# -*- coding: utf-8 -*-
"""
Created on Sat Apr 26 12:55:44 2014
@author: princengoc
Training using pybrain
"""
from gridcells import Gridcode, ncr
gc = Gridcode((9, 10, 11, 13), 0.5)
gc.computeBinaryDict()
input = gc.getInput()
# take some collection of cliques as targets
from cliqueFinder import randomClique
V = 16
targets = []
vertexList = set()
ctr = 0
while ctr < gc.r:
clique, vx = randomClique(V)
if vx not in vertexList:
vertexList.add(vx)
targets.append(clique.tolist())
ctr = ctr + 1
# using pybrain
# test their xor example
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 26 12:56:51 2014
@author: princengoc
Train using pylearn2. We shall use the word representation.
Also, we represent a clique by its set of vertices, rather than its
set of edges.
"""
from gridcells import Gridcode, ncr
from trainOneNode import softMaxFit
import numpy as np
gc = Gridcode((9, 10, 11, 13), 0.5)
gc.computeWordDict()
#take some collection of cliques as targets
from cliqueFinder import randomVertex
V = 14
targets = []
vertexList = []
ctr = 0
vxlist = None
while ctr < gc.r:
vxlist, vxvec = randomVertex(V, frac = 0.5, vxlist = vxlist)
if(vxvec not in targets):
targets.append(vxvec)
vertexList.append(vxlist)
ctr = ctr + 1
