def generate(number=10,r=0.5): """Function to generate and return a random tree with fixed number of internal nodes added. The root has to be a scalarizing node. Biasing of the tree towards (or away from) binary nodes can be controlled with the parameter r.""" root = ScalarNode(CGAFunctions.DataMethodFactory().getScalar()) tree = AlgorithmTree(root) while number > 0: tNode = random.choice(tree.getTermini()) fNode = CGAGenerator._getRandomFunctionalNode(r) CGAGenerator._extend(tNode,fNode) number -= 1 return tree
def expgenerate(p,r=0.5): """Generates and returns a random tree. Current tree is extended at a *single* node with probability p, and process terminates with probability 1-p.""" root = ScalarNode(CGAFunctions.DataMethodFactory().getScalar()) tree = AlgorithmTree(root) extend = True while extend: tNode = random.choice(tree.getTermini()) fNode = CGAGenerator._getRandomFunctionalNode(r) CGAGenerator._extend(tNode,fNode) if uniform() < p: extend = True else: extend = False return tree
def setUp(self):
self.methodFactory = CGAFunctions.DataMethodFactory()
# set up the tree
self.root = ScalarNode(self.methodFactory.getScalar('sum_ij'))
self.node1 = BinaryNode(self.methodFactory.getBinary('add'))
self.node2 = BinaryNode(self.methodFactory.getBinary('subtract'))
self.node3 = UnaryNode(self.methodFactory.getUnary('log'))
# immutable data - stored
self.constant1 = DataNode(self.methodFactory.getData('e'))
self.constant2 = DataNode(self.methodFactory.getData('pi'))
self.constant3 = DataNode(self.methodFactory.getData('1/N'))
# set up the tree
self.testTree = AlgorithmTree(self.root)
self.root.setChildren(self.node1)
self.node1.setChildren(self.node2,self.node3)
self.node2.setChildren(self.constant1,self.constant2)
self.node3.setChildren(self.constant3)
class CGAGeneratorTests(unittest.TestCase):
def setUp(self):
self.methodFactory = CGAFunctions.DataMethodFactory()
# set up the tree
self.root = ScalarNode(self.methodFactory.getScalar('sum_ij'))
self.node1 = BinaryNode(self.methodFactory.getBinary('add'))
self.node2 = BinaryNode(self.methodFactory.getBinary('subtract'))
self.node3 = UnaryNode(self.methodFactory.getUnary('log'))
# immutable data - stored
self.constant1 = DataNode(self.methodFactory.getData('e'))
self.constant2 = DataNode(self.methodFactory.getData('pi'))
self.constant3 = DataNode(self.methodFactory.getData('1/N'))
# set up the tree
self.testTree = AlgorithmTree(self.root)
self.root.setChildren(self.node1)
self.node1.setChildren(self.node2,self.node3)
self.node2.setChildren(self.constant1,self.constant2)
self.node3.setChildren(self.constant3)
def testExtend(self):
print "\n\n----- testing extension -----"
print 'Tree before extension (node,id): '
self.testTree()
newNode = BinaryNode(self.methodFactory.getBinary('multiply'))
CGAGenerator._extend(self.constant1,newNode)
print 'Tree after extension (node,id): '
self.testTree()
def testGrow(self):
print "\n\n----- testing grow(tree,tNode) -----"
print "Tree before extension (node,id): "
self.testTree()
CGAGenerator.grow(self.testTree,self.constant2)
print "Tree after extension (node,id): "
self.testTree()
def testDelete(self):
print "\n\n----- testing deletion -----"
print 'Tree before deletion (node, id): '
self.testTree()
CGAGenerator._delete(self.node2)
print 'Tree after deletion (node, id): '
self.testTree()
def testPrune(self):
print "\n\n----- testing prune(tree,node) -----"
print "Tree before pruning (node,id): "
self.testTree()
CGAGenerator.prune(self.testTree,self.node3)
