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Nov 8th, 2017
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  1.  #
  2.             #BEGIN Regularization section
  3.             #
  4.             min_err, w_hat = Inf, Inf
  5.             for lamb in vcat([2.0^x for x in -1:4],0)
  6.                 #find the best w_hat_lam
  7.                 w_hat_lam = (svd_X[:Vt]'*Diagonal(svd_X[:S])^2*svd_X[:Vt]+lamb*eye(size(X_train,2)) )^(-1)*svd_X[:Vt]'*Diagonal(svd_X[:S])*svd_X[:U]'*y_train
  8.                #use this to predict error on the k holdout
  9.                preds = sign.(X_test_k*w_hat_lam)
  10.                
  11.                error_lam = num_mistakes(y_test_k , preds)/16
  12.                if error_lam < min_err
  13.                    min_err = error_lam
  14.                    w_hat = w_hat_lam
  15.                end
  16.            end
  17.            preds_actual = sign.(X_test*w_hat)
  18.            error_rate_sum_reg += num_mistakes(y_test , preds_actual)/16
  19.        end
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