TinghuiWang / pyActLearn

                for i in range(window, x.shape[0]):

                    result[i] = model.predict(x[i-window+1:i+1])[-1]

        else:

            if window == -1:

                result = self.decode(x, init_prob)

            else:

                result = np.zeros(x.shape[0], dtype=np.int)

                result[0:window] = self.decode(x[0:window], init_prob)

                for i in range(window, x.shape[0]):

                    result[i] = self.decode(x[i-window+1:i+1], init_prob)[-1]

        return result

    def predict_prob(self, x, init_prob=None, window=-1):

            model.startprob_ = init_prob

            model.emissionprob_ = self.B

            model.transmat_ = self.A

            if window == -1:

                result = model.predict(x)

            else:

                result = np.zeros(x.shape[0], dtype=np.int)

                result[0:window] = model.predict(x[0:window])

                for i in range(window, x.shape[0]):

                    result[i] = model.predict(x[i-window+1:i+1])[-1]

        else:

            if window == -1:

                result = self.decode(x, init_prob)