This is documentation for an old release of SciPy (version 0.9.0). Read this page Search for this page in the documentation of the latest stable release (version 1.15.1).

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.