def get_t_and_mu(T, D):
""" returns three lists, the first is a list of numpy column vectors of data points t
with missing data removed. The second is a list of mean vectors corresponding to the list of t-vectors
the third is a list of lists with the indices at which data were missing in the t-vectors"""
T_boole = np.isnan(T)
N = T.shape[1]
mu = calc_mean_T(T)
data_is_missing = np.any(T_boole)
if data_is_missing:
t_list = []
mu_list = []
nan_indices_list = N * [[]]
for i in range(0, N):
t_i_missing = T[:, i]
for j in range(0, D):
if np.isnan(T[j, i]):
copy = nan_indices_list[i].copy()
copy.append(j)
nan_indices_list[i] = copy
nan_indices = nan_indices_list[i]
t_i_removed = np.delete(t_i_missing, nan_indices)
mu_i_removed = np.delete(mu, nan_indices)
t_list.append(t_i_removed)
mu_list.append(mu_i_removed)
else:
t_list = []
mu_list = []
nan_indices_list = N * [[]]
for i in range(0, N):
t_list.append(T[:, i].reshape(D, 1))
mu_list.append(mu)
return t_list, mu_list, nan_indices_list
def generate(data):
# Randomize the percentage of program which is serial. Store the information in params.
p = [5, 10, 20, 25, 50, 100]
idx = random.choice(range(6))
percentage = p[idx]
data['params']['percentage'] = percentage
# Compute the corresponding solutions for each of the possible percentages. Determine the correct ones. Store the information in params.
solutions = [100 / i for i in p]
solution = solutions[idx]
data['params']['solution'] = solution
# Store the incorrect solutions in params. "i" is short-hand for incorrect.
copy = []
for i in range(6):
copy.append(solutions[i])
copy.remove(solution)
i0 = copy[0]
i1 = copy[1]
i2 = copy[2]
i3 = copy[3]
i4 = copy[4]
data['params']['i0'] = i0
data['params']['i1'] = i1
data['params']['i2'] = i2
data['params']['i3'] = i3
data['params']['i4'] = i4
def astar(init, goal, network):
path = []
open = []
closed = []
### YOUR CODE GOES BELOW HERE ###
open.append((0, list(), init))
while not len(open) == 0:
item = min(open)
open.remove(item)
if item[2] not in closed:
if goal == item[2]:
return item[1], closed
closed.append(item[2])
for edge in network:
if item[2] == edge[0]:
copy = item[1][:]
copy.append(edge[1])
open.append(
(item[0] + euDis(edge[1], goal), copy, edge[1]))
elif item[2] == edge[1]:
copy = item[1][:]
copy.append(edge[0])
open.append(
(item[0] + euDis(edge[0], goal), copy, edge[0]))
### YOUR CODE GOES ABOVE HERE ###
return path, closed
def __add__(self, value):
first_element = self.__values[0]
copy = LinkedList(first_element)
for node in self.__values[1:]:
copy.append(node)
copy.append(value)
return copy
def duplicatePuzzle(self):
copy = []
for i in range(len(self.puzzle)):
temp = []
for j in range(len(self.puzzle)):
temp.append(self.puzzle[i][j])
copy.append(temp)
return copy
def inventory_copy(self): # make a copy of current inventory state
copy = []
for i in self.inventory:
c = Inventory(i.name, i.quantity)
copy.append(c)
return copy
def duplicate(self):
copy = LinkedList()
node1 = self.head
while node1:
node = node1.value
copy.append(node)
node1 = node1.next
return copy
def copy(self, puzzle):
copy = []
for i in range(0, len(puzzle)):
temp = []
for j in range(0, len(puzzle)):
temp.append(puzzle[i][j])
copy.append(temp)
return copy
def getCopy(self):
copy = []
for i in range(len(self.puzzle)):
temp = []
for j in range(len(self.puzzle)):
temp.append(self.puzzle[i][j])
copy.append(temp)
return copy
def copy(self):
copy = []
for i in self.board:
line = []
for j in i:
line.append(j)
copy.append(line)
return TilePuzzle(copy)
def load_file(input_file: str) -> [str]:
with open(input_file) as data_file:
data = data_file.readlines()
copy = []
for line in data:
line = line.rstrip("\n")
line = list(line)
copy.append(line)
return copy
def getContents(self):
"""Returns: a new list containing copies of the points in this cluster.
