def main():
initialize()
eventHandler = EventHandler()
frameRateHandler = FrameRateHandler(60)
squareSize = 100
totalSize = squareSize * 3
offsetY = squareSize
offsetX = 0
backColor = Color(0, 0, 0)
mainSurface = pygame.display.set_mode(
(totalSize + offsetX, totalSize + offsetY))
gameBoard = GameBoard(0, offsetY, eventHandler, mainSurface)
while True:
frameRateHandler.updateStart()
eventHandler.update()
if eventHandler.quit or eventHandler.keys.release[K_ESCAPE]:
break
gameBoard.update()
mainSurface.fill(backColor)
gameBoard.draw()
pygame.display.flip()
frameRateHandler.updateEnd()
def start_simulation():
global param,OD_pairs,edges,edge_pairs,OD,demand_input,demand_data,msa_flow,destination_data
for i in range(len(param)):
for j in range(5):
param[i][j] = param[i][j].get() # Converting the gui object into its corresponding value
for i in range(len(OD_pairs)):
for j in range(2):
OD_pairs[i][j] = OD_pairs[i][j].get() # Converting the gui object into its corresponding value
# GUI related
alist = root.winfo_children()
for item in alist:
if(item.winfo_children()):
alist.extend(item.winfo_children())
for item in alist:
item.destroy()
# Updating the edge dictionary storing length width density and cell number corresponding to a particular link
for i in range(len(param)):
n1,n2,l,w,k = int(param[i][0]),int(param[i][1]),float(param[i][2]),float(param[i][3]),int(param[i][4])
edges[n1][n2] = [l,w,k,int(math.ceil(l/0.225))]
edge_pairs.append([n1,n2])
incoming_links[n2].append(i) #Adding i to the incoming links corresponding to node n2
outgoing_links[n1].append(i) #Adding i to the outgoing links corresponding to node n1
for i in range(len(OD_pairs)):
o,d = map(int,(OD_pairs[i][0],OD_pairs[i][1]))
OD.append([o,d])
for i in OD:
demand_input[i[0]][i[1]] = [0, 0, 0, 0]
# Opening the demand1.txt file that contains the class wise demand data for each O-D pair separated by space
file = open('demand.txt', 'r')
demand = file.readlines()
for i in range(len(demand)):
demand[i] = list(map(int,demand[i].split())) #Storing the first line corresponding to demand1 file
for i in range(len(OD)):
for j in range(len(demand)):
demand_data[tuple(OD[i])].append(demand[j][i * 4:(i + 1) * 4]) #Storing four values at a time and appending that to the corresponding O-D pair in demand_data dictionary
for i in OD:
for j in range(simtime):
msa_flow[(i[0],i[1])].append([0,0,0,0]) #Initializing the msa flow
for i in OD:
destination_data[i[1]] = [0,0,0,0] #initializing the destination flow corresponding to each destination node that stores the flow coming out of the network at every destination node
initialize() #Calling the initialize function
OD_dictionary() #Calling the OD_dictionary function
linkpath_calculation() #Calling the link_path calculation function
flow_visual() # Calling the flow visualization method
def main():
initialize()
eventHandler = EventHandler()
frameRateHandler = FrameRateHandler(60)
meshDict = loadMeshes()
backColor = Color(0, 0, 0)
mainSurface = pygame.display.set_mode((1280, 720))
objectHandler = ObjectHandler(eventHandler, frameRateHandler, mainSurface, meshDict)
interfaceHandler = InterfaceHandler(objectHandler)
tankMesh = RawMesh([
("body", [(8, 0), (40, 0), (48, 8), (48, 40), (40, 48), (8, 48), (0, 40), (0, 8)]),
("gun", [(0, 0), (45, 0), (45, 10), (0, 10)], (5, 5))
])
playerMesh = Mesh(tankMesh, (0, 0))
while True:
frameRateHandler.updateStart()
eventHandler.update()
if eventHandler.quit or eventHandler.keys.release[K_ESCAPE]:
break
objectHandler.update()
mainSurface.fill(backColor)
objectHandler.draw()
interfaceHandler.draw()
pygame.display.flip()
frameRateHandler.updateEnd()
def DoubleMoonTest():
myann = NeuralNetwork('Dong Dong Network')
myann.model.SetInputLayer(2)
myann.model.SetOutputLayer('SoftMax')
myann.model.SetFullConnectLayer([(15,'ReLu'),(2,None)])
Na = 500
Nb = 500
(x,y) = initialize(10,6,-4,Na,Nb)
x = x.transpose()
yy = np.zeros([Na+Nb,2])
for i in range(Na+Nb):
if y[0,i]==1:
yy[i,0] = 1
else:
yy[i,1] = 1
myann.setSamples(x,yy)
myann.MiniBatch_Train(700,2000)
def DoubleMoonTest():
myann = NeuralNetwork('Dong Dong Network')
