Hello:
The title kind of says it all. The gmm.model_.covariances variable is None for some reason for both of these models. I don't think this was the case in the past so maybe one of the last commits had a bug. Unless this is intentional?
Here is a minal working example:
from pycave.bayes import GaussianMixture
import torch
import numpy as np
import pycave
import random
import time
#Set seed
seed = 0
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
random.seed(seed)
#Inputs
d = 10
k = 1
n = 1000
#Data
x = torch.randn(n,d)
#Fit PyCave GMM
gmm = GaussianMixture(num_components=k,
covariance_type='diag',
init_strategy='kmeans',
trainer_params={'gpus':1,'enable_progress_bar':True, 'logger':True},
covariance_regularization=1e-6)
gmm = gmm.fit(x)
print(type(gmm.model_.covariances))
Hello:
The title kind of says it all. The gmm.model_.covariances variable is None for some reason for both of these models. I don't think this was the case in the past so maybe one of the last commits had a bug. Unless this is intentional?
Here is a minal working example:
from pycave.bayes import GaussianMixture
import torch
import numpy as np
import pycave
import random
import time
#Set seed
seed = 0
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
random.seed(seed)
#Inputs
d = 10
k = 1
n = 1000
#Data
x = torch.randn(n,d)
#Fit PyCave GMM
gmm = GaussianMixture(num_components=k,
covariance_type='diag',
init_strategy='kmeans',
trainer_params={'gpus':1,'enable_progress_bar':True, 'logger':True},
covariance_regularization=1e-6)
gmm = gmm.fit(x)
print(type(gmm.model_.covariances))