EM Class

Create an Expectation Maximization model

The number of mixtures

The type of the mixture covariation matrices

Pointer to the unmanaged object

Termination criteria of the procedure. EM algorithm stops either after a certain number of iterations (term_crit.num_iter), or when the parameters change too little (no more than term_crit.epsilon) from iteration to iteration

The dispose function that implements IDisposable interface

Release the memory associated with this EM model

Determines whether the specified Object is equal to the current Object.

Destructor

Serves as a hash function for a particular type.

Gets the type of the current instance.

Creates a shallow copy of the current Object.

Predict the probability of the samples

Release the managed resources. This function will be called during the disposal of the current object. override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object

Returns a string that represents the current object.

Estimate the Gaussian mixture parameters from a samples set. This variation starts with Expectation step. You need to provide initial means of mixture components. Optionally you can pass initial weights and covariance matrices of mixture components.

Estimate the Gaussian mixture parameters from a samples set. This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm. Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure, and optionally computes the output "class label" for each sample. The trained model can be used further for prediction, just like any other classifier.

A pointer to the unmanaged object

Clear the algorithm

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Returns true if the Algorithm is empty. e.g. in the very beginning or after unsuccessful read.

Loads algorithm from the file

Loads algorithm from a String

Predicts response(s) for the provided sample(s)

Reads algorithm parameters from a file storage.

Save the algorithm to file

Save the algorithm to a string

Trains the statistical model.

Trains the statistical model.

Stores algorithm parameters in a file storage

Stores algorithm parameters in a file storage