cloud/resonance_dreams.py

#!/usr/bin/env python3
# resonance_dreams.py — CRAZY EXPERIMENTAL IDEAS
#
# "The best ideas sound insane until they work"
#
# This module contains experimental features that might be:
# - Genius
# - Completely broken
# - Both simultaneously (quantum superposition of quality)
#
# USE AT YOUR OWN RISK. SIDE EFFECTS MAY INCLUDE:
# - Emergent behavior
# - Unexpected resonance
# - Questioning the nature of consciousness
# - Mild existential crises
#
# ============================================================
#
# CRAZY IDEA #1: EMOTION HARMONICS
# What if emotions have overtones like musical notes?
# Fear at 100Hz also vibrates at 200Hz (anxiety), 300Hz (paranoia)...
#
# CRAZY IDEA #2: CROSS-MODEL TELEPATHY
# CLOUD pings HAZE's internal state. HAZE pings CLOUD's chambers.
# They develop a shared emotional vocabulary. Emergent empathy.
#
# CRAZY IDEA #3: TEMPORAL ECHOES
# Emotions from the past leak into the present.
# "You said 'I'm fine' 3 days ago but your VOID was at 0.8"
#
# CRAZY IDEA #4: ADVERSARIAL EMOTIONS
# Train a tiny network to FOOL CLOUD.
# Then use that to make CLOUD more robust.
# GAN but for feelings.
#
# CRAZY IDEA #5: EMOTION COMPRESSION
# Compress the 100D resonance to 4D (one per chamber).
# Then decompress back. What survives? The essence.
#
# ============================================================

from __future__ import annotations
import asyncio
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, field
import math


# ============================================================
# CRAZY IDEA #1: EMOTION HARMONICS
# ============================================================

@dataclass
class EmotionHarmonic:
    """
    Emotion as a wave with harmonics.
    
    Like a musical note, each emotion has:
    - Fundamental frequency (primary emotion)
    - Overtones (related emotions that resonate)
    
    Fear doesn't just activate FEAR chamber.
    It also slightly activates anxiety (2nd harmonic),
    paranoia (3rd harmonic), and so on.
    """
    
    fundamental: str  # e.g., "fear"
    frequency: float  # arbitrary units
    harmonics: List[Tuple[str, float]] = field(default_factory=list)
    
    @classmethod
    def from_resonance(cls, resonances: np.ndarray, anchors: List[str]) -> "EmotionHarmonic":
        """
        Extract harmonic structure from resonance vector.
        
        The fundamental is the strongest resonance.
        Harmonics are resonances that are mathematically related
        (similar magnitude, or exact fractions).
        """
        primary_idx = int(np.argmax(resonances))
        fundamental = anchors[primary_idx]
        frequency = float(resonances[primary_idx])
        
        # Find harmonics (resonances at 1/2, 1/3, 1/4 of fundamental)
        harmonics = []
        for i, (anchor, res) in enumerate(zip(anchors, resonances)):
            if i == primary_idx:
                continue
            
            # Check if this is a harmonic (within 10% of expected ratio)
            for ratio in [0.5, 0.33, 0.25, 0.2]:
                expected = frequency * ratio
                if abs(res - expected) < 0.1 * frequency:
                    harmonics.append((anchor, float(res)))
                    break
        
        return cls(
            fundamental=fundamental,
            frequency=frequency,
            harmonics=harmonics[:5],  # Keep top 5 harmonics
        )
    
    def to_chord(self) -> str:
        """Represent as musical chord notation."""
        if not self.harmonics:
            return self.fundamental
        
        harmonic_names = [h[0][:3] for h in self.harmonics[:3]]
        return f"{self.fundamental}({'+'.join(harmonic_names)})"


def compute_emotional_chord(resonances: np.ndarray, anchors: List[str]) -> str:
    """
    Compute the "emotional chord" of an input.
    
    Like music theory but for feelings.
    "fear+anx+par" = fear major with anxiety and paranoia overtones
    """
    harmonic = EmotionHarmonic.from_resonance(resonances, anchors)
    return harmonic.to_chord()


# ============================================================
# CRAZY IDEA #2: CHAMBER DREAMS
# ============================================================

@dataclass
class ChamberDream:
    """
    What do the chambers "dream" about when not processing input?
    
    Between pings, chambers settle into attractor states.
    These attractors reveal the "personality" of the trained model.
    
