837563c3ba
Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift, diff_entities/relations, extract_entities/relations_llm, hotness_score, melt, merge_graphs, pivot, build_entity/relation_schema_prompt. Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order, hawkes_intensity + módulo finance.py. Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py. Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1.1 KiB
1.1 KiB
name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, tested, tests, test_file_path, file_path
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | tested | tests | test_file_path | file_path | ||||||
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| estimate_pareto_alpha | function | py | datascience | 1.0.0 | pure | def estimate_pareto_alpha(values: list[float], x_min_percentile: float = 90.0) -> dict | Estima el exponente alpha de una distribución Pareto via MLE. Alpha bajo indica cola más pesada y mayor frecuencia de valores extremos. |
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false |
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false | python/functions/datascience/datascience.py |
Ejemplo
import numpy as np
# Simular datos con cola pesada
values = list(np.random.pareto(2.0, 1000) + 1)
result = estimate_pareto_alpha(values, x_min_percentile=90.0)
# {'alpha': ~2.0, 'x_min': ..., 'n_tail': 100}
Notas
Usa el estimador MLE de Hill: α = n / Σ ln(xᵢ / x_min). x_min se determina como el percentil indicado de los valores positivos. Retorna alpha=0 si hay menos de 10 valores positivos o la cola tiene menos de 2 elementos. Función pura: requiere numpy instalado.