[mlpack-svn] r10089 - mlpack/trunk/src/mlpack/methods/linear_regression

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

Author: jcline3
Date: 2011-10-31 11:41:27 -0400 (Mon, 31 Oct 2011)
New Revision: 10089

Modified:
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_main.cpp
   mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp
Log:
Fix braces, #153


Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp	2011-10-31 15:26:59 UTC (rev 10088)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.cpp	2011-10-31 15:41:27 UTC (rev 10089)
@@ -27,7 +27,9 @@
   parameters.load(filename);
 }
 
-LinearRegression::~LinearRegression() {}
+LinearRegression::~LinearRegression()
+{
+}
 
 void LinearRegression::predict(arma::rowvec& predictions, const arma::mat& points)
 {
@@ -39,15 +41,18 @@
 
   predictions.set_size(n_cols);
   predictions += parameters(0);
-  for(size_t i = 1; i < n_rows; ++i) {
-    for(size_t j = 0; j < n_cols; ++j) {
+  for(size_t i = 1; i < n_rows; ++i)
+  {
+    for(size_t j = 0; j < n_cols; ++j)
+    {
       predictions(j) += parameters(i) * points(i-1,j);
       
     }
   }
 }
 
-arma::vec LinearRegression::getParameters() {
+arma::vec LinearRegression::getParameters()
+{
   return parameters;
 }
 

Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp	2011-10-31 15:26:59 UTC (rev 10088)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression.hpp	2011-10-31 15:41:27 UTC (rev 10089)
@@ -8,7 +8,8 @@
 /** 
  *  A simple linear regresion algorithm using ordinary least squares.
  */
-class LinearRegression {
+class LinearRegression
+{
   public:
     /** Creates the model.
      *  @param predictors X, matrix of data points to create B with.

Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_main.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_main.cpp	2011-10-31 15:26:59 UTC (rev 10088)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_main.cpp	2011-10-31 15:41:27 UTC (rev 10089)
@@ -13,7 +13,8 @@
 PROGRAM_INFO("Simple Linear Regression", "An implementation of simple linear \
 regression using ordinary least squares.", "linear_regression");
 
-int main(int argc, char* argv[]) {
+int main(int argc, char* argv[])
+{
 
   arma::vec B;
   arma::colvec responses;
@@ -33,21 +34,25 @@
   size_t n_cols = file.n_cols,
          n_rows = file.n_rows;
 
-  if(response_name == "") {
+  if(response_name == "")
+  {
     predictors = file.submat(0,0, n_rows-2, n_cols-1);
     // The initial predictors for y, Nx1
     responses = arma::trans(file.row(n_rows-1));
     --n_rows;
   }
-  else {
+  else
+  {
     predictors = file;
     // The initial predictors for y, Nx1
     responses.load(response_name.c_str(), arma::auto_detect, false, true);
-    if(responses.n_rows > 1) {
+    if(responses.n_rows > 1)
+    {
       std::cerr << "Error: The responses must have one column.\n";
       return 0;
     }
-    if(responses.n_cols != n_cols) {
+    if(responses.n_cols != n_cols)
+    {
       std::cerr << "Error: The responses must have the same number of rows as\
  the training file.\n";
       return 0;
@@ -55,7 +60,8 @@
   }
 
   points.load(test_name.c_str(), arma::auto_detect, false, true);
-  if(points.n_rows != n_rows) {
+  if(points.n_rows != n_rows)
+  {
       std::cerr << "Error: The test data must have the same number of cols as\
  the training file.\n";
       return 0;

Modified: mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp	2011-10-31 15:26:59 UTC (rev 10088)
+++ mlpack/trunk/src/mlpack/methods/linear_regression/linear_regression_test.cpp	2011-10-31 15:41:27 UTC (rev 10089)
@@ -8,7 +8,8 @@
  * Creates two 10x3 random matrices and one 10x1 "results" matrix.
  * Finds B in y=BX with one matrix, then predicts against the other.
  */
-BOOST_AUTO_TEST_CASE(LinearRegressionTest) {
+BOOST_AUTO_TEST_CASE(LinearRegressionTest)
+{
   // predictors, points are 10x3 matrices
   arma::mat predictors, points;
 
@@ -28,7 +29,8 @@
   // Create y
   responses.zeros(10);
   // Create a second "class" for the first cluster of points
-  for(size_t i = 0; i < 5; ++i) {
+  for(size_t i = 0; i < 5; ++i)
+  {
     responses(i) = 1;
   }
   responses += 1; // "classes" are 2,1
@@ -42,7 +44,8 @@
   // Output result and verify we have less than .5 error from "correct" value
   // for each point
   std::cout << points << '\n' << predictions << '\n';
-  for(size_t i = 0; i < predictions.n_cols; ++i) {
+  for(size_t i = 0; i < predictions.n_cols; ++i)
+  {
     assert( fabs(predictions(i) - responses(i)) < .5);
   }
 }