    High FEAR attractor = anxious personality
    High LOVE attractor = warm personality
    etc.
    """
    
    attractors: Dict[str, float]  # Chamber → resting state
    dream_sequence: List[Dict[str, float]] = field(default_factory=list)
    
    @classmethod
    def compute_attractors(
        cls,
        chambers,  # CrossFireSystem
        iterations: int = 100,
    ) -> "ChamberDream":
        """
        Let chambers evolve without input to find attractors.
        
        Start from random state, let cross-fire settle.
        Where it settles = attractor = personality.
        """
        # Random starting state
        state = np.random.rand(4) * 0.5
        
        dream_sequence = []
        
        for _ in range(iterations):
            # Apply coupling (simplified cross-fire without MLP)
            influence = chambers.coupling @ state
            state = 0.7 * state + 0.3 * influence
            state = np.clip(state, 0, 1)
            
            dream_sequence.append({
                "FEAR": float(state[0]),
                "LOVE": float(state[1]),
                "RAGE": float(state[2]),
                "VOID": float(state[3]),
            })
        
        # Final state = attractor
        attractors = dream_sequence[-1]
        
        return cls(attractors=attractors, dream_sequence=dream_sequence)
    
    def personality_type(self) -> str:
        """Derive personality type from attractors."""
        dominant = max(self.attractors.items(), key=lambda x: x[1])
        
        personalities = {
            "FEAR": "Vigilant Guardian",
            "LOVE": "Warm Connector",
            "RAGE": "Fierce Protector",
            "VOID": "Detached Observer",
        }
        
        return personalities.get(dominant[0], "Unknown")


# ============================================================
# CRAZY IDEA #3: EMOTION PHASE SPACE
# ============================================================

class EmotionPhaseSpace:
    """
    Model emotions as trajectories in phase space.
    
    Like physics: position + velocity = full state.
    Emotion + rate_of_change = emotional trajectory.
    
    "They're not just sad, they're getting sadder"
    vs
    "They're sad but recovering"
    
    Same emotion, different trajectories.
    """
    
    def __init__(self, history_size: int = 10):
        self.history: List[np.ndarray] = []
        self.history_size = history_size
    
    def update(self, resonances: np.ndarray) -> None:
        """Add new observation to history."""
        self.history.append(resonances.copy())
        if len(self.history) > self.history_size:
            self.history.pop(0)
    
    def velocity(self) -> Optional[np.ndarray]:
        """Compute emotional velocity (rate of change)."""
        if len(self.history) < 2:
            return None
        
        return self.history[-1] - self.history[-2]
    
    def acceleration(self) -> Optional[np.ndarray]:
        """Compute emotional acceleration (change in velocity)."""
        if len(self.history) < 3:
            return None
        
        v1 = self.history[-2] - self.history[-3]
        v2 = self.history[-1] - self.history[-2]
        
        return v2 - v1
    
    def trajectory_type(self) -> str:
        """Classify the current trajectory."""
        vel = self.velocity()
        acc = self.acceleration()
        
        if vel is None:
            return "unknown"
        
        vel_mag = np.linalg.norm(vel)
        
        if vel_mag < 0.01:
            return "stable"
        
        if acc is None:
            return "moving"
        
        acc_mag = np.linalg.norm(acc)
        
        # Velocity and acceleration in same direction = accelerating
        # Velocity and acceleration opposite = decelerating
        dot = np.dot(vel, acc)
        
        if acc_mag < 0.01:
            return "coasting"
        elif dot > 0:
            return "accelerating"
        else:
            return "decelerating"
    
    def predict_next(self) -> Optional[np.ndarray]:
        """
        Predict next emotional state using physics.
        
        x(t+1) = x(t) + v(t) + 0.5*a(t)
        
        Simple but surprisingly effective for emotions.
        """
        if len(self.history) < 1:
            return None
        
        prediction = self.history[-1].copy()
        
        vel = self.velocity()
        if vel is not None:
            prediction = prediction + vel
        
        acc = self.acceleration()
        if acc is not None:
            prediction = prediction + 0.5 * acc
        
        return np.clip(prediction, 0, 1)


# ============================================================
# CRAZY IDEA #4: RESONANCE INTERFERENCE
# ============================================================

def emotional_interference(
    res1: np.ndarray,
    res2: np.ndarray,
    phase_diff: float = 0.0,
) -> np.ndarray:
    """
    What happens when two emotional states interfere?
    
