Пример #1
0
#for i in xrange(i+1,i+1+15):
#  data.append(ProblogExample(i,"throws(john),breaks"))
#for i in xrange(i+1,i+1+10):
#  data.append(ProblogExample(i,"throws(john)"))

#i = 0
#for i in xrange(i+1,i+1+23):
#  data.append(ProblogExample(i,"throws(mary),throws(john),breaks"))
#for i in xrange(i+1,i+1+2):
#  data.append(ProblogExample(i,"throws(mary),throws(john),problog_not(breaks)"))
#for i in xrange(i+1,i+1+15):
#  data.append(ProblogExample(i,"problog_not(throws_mary),throws(john),breaks"))
#for i in xrange(i+1,i+1+10):
#  data.append(ProblogExample(i,"problog_not(throws_mary),throws(john),problog_not(breaks)"))

#To make it work, be sure the proofs only contain positive information
#if necessary, marginalize variables
data.append(ProblogExample(0,"breaks",weight=0.76))
data.append(ProblogExample(1,"throws(mary)",weight=0.5))
data.append(ProblogExample(2,"throws(john)",weight=1))
data.append(ProblogExample(3,"throws(john),throws(mary),breaks",weight=0.46))
#We give the weights, so don't calculate them
cc.weight=False

runmodel = cc.compileCode(cpcode,otherexamples=data)
runmodel.iterations = 500
result = runmodel.run()
g = GnuplotDrawer()
g.draw(result)
pprint.pprint(result.probs[-1])
Пример #2
0
d = Dataset(["a","b","c"])
d.addData(["laag","laag","hoog"])
d.addData(["laag","hoog","laag"])
d.addData(["hoog","laag","laag"])
d.addData(["hoog","hoog","hoog"])

def runCode(generator):
  cpcode = generator.generate(m)
  cc = CPCompiler()
  runmodel = cc.compileCode(cpcode,d)
  runmodel.iterations = 1000
  result = runmodel.run()
  return result

resnonlin = runCode(NonLinearCPLogicGenerator())
reslin = runCode(LinearCPLogicGenerator())

print "Niet-lineair:"
resnonlin.printReport()
g = GnuplotDrawer()
g.draw(resnonlin)
t = TableResultInterpreter()
r = t.interprete(m,resnonlin.latest())
ltri = LatexTableResultInterpreter(r)
print ltri.out()
print "Lineair:"
reslin.printReport()
g = GnuplotDrawer()
g.draw(reslin,prefix="lin-")
Пример #3
0
d = d.makeDiff()
d = d.discretise()

def runCode(generator):
  cpcode = generator.generate(m)
  cc = CPCompiler()
  runmodel = cc.compileCode(cpcode,d)
  runmodel.iterations = 1000
  result = runmodel.run()
  return result

reslin = runCode(LinearCPLogicGenerator())
resmaj = runCode(MajorityLinearCPLogicGenerator())

print "Gelijke verdeling:"
reslin.printReport()
g = GnuplotDrawer()
g.draw(reslin)
print "Meerderheidsverdeling:"
resmaj.printReport()
g = GnuplotDrawer()
g.draw(resmaj,prefix="maj-")

import tempfile
datafile = tempfile.NamedTemporaryFile()
for p in zip(reslin.sse, resmaj.sse):
  datafile.file.write("\t".join([str(num) for num in p]) + "\n")
datafile.file.flush()
g.callGnuplot(2,datafile.name,"fits.png")
datafile.close()