def test_createAndLableTree(self):
''' Create new tree with new ids starting from 0'''
print "---------- test createAndLabelTree 1--------"
no1dN = []
points = numpy.array([[0.0, 0.0], [0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0, 0, 6, no1dN)
tree1 = kdtree.createNewTree(no1dN)
label=0
for n in kdtree.level_order(tree1):
self.assertIsNotNone(kdtree.getNode(tree1, label), "1: node with label: "+ str(label) + " not found in tree")
label+=1
kdtree.visualize(tree1)
print "---------- test createAndLabelTree 2--------"
no2dN = []
points = numpy.array([[0.0, 0.01], [0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0, 0, 6, no2dN)
tree2 = kdtree.createNewTree(no2dN)
kdtree.visualize(tree2)
label=0
for n in kdtree.level_order(tree2):
self.assertIsNotNone(kdtree.getNode(tree2, label), "2: node with label: "+ str(label) + " not found in tree")
label+=1
self.assertNotEqual(tree1, tree2, "trees have to be different")
def test_compare_old_to_new_method_to_create_trees(self):
"""
tree created with old method should be equal to tree created with new method
"""
nodes = util.generate_sequence_of_points(2, 2)
tree1 = kdtree.createNewTree(nodes)
kdtree.visualize(tree1)
sel_axis = (lambda axis: axis)
tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)
tree2.split2([0.25, 0.5], axis = 1)
tree2.split2([0.75, 0.5], axis = 1)
#left
tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)
tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)
#right
tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)
tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)
kdtree.visualize(tree2)
for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):
self.assertEqual(n[0].data, n[1].data, "elements not equal")
if n[0].data is not None and n[1].data is not None:
self.assertEqual(n[0].axis, n[1].axis, "elements not equal")
def test_add_node_and_pickle_tree(self):
print "-------------- test_pickle_tree ---------------"
nodes = []
netDimension = 3
levels = 3
sequence = ["".join(seq) for seq in itertools.product("01", repeat=netDimension)]
points_temp= numpy.array([list(s) for s in sequence])
points = numpy.array([map(float, f) for f in points_temp])
util.splitN(points, 0, 0, levels, nodes)
tree = kdtree.createNewTree(nodes)
for i in range(10):
tree.split2([random.random(), random.random(), random.random()], axis=random.randint(0, netDimension-1))
points_tree = [(d.data, d.axis) for d in kdtree.level_order(tree) if d.data is not None]
kdtree.visualize(tree)
kdtree.save( tree, "save_tree_test.pkl" )
tree_loaded = kdtree.load("save_tree_test.pkl")
points_tree_loaded = [(d.data, d.axis) for d in kdtree.level_order(tree_loaded) if d.data is not None]
numpy.testing.assert_array_equal(points_tree, points_tree_loaded, "trees not equal?")
def test_showQ(self):
import matplotlib.pyplot as plt
import time
print "---------- DisplayTreeTest ----------"
#plt.figure(self.fig_values.number)
maxLevel=2
no1dN = []
points = numpy.array([[0.0, 0.0], [0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0, 0, maxLevel, no1dN)
tree = kdtree.createNewTree(no1dN)
numberOfStates= tree.getHighestNodeId
numberOfActions = 4
kdtree.visualize(tree)
Q = numpy.ones((100,numberOfActions))
n = tree.get_path_to_best_matching_node([0.75, 0.75])[-1]
print n.label
n.split2([0.85, 0.75], axis=0, sel_axis = (lambda axis: axis))
kdtree.visualize(tree)
# only leaves are shown!
# States are positioned like in the tree i.e. xy axes splits in tree represent xy position in coord system
# 0, 0 is bottom left, x increases to the right
# action 0 is to the left
# Q[State][action]
Q[3][0] = 0 # bottom left, action left
# Q[5][1] = 0.1 # above Q[2] (in y direction), right
# Q[58][2] = 0.1 #right top corner, down
# Q[4][0] = 0.5
kdtree.plotQ2D(tree, min_coord=[0, 0], max_coord=[1, 1],Values = Q, plt=plt, plot="Q")
time.sleep(5)
def test_splitNode(self):
''' find the best matching node and split it, then find the best matching node again.
