我很难让我的形状适用于Dirichlet过程高斯混合模型。我的数据observations
具有形状(number of samples, number of dimensions)
。每个高斯平均值应取自各向同性先验,每个高斯协方差应为单位矩阵。我以为我的设置正确,但出现以下错误:
Input dimension mis-match. (input[0].shape[1] = 13, input[1].shape[1] = 2)
我的代码是:
import numpy as np
import pymc3 as pm
import theano.tensor as tt
num_obs, obs_dim = observations.shape
max_num_clusters = 13
def stick_breaking(beta):
portion_remaining = tt.concatenate([[1], tt.extra_ops.cumprod(1 - beta)[:-1]])
return beta * portion_remaining
with pm.Model() as model:
w = pm.Deterministic("w", stick_breaking(beta))
cluster_means = pm.MvNormal(f'cluster_means',
mu=pm.floatX(np.zeros(obs_dim)),
cov=pm.floatX(gaussian_mean_prior_cov_scaling * np.eye(obs_dim)),
shape=(max_num_clusters, obs_dim))
comp_dists = pm.MvNormal.dist(mu=cluster_means,
cov=gaussian_cov_scaling * np.eye(obs_dim),
shape=(max_num_clusters, obs_dim))
obs = pm.Mixture(
"obs",
w=w,
comp_dists=comp_dists,
observed=observations,
shape=obs_dim)
有人能解释一下如何让这些形状工作吗
目前没有回答
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