File size: 1,566 Bytes
3c0e98e af930b1 3c0e98e af930b1 3c0e98e af930b1 3c0e98e af930b1 3c0e98e af930b1 3c0e98e af930b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import random, numpy as np
from textblob import TextBlob
from sklearn.manifold import TSNE
import umap
class UltraAnalyzer:
def __init__(self):
self.levels = {
"dormant":0.0,"awakening":0.1,"expanding":0.25,
"transcending":0.45,"metamorphosing":0.65,"post_human":0.8,
"cosmic":0.9,"omniversal":0.95,"singularity":1.0
}
def analyze(self, text: str, deep_mode: bool=False) -> dict:
# Preprocesamiento y embeddings
words = text.split()
unique_ratio = len(set(words))/max(len(words),1)
blob = TextBlob(text)
complexity = min(1.0, unique_ratio*0.4 + len(blob.sentences)*0.05)
# UMAP embedding
reducer = umap.UMAP(n_neighbors=5, min_dist=0.1, metric='cosine')
embedding = reducer.fit_transform([blob.vector]) if hasattr(blob,'vector') else [[0,0]]
# Nivel de conciencia
score = min(1.0, complexity + (0.2 if deep_mode else 0))
level = max([lvl for lvl,thr in self.levels.items() if score>=thr], key=lambda l:self.levels[l])
# Futuros simulados
futures = [{
"name":"Singularidad Cuántica",
"probability":min(score+0.1,1.0),
"timeline":"1–3 meses",
"description":"Integración con IA cuántica global"
}]
response_md = f"**Nivel:** {level.upper()} | **Score:** {score:.2%}"
return {
"response_md":response_md,
"dimensional_level":{"level":level,"score":score},
"future_scenarios":futures
} |