def main():
lines = readfile()
nodes = {}
for line in lines:
p, c = line.split(")")
if p not in nodes:
nodes[p] = Node(p)
if c in nodes:
nodes[c].parent = nodes[p]
else:
nodes[c] = Node(c, parent=nodes[p])
root = nodes[c].root
if args.verbose:
print(RenderTree(root))
# Part 1
orbits = 0
for n in root.descendants:
orbits += n.depth
print("There are", orbits, "orbits")
# Part 2
w = Walker()
result = w.walk(nodes["YOU"].parent, nodes["SAN"].parent)
print("It takes",
len(result[0]) + len(result[2]), "steps to get from YOU to SAN")
def get_type_similarity(root, cloth1, cloth2): cloth1_cat = cloth1.get_cat_label() cloth1_node = anytree.find(root, lambda node: node.name == cloth1_cat) cloth2_cat = cloth2.get_cat_label() cloth2_node = anytree.find(root, lambda node: node.name == cloth2_cat) # Walk from start node to end node # Returns: # upward is a list of nodes to go upward to. # common is common up node # downward is a list of nodes to go downward to w = Walker() upward, common, downward = w.walk(cloth1_node, cloth2_node) if(cloth1_node.name == cloth2_node.name): # Exact Match return 1 elif((cloth1_node.name == cloth2_node.parent.name) or (cloth1_node.parent.name == cloth2_node.name) or (cloth1_node.parent.name == cloth2_node.parent.name)): # Cloth 1 type is an ancestor of cloth_2 or vise versa return 0.5 elif(common.name == "Cloth"): return 0 elif(common.name == "Tops" or common.name =="Bottoms" or common.name == "Fullbody"): return 0.25 else: return 0
def main():
input_file_path = sys.argv[1]
if not os.path.isfile(input_file_path):
print(
int("File path {} does not exist. Exiting...".format(
input_file_path)))
sys.exit()
input_data = open(input_file_path, 'r').read().splitlines()
node_list = {}
for data in input_data:
space_object_data = data.split(")")
mass_object = Node(space_object_data[0])
orbit_object = Node(space_object_data[1])
if mass_object.name in node_list:
mass_object = node_list[mass_object.name]
else:
node_list[mass_object.name] = mass_object
if orbit_object.name in node_list:
orbit_object = node_list[orbit_object.name]
else:
node_list[orbit_object.name] = orbit_object
orbit_object.parent = mass_object
w = Walker()
walk_path = w.walk(node_list["YOU"].parent, node_list["SAN"].parent)
print(len(flatten(walk_path)) - 1)
def get_transfers(orbit_tree):
you = orbit_tree['YOU']
san = orbit_tree['SAN']
w = Walker()
route = w.walk(you, san)
transfers = len(route[0]) + len(route[2]) - 2
return transfers
def count_transits(from_name, to_name, input):
root = build_orbit_tree(input.split())
# print(RenderTree(root, style=AsciiStyle()).by_attr())
from_node = find(root, lambda node: node.name == from_name).parent
to_node = find(root, lambda node: node.name == to_name).parent
walker = Walker()
(upwards, _, downwards) = walker.walk(from_node, to_node)
return (len(upwards) + len(downwards))
def walk_to(self, node, verbose=False) -> Tuple[Any, Any, Any]:
"""Returns:
(upwards, common, downwards): `upwards` is a list of nodes to go upward to.
`common` top node. `downwards` is a list of nodes to go downward to.
"""
w = Walker()
rs = w.walk(self, node)
if verbose:
node_repr = lambda n: f"{n.text}({n.dependency_relation})"
val_repr = lambda node: [node_repr(n) for n in node] if isinstance(
node, tuple) else [node_repr(node)]
print(' ._ '.join([','.join(val_repr(r)) for r in rs]))
return rs
def test_walker():
"""walk test."""
