You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: submit.m
    path of file: ./repos/Coding-Guide/Notes/ml/coursera/machine-learning-ex5/ex5
    
    the code of the file until where you have to start completion: function submit()
  addpath('./lib');

  conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
  conf.itemName = 'Regularized Linear Regression and Bias/Variance';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'linearRegCostFunction.m' }, ...
      'Regularized Linear Regression Cost Function', ...
    }, ...
    { ...
      '2', ...
      { 'linearRegCostFunction.m' }, ...
      'Regularized Linear Regression Gradient', ...
    }, ...
    { ...
      '3', ...
      { 'learningCurve.m' }, ...
      'Learning Curve', ...
    }, ...
    { ...
      '4', ...
      { 'polyFeatures.m' }, ...
      'Polynomial Feature Mapping', ...
    }, ...
    { ...
      '5', ...
      { 'validationCurve.m' }, ...
      'Validation Curve', ...
    }, ...
  };
  conf.output = @output;


    
function submit()
  addpath('./lib');

  conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
  conf.itemName = 'Regularized Linear Regression and Bias/Variance';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'linearRegCostFunction.m' }, ...
      'Regularized Linear Regression Cost Function', ...
    }, ...
    { ...
      '2', ...
      { 'linearRegCostFunction.m' }, ...
      'Regularized Linear Regression Gradient', ...
    }, ...
    { ...
      '3', ...
      { 'learningCurve.m' }, ...
      'Learning Curve', ...
    }, ...
    { ...
      '4', ...
      { 'polyFeatures.m' }, ...
      'Polynomial Feature Mapping', ...
    }, ...
    { ...
      '5', ...
      { 'validationCurve.m' }, ...
      'Validation Curve', ...
    }, ...
  };
  conf.output = @output;

  submitWithConfiguration(conf);
end

function out = output(partId, auxstring)
  % Random Test Cases
  X = [ones(10,1) sin(1:1.5:15)' cos(1:1.5:15)'];
  y = sin(1:3:30)';
  Xval = [ones(10,1) sin(0:1.5:14)' cos(0:1.5:14)'];
  yval = sin(1:10)';
  if partId == '1'
    [J] = linearRegCostFunction(X, y, [0.1 0.2 0.3]', 0.5);
    out = sprintf('%0.5f ', J);
  elseif partId == '2'
    [J, grad] = linearRegCostFunction(X, y, [0.1 0.2 0.3]', 0.5);
    out = sprintf('%0.5f ', grad);
  elseif partId == '3'
    [error_train, error_val] = ...
        learningCurve(X, y, Xval, yval, 1);
    out = sprintf('%0.5f ', [error_train(:); error_val(:)]);
  elseif partId == '4'
    [X_poly] = polyFeatures(X(2,:)', 8);
    out = sprintf('%0.5f ', X_poly);
  elseif partId == '5'
    [lambda_vec, error_train, error_val] = ...
        validationCurve(X, y, Xval, yval);
    out = sprintf('%0.5f ', ...
        [lambda_vec(:); error_train(:); error_val(:)]);
  end