scipy.maxentropy.maxentropyΒΆ

Functions

arrayexp(x) Returns the elementwise antilog of the real array x.
asarray(a[, dtype, order]) Convert the input to an array.
columnmeans(A) This is a wrapper for general dense or sparse dot products.
columnvariances(A) This is a wrapper for general dense or sparse dot products.
flatten(a) Flattens the sparse matrix or dense array/matrix ‘a’ into a
innerprod(A, v) This is a wrapper around general dense or sparse dot products.
innerprodtranspose(A, v) This is a wrapper around general dense or sparse dot products.
logsumexp(a) Compute the log of the sum of exponentials log(e^{a_1}+...e^{a_n})
norm(a[, ord]) Matrix or vector norm.
sparsefeaturematrix(f, sample[, format]) Returns an (m x n) sparse matrix of non-zero evaluations of the scalar or vector functions f_1,...,f_m in the list f at the points x_1,...,x_n in the sequence ‘sample’.

Classes

basemodel() A base class providing generic functionality for both small and large maximum entropy models.
bigmodel() A maximum-entropy (exponential-form) model on a large sample space.
conditionalmodel(F, counts, numcontexts) A conditional maximum-entropy (exponential-form) model p(x|w) on a discrete sample space.
model([f, samplespace]) A maximum-entropy (exponential-form) model on a discrete sample space.

Exceptions

DivergenceError(message) Exception raised if the entropy dual has no finite minimum.

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