heat.exponential
This module computes exponential and logarithmic operations.
Module Contents
- exp(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Calculate the exponential of all elements in the input array. Result is a
DNDarrayof the same shape asx.- Parameters:
Examples
>>> ht.exp(ht.arange(5)) DNDarray([ 1.0000, 2.7183, 7.3891, 20.0855, 54.5981], dtype=ht.float32, device=cpu:0, split=None)
- expm1(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Calculate \(exp(x) - 1\) for all elements in the array. Result is a
DNDarrayof the same shape asx.- Parameters:
Examples
>>> ht.expm1(ht.arange(5)) + 1. DNDarray([ 1.0000, 2.7183, 7.3891, 20.0855, 54.5981], dtype=ht.float64, device=cpu:0, split=None)
- exp2(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Calculate the exponential of two of all elements in the input array (\(2^x\)). Result is a
DNDarrayof the same shape asx.- Parameters:
Examples
>>> ht.exp2(ht.arange(5)) DNDarray([ 1., 2., 4., 8., 16.], dtype=ht.float32, device=cpu:0, split=None)
- log(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Natural logarithm, element-wise. The natural logarithm is the inverse of the exponential function, so that \(log(exp(x)) = x\). The natural logarithm is logarithm in base e. Result is a
DNDarrayof the same shape asx. Negative input elements are returned as NaN.- Parameters:
Examples
>>> ht.log(ht.arange(5)) DNDarray([ -inf, 0.0000, 0.6931, 1.0986, 1.3863], dtype=ht.float32, device=cpu:0, split=None)
- log2(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Compute the logarithm to the base 2 (\(log_2(x)\)), element-wise. Result is a
DNDarrayof the same shape asx. Negative input elements are returned as NaN.- Parameters:
Examples
>>> ht.log2(ht.arange(5)) DNDarray([ -inf, 0.0000, 1.0000, 1.5850, 2.0000], dtype=ht.float32, device=cpu:0, split=None)
- log10(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Compute the logarithm to the base 10 (\(log_{10}(x)\)), element-wise. Result is a
DNDarrayof the same shape asx. Negative input elements are returned as NaN.- Parameters:
Examples
>>> ht.log10(ht.arange(5)) DNDarray([ -inf, 0.0000, 0.3010, 0.4771, 0.6021], dtype=ht.float32, device=cpu:0, split=None)
- log1p(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Return the natural logarithm of one plus the input array, element-wise. Result is a
DNDarrayof the same shape asx. Negative input elements are returned as NaN.- Parameters:
Examples
>>> ht.log1p(ht.arange(5)) DNDarray([0.0000, 0.6931, 1.0986, 1.3863, 1.6094], dtype=ht.float32, device=cpu:0, split=None)
- logaddexp(x1: heat.core.dndarray.DNDarray, x2: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Calculates the logarithm of the sum of exponentiations \(log(exp(x1) + exp(x2))\) for each element \({x1}_i\) of the input array x1 with the respective element \({x2}_i\) of the input array x2.
- Parameters:
x1 (DNDarray) – first input array. Should have a floating-point data type.
x2 (DNDarray) – second input array. Must be compatible with x1. Should have a floating-point data type.
out (DNDarray, optional) – A location in which to store the results. If provided, it must have a broadcastable shape. If not provided or set to
None, a fresh array is allocated.
See also
logaddexp2()Logarithm of the sum of exponentiations of inputs in base-2.
Examples
>>> ht.logaddexp(ht.array([-1.0]), ht.array([-1.0, -2, -3])) DNDarray([-0.3069, -0.6867, -0.8731], dtype=ht.float32, device=cpu:0, split=None)
- logaddexp2(x1: heat.core.dndarray.DNDarray, x2: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Calculates the logarithm of the sum of exponentiations in base-2 \(log2(exp(x1) + exp(x2))\) for each element \({x1}_i\) of the input array x1 with the respective element \({x2}_i\) of the input array x2.
- Parameters:
x1 (DNDarray) – first input array. Should have a floating-point data type.
x2 (DNDarray) – second input array. Must be compatible with x1. Should have a floating-point data type.
out (DNDarray, optional) – A location in which to store the results. If provided, it must have a broadcastable shape. If not provided or set to
None, a fresh array is allocated.
See also
logaddexp()Logarithm of the sum of exponentiations of inputs.
Examples
>>> ht.logaddexp2(ht.array([-1.0]), ht.array([-1.0, -2, -3])) DNDarray([ 0.0000, -0.4150, -0.6781], dtype=ht.float32, device=cpu:0, split=None)
- sqrt(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Return the non-negative square-root of a tensor element-wise. Result is a
DNDarrayof the same shape asx. Negative input elements are returned as NaN.- Parameters:
Examples
>>> ht.sqrt(ht.arange(5)) DNDarray([0.0000, 1.0000, 1.4142, 1.7321, 2.0000], dtype=ht.float32, device=cpu:0, split=None) >>> ht.sqrt(ht.arange(-5, 0)) DNDarray([nan, nan, nan, nan, nan], dtype=ht.float32, device=cpu:0, split=None)
- square(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) heat.core.dndarray.DNDarray
Return a new tensor with the squares of the elements of input.
- Parameters:
x (DNDarray) – The array for which to compute the squares.
out (DNDarray, optional) – A location in which to store the results. If provided, it must have a broadcastable shape. If not provided or set to
None, a fresh array is allocated.Examples
--------
ht.random.rand(4) (>>> a =)
a (>>>)
DNDarray([0.8654 (0, split=None))
0.1432 (0, split=None))
0.9164 (0, split=None))
0.6179] (0, split=None))
dtype=ht.float32 (0, split=None))
device=cpu (0, split=None))
ht.square(a) (>>>)
DNDarray([0.7488 (0, split=None))
0.0205 (0, split=None))
0.8397 (0, split=None))
0.3818] (0, split=None))
dtype=ht.float32
device=cpu