The result is a list of list of numbers. It has to be computed from
the indices.
"""
copy = []
for i in range(len(self.getIndices())):
copy.append(self._dataset.getPoint(self.getIndices()[i]))
return copy
def deep_copy(obj):
'''Return a deep (i.e., non-aliased at all) copy of obj, a nested list of
integers'''
#Base case:
if type(obj) != list:
return obj
copy = []
for elem in obj:
#The leap of faith: assume that deep_copy works!
copy.append(deep_copy(elem))
return copy
def convertInf(graph, inf_val):
copy = []
for i in range(len(graph)):
row = []
for j in range(len(graph)):
if graph[i][j] == float(inf_val):
row.append(0)
else:
row.append(graph[i][j])
copy.append(row)
return copy
def resample(self):
self.normalize()
weights = [p.weight for p in self._particles]
total_weight = sum(weights)
choice = np.random.choice(self._particles,
size=self.num_particles,
replace=True,
p=weights / total_weight)
copy = []
for p in choice:
copy.append(Particle(x=p.x, y=p.y, theta=p.theta, ln_p=p.weight))
self.particles = copy
def make_copy(self):
"""
Returns copy of Field.field from self
Return: [[Cell]]
"""
copy = []
for i in range(self.size):
copy.append([])
for i, row in enumerate(self.field):
for cell in row:
copy[i].append(Cell(cell.id, cell.direction, cell.vehicle_size))
return copy
def build_residual_graph(graph, flow):
victory = change_to_dict_of_dict(graph)
length = len(graph)
# keep track of all the forward edge: capacity - flow
lst = []
for num in victory:
adjacent = victory[num]
for elem in adjacent:
capacity = victory[num][elem]
flowing = flow[num][elem]
weight = capacity - flowing
victory[num][elem] = weight
lst.append([num, elem])
# keep track of all the backward edge: - f #
for edge in lst:
node1 = edge[0]
node2 = edge[1]
flowing = flow[node1][node2]
if flowing == 0:
continue
else:
victory[node2][node1] = -flowing
# Remove all the 0 residual capacity #
my_dict = {}
for i in victory:
adjacent = victory[i]
my_dict[num] = None
temp = {}
for j in adjacent:
residual_capacity = adjacent[j]
if residual_capacity != 0:
temp[j] = residual_capacity
else:
continue
my_dict[i] = temp
# sort dictionary in ascending order #
final = {}
for num in range(length):
final[num] = my_dict[num]
updated_graph = []
for x in final:
adjacent = final[x]
copy = []
for y in adjacent:
edge = [y, adjacent[y]]
copy.append(edge)
updated_graph.append(copy)
return updated_graph
def removeFullRows(app):
copy, counter = [], 0
for row in app.board:
#checkRow is a helper functions that tells us if the row is filled
if checkRow(app, row) == False:
copy.append(row)
else:
counter += 1
#adding the empty rows(replacing those that were removed)
for c in range(counter):
copy.insert(0, [app.emptyColor] * app.cols)
app.board = copy
app.score += counter
def _create_clone(self, element):
if isinstance(element, NavigableString):
return type(element)(element)
copy = Tag(None, element.builder, element.name, element.namespace, element.nsprefix)
# work around bug where there is no builder set
# https://bugs.launchpad.net/beautifulsoup/+bug/1307471
copy.attrs = dict(element.attrs)
for attr in ('can_be_empty_element', 'hidden'):
setattr(copy, attr, getattr(element, attr))
for child in element.contents:
copy.append(self._create_clone(child))
return copy
def preprocess(self, data):
shape = np.array(data).shape
# If list of flat lists
if shape[0] > shape[1]:
