KNN Cross Entropy Estimators.

1 | ```
crossentropy(X, Y, k=10, algorithm=c("kd_tree", "cover_tree", "brute"))
``` |

`X` |
an input data matrix. |

`Y` |
an input data matrix. |

`k` |
the maximum number of nearest neighbors to search. The default value is set to 10. |

`algorithm` |
nearest neighbor search algorithm. |

If `p(x)`

and `q(x)`

are two continuous probability density functions,
then the cross-entropy of `p`

and `q`

is defined as
*H(p;q) = E_p[-\log q(x)]*.

a vector of length `k`

for crossentropy estimates using `1:k`

nearest neighbors, respectively.

Shengqiao Li. To report any bugs or suggestions please email: shli@stat.wvu.edu.

S. Boltz, E. Debreuve and M. Barlaud (2007).
“kNN-based high-dimensional Kullback-Leibler distance for tracking”.
*Image Analysis for Multimedia Interactive Services, 2007. WIAMIS '07. Eighth International Workshop on*.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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