There are many articles in AskPython detailing the various functions that the *numpy *library of Python has got to offer. In this article, we shall explore one which combines the logarithmic and exponential functions to the base of 2 â€“ the *logaddexp2( ) *function. Letâ€™s get things started by first importing the *numpy *library using the following code.

```
import numpy as np
```

Thereafter, we shall explore further the *logaddexp2( ) *function through each of the following sections.

**Syntax of the***logaddexp2( )*function**Why use***log**addexp2(a,b)*instead of*log2(2**a + 2**b)*?**Using***log**addexp2( )*on N-Dimensional Arrays

**Syntax ofÂ the*** logaddexp2( )*Â function

*logaddexp2( )*Â function

The *logaddexp2( )* function adds a pair of 2â€™s raised to the power of input scalars or arrays and then calculates the natural logarithm of the resulting value again with the base of 2. Following is the syntax of the *logaddexp2( ) *function which contains both the mandatory and the optional inputs required for its functioning.

```
numpy.logaddexp2(x1, x2, out=None, *, where=True, dtype=None)
```

where,

Scalars or N-dimensional arrays which are to be used with base 2*x1, x2 â€“*an optional construct set to*out â€“**none*by default, but could be used to store the results in the desired array which is of the same length as the outputkwargs or keyword argument which is an optional construct used to pass keyword variable length of argument to a function***â€“an optional construct which is used to calculate the universal function (ufunc) at the given position when set to*where â€“**True*(default setting) or not calculate when set to*False*an optional construct used to specify the data type which is being used*dtype â€“*

**Why use ***logaddexp2(a,b) *instead of *log2(2**a + 2**b)*?

*logaddexp2(a,b)*instead of

*log2(2**a + 2**b)*?

There are independent functions within the *numpy *library that can help in deducing the required results. It can be done by summing up the inputs raised to the power of â€˜2â€™ & then deducing the logarithm of the sum to the base of 2. So, letâ€™s find out why thereâ€™s an exclusive function to perform the same set of operations.

```
a = 1.03
b = 0.0006
np.log2(2**a + 2**b)
np.logaddexp2(a,b)
```

Following are the results of the above code.

Seems that the results are identical. But, what if we reduce *â€˜xâ€™ *a tad bit lesser and repeat the same? Letâ€™s find out what happens then!

```
a = 1.03e-5
b = 6e-7
np.log2(2**a + 2**b)
np.logaddexp2(a,b)
```

One can see that there is an additional decimal after the last digit with the *logaddexp2( ) *function. This serves as a great utility during machine learning applications which involve minuscule probabilities of events. These probabilities might be very small that they can exceed the normal range of floating point numbers. Since the *logaddexp2( ) *function uses the base of 2 for the results to be more accurate.

**Using ***logaddexp2( ) *on N-Dimensional Arrays

*logaddexp2( )*on N-Dimensional Arrays

In this section, we shall demonstrate the usage of *logaddexp2( ) *function only at select positions of an N-dimensional array.

```
ar1 = np.array([[1e-10, 8e-3, 0.09],
[5.007, 33e-5, 2e-7]])
ar2 = np.array([[2e-6, 10e-3, 9e-4],
[0.006, 4e-9, 7e-6]])
np.logaddexp2(ar1, ar2, where = [[False, True, True],
[True, True, False]])
```

The usage of *where *option has made valid results be returned only in the positions given as *True *& those elsewhere as erroneous. Letâ€™s assign this to an output array to make believe it.

```
r = np.zeros((2,3))
np.logaddexp2(ar1, ar2, where = [[False, True, True],
[True, True, False]], out = r)
```

With a keen eye, one can observe that only the values returned by the function remain whilst those that are erroneous are replaced with zeros.

**Conclusion**

Now that we have reached the end of this article, hope it has elaborated on how to use theÂ *logaddexp2( )Â *function from theÂ *numpyÂ *library. Hereâ€™s another article that details the usage of *logaddexp( ) *function from theÂ *numpyÂ *library in Python. There are numerous other enjoyable and equally informative articles in AskPython that might be of great help to those who are looking to level up in Python.Â *Audere est faucere*!