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# What is the numpy.logaddexp2() function from NumPy in Python? Onyejiaku Theophilus Chidalu

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### Overview

The function logaddexp() of numpy in Python returns the logarithm of the sum of exponentiations of x1 and x2 inputs in base-2.

It can be expressed mathematically as:

logaddexp(x1, x2) = log2($2^{x1}$ + $2^{x2}$)

### Syntax

logaddexp2(x1, x2, out)


### Parameters

The logaddexp2() function takes the following parameter values:

• x1, x2: This represents the input arrays or scalers.
• out: This represents a location where the result is stored. This is optional.

### Return value

The logaddexp2() function returns the logarithm of the sum of the exponentiations of x1 and x2 in base-2.

### Example

from numpy import logaddexp2# creating our input valuesx = 2y  = 3# calling the logaddexp2() functionmyresult  = logaddexp2(x, y)print(myresult)
Implementing the logaddexp2() function

### Explanation

• Line 1: We import the logaddexp2 from numpy module.
• Lines 3 and 4: We create variables x and y.
• Line 7: We call the logaddexp2() function on the variables x and y. The result is assigned to a variable myresult.
• Line 9: We print the variable myresult.

### Application

The function logaddexp2() is useful in statistics, especially when the probability of an event is so tiny that it exceeds the range of regular floating-point numbers. In such cases, the logarithm of the calculated probability is stored. The function allows storing the added probabilities in such a fashion.

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CONTRIBUTOR Onyejiaku Theophilus Chidalu

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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