CODEWITHSUNDEEP
randomscikit-learngmm
Yiftach
Yiftach

Reputation: 347

sklearn - sample from GaussianMixture without fitting

I would like to use a GaussianMixture for generating random data. The parameters should not be learnt from data but supplied.

GaussianMixture allows supplying inital values for weights, means, precisions, but calling "sample" is still not possible.

Example:

import numpy as np
from sklearn.mixture import GaussianMixture
d = 10
k = 2
_weights = np.random.gamma(shape=1, scale=1, size=k)
data_gmm = GaussianMixture(n_components=k, 
                           weights_init=_weights / _weights.sum(),
                           means_init=np.random.random((k, d)) * 10,
                           precisions_init=[np.diag(np.random.random(d)) for _ in range(k)])
data_gmm.sample(100)

This throws:

NotFittedError: This GaussianMixture instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

I've tried:

Any help would be appreciated.

Upvotes: 0

Views: 795

Answers (1)

Antoine Dubuis
Antoine Dubuis

Reputation: 5304

You can set all the attributes manually so you don't have to fit the GaussianMixture.

You need to set weights_, means_, covariances_ as follow:

import numpy as np
from sklearn.mixture import GaussianMixture
d = 10
k = 2
_weights = np.random.gamma(shape=1, scale=1, size=k)
data_gmm = GaussianMixture(n_components=k)
data_gmm.weights_ = _weights / _weights.sum()
data_gmm.means_ = np.random.random((k, d)) * 10
data_gmm.covariances_ = [np.diag(np.random.random(d)) for _ in range(k)]
data_gmm.sample(100)

NOTE: You might need to modify theses parameters values according to your usecase.

Upvotes: 1

Related Questions