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The functions available in the special package are universal functions, which follow broadcasting and automatic array looping.
Let us look at some of the most frequently used special functions −
- Cubic Root Function
- Exponential Function
- Relative Error Exponential Function
- Log Sum Exponential Function
- Lambert Function
- Permutations and Combinations Function
- Gamma Function
Let us now understand each of these functions in brief.
Cubic Root Function
The syntax of this cubic root function is – scipy.special.cbrt(x). This will fetch the element-wise cube root of x.
Let us consider the following example.
from scipy.special import cbrt res = cbrt([10, 9, 0.1254, 234]) print res
The above program will generate the following output.
[ 2.15443469 2.08008382 0.50053277 6.16224015]
Exponential Function
The syntax of the exponential function is – scipy.special.exp10(x). This will compute 10**x element wise.
Let us consider the following example.
from scipy.special import exp10 res = exp10([2, 9]) print res
The above program will generate the following output.
[1.00000000e+02 1.00000000e+09]
Relative Error Exponential Function
The syntax for this function is – scipy.special.exprel(x). It generates the relative error exponential, (exp(x) – 1)/x.
When x is near zero, exp(x) is near 1, so the numerical calculation of exp(x) – 1 can suffer from catastrophic loss of precision. Then exprel(x) is implemented to avoid the loss of precision, which occurs when x is near zero.
Let us consider the following example.
from scipy.special import exprel res = exprel([-0.25, -0.1, 0, 0.1, 0.25]) print res
The above program will generate the following output.
[0.88479687 0.95162582 1. 1.05170918 1.13610167]
Log Sum Exponential Function
The syntax for this function is – scipy.special.logsumexp(x). It helps to compute the log of the sum of exponentials of input elements.
Let us consider the following example.
from scipy.special import logsumexp import numpy as np a = np.arange(10) res = logsumexp(a) print res
The above program will generate the following output.
9.45862974443
Lambert Function
The syntax for this function is – scipy.special.lambertw(x). It is also called as the Lambert W function. The Lambert W function W(z) is defined as the inverse function of w * exp(w). In other words, the value of W(z) is such that z = W(z) * exp(W(z)) for any complex number z.
The Lambert W function is a multivalued function with infinitely many branches. Each branch gives a separate solution of the equation z = w exp(w). Here, the branches are indexed by the integer k.
Let us consider the following example. Here, the Lambert W function is the inverse of w exp(w).
from scipy.special import lambertw w = lambertw(1) print w print w * np.exp(w)
The above program will generate the following output.
(0.56714329041+0j) (1+0j)
Permutations & Combinations
Let us discuss permutations and combinations separately for understanding them clearly.
Combinations − The syntax for combinations function is – scipy.special.comb(N,k). Let us consider the following example −
from scipy.special import comb res = comb(10, 3, exact = False,repetition=True) print res
The above program will generate the following output.
220.0
Note − Array arguments are accepted only for exact = False case. If k > N, N < 0, or k < 0, then a 0 is returned.
Permutations − The syntax for combinations function is – scipy.special.perm(N,k). Permutations of N things taken k at a time, i.e., k-permutations of N. This is also known as “partial permutations”.
Let us consider the following example.
from scipy.special import perm res = perm(10, 3, exact = True) print res
The above program will generate the following output.
720
Gamma Function
The gamma function is often referred to as the generalized factorial since z*gamma(z) = gamma(z+1) and gamma(n+1) = n!, for a natural number ‘n’.
The syntax for combinations function is – scipy.special.gamma(x). Permutations of N things taken k at a time, i.e., k-permutations of N. This is also known as “partial permutations”.
The syntax for combinations function is – scipy.special.gamma(x). Permutations of N things taken k at a time, i.e., k-permutations of N. This is also known as “partial permutations”.
from scipy.special import gamma res = gamma([0, 0.5, 1, 5]) print res
The above program will generate the following output.
[inf 1.77245385 1. 24.]
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