EMGMM
EM-GMM implementation. Inspired by the work of Maël Fabien: https://github.com/maelfabien/EM_GMM_HMM
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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EMGMM.type
Members list
Value members
Concrete methods
Compute the log likelihood (used for e step).
Compute the log likelihood (used for e step).
Value parameters
- covariances
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covariances of the components (clusters)
- means
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means of the components (clusters)
- weights
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weights of the components (clusters)
- x
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data points
Attributes
Estimate a covariance matrix, given data.
Estimate a covariance matrix, given data.
Value parameters
- columns
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number of columns of the points
- x
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data points
Attributes
2d matrix dot product.
2d matrix dot product.
Value parameters
- A
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matrix A
- B
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matrix B
Attributes
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 parameters
- covariances
-
covariances of the components (clusters)
- means
-
means of the components (clusters)
- weights
-
weights of the components (clusters)
- x
-
data points
Attributes
Full covariance Gaussian Mixture Model, trained using Expectation Maximization.
Full covariance Gaussian Mixture Model, trained using Expectation Maximization.
Value parameters
- columns
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number of data columns
- x
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data points
Attributes
M-step, update parameters.
M-step, update parameters.
Value parameters
- X
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data points