def jsd_diss(self, w1, mu1, cov1, w2, mu2, cov2):
"""
Calculates Jensen-Shannon divergence of two gmm's
:param gmm_p: mixture.GaussianMixture
:param gmm_q: mixture.GaussianMixture
:param sample_count: number of monte carlo samples to use
:return: Jensen-Shannon divergence
"""
gmm_p = GaussianMixture(n_components=n_components,
covariance_type="full")
gmm_p.weights_ = w1
gmm_p.covariances_ = cov1
gmm_p.means_ = mu1
gmm_p.n_components = 1
gmm_p.precisions_cholesky_ = _compute_precision_cholesky(cov1, "full")
gmm_q = GaussianMixture(n_components=n_components,
covariance_type="full")
gmm_q.weights_ = w2
gmm_q.covariances_ = cov2
gmm_q.means_ = mu2
gmm_q.n_components = 1
gmm_q.precisions_cholesky_ = _compute_precision_cholesky(cov2, "full")
X = gmm_p.sample(sample_count)[0]
log_p_X = gmm_p.score_samples(X)
log_q_X = gmm_q.score_samples(X)
log_mix_X = np.logaddexp(log_p_X, log_q_X)
Y = gmm_q.sample(sample_count)[0]
log_p_Y = gmm_p.score_samples(Y)
log_q_Y = gmm_q.score_samples(Y)
log_mix_Y = np.logaddexp(log_p_Y, log_q_Y)
# black magic?
return (log_p_X.mean() -
(log_mix_X.mean() - np.log(2)) + log_q_Y.mean() -
(log_mix_Y.mean() - np.log(2))) / 2
def gmm_combine(gmms, weights=None, params=None, good_idx=None,
gmms_means=None, gmms_variances=None, gmms_weights=None, gmms_zps=None):
"""
Method to combine Gaussian Mixture Models. This function create a new GaussianMixture instance and adds all
mixture components from the input models.
Parameters
----------
gmms
List of GaussianMixture instances to combine.
weights : iterable, optional
Weights for each GaussianMixture instance in the input list.
params : dict, optional
GaussianMixture parameter dictionary for faster instantiation.
good_idx : iterable, optional
Boolean array or list for masking. True indicates a good entry in the input list, False a bad entry.
gmms_means : np.ndarray, optional
The means of all components for all input GMMs in an array. Can be used to speed up the combination process-
gmms_variances : np.ndarray, optional
Same as gmms_means, but for all variances of all components.
gmms_weights : np.ndarray, optional
Same as gmms_means, but for all weights of all components.
gmms_zps : np.ndarray, optional
Zero point for all models. If None is given, no shift is applied.
Returns
-------
GaussianMixture
Combined GaussianMixture instance.
"""
# Dummy checks
if not isinstance(gmms, Iterable):
raise ValueError("Models must be provided as an iterable")
# Set good_idx
if good_idx is None:
good_idx = [True for _ in range(len(gmms))]
# Extract good GMMs
gmms = gmms[good_idx]
# Set weights to unity if not specified
if weights is None:
weights = [1 for _ in range(len(gmms))]
else:
weights = weights[good_idx]
# Return None if weights are all bad
if np.sum(np.isfinite(weights)) == 0:
return None
# Set zeropoints if not specified
if gmms_zps is None:
gmms_zps = [0. for _ in range(len(gmms))]
else:
gmms_zps = gmms_zps[good_idx]
# Get parameters if not set from first entry
if params is None:
params = gmms[0].get_params()
# Dummy check
if np.sum([isinstance(g, GaussianMixture) for g in gmms]) != len(gmms):
raise ValueError("Must only supply GaussianMixture instances")
# Instantiate combined GMM
gmm_combined = GaussianMixture(**params)
# Build combined components from supplied models if not given as attributes
if gmms_means is None or gmms_variances is None or gmms_weights is None:
gmm_combined_means = gmms[0].means_ + gmms_zps[0]
gmm_combined_variances = gmms[0].covariances_
gmm_combined_weights = gmms[0].weights_ * weights[0]
for gmm, w, zp in zip(gmms[1:], weights[1:], gmms_zps[1:]):
gmm_combined_means = np.vstack([gmm_combined_means, gmm.means_ + zp])
gmm_combined_variances = np.vstack([gmm_combined_variances, gmm.covariances_])
gmm_combined_weights = np.hstack([gmm_combined_weights, gmm.weights_ * w])
# If the attributes are provided, extract the parameters directly (much faster)
else:
gmm_combined_means = np.vstack(gmms_means[good_idx] + gmms_zps)
gmm_combined_variances = np.vstack(gmms_variances[good_idx])
gmm_combined_weights = np.hstack(gmms_weights[good_idx] * weights)
# Add attributes to new mixture
gmm_combined.n_components = len(gmm_combined_means)
gmm_combined.means_ = gmm_combined_means
gmm_combined.covariances_ = gmm_combined_variances
gmm_combined.weights_ = gmm_combined_weights / np.sum(gmm_combined_weights)
gmm_combined.precisions_ = np.linalg.inv(gmm_combined.covariances_)
gmm_combined.precisions_cholesky_ = np.linalg.cholesky(gmm_combined.precisions_)
# Add attribute to store number of input models used to create this model
gmm_combined.n_models = len(gmms)
# Return new GMM
return gmm_combined
