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"""
some basic language objects
if you don't specify a node_type for these, it is usually a good idea to
not allow crossovers at these nodes. That would allow two (maybe different)
list_range nodes to exchange places.
"""
from numpy import log10
import tree
import gene
#import weakdict
#class opt_object(tree.tree_node,weakdict.WeakValue):
# opt_dict = weakdict.WeakDict()
# def __init__(self,node_type, sub_nodes):
# tree.tree_node.__init__(self,sub_nodes,node_type=node_type)
# weakdict.WeakValue.__init__(self)
# opt_object.opt_dict[id(self)] = self
# def _create(self,parent = None):
# new = tree.tree_node._create(self,parent)
# weakdict.WeakValue.__init__(new)
# self._WeakValue__cid = None #force a reset of the __cid value
# weakdict.WeakValue.__init__(new) #now get a new value for it
# opt_object.opt_dict[id(new)] = new
# return new
# def __del__(self):
# tree.tree_node.__del__(self)
# weakdict.WeakValue.__del__(self)
class opt_object(tree.tree_node):
def __init__(self,node_type, sub_nodes):
tree.tree_node.__init__(self,sub_nodes,node_type=node_type)
class float_range(gene.float_gene,opt_object):
optimize = 1
def __init__(self,bounds,node_type='float_range', sub_nodes = 0):
opt_object.__init__(self,node_type,sub_nodes)
gene.float_gene.__init__(self,bounds[:2])
tree.tree_node.__init__(self,sub_nodes,node_type=node_type)
if(len(bounds) == 3): self.default = bounds[2]
else: self.default = (bounds[0] + bounds[1])/2.
self._value = self.default
print self._value
def clone(self, parent=None):
return tree.tree_node.clone(self,parent)
# def mutate(self):
# m = gene.float_gene.mutate(self)
# if(m and self.parent): self.parent.recalc(force_parent=1)
# return m
def scale(self,sc):
self.bounds = (self.bounds[0]*sc,self.bounds[1]*sc)
self.default = self.default*sc
self._value = self._value*sc
def defaultize(self):
self._value = self.default
for child in self._children:
child.defaultize()
def create(self, parent):
new = tree.tree_node.create(self,parent)
new.initialize()
gene.float_gene.initialize(new)
return new
def __del__(self):
# gene.float_gene.__del__(self)
opt_object.__del__(self)
def __repr__(self):
try:
val = self.value()
if val < .01 or val > 1000:
v = "%4.3e" % self.value()
else:
v = "%4.3f" % self.value()
except gene.GAError:
v = 'not initialized'
self.label = '%s = %s' % (self.node_type, v)
return tree.tree_node.__repr__(self)
class log_float_range(gene.log_float_gene,opt_object):
optimize = 1
def __init__(self,bounds,node_type='log_float_range', sub_nodes = 0):
gene.log_float_gene.__init__(self,bounds[:2])
opt_object.__init__(self,node_type,sub_nodes)
if len(bounds) == 3:
self.default = bounds[2]
else:
self.default = (bounds[0] + bounds[1])/2.
self._value = log10(self.default)
def clone(self, parent=None):
return tree.tree_node.clone(self,parent)
def mutate(self):
m=gene.log_float_gene.mutate(self)
if m and self.parent:
self.parent.recalc(force_parent=1)
return m
def scale(self,sc):
self.default = self.default*sc
sc = log10(sc)
self.bounds = (self.bounds[0]*sc,self.bounds[1]*sc)
self._value = self._value*sc
def defaultize(self):
self._value = self.default
for child in self._children:
child.defaultize()
def create(self,parent):
new = tree.tree_node.create(self,parent)
new.initialize()
gene.log_float_gene.initialize(new)
return new
def __del__(self):
# gene.log_float_gene.__del__(self)
opt_object.__del__(self)
def __repr__(self):
try:
val = self.value()
if val < .01 or val > 1000:
v = "%4.3e" % self.value()
else:
v = "%4.3f" % self.value()
except gene.GAError:
v = 'not initialized'
self.label = '%s = %s' % (self.node_type, v)
return tree.tree_node.__repr__(self)
class list_range(gene.list_gene,opt_object):
optimize = 1
def __init__(self, allele_set, node_type='list_range', default=None, sub_nodes = 0):
gene.list_gene.__init__(self,allele_set)
opt_object.__init__(self,node_type,sub_nodes)
gene.list_gene.initialize(self) # prevents trouble in tree generation
if default:
self.default = default
else:
self.default = allele_set[int(len(allele_set)/2.)] #the center item
self._value = self.default
def clone(self, parent=None):
return tree.tree_node.clone(self,parent)
def scale(self,sc):
for i in range(len(self.allele_set)):
self.allele_set[i] = self.allele_set[i] *sc
self.default = self.default*sc
self._value = self._value*sc
def defaultize(self):
self._value = self.default
for child in self._children:
child.defaultize()
def create(self,parent):
new = tree.tree_node.create(self,parent)
new.initialize()
gene.list_gene.initialize(new)
return new
def __del__(self):
# gene.list_gene.__del__(self)
opt_object.__del__(self)
def __repr__(self):
self.label = '%s = %s' % (self.node_type, self.value())
return tree.tree_node.__repr__(self)
class val(gene.frozen,opt_object):
optimize = 0
def __init__(self, val, node_type='val', sub_nodes=0):
gene.frozen.__init__(self,val)
opt_object.__init__(self,node_type,sub_nodes)
def clone(self,parent = None):
return tree.tree_node.clone(self,parent)
def scale(self,sc):
self._value = self._value*sc
def defaultize(self):
pass
def create(self,parent):
new = tree.tree_node.create(self,parent)
new.initialize()
return new
def __del__(self):
# gene.frozen.__del__(self)
opt_object.__del__(self)
def __repr__(self):
self.label = '%s = %s' % (self.node_type, self.value())
return tree.tree_node.__repr__(self)
# These two routines are useful for picking off or replacing the nodes in a tree
# that should be that should be numerically optimized. They are helpful if your
# interested in using a gradient method to optimize some of the paramters of the
# array
def pick_numbers(node):
start = []; lower = []; upper =[];
for child in node.children():
s, l, u = pick_numbers(child)
start = start + s
lower = lower + l
upper = upper + u
#for now only works with float_genes
if hasattr(node,'optimize') and node.optimize == 1:
s = node._value
l,u = node.bounds
start = start + [s]
lower = lower + [l]
upper = upper + [u]
else:
print 'no opt:', node.__class__
return start, lower, upper
def put_numbers(node,vals, index = 0):
for child in node.children():
index = put_numbers(child,vals,index)
if hasattr(node,'optimize') and node.optimize == 1:
s = node._value = vals[index]
index = index + 1
return index
# Grab the numerical nodes that need to be optimized so that you can directly
# manipulate them
def pick_optimize_nodes(node):
nodes = [];
for child in node.children():
nodes = nodes + pick_optimize_nodes(child)
#for now only works with float_genes
if hasattr(node,'optimize') and node.optimize == 1:
nodes = nodes + [node]
return nodes
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