Symbolic regression - Multiple variables

Extension to multiple variables is straight-forward.

For example consider the \(f(x,y) = ln(x^2 + y^2)\) function:

ln(x·x + y·y)

We only need to add a column to the input data:

std::istringstream training(R"(
  -2.079, 0.25, 0.25
  -0.693, 0.50, 0.50
   0.693, 1.00, 1.00
   0.000, 0.00, 1.00
   0.000, 1.00, 0.00
   1.609, 1.00, 2.00
   1.609, 2.00, 1.00
   2.079, 2.00, 2.00
)");

and a function to the function set:

prob.insert<real::ln>();
(for your ease the above code is in the examples/symbolic_regression/symbolic_regression01.cc file)

and what we get is:

[INFO] Reading dataset from input stream...
[INFO] Setting up terminals...
[INFO] ...terminals ready. Variables: `X1` `X2`
[INFO] ...dataset read. Examples: 8, categories: 1, features: 2, classes: 0
[INFO] Number of layers set to 1
[INFO] Population size set to 100
       0:     -51.9232 (   0.196)                 
       2:     -23.9684 (   0.587)                 
       5:     -20.1796 (   1.206)                 
      29:     -20.1796 (   6.604)                 
      38:     -12.9331 (   8.893)                 
      43:   -0.0174211 (  10.204)                 
[INFO] Evolution completed at generation: 102. Elapsed time: 28.313

CANDIDATE SOLUTION
log(((X2*X2)+(X1*X1)))

FITNESS
-0.0174211

PROCEED TO PART 2→