EMGMM
EM-GMM implementation. Inspired by the work of Maƫl Fabien: https://github.com/maelfabien/EM_GMM_HMM
Value members
Concrete methods
Compute the log likelihood (used for e step).
Compute the log likelihood (used for e step).
- Value Params
- covariances
covariances of the components (clusters)
- means
means of the components (clusters)
- weights
weights of the components (clusters)
- x
data points
Estimate a covariance matrix, given data.
Estimate a covariance matrix, given data.
- Value Params
- columns
number of columns of the points
- x
data points
E-step: compute responsibilities, update resp matrix so that resp[j, k] is the responsibility of cluster k for data point j, to compute likelihood of seeing data point j given cluster k.
E-step: compute responsibilities, update resp matrix so that resp[j, k] is the responsibility of cluster k for data point j, to compute likelihood of seeing data point j given cluster k.
- Value Params
- covariances
covariances of the components (clusters)
- means
means of the components (clusters)
- weights
weights of the components (clusters)
- x
data points
Full covariance Gaussian Mixture Model, trained using Expectation Maximization.
Full covariance Gaussian Mixture Model, trained using Expectation Maximization.
- Value Params
- columns
number of data columns
- x
data points
M-step, update parameters.
M-step, update parameters.
- Value Params
- X
data points