Uniform Sampling
This function generates a Uniform sampling between \(a\) (minimum) and \(b\) (maximum).
u = uniform_sampling(parameters, method, n_samples, seed)
Input variables
| Name | Description | Type |
|---|---|---|
parameters | Dictionary of parameters for uniform distribution. Keys:
| dictionary |
method | Sampling method. Supports the following values:
| string |
n_samples | Number of samples | integer |
seed | Seed for random number generation. Use None for a random seed | integer or none |
Output variables
| Name | Description | Type |
|---|---|---|
u | Random samples | list |
Example 1
In this example, we will use the uniform_sampling function from the parepy_toolbox to generate two random samples (\(n=400\)) following a uniform distribution. The first set is sampled using the Monte Carlo Sampling (MCS) method, and the second using the Latin Hypercube Sampling (LHS) method. The range for both distributions is defined as \([10, 20]\). The results are visualized using histograms with Kernel Density Estimates (KDE) plotted (using matplotlib lib) side-by-side for comparison.
# Library
import matplotlib.pyplot as plt
from parepy_toolbox import uniform_sampling
# Sampling
n = 400
x = uniform_sampling({'min': 10, 'max': 20}, 'mcs', n)
y = uniform_sampling({'min': 10, 'max': 20}, 'lhs', n)
# Plot
fig, axes = plt.subplots(1, 2, figsize=(7, 3))
sns.histplot(x, kde=True, bins=30, color='blue', ax=axes[0], alpha=0.6, edgecolor='black')
axes[0].set_title('MCS Sampling')
axes[0].set_xlabel('Values')
axes[0].set_ylabel('Densidade')
sns.histplot(y, kde=True, bins=30, color='green', ax=axes[1], alpha=0.6, edgecolor='black')
axes[1].set_title('LHS Sampling')
axes[1].set_xlabel('Valores')
axes[1].set_ylabel('Densidade')
plt.tight_layout()
plt.show()
Figure 1. Uniform variable example.
Example 2
In this example, we will use the uniform_sampling function from the parepy_toolbox to generate two random samples (\(n=3\)) following a uniform distribution. Using the Monte Carlo algorithm and the specific seed (seed=25), we generated 3 times and compared the results.
from parepy_toolbox import uniform_sampling
# Sampling
n = 3
x0 = uniform_sampling({'min': 10, 'max': 20}, 'mcs', n, 25)
x1 = uniform_sampling({'min': 10, 'max': 20}, 'mcs', n, 25)
x2 = uniform_sampling({'min': 10, 'max': 20}, 'mcs', n, 25)
print(x0, '\n', x1, '\n', x2)
[11.607212332320078, 15.003120351710036, 12.16598464462817]
[11.607212332320078, 15.003120351710036, 12.16598464462817]
[11.607212332320078, 15.003120351710036, 12.16598464462817]
Note that using the seed 25 by 3 times, we can generate the same values in a random variable.