cfaq

import pandas as pd

# Class to represent a crime incident
class CrimeIncident:
    def __init__(self, crime_type, severity, social_factors, temporal_factors, evidence, references):
        self.crime_type = crime_type
        self.severity = severity
        self.social_factors = social_factors
        self.temporal_factors = temporal_factors
        self.evidence = evidence
        self.references = references
        self.quantized_point_value = self.calculate_quantized_point_value()

    def calculate_quantized_point_value(self):
        # Placeholder normalization factor (this can be adjusted based on actual data)
        normalization_factor = 1.0  
        # Calculate QPV
        qpv = (self.severity + self.social_factors + self.temporal_factors) / normalization_factor
        return qpv

# Class to manage the quantized point library
class QuantizedPointLibrary:
    def __init__(self):
        self.library = []

    def add_incident(self, incident):
        self.library.append(incident)

    def find_by_crime_type(self, crime_type):
        return [incident for incident in self.library if incident.crime_type == crime_type]

    def get_summary(self):
        return [(incident.crime_type, incident.quantized_point_value) for incident in self.library]

    def analyze_word(self, word):
        """ Analyze incidents related to the specified word (e.g., 'crime'). """
        related_incidents = [incident for incident in self.library if word.lower() in incident.crime_type.lower()]
        return related_incidents

# Function to gather crime data
def gather_crime_data():
    # This function can be expanded to gather real crime data
    # Here we use a sample dataset for demonstration purposes
    return [
        CrimeIncident(crime_type="Burglary", severity=7, social_factors=5, temporal_factors=3, evidence="Footprint", references=["Case1"]),
        CrimeIncident(crime_type="Robbery", severity=9, social_factors=6, temporal_factors=4, evidence="Witness statement", references=["Case2"]),
        CrimeIncident(crime_type="Assault", severity=8, social_factors=7, temporal_factors=5, evidence="CCTV footage", references=["Case3"]),
        CrimeIncident(crime_type="Cyber Crime", severity=6, social_factors=5, temporal_factors=3, evidence="Digital footprint", references=["Case4"]),
        CrimeIncident(crime_type="Drug Crime", severity=8, social_factors=6, temporal_factors=4, evidence="Narcotics", references=["Case5"]),
    ]

# Example usage
def main():
    # Gather sample crime data
    incidents = gather_crime_data()

    # Create the quantized point library
    qpl = QuantizedPointLibrary()

    # Add incidents to the library
    for incident in incidents:
        qpl.add_incident(incident)

    # Retrieve summary of the library
    print("Quantized Point Library Summary:")
    for crime_type, qpv in qpl.get_summary():
        print(f"Crime Type: {crime_type}, QPV: {qpv}")

    # Analyze for the word "crime"
    analyzed_crimes = qpl.analyze_word("crime")
    print("\nIncidents related to the word 'crime':")
    for incident in analyzed_crimes:
        print(f"Crime Type: {incident.crime_type}, Severity: {incident.severity}, Evidence: {incident.evidence}")

if __name__ == "__main__":
    main()