from semcplogic.cpcompiler import CPCompiler
from semcplogic.problogresult import GnuplotDrawer
from semcplogic.dataset import Dataset
from semcplogic.cpmodel import TableResultInterpreter
from experiments.latexreport import LatexTableResultInterpreter
b = ModelBuilder()
b.addNode("a")
b.addNode("b")
b.addNode("c")
b.setInfluence("a","c",1)
b.setInfluence("b","c",1)
m = b.consume()
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())
b = ModelBuilder()
b.addNode("a")
b.addNode("b")
b.setInfluence("a","b",1)
m = b.consume()
levels = ["l1","l2","l3","l4","l5"]
m.nodes["a"].setLevels(levels)
m.nodes["b"].setLevels(levels)
d = m.sample(200)
newdata = [[a,b - a + a*a] for [a,b] in d.data]
d2 = Dataset(["a","b"])
for p in newdata:
d2.addData(p)
d2 = d2.makeDiff()
d2 = d2.discretise({"a":levels,"b":levels})
def runCode(generator):
cpcode = generator.generate(m)
cc = CPCompiler()
runmodel = cc.compileCode(cpcode,d2)
runmodel.iterations = 500
result = runmodel.run()
return result
resnonlin = runCode(NonLinearCPLogicGenerator())
reslin = runCode(LinearCPLogicGenerator())
print "Niet-lineair:"