myann.model.SetInputLayer(2)
myann.model.SetOutputLayer('SoftMax')
myann.model.SetFullConnectLayer([(15, 'ReLu'), (2, None)])
Na = 500
Nb = 500
(x, y) = initialize(10, 6, -4, Na, Nb)
x = x.transpose()
yy = np.zeros([Na + Nb, 2])
for i in range(Na + Nb):
if y[0, i] == 1:
yy[i, 0] = 1
else:
yy[i, 1] = 1
myann.setSamples(x, yy)
myann.MiniBatch_Train(700, 2000)
def DoubleMoonTest():
myann = NeuralNetwork('Dong Dong Network')
myann.model.SetInputLayer(2)
myann.model.SetOutputLayer('SoftMax')
myann.model.SetFullConnectLayer([(15,'ReLu'),(2,None)])
Na = 500
Nb = 500
(x,y) = initialize(10,6,-4,Na,Nb)
#y[y==0]=-1 #use tanh to classify
yy = np.zeros([2,Na+Nb])
for i in range(Na+Nb):
if y[0,i]==1:
yy[0,i] = 1
else:
yy[1,i] = 1
myann.setSamples(x,yy)
#myann.setSamples(x,y)
myann.MiniBatch_Train(700,2000)
def main():
# Prompts users for a file name containing the graph structure.
graph_name = input('Graph Structure File: ')
node_list = read_graph(graph_name)
# Prompts users for a file name containing the possibles values of the nodes.
val_name = input('Node Value File: ')
node_list = read_val(val_name, node_list)
# Prompts users for a file name containing the probabilities.
prob_name = input('Probability File: ')
node_list = read_prob(prob_name, node_list)
# Initializes the graph structure.
evidence, node_list = initialize(node_list)
exit_flag = False
while not exit_flag:
# Resets the nodes and evidence to their initial values.
node_list = read_graph(graph_name)
node_list = read_val(val_name, node_list)
node_list = read_prob(prob_name, node_list)
evidence, node_list = initialize(node_list)
# Prompts the user for a node and value of interest.
int_node = input('Input the node of interest and its value (ex: A1): ')
# Prompts the user for all the evidence.
evi_nodes = input('Input all evidence nodes (ex: B1 C0 D1). If there is no evidence, type None: ')
if evi_nodes == 'None':
# Gets the name and value associated with the node of interest.
interest_name, interest_val = name_val(int_node)
# Since there is no evidence, we need only the marginal distribution given no evidence, which we
# initialized as 'Empty.'
cond_prob = node_list[interest_name].cond[interest_val]['Empty']
print('The conditional probability of', int_node, 'given no evidence is', '{0:0.4f}.'.format(cond_prob))
else:
# Gets the name and value associated with the node of interest.
interest_name, interest_val = name_val(int_node)
# Updates the network for every evidence given.
evidence_list = evi_nodes.split(' ')
for e in evidence_list:
evidence, node_list = update(e, node_list, evidence)
# Creates a list of the evidence.
evi_list = list()
for v in evidence:
evi_list.append(v + str(evidence[v]))
# Gets the conditional probability given the evidence.
try:
cond_prob = node_list[interest_name].cond[interest_val][tuple(sorted(evi_list))]
# If the exact conditional probability doesn't exist, then a message wasn't sent, so we have to
# use a different metric.
except KeyError:
# Gets a list of all the conditional probabilities.
cond_list = list(node_list[interest_name].cond[interest_val].keys())
# Scores each conditional probability by the number of shared indices with the evidence.
score_dict = score(evi_list, cond_list)
# The one with the most amount of similar indices is the one that we want.
condition = max(score_dict, key=score_dict.get)
cond_prob = node_list[interest_name].cond[interest_val][condition]
print('The conditional probability of', int_node, 'given', evi_nodes, 'is', '{0:0.4f}.'.format(cond_prob))
# Cycling exit prompt, for convenience.
exit_prompt = input('Would you like to make another query? (Y or N): ')
if exit_prompt == 'Y':
exit_flag = False
else:
exit_flag = True
import pandas as pd
import sys
import time
input_file = ('''/Users/edz/Documents/Princeton/Senior/ORF411'''
'''/OJ/not_grapefruit/Results/notgrapefruit2016.xlsm''')
# We also need the Results file of the previous year in order to
# initialize the inventory.
last_year_file = ('''/Users/edz/Documents/Princeton/Senior/ORF411'''
'''/OJ/not_grapefruit/Results/notgrapefruit2015.xlsm''')
print 'Initializing...'
