def main():
#make a neural network with set architecture
arch = (2,4,1)
nn = Neural_Network(arch)
#XOR input data
X_train = np.array( [ [0,0], [0,1], [1,0], [1,1] ] )
#XOR output data
y_train = np.array( [[0],[1],[1],[0]] )
#set max iterations, learning rate, and convergence threshold
iters, lr, threshold = 5000, 1, 0.00001
#train the network
J_Hist = nn.train(X_train, y_train, alpha = lr, maxIter = iters, convergenceThreshold = threshold)
#forward propagate to get a prediction from the network
result = nn.forwardProp(X_train)
#print some nice information
print("\nUnfiltered Prediction:\n", result)
print("Final Prediction:\n", result >= 0.5, '\n')
print("Random init cost: ", round(J_Hist[0], 5), ", Final cost: ", round(J_Hist[-1], 5))
print("Cost reduction from random init: ", round(J_Hist[0] - J_Hist[-1], 5), '\n')
#set up subplots for the cost history and decision boundary
figure, plots = plt.subplots(ncols=2)
figure.suptitle('Neural Network Learning of XOR') #supertitle
figure.tight_layout(pad=2.5, w_pad=1.5, h_pad=0) #fix margins
drawCostHistory(J_Hist, plots[0])
drawDecisionBoundary(nn, plots[1], seperation_coefficient = 50, square_size = 1, allowNegatives = False)
#show the cool graphs :)
plt.show()
