Cara menggunakan random triangular python

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triangular() is an inbuilt method of the random module. It is used to return a random floating point number within a range with a bias towards one extreme.

Syntax : random.triangular(low, high, mode)

Parameters :
low : the lower limit of the random number
high : the upper limit of the random number
mode : additional bias; low < mode < high

if the parameters are (10, 100, 20) then due to the bias, most of the random numbers generated will be closer to 10 as opposed to 100.

Returns : a random floating number

Example 1:

import random

low = 10

high = 100

mode = 20

print(random.triangular(low, high, mode))

Output :

22.614510550239572

Example 2: If we generate the number multiple times we can probably identify the bias.

import random

low = 10

high = 100

mode = 20

for i in range(10):

    print(random.triangular(low, high, mode))

Output :

58.645768016894735
46.690692250503226
33.57590419190895
52.331804090351305
33.09451214875767
12.03845752596168
32.816080679206294
20.4739124559502
82.49208123077557
63.511062284733015

Example 3: We can visualize the triangular pattern by plotting a graph.

import random

import matplotlib.pyplot as plt

nums = []

low = 10

high = 100

mode = 20

for i in range(10000):

    temp = random.triangular(low, high, mode)

    nums.append(temp)

plt.hist(nums, bins = 200)

plt.show()

Output :

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With the help of numpy.random.triangular() method, we can get the random samples from triangular distribution from interval [left, right] and return the random samples by using this method.

Syntax : numpy.random.triangular(left, mode, right, size=None)

Parameters :

1) left – lower limit of the triangle.

2) mode – peak value of the distribution.

3) right – upper limit of the triangle.

4) size – total number of samples required.

Return : Return the random samples as numpy array.

Example #1 :

In this example we can see that by using numpy.random.triangular() method, we are able to get the random samples of triangular distribution and return the numpy array.

Python3

import numpy as np

import matplotlib.pyplot as plt

gfg = np.random.triangular(-5, 0, 5, 5000)

plt.hist(gfg, bins = 50, density = True)

plt.show()

Output :

Example #2 :

Python3

import numpy as np

import matplotlib.pyplot as plt

gfg = np.random.triangular(-10, 8, 10, 15000)

plt.hist(gfg, bins = 100, density = True)

plt.show()

Output :