Class GeneralExpansionNode

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.