# cross.entropy.md

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| ------------- | --------------: |
| cross.entropy | R Documentation |

### Cross Entropy

#### Description

A generic S3 function to compute the *cross entropy* score for a\
classification model. This function dispatches to S3 methods in`cross.entropy()` and performs no input validation. If you supply NA\
values or vectors of unequal length (e.g. `length(x) != length(y)`), the\
underlying `C++` code may trigger undefined behavior and crash your `R`\
session.

**Defensive measures**

Because `cross.entropy()` operates on raw pointers, pointer-level faults\
(e.g. from NA or mismatched length) occur before any `R`-level error\
handling. Wrapping calls in `try()` or `tryCatch()` will *not* prevent`R`-session crashes.

To guard against this, wrap `cross.entropy()` in a "safe" validator that\
checks for NA values and matching length, for example:

{% code overflow="wrap" lineNumbers="true" %}

```r
safe_cross.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  cross.entropy(x, y, ...)
}
```

{% endcode %}

Apply the same pattern to any custom metric functions to ensure input\
sanity before calling the underlying `C++` code.

#### Usage

```r
## Generic S3 method
## for Cross Entropy
cross.entropy(...)
```

#### Arguments

| `...` | <p>Arguments passed on to <code>cross.entropy.matrix</code></p><p><code>pk,qk</code></p><p>A pair of \<double> matrices of length <code>n</code> of emprical probabilities <code>p</code> and estimated probabilities <code>q</code>.</p><p><code>dim</code></p><p>An \<integer> value of length 1 (Default: 0). Defines the<br>dimension along which to calculate the entropy (0: total, 1: row-wise,<br>2: column-wise).</p><p><code>normalize</code></p><p>A \<logical>-value (default: TRUE). If TRUE, the mean<br>cross-entropy across all observations is returned; otherwise, the sum of<br>cross-entropies is returned.</p> |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

#### Value

A \<double> value or vector:

* A single \<double> value (length 1) if `dim == 0`.
* A \<double> vector with length equal to the length of columns if`dim == 1`.
* A \<double> vector with length equal to the length of rows if`dim == 2`.

#### References

MacKay, David JC. Information theory, inference and learning algorithms.\
Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for\
evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for\
scientific computing in Python." Nature methods 17.3 (2020): 261-272.

#### Examples

```r
## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
cross.entropy(
 pk = pk,
 qk = qk
)


```

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