GMM Sample Generator
>>> from mlpack import gmm_generate
This program is able to generate samples from a pre-trained GMM (use gmm_train to train a GMM). The pre-trained GMM must be specified with the 'input_model' parameter. The number of samples to generate is specified by the 'samples' parameter. Output samples may be saved with the 'output' output parameter.
The following command can be used to generate 100 samples from the pre-trained GMM 'gmm' and store those generated samples in 'samples':
>>> gmm_generate(input_model=gmm, samples=100)
>>> samples = output['output']
- input_model (mlpack.GMMType): [required] Input GMM model to generate samples from.
- samples (int): [required] Number of samples to generate. Default value 0.
- copy_all_inputs (bool): If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code.
- seed (int): Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
- verbose (bool): Display informational messages and the full list of parameters and timers at the end of execution.
The return value from the binding is a dict containing the following elements:
- output (numpy matrix, float dtype): Matrix to save output samples in.