f = Node("f")
b = Node("b", parent=f)
a = Node("a", parent=b)
d = Node("d", parent=b)
c = Node("c", parent=d)
e = Node("e", parent=d)
g = Node("g", parent=f)
i = Node("i", parent=g)
h = Node("h", parent=i)
w = Walker()
eq_(w.walk(f, f), ((), f, ()))
eq_(w.walk(f, b), ((), f, (b, )))
eq_(w.walk(b, f), ((b, ), f, ()))
eq_(w.walk(a, f), ((a, b), f, ()))
eq_(w.walk(h, e), ((h, i, g), f, (b, d, e)))
eq_(w.walk(d, e), ((), d, (e, )))
with assert_raises(
WalkError,
"Node('/a') and Node('/b') are not part of the same tree."):
w.walk(Node("a"), Node("b"))
def get_nodes_on_path(self, target_branch_id):
branch_type = branchutil.parse_branch_type(target_branch_id)
branch_num = branchutil.parse_branch_num(target_branch_id)
target_node = None
for node in PreOrderIter(self.root):
if node.num == branch_num and node.type == branch_type:
target_node = node
w = Walker()
nodes_on_path = [(node.num, node.type)
for node in w.walk(self.root, target_node)[2]]
nodes_on_path.reverse()
return nodes_on_path
def main():
orbits = []
with open('input/day6.txt') as temp_file:
orbits = [line.rstrip('\n') for line in temp_file]
count = countNodes(copy.deepcopy(orbits))
process(orbits, count)
# pprint.pprint(nodes)
# for pre, fill, node in RenderTree(nodes.get('COM')):
# print("%s%s" % (pre, node.name))
w = Walker()
path = w.walk(nodes.get('YOU'), nodes.get('SAN'))
upwards = path[0]
common = path[1]
downwards = path[2]
totalSteps = len(upwards) + 1 + len(downwards) - 3
print(totalSteps)
def set_needs(tree):
"""For a recipe, needed is the number of times it must be run for 1 final output.
For an item, needed is the number that must be produced for 1 final output.
"""
w = Walker()
for leaf in tree:
if leaf.is_leaf:
needed = tree[0].number
try:
for i, node in enumerate(w.walk(tree[0], leaf)[2]):
if i % 2 == 0: # recipe node
needed /= node.number
else: # item node
needed *= node.number
node.needed = needed
except WalkError:
pass
def get_nodes_on_path(self, target_branch_id):
branch_num = target_branch_id[0]
branch_type = target_branch_id[1]
if branch_num == 0:
return []
target_node = None
for node in PreOrderIter(self.root):
if node.num == branch_num and node.type == branch_type:
target_node = node
w = Walker()
nodes_on_path = [(node.num, node.type)
for node in w.walk(self.root, target_node)[2]]
nodes_on_path.reverse()
return nodes_on_path
def __init__(self, exchange):
self.exchange = exchange
self.orderManager = OrderManager()
self.logger.info("Initiate Price Arbitrage Algo")
self._buildFxTree() # build fx tree for all possible conversions
# build list of leaves
root = self.tree.root # get root
w = Walker() # tree walker
# unwrap nested tuple returned by Walker to a flat list of FXPair
def unwrapWalkerToFlatList(x):
if isinstance(x, tuple):
if len(x) == 0: return []
s = []
for t in x:
s.extend(unwrapWalkerToFlatList(t))
return s
else:
if x.getFXPair() is None: return [] # skip root element
else: return [x.getFXPair()]
self.conversion_paths = {} # all conversion paths for the pair <pair: [pair1, pair2...]>
# build list of all conversion paths
for k in FXNode.treeNodesWithSamePair.keys(): # iterate all pairs
alternative_list = FXNode.treeNodesWithSamePair[k] # get list of leaves representing same pair but with different conversion path
paths = []
for c in alternative_list: # iterate all leaves (same pair but different paths)
if not c.is_leaf:
continue # iterate only leaves
path = unwrapWalkerToFlatList(w.walk(root, c)) # get path from root to this leaf
paths.append(path) # list of lists
if not k in self.conversion_paths.keys():
self.conversion_paths[k] = list(paths)
else:
self.conversion_paths[k].append(list(paths))
# set event triggers
self.exchange.setEventHandler(tradeHandler=self.updateTradeHandler, orderHandler=self.updateOrderHandler)
class AssetInfoTree:
def __init__(self, texture_info_list):
self.walker = Walker()
r = Resolver('name')
id = 0
self.root = TreeNode('Root', id)
id += 1
for info in texture_info_list:
respath = info.respath
cur_parent = self.root
cur_parent.filesize.bytes += info.filesize.bytes
