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-")
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)