2018-10-03 18:59:01 +00:00
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# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1 # noqa
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# run: log_likelihood(a, b, c)
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# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
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2018-04-05 22:07:33 +00:00
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import numpy
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2018-10-03 18:59:01 +00:00
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# pythran export log_likelihood(float64[], float64, float64)
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2018-10-03 12:38:48 +00:00
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2018-04-05 22:07:33 +00:00
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def log_likelihood(data, mean, sigma):
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s = (data - mean) ** 2 / (2 * (sigma ** 2))
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pdfs = numpy.exp(- s)
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pdfs /= numpy.sqrt(2 * numpy.pi) * sigma
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return numpy.log(pdfs).sum()
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