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- .claude/CLAUDE.md - .claude/commands/subagentes.md - .claude/rules/INDEX.md - .mcp.json - bash/functions/cybersecurity/analyze_dns.md - bash/functions/cybersecurity/audit_http_headers.md - bash/functions/cybersecurity/audit_ssh_config.md - bash/functions/cybersecurity/check_firewall.md - bash/functions/cybersecurity/detect_suspicious_users.md - bash/functions/cybersecurity/encrypt_file.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
39 lines
1.0 KiB
Markdown
39 lines
1.0 KiB
Markdown
---
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name: pearson
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kind: function
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lang: py
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domain: datascience
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version: "1.0.0"
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purity: pure
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signature: "def pearson(xs: list, ys: list) -> float"
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description: "Calcula el coeficiente de correlacion de Pearson entre dos listas de floats."
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tags: [statistics, correlation, python, pendiente-usar]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: [math]
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params:
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- name: xs
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desc: "lista de valores numericos de la primera variable (ej: [1, 2, 3])"
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- name: ys
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desc: "lista de valores numericos de la segunda variable, misma longitud que xs"
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output: "coeficiente de correlacion de Pearson en rango [-1, 1]. 1.0=correlacion perfecta positiva, -1.0=negativa, 0.0=sin correlacion"
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tested: false
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tests: []
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test_file_path: ""
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file_path: "python/functions/datascience/datascience.py"
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---
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## Ejemplo
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```python
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r = pearson([1, 2, 3], [2, 4, 6])
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# r = 1.0
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```
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## Notas
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Usa solo math stdlib. No requiere numpy. Retorna 0.0 si las listas tienen longitud diferente, estan vacias, o la desviacion es cero.
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