def setup_func():
global T, tv, nb_trees, tree_list, mtg_name, g
# build some random initial tree
stat_tool.plot.DISABLE_PLOT = True
inf_bound = 0
sup_bound = 3
distrib = stat_tool.Uniform(inf_bound, sup_bound)
max_depth = 3
max_size = 10
nbtrees = 40
# define a set of trees
tree_list = []
tv = [1., 0, 1, 2.] # trees.TreeValue([1., 0])
R = trees.TreeStructure(distrib, max_size, max_depth)
tmp_tree = trees.Tree(tv, R)
n = 1
tree_list.append(trees.Tree(tmp_tree))
while n < nbtrees:
n = n + 1
R.Simulate(distrib, max_size, max_depth)
tmp_tree = trees.Tree(tv, R)
tree_list.append(trees.Tree(tmp_tree))
distrib_list = []
for i in range(tmp_tree.NbInt()):
distrib_list.append(distrib)
for n in range(len(tree_list)):
tree_list[n].Simulate(distrib_list)
T = trees.Trees(tree_list)
nb_trees = nbtrees
mtg_name = "data/sample_mtg_forest.mtg"
g = build_mtg()
return T, tv, nb_trees, tree_list, mtg_name, g
def test_tree_structures():
"""Test TreeStructure class"""
msg1 = "Bad number of vertices for TreeStructure: "
msg2 = "Bad correspondence from MTG to Tree vertices: "
mtg_t = test_mtg_build()
TrMTG = mtg_t.Tree(0)
# Copy tree structures
Tr = trees.TreeStructure(TrMTG)
assert Tr.Size() == TrMTG.Size(), msg1 + str(Tr.Size())
assert Tr.TreeVertex() == TrMTG.TreeVertex(), msg2
assert Tr.MTGVertex() == TrMTG.MTGVertex(), msg2
# Build trees with same structures as mtg_t
tree_list = []
attributes = mtg_t.Attributes()
for t in range(mtg_t.NbTrees()):
TrMTG2 = mtg_t.Tree(t)
Tr = trees.TreeStructure(TrMTG2)
tree_list += [trees.Tree([0], Tr)]
T0 = trees.Trees(tree_list, attribute_names=[attributes[0]])
Tr = T0.Tree(0)
assert Tr.Size() == TrMTG.Size(), msg1 + str(Tr.Size())
assert Tr.TreeVertex() == TrMTG.TreeVertex(), msg2
assert Tr.MTGVertex() == TrMTG.MTGVertex(), msg2
ident, inf_bound, sup_bound, parameter, probability)
print "Distribution used for the number of children and the tree attributes:"
print distrib
max_depth = 3
max_size = 10
nbtrees = 4
# test the initialization and the estimation of a HMT
print "Test the initialization and the estimation of a HMT"
# define a set of trees
tree_list = []
tv = [1., 0, 1, 2.] # trees.TreeValue([1., 0])
R = trees.TreeStructure(distrib, max_size, max_depth)
tmp_tree = trees.Tree(tv, R)
n = 1
tree_list.append(trees.Tree(tmp_tree))
while n < nbtrees:
n = n + 1
R.Simulate(distrib, max_size, max_depth)
tmp_tree = trees.Tree(tv, R)
tree_list.append(trees.Tree(tmp_tree))
distrib_list = []
for i in range(tmp_tree.NbInt()):
distrib_list.append(distrib)
for n in range(len(tree_list)):
tree_list[n].Simulate(distrib_list)
sup_bound = 3
distrib = stat_tool.Uniform(inf_bound, sup_bound)
nbtrees = 40
max_depth = 3
max_size = 20
n = 5
init()
print distrib
################################
# TreeStructure
################################
D = trees.TreeStructure()
print "Default tree structure: ", D
print "A random tree structure R:"
R = trees.TreeStructure(distrib)
print R
print "(size, depth) = (", R.Size(), ", ", R.Depth(), ")."
R.Simulate(distrib, max_size, max_depth)
print "A second realization of R: "
print R
# TreeStructure.SelectSubTree
ST = R.SelectSubTree(1)
print "Extract subtree rooted at vertex 1 from R:"
print ST
# pruning
ST = R.SelectSubTree(1, False)
print "Prune subtree rooted at vertex 1 from R:"
import openalea.stat_tool as stat_tool
import openalea.tree_statistic.trees as trees
inf_bound = 1
sup_bound = 3
probability = 0.6
children_distrib = stat_tool.Uniform(inf_bound, sup_bound)
# distribution of the children
attributes_distrib = stat_tool.Uniform(0, 10)
# distribution of the attributes
max_depth = 3
max_size = 10
nbtrees = 2
# defining a set of trees
tree_list = []
# simulation of the structure
R = trees.TreeStructure(children_distrib, max_size, max_depth)
tmp_tree = trees.Tree([1., 0], R)
n = 1
tree_list.append(trees.Tree(tmp_tree))
while n < nbtrees:
n = n+1
R.Simulate(children_distrib, max_size, max_depth)
tmp_tree = trees.Tree([1., 0], R)
tree_list.append(trees.Tree(tmp_tree))
distrib_list = []
# simulation of the labels
for i in range(tmp_tree.NbInt()):
distrib_list.append(attributes_distrib)
for n in range(len(tree_list)):
