cfaID

import numpy as np

class CriminalActivityIdentifier:
    def __init__(self):
        self.data = {}
        self.energy_scores = {}
        self.dimensions = {}

    def add_data(self, dimension, label, value):
        """Add data to a specific dimension."""
        if dimension not in self.data:
            self.data[dimension] = {}
        self.data[dimension][label] = value

    def apply_dimensional_mapping(self):
        """Map data to corresponding dimensions using provided expressions."""
        for dim, values in self.data.items():
            if dim == 3:
                # Example: 3D crime scene mapping
                self.dimensions[dim] = {label: value for label, value in values.items()}
            elif dim == 1:
                # Example: Normalizing 1D data (e.g., time)
                self.dimensions[dim] = {label: value / value for label, value in values.items()}
            # Add more dimensional mappings as necessary

    def calculate_energy(self):
        """Calculate energy for scenarios based on the input data."""
        for dim, values in self.dimensions.items():
            if dim == 3:
                # Example: E=mc²/2πr² for 3D scenarios
                for label, value in values.items():
                    mass = value
                    c = 3e8  # Speed of light
                    r = 2 * np.pi * value
                    energy = (mass * c ** 2) / (2 * np.pi * r ** 2)
                    self.energy_scores[label] = energy
            elif dim == 1:
                # Example: Energy calculation for 1D data
                for label, value in values.items():
                    energy = value ** 2  # Simple energy function
                    self.energy_scores[label] = energy
            # Add more energy calculations as necessary

    def analyze_scenarios(self):
        """Analyze and print potential scenarios based on energy and inequality expressions."""
        for label, energy in self.energy_scores.items():
            if energy > 1e18:  # Arbitrary threshold for a significant event
                print(f"High energy detected for {label}: Potentially significant evidence.")
            else:
                print(f"Low energy for {label}: Less likely to be significant.")
    
    def identify_anomalies(self):
        """Identify anomalies or outliers."""
        anomalies = []
        for dim, values in self.dimensions.items():
            for label, value in values.items():
                if value >= np.inf or value == np.pi:
                    anomalies.append((label, value))
        if anomalies:
            print("Anomalies detected:")
            for label, value in anomalies:
                print(f" - {label} with value {value}")
        else:
            print("No anomalies detected.")

# Example usage
identifier = CriminalActivityIdentifier()

# Adding example data for different dimensions
identifier.add_data(3, "crime_scene_3D", 5.5)
identifier.add_data(1, "time_factor", 12.0)
identifier.add_data(0, "zero_dimensional_data", 0)

# Applying dimensional mappings and calculations
identifier.apply_dimensional_mapping()
identifier.calculate_energy()

# Analyzing scenarios and identifying anomalies
identifier.analyze_scenarios()
identifier.identify_anomalies()