Random python choices weights

Random sampling in numpy | ranf() function
Random sampling in numpy | random() function
Random sampling in numpy | random_sample() function
Random sampling in numpy | sample() function
Random sampling in numpy | random_integers() function
Random sampling in numpy | randint() function
numpy.random.choice() in Python
How to choose elements from the list with different probability using NumPy?
How to get weighted random choice in Python?
numpy.random.shuffle() in python
numpy.random.geometric() in Python
numpy.random.permutation() in Python

Sorting and Searching in NumPy Array

Universal Functions

Working With Images

Projects and Applications with NumPy

Introduction

Creating NumPy Array

Numpy | Array Creation
numpy.arange() in Python
numpy.zeros() in Python
Create a Numpy array filled with all ones
numpy.linspace() in Python
numpy.eye() in Python
Creating a one-dimensional NumPy array
How to create an empty and a full NumPy array?
Create a Numpy array filled with all zeros | Python
How to generate 2-D Gaussian array using NumPy?
How to create a vector in Python using NumPy
Python | Numpy fromrecords() method

NumPy Array Manipulation

Copy and View in NumPy Array
How to Copy NumPy array into another array?
Appending values at the end of an NumPy array
How to swap columns of a given NumPy array?
Insert a new axis within a NumPy array
numpy.hstack() in Python
numpy.vstack() in python
Joining NumPy Array
Combining a one and a two-dimensional NumPy Array
Python | Numpy np.ma.concatenate() method
Python | Numpy dstack() method
Splitting Arrays in NumPy
How to compare two NumPy arrays?
Find the union of two NumPy arrays
Find unique rows in a NumPy array
Python | Numpy np.unique() method
numpy.trim_zeros() in Python

Matrix in NumPy

Matrix manipulation in Python
numpy matrix operations | empty() function
numpy matrix operations | zeros() function
numpy matrix operations | ones() function
numpy matrix operations | eye() function
numpy matrix operations | identity() function
Adding and Subtracting Matrices in Python
Matrix Multiplication in NumPy
Numpy ndarray.dot() function | Python
NumPy | Vector Multiplication
How to calculate dot product of two vectors in Python?
Multiplication of two Matrices in Single line using Numpy in Python
Python | Numpy np.eigvals() method
How to Calculate the determinant of a matrix using NumPy?
Python | Numpy matrix.transpose()
Python | Numpy matrix.var()
Compute the inverse of a matrix using NumPy

Operations on NumPy Array

Reshaping NumPy Array

Reshape NumPy Array
Python | Numpy matrix.resize()
Python | Numpy matrix.reshape()
NumPy Array Shape
Change the dimension of a NumPy array
numpy.ndarray.resize() function – Python
Flatten a Matrix in Python using NumPy
numpy.moveaxis() function | Python
numpy.swapaxes() function | Python
Python | Numpy matrix.swapaxes()
numpy.vsplit() function | Python
numpy.hsplit() function | Python
Numpy MaskedArray.reshape() function | Python
Python | Numpy matrix.squeeze()

Indexing NumPy Array

Arithmetic operations on NumPyArray

Linear Algebra in NumPy Array

Random sampling in numpy | ranf() function
Random sampling in numpy | random() function
Random sampling in numpy | random_sample() function
Random sampling in numpy | sample() function
Random sampling in numpy | random_integers() function
Random sampling in numpy | randint() function
numpy.random.choice() in Python
How to choose elements from the list with different probability using NumPy?
How to get weighted random choice in Python?
numpy.random.shuffle() in python
numpy.random.geometric() in Python
numpy.random.permutation() in Python

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Sorting and Searching in NumPy Array

Universal Functions

Working With Images

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Weighted Random Choice Using Python

Use the random.choices() Function to Generate Weighted Random Choices
Use the numpy.random.choice() Function to Generate Weighted Random Choices

In Python, we can easily generate random numbers using Random and NumPy libraries.

Selecting random elements from a list or an array by the probable outcome of the element is known as Weighted Random Choices. The selection of an element is determined by assigning a probability to each element present. Sometimes more than one element is also selected from the list of the elements made.

In this tutorial, we will discuss how to generate weighted random choices in Python.

Use the random.choices() Function to Generate Weighted Random Choices

Here, the random module of Python is used to make random numbers.

In the choices() function, weighted random choices are made with a replacement. It is also known as the weighted random sample with replacement. Also, in this function, weights play an essential role. Weights define the probable outcome of the selection of each element. There are two types of weights:

Choose Elements With Relative Weights

The weights parameter defines the relative weights. The probable outcome is different for each element in the list. If the probable outcome for each element has been fixed using the relative weights, then the selections are made based on the relative weights only.

import random  List = [12, 24, 36, 48, 60, 72, 84] print(random.choices(List, weights=(30, 40, 50 , 60, 70, 80, 90), k=7))

Here each element in the list is given its own weight i.e, probable outcome. Also, k in the above example is the number of elements needed from the given list.

Here, the total sum of weights is not 100 because they are relative weights and not percentages. The number 84 has occurred three times as it has the highest weight of all weights. So the probability of its occurrence will be the highest.

Choose Elements With Cumulative Weights

The cum_weight parameter is used to define the cumulative weights. The cumulative weight of an element is determined by the weight of the preceding element plus the relative weight of that element. For example, the relative weights [10, 20, 30, 40] are equivalent to the cumulative weights [10, 30, 60, 100]

import random  List = [13, 26, 39, 52, 65] print(random.choices(List, cum_weights=(10, 30, 60, 100, 150), k=5))

Here also, the number 65 occurs more than any other number as it has the highest weight.

Use the numpy.random.choice() Function to Generate Weighted Random Choices

For generating random weighted choices, NumPy is generally used when a user is using the Python version less than 3.6.

Here, numpy.random.choice is used to determine the probability distribution. In this method, random elements of 1D array are taken, and random elements of a numpy array are returned using the choice() function.

import numpy as np  List = [500,600,700,800] sNumbers = np.random.choice(List, 4, p=[0.10,0.20,0.30,0.40]) print(sNumbers)

Here, the probability should be equal to 1. The number 4 represents the size of the list.

Lakshay Kapoor is a final year B.Tech Computer Science student at Amity University Noida. He is familiar with programming languages and their real-world applications (Python/R/C++). Deeply interested in the area of Data Sciences and Machine Learning.

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Random python choices weights