# Example of estimating coefficients

# Make a prediction
def predict(row, coefficients):
	yhat = coefficients[0]
	for i in range(len(row)-1):
		yhat += coefficients[i + 1] * row[i]
	return yhat

# Estimate linear regression coefficients using stochastic gradient descent
def coefficients_sgd(train, l_rate, n_epoch):
	coef = [0.0 for i in range(len(train[0]))]
	for epoch in range(n_epoch):
		sum_error = 0
		for row in train:
			yhat = predict(row, coef)
			error = yhat - row[-1]
			sum_error += error**2
			coef[0] = coef[0] - l_rate * error
			for i in range(len(row)-1):
				coef[i + 1] = coef[i + 1] - l_rate * error * row[i]
		print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error))
	return coef

# Calculate coefficients
dataset = [[1, 1], [2, 3], [4, 3], [3, 2], [5, 5]]
l_rate = 0.001
n_epoch = 50
coef = coefficients_sgd(dataset, l_rate, n_epoch)
print(coef)