dpnp.logaddexp
- dpnp.logaddexp(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)
Calculates the natural logarithm of the sum of exponents for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.
This function calculates log(exp(x1) + exp(x2)) more accurately for small values of x.
For full documentation refer to
numpy.logaddexp
.- Parameters:
x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have a real-valued floating-point data type. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have a real-valued floating-point data type. Both inputs x1 and x2 can not be scalars at the same time. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type. Default:
None
.order ({"C", "F", "A", "K"}, optional) -- Memory layout of the newly output array, if parameter out is
None
. Default:"K"
.
- Returns:
out -- An array containing the element-wise results. The data type of the returned array is determined by the Type Promotion Rules.
- Return type:
dpnp.ndarray
Limitations
Parameters where and subok are supported with their default values. Keyword arguments kwargs are currently unsupported. Otherwise
NotImplementedError
exception will be raised.See also
dpnp.log
Natural logarithm, element-wise.
dpnp.exp
Exponential, element-wise.
dpnp.logaddexp2
Logarithm of the sum of exponentiation of inputs in base-2, element-wise.
dpnp.logsumexp
Logarithm of the sum of exponents of elements in the input array.
Examples
>>> import dpnp as np >>> prob1 = np.log(np.array(1e-50)) >>> prob2 = np.log(np.array(2.5e-50)) >>> prob12 = np.logaddexp(prob1, prob2) >>> prob12 array(-113.87649168) >>> np.exp(prob12) array(3.5e-50)