Source code for nnfbp.Reductors

#Copyright 2013 Centrum Wiskunde & Informatica, Amsterdam
#Author: Daniel M. Pelt
#This file is part of the PyNN-FBP, a Python implementation of the
#NN-FBP tomographic reconstruction method.
#PyNN-FBP is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#PyNN-FBP is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#GNU General Public License for more details.
#You should have received a copy of the GNU General Public License
#along with PyNN-FBP. If not, see <>.

import math
import numpy as np

[docs]class Reductor(object): '''Base object of a ``Reductor``, that takes input data and reduces it. Implementing objects should define `outSize`, the number of elements after reduction, and a ``filters`` :class:`numpy.ndarray` of size ``(inSize,outSize)``, where each row is a basis vector. :param inSize: Input size of vectors. :type inSize: :class:`int` ''' def __init__(self,inSize): self.size = inSize self.inSize = self.size
[docs] def getFilter(self,weights): '''Returns actual FBP filters, given the resulting weights of a trained neural network.''' return,weights)
[docs]class IdentityReductor(Reductor): '''An implementation of a ``Reductor`` that performs no reduction at all.''' def __init__(self,size): Reductor.__init__(self,size) self.filters = np.zeros((self.size,self.size))"Identity" for i in xrange(self.size): self.filters[i,i] = 1 self.outSize = self.size
[docs]class LogSymReductor(Reductor): '''An implementation of a ``Reductor`` with exponentially growing bin widths, and symmetric bins. :param nLinear: Number of bins of width 1 before starting exponential growth.' :type nLinear: :class:`int` ''' def __init__(self,size,nLinear=2): Reductor.__init__(self,size)"LogSym" self.indices = np.array(np.floor(np.log2(np.abs(np.arange(self.size)-(self.size-1)/2)+1)),dtype=np.int32) self.indices = self.indices+nLinear mid = (self.size-1)/2 self.indices[mid]=0 for q in xrange(nLinear): self.indices = np.insert(self.indices, (mid,mid+1), nLinear-q) self.indices = np.delete(self.indices, (0,self.indices.shape[0]-1)) nFilt = np.max(self.indices)+1 self.filters = np.zeros((self.size,nFilt)) for i in xrange(nFilt): self.filters[:,i][self.indices==i] = 1 self.outSize = nFilt