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# scipy.maxentropy.maxentropy

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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