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KOOREE V11 — Cascade Analysis (Simplified)
Focus on cascade tracking, not matrix complexity.
Pre-allocate matrices to avoid dynamic growth issues.
"""
import numpy as np
import time
import json
PHI = (1 + np.sqrt(5)) / 2
C_LIGHT = 3e8
EARTH_RADIUS = 6.371e6
EARTH_CIRCUMFERENCE = 2 * np.pi * EARTH_RADIUS
SCHUMANN_FUNDAMENTAL = C_LIGHT / EARTH_CIRCUMFERENCE
DT = 1e-3
MAX_NEURONS = 200 # Pre-allocate for up to 200 neurons
class SignalGenerator:
def __init__(self, seed=42):
self.rng = np.random.default_rng(seed)
self.x_logistic = 0.1
self.r_logistic = 3.9
self.pink_state = 0.0
def get_signal(self, t):
phi_sig = np.sin(2 * np.pi * PHI * t)
schumann_sig = sum(np.sin(2 * np.pi * SCHUMANN_FUNDAMENTAL * n * t) for n in range(1, 4)) / 3
self.x_logistic = self.r_logistic * self.x_logistic * (1 - self.x_logistic)
logistic_sig = self.x_logistic
pink_noise = self.rng.normal(0, 0.1)
self.pink_state = 0.9 * self.pink_state + 0.1 * pink_noise
return np.array([phi_sig, schumann_sig, logistic_sig, self.pink_state])
class ResonanceTimeline:
def __init__(self, timeline_id, dt=DT, seed=None):
self.timeline_id = timeline_id
self.dt = dt
rng = np.random.default_rng(seed)
# Pre-allocate for MAX_NEURONS
self.n_neurons = 47
self.tau = np.zeros(MAX_NEURONS)
self.tau[:47] = rng.uniform(10e-3, 25e-3, 47)
self.activity = np.zeros(MAX_NEURONS)
self.firing_rate = np.zeros(MAX_NEURONS)
self.prev_activity = np.zeros(MAX_NEURONS)
self.W_in = np.zeros((MAX_NEURONS, 4))
self.W_in[:47] = rng.uniform(0.05, 0.15, (47, 4))
self.W_rec = np.zeros((MAX_NEURONS, MAX_NEURONS))
self.W_rec[:47, :47] = rng.uniform(-0.1, 0.1, (47, 47))
np.fill_diagonal(self.W_rec[:47, :47], 0)
self.eta = 0.002
self.phase = 0.0
self.frequency = 0.0
self.mean_firing = 0.0
# Cascade tracking
self.births = []
self.birth_threshold = 0.3
self.residual_60s = []
self.last_birth_time = -1000
self.birth_count = 0
def step(self, signal_4d, resonance_coupling=0.0):
n = self.n_neurons
I_in = np.dot(self.W_in[:n], signal_4d)
I_rec = np.dot(self.W_rec[:n, :n], self.prev_activity[:n])
I_resonance = resonance_coupling * np.sin(self.phase)
I_total = I_in + 0.5 * I_rec + 0.1 * I_resonance
dA = (-self.activity[:n] + I_total) / self.tau[:n]
self.activity[:n] += dA * self.dt
self.firing_rate[:n] = np.maximum(0, self.activity[:n])
spikes = (self.activity[:n] > 0.5).astype(float)
dW_in = self.eta * spikes[:, None] * signal_4d[None, :]
self.W_in[:n] += dW_in
self.W_in[:n] = np.clip(self.W_in[:n], 0, 1.0)
self.mean_firing = self.firing_rate[:n].mean()
self.frequency = self.mean_firing * 2 * np.pi
self.phase += self.frequency * self.dt
self.phase = self.phase % (2 * np.pi)
processed = np.dot(self.W_in[:n], signal_4d)
residual = np.abs(signal_4d.sum() - processed.sum())
self.residual_60s.append(residual)
if len(self.residual_60s) > 60000:
self.residual_60s.pop(0)
self.prev_activity[:n] = self.activity[:n].copy()
return spikes, residual, self.phase, self.frequency
def check_neurogenesis(self, current_time):
if len(self.residual_60s) < 60000:
return False
if current_time - self.last_birth_time < 300:
return False
if self.n_neurons >= MAX_NEURONS:
return False
mean_residual = np.mean(self.residual_60s)
if mean_residual > self.birth_threshold:
self.birth_count += 1
new_idx = self.n_neurons
# Record birth
birth_info = {
'timeline_id': self.timeline_id,
'birth_order': self.birth_count,
'time': current_time,
'neuron_index': new_idx,
'residual': mean_residual,
'phase': self.phase,
'frequency': self.frequency,
'input_weights': self.W_in[new_idx, :4].tolist(),
}
self.births.append(birth_info)
