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.