ProbabilisticModel.py 文件源码

def sample(self, n=1):
        counts = np.random.multinomial(n, self.weights, size=1)
        samples = np.empty((n, len(self.means[0])))

        k = 0
        for i in range(len(self.means)):
            for j in range(len(self.means[i])):
                samples[k:k+counts[i], j] = stats.norm.rvs(loc=self.means[i, j], scale=self.stds[i, j], size=counts[i])
            k += counts[i]

        np.shuffle(samples)

        return samples