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. |