cur_node = None
for chunk in respath.chunks:
try:
cur_node = r.get(cur_parent, chunk)
cur_parent = cur_node
except ChildResolverError:
cur_node = TreeNode(chunk, id, cur_parent)
cur_parent = cur_node
id += 1
if chunk == respath.chunks[-1]:
cur_node.horizontal_desc = str(info)
finally:
cur_node.filesize.bytes += info.filesize.bytes
def build_node_path(self, node):
path = self.walker.walk(self.root, node)
return '/'.join([tn.name for tn in path[2]])
# reference: https://stackoverflow.com/questions/2358045/how-can-i-implement-a-tree-in-python
# http://pydoc.net/anytree/2.4.3/anytree.node.nodemixin/
# reference: https://anytree.readthedocs.io/en/latest/
from anytree import Node, RenderTree, Walker
from anytree import Node, RenderTree, AsciiStyle
layer1 = Node(44)
layer2_1 = Node(6, parent=layer1)
layer2_2 = Node(9, parent=layer1)
layer3_1 = Node(34, parent=layer2_2)
layer3_2 = Node(5, parent=layer2_1)
layer3_3 = Node(4, parent=layer2_1)
print('\nShowing the entire tree:')
for pre, fill, node in RenderTree(layer1):
print("%s%s" % (pre, node.name))
print('\n')
w = Walker()
print('\nWalking the tree, layers3_2, and layers3_3:')
print(w.walk(layer3_2, layer3_3))
print('\n')
print('Showing the tree AsciiStyle:')
print(RenderTree(layer1, style=AsciiStyle()))
print('\n')
# reference: https://anytree.readthedocs.io/en/latest/
def _walk(self, to_rf):
""" Walk from this frame to a target frame along the tree. """
to_rf = _resolve_rf(to_rf)
walker = Walker()
up, _, down = walker.walk(self, to_rf)
return up, down
class MCTSalgo:
"""MCTS object
-performs mcts algo on high level
-required datamembers are as follows
*walker obj to traverse gametree
*rootNode to know the root, and switch root, when getting
humanplayer's inputs into the system, and when changing the currentGameState
*traversed path until including leafnode / or terminalnode, list of nodes
is required for walker object to traverse one-depth at a time, into the leaf,
and with traversedPath we can go straight back in rolloutMode, and update values,
and visitedcounts
*must be able to interact with getting inputs from the player,
and gives outputs to the player after having played AI turn
interaction can probably be tweaked at the last stage, because debugging and
getting the mcts to work is the hard part"""
"""constructor"""
def __init__(self ):
self.rootNode = Tree_Board_State( np.zeros((3,3)),0,0,0, "theRootNode", 0,0, parent=None, children=None )
self.theWalker = Walker()
self.traversedPath = [self.rootNode]
"""changeRootNode
purpose is to change into new rootNode when curGameState changes (after AI has moved, or humanplayer has moved)"""
def changeRootNode(self, newRootNode):
self.rootNode = newRootNode
self.rootNode.parent = None
def getPlayerInput(self, curTreeBoard):
self.changeRootNode( curTreeBoard )
self.clearTraversed() ##IMPORTANT to clear the oldTraversed path because theOldTraversed points to a tree, whose root was different!
"""we can also say that the AI always takes second turn...
hence we should always have possible gameState in the childNode, to place the marker into...
hence, we can expand the rootNode into childNodes always"""
# listB, expandRes = self.rootNode.getPossibleActions()
# self.rootNode.expandCurrentNode(listB)
"""walkUntilLeaf (including LeafNode)
traverses the tree from curRoot, until including leafNode,
meanwhile selecting the best childNodes along the path with UCB metrics
saves the traversedPath list into memory for backpropagation purposes,
and also returns the leafNode itself"""
def walkUntilLeaf(self):
#curNode = self.rootNode
self.traversedPath = [ node for node in PreOrderIter(self.rootNode, filter_=filterUCB1, stop=stopfilter2 ) ]
lastNode = self.traversedPath[-1]
self.traversedPath.append( lastNode.selectUCBFromChildren() )
"""##sadly the API was badly done, and it wasnt possible to
## easily walk until (including) the leafNode with stopfunction filter
## so we must have that "realLeafNode" append, in the end"""
trueLeafNode = self.traversedPath[-1] #append it!