print "Tree after pruning (node,id): "
self.testTree()
def testNonRootReplace(self):
print "\n\n----- testing non-root replacement -----"
print 'Tree before replacement (node,id): '
self.testTree()
newNode = BinaryNode(self.methodFactory.getBinary('multiply'))
CGAGenerator._replace(self.testTree, self.node1, newNode)
print 'Tree after replacement (node,id): '
self.testTree()
def testRootReplace(self):
print "\n\n----- testing replacement of root node -----"
print 'Tree before root replacement (node,id): '
self.testTree()
print 'Root : ', self.testTree.root
print 'Root header : ', self.testTree.root.getHeader()
newNode = ScalarNode(self.methodFactory.getScalar('tr'))
CGAGenerator._replace(self.testTree,self.testTree.root,newNode)
print 'Tree after root replacement (node, id): '
self.testTree()
print 'Root : ', self.testTree.root
print 'Root header : ', self.testTree.root.getHeader()
def testSwap(self):
print "\n\n----- testing subtree swap -----"
root = ScalarNode(self.methodFactory.getScalar('tr'))
node1 = UnaryNode(self.methodFactory.getUnary('log'))
node2 = BinaryNode(self.methodFactory.getBinary('multiply'))
constant1 = DataNode(self.methodFactory.getData('1/N'))
constant2 = DataNode(self.methodFactory.getData('e'))
tree = AlgorithmTree(root)
root.setChildren(node1)
node1.setChildren(node2)
node2.setChildren(constant1,constant2)
print 'Trees before the swap (node,id): '
print 'Tree One:'
self.testTree()
print 'Tree Two:'
tree()
CGAGenerator._swap(self.node2, node1)
print 'Trees after the swap (node,id): '
print 'Tree One:'
self.testTree()
print 'Tree Two:'
tree()
def testCrossover(self):
print "\n\n----- testing single_crossover(tree1Node, tree2Node) -----"
root = ScalarNode(self.methodFactory.getScalar('tr'))
node1 = UnaryNode(self.methodFactory.getUnary('log'))
node2 = BinaryNode(self.methodFactory.getBinary('multiply'))
constant1 = DataNode(self.methodFactory.getData('1/N'))
constant2 = DataNode(self.methodFactory.getData('e'))
tree = AlgorithmTree(root)
root.setChildren(node1)
node1.setChildren(node2)
node2.setChildren(constant1,constant2)
print 'Trees before the swap (node,id): '
print 'Tree One:'
self.testTree()
print 'Tree Two:'
tree()
CGAGenerator.single_crossover(self.node2, node1)
print 'Trees after the swap (node,id): '
print 'Tree One:'
self.testTree()
print 'Tree Two:'
tree()
def testGenerate(self):
print "\n\n----- testing generate(tree) -----"
tree = CGAGenerator.generate(3)
tree()
def testExpGenerate(self):
print "\n\n----- testing expgenerate(tree) -----"
tree = CGAGenerator.expgenerate(0.85)
tree()
def testPointMutate(self):
print "\n\n----- testing point_mutate(tree,node) -----"
print "Tree before mutation (node,id): "
self.testTree()
CGAGenerator.point_mutate(self.testTree,random.choice(self.testTree.getNodes()))
print "Tree after mutation (node,id): "
self.testTree()
def testRepeatedPointMutate(self):
print "\n\n----- testing repeated runs of point_mutate(tree,node) -----"
print "Tree before mutation (node,id): "
self.testTree()
for i in xrange(0,10000):
CGAGenerator.point_mutate(self.testTree, random.choice(self.testTree.getNodes()))
print "Tree after mutation (node,id): "
self.testTree()
def testSpecialTreeGeneration(self):
print "\n\n----- testing generation of 'special' trees -----"
treeset = ['MI','OMES','DOES NOT EXIST']
for t in treeset:
tree = CGAGenerator.generate_special_tree(t)
if tree is not None:
print '%s : %s', (t,tree.getString())
else:
print 'Tree %s does not exist.' % t