mbg.py

mbg.py

        self.init_plot(self.FEATURES)

        has_converged = False
        iter = 1

        while not has_converged:
            print(f"iter {iter}")

            minibatch = random.sample(range(0, self.DATAPOINTS), self.MINIBATCH_SIZE)
            for k in range(self.FEATURES):
                total = 0
                for i in minibatch:
                    total += self.x[i][k] * self.conditional_prob(1, i) - self.y[i]
                gradient = 1.0 / self.DATAPOINTS * total
                self.gradient[k] = gradient

            self.update_plot(np.sum(np.square(self.gradient)))
            for k in range(0, self.FEATURES):
                self.theta[k] -= self.LEARNING_RATE * self.gradient[k]

            has_converged = all(abs(i) < self.CONVERGENCE_MARGIN for i in self.gradient)

            iter += 1