start = time.time()
initial_inventory = initialize_inventory(last_year_file)
storages, processing_plants, groves, markets, decisions = initialize(
input_file, initial_inventory)
print 'Initialization took {0}'.format(time.time() - start)
#### Test Storage
s = storages['S51']
# Test adding products
s.add_product('ORA', 500)
assert s.inventory['ORA'] == [500, 0, 0, 0]
s.add_product('POJ', 200)
assert s.inventory['POJ'] == [200, 0, 0, 0, 0, 0, 0, 0]
# Test aging
s.age()
assert s.inventory['ORA'] == [0, 500, 0, 0]
assert s.inventory['POJ'] == [0, 200, 0, 0, 0, 0, 0, 0]
# 'S59': {
# 'POJ': 82.316,
# 'FCOJ': 77.9730,
# 'ROJ': 86.51086
# },
# 'S73': {
# 'POJ': 122.47359,
# 'FCOJ': 101.586,
# 'ROJ': 69.24297
# }
# }
print 'Initializing...'
start = time.time()
initial_inventory, scheduled_to_ship_in = initialize_inventory(last_year_file)
storages, processing_plants, groves, markets, decisions = initialize(
input_file, initial_inventory)
print 'Initialization took {0}'.format(time.time() - start)
sales = {'ORA': 0, 'POJ': 0, 'ROJ': 0, 'FCOJ': 0}
revenues = {'ORA': 0, 'POJ': 0, 'ROJ': 0, 'FCOJ': 0}
cost = {
'purchase': {
'raw': 0,
'futures': {
'ORA': 0,
'FCOJ': 0
}
},
'manufacturing': {
'POJ': 0,
'FCOJ': 0,
w = 84 # links width
h = 12 # links height
init_angle = np.pi / 18
sigma_p = 5
sigma_th = 10 * np.pi / 180
sigma_s = 2
eps = 1e-5
## Initialize graph
[pairs, node_ids, neighbor_dict, num_pairs] = initGraph()
# initialize a message for each node
displayImage_msg = observation.copy()
msg_prev = dict()
for pair in pairs:
msg_prev[pair], gt_circle, gt_links = initialize(
pair[1], num_components, init_angle, w, h)
displayMessages(msg_prev[pair], labelFromId(pair[1]), displayImage_msg)
cv2.imwrite("initialize.jpg", displayImage_msg)
# iterate to converge
for i in range(num_iterations):
displayImage_msg = observation.copy()
# NBP update of nonparametric message t -> s
msg_curr = {}
for pair in pairs:
print("iter: {0}, pair:".format(i), pair, flush=True)
no_u = False
t_id = pair[0] # t node
s_id = pair[1] # s node
t_label = labelFromId(t_id)
setPanel(3,(0,0,0))
setPanel(4,(0,0,0))
# Already created savings-accounts.
# <Food and beverage> <Living costs> <Housing> <Transportation> <Leisure time>
accounts = ["50000000102", "50000000103","50000000104","50000000105","50000000106"]
#
accounts_dummy = ["50000000108","50000000109","50000000110","50000000111","50000000112"]
customer = "01011900123"
customer_dummy = "01011900124"
initialize(accounts, accounts_dummy, customer, customer_dummy)
last_balance = [1,1,1,1,1]
current_balance = [0,0,0,0,0]
print("The Matrix is now initialized.")
print("Welcome.")
print([getBalance(customer, acc) for acc in accounts])
while True:
try:
#Poll account balances
current_balance = [getBalance(customer, acc) for acc in accounts]
for i in range(0,N):
if current_balance[i] != last_balance[i]:
print('Account balance update for Account:',i, account_categories[i])
# Make sure you replace the API and/or APP key below
# with the ones for your account
import initialize
import api
from datadog import initialize, api
options = {
'api_key': 'd15bc0c919de4948552c9fd532b03620',
}
initialize(**options)
print api.Tag.get_all()