Python Data Science NumPy Random Logistic Data Distribution

It is used extensively in machine learning in logistic regression, neural networks etc.

It has the following three parameters

1. loc - mean, where the peak is. Default 0.

2. scale - standard deviation, the flatness of distribution. Default 1.

3. size - The shape of the returned array.

Example 1: Draw 2x4 samples from a logistic distribution with mean at 1 and stddev 2.0.

Code

from numpy import random

x = random.logistic(loc=1, scale= 2, size=(2, 4))

print(x)

the output will be

[[ 7.21760762 5.27472721 6.79142466 4.59750802]

[-0.49735547 10.66263062 5.69171819 -6.00562739]]

Note: the output may differ every time the code is run because of random generation.

Visualization of Logistic Distribution

Example 2

Code

from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns

sns.distplot(random.
logistic(size=100000), hist=False)

plt.show()

the output will be

Difference Between Logistic and Normal Distribution

Both distributions are near identical, but logistic distribution has more area under the tails, meaning it represents more possibility of occurrence of an event further away from mean.

For higher value of scale (standard deviation) the normal and logistic distributions are near identical apart from the peak.

Example 3.

Code

from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns

sns.distplot(random.
normal(scale=2, size=3000000), hist=False)
sns.distplot(random.
logistic(size=3000000), hist=False)

plt.show()

the output will be