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Perform Independent Component Analysis using the TDSEP algorithm. Note that TDSEP, as implemented in this Node, is an online algorithm, i.e. it is suited to be trained on huge data sets, provided that the training is done sending small chunks of data for each time.
Reference: Ziehe, Andreas and Muller, KlausRobert (1998). TDSEP an efficient algorithm for blind separation using time structure. in Niklasson, L, Boden, M, and Ziemke, T (Editors), Proc. 8th Int. Conf. Artificial Neural Networks (ICANN 1998).
Internal variables of interest
 self.white
 The whitening node used for preprocessing.
 self.filters
 The ICA filters matrix (this is the transposed of the projection matrix after whitening).
 self.convergence
 The value of the convergence threshold.








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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Input arguments:
limit  convergence threshold.

Stop the training phase. If the node is used on large datasets it may be wise to first learn the covariance matrices, and then tune the parameters until a suitable parameter set has been found (learning the covariance matrices is the slowest part in this case). This could be done for example in the following way (assuming the data is already white): >>> covs=[mdp.utils.DelayCovarianceMatrix(dt, dtype=dtype) ... for dt in lags] >>> for block in data: ... [covs[i].update(block) for i in range(len(lags))] You can then initialize the ISFANode with the desired parameters, do a fake training with some random data to set the internal node structure and then call stop_training with the stored covariance matrices. For example: >>> isfa = ISFANode(lags, .....) >>> x = mdp.numx_rand.random((100, input_dim)).astype(dtype) >>> isfa.train(x) >>> isfa.stop_training(covs=covs) This trick has been used in the paper to apply ISFA to surrogate matrices, i.e. covariance matrices that were not learnt on a real dataset.

Stop the training phase. If the node is used on large datasets it may be wise to first learn the covariance matrices, and then tune the parameters until a suitable parameter set has been found (learning the covariance matrices is the slowest part in this case). This could be done for example in the following way (assuming the data is already white): >>> covs=[mdp.utils.DelayCovarianceMatrix(dt, dtype=dtype) ... for dt in lags] >>> for block in data: ... [covs[i].update(block) for i in range(len(lags))] You can then initialize the ISFANode with the desired parameters, do a fake training with some random data to set the internal node structure and then call stop_training with the stored covariance matrices. For example: >>> isfa = ISFANode(lags, .....) >>> x = mdp.numx_rand.random((100, input_dim)).astype(dtype) >>> isfa.train(x) >>> isfa.stop_training(covs=covs) This trick has been used in the paper to apply ISFA to surrogate matrices, i.e. covariance matrices that were not learnt on a real dataset.

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