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Expands the input samples by applying to them one or more functions provided. The functions to be applied are specified by a list [f_0, ..., f_k], where f_i, for 0 <= i <= k, denotes a particular function. The input data given to these functions is a two-dimensional array and the output is another two-dimensional array. The dimensionality of the output should depend only on the dimensionality of the input. Given a two-dimensional input array x, the output of the node is then [f_0(x), ..., f_k(x)], that is, the concatenation of each one of the computed arrays f_i(x). This node has been designed to facilitate nonlinear, fixed but arbitrary transformations of the data samples within MDP flows. **Example**:: >>> import mdp >>> from mdp import numx >>> def identity(x): return x >>> def u3(x): return numx.absolute(x)**3 #A simple nonlinear transformation >>> def norm2(x): #Computes the norm of each sample returning an Nx1 array >>> return ((x**2).sum(axis=1)**0.5).reshape((-1,1)) >>> x = numx.array([[-2., 2.], [0.2, 0.3], [0.6, 1.2]]) >>> gen = mdp.nodes.GeneralExpansionNode(funcs=[identity, u3, norm2]) >>> print(gen.execute(x)) >>> [[-2. 2. 8. 8. 2.82842712] >>> [ 0.2 0.3 0.008 0.027 0.36055513] >>> [ 0.6 1.2 0.216 1.728 1.34164079]] Original code contributed by Alberto Escalante.
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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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Short argument description: ``funcs`` list of functions f_i that realize the expansion.
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Process the data contained in `x`. If the object is still in the training phase, the function `stop_training` will be called. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_execute` to implement their execution phase. The docstring of the `_execute` method overwrites this docstring.
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The expanded dim is computed by directly applying the expansion functions f_i to a zero input of dimension n.
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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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Return the individual output sizes of each expansion function when the input has lenght n. |
Calculate a pseudo inverse of the expansion using scipy.optimize. ``use_hint`` when calculating a pseudo inverse of the expansion, the hint determines the starting point for the approximation. For details on this parameter see the function ``invert_exp_funcs2`` in ``mdp.utils.routines.py``. This method requires scipy. |
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