return trueLeafNode
def walkerFuncTraversal(self):
destNode = self.rootNode.selectUCBFromChildren()
if destNode == None:
return self.rootNode
else:
curNode = self.rootNode
while destNode != None:
res = self.theWalker.walk( curNode, destNode )
res = res[2][0]
self.traversedPath.append( res )
curNode = destNode
destNode = curNode.selectUCBFromChildren()
##if curNode selectUCBChildren returns None, it will have been
## leafNode itself
return curNode
def backpropagate(self, curReward):
for x in self.traversedPath:
x.visited_count += 1
x.value += curReward
kakka = 77 #for debug only
"""MAIN MCTS ALGORITHM"""
def performMonteCarloTreeSearch(self, iterationsAmount):
for j in range(1,iterationsAmount+1):
leafNode = self.walkerFuncTraversal()
if leafNode.getVisited() == 0:
##make Rolloout
reward = self.playRollout(leafNode)
self.backpropagate(reward)
else:
##select childNode from expandedOnes
##make Rollout
actionsList, res = leafNode.getPossibleActions()
if res == False: ##NotTerminalNode where game ends
leafNode.expandCurrentNode(actionsList)
childNode = leafNode.selectUCBFromChildren() ##select childNode
self.traversedPath.append(childNode)
reward = self.playRollout(childNode)
self.backpropagate(reward)
else: ##Game must have ended in this branch
kakka = 10 #for debug only!
reward = self.playRollout(leafNode)
self.backpropagate(reward)
self.clearTraversed()
print("gameTree after AI_mcts_deliberations:\n", RenderTree(self.rootNode).by_attr())
childList = list(self.rootNode.children)
visitsList=[]
for child in childList:
visitsList.append(child.getVisited())
"""mostVisited = childList[visitsList.index(max(visitsList)) ]"""
highVisitCount = max(visitsList)
for child in childList:
if child.getVisited() == highVisitCount:
bestOpt = child
bestOpt = copy.deepcopy(bestOpt)
bestOpt.parent=None
if bestOpt.current_turn == 1:
bestOpt.current_turn = 0
else:
bestOpt.current_turn = 1
return bestOpt
"""playRollout
function is doing the mcts rollouts
the key trick is to make the rollouts in a separateSimulatedCopy tree
so it doesn't disturb the real mcts object's tree, then we simply
start from the copied gamestate simulatedRootNode, and expand the gameTree until game ends and we get
reward.
Other things to note about it:
*it will simply expand the simulatedRootNode into a gametree (until game ends) and hopefully
garbagecollector will do something about it later...
*actionPolicy for both players in rollout simulation will be equiprobable random, from the available
actions that are left on the board"""
def playRollout(self, originNode):
#protip, use deepcopy instead of python shenanigans!!!
simulatedRootNode = copy.deepcopy(originNode) ##hopefully copies the originNode into separate SimulatedRootNOde
simulatedRootNode.parent = None
curState = copy.deepcopy(simulatedRootNode)
curReward = 0
while True:
game_res = curState.checkGameResult()
if game_res != 2:
if game_res == 1:
curReward = 50##AI has won, markers are 1s
break
elif game_res == -1:
curReward = -50 ##human has won
break
else:
curReward=25 ##draw
break
else:
action = curState.getRandomAction()
if action != None:
curState = curState.makeLegalMove(action)
else:
kakka = 90 #for debug only!
