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# cross.entropy.md
| | |
| ------------- | --------------: |
| 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
| `...` | Arguments passed on to cross.entropy.matrix
pk,qk
A pair of \ matrices of length n of emprical probabilities p and estimated probabilities q.
dim
An \ value of length 1 (Default: 0). Defines the
dimension along which to calculate the entropy (0: total, 1: row-wise,
2: column-wise).
normalize
A \-value (default: TRUE). If TRUE, the mean
cross-entropy across all observations is returned; otherwise, the sum of
cross-entropies is returned.
|
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
#### Value
A \ value or vector:
* A single \ value (length 1) if `dim == 0`.
* A \ vector with length equal to the length of columns if`dim == 1`.
* A \ 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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