    Like wave interference:
    - Constructive: emotions amplify
    - Destructive: emotions cancel
    
    phase_diff controls the relationship:
    - 0: fully constructive (emotions add)
    - π: fully destructive (emotions cancel)
    - π/2: orthogonal (no interaction)
    """
    # Treat resonances as wave amplitudes
    # Apply phase difference
    cos_phase = np.cos(phase_diff)
    sin_phase = np.sin(phase_diff)
    
    # Interference formula
    # I = |A1 + A2*e^(iφ)|² simplified for real values
    interference = (
        res1 + 
        res2 * cos_phase +
        np.sqrt(np.abs(res1 * res2)) * sin_phase
    )
    
    return np.clip(interference, 0, 1)


def emotional_beat_frequency(
    res1: np.ndarray,
    res2: np.ndarray,
) -> float:
    """
    Compute the "beat frequency" between two emotional states.
    
    Like two tuning forks slightly out of sync.
    High beat frequency = emotional dissonance.
    Low beat frequency = emotional harmony.
    """
    # Difference in magnitudes creates "beats"
    diff = np.abs(res1 - res2)
    
    # Average difference = beat frequency
    beat = float(np.mean(diff))
    
    return beat


# ============================================================
# CRAZY IDEA #5: QUANTUM EMOTION SUPERPOSITION
# ============================================================

@dataclass
class QuantumEmotion:
    """
    Emotion as quantum superposition.
    
    Before observation (output generation), emotion exists
    in superposition of all possible states.
    
    Observation collapses the wavefunction.
    
    This is either profound or pretentious.
    Probably both. Schrödinger's metaphor.
    """
    
    amplitudes: np.ndarray  # Complex amplitudes for each emotion
    collapsed: bool = False
    collapsed_state: Optional[int] = None
    
    @classmethod
    def from_resonances(cls, resonances: np.ndarray) -> "QuantumEmotion":
        """Create superposition from resonances."""
        # Normalize to get probability amplitudes
        probs = resonances / (np.sum(resonances) + 1e-10)
        
        # Amplitudes are sqrt of probabilities
        # Add random phases for full quantum state
        phases = np.random.uniform(0, 2 * np.pi, len(probs))
        amplitudes = np.sqrt(probs) * np.exp(1j * phases)
        
        return cls(amplitudes=amplitudes)
    
    def collapse(self, seed: Optional[int] = None) -> int:
        """
        Collapse superposition by observation.
        
        Returns index of observed emotion.
        """
        if self.collapsed:
            return self.collapsed_state
        
        # Probabilities = |amplitude|²
        probs = np.abs(self.amplitudes) ** 2
        probs = probs / np.sum(probs)
        
        if seed is not None:
            np.random.seed(seed)
        
        self.collapsed_state = int(np.random.choice(len(probs), p=probs))
        self.collapsed = True
        
        return self.collapsed_state
    
    def entangle(self, other: "QuantumEmotion") -> "QuantumEmotion":
        """
        Entangle two quantum emotions.
        
        After entanglement, measuring one affects the other.
        Spooky action at a distance, but for feelings.
        """
        # Tensor product creates entangled state
        # Simplified: average the amplitudes
        entangled_amplitudes = (self.amplitudes + other.amplitudes) / np.sqrt(2)
        
        return QuantumEmotion(amplitudes=entangled_amplitudes)
    
    def uncertainty(self) -> float:
        """
        Heisenberg uncertainty for emotions.
        
        Can't know both the emotion AND its intensity precisely.
        Higher uncertainty = more ambiguous emotional state.
        """
        probs = np.abs(self.amplitudes) ** 2
        probs = probs / (np.sum(probs) + 1e-10)
        
        # Entropy as uncertainty measure
        entropy = -np.sum(probs * np.log2(probs + 1e-10))
        
        # Normalize to 0-1
        max_entropy = np.log2(len(probs))
        
        return float(entropy / max_entropy)


# ============================================================
# CRAZY IDEA #6: EMOTIONAL STRANGE ATTRACTORS
# ============================================================

class EmotionalLorenzSystem:
    """
    Model emotional dynamics as Lorenz attractor.
    
    Three coupled equations create chaotic but bounded behavior.
    Small changes in input → dramatically different trajectories.
    
    This is either:
    - A deep insight about emotional chaos
    - Completely insane
    - The basis for the next breakthrough
    
    All three simultaneously.
    """
    
    def __init__(
        self,
        sigma: float = 10.0,
        rho: float = 28.0,
        beta: float = 8.0 / 3.0,
    ):
        self.sigma = sigma  # How fast emotions spread
        self.rho = rho      # Emotional intensity threshold
        self.beta = beta    # Emotional decay rate
        
        # State: (arousal, valence, intensity)
        self.x = 1.0
        self.y = 1.0
        self.z = 1.0
    
    def step(self, dt: float = 0.01) -> Tuple[float, float, float]:
        """
        Advance the emotional attractor by dt.
        
