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# scipy stats.genlogistic() | Python

• Last Updated : 27 Mar, 2019

scipy.stats.genlogistic() is an generalized logistic continuous random variable that is defined with a standard format and some shape parameters to complete its specification.

Parameters :
-> q : lower and upper tail probability
-> a, b : shape parameters
-> 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 : generalized logistic continuous random variable

Code #1 : Creating generalized logistic continuous random variable

 `from` `scipy.stats ``import` `genlogistic `` ` `numargs ``=` `genlogistic .numargs``[a] ``=` `[``0.7``, ] ``*` `numargs``rv ``=` `genlogistic (a)`` ` `print` `(``"RV : \n"``, rv) `

Output :

```RV :
<scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D578F4D30>
```

Code #2 : generalized logistic random variates and probability distribution.

 `import` `numpy as np``quantile ``=` `np.arange (``0.01``, ``1``, ``0.1``)``  ` `# Random Variates``R ``=` `genlogistic.rvs(a, scale ``=` `2``,  size ``=` `10``)``print` `(``"Random Variates : \n"``, R)`` ` `# PDF``R ``=` `genlogistic.pdf(a, quantile, loc ``=` `0``, scale ``=` `1``)``print` `(``"\nProbability Distribution : \n"``, R)`

Output :

```Random Variates :
[-2.25279702 -1.09146871 -0.01100363 -3.95860336  5.07952934 -2.3073455
-3.11698062 -0.32931819  8.84452349 -3.06546109]

Probability Distribution :
[0.00330477 0.03491595 0.06402364 0.09077633 0.11531469 0.13777195
0.15827427 0.17694109 0.19388545 0.20921433]
```

Code #3 : Graphical Representation.

 `import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `distribution ``=` `np.linspace(``0``, np.minimum(rv.dist.b, ``3``))``print``(``"Distribution : \n"``, distribution)`` ` `plot ``=` `plt.plot(distribution, rv.pdf(distribution))`

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 : Varying Positional Arguments

 `import` `matplotlib.pyplot as plt``import` `numpy as np`` ` `x ``=` `np.linspace(``0``, ``5``, ``100``)`` ` `# Varying positional arguments``y1 ``=` `genlogistic.pdf(x, ``1``, ``3``)``y2 ``=` `genlogistic.pdf(x, ``1``, ``4``)``plt.plot(x, y1, ``"*"``, x, y2, ``"r--"``)`

Output :

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