[mlpack-svn] r15845 - mlpack/trunk/src/mlpack/methods/linear_regression

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: rcurtin
Date: Thu Sep 26 15:37:45 2013
New Revision: 15845

Log:
Avoid an additional matrix copy (insert_rows() and shed_rows() are a copy each
time).  Also make the input matrix const.


Modified:
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp

Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp	(original)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp	Thu Sep 26 15:37:45 2013
@@ -9,7 +9,7 @@
 using namespace mlpack;
 using namespace mlpack::regression;
 
-LinearRegression::LinearRegression(arma::mat& predictors,
+LinearRegression::LinearRegression(const arma::mat& predictors,
                                    const arma::colvec& responses,
                                    const double lambda) :
     lambda(lambda)
@@ -26,38 +26,46 @@
   const size_t nCols = predictors.n_cols;
 
   // Here we add the row of ones to the predictors.
-  arma::rowvec ones;
-  ones.ones(nCols);
-  predictors.insert_rows(0, ones);
-
-  // We set the parameters to the correct size and initialize them to zero.
-  parameters.zeros(nCols);
-
-  // Now, add the identity matrix to the predictors (this is equivalent to ridge
-  // regression).  See http://math.stackexchange.com/questions/299481/ for more
-  // information.
-  if (lambda > 0)
+  arma::mat p;
+  if (lambda == 0.0)
   {
-    predictors.insert_cols(nCols, predictors.n_rows);
-    predictors.cols(nCols, predictors.n_cols - 1) =
+    p.set_size(predictors.n_rows + 1, nCols);
+    p.submat(1, 0, p.n_rows - 1, nCols - 1) = predictors;
+    p.row(0).fill(1);
+  }
+  else
+  {
+    // Add the identity matrix to the predictors (this is equivalent to ridge
+    // regression).  See http://math.stackexchange.com/questions/299481/ for
+    // more information.
+    p.set_size(predictors.n_rows + 1, nCols + predictors.n_rows);
+    p.submat(1, 0, p.n_rows - 1, nCols - 1) = predictors;
+    p.row(0).fill(1);
+    p.submat(1, nCols, p.n_rows - 1, nCols + predictors.n_rows - 1) =
         lambda * arma::eye<arma::mat>(predictors.n_rows, predictors.n_rows);
   }
 
   // We compute the QR decomposition of the predictors.
   // We transpose the predictors because they are in column major order.
   arma::mat Q, R;
-  arma::qr(Q, R, arma::trans(predictors));
+  arma::qr(Q, R, arma::trans(p));
 
   // We compute the parameters, B, like so:
   // R * B = Q^T * responses
   // B = Q^T * responses * R^-1
-  arma::solve(parameters, R, arma::trans(Q) * responses);
-
-  // We now remove the row of ones we added so the user's data is unmodified.
-  predictors.shed_row(0);
-  if (lambda > 0)
+  // If lambda > 0, then we must add a bunch of empty responses.
+  if (lambda == 0.0)
   {
-    predictors.shed_cols(nCols, predictors.n_cols - 1);
+    arma::solve(parameters, R, arma::trans(Q) * responses);
+  }
+  else
+  {
+    // Copy responses into larger vector.
+    arma::vec r(nCols + predictors.n_rows);
+    r.subvec(0, nCols - 1) = responses;
+    r.subvec(nCols, nCols + predictors.n_rows - 1).fill(0);
+
+    arma::solve(parameters, R, arma::trans(Q) * r);
   }
 }
 

Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp	(original)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp	Thu Sep 26 15:37:45 2013
@@ -26,7 +26,7 @@
    * @param predictors X, matrix of data points to create B with.
    * @param responses y, the measured data for each point in X
    */
-  LinearRegression(arma::mat& predictors,
+  LinearRegression(const arma::mat& predictors,
                    const arma::vec& responses,
                    const double lambda = 0);
 
@@ -82,6 +82,11 @@
   //! Modify the parameters (the b vector).
   arma::vec& Parameters() { return parameters; }
 
+  //! Return the Tikhonov regularization parameter for ridge regression.
+  double Lambda() const { return lambda; }
+  //! Modify the Tikhonov regularization parameter for ridge regression.
+  double& Lambda() { return lambda; }
+
  private:
   /**
    * The calculated B.
@@ -89,7 +94,10 @@
    */
   arma::vec parameters;
 
-  //! The lambda parameter for ridge regression (0 for linear regression).
+  /**
+   * The Tikhonov regularization parameter for ridge regression (0 for linear
+   * regression).
+   */
   double lambda;
 };