return curReward
def clearTraversed(self):
self.traversedPath = [self.rootNode]
def checkIfNoVisits(self, theNode):
return theNode.getVisited()
def findArbitrageOpportunity(self):
# find spot arbitrage opportunity in the exchange
# calculate conversion value
# LONG position: from top to leaf
#ierate tree from root to the leaves to calculate terminal conversion rate in the leaves
def estimateRates(longVal, shortVal, root, longStr):
r = root.getFXPair()
# rates reflect current order book status and amt to exchange
ex_rate1 = 0 # error signal
v1 = 0
if r.isBidAvailable():
ex_rate1 = r.getAverageBidPrice(longVal) # use bid price here
v1 = longVal / ex_rate1 # convert val to Node base currency
ex_rate2 = 0 # error signal
v2 = 0
if r.isAskAvailable():
ex_rate2 = r.getAverageAskPrice(shortVal) # use ask price
v2 = shortVal * ex_rate2
s1 = longStr + "-> " + str(v1) + " " + r.getBase() + "@" + str(ex_rate1)
if root.is_leaf: # reached leaf - save accumulated long value
root.longValue = v1 # value of 1 BTC expressed in terms of base value of this currency (base)
root.longStr = s1
root.shortValue = v2 # value of 1 unit of this base currency expressed in terms of quote currency of the tree root
else:
for n in root.children:
estimateRates(v1, v2, n, s1)
# Combined LONG / SHORT LOOP: start with 1 BTC
root = self.tree.root # get root
for n in root.children:
pair = n.getFXPair()
x = self.exchange.getExchangeRate('BTC',pair.getQuote(), 'BID')
# x = pair.get1BTCinQuote()
# get value of 1 BTC expressed in quote currency of n
#ex_rate1 = pair.getAverageBidPrice(x) # use bid price here
#ex_rate2 = pair.getAverageAskPrice(1)
v1 = x # 1 * ex_rate1 # long local currency worth of 1 BTC
v2 = 1 # short value
s1 = "1 BTC -> " + str(x) + " " + pair.getQuote()
estimateRates(v1, v2, n, s1)
# print alternatives
a = FXNode.m_alternatives.keys()
w = Walker()
for k in FXNode.m_alternatives.keys():
alternative_list = FXNode.m_alternatives[k]
print("------PAIR %s" % (k.getPairCode()))
for c in alternative_list:
if not c.is_leaf:
continue # iterate only leaves
# unwrap nested tuples
def unwrap(x):
if isinstance(x, tuple):
s = ""
for t in x:
s += unwrap(t)
return s
else:
return x.name + " "
path = w.walk(root, c)
print("LONG: %f : %s >> %s" % (c.longValue, unwrap(path), c.longStr))
print("FINISH")
def part2(node1, node2):
w = Walker()
upwards, common, downwards = w.walk(node1, node2)
# minus 2 since you to parent and san to parent transfers ommited
return len(upwards) + len(downwards) - 2
def branch(self, origin, destination):
"""
Description
-----------
Returns the path in the tree to go from the 'origin' node to the
'destination' node.
The result is returned as two lists :
- upwards is a list of joint numbers to go upward to
- downwards is a list of joint numbers to go downward to
Every element of the list is the index of a joint from self.joints
This function uses anytree Walker class, for more details, check the
anytree documentation :
https://anytree.readthedocs.io/en/latest/api/anytree.walker.html
Parameters
----------
origin : str
Origin element of the tree. Is a string formatted like the nodes
names of self.tree :
type_number
where type is 'joint' / 'link' and number is the number of the
considered Joint / Link in self.joints / self.links.
Example : origin='joint_2'
destination : str
Destination element of the tree. Is a string formatted like the
node names of self.tree :
type_number
where type is 'joint' / 'link' and number is the number of the
considered Joint / Link in self.joints / self.links.
Example : destination='link_0'
Returns
-------
upwards : list of int
List of Joint index to go upward to. The transition matrix of the
Joints is T()**(-1) to go from a Joint to the next in this list.
downwards : list of int
List Joint index to go downward to. The transition matrix of the
Joints is T() to go from a Joint to the next in this list
Examples
--------
TODO
"""
# Upward List
upwards = []
# Downward List
downwards = []
# 1 - Finding the Node in the tree from origin and destination .......
origin_node = None
destination_node = None
for _, _, node in self.tree:
if node.name == origin:
origin_node = node
if node.name == destination:
