def getTree():
dich1 = statements.get_negation(statements.get_moreThan(statements.get_statement(statements.op_takeValue,['MinIntensity']),0.7))
dich11 = statements.get_negation(statements.get_moreThan(statements.get_statement(statements.op_takeValue,['MinIntensity']),0.35))
tree = treeNode.treeNode([],[])
tree.dichotomy = dich1
tree.isTerminal = False
tree.childNegative = treeNode.treeNode([],[])
tree.childNegative.result = [False,False]
tree.childPositive = treeNode.treeNode([],[])
tree.childPositive.dichotomy = dich11
tree.childPositive.isTerminal = False
tree.childPositive.childNegative = treeNode.treeNode([],[])
tree.childPositive.childNegative.result = [True,False]
tree.childPositive.childPositive = treeNode.treeNode([],[])
tree.childPositive.childPositive.result = [True,True]
return tree
def getTree(boolStatements,numStatements,keyStatements,samples,nodesCount,optimisation=False,deadline = 0.005,isMajorant = False):
'''dijkstra-inspired classifier'''
#expand in order of replacement potential descension
if not (keyStatements is list): keyStatements = list(keyStatements)
if not (samples is set): samples = set(samples)
treeRoot = treeNode(samples, keyStatements,isMajorant)
treeRoot.update()
openList = [treeRoot]
budget = nodesCount
while budget > 0 :
budget -=2
if openList ==[]: break
current = openList.pop(0) #must be sorted
divisor = factor_connection.getBestLink(keyStatements,current.samples,boolStatements,numStatements,False,True)
if divisor == False: continue;##this was absent in the original build.
expand(current,boolStatements, numStatements,keyStatements,divisor)
newNodes = []
if current.getInformationGain() < deadline:
continue
if current.childPositive.entropy >= deadline:
newNodes.append(current.childPositive)
if current.childNegative.entropy >= deadline:
newNodes.append(current.childNegative)
for node in newNodes:
repPotential = node.getReplacementPotential()
for cmpInd in range(len(openList)):
if repPotential > openList[cmpInd].getReplacementPotential():
openList.insert(cmpInd,node)
break
else:
openList.append(node)
if optimisation:
treeRoot.optimize()
return treeRoot
