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hackassin
GitHub Repository: hackassin/Coursera-Machine-Learning
 Path: blob/master/Week 3/Programming Assignment - 2/machine-learning-ex2/ex2/submit.m
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function submit()

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  addpath('./lib');

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  conf.assignmentSlug = 'logistic-regression';

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  conf.itemName = 'Logistic Regression';

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  conf.partArrays = { ...

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    { ...

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      '1', ...

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      { 'sigmoid.m' }, ...

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      'Sigmoid Function', ...

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    }, ...

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    { ...

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      '2', ...

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      { 'costFunction.m' }, ...

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      'Logistic Regression Cost', ...

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    }, ...

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    { ...

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      '3', ...

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      { 'costFunction.m' }, ...

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      'Logistic Regression Gradient', ...

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    }, ...

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    { ...

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      '4', ...

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      { 'predict.m' }, ...

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      'Predict', ...

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    }, ...

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    { ...

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      '5', ...

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      { 'costFunctionReg.m' }, ...

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      'Regularized Logistic Regression Cost', ...

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    }, ...

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    { ...

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      '6', ...

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      { 'costFunctionReg.m' }, ...

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      'Regularized Logistic Regression Gradient', ...

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    }, ...

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  };

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  conf.output = @output;

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  submitWithConfiguration(conf);

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end

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function out = output(partId, auxstring)

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  % Random Test Cases

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  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];

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  y = sin(X(:,1) + X(:,2)) > 0;

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  if partId == '1'

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    out = sprintf('%0.5f ', sigmoid(X));

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  elseif partId == '2'

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    out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y));

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  elseif partId == '3'

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    [cost, grad] = costFunction([0.25 0.5 -0.5]', X, y);

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    out = sprintf('%0.5f ', grad);

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  elseif partId == '4'

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    out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X));

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  elseif partId == '5'

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    out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1));

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  elseif partId == '6'

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    [cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1);

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    out = sprintf('%0.5f ', grad);

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  end 

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end

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