quhabiton.py

import numpy as np
from qiskit import QuantumCircuit, Aer, execute
from qiskit.circuit.library import ZZFeatureMap
from qiskit.algorithms.optimizers import COBYLA
from qiskit_machine_learning.algorithms import VQC
from qiskit.utils import QuantumInstance
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import networkx as nx

class QuHabiton:
    def __init__(self, name, difficulty, frequency):
        self.name = name
        self.difficulty = difficulty
        self.frequency = frequency
        self.success_rate = 0.0
        self.score = 0.0
        self.amplitude = self._initialize_amplitude()
        self.spin = self._calculate_spin()
        self.charge = self._calculate_charge()
        self.entanglement = {}

    def _initialize_amplitude(self):
        theta = np.pi / (2 * self.difficulty)
        return complex(np.cos(theta), np.sin(theta))

    def _calculate_spin(self):
        return 1 / (1 + np.exp(-self.frequency / self.difficulty))

    def _calculate_charge(self):
        return (self.frequency - self.difficulty) / 10

    def update_amplitude(self):
        theta = np.pi * self.success_rate / (2 * self.difficulty)
        self.amplitude = complex(np.cos(theta), np.sin(theta))

    def get_success_probability(self):
        return abs(self.amplitude) ** 2

    def measure(self):
        prob_success = self.get_success_probability()
        return np.random.random() < prob_success

    def update(self, success):
        self.success_rate = (self.success_rate * self.frequency + int(success)) / (self.frequency + 1)
        self.update_amplitude()
        if success:
            self.amplitude *= 1.1
        else:
            self.amplitude *= 0.9
        self.amplitude /= abs(self.amplitude)  # Normalize

    def entangle(self, other_habit, strength):
        self.entanglement[other_habit.name] = strength
        other_habit.entanglement[self.name] = strength

    def apply_entanglement(self):
        for habit_name, strength in self.entanglement.items():
            self.amplitude += strength * 0.1j
        self.amplitude /= abs(self.amplitude)  # Normalize

class QuHabitonSystem:
    def __init__(self, n_dimensions=5):
        self.habits = {}
        self.n_dimensions = n_dimensions
        self.distance_matrix = None
        self.embedded_points = None
        self.scaler = StandardScaler()

    def add_habit(self, name, difficulty, frequency):
        self.habits[name] = QuHabiton(name, difficulty, frequency)

    def entangle_habits(self, habit1_name, habit2_name, strength):
        self.habits[habit1_name].entangle(self.habits[habit2_name], strength)

    def simulate_day(self):
        for habit in self.habits.values():
            habit.apply_entanglement()
            success = habit.measure()
            habit.update(success)
        self._update_scores()

    def _update_scores(self):
        self._update_manifold()
        topological_factor = self._calculate_topological_factor()
        for habit in self.habits.values():
            manifold_factor = self._calculate_manifold_factor(habit)
            habit.score += habit.get_success_probability() * manifold_factor * topological_factor

    def _update_manifold(self):
        habit_vectors = np.array([[h.difficulty, h.frequency, h.success_rate, h.score, h.get_success_probability()] for h in self.habits.values()])
        normalized_vectors = self.scaler.fit_transform(habit_vectors)
        self.distance_matrix = squareform(pdist(normalized_vectors))
        mds = MDS(n_components=self.n_dimensions, dissimilarity='precomputed', random_state=42, max_iter=300, n_init=10)
        self.embedded_points = mds.fit_transform(self.distance_matrix)

    def _calculate_manifold_factor(self, habit):
        if len(self.habits) < 2:
            return 1.0
        habit_vector = np.array([habit.difficulty, habit.frequency, habit.success_rate, habit.score, habit.get_success_probability()])
        distances = np.linalg.norm(self.embedded_points - habit_vector, axis=1)
        nearest_distance = np.min(distances[distances > 0])
        return 1 + 1 / (1 + nearest_distance)

    def _calculate_topological_factor(self):
        if len(self.habits) < 3:
            return 1.0
        diagrams = ripser(self.embedded_points)['dgms']
        persistence = np.sum([np.sum(diag[:, 1] - diag[:, 0]) for diag in diagrams])
        return 1 + persistence / len(self.habits)

    def visualize_bloch_sphere(self):
        fig = plt.figure(figsize=(12, 8))
        ax = fig.add_subplot(111, projection='3d')
        
        for habit in self.habits.values():
            theta = 2 * np.arccos(habit.amplitude.real)
            phi = np.arctan2(habit.amplitude.imag, habit.amplitude.real)
            x = np.sin(theta) * np.cos(phi)
            y = np.sin(theta) * np.sin(phi)
            z = np.cos(theta)
            ax.scatter(x, y, z, label=habit.name)
        
        u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
        x = np.cos(u)*np.sin(v)
        y = np.sin(u)*np.sin(v)
        z = np.cos(v)
        ax.plot_wireframe(x, y, z, color="gray", alpha=0.1)
        
        ax.set_xlabel("X")
        ax.set_ylabel("Y")
        ax.set_zlabel("Z")
        ax.legend()
        plt.title("QuHabitons on Bloch Sphere")
        plt.show()

    def visualize_entanglement(self):
        G = nx.Graph()
        for habit in self.habits.values():
            G.add_node(habit.name, spin=habit.spin, charge=habit.charge)
            for other_habit, strength in habit.entanglement.items():
                G.add_edge(habit.name, other_habit, weight=strength)