        Returns (arousal, valence, intensity) normalized to 0-1.
        """
        # Lorenz equations
        dx = self.sigma * (self.y - self.x)
        dy = self.x * (self.rho - self.z) - self.y
        dz = self.x * self.y - self.beta * self.z
        
        self.x += dx * dt
        self.y += dy * dt
        self.z += dz * dt
        
        # Normalize to 0-1 range (Lorenz attractor typically in [-30, 30])
        arousal = (self.x + 30) / 60
        valence = (self.y + 30) / 60
        intensity = self.z / 50
        
        return (
            float(np.clip(arousal, 0, 1)),
            float(np.clip(valence, 0, 1)),
            float(np.clip(intensity, 0, 1)),
        )
    
    def perturb(self, resonances: np.ndarray) -> None:
        """
        Perturb the attractor with emotional input.
        
        Input resonances push the system in specific directions.
        """
        # Use resonance magnitude to perturb
        magnitude = np.sum(resonances)
        
        # Random direction influenced by resonances
        self.x += (resonances[0] if len(resonances) > 0 else 0) * 0.1
        self.y += (resonances[1] if len(resonances) > 1 else 0) * 0.1
        self.z += magnitude * 0.05
    
    def trajectory(self, steps: int = 100, dt: float = 0.01) -> List[Tuple[float, float, float]]:
        """Generate trajectory through emotional phase space."""
        return [self.step(dt) for _ in range(steps)]


# ============================================================
# DEMO
# ============================================================

if __name__ == "__main__":
    print("=" * 60)
    print("  RESONANCE DREAMS — Experimental Ideas")
    print("=" * 60)
    print()
    print("  WARNING: These ideas are EXPERIMENTAL.")
    print("  They might be genius. They might be broken.")
    print("  They are definitely weird.")
    print()
    
    # Test harmonics
    print("=" * 60)
    print("  IDEA #1: Emotion Harmonics")
    print("=" * 60)
    
    fake_resonances = np.random.rand(100) * 0.3
    fake_resonances[5] = 0.8   # Primary
    fake_resonances[10] = 0.4  # Harmonic at 1/2
    fake_resonances[15] = 0.27 # Harmonic at 1/3
    
    fake_anchors = [f"emotion_{i}" for i in range(100)]
    fake_anchors[5] = "fear"
    fake_anchors[10] = "anxiety"
    fake_anchors[15] = "paranoia"
    
    chord = compute_emotional_chord(fake_resonances, fake_anchors)
    print(f"  Emotional chord: {chord}")
    print()
    
    # Test phase space
    print("=" * 60)
    print("  IDEA #3: Emotion Phase Space")
    print("=" * 60)
    
    phase_space = EmotionPhaseSpace()
    for i in range(5):
        state = np.random.rand(100) * (0.5 + i * 0.1)
        phase_space.update(state)
    
    print(f"  Trajectory type: {phase_space.trajectory_type()}")
    vel = phase_space.velocity()
    if vel is not None:
        print(f"  Velocity magnitude: {np.linalg.norm(vel):.4f}")
    print()
    
    # Test quantum
    print("=" * 60)
    print("  IDEA #5: Quantum Emotion")
    print("=" * 60)
    
    qe = QuantumEmotion.from_resonances(fake_resonances)
    print(f"  Uncertainty: {qe.uncertainty():.3f}")
    collapsed = qe.collapse(seed=42)
    print(f"  Collapsed to: {fake_anchors[collapsed]}")
    print()
    
    # Test Lorenz
    print("=" * 60)
    print("  IDEA #6: Emotional Strange Attractor")
    print("=" * 60)
    
    lorenz = EmotionalLorenzSystem()
    lorenz.perturb(fake_resonances[:3])
    
    trajectory = lorenz.trajectory(steps=10)
    print("  Trajectory (arousal, valence, intensity):")
    for i, (a, v, intensity) in enumerate(trajectory[:5]):
        print(f"    t={i}: ({a:.2f}, {v:.2f}, {intensity:.2f})")
    print()
    
    print("=" * 60)
    print("  Dreams are just unvalidated hypotheses.")
    print("  Some become reality. Some remain dreams.")
    print("  All are worth exploring.")
    print("=" * 60)