* This example implements a basic Linear Regression to solve the y = theta0 + theta1*x problem using batch gradient descent algorithm.
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* <p>
* Linear Regression with BGD(batch gradient descent) algorithm is an iterative clustering algorithm and works as follows:<br>
* Giving a data set and target set, the BGD try to find out the best parameters for the data set to fit the target set.
* In each iteration, the algorithm computes the gradient of the cost function and use it to update all the parameters.
* The algorithm terminates after a fixed number of iterations (as in this implementation)
* With enough iteration, the algorithm can minimize the cost function and find the best parameters
* This is the Wikipedia entry for the <a href = "http://en.wikipedia.org/wiki/Linear_regression">Linear regression</a> and <a href = "http://en.wikipedia.org/wiki/Gradient_descent">Gradient descent algorithm</a>.
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* <p>
* This implementation works on one-dimensional data. And find the two-dimensional theta.<br>
* It find the best Theta parameter to fit the target.
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* <p>
* Input files are plain text files and must be formatted as follows:
* <ul>
* <li>Data points are represented as two double values separated by a blank character. The first one represent the X(the training data) and the second represent the Y(target).
* Data points are separated by newline characters.<br>
* For example <code>"-0.02 -0.04\n5.3 10.6\n"</code> gives two data points (x=-0.02, y=-0.04) and (x=5.3, y=10.6).
