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- import numba
- @numba.njit(parallel=True)
- def media(valores):
- soma = 0
- for i in numba.prange(len(valores)):
- soma += valores[i]
- return soma / float(len(valores))
- @numba.njit(parallel=True)
- def covariancia(x, media_x, y, media_y):
- covar = 0.0
- for i in numba.prange(len(x)):
- covar += (x[i] - media_x) * (y[i] - media_y)
- return covar
- @numba.njit(parallel=True)
- def variancia(valores, media):
- soma = 0
- for i in numba.prange(len(valores)):
- soma += (valores[i] - media) ** 2
- return soma
- @numba.jit
- def coef_regressao_linear(x, y):
- x_media = media(x)
- y_media = media(y)
- b1 = covariancia(x, x_media, y, y_media) / variancia(x, x_media)
- b0 = y_media - b1 * x_media
- return [b0, b1]
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