new.m

%% Initialization
%clear ; close all; clc


%% Setup the parameters you will use for this exercise
input_layer_size  = 2;  % x1 and x2
hidden_layer_size = 10;   % 5 hidden units
num_labels = 2;          % 2 labels, 0 and 1   
lambda = 1;     %for regularization
                         

%% Load Data
%  The first two columns contains X and the third column
%  contains label.
data = load('datac_118.txt');
X = data(:, [1, 2]); y = data(:, 3);

m = size(X, 1);

%% ==================== Plotting ====================


fprintf(['Plotting data \n']);

plotData(X, y);

% Put some labels 
hold on;
% Labels and Legend
xlabel('Feature 1')
ylabel('Feature 2')

% Specified in plot order
%legend('Label-0', 'Label-1')
hold off;

fprintf('\nProgram paused. Press enter to continue.\n');
pause;


%% ================  Initializing Pameters ================


fprintf('\nInitializing Neural Network Parameters ...\n')

initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);

% Unroll parameters
initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];

%J = new1(nn_params, input_layer_size, hidden_layer_size, ...
%                   num_labels, X, y, lambda);




%% ===================  Training NN ===================

%
fprintf('\nTraining Neural Network... \n')

%  We can change the MaxIter to a larger
%  value to see how more training helps.
options = optimset('MaxIter', 60);

%  You should also try different values of lambda
lambda = 0;

% Create "short hand" for the cost function to be minimized
costFunction = @(p) nnCostFunction(p, ...
                                   input_layer_size, ...
                                   hidden_layer_size, ...
                                   num_labels, X, y, lambda);

% Now, costFunction is a function that takes in only one argument (the
% neural network parameters)
[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);

% Obtain Theta1 and Theta2 back from nn_params
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
                 hidden_layer_size, (input_layer_size + 1));

Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
                 num_labels, (hidden_layer_size + 1));

fprintf('Program paused. Press enter to continue.\n');
pause;







%% ================= Implement Predict =================


pred = predict1(Theta1, Theta2, X);

fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);