Check if point lies in new generated node'''
print "---------- test splitNode --------"
#create tree with 2 levels
listSplitPoints = []
points = numpy.array([[0.0, 0.0],[0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0,0,5, listSplitPoints)
tree2dN = kdtree.createNewTree(listSplitPoints)
util.activate(tree2dN, 2)
#points
point1 = [0.9,0.1]
point2 = [0.1,0.9]
kdtree.visualize(tree2dN)
# split
print "found: ", tree2dN.get_path_to_best_matching_node(point1)[-1]
tree2dN.get_path_to_best_matching_node(point1)[-1].activate_subnodes()
kdtree.visualize(tree2dN)
tree2dN.get_path_to_best_matching_node(point1)[-1].activate_subnodes()
kdtree.visualize(tree2dN)
print "data: ", tree2dN.get_path_to_best_matching_node(point1)[-1].data
self.assertEqual( tree2dN.get_path_to_best_matching_node(point1)[-1].data, [0.875, 0.125], "wrong node")
tree2dN.get_path_to_best_matching_node(point2)[-1].activate_subnodes()
tree2dN.get_path_to_best_matching_node(point2)[-1].activate_subnodes()
self.assertEqual( tree2dN.get_path_to_best_matching_node(point2)[-1].data, [0.125, 0.875], "wrong node")
del tree2dN
def test_add_empty_nodes_with_label_when_splitting(self):
"""
When a node is split along a certain axis, then this split should be active immediately.
Create tree, split right and left node, try to find matching node
"""
print "----- test_add_empty_nodes_with_label_when_splitting -----"
sel_axis = (lambda axis: axis)
#create tree, first node splits in x direction
tree = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)
kdtree.visualize(tree)
point_left = [0.4, 0.5]
tree.split2(point_left, axis = 0)
kdtree.visualize(tree)
point1 = [0.3, 0.5]
found_node = tree.get_path_to_leaf(point1)[-1]
correct_node1 = 3
self.assertEqual(found_node.label, correct_node1, "Not correct node found")
point_right = [0.6, 0.5]
tree.split2(point_right, axis = 1)
kdtree.visualize(tree)
point2 = [0.6, 0.7]
found_node = tree.get_path_to_leaf(point2)[-1]
correct_node2 = 6
self.assertEqual(found_node.label, correct_node2, "Not correct node found")
print "----- end: test_add_empty_nodes_with_label_when_splitting -----"
def test_plotTree(self):
# function to chose next spillting axis
sel_axis = (lambda axis: axis)
#create tree, first node splits in x direction
tree = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)
tree.split2([0.4, 0.5], axis = 0, sel_axis = sel_axis)
#add right node root node and left node to new node
tree.split2([0.6, 0.5], axis = 1, sel_axis = sel_axis)
tree.split2([0.7, 0.4], axis = 0, sel_axis = sel_axis)
print "node before: ", tree.get_path_to_best_matching_node([0.3, 0.5])[-1].label
print "node before: ", tree.get_path_to_best_matching_node([0.3, 0.5])[-1].label
#add a node
tree.split2([0.3, 0.6], axis = 1, sel_axis = sel_axis)
print "node after: ", tree.get_path_to_best_matching_node([0.3, 0.5])[-1].label
print "node after: ", tree.get_path_to_best_matching_node([0.3, 0.5])[-1].label
kdtree.visualize(tree)
# img=mpimg.imread("test_unconstraint_tree.png")
# plt.imshow(img)
# Compare to image test_unconstraint_tree.png
kdtree.plot2D(tree, plt=plt)
def test_numberOfNodes(self):
highestlevel = 4
numberOfStates= 2**(highestlevel+2)-1
no1dN = []
points = numpy.array([[0.0, 0.0], [0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0, 0, highestlevel, no1dN)
tree = kdtree.createNewTree(no1dN)
self.assertEqual(tree.getHighestNodeId, numberOfStates, "created and expected number of states does not match")
def test_getNode(self):
print "---------- test getNode --------"
listSplitPoints = []
points = numpy.array([[0.0, 0.0],[0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0,0,6, listSplitPoints)
