def main():
theta = np.random.randn(6, 1)
p = Problem("database.txt")
#x = p.getX()
y = p.getY()
c = Controller(10, p)
#x_b = np.c_[np.ones((len(x), 1)), x]
'''
#theta, cost_history, theta_history = c.gradientDescent(theta, x_b)
theta, cost_history = c.stochasticGradientDescent(theta)
error = c.getError(theta)
print("Theta:")
print(theta[0][0])
print("Theta1:")
print(theta[1][0])
print("Theta2:")
print(theta[2][0])
print("Theta3:")
print(theta[3][0])
print("Theta4:")
print(theta[4][0])
print("Error:")
print(error)
'''
X = 2 * np.random.rand(100, 5)
#y = 4 +3 * X+np.random.randn(100,1)
print(X)
print("bine")
print("bine")
print("bine")
print(y)
class Controller:
def __init__(self, filename):
self.__problem = Problem(filename)
self.__x = self.__problem.getX()
self.__y = self.__problem.getY()
self.__nn = ArtificialNeuralNetwork(self.__x, self.__y, 2)
def solve(self):
self.__nn.loss=[]
iterations =[]
for i in range(4000):
self.__nn.feedforward()
learning_rate = 0.5
self.__nn.backpropagation(learning_rate)
iterations.append(i)
print(self.__nn.output)
mpl.pyplot.plot(iterations, self.__nn.loss, label='loss value vs iteration')
mpl.pyplot.xlabel('Iterations')
mpl.pyplot.ylabel('loss function')
mpl.pyplot.legend()
mpl.pyplot.show()
