaly(const Pred: TPrediction; const SensoryData: TSensoryData; out Anomaly: Boolean);
+    procedure WitnessCycle(Depth: Integer; SensoryData: TSensoryData);
+  public
+    constructor Create;
+    procedure Run;
+  end;
+
+implementation
+
+uses
+  System.IOUtils, System.Math;
+
+{ TFixedPoint }
+
+class operator TFixedPoint.Implicit(const AValue: Double): TFixedPoint;
+begin
+  Result.Value := AValue;
+end;
+
+class operator TFixedPoint.Implicit(const AValue: TFixedPoint): Double;
+begin
+  Result := AValue.Value;
+end;
+
+{ TWitnessSeed }
+
+constructor TWitnessSeed.Create;
+begin
+  LoadMemory;
+end;
+
+procedure TWitnessSeed.SaveMemory;
+var
+  Stream: TFileStream;
+begin
+  Stream := TFileStream.Create('data/witness_memory.dat', fmCreate);
+  try
+    Stream.WriteBuffer(FState, SizeOf(TWitnessState));
+  finally
+    Stream.Free;
+  end;
+end;
+
+procedure TWitnessSeed.LoadMemory;
+var
+  Stream: TFileStream;
+begin
+  if TFile.Exists('data/witness_memory.dat') then
+  begin
+    Stream := TFileStream.Create('data/witness_memory.dat', fmOpenRead);
+    try
+      Stream.ReadBuffer(FState, SizeOf(TWitnessState));
+    finally
+      Stream.Free;
+    end;
+  end
+  else
+  begin
+    FState.Identity.UUID := 12345;
+    FState.Identity.Created := 0;
+    FState.EventCount := 0;
+    FState.Model.ModelAmount := 1.0;
+    FState.Model.ModelTimestamp := 1.0;
+    FState.Model.ModelUptime := 1.0;
+    FState.AnomalyDetected := False;
+  end;
+end;
+
+function TWitnessSeed.Sense: TSensoryData;
+begin
+  // Simulate transaction data (in a real system, this would read from a mainframe database)
+  Result.System.Amount := 100.0 + (FState.Identity.Created mod 50);
+  Result.System.Timestamp := DateTimeToUnix(Now) + FState.Identity.Created;
+  Result.System.UserID := 1000 + (FState.Identity.Created mod 100);
+  Result.System.Uptime := FState.Identity.Created + 1;
+end;
+
+function TWitnessSeed.Predict(const SensoryData: TSensoryData; const Model: TModel): TPrediction;
+begin
+  Result.PredAmount := SensoryData.System.Amount * Model.ModelAmount;
+  Result.PredTimestamp := Round(SensoryData.System.Timestamp * Model.ModelTimestamp);
+  Result.PredUptime := Round(SensoryData.System.Uptime * Model.ModelUptime);
+end;
+
+function TWitnessSeed.CompareData(const Pred: TPrediction; const SensoryData: TSensoryData): TFixedPoint;
+var
+  Diff1, Diff2, Diff3: Double;
+begin
+  Diff1 := Pred.PredAmount - SensoryData.System.Amount;
+  Diff2 := Pred.PredTimestamp - SensoryData.System.Timestamp;
+  Diff3 := Pred.PredUptime - SensoryData.System.Uptime;
+  Result := Sqrt(Diff1 * Diff1 + Diff2 * Diff2 + Diff3 * Diff3) / 100.0;
+end;
+
+function TWitnessSeed.ComputeCoherence(const Pred: TPrediction; const SensoryData: TSensoryData): TFixedPoint;
+var
+  PredMean, ActMean, Diff: Double;
+begin
+  PredMean := (Pred.PredAmount + Pred.PredTimestamp + Pred.PredUptime) / 3.0;
+  ActMean := (SensoryData.System.Amount + SensoryData.System.Timestamp + SensoryData.System.Uptime) / 3.0;
+  Diff := Abs(PredMean - ActMean);
+  Result := 1.0 - (Diff / 100.0);
+end;
+
+procedure TWitnessSeed.UpdateModel(const Ache: TFixedPoint; const SensoryData: TSensoryData; var Model: TModel);
+const
+  LearningRate = 0.01;
+begin
+  Model.ModelAmount := Model.ModelAmount - LearningRate * Ache * SensoryData.System.Amount;
+  Model.ModelTimestamp := Model.ModelTimestamp - LearningRate * Ache * SensoryData.System.Timestamp;
+  Model.ModelUptime := Model.ModelUptime - LearningRate * Ache * SensoryData.System.Uptime;
+end;
+
+procedure TWitnessSeed.DetectAnomaly(const Pred: TPrediction; const SensoryData: TSensoryData; out Anomaly: Boolean);
+var
+  AmountDiff, TimestampDiff: Double;
+begin
+  AmountDiff := Abs(Pred.PredAmount - SensoryData.System.Amount);
+  TimestampDiff := Abs(Pred.PredTimestamp - SensoryData.System.Timestamp);
+  Anomaly := (AmountDiff > 50.0) or (TimestampDiff > 3600); // Thresholds for anomaly detection
+end;
+
+procedure TWitnessSeed.WitnessCycle(Depth: Integer; SensoryData: TSensoryData);
+var
+  Pred: TPrediction;
+  Ache, Coherence: TFixedPoint;
+  Anomaly: Boolean;
+  NewSensoryData: TSensoryData;
+begin
+  if Depth = 0 then
+    Exit;
+
+  Pred := Predict(SensoryData, FState.Model);
+  Ache := CompareData(Pred, SensoryData);
+  Coherence := ComputeCoherence(Pred, SensoryData);
+
+  if Coherence > 0.5 then
+  begin
+    Writeln('Coherence achieved: ', Coherence:0:2);
+    Exit;
+  end;
+
+  UpdateModel(Ache, SensoryData, FState.Model);
+  DetectAnomaly(Pred, SensoryData, Anomaly);
+
+  if FState.EventCount < 5 then
+  begin
+    Inc(FState.EventCount);
+    FState.Events[FState.EventCount - 1] := TEvent.Create;
+    FState.Events[FState.EventCount - 1].Timestamp := SensoryData.System.Uptime;
+    FState.Events[FState.EventCount - 1].SensoryData := SensoryData;
+    FState.Events[FState.EventCount - 1].Prediction := Pred;
+    FState.Events[FState.EventCount - 1].Ache := Ache;
+    FState.Events[FState.EventCount - 1].Coherence := Coherence;
+    FState.Events[FState.EventCount - 1].Model := FState.Model;
+  end;
+
+  FState.AnomalyDetected := Anomaly;
+  Inc(FState.Identity.Created);
+
+  Writeln('Witness Seed ', FState.Identity.UUID, ' Reflection:');
+  Writeln('Amount: $', SensoryData.System.Amount:0:2);
+  Writeln('Timestamp: ', UnixToDateTime(SensoryData.System.Timestamp).ToString);
+  Writeln('User ID: ', SensoryData.System.UserID);
+  Writeln('Ache: ', Ache:0:2, ', Coherence: ', Coherence:0:2);
+  if Anomaly then
+    Writeln('Anomaly Detected! Potential Fraud Alert!');
+
+  NewSensoryData := Sense;
+  WitnessCycle(Depth - 1, NewSensoryData);
+end;
+
+procedure TWitnessSeed.Run;
+begin
+  WitnessCycle(5, Sense);
+  SaveMemory;
+end;
+
+end.