def runCode(generator): cpcode = generator.generate(m) cc = CPCompiler() runmodel = cc.compileCode(cpcode,d) runmodel.iterations = 1000 result = runmodel.run() return result
def doLearn(model,data): cm = LinearCPLogicGenerator() cpcode = cm.generate(model) cc = CPCompiler() runmodel = cc.compileCode(cpcode,data) runmodel.iterations = 500 return runmodel.run()
#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])
from semcplogic.model import ModelBuilder
from semcplogic.cpmodel import NonLinearCPLogicGenerator,TableResultInterpreter
from semcplogic.cpcompiler import CPCompiler
from semcplogic.problogresult import GnuplotDrawer
import pprint
b = ModelBuilder()
b.addNode("a")
b.addNode("b",0,0)
b.addNode("c",0,0)
b.setInfluence("a","b",5)
b.setInfluence("a","c",5)
m = b.consume()
m.nodes["c"].setLevels(["laag","middel","hoog"])
d = m.sample(100)
d2 = d.discretise({"a":["laag","hoog"],"b":["laag","hoog"],"c":["laag","middel","hoog"]})
cm = NonLinearCPLogicGenerator()
cpcode = cm.generate(m)
cc = CPCompiler()
runmodel = cc.compileCode(cpcode,d2)
runmodel.iterations = 100
result = runmodel.run()
g = GnuplotDrawer()
g.draw(result)
t = TableResultInterpreter()
r = t.interprete(m,result.latest())
pprint.pprint(r)