copy = []
for i in range(shape[0]):
copy.append(
Process.multiple_methods(argument=data[i], methods=[Process.logarithm, Process.moveable_lines]))
return copy
# If one single
else:
return Process.multiple_methods(argument=data, methods=[Process.logarithm, Process.moveable_lines])
def all_options(cells_left):
if cells_left == 1:
return [[0], [1]]
extension = []
arrays = all_options(cells_left - 1)
for array in arrays: #in every configuration append 0, and also have a copy where 1's are appended instead of 0's
copy = array[:]
copy.append(1)
array.append(0)
extension.append(copy)
arrays.extend(extension)
return arrays
def updateDicts(nodesByClusterID, maxID):
copy = []
for node in nodesByClusterID[maxID]:
copy.append(node.getID())
del nodesByClusterID[node.getID()]
for ID in copy:
for nodeID in nodesByClusterID.keys():
for n in nodesByClusterID[nodeID]:
if n.getID() == ID:
nodesByClusterID[nodeID].remove(n)
del copy
return nodesByClusterID
def updateDicts(nodesByClusterID,maxID):
copy=[]
for node in nodesByClusterID[maxID]:
copy.append(node.getID())
del nodesByClusterID[node.getID()]
for ID in copy:
for nodeID in nodesByClusterID.keys():
for n in nodesByClusterID[nodeID]:
if n.getID()==ID:
nodesByClusterID[nodeID].remove(n)
del copy
return nodesByClusterID
def remove_item_nested_list(nested_gff, remove_string):
"""removes an item from nested_gff and returns an item that you do want
:nested_gff: TODO
:returns: TODO
"""
copy = []
for gene in nested_gff:
smaller_list = []
for hit in gene:
if remove_string in hit:
pass
else:
smaller_list.append(hit)
copy.append(smaller_list)
return copy
def updateDicts(nodesByClusterID, maxID, clusters, nodes):
copy = []
for nodeID in nodesByClusterID[maxID]: #burayi bir kontrol et bakalim
copy.append(nodeID)
clusters[maxID].append(nodes[nodeID])
del nodesByClusterID[nodeID]
for nodeID in nodesByClusterID.keys():
copy2 = []
for n in nodesByClusterID[nodeID]:
copy2.append(n)
for node in copy2:
for ID in copy:
if node == ID:
nodesByClusterID[nodeID].remove(node)
del copy2
del copy
return nodesByClusterID, clusters
def updateDicts(nodesByClusterID,maxID,clusters,nodes):
copy=[]
for nodeID in nodesByClusterID[maxID]: #burayi bir kontrol et bakalim
copy.append(nodeID)
clusters[maxID].append(nodes[nodeID])
del nodesByClusterID[nodeID]
for nodeID in nodesByClusterID.keys():
copy2=[]
for n in nodesByClusterID[nodeID]:
copy2.append(n)
for node in copy2:
for ID in copy:
if node==ID:
nodesByClusterID[nodeID].remove(node)
del copy2
del copy
return nodesByClusterID,clusters
def __init__(self, params, dbType, dataTypeConverter, outparams=None):
names = {}
copy = []
for p in params:
if isinstance(p, OutParam):
if outparams:
value = outparams.pop(0)
else:
value = p.value()
if p.dataType is not None:
typeCode = dataTypeConverter.convertType(
dbType, p.dataType)
value = dataTypeConverter.convertValue(
dbType, p.dataType, typeCode, value)
copy.append(value)
if p.name is not None:
names[p.name] = value
else:
copy.append(p)
super(OutParams, self).__setattr__("config", copy)
super(OutParams, self).__setattr__("names", names)
def copy_nodes(self, state):
"""
Returns a 'deep' copy of list of nodes.
Parameters
----------
state: a list
A list containing Node objects representing a wire path.
Returns
-------
list
A list containing 'deep' copy of the nodes in the original list.