tree = kdtree.createNewTree(listSplitPoints)
util.activate(tree, 6)
kdtree.visualize(tree)
nodeLabel = 117
node = kdtree.getNode(tree, nodeLabel)
self.assertEqual( node.label, nodeLabel, "returned wrong node")
del tree
def __init__(self):
nodes = []
points = numpy.array([[0.0, 0.0], [0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0, 0, 4, nodes)
#print "nodes:", nodes
print "Number of node: ", len(nodes)
self.tree = kdtree.createNewTree(nodes)
util.activate(self.tree, 3)
self.fig, self.ax = plt.subplots()
self.fig2, self.ax2 = plt.subplots()
def test_create_tree_create_new_tree_with_data_from_first(self):
nodes = []
netDimension = 2
levels = 3
sequence = ["".join(seq) for seq in itertools.product("01", repeat=netDimension)]
points_temp= numpy.array([list(s) for s in sequence])
points = numpy.array([map(float, f) for f in points_temp])
util.splitN(points, 0, 0, levels, nodes)
#print "nodes:", nodes
tree = kdtree.createNewTree(nodes)
# kdtree.visualize(tree)
points_tree = [(d.data, d.axis) for d in kdtree.level_order(tree) if d.data is not None]
points_tree_copy = list(points_tree)
tree1 = kdtree.createNewTree([d[0] for d in points_tree]) # points_tree1 is changed
# kdtree.visualize(tree1)
points_tree2 = [(d.data, d.axis) for d in kdtree.level_order(tree1) if d.data is not None]
numpy.testing.assert_array_equal(points_tree_copy, points_tree2, "trees not equal?")
def test_numberOfActiveStates(self):
"""only temporary, active property will disapear in future"""
highestlevel = 4
numberOfStates= 2**(highestlevel+2)-1
no1dN = []
points = numpy.array([[0.0, 0.0], [0.0, 1.0], [ 1.0, 0.0], [1.0, 1.0]])
util.splitN(points, 0, 0, highestlevel, no1dN)
tree = kdtree.createNewTree(no1dN)
util.activate(tree, highestlevel+1)
activeNodes = len([n for n in kdtree.level_order(tree) if n.active])
print "activeNodes: ", activeNodes, " numberOfStates: ", numberOfStates
self.assertEqual(activeNodes, numberOfStates, "not the correct number of nodes active")
def test_createTreeRoot(self):
'''
Created tree should have a root and two empty nodes with a label'''
sel_axis = (lambda axis: axis)
tree = kdtree.createNewTree([[0.5, 0.5, 0.5]],axis = 1, sel_axis= sel_axis)
# kdtree.visualize(tree)
self.assertTrue(tree.label == 2, "left label is not 2")
self.assertTrue(tree.axis == 1, "left label is not 2")
self.assertTrue(tree.left is not None, "left node of root is missing")
self.assertTrue(tree.left.label == 0, "left label is not 0")
self.assertTrue(tree.right is not None, "right node of root is missing")
self.assertTrue(tree.right.label == 1, "right label is not 1")
def test_createTree(self):
'''
Created tree and split left and right node'''
sel_axis = (lambda axis: axis)
tree = kdtree.createNewTree([[0.5, 0.5, 0.5]],axis = 1, sel_axis= sel_axis)
#add right node
point_right = [0.4, 0.5, 0.5]
tree.split2(point_right, axis = 2)
self.assertTrue(tree.right.data == point_right, "right node data not set")
self.assertTrue(tree.right.axis == 2, "right node axis not set")
self.assertTrue(tree.right.label == 1, "right node label wrong")
self.assertTrue(tree.right.right is not None, "right node chikd missing")
self.assertTrue(tree.right.left is not None, "right node chikd missing")
#add left node
point_left = [0.4, 0.4, 0.5]
tree.split2(point_left, axis = 2)
self.assertTrue(tree.left.data == point_left, "left node data not set")
self.assertTrue(tree.left.axis == 2, "left node axis not set")
self.assertTrue(tree.left.label == 0, "left node label wrong")
self.assertTrue(tree.left.left is not None, "left node chikd missing")
self.assertTrue(tree.left.left is not None, "left node chikd missing")
