cfati

import numpy as np
from sklearn.cluster import DBSCAN
from scipy.spatial import ConvexHull

# Mock data structure for crime data points
class CrimeDataPoint:
    def __init__(self, x, y, time, digital_trace, psychological, social_network, past_behavior):
        self.x = x  # spatial X coordinate
        self.y = y  # spatial Y coordinate
        self.time = time  # timestamp of the crime or event
        self.digital_trace = digital_trace  # digital footprint of the person
        self.psychological = psychological  # psychological profile
        self.social_network = social_network  # social network connections
        self.past_behavior = past_behavior  # history of behavior/criminal activities

# Mock dataset of quantized points (for simplicity)
crime_data_points = [
    CrimeDataPoint(1, 2, "2023-09-21T12:00", "trace1", "profile1", "network1", "behavior1"),
    CrimeDataPoint(2, 3, "2023-09-22T13:00", "trace2", "profile2", "network2", "behavior2"),
    CrimeDataPoint(3, 5, "2023-09-23T14:00", "trace3", "profile3", "network3", "behavior3")
]

# Step 1: Quantized Point Analysis
def quantize_points(crime_data):
    quantized_points = []
    for point in crime_data:
        # Extract necessary quantized attributes like x, y, and time
        quantized_points.append([point.x, point.y])
    return np.array(quantized_points)

# Step 2: Ten-Dimensional Data Processing
def process_ten_dimensions(crime_data):
    processed_values = []
    for point in crime_data:
        # Process across multiple dimensions (for simplicity, we concatenate feature sets)
        dimensions = [point.x, point.y, len(point.digital_trace), len(point.psychological), 
                      len(point.social_network), len(point.past_behavior)]
        processed_values.append(dimensions)
    return np.array(processed_values)

# Step 3: Polygon Extrapolation for AI Accuracy
def polygon_extrapolation(quantized_points):
    # Use Convex Hull to generate a polygon from quantized points
    if len(quantized_points) >= 3:
        hull = ConvexHull(quantized_points)
        return hull
    else:
        return None

# Integration function that interconnects all three processes
def crimefinder_integration(crime_data):
    # 1. Perform quantized point analysis
    quantized_points = quantize_points(crime_data)
    
    # 2. Process the data across ten dimensions
    ten_dimensional_values = process_ten_dimensions(crime_data)
    
    # 3. Apply polygon extrapolation for pattern recognition
    polygon = polygon_extrapolation(quantized_points)
    
    # Return results for further AI-driven prediction
    return quantized_points, ten_dimensional_values, polygon

# Testing the integration
quantized_points, ten_dimensional_values, polygon = crimefinder_integration(crime_data_points)

# Output: Print the results
print("Quantized Points:\n", quantized_points)
print("\nTen-Dimensional Processed Values:\n", ten_dimensional_values)
if polygon:
    print("\nPolygon (Convex Hull Vertices):\n", polygon.vertices)
else:
    print("\nNot enough points for a polygon.")