def WriteInitialPopulation2DB(popsize,root_node,mintreesize,maxtreesize,buildmethod,dbname,tablename):
"""
Function: WriteInitialPopulation2DB
====================================
create a new population of randomly generated trees and write them to a database
@param popsize: size of the population
@param root_node: specify the root node and its arity (nb of children). e.g. (0,2,'root')
@param mintreesize: min tree depth (at the moment only 2 working)
@param maxtreesize: max tree depth (At least 2)
@param buildmethod: which Koza method is used to build the trees (either
'AddHalfNode' or 'AddFullNode' or 'AddGrowNodeMin' respectively for
Half, Full, or Ramped Half-n-Half)
@param dbname: path to database e.g. r'D:\3d_work\pythongp\pySTGP_0.51\src\pop_db'
@param tablename: name of the database table
"""
con = sqlite.connect(dbname)
con.execute("create table %s(o_id INTEGER PRIMARY KEY,tree TEXT,\
tree_mapping TEXT, treedepth INTEGER, evaluated INTEGER, fitness FLOAT)"%tablename)
trees=[]
i=0
while i <popsize:
# create a tree individual
method = getattr(buildtree.buildTree(),buildmethod)
my_tree = method(root_node,0,mintreesize,maxtreesize)
if my_tree not in trees:
# my_tree = method(root_node,0,mintreesize,maxtreesize)
trees.append(my_tree)
# create its tree mapping
my_tree_indices=crossutil.GetIndicesMappingFromTree(my_tree)
# get depth of the tree
depth=crossutil.GetDepthFromIndicesMapping(my_tree_indices)
# get fitness of the tree
resultfitness=settings.FitnessFunction(my_tree)
#myfitness=evalfitness.EvalTreeForAllInputSets(my_tree,xrange(settings.nb_eval))
#myfitness=evalfitness.EvalTreeForOneListInputSet(my_tree)
#print myfitness
#resultfitness=evalfitness.FinalFitness(myfitness)
#resultfitness=evalfitness.FinalFitness2(myfitness)
#resultfitness=evalfitness.FinalFitness4(myfitness)
#print resultfitness
con.execute("insert into %s(o_id,tree,tree_mapping,treedepth,evaluated,fitness) values (NULL,?,?,?,?,?)"%tablename,(buffer(marshal.dumps(my_tree,-1)),buffer(marshal.dumps(my_tree_indices,-1)),depth,1,resultfitness))
i=i+1
con.commit()
con.close()
#final_output= sum([(math.fabs(settings.ideal_results[x][y]-intermediate_outputs[2][x][y]),math.fabs(settings.ideal_results[x][y]-intermediate_outputs[1][x][y])) [intermediate_outputs[0][x][y]] for x in xrange(len(intermediate_outputs[1])) for y in xrange(len(intermediate_outputs[1][x]))])
#for i in xrange( len(result)):
# for j in xrange( len(result[i])):
# print result[i][j]
return final_output
if __name__ == '__main__':
#for i in xrange(2000):
#a=buildtree.buildTree().AddFullNode((0,3,'root'),0,2,8)
#a=buildtree.buildTree().AddFullNode((0,3,'root'),0,8)
a=buildtree.buildTree().AddFullNode((0,2,'root'),0,8)
#print a
#print i
#print r
#print FinalFitness_tutorial9(EvalTreeForOneListInputSet_tutorial9(a))
#t1 = timeit.Timer('evalfitness.EvalTreeForOneListInputSet_tutorial9(a)' , 'from __main__ import a ;import evalfitness')
t2 = timeit.Timer('evalfitness.FinalFitness_tutorial9(evalfitness.EvalTreeForOneListInputSet_tutorial9(a))' , 'from __main__ import a ;import evalfitness')
#print t1.timeit(100)
print t2.timeit(1000)
#print FinalFitness_tutorial8(EvalTreeForOneListInputSet_tutorial8(a))
#print FinalFitness3(EvalTreeForAllInputSets(a,xrange(20)))
#print FinalFitness4(EvalTreeForOneListInputSet(a))
second_p_cross = Koza1PointCrossover(maxdepth, cp2_copy_parent1,
cp2_copy_parent2, cp2_copy_p1_map,
cp2_copy_p2_map,
cp2_copy_p1_depth,
cp2_copy_p2_depth)
if second_p_cross[0] == [1, 1, 1, 1]:
break
return second_p_cross
if __name__ == '__main__':
# testing 1-point crossover capability
# by generating 2 offsprings
for i in xrange(10000):
a = buildtree.buildTree().AddHalfNode((0, 2, 'root'), 0, 3, 7)
b = buildtree.buildTree().AddHalfNode((0, 2, 'root'), 0, 3, 7)
