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Compute the eta values of the normalized training data. The delta value of a signal is a measure of its temporal variation, and is defined as the mean of the derivative squared, i.e. delta(x) = mean(dx/dt(t)^2). delta(x) is zero if x is a constant signal, and increases if the temporal variation of the signal is bigger. The eta value is a more intuitive measure of temporal variation, defined as eta(x) = T/(2*pi) * sqrt(delta(x)) If x is a signal of length T which consists of a sine function that accomplishes exactly N oscillations, then eta(x)=N. EtaComputerNode normalizes the training data to have unit variance, such that it is possible to compare the temporal variation of two signals independently from their scaling. Reference: Wiskott, L. and Sejnowski, T.J. (2002). Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4):715-770. Important: if a data chunk is tlen data points long, this node is going to consider only the first tlen-1 points together with their derivatives. This means in particular that the variance of the signal is not computed on all data points. This behavior is compatible with that of SFANode. This is an analysis node, i.e. the data is analyzed during training and the results are stored internally. Use the functions 'get_eta' to access them.
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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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If the input dimension and the output dimension are unspecified, they will be set when the 'train' or 'execute' method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of 'train' or 'execute'. Every subclass must take care of up- or down-casting the internal structures to match this argument (use _refcast private method when possible).
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Return the list of dtypes supported by this node.
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Return the eta values of the data received during the training phase. If the training phase has not been completed yet, call stop_training. Input arguments: t -- Sampling frequency in Hz The original definition in (Wiskott and Sejnowski, 2002) is obtained for t=self._tlen, while for t=1 (default), this corresponds to the beta-value defined in (Berkes and Wiskott, 2005). |
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