问题
I'm using scikit-learn for GMM training and am trying to vary the number of mixture components by looping over a list of integers. But when I print my resulting models I only get the ones with 3 mixture components, or whatever I put as the last item in my list.
This is my code:
from sklearn.mixture import GMM
class_names = ['name1','name2','name3']
covs = ['spherical', 'diagonal', 'tied', 'full']
num_comp = [1,2,3]
models = {}
for c in class_names:
models[c] = dict((covar_type,GMM(n_components=num,
covariance_type=covar_type, init_params='wmc',n_init=1, n_iter=10)) for covar_type in covs for num in num_comp)
print models
Can someone help please? Many thanks in advance!
回答1:
This happens because in the expression:
dict((covar_type,GMM(n_components=num,
covariance_type=covar_type, init_params='wmc',n_init=1, n_iter=10)) for covar_type in covs for num in num_comp)
You are using the same covar_type as key over all iterations, thus rewriting the same element.
If we write the code in a more readable way, this is what it's happening:
data = dict()
for covar_type in covs:
for num in num_comp:
# covar_type is the same for all iterations of this loop
# hence only the last one "survives"
data[covar_type] = GMM(...)
If you want to keep all the values you should use a list of values instead of a single value or change the key.
For the list of values:
data = dict()
for covar_type in covs:
data[covar_type] = values = []
for num in num_comp:
values.append(GMM(...))
For different keys:
data = dict()
for covar_type in covs:
for num in num_comp:
data[(covar_type, num)] = GMM(...)
来源:https://stackoverflow.com/questions/20398242/python-list-iteration-only-returns-last-value