  # scipy stats.halfcauchy () | python

NumPy | Python Methods and Functions

scipy.stats.halfcauchy () is a continuous Half-Cauchy random variable that is defined in a standard format and with some shape parameters to complete its specification.

Parameters:
- & gt; q: lower and upper tail probability
- & gt; x: quantiles
- & gt; loc: [optional] location parameter. Default = 0
- & gt; scale: [optional] scale parameter. Default = 1
- & gt; size: [tuple of ints, optional] shape or random variates.
- & gt; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: Half-Cauchy continuous random variable

Code # 1: Create continuous random variable random variable with half Cauchy

 ` from ` ` scipy.stats ` ` import ` ` halfcauchy `   ` numargs ` ` = ` ` halfcauchy.numargs ` ` [] ` ` = ` ` [` ` 0.7 ,] * numargs `` rv = halfcauchy)    `` < code class = "functions"> print ` ` (` ` "RV:" ` `, rv) `

Output:

` RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A272470 & gt; `

Code # 2: semi-cats of random variables and probability distribution

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

Output:

` Random Variates: [6.99019514 4.03402743 6.59099197 2.54849344 5.22950683 0.02399243 0.43431935 2.38057697 8.43432847 10.53182273] Probability Distribution: [ 0.63655612 0.62900877 0.60973065 0.58080446 0.54500451 0.50521369 0.46397476 0.42325628 0.38440902 0.34824122] `

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 (distributi on)) `

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 ` ` = ` ` halfcauchy .pdf (x, ` ` 1 ` `, ` ` 3 ` ` ) ` ` y2 ` ` = ` ` halfcauchy .pdf ( x, ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` ` "*" ` `, x, y2, ` ` "r--" ` `) `

Output: