multivariate_normal distribution api 设计文档 #320
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dasenCoding
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dasenCoding
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cxxly
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Nov 3, 2022
| # 二、飞桨现状 | ||
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| - 目前 飞桨没有 API `paddle.distribution.MultivariateNormal` | ||
| - API `paddle.distribution.Normal`的代码开发风格可以作为`paddle.distribution.MultivariateNormal` 的主要参考。 |
cxxly
reviewed
Nov 4, 2022
| - rsample(value)：重参数化采样 | ||
| ```python | ||
| rsample(value) | ||
| ``` |
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补充rsample设计思路
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cxxly
reviewed
Nov 4, 2022
| ```python | ||
| 1 / 2 * paddle.log(paddle.pow(2 * math.pi * math.e, value.shpe.pop(1)) * paddle.linalg.det(self.convariance_matrix)) | ||
| ``` | ||
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在kl.py中注册 MultivariateNormal KL散度计算逻辑
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cxxly
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Nov 4, 2022
| * 均值、方差、标准差通过Numpy计算相应值，对比MultivariateNormal类中相应property的返回值，若一致即正确； | ||
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| * 采样方法除验证其返回的数据类型及数据形状是否合法外，还需证明采样结果符合MultivariateNormal分布。验证策略如下：随机采样30000个laplace分布下的样本值，计算采样样本的均值和方差，并比较同分布下`scipy.stats.multivariate_normal`返回的均值与方差，检查是否在合理误差范围内；同时通过Kolmogorov-Smirnov test进一步验证采样是否属于multivariate_normal分布，若计算所得ks值小于0.1，则拒绝不一致假设，两者属于同一分布； | ||
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laplace -> MultivariateNormal
done
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