"""
copy = []
for node in state:
node_copy = node.get_copy()
copy.append(node_copy)
return copy
def __init__(self, params, dbType, dataTypeConverter, outparams=None):
names = {}
copy = []
for p in params:
if isinstance(p, OutParam):
if outparams:
value = outparams.pop(0)
else:
value = p.value()
if p.dataType is not None:
typeCode = dataTypeConverter.convertType(
dbType, p.dataType)
value = dataTypeConverter.convertValue(
dbType, p.dataType, typeCode, value)
copy.append(value)
if p.name is not None:
names[p.name] = value
else:
copy.append(p)
super(OutParams, self).__setattr__("config", copy)
super(OutParams, self).__setattr__("names", names)
def check():
p = preprocessing()
letters = generateLetters()
result = {}
try:
print(sys.getsizeof(result))
except AttributeError:
print("sys.getsizeof exists in Python ")
for word in p:
if is_word_possible(word, letters):## returns True if any letter is in word
length = len(word)
try:
copy = result[length]## keeps track of everything in result at that word size
except KeyError:
copy = []
copy.append(word)
result[length] = copy##re appends result with the new word for that word size
##le = max(len(x) for x in result)
#print(result)
print(result[sorted(result.keys())[-1]])##prints the longest word/words found if same length
def analyse(self): #分析剩下的3n张牌是否满足胡牌条件
if len(self.handtiles) == 0:
copy = []
for meld in self.result:
copy.append(meld)
for meld in self.melds:
copy.append(meld)
self.allResult.append(copy)
self.back()
return
color = self.handtiles[0].color
number = self.handtiles[0].number
if (self.total[color][number] in [1, 2]):
if ((color in [4, 5]) or number + 2 > 9
or self.total[color][number + 1] == 0
or self.total[color][number + 2] == 0):
self.back()
return
else:
self.totalToResult_shun(color, number)
self.analyse()
elif (self.total[color][number] == 3):
self.totalToResult_ke(color, number)
self.analyse()
elif (self.total[color][number] == 4):
if ((color in [4, 5]) or number + 2 > 9
or self.total[color][number + 1] == 0
or self.total[color][number + 2] == 0):
self.back()
return
else:
self.totalToResult_shun(color, number)
self.totalToResult_ke(color, number)
self.analyse()
self.back()
def copyboard(B):
copy = []
for row in B:
copy.append(row)
return copy
def read(f):
copy = []
for line in f:
for c in line:
copy.append(c)
return copy
def duplicate_paths(self, paths): copy = [] for path in paths: copy.append(path.Duplicate()) return tuple(copy)
if copy[r][c] == '#' and sum_neighbours(copy, r, c) >= 4:
if changed == False:
changed = True
return False
elif copy[r][c] == '#' and sum_neighbours(copy, r, c) < 4:
return True
return False
copy = []
while changed:
copy = []
for i in range(len(layout)):
copy.append([])
for j in range(len(layout[i])):
copy[i].append(layout[i][j])
changed = False
for i in range(len(copy)):
for j in range(len(copy[i])):
if not copy[i][j] == '.':
if becomes_occupied(copy, i, j):
layout[i][j] = '#'
else:
layout[i][j] = 'L'
res = 0
for i in range(len(layout)):
for j in range(len(layout[i])):
def get_schedules(data):
timeout = 15 # in seconds
activities = {}
itineraries = []
size = data["size"]
# Build a dict of activity data
for i in range(size):
activity = data["activities"][i]
activity["slots"] = [e.replace(" (", "_").replace(")", "") for e in activity["slots"]]
activities[i] = {"code" : activity["code"], "name" : activity["name"], "category" : activity["category"],
"slots" : [enums.Weeks[e].value for e in activity["slots"]], "length" : int(activity["length"])}
# First activity
first = activities[0]
# Initialize itineraries with first activity
for slot in first["slots"]:
length = first["length"]
weeks = [(slot + i) for i in range(length)]
itineraries.append([(0, weeks)])
# Prevent infinite looping
t1 = datetime.now()
# For each activity after the first
for i in range(1, size):
new_itineraries = []
slots = activities[i]["slots"]
length = activities[i]["length"]
# For each itinerary already in the list
for j in range(len(itineraries)):
# For each slot in the current activity
for slot in slots:
weeks = [slot + k for k in range(length)]
conflict = False
# For each activity in the current itinerary
for activity in itineraries[j]:
# For each week in the current activity's length
for week in weeks:
# Check for time conflict
if week in activity[1]:
conflict = True
# Add activity if no conflict
if not conflict:
copy = itineraries[j].copy()
copy.append((i, weeks))
new_itineraries.append(copy)
# Check timeout
if (datetime.now() - t1).seconds > timeout:
return {"success" : False, "error" : "Operation timed out."}
# Only interested in up to date itineraries
itineraries = new_itineraries
ret_val = {"success": True, "itineraries" : []}
# For each itinerary
for itinerary in itineraries:
new_itinerary = []
# For each activity in the itinerary
for activity in itinerary:
# Set activity info
new_activity = activities[activity[0]].copy()
new_activity["slots"] = activity[1]
new_itinerary.append(new_activity)
# Add itinerary to return array
ret_val["itineraries"].append(new_itinerary)
ret_val["size"] = len(ret_val["itineraries"])
return ret_val