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Extract the slowly varying components from the input data. More information about Slow Feature Analysis can be found in Wiskott, L. and Sejnowski, T.J., Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4):715-770 (2002). Internal variables of interest: self.avg -- Mean of the input data (available after training) self.sf -- Matrix of the SFA filters (available after training) self.d -- Delta values corresponding to the SFA components (generalized eigenvalues). (See the docs of the 'get_eta_values' method for more information)
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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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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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Compute the output of the slowest functions. if 'range' is a number, then use the first 'range' functions. if 'range' is the interval=(i,j), then use all functions between i and j.
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Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
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Compute the output of the slowest functions. if 'range' is a number, then use the first 'range' functions. if 'range' is the interval=(i,j), then use all functions between i and j.
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Return the eta values of the slow components learned during the training phase. If the training phase has not been completed yet, call stop_training. 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. Input arguments: t -- Sampling frequency in Hz The original definition in (Wiskott and Sejnowski, 2002) is obtained for t = number of training data points, while for t=1 (default), this corresponds to the beta-value defined in (Berkes and Wiskott, 2005). |
Compute the linear approximation of the time derivative. |
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