bigmodel.crossentropy(fx, log_prior_x=None, base=2.718281828459045)

Returns the cross entropy H(q, p) of the empirical distribution q of the data (with the given feature matrix fx) with respect to the model p. For discrete distributions this is defined as:

H(q, p) = - n^{-1} sum_{j=1}^n log p(x_j)

where x_j are the data elements assumed drawn from q whose features are given by the matrix fx = {f(x_j)}, j=1,...,n.

The ‘base’ argument specifies the base of the logarithm, which defaults to e.

For continuous distributions this makes no sense!

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