Beispiel #1
0
def test_distributions_mgd_random_sample():
    mu = numpy.array([5., 1.])
    cov = numpy.eye(2)
    d = MultivariateGaussianDistribution(mu, cov)

    x = numpy.array([[5.441227, 0.66913], [7.430771, 0.747908],
                     [5.10961, 2.582481]])

    assert_array_almost_equal(d.sample(3, random_state=5), x)
    assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_distributions_mgd_random_sample():
	mu = numpy.array([5., 1.])
	cov = numpy.eye(2)
	d = MultivariateGaussianDistribution(mu, cov)

	x = numpy.array([[5.441227, 0.66913 ],
			         [7.430771, 0.747908],
			         [5.10961 , 2.582481]])

	assert_array_almost_equal(d.sample(3, random_state=5), x)
	assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
Beispiel #3
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    def sample(self, n=10):
        try:
            X1 = np.stack(self.model.sample(int(np.round(0.8 * n))))
            # np.round 取最近的整数
            # 从多元高斯分布中采样8个点
            if self.dim == 2:
                mean = np.mean(X1, axis=0)
                # 由X1的采样点计算均值
                std = np.diag([1.0, 1.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                # 构建一个新的多元高斯分布
                X2 = np.stack(gmm.sample(int(np.round(0.1 * n))))
                # 采样1个点

                mean = np.mean(X1, axis=0)
                std = np.diag([1e-3, 1e-3])  # 标准差不同了
                gmm = MultivariateGaussianDistribution(mean, std)
                X3 = np.stack(gmm.sample(int(np.round(0.1 * n))))
                # 又采样了一个点

            else:
                mean = np.mean(X1, axis=0)
                std = np.diag([1.0, 1.0, 1e-3])
                gmm = MultivariateGaussianDistribution(mean, std)
                X2 = np.stack(gmm.sample(int(np.round(0.1 * n))))

                mean = np.mean(X1, axis=0)
                std = np.diag([1e-3, 1e-3, 10.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X3 = np.stack(gmm.sample(int(np.round(0.1 * n))))

            X = np.concatenate((X1, X2, X3))  # 多尺度采样?

        except ValueError:
            print("exception caught on sampling")
            if self.dim == 2:
                mean = np.zeros(self.dim)
                std = np.diag([1.0, 1.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X = gmm.sample(int(n))
            else:
                mean = np.zeros(self.dim)
                std = np.diag([1.0, 1.0, 5.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X = gmm.sample(int(n))
        return X
Beispiel #4
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    def sample(self, n=10):
        try:
            X1 = np.stack(self.model.sample(int(np.round(0.8 * n))))
            if self.dim == 2:
                mean = np.mean(X1, axis=0)
                std = np.diag([1.0, 1.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X2 = np.stack(gmm.sample(int(np.round(0.1 * n))))

                mean = np.mean(X1, axis=0)
                std = np.diag([1e-3, 1e-3])
                gmm = MultivariateGaussianDistribution(mean, std)
                X3 = np.stack(gmm.sample(int(np.round(0.1 * n))))

            else:
                mean = np.mean(X1, axis=0)
                std = np.diag([1.0, 1.0, 1e-3])
                gmm = MultivariateGaussianDistribution(mean, std)
                X2 = np.stack(gmm.sample(int(np.round(0.1 * n))))

                mean = np.mean(X1, axis=0)
                std = np.diag([1e-3, 1e-3, 10.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X3 = np.stack(gmm.sample(int(np.round(0.1 * n))))

            X = np.concatenate((X1, X2, X3))

        except ValueError:
            print("exception caught on sampling")
            if self.dim == 2:
                mean = np.zeros(self.dim)
                std = np.diag([1.0, 1.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X = gmm.sample(int(n))
            else:
                mean = np.zeros(self.dim)
                std = np.diag([1.0, 1.0, 5.0])
                gmm = MultivariateGaussianDistribution(mean, std)
                X = gmm.sample(int(n))
        return X