from jyimportlib import importjar, importbin, importdir
importbin()
importjar("srldb.jar")
importjar("tumutils.jar")
importjar("weka_fipm.jar")
#importdir("../WEKA/bin")
from java.util import Vector, HashMap
from java.lang import String, Double
import jarray
from weka.classifiers.trees import J48, DecisionStump, RandomForest
from weka.classifiers.rules import OneR
from weka.classifiers.functions import SMO
from weka.classifiers.trees.j48 import Rule
from weka.classifiers.meta import MultiBoostAB, AdaBoostM1, RandomCommittee, Bagging
from weka.core import Attribute, FastVector, Instance, Instances
class WekaClassifier(object):
def __init__(self, numericAttributes=None):
self.attName2Domain = {}
self.numericAttributes = []
if numericAttributes is not None:
self.numericAttributes = list(numericAttributes)
self.instances = []
def setDomain(self, attName, domain):
domain = set(
domain
) #remove duplicates, make sure it is set if things are added later on
self.attName2Domain[attName] = domain
from jyimportlib import importjar, importbin, importdir
importjar("jahmm.jar")
from be.ac.ulg.montefiore.run.jahmm import OpdfGaussian, ObservationReal
from java.util import Vector
from java.lang import Double
class DiscreteDist(object):
def __init__(self, varName, values):
self.values = values
self.varName = varName
self.dist = [1.0 for i in xrange(len(values))]
def normalize(self):
Z = sum(self.dist)
self.dist = map(lambda x: x / Z, self.dist)
def addExample(self, d):
idx = self.values.index(d[self.varName])
self.dist[idx] += 1.0
def finish(self):
self.normalize()
def density(self, v):
return self.dist[self.values.index(v)]
class ContDist(object):
def __init__(self, varName):
import sys
import os
from jyimportlib import importjar, importbin
importbin()
importjar("srldb.jar")
importjar("tumutils.jar")
importjar("bnj.jar")
importjar("bnj_res.jar")
importjar("log4j-1.2.9.jar")
importjar("ssj.jar")
importjar("optimization.jar")
importjar("yprolog.jar")
from probcog.srl.directed.inference import BLNinfer
def infer(network, decls, logic, algorithm, evidence, queries, cwPreds = None, maxSteps = None, args=None):
'''
network: the model's fragment network (filename)
decls: the model's declarations (filename)
logic: the model's logical constraints (filename)
algorithm: an algorithm name (use printInferenceAlgorithmList())
evidence: an evidence database (filename)
queries: a comma-separated list of queries (predicate names or fully or partially grounded atoms)
cwPreds: a comma-separated list of closed-world predicates
maxSteps: maximum number of steps to take
args: list of additional arguments to pass (see BLNinfer help)
returns the BLNinfer object, which can be used to immediately obtain the results via .getResults() and other information
'''
a = ['-ia', algorithm, '-x', network, '-b', decls, '-l', logic, '-e', evidence, '-q', queries]
if cwPreds is not None:
a.extend(["-cw", cwPreds])
from jyimportlib import importjar, importbin, importdir
importjar("jahmm.jar")
from be.ac.ulg.montefiore.run.jahmm import OpdfGaussian, ObservationReal
from java.util import Vector
from java.lang import Double
class DiscreteDist(object):
def __init__(self, varName, values):
self.values = values
self.varName = varName
self.dist = [1.0 for i in xrange(len(values))]
def normalize(self):
Z = sum(self.dist)
self.dist = map(lambda x: x / Z, self.dist)
def addExample(self, d):
idx = self.values.index(d[self.varName])
self.dist[idx] += 1.0
def finish(self):
self.normalize()
def density(self, v):
return self.dist[self.values.index(v)]
class ContDist(object):
def __init__(self, varName):
from jyimportlib import importjar, importbin, importdir
importbin()
importjar("srldb.jar")
importjar("tumutils.jar")
importjar("weka_fipm.jar")
#importdir("../WEKA/bin")
from java.util import Vector, HashMap
from java.lang import String, Double
import jarray
from weka.classifiers.trees import J48, DecisionStump, RandomForest
from weka.classifiers.rules import OneR
from weka.classifiers.functions import SMO;
from weka.classifiers.trees.j48 import Rule;
from weka.classifiers.meta import MultiBoostAB, AdaBoostM1, RandomCommittee, Bagging
from weka.core import Attribute, FastVector, Instance, Instances
class WekaClassifier(object):
def __init__(self, numericAttributes = None):
self.attName2Domain = {}
self.numericAttributes = []
if numericAttributes is not None:
self.numericAttributes = list(numericAttributes)
self.instances = []
def setDomain(self, attName, domain):
domain = set(domain) #remove duplicates, make sure it is set if things are added later on
self.attName2Domain[attName] = domain
def setNumericAttribute(self, attName):
import sys
import os
from jyimportlib import importjar, importbin
importbin()
importjar("srldb.jar")
importjar("tumutils.jar")
importjar("bnj.jar")
importjar("bnj_res.jar")
importjar("log4j-1.2.9.jar")
importjar("ssj.jar")
importjar("optimization.jar")
importjar("yprolog.jar")
from probcog.srl.directed.inference import BLNinfer
def infer(network,
decls,
logic,
algorithm,
evidence,
queries,
cwPreds=None,
maxSteps=None,
args=None):
'''
network: the model's fragment network (filename)
decls: the model's declarations (filename)
logic: the model's logical constraints (filename)
algorithm: an algorithm name (use printInferenceAlgorithmList())
evidence: an evidence database (filename)
