[mlpack] 05/149: New distribution (a combination of LinearRegression and GaussianDistribution) for implementing HMM Regression.

[Barak A. Pearlmutter]

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bap pushed a commit to branch svn-trunk
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commit 7b41f59d6d5b66a055f7b58eedbc5d66983c74ec
Author: michaelfox99 <michaelfox99 at 9d5b8971-822b-0410-80eb-d18c1038ef23>
Date:   Sat Sep 13 15:20:21 2014 +0000

    New distribution (a combination of LinearRegression and GaussianDistribution) for implementing HMM Regression.
    
    
    git-svn-id: http://svn.cc.gatech.edu/fastlab/mlpack/trunk@17178 9d5b8971-822b-0410-80eb-d18c1038ef23
---
 src/mlpack/core/dists/hmm_regression.hpp | 89 ++++++++++++++++++++++++++++++++
 1 file changed, 89 insertions(+)

diff --git a/src/mlpack/core/dists/hmm_regression.hpp b/src/mlpack/core/dists/hmm_regression.hpp
new file mode 100644
index 0000000..3b17cac
--- /dev/null
+++ b/src/mlpack/core/dists/hmm_regression.hpp
@@ -0,0 +1,89 @@
+/**
+ * @file hmm_regression.hpp
+ * @author Michael Fox
+ *
+ * Implementation of conditional Gaussian distribution for HMM regression (HMMR)
+ */
+#ifndef __MLPACK_METHODS_HMM_DISTRIBUTIONS_CONDITIONAL_GAUSSIAN_DISTRIBUTION_HPP
+#define __MLPACK_METHODS_HMM_DISTRIBUTIONS_CONDITIONAL_GAUSSIAN_DISTRIBUTION_HPP
+
+#include <mlpack/core.hpp>
+#include <mlpack/methods/linear_regression/linear_regression.hpp>
+
+namespace mlpack {
+namespace distribution {
+
+/**
+ * A class that represents a univariate conditionally Gaussian distribution.
+ * Can be used as an emission distribution with the hmm class to implement HMM
+ * regression (HMMR) as described in
+ * https://www.ima.umn.edu/preprints/January1994/1195.pdf
+ * The hmm observations should have the dependent variable in the first row,
+ * with the independent variables in the other rows. 
+ */
+class HMMRegression
+{
+ private:
+  //! Regression function for representing conditional mean.   
+  regression::LinearRegression rf;   
+  //! Error distribution
+  GaussianDistribution err;
+  
+ public:
+  /**
+   * Default constructor, which creates a Gaussian with zero dimension.
+   */
+  HMMRegression() { /* nothing to do */ }
+
+  /**
+   * Create a Conditional Gaussian distribution with conditional mean function
+   * obtained by running RegressionFunction on predictors, responses. 
+   * 
+   * @param predictors Matrix of predictors (X).
+   * @param responses Vector of responses (y).
+   */
+  HMMRegression(const arma::mat& predictors,
+                                  const arma::vec& responses) :
+      rf(regression::LinearRegression(predictors, responses))    
+  {
+      err = GaussianDistribution(1);
+      err.Covariance() = rf.ComputeError(predictors, responses);
+  }
+
+  /**
+   * Returns a string representation of this object.
+   */
+  std::string ToString() const;
+ 
+  // Return regression function
+  const regression::LinearRegression& Rf() {return rf;}
+  
+  /**
+   * Estimate parameters using provided observation weights
+   *
+   * @param weights probability that given observation is from distribution
+   */
+  void Estimate(const arma::mat& observations, const arma::vec& weights);
+  
+  /**
+  * Evaluate probability density function of given observation  
+  *
+  * @param observation point to evaluate probability at
+  */
+  double Probability(const arma::vec& observation) const;
+  
+   //! Return the parameters (the b vector).
+  const arma::vec& Parameters() const { return rf.Parameters(); }
+
+  //! Return the dimensionality (2)
+  static const size_t Dimensionality() { return 2; }
+};
+
+
+}; // namespace distribution
+}; // namespace mlpack
+
+//Include implmentation
+#include "hmm_regression_impl.hpp"
+
+#endif

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