scipy.stats.fisk () — it is a continuous random variable fisk . It is also known as the logistic-logistic distribution and equates to the Burr distribution with d == 1 and is defined by a standard format and some form parameters to complete its specification.
Parameters: q: lower and upper tail probability x: quantiles loc: [optional] location parameter. Default = 0 scale: [optional] scale parameter. Default = 1 size: [tuple of ints, optional] shape or random variates. moments: [optional] composed of letters [’mvsk ’]; ’m’ = mean, ’v’ = variance, ’s’ = Fisher’s skew and ’k’ = Fisher’s kurtosis. (default = ’mv’).
Results: fisk continuous random variable
Code # 1: Generating a continuous random variable
from
scipy.stats
import
fisk
numargs
=
fisk.numargs
[a]
=
[
0.7
,]
*
numargs
rv
=
fisk (a)
print
( "RV:"
, rv)
Output:
RV: "scipy.stats ._distn_infrastructure.rv_frozen object at 0x0000018D568102B0" Code # 2: risk random variables and probability distributions.
import
numpy as np
quantile
=
np.arange (
0.01
,
1
,
0.1
)
# Random Variants
R
=
fisk.rvs (a, scale
=
2
, size
=
10
)
print
(
"Random Variates:"
, R)
# PDF
R
=
fisk.pdf (a, quantile, loc
=
0
, scale
=
1
)
print
(
"Probability Distribution:"
, R)
Output:
Random Variates: [7.79438195 3.97977194 3.20802248 3.02623867 9.36996936 8.54462365 0.47436888 0.4645239 2.1188909 1.49435511] Probability Distribution: [0.00357142 0.0392706 0.07489491 0.11037659 0.1456485 0.18064439 0.21529915 0.2495491 0.28333225 0.3 1658852] Code # 3: Graphic representation.
import
numpy as np
import
matplotlib.pyplot as plt
distribution
=
np.linspace (
0
, np.minimum (rv.dist.b,
3
))
print
(
"Distribution:"
, distribution)
plot
=
plt.plot (distribution, rv.pdf (distribution))
< / p>
Output:
Distribution : [0. 0.06122449 0.12244898 0.18367347 0.24489796 0.30612245 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449 0.67346939 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633 1.10204082 1.16326531 1.2244898 1.28571429 1.34693878 1.40816327 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714 2.20408163 2.26530612 2.32653061 2.3877551 2.44897959 2.51020408 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102 2.93877551 3. ]
Code # 4: Various Positional Arguments
import
matplotlib. pyplot as plt
import
numpy as np
x
=
np.linspace (
0
,
5
,
100
)
# Various positional arguments
y1
=
fisk.pdf (x,
1
,
3
)
y2
=
fisk.pdf (x,
1
,
4
)
plt.plot (x, y1,
"*"
, x, y2,
"r--"
)
Output:
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