        pos = nx.spring_layout(G)
        plt.figure(figsize=(12, 8))
        nx.draw_networkx_nodes(G, pos, node_color=[h.charge for h in self.habits.values()], 
                               node_size=[h.spin*1000 for h in self.habits.values()], 
                               cmap=plt.cm.RdYlBu)
        nx.draw_networkx_edges(G, pos, width=[G[u][v]['weight']*5 for u,v in G.edges()])
        nx.draw_networkx_labels(G, pos)
        plt.title("QuHabiton Entanglement Network")
        plt.axis('off')
        plt.show()

    def visualize_manifold(self):
        if len(self.habits) < 2:
            print("Need at least 2 habits to visualize.")
            return
        
        plt.figure(figsize=(10, 8))
        plt.scatter(self.embedded_points[:, 0], self.embedded_points[:, 1])
        for i, habit in enumerate(self.habits.values()):
            plt.annotate(habit.name, (self.embedded_points[i, 0], self.embedded_points[i, 1]))
        plt.title('Habit Manifold Visualization')
        plt.show()

    def visualize_topology(self):
        if len(self.habits) < 3:
            print("Need at least 3 habits for topological analysis.")
            return
        
        diagrams = ripser(self.embedded_points)['dgms']
        plot_diagrams(diagrams, show=True)

class QuHabitonQML(QuHabitonSystem):
    def __init__(self, n_dimensions=5):
        super().__init__(n_dimensions)
        self.qml_model = None
        self.prediction_history = []

    def train_qml_model(self):
        # Prepare data
        X = np.array([[h.difficulty, h.frequency, h.success_rate, h.score, h.get_success_probability()] 
                      for h in self.habits.values()])
        y = np.array([h.measure() for h in self.habits.values()])
        
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

        # Define quantum circuit
        feature_map = ZZFeatureMap(feature_dimension=5, reps=2)
        ansatz = QuantumCircuit(5)
        for i in range(5):
            ansatz.ry(np.pi/2, i)
            ansatz.cx(i, (i+1)%5)

        # Create VQC instance
        qi = QuantumInstance(Aer.get_backend('aer_simulator'), shots=1024)
        optimizer = COBYLA(maxiter=100)
        self.qml_model = VQC(feature_map=feature_map,
                             ansatz=ansatz,
                             optimizer=optimizer,
                             quantum_instance=qi)

        # Train model
        self.qml_model.fit(X_train, y_train)

        # Evaluate model
        score = self.qml_model.score(X_test, y_test)
        print(f"QML Model Accuracy: {score}")

    def predict_future(self, days=4):
        if self.qml_model is None:
            self.train_qml_model()

        predictions = []
        current_state = np.array([[h.difficulty, h.frequency, h.success_rate, h.score, h.get_success_probability()] 
                                  for h in self.habits.values()])
        
        for _ in range(days):
            day_prediction = self.qml_model.predict(current_state)
            predictions.append(day_prediction)
            
            # Update current_state based on predictions
            for i, habit in enumerate(self.habits.values()):
                habit.update(bool(day_prediction[i]))
            current_state = np.array([[h.difficulty, h.frequency, h.success_rate, h.score, h.get_success_probability()] 
                                      for h in self.habits.values()])

        self.prediction_history.append(predictions)
        return predictions

    def calculate_future_score(self, predictions):
        future_scores = {}
        for name, habit in self.habits.items():
            future_success_rate = sum(pred[i] for pred in predictions) / len(predictions)
            future_score = habit.score + (future_success_rate * habit.difficulty * habit.frequency)
            future_scores[name] = future_score
        return future_scores

    def update_scores_with_prediction(self):
        predictions = self.predict_future()
        future_scores = self.calculate_future_score(predictions)
        
        for name, habit in self.habits.items():
            prediction_factor = future_scores[name] / habit.score if habit.score != 0 else 1
            habit.score *= (1 + prediction_factor) / 2  # Blend current score with predicted future score

    def simulate_day(self):
        super().simulate_day()
        self.update_scores_with_prediction()

    def visualize_predictions(self):
        if not self.prediction_history:
            print("No predictions available. Run predict_future() first.")
            return

        latest_prediction = self.prediction_history[-1]
        habit_names = list(self.habits.keys())

        plt.figure(figsize=(12, 6))
        for i, habit in enumerate(habit_names):
            plt.plot(range(1, 5), [pred[i] for pred in latest_prediction], label=habit, marker='o')

        plt.title("4-Day Habit Success Predictions")
        plt.xlabel("Day")
        plt.ylabel("Predicted Success (0 or 1)")
        plt.legend()
        plt.yticks([0, 1])
        plt.grid(True)
        plt.show()

# Example usage
system = QuHabitonQML()
system.add_habit("Meditation", difficulty=5, frequency=1)
system.add_habit("Exercise", difficulty=7, frequency=3)
system.add_habit("Reading", difficulty=3, frequency=1)
system.add_habit("Coding", difficulty=6, frequency=2)

system.entangle_habits("Meditation", "Exercise", 0.5)
system.entangle_habits("Reading", "Coding", 0.7)

for _ in range(30):  # Simulate for 30 days
    system.simulate_day()

system.predict_future()
system.visualize_predictions()

for name, habit in system.habits.items():
    print(f"{name}: Score = {habit.score:.2f}, Success Rate = {habit.success_rate:.2%}, Success Probability = {habit.get_success_probability():.2%}")