// Perform nonlinear regression to fit the function:
//  f(x,y) = exp(-(x^2+y^2)/1000) * cos(3(x+y)/pi)

// Load in data set
data = new data.Dataset
data import ./datasets/simple-data.txt

// Define the function to be fit. Notation:
//  Variables are defined by #{<name>}. 
//  If <name> begins with "a:", the variable is a dependent variable.
//  Values of dependent variables are linked to values of attributes in the dataset
model = new models.regression.NonlinearRegressionExpr exp(-1 * (#{a:x} ^ 2 + #{a:y} ^ 2) / #{a}) * cos(#{b} * (#{a:x} + #{a:y}) / pi)
model nonlinear guess a 1
model nonlinear guess b 1
model nonlinear maxiter 1000

// Evaluate predictive ability
model train $data

// Print out the model and fit results
print model model
print model training stats
	
exit