[mlpack-git] master: Update documentation and use Train() not Estimate() for distributions. (e444bd0)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Fri Dec 18 11:43:06 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/5ba11bc90223b55eecd5da4cfbe86c8fc40637a5...df229e45a5bd7842fe019e9d49ed32f13beb6aaa
>---------------------------------------------------------------
commit e444bd07cd884a1fc44055e08ece20e419888ec0
Author: Ryan Curtin <ryan at ratml.org>
Date: Wed Dec 16 21:02:00 2015 +0000
Update documentation and use Train() not Estimate() for distributions.
>---------------------------------------------------------------
e444bd07cd884a1fc44055e08ece20e419888ec0
src/mlpack/methods/hmm/hmm.hpp | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/src/mlpack/methods/hmm/hmm.hpp b/src/mlpack/methods/hmm/hmm.hpp
index de375da..da03d9b 100644
--- a/src/mlpack/methods/hmm/hmm.hpp
+++ b/src/mlpack/methods/hmm/hmm.hpp
@@ -35,12 +35,12 @@ namespace hmm /** Hidden Markov Models. */ {
* double Probability(const DataType& observation) const;
*
* // Estimate the distribution based on the given observations.
- * void Estimate(const std::vector<DataType>& observations);
+ * void Train(const std::vector<DataType>& observations);
*
* // Estimate the distribution based on the given observations, given also
* // the probability of each observation coming from this distribution.
- * void Estimate(const std::vector<DataType>& observations,
- * const std::vector<double>& probabilities);
+ * void Train(const std::vector<DataType>& observations,
+ * const std::vector<double>& probabilities);
* };
* @endcode
*
@@ -71,7 +71,7 @@ namespace hmm /** Hidden Markov Models. */ {
* Once initialized, the HMM can evaluate the probability of a certain sequence
* (with LogLikelihood()), predict the most likely sequence of hidden states
* (with Predict()), generate a sequence (with Generate()), or estimate the
- * probabilities of each state for a sequence of observations (with Estimate()).
+ * probabilities of each state for a sequence of observations (with Train()).
*
* @tparam Distribution Type of emission distribution for this HMM.
*/
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