# Initialize new neuron
rng = np.random.default_rng(42 + self.timeline_id + self.birth_count)
self.tau[new_idx] = rng.uniform(10e-3, 25e-3)
self.W_in[new_idx] = rng.uniform(0.05, 0.15, 4)
self.W_rec[new_idx, :self.n_neurons] = rng.uniform(-0.1, 0.1, self.n_neurons)
self.W_rec[:self.n_neurons, new_idx] = rng.uniform(-0.1, 0.1, self.n_neurons)
self.W_rec[new_idx, new_idx] = 0 # No self-connection
self.n_neurons += 1
self.birth_threshold += 0.05
self.last_birth_time = current_time
return True
return False
class CascadeAnalyzer:
def __init__(self, n_timelines=10, dt=DT):
self.n_timelines = n_timelines
self.dt = dt
self.timelines = [ResonanceTimeline(i, dt=dt, seed=42+i) for i in range(n_timelines)]
self.signal_gen = SignalGenerator(seed=42)
self.coupling_strength = 0.1
self.bifurcation_sequence = []
def step(self, t):
signal_4d = self.signal_gen.get_signal(t)
avg_phase = np.mean([tl.phase for tl in self.timelines])
shared_resonance = np.sin(avg_phase)
births_this_step = []
for timeline in self.timelines:
resonance_coupling = self.coupling_strength * shared_resonance
spikes, residual, phase, freq = timeline.step(signal_4d, resonance_coupling)
if timeline.check_neurogenesis(t):
births_this_step.append({
'timeline_id': timeline.timeline_id,
'time': t,
'residual': residual,
})
return births_this_step
def run(self, duration_seconds):
n_steps = int(duration_seconds / self.dt)
total_births = 0
print(f"Running cascade analysis for {duration_seconds:.0f}s...")
print(f"({n_steps} timesteps)")
wall_start = time.time()
last_print = 0
for step in range(n_steps):
t = step * self.dt
births = self.step(t)
if births:
total_births += len(births)
self.bifurcation_sequence.append({
't': t,
'births': births,
'n_births': len(births),
})
if t - last_print > 10:
elapsed = time.time() - wall_start
print(f" t={t:.1f}s | Total births: {total_births} | "
f"Bifurcation events: {len(self.bifurcation_sequence)} | wall={elapsed:.1f}s")
last_print = t
print(f"Done. Total births: {total_births}")
return {
'total_births': total_births,
'bifurcation_sequence': self.bifurcation_sequence,
'timeline_births': [birth for tl in self.timelines for birth in tl.births],
'final_neuron_counts': [tl.n_neurons for tl in self.timelines],
}
if __name__ == '__main__':
print("=" * 70)
print("KOOREE V11 — CASCADE ANALYSIS (SIMPLIFIED)")
print("=" * 70)
analyzer = CascadeAnalyzer(n_timelines=10, dt=DT)
print("\n" + "=" * 70)
print("RUNNING CASCADE ANALYSIS")
print("=" * 70 + "\n")
history = analyzer.run(duration_seconds=300)
print("\n" + "=" * 70)
print("CASCADE ANALYSIS RESULTS")
print("=" * 70)
print(f"\nTotal neurogenesis events: {history['total_births']}")
print(f"Bifurcation events: {len(history['bifurcation_sequence'])}")
print(f"\nFinal neuron counts:")
print(f" Mean: {np.mean(history['final_neuron_counts']):.1f}")
print(f" Min: {min(history['final_neuron_counts'])}")
print(f" Max: {max(history['final_neuron_counts'])}")
if history['bifurcation_sequence']:
print(f"\nFirst 20 bifurcation events:")
for i, event in enumerate(history['bifurcation_sequence'][:20]):
print(f" {i+1}. t={event['t']:.1f}s | {event['n_births']} simultaneous births")
print(f"\nFirst 30 births across all timelines:")
for i, birth in enumerate(history['timeline_births'][:30]):
print(f" {i+1}. Timeline {birth['timeline_id']} | Birth #{birth['birth_order']} | "
f"t={birth['time']:.1f}s | Residual: {birth['residual']:.1f}")
with open('/home/ubuntu/v11_cascade_analysis.json', 'w') as f:
json.dump(history, f, indent=2)
print("\nResults saved to v11_cascade_analysis.json")
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