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NumPy
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schedule Jul 1, 2022
Last updated
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Numpy's `logaddexp(~)` method computes `log(exp(x1)+exp(x2))`, where `x1` and `x2` are the input arrays. This expression is useful because it often crops up in statistics.

# Parameters

1. `x1` | `array-like`

The first input array.

2. `x1` | `array-like`

The second input array.

3. `out` | `Numpy array` | `optional`

Instead of creating a new array, you can place the computed result into the array specified by `out`.

4. `where` | `array` of `boolean` | `optional`

Values that are flagged as False will be ignored, that is, their original value will be uninitialized. If you specified the out parameter, the behavior is slightly different - the original value will be kept intact. Since this is a source of confusion for many, check examples below.

# Return value

A Numpy array that contains the result of `log(p+1)` where `p` is each value in the input array.

# Examples

## Basic usage

``` np.logaddexp([1,2],[3,4]) array([3.12692801, 4.12692801]) ```

## Specifying an output array

``` a = np.zeros(2)np.logaddexp([1,2], [3,4], out=a)a array([3.12692801, 4.12692801]) ```

Here, we've output the result into array `a`.

``` np.logaddexp([1,2,3], [4,5,6], where=[False, True, False]) array([20.00000011, 5.04858735, 6.04858735]) ```

Here, only the second number was used for calculation since it has a corresponding boolean of `True` in the mask. You should notice how the values with `False` yielded strange results - in fact, you should disregard them because they are uninitialized numbers that are of no practical use.

Now, if you specified the out parameter, instead of uninitalized values, the original values will be left intact:

``` a = np.zeros(3)np.logaddexp([1,2,3], [4,5,6], out=a, where=[False, True, False])a array([0. , 5.04858735, 0. ]) ```