Exemplo n.º 1
0
    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
Exemplo n.º 2
0
 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
Exemplo n.º 3
0
 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()
Exemplo n.º 4
0
    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")
Exemplo n.º 5
0
#  
# fig = plt.figure()
# 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)
Exemplo n.º 6
0
 def update(self, network):
     result = self.bias
     for i in range(len(self.connections)):
         n = network[self.connections[i]]
         result += n.get_value(network) * self.weights[i]
     self.value = activate(result, self.activation)
Exemplo n.º 7
0
# print "no2dN:", no2dN  
# print "Number of nodes2dN: ", len(no2dN)
# 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()