#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])
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-")
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()