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import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import IsolationForest
from transformers import pipeline

# Define Fact Referencers
class FactReferencer:
    def __init__(self, data):
        self.data = data

    def inside_fact(self, context):
        relevant_facts = [fact for fact in self.data if self._is_relevant(fact, context)]
        return relevant_facts

    def outside_fact(self, external_data):
        impact_facts = [fact for fact in self.data if self._assess_impact(fact, external_data)]
        return impact_facts

    def specific_fact(self, scope):
        specific_facts = [fact for fact in self.data if self._is_specific(fact, scope)]
        return specific_facts

    def _is_relevant(self, fact, context):
        return fact['context'] == context

    def _assess_impact(self, fact, external_data):
        return fact['external_influence'] in external_data

    def _is_specific(self, fact, scope):
        return fact['scope'] == scope

# Criminal Activity Identifier with AI
class CriminalActivityIdentifierAI:
    def __init__(self):
        self.data = []
        self.text_data = []
        self.labels = []
        self.features = []
        self.text_features = []
        self.model = None
        self.text_classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
        self.isolation_forest = IsolationForest(contamination=0.1)

    def add_numerical_data(self, features, label):
        self.features.append(features)
        self.labels.append(label)
    
    def add_textual_data(self, text, label):
        self.text_data.append(text)
        sentiment_result = self.text_classifier(text)
        sentiment_score = sentiment_result[0]['score']
        sentiment_label = 1 if sentiment_result[0]['label'] == 'POSITIVE' else 0
        self.text_features.append([sentiment_score, sentiment_label])
        self.labels.append(label)
    
    def calculate_energy(self, features):
        return np.sum(features)

    def train_model(self):
        combined_features = [np.concatenate([num_feat, text_feat]) for num_feat, text_feat in zip(self.features, self.text_features)]
        X_train, X_test, y_train, y_test = train_test_split(combined_features, self.labels, test_size=0.3, random_state=42)
        self.model = RandomForestClassifier(n_estimators=100, random_state=42)
        self.model.fit(X_train, y_train)
        predictions = self.model.predict(X_test)
        accuracy = accuracy_score(y_test, predictions)
        print(f"Model Accuracy: {accuracy * 100:.2f}%")

    def predict_truth_lie(self, features, text):
        sentiment_result = self.text_classifier(text)
        sentiment_score = sentiment_result[0]['score']
        sentiment_label = 1 if sentiment_result[0]['label'] == 'POSITIVE' else 0
        combined_features = np.concatenate([features, [sentiment_score, sentiment_label]])
        prediction = self.model.predict([combined_features])
        return "Truth" if prediction == 1 else "Lie"

    def detect_anomalies(self, features):
        anomalies = self.isolation_forest.fit_predict(features)
        anomaly_indices = np.where(anomalies == -1)
        return anomaly_indices

    def simulate_markers(self, truth_features, lie_features):
        truth_positions = [np.sum(feat) for feat in truth_features]
        lie_positions = [np.sum(feat) for feat in lie_features]

        plt.figure(figsize=(10, 6))
        plt.scatter(truth_positions, np.zeros_like(truth_positions), c='green', label='Truth Markers', s=100)
        plt.scatter(lie_positions, np.zeros_like(lie_positions), c='red', label='Lie Markers', s=100)
        plt.axhline(0, color='black', linewidth=1)
        plt.title('Truth vs Lie Markers Distribution with AI and ML')
        plt.xlabel('Position')
        plt.yticks([])
        plt.legend()
        plt.grid(True)
        plt.show()

# Carbon Wavelength Measurement and Polygon Drawing
def calculate_wavelength(energy):
    speed_of_light = 3e8  # m/s
    return speed_of_light / energy

def get_carbon_energy(atomic_weight):
    return 12.011 / atomic_weight

def map_to_zero_point_space(emissions_data):
    mapped_points = []
    for weight, emission in emissions_data:
        energy = get_carbon_energy(weight)
        wavelength = calculate_wavelength(energy)
        mapped_point = [wavelength, emission]
        mapped_points.append(mapped_point)
    return mapped_points

def draw_polygons(mapped_points):
    x_coords = [point[0] for point in mapped_points]
    y_coords = [point[1] for point in mapped_points]

    plt.figure(figsize=(10, 6))
    plt.plot(x_coords + [x_coords[0]], y_coords + [y_coords[0]], 'o-', label='Carbon Emissions Polygon')
    plt.xlabel('Wavelength')
    plt.ylabel('Emission Value')
    plt.title('Polygon of Carbon Emissions in Zero-Point Space')
    plt.legend()
    plt.grid(True)
    plt.show()

# Language Model for Polygon Descriptions
def generate_polygon_description(polygon_points):
    description_model = pipeline("text-generation", model="gpt-3.5-turbo")
    x_coords = [point[0] for point in polygon_points]
    y_coords = [point[1] for point in polygon_points]
    description = description_model(f"The polygon has the following coordinates: {list(zip(x_coords, y_coords))}.")
    return description[0]['generated_text']

# Example Usage
# Sample Data for Fact Referencer
data = [
    {'context': 'local', 'external_influence': 'global', 'scope': 'factory'},
    {'context': 'global', 'external_influence': 'local', 'scope': 'region'},
]
fact_referencer = FactReferencer(data)
context = 'local'
external_data = ['global']
scope = 'factory'
inside_facts = fact_referencer.inside_fact(context)
outside_facts = fact_referencer.outside_fact(external_data)
specific_facts = fact_referencer.specific_fact(scope)
print("Inside Facts:", inside_facts)
print("Outside Facts:", outside_facts)
print("Specific Facts:", specific_facts)

# Example Data for CriminalActivityIdentifierAI
identifier_ai = CriminalActivityIdentifierAI()
truth_data = [
    ([5.5, 12.0, 0.9], "The suspect was seen leaving the scene at 3 PM."),
    ([6.1, 11.5, 1.2], "Witnesses confirmed that the event occurred at the stated time.")
]
lie_data = [
    ([2.5, 25.0, 0.3], "The suspect claimed to be at home, but no one can verify."),
    ([3.0, 22.0, 0.4], "The story doesn't match the evidence found at the scene.")
]
for features, text in truth_data:
    identifier_ai.add_numerical_data(features, label=1)
    identifier_ai.add_textual_data(text, label=1)
for features, text in lie_data:
    identifier_ai.add_numerical_data(features, label=0)
    identifier_ai.add_textual_data(text, label=0)
identifier_ai.train_model()
new_features = [5.7, 12.1, 0.95]
new_text = "The suspect was seen near the area but denies being involved."
prediction = identifier_ai.predict_truth_lie(new_features, new_text)
print(f"Prediction for new data: {prediction}")
anomalies = identifier_ai.detect_anomalies(identifier_ai.features)
print(f"Anomalies detected at indices: {anomalies}")
identifier_ai.simulate_markers([f[0] for f in truth_data], [f[0] for f in lie_data])

# Carbon Emissions Polygon
emissions_data = [
    (12.011, 100),
    (12.011, 150),
    (12.011, 200),
    (12.011, 250)
]
mapped_points = map_to_zero_point_space(emissions_data)
draw_polygons(mapped_points)

# Generate Polygon Description
description = generate_polygon_description(mapped_points)
print("Polygon Description:", description)