def toMaximize(fit_params):
self.CONSTANT_VALUES = fit_params.tolist() # set these
# And return the original likelihood, which by get_function_responses above uses this
constant_prior = sum(
map(lambda x: normlogpdf(x, 0.0, CONSTANT_SD),
self.CONSTANT_VALUES))
return -(GaussianLOTHypothesis.compute_likelihood(self, data) +
constant_prior)
def to_maximize(fit_params):
self.CONSTANT_VALUES = fit_params.tolist() # set these
# And return the original likelihood, which by get_function_responses above uses this
constant_prior = sum(map(lambda x: normlogpdf(x,0.0,CONSTANT_SD), self.CONSTANT_VALUES))
return -(GaussianLOTHypothesis.compute_likelihood(self, data) + constant_prior)
