[mlpack-svn] r10201 - mlpack/trunk/src/mlpack/methods/linear_regression

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

Author: jcline3
Date: 2011-11-08 22:40:34 -0500 (Tue, 08 Nov 2011)
New Revision: 10201

Modified:
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp
Log:
comments


Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp	2011-11-09 01:42:26 UTC (rev 10200)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp	2011-11-09 03:40:34 UTC (rev 10201)
@@ -6,18 +6,30 @@
 LinearRegression::LinearRegression(arma::mat& predictors,
   const arma::colvec& responses)
 {
+
+  /*
+   * We want to calculate the a_i coefficients of:
+   * \sum_{i=0}^n (a_i * x_i^i)
+   * We add a row of ones to get a_0, where x_0^0 = 1, the intercept.
+   */
+
+  // The number of columns and rows
   size_t n_cols, n_rows;
 
   n_cols = predictors.n_cols;
   n_rows = predictors.n_rows;
 
+  // Add a row of ones, to get the intercept
   arma::rowvec ones;
   ones.ones(n_cols);
   predictors.insert_rows(0,ones);
+  // We have an additional row, now
   ++n_rows;
 
-  parameters.set_size(n_cols);
+  // Set the parameters to the correct size, all zeros.
+  parameters.zeros(n_cols);
 
+  // inverse( A^T * A ) * A^T * responses, where A = predictors
   parameters = arma::inv((predictors * arma::trans(predictors))) *
     predictors * responses;
 }
@@ -33,18 +45,22 @@
 
 void LinearRegression::predict(arma::rowvec& predictions, const arma::mat& points)
 {
+  // The number of columns and rows
   size_t n_cols, n_rows;
   n_cols = points.n_cols;
   n_rows = points.n_rows;
 
+  // Sanity check
   assert(n_rows == parameters.n_rows - 1);
 
   predictions.zeros(n_cols);
-  predictions += parameters(0);
+  // Set to a_0
+  predictions = parameters(0);
   for(size_t i = 1; i < n_rows; ++i)
   {
     for(size_t j = 0; j < n_cols; ++j)
     {
+      // Add in the next term: a_i * x_i
       predictions(j) += parameters(i) * points(i-1,j);
 
     }