Train polynomial regression and comput train error (m = 1 to 12) and cross validation error.

coursera-stanford-machine-learning-class-week6-compute-train-polynomial-regression-and-compute-error.m

% Octave console output
Iteration    14 | Cost: 1.232595e-32
Iteration    25 | Cost: 4.108651e-32
Iteration    11 | Cost: 3.910038e-28
Iteration   200 | Cost: 5.989594e-08
Iteration   200 | Cost: 8.797460e-04
warning: division by zero.107198e+01
Iteration   200 | Cost: 4.639732e-02
Iteration   200 | Cost: 6.939729e-02
warning: division by zero.922449e+01
Iteration   200 | Cost: 1.814619e-01
Iteration   200 | Cost: 1.626512e-01
Iteration   200 | Cost: 1.240625e-01
Iteration   200 | Cost: 1.354165e-01
Polynomial Regression (lambda = 0.000000)

# Training Examples	Train Error	Cross Validation Error
  	1		0.000000	331.806752
  	2		0.000000	160.121510
  	3		0.000000	61.754825
  	4		0.000000	61.928895
  	5		0.000000	6.604738
  	6		0.000880	10.065414
  	7		0.046397	7.260759
  	8		0.069397	7.098868
  	9		0.181462	7.725792
  	10		0.162651	8.719869
  	11		0.124062	9.822221
  	12		0.135417	12.486147