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)
b.addNode(var)
b.setInfluence("xlag","wp",1)
b.setInfluence("x","wp",1)
b.setInfluence("a","wp",1)
b.setInfluence("klag","i",1)
b.setInfluence("plag","i",1)
b.setInfluence("p","i",1)
b.setInfluence("plag","c",1)
b.setInfluence("p","c",1)
b.setInfluence("wg","c",1)
b.setInfluence("wp","c",1)
m = b.consume()
data = dataset.fromCSV("experiments/kleindata.csv")
data = data.makeDiff()
data = data.discretise()
lincp = NonLinearCPLogicGenerator()
cpcode = lincp.generate(m)
cc = CPCompiler()
runmodel = cc.compileCode(cpcode,data)
runmodel.iterations = 10000
result = runmodel.run()
g = GnuplotDrawer()
g.draw(result)
t = TableResultInterpreter()
r = t.interprete(m,result.latest())
ltri = LatexTableResultInterpreter(r)
print ltri.out()