a_map = crossutil.GetIndicesMappingFromTree(a)
b_map = crossutil.GetIndicesMappingFromTree(b)
a_depth = crossutil.GetDepthFromIndicesMapping(a_map)
b_depth = crossutil.GetDepthFromIndicesMapping(b_map)
#r=Koza1PointCrossover(10,a,b,a_map,b_map,a_depth,b_depth)
#print r
#r2=Koza2PointsCrossover(10,a,b,a_map,b_map,a_depth,b_depth)
#print r2
#print a
#print b
r2 = Koza1PointCrossover(15, a, b, a_map, b_map, a_depth, b_depth)
#if r2[1][1][0]!=(2, 2, 'adf1'):
print r2
#if r2[0][0]==1 and r2[0][1]==1:
def Mutate(maxdepth, parent, p1_map, p1_depth):
"""
Function: Mutate
=================
create a mutated individual from a parent tree using Koza styled mutation
@param maxdepth: maximum depth of the mutated offspring
@param parent: parent tree e.g. a=buildtree.buildTree().AddHalfNode((0,2,'root'),0,2,7)
@param p1_map: parent tree index mapping e.g a_map=crossutil.get_indices_mapping_from_tree(a)
@param p1_depth: parent tree depth e.g. a_depth=crossutil.get_depth_from_indices_mapping(a_map)
@return: a tuple containing two elements.
- The first one is a boolean indicating if the mutated tree is identical to the parent
(if identical, returns True)
- The second one is the mutated tree
"""
# get a random depth for parent1
if p1_depth >= 1:
p1_mutation_depth = 1
else:
p1_mutation_depth = random.randint(1, p1_depth - 1)
# get a random depth in p2 such that the resulting
# offspring lenght is <= offspring maxdepth
mychoice1=crossutil.UnpackIndicesFromList(\
crossutil.GetPackedListIndicesAtDepth(p1_map,p1_mutation_depth))
p1_point = random.choice(mychoice1)
parent1_clone = copy.deepcopy(parent)
exec("fragment_p1=parent1_clone%s" %
crossutil.IndexLstToIndexStr2(p1_point))
# first we need to extract the top node of each subtree
if isinstance(fragment_p1, list):
firstnode_p1 = fragment_p1[0]
if isinstance(fragment_p1, tuple):
firstnode_p1 = fragment_p1
# get the parent node of each sub tree (context of each parent)
subtree1_parent_s = crossutil.IndexLstToIndexStr2(p1_point)
# if the first node is not an ADF, the string version of the
# index of the subtree is just the index of upper node in the tree
if firstnode_p1[0] != 2:
subtree1_parent_s = subtree1_parent_s[:-3]
# get the subtree using the index we just obtained
exec("subtree1_parent=parent1_clone%s" % subtree1_parent_s)
# get the flat list of permitted nodes for the parent tree
# for that first get the list of permitted branch nodes...
context_p1 = settings.treeRules[subtree1_parent[0][2]]
context = copy.deepcopy(context_p1)
# and extend to it the list of permitted leaf nodes
context[p1_point[-1] - 1][0].extend(context[p1_point[-1] - 1][1])
if len(context[p1_point[-1] - 1][0]) > 1 and firstnode_p1[0] == 2:
context[p1_point[-1] - 1][0].remove(firstnode_p1)
# get the context from grammar rules for each parent
# min_mutation_depth for the subtree has to be extracted by looking when is the next child with a terminal
min_mutation_depth = 1
#print context[p1_point[-1]-1][0]
flag = random.choice(context[p1_point[-1] - 1][0])
if not settings.treeRules[subtree1_parent[0][2]][p1_point[-1] - 1][1]:
min_mutation_depth = 2
#print flag
mutant_fragment=buildtree.buildTree().AddHalfNode(\
random.choice(context[p1_point[-1]-1][0]) ,p1_mutation_depth,p1_mutation_depth+min_mutation_depth,maxdepth)
# make sure that the mutant fragment is different from the previous fragment
if len(mutant_fragment) == 1 and isinstance(mutant_fragment[0], tuple):
mutant_fragment = mutant_fragment[0]
exec("parent1_clone%s=mutant_fragment" %
crossutil.IndexLstToIndexStr2(p1_point))
identical = False
if mutant_fragment == fragment_p1:
identical = True
# no branch nor leaf compatible from fragment to parent nodes
return (identical, parent1_clone)