Sam Perry
38 lines
994 B
Matlab
38 lines
994 B
Matlab
%% This loads our data
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[X, y] = load_data_ex2();
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y
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%% Normalise and initialize.
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[X, mean_vec, std_vec] = normalise_features(X);
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%after normalising we add the bias
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X = [ones(size(X, 1), 1), X];
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%initialise theta
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theta = [0.0, 0.0, 0.0];
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% Learning rate
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alpha = 0.2;
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% Number of iterations for calculating theta values in gradient descent
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iterations = 100;
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% Perform gradient descent on data to calculate the values for theta that make
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% the best hypothesis function.
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t = gradient_descent(X, y, theta, alpha, iterations, false)
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% Declare new pair of input features with which to calculate a predicted output
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% value.
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input_size = 3000.0;
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input_bedrooms = 4.0;
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% Normalize features
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is_norm = (input_size - mean_vec(1)) / std_vec(1)
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ib_norm = (input_bedrooms - mean_vec(2)) / std_vec(2)
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% Use values for theta generated by gradient descent to predict the output
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prediction = t(1) + t(2)*is_norm + t(3)*ib_norm;
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% Round and print output
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round(prediction)
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disp 'Press enter to exit!';
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pause;
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