# Integrazione GPT – Generazione Automatica delle Φ
"""
Funzionalità sperimentale: generazione automatica di trasformazioni Φ_{ijkl} a partire da testo naturale, utilizzando modelli GPT.
Attualmente implementata come placeholder tramite matching lessicale su keyword.
"""
import random
import numpy as np
from scipy.spatial.distance import directed_hausdorff
try:
import matplotlib.pyplot as plt
except ImportError:
import subprocess
import sys
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'matplotlib'])
import matplotlib.pyplot as plt
def generate_phi_from_text(prompt_text):
def phi_generated(z, P=complex(0.5, 0.5), lambda_linear=0.1):
if "opposti" in prompt_text and "attrazione" in prompt_text:
return P + lambda_linear * (z - P)
elif "riflessione" in prompt_text:
return P - z
elif "espansione" in prompt_text:
return z * (1 + lambda_linear)
else:
return z
return Transformation(phi_generated)
def map_semantic_trajectory(concepts):
semantic_map = set()
angle_step = 2 * np.pi / len(concepts)
radius = 1.0
for i, concept in enumerate(concepts):
angle = i * angle_step
point = radius * np.exp(1j * angle)
semantic_map.add(point)
return semantic_map
class SystemParameters:
def __init__(self, iterations=10000, transition_threshold=0.005, lambda_linear=0.1,
P=complex(0.5, 0.5), alpha=0.4, beta=0.4, gamma=0.2, blend_iterations=5,
scale_factor_A=0.5, scale_factor_B=0.5, offset_A=1j, offset_B=1):
self.iterations = iterations
self.transition_threshold = transition_threshold
self.lambda_linear = lambda_linear
self.P = P
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.blend_iterations = blend_iterations
self.scale_factor_A = scale_factor_A
self.scale_factor_B = scale_factor_B
self.offset_A = offset_A
self.offset_B = offset_B
class Transformation:
def __init__(self, func, **kwargs):
self.func = func
self.kwargs = kwargs
def apply(self, z):
return self.func(z, **self.kwargs)
class DNDTensorField:
def __init__(self, params):
self.params = params
self.R = {complex(0, 0)}
self.all_points = set(self.R)
self.linear_phase = True
self.blend_phase = False
self.blend_counter = 0
self.generated_phi = []
def T_A(self, z):
return z * self.params.scale_factor_A + self.params.offset_A
def T_B(self, z):
return (z + self.params.offset_B) * self.params.scale_factor_B
def run_linear_phase(self):
R_next = {z + self.params.lambda_linear * (self.params.P - z) for z in self.R}
self.all_points.update(R_next)
self.R = R_next
def run_fractal_phase(self):
R_next = set()
for z in self.R:
if random.random() < 0.5:
R_next.add(self.T_A(z))
else:
R_next.add(self.T_B(z))
self.all_points.update(R_next)
self.R = R_next
def run_blended_phase(self):
linear_R = {z + self.params.lambda_linear * (self.params.P - z) for z in self.R}
fractal_R = set()
for z in self.R:
if random.random() < 0.5:
fractal_R.add(self.T_A(z))
else:
fractal_R.add(self.T_B(z))
blend_factor = self.blend_counter / max(1, self.params.blend_iterations)
blended_R = set()
for z in linear_R:
if random.random() < blend_factor:
blended_R.add(z)
for z in fractal_R:
if random.random() < (1 - blend_factor):
blended_R.add(z)
self.all_points.update(blended_R)
self.R = blended_R
self.blend_counter += 1
def add_phi_from_prompt(self, prompt):
phi = generate_phi_from_text(prompt)
self.generated_phi.append(phi)
def run_generated_phase(self):
R_next = set()
for phi in self.generated_phi:
R_next.update({phi.apply(z) for z in self.R})
self.all_points.update(R_next)
self.R = R_next
def is_stable_hausdorff(self, R_t, R_t1):
try:
R_t_arr = np.array([(z.real, z.imag) for z in R_t])
R_t1_arr = np.array([(z.real, z.imag) for z in R_t1])
if len(R_t_arr) == 0 or len(R_t1_arr) == 0:
return False
dist1 = directed_hausdorff(R_t_arr, R_t1_arr)[0]
dist2 = directed_hausdorff(R_t1_arr, R_t_arr)[0]
return max(dist1, dist2) < self.params.transition_threshold
except:
return False
def run(self):
for t in range(self.params.iterations):
prev_R = self.R.copy()
if self.linear_phase:
self.run_linear_phase()
if self.is_stable_hausdorff(prev_R, self.R):
self.linear_phase = False
self.blend_phase = True
elif self.blend_phase and self.blend_counter < self.params.blend_iterations:
self.run_blended_phase()
elif self.generated_phi:
self.run_generated_phase()
else:
self.run_fractal_phase()
def visualize_tensor(self):
x_vals = [z.real for z in self.all_points]
y_vals = [z.imag for z in self.all_points]
plt.figure(figsize=(8, 8))
plt.scatter(x_vals, y_vals, s=1, color="blue")
plt.title("DNDTensorField – Mappa del Campo Tensoriale")
plt.xlabel("Re(z)")
plt.ylabel("Im(z)")
plt.grid(True)
plt.show()
# Risultante del Modulo `DNDTensorField` come Osservatore Logico
## Equazione Cardine
R(t+1) = min_{Φ_{ijkl}} [Σ T_{ijkl} · Φ_{ijkl}(A_i, B_j, P_k, λ_l, O)] → R*
## Descrizione
Il sistema genera una Risultante R* come configurazione minima coerente nel Continuum NT, osservata attraverso l’evoluzione di trasformazioni Φ modulari. Ogni Φ rappresenta un’interazione assiomatica tra elementi del modello D-ND (A, B, P), modulata da curvatura λ e osservatore O.
## Significati Informazionali
- Φ_{ijkl} → Operatore assiomatico modulare
- T_{ijkl} → Densità logica nel campo osservato
- Cluster → Zone ad alta coerenza (verità locali)
- Re(z), Im(z) → Coordinate assiomatiche nel piano logico
- R* → Output autologico, sintesi emergente coerente