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# scipy stats.erlang () | python

scipy.stats.erlang (): is a continuous random Erlang variable that is defined by a standard format and some form parameters to complete its specification. this is a special case of the gamma distribution.

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: erlang continuous random variable

Code # 1: Generating a continuous random variable Erlang

 ` from ` ` scipy.stats ` ` import ` ` erlang `   ` numargs ` ` = ` ` erlang.numargs ` ` [a] ` ` = ` ` [` ` 0.6 ` `,] ` ` * ` ` numargs ` ` rv ` ` = ` ` erlang (a) ` ` `  ` print ` ` (` ` "RV:" ` `, rv) `

Output:

` RV : "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D544FBC88" `

Code # 2: Erlang Random Variables and Probability Distribution.

Output:

` Random Variates: [5.65708510e + 00 5.16045580e + 00 1.02056956e-01 3.64349340 e-01 5.65593073e + 00 2.27100280e + 00 9.77623414e-04 2.01994399e-01 8.84331471e-01 2.20817630e + 00] Probability Distribution: [0.01, 0.11, 0.21, 0.31, 0.41, 0.51, 0.61, 0.71, 0.8 1, 0.91] `

Code # 3: Graphic representation.

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) `   ` # Random Variants ` ` R ` ` = ` ` erlang.rvs (a, scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) ` ` print ( "Random Variates:" , R) ``   # PDF R = erlang.pdf (a, quantile, loc = 0 , scale = 1 ) print ( "Probability Distribution:" , R) `
 ` import ` ` numpy as np ` ` import ` ` matplotlib.pyplot as plt `   ` distribution ` ` = ` ` np.linspace (` ` 0 ` `, np.minimum (rv.dist.b , ` ` 5 ` `)) ` ` print ` ` (` ` "Distribution:" ` `, distribution) `   ` plot ` ` = ` ` plt.plot (distribution, rv.pdf (distribution)) ` < / p>

Output:

` Distribution : Distribution: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] `

Code # 4: Various Positional Arguments

 ` import ` ` matplotlib. pyplot as plt ` ` import ` numpy as np   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` erlang.pdf (x, ` ` 2 ` `, ` ` 6 ` `) ` ` y2 ` ` = ` ` erlang.pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * " , x, y2, "r--" ) `` `

Output:

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