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_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_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_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_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 example_kdtree():
# An example of how to use kdtree
print("*" * 60)
print( "*" * 15, "An Example of kdtree's Usage", "*" * 15)
print( "*" * 60)
point = [(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2), (8, 8)]
point1 = []
for i in point:
point1.append({0: i[0], 1: i[1]})
print ("point list")
print (point1)
# Create a kdtree
root = kdtree.create(point1, dimensions=2)
# Visualize the kdtree
print ("visualize the kd-tree: ")
kdtree.visualize(root)
# Search for k-nearsest neighbor by given p-Minkowski distance
f = ds.EuclideanDistance
ans = root.search_knn(point={0: 7, 1: 3}, k=4, dist=f)
print("The 3 nearest nodes to point (7, 3) are:")
print(ans)
print("The nearest node to the point is:")
print(ans[0][0].data)
ans = root.search_inrange(point={0: 7, 1: 3}, r=4, dist=f)
print("the nodes in range are:")
print(ans)
print("The nearest node to the point is:")
print(ans[0][0].data)
def visual(self):
global data, dim, vtree, new, tree, type, newtree
print(
colored(
"\n----------------------------\nV I S U A L\n----------------------------",
"red"))
kdtree.visualize(vtree)
input("Press Enter to continue...\n")
tree.visual_tree()
input("Press Enter to continue...\n")
main_menu(tree)
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 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 example_kdtree():
# An example of how to use kdtree
print "*" * 60
print "*" * 15, "An Example of kdtree's Usage", "*" * 15
print "*" * 60
point = [(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2), (8, 8)]
point1 = []
for i in point:
point1.append({1: i[0], 2: i[1]})
print "point list"
print point1
# Create a kdtree
root = kdtree.create(point1, dimensions=2)
# Visualize the kdtree
print "visualize the kd-tree: "
kdtree.visualize(root)
# Search for k-nearsest neighbor by given p-Minkowski distance
f = ds.EuclideanDistance
ans = root.search_knn(point={1: 7, 2: 3}, k=10, dist=f)
print "The 3 nearest nodes to point (7, 3) are:"
print ans
print "The nearest node to the point is:"
print ans[0][0].data
def example_kdtree():
# An example of how to use kdtree
print "*" * 60
print "*" * 15, "An Example of kdtree's Usage", "*" * 15
print "*" * 60
point = [(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2), (8, 8)]
point1 = []
for i in point:
point1.append({1: i[0], 2: i[1]})
print "point list"
print point1
# Create a kdtree
root = kdtree.create(point1, dimensions=2)
# Visualize the kdtree
print "visualize the kd-tree: "
kdtree.visualize(root)
# Search for k-nearsest neighbor by given p-Minkowski distance
f = ds.EuclideanDistance
ans = root.search_knn(point={1: 7, 2: 3}, k=10, dist=f)
print "The 3 nearest nodes to point (7, 3) are:"
print ans
print "The nearest node to the point is:"
print ans[0][0].data
tree = tree.remove( (5, 4, 3) ) # Retrieving the Tree in inorder print(list(tree.inorder())) # Retrieving the Tree in level order print(list(kdtree.level_order(tree))) # Find the nearest node to the location (1, 2, 3) tree.search_nn( (1, 2, 3) ) # Add a point to make the tree more interesting tree.add( (10, 2, 1) ) # Visualize the Tree kdtree.visualize(tree) # Take the right subtree of the root subtree = tree.right # and detatch it tree.right = None kdtree.visualize(tree) kdtree.visualize(subtree) # and re-attach it tree.right = subtree kdtree.visualize(tree) # Add a node to make the tree unbalanced
def visualize_kdtree(self):
"""
Visualize the kdtree.