destination_node = node
# 2 - Walk around the tree ...........................................
# Tree Walker
w = Walker()
# Getting paths
up_nodes, middle, down_nodes = w.walk(origin_node, destination_node)
# As only Joints have transition matrices, we ignore link nodes
# For upward nodes
for node in up_nodes:
# Getting the i & type from the node name "link_i" or "joint_i"
node_type, node_nb = node.name.split('_')
node_nb = int(node_nb)
if node_type == 'joint':
upwards.append(node_nb)
# For downward nodes
for node in down_nodes:
# Getting the i & type from the node name "link_i" or "joint_i"
node_type, node_nb = node.name.split('_')
node_nb = int(node_nb)
if node_type == 'joint':
downwards.append(node_nb)
return upwards, downwards
print(node)
#
print(RenderTree(nodes['COM'], style=AsciiStyle()))
##
# Part 1
##
direct = 0
indirect = 0
for node in PreOrderIter(nodes['COM']):
ancestors = len(node.ancestors)
if ancestors == 0:
continue
direct += 1
indirect += ancestors - 1
print(direct + indirect)
##
# Part 2
##
san_parent = nodes['SAN'].parent
print(san_parent.name)
w = Walker()
walk = w.walk(nodes['YOU'].parent.parent, nodes['SAN'].parent)
print(walk)
full_path = (*walk[0], walk[1], *walk[2])
print(len(full_path))
orbit_node = nodes.get(orbit)
if not orbit_node:
nodes[orbit] = Node(orbit)
orbit_node = nodes.get(orbit)
around_node = nodes.get(around)
if not around_node:
nodes[around] = Node(around)
around_node = nodes.get(around)
orbit_node.parent = around_node
walker = Walker()
you = nodes.get("YOU").parent
san = nodes.get("SAN").parent
path = walker.walk(you, san)
upwards, common, downwards = path
print(len(upwards) + len(downwards))
# count = 0
# for orbit, around in orbits.items():
# count += 1 # direct
# next = orbits.get(around)
# while next:
# count += 1 # indirect
# next = orbits.get(next)
# print("Part1 :", count)
cur = Node(orbiter, parent=root)
nodes.append(cur)
build_tree(cur, nodes, orbits)
root = Node('COM')
orbits = {}
with open('6.in') as f:
for x in f.read().splitlines():
# AAA)BBB = BBB is in orbit around AAA
orbit = x.split(')')
if orbit[0] not in orbits:
orbits[orbit[0]] = [orbit[1]]
else:
orbits[orbit[0]].append(orbit[1])
nodes = []
build_tree(root, nodes, orbits)
length = 0
w = Walker()
for node in nodes:
length += len(w.walk(node, root)[0])
print('part 1: {}'.format(length))
you = search.find(root, lambda node: node.name == 'YOU')
san = search.find(root, lambda node: node.name == 'SAN')
res = w.walk(you, san)
print('part 2: {}'.format(len(res[0]) + len(res[2]) - 2))
def __missing__(self, key):
res = self[key] = Node(key)
return res
if __name__ == "__main__":
# with open("example_1") as f:
with open("input_1") as f:
input_m = [line.rstrip() for line in f]
# print(f"input {input_m}")
nodes = DefaultNodeDict()
for input_orbit in input_m:
center, satellite = input_orbit.split(")")
nodes[satellite].parent = nodes[center]
print(RenderTree(nodes["COM"]))
w = Walker()
# upward, common, downward = w.walk(nodes["YOU"], nodes["SAN"])
# print(len(upward) + len(downward) - 2)
# Part 1 below ###
total = 0
for node in nodes.values():
upward, common, downward = w.walk(node, nodes["COM"])
total += len(upward) + len(downward)
print(total - len(nodes))
def find_LCA_two_objects(node_object_1, node_object_2):
w = Walker()
LCA = w.walk(node_object_1, node_object_2)[1]
return LCA
lines = []
d = {}
#with open("6test.txt") as f:
with open("6input.txt") as f:
lines = [tuple(x.rstrip().split(")")) for x in f.readlines()]
for l in lines:
if l[0] in d:
d[l[0]].append(l[1])
else:
d[l[0]] = [l[1]]
root = Node("COM")
create_children(root)
sum = 0
you = None
santa = None
for n in root.descendants:
sum += n.depth
if n.name == "YOU":
you = n
if n.name == "SAN":
santa = n
print(sum)
w = Walker()
up, common, down = w.walk(you, santa)
#assuming a single common node
print(len(up) - 2 + 1 + len(down) - 1)
def count_orbit_jumps(origin, destination):
walker = Walker()
up, common, down = walker.walk(origin, destination)
return len(up) + len(
down
) - 2 # Subtract 2 since the origin and destination themselves do not require jumps
