scipy.special.entr#
- scipy.special.entr(x, out=None) = <ufunc 'entr'>#
- Elementwise function for computing entropy. \[\begin{split}\text{entr}(x) = \begin{cases} - x \log(x) & x > 0 \\ 0 & x = 0 \\ -\infty & \text{otherwise} \end{cases}\end{split}\]- Parameters:
- xndarray
- Input array. 
- outndarray, optional
- Optional output array for the function values 
 
- Returns:
- resscalar or ndarray
- The value of the elementwise entropy function at the given points x. 
 
 - See also - Notes - Added in version 0.15.0. - This function is concave. - The origin of this function is in convex programming; see [1]. Given a probability distribution \(p_1, \ldots, p_n\), the definition of entropy in the context of information theory is \[\sum_{i = 1}^n \mathrm{entr}(p_i).\]- To compute the latter quantity, use - scipy.stats.entropy.- entrhas experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable- SCIPY_ARRAY_API=1and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.- Library - CPU - GPU - NumPy - ✅ - n/a - CuPy - n/a - ✅ - PyTorch - ✅ - ✅ - JAX - ✅ - ✅ - Dask - ✅ - n/a - See Support for the array API standard for more information. - References [1]- Boyd, Stephen and Lieven Vandenberghe. Convex optimization. Cambridge University Press, 2004. DOI:https://doi.org/10.1017/CBO9780511804441