# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1  # noqa
# run: log_likelihood(a, b, c)
# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
import numpy

# pythran export log_likelihood(float64[], float64, float64)


def log_likelihood(data, mean, sigma):
    s = (data - mean) ** 2 / (2 * (sigma ** 2))
    pdfs = numpy.exp(- s)
    pdfs /= numpy.sqrt(2 * numpy.pi) * sigma
    return numpy.log(pdfs).sum()