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()