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Perform Independent Component Analysis using the JADE algorithm. Note that JADE is a batch-algorithm. This means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples. JADE does not support the telescope mode. Main references: * Cardoso, Jean-Francois and Souloumiac, Antoine (1993). Blind beamforming for non Gaussian signals. Radar and Signal Processing, IEE Proceedings F, 140(6): 362-370. * Cardoso, Jean-Francois (1999). High-order contrasts for independent component analysis. Neural Computation, 11(1): 157-192. Original code contributed by: Gabriel Beckers (2008). History: - May 2005 version 1.8 for MATLAB released by Jean-Francois Cardoso - Dec 2007 MATLAB version 1.8 ported to Python/NumPy by Gabriel Beckers - Feb 15 2008 Python/NumPy version adapted for MDP by Gabriel Beckers
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_train_seq List of tuples:: |
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dtype dtype |
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input_dim Input dimensions |
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output_dim Output dimensions |
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supported_dtypes Supported dtypes |
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Input arguments: General: whitened -- Set whitened == True if input data are already whitened. Otherwise the node will whiten the data itself white_comp -- If whitened == False, you can set 'white_comp' to the number of whitened components to keep during the calculation (i.e., the input dimensions are reduced to white_comp by keeping the components of largest variance). white_parm -- a dictionary with additional parameters for whitening. It is passed directly to the WhiteningNode constructor. Ex: white_parm = { 'svd' : True } limit -- convergence threshold. Specific for JADE: max_it -- maximum number of iterations
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This is the core routine of the ICANode. Each subclass must define this function to return the achieved convergence value. This function is also responsible for setting the ICA filters matrix self.filters. Note that the matrix self.filters is applied to the right of the matrix containing input data. This is the transposed of the matrix defining the linear transformation.
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