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Onyejiaku Theophilus Chidalu

Grokking Modern System Design Interview for Engineers & Managers

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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($2^{x1}$ + $2^{x2}$)

```
logaddexp2(x1, x2, out)
```

The `logaddexp2()`

function takes the following parameter values:

: This represents the input arrays or scalers.`x1, x2`

: This represents a location where the result is stored. This is optional.`out`

The `logaddexp2()`

function returns the logarithm of the sum of the exponentiations of `x1`

and `x2`

in base-2.

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

Implementing the logaddexp2() function

**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`

.

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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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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