mlpack_radical - radical
mlpack_radical [-h] [-v]
An implementation of RADICAL, a method for independentcomponent analysis (ICA). Assuming that we have an input matrix X, thegoal is to find a square unmixing matrix W such that Y = W * X and the dimensions of Y are independent components. If the algorithm is runningparticularly slowly, try reducing the number of replicates.
The input matrix to perform ICA on should be specified with the ’--input_file (-i)’ parameter. The output matrix Y may be saved with the ’--output_ic_file (-o)’ output parameter, and the output unmixing matrix W may be saved with the ’--output_unmixing_file (-u)’ output parameter.
For example, to perform ICA on the matrix ’X.csv’ with 40 replicates, saving the independent components to ’ic.csv’, the following command may be used:
$ radical --input_file X.csv --replicates 40 --output_ic_file ic.csv
--input_file (-i) [string]
Input dataset for ICA.
--angles (-a) [int]
Number of angles to consider in brute-force search during Radical2D. Default value 150.
--help (-h) [bool]
Default help info.
Get help on a specific module or option. Default value ’’. --noise_std_dev (-n) [double] Standard deviation of Gaussian noise. Default value 0.175.
--objective (-O) [bool]
If set, an estimate of the final objective function is printed.
--replicates (-r) [int]
Number of Gaussian-perturbed replicates to use (per point) in Radical2D. Default value 30.
--seed (-s) [int]
Random seed. If 0, ’std::time(NULL)’ is used. Default value 0.
--sweeps (-S) [int]
Number of sweeps; each sweep calls Radical2D once for each pair of dimensions. Default value
--verbose (-v) [bool]
Display informational messages and the full list of parameters and timers at the end of execution.
--version (-V) [bool]
Display the version of mlpack.
--output_ic_file (-o) [string] Matrix to save independent components to. Default value ’’. --output_unmixing_file (-u) [string] Matrix to save unmixing matrix to. Default value ’’.
For further information, including relevant papers, citations, and theory, For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your consult the documentation found at http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK. DISTRIBUTION OF MLPACK.