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Expand input space with Gaussian Radial Basis Functions (RBFs). The input data is filtered through a set of unnormalized Gaussian filters, i.e., y_j = exp(-0.5/s_j * ||x - c_j||^2) for isotropic RBFs, or more in general y_j = exp(-0.5 * (x-c_j)^T S^-1 (x-c_j)) for anisotropic RBFs.
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__metaclass__ This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc... |
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_train_seq List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ... |
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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: centers -- Centers of the RBFs. The dimensionality of the centers determines the input dimensionality; the number of centers determines the output dimensionalities sizes -- Radius of the RBFs. 'sizes' is a list with one element for each RBF, either a scalar (the variance of the RBFs for isotropic RBFs) or a covariance matrix (for anisotropic RBFs). If 'sizes' is not a list, the same variance/covariance is used for all RBFs.
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Return the list of dtypes supported by this node.
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Return True if the node can be inverted, False otherwise.
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Return True if the node can be trained, False otherwise.
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