"""
kdtree.visualize(self.kdtree)
# tree2dN = kdtree.create(no2dN) # kdtree.visualize(tree2dN) # kdtree.plot2D(tree2dN) no3dN = [] #points = numpy.array([[0.0, 0.0, 0.0],[0.0 , 0.0, 1.0], [ 0.0, 1.0, 0.0], [ 0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0]]) points = numpy.array([[0.0, 0.0, 0.0, 0.0],[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0], [0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 1.0], [1.0, 0.0, 1.0, 0.0], [1.0, 0.0, 1.0, 1.0], [1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 1.0], [1.0, 1.0, 1.0, 0.0], [1.0, 1.0, 1.0, 1.0]]) util.splitN(points, 0,0,5, no3dN) #print "no3dN:", no3dN print "Number of nodes3dN: ", len(no3dN) point = [1,0,0,0] tree3dN = kdtree.create(no3dN) util.activate(tree3dN, 2) print tree3dN.get_path_to_best_matching_node(point)[-1].label kdtree.visualize(tree3dN) #kdtree.plot2D(tree3dN) # # # no3dN_test = [] # points = numpy.array([[0.0, 0.0, 0.0],[0.0 , 0.0, 1.0], [ 0.0, 1.0, 0.0], [ 0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0]]) # splitN_test(points, 0,0,7, no3dN_test) # # assert all(x in no3dN_test for x in no3dN), "NOT EQUAL !!!" #kdtree.plot2D(tree) # print [ n.label for n in tree.get_path_to_best_matching_node((0.1,0.1))] # tree.get_path_to_best_matching_node((0.1,0.1))[-1].split() # # print [ n.label for n in tree.get_path_to_best_matching_node((0.1,0.1))]
print "no:", no
print "Number of nodes: ", len(no)
tree = kdtree.create(no)
#kdtree.visualize(tree)
#print [ n.label for n in tree.get_path_to_best_matching_node((0.6,0.4))]
print [ str(n.label) +" " + str(n.height()) for n in tree.get_path_to_best_matching_node((0.6, 0.4))]
print [ n.label for n in tree.get_path_to_best_matching_node((0.6, 0.1))]
print [ n.label for n in tree.get_path_to_best_matching_node((0.175, 0.6))]
kdtree.visualize(tree)
# unbalanced tree
tree2=kdtree.create([ (1, 2), (2, 3) ])
node=tree2.search_nn((1.1,2.1))
print node[0].label
node[0].add((1,1))
kdtree.visualize(tree2)
print "tree2.height(): ", tree2.height()
print "node[0].level(tree2): ", node[0].level(tree2)
print "tree2.level(tree2): ", tree2.level(tree2)
import kdtree
if __name__ == '__main__':
t = kdtree.create([[1, 1], [5, 4], [6, 1]])
#t = kdtree.create([[500,0], [ 0,10000], [ 1000,10001]])
kdtree.visualize(t)
print(t.search_nn([4, 1]))
#print(t.search_nn([ 1000,5000]))
def visualize_kdtree(self):
"""
Visualize the kdtree.
"""
kdtree.visualize(self.kdtree)
# ax = fig.add_subplot(111) # patch = patches.PathPatch(path, facecolor='orange', lw=2) # ax.add_patch(patch) # ax.set_xlim(-2,2) # ax.set_ylim(-2,2) # plt.show() no2dN = [] 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, no2dN) tree2dN = kdtree.create(no2dN) util.activate(tree2dN, 4) kdtree.visualize(tree2dN) V = numpy.random.rand(len(no2dN),1) kdtree.plotQ2D(tree2dN, Values=V) #kdtree.plot2D(tree2dN) #kdtree.plot2DUpdate(tree2dN) # # # no3dN_test = [] # points = numpy.array([[0.0, 0.0, 0.0],[0.0 , 0.0, 1.0], [ 0.0, 1.0, 0.0], [ 0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0]]) # splitN_test(points, 0,0,7, no3dN_test) # # assert all(x in no3dN_test for x in no3dN), "NOT EQUAL !!!"
