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October 6, 2025 18:27
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Calculos prob_gaussiana
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| import math | |
| import numpy as np | |
| ## Calcula a probabilidade de uma observação x em uma distribuição normal | |
| # Caso positivo | |
| mean_pos = [0.517, 0.423] | |
| cov_pos = [[0.0086335, -0.006541], [-0.006541, 0.0196335]] | |
| x7 = [0.45, 0.80] | |
| mean_neg = [0.49, 0.733] | |
| cov_neg = [[0.0037, 0.0136], [0.0136, 0.0517335]] | |
| x8 = [0.5, 0.7] | |
| def probabilidade_gaussiana(x, mean, cov): | |
| d = len(x) | |
| x = np.array(x) | |
| mean = np.array(mean) | |
| cov = np.array(cov) | |
| coeff = 1 / (np.sqrt((2 * np.pi) ** d * np.linalg.det(cov))) | |
| exponent = -0.5 * (x - mean).T @ np.linalg.inv(cov) @ (x - mean) | |
| return coeff * np.exp(exponent) | |
| prob_pos = probabilidade_gaussiana(x7, mean_pos, cov_pos) | |
| prob_neg = probabilidade_gaussiana(x7, mean_neg, cov_neg) | |
| print(f"Probabilidade de x7 (classe positiva): {prob_pos}") | |
| print(f"Probabilidade de x7 (classe negativa): {prob_neg}") | |
| prob_pos = probabilidade_gaussiana(x8, mean_pos, cov_pos) | |
| prob_neg = probabilidade_gaussiana(x8, mean_neg, cov_neg) | |
| print(f"Probabilidade de x8 (classe positiva): {prob_pos}") | |
| print(f"Probabilidade de x8 (classe negativa): {prob_neg}") |
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