comparison armadillo-3.900.4/examples/example_lsq.cpp @ 49:1ec0e2823891

Switch to using subrepo copies of qm-dsp, nnls-chroma, vamp-plugin-sdk; update Armadillo version; assume build without external BLAS/LAPACK

+// Tutorial for linear least square fitting
+// Author: Pierre Moulon
+// Date: 8 December 2009
+// Objective:
+// Fit a 2D line with to a set of points
+// Specifically, find a and b in the model y = ax + b
+// 
+// Direct application of the example in:
+// http://en.wikipedia.org/wiki/Linear_least_squares#Motivational_example
+
+
+#include <iostream>
+
+#include "armadillo"
+
+using namespace arma;
+using namespace std;
+
+
+
+int main(int argc, char** argv)
+  {
+  // points to which we will fit the line
+  mat data = "1 6; 2 5; 3 7; 4 10";
+
+  cout << "Points used for the estimation:" << endl;
+  cout << data << endl;
+
+  // Build matrices to solve Ax = b problem:
+  vec b(data.n_rows);
+  mat C(data.n_rows, 2);
+
+  for(u32 i=0; i<data.n_rows; ++i)
+    {
+    b(i)   = data(i,1);
+    
+    C(i,0) = 1;
+    C(i,1) = data(i,0);
+    }
+  
+  cout << "b:" << endl;
+  cout << b << endl;
+  
+  cout << "Constraint matrix:" << endl;
+  cout << C << endl;
+  
+  // Compute least-squares solution:
+  vec solution = solve(C,b);
+  
+  // solution should be "3.5; 1.4"
+  cout << "solution:" << endl;
+  cout << solution << endl;
+  
+
+  cout << "Reprojection error:" << endl;
+  
+  for(u32 i=0; i<data.n_rows; ++i)
+    {
+    cout << "  residual: " << ( data(i,1) - (solution(0) + solution(1) * data(i,0)) ) << endl;
+    }
+    
+  return 0;
+  }
+