class ForagingModel(Model): """A environemnt to model foraging environment.""" def __init__(self, N, width, height, grid=10, iter=100000, seed=None, name='SForaging', viewer=False, parent=None, ratio=1.0): """Initialize the attributes.""" if seed is None: super(ForagingModel, self).__init__(seed=None) else: super(ForagingModel, self).__init__(seed) # Create a unique experiment id self.runid = datetime.datetime.now().timestamp() self.runid = str(self.runid).replace('.', '') # Create the experiment folder # If parent folder exits create inside it if parent is not None and Path(parent).is_dir(): self.pname = parent + '/' + self.runid + '-' + str(ratio) else: self.pname = '/'.join(os.getcwd().split( '/')[:-2]) + '/results/' + self.runid + '-' + str(iter) + name # Define some parameters to count the step self.stepcnt = 1 self.iter = iter # UI self.viewer = viewer # Create db connection connect = Connect('swarm', 'swarm', 'swarm', 'localhost') self.connect = connect.tns_connect() # Fill out the experiment table self.experiment = Experiment(self.connect, self.runid, N, seed, name, iter, width, height, grid) self.experiment.insert_experiment() # Get the primary key of the experiment table for future use self.sn = self.experiment.sn # Create a folder to store results os.mkdir(self.pname) # Number of agents self.num_agents = N # Environmental grid size self.grid = Grid(width, height, grid) # Schedular to active agents self.schedule = SimultaneousActivation(self) # Empty list of hold the agents self.agents = [] def create_agents(self, random_init=True, phenotypes=None): """Initialize agents in the environment.""" # This is abstract class. Each class inherting this # must define this on its own pass def create_environment_object(self, jsondata, obj): """Create env from jsondata.""" name = obj.__name__.lower() temp_list = [] i = 0 for json_object in jsondata[name]: location = (json_object["x"], json_object["y"]) if "q_value" in json_object: temp_obj = obj(i, location, json_object["radius"], q_value=json_object["q_value"]) else: temp_obj = obj(i, location, json_object["radius"]) self.grid.add_object_to_grid(location, temp_obj) temp_list.append(temp_obj) i += 1 return temp_list def build_environment_from_json(self): """Build env from jsondata.""" jsondata = JsonData.load_json_file(filename) # Create a instance of JsonData to store object that # needs to be sent to UI self.render = JsonData() self.render.objects = {} # First create the agents in the environment for name in jsondata.keys(): obj = eval(name.capitalize()) self.render.objects[name] = self.create_environment_object( jsondata, obj) self.hub = self.render.objects['hub'][0] self.total_food_units = 0 self.foods = [] try: self.site = self.render.objects['sites'][0] for i in range(self.num_agents * 1): f = Food(i, location=self.site.location, radius=self.site.radius) f.agent_name = None self.grid.add_object_to_grid(f.location, f) self.total_food_units += f.weight f.phenotype = dict() self.foods.append(f) except KeyError: pass def step(self): """Step through the environment.""" # Next step self.schedule.step() # Increment the step count self.stepcnt += 1 def gather_info(self): """Gather information from all the agents.""" # diversity = np.ones(len(self.agents)) exploration = np.ones(len(self.agents)) foraging = np.ones(len(self.agents)) fittest = np.ones(len(self.agents)) prospective = np.ones(len(self.agents)) for id in range(len(self.agents)): # diversity[id] = self.agents[id].diversity_fitness # exploration[id] = self.agents[id].exploration_fitness() # foraging[id] = self.agents[id].food_collected # fittest[id] = self.agents[id].individual[0].fitness # prospective[id] = self.agents[id].carrying_fitness() exploration[id] = self.agents[id].exploration_fitness() foraging[id] = self.agents[id].food_collected fittest[id] = self.agents[id].individual[0].fitness prospective[id] = self.agents[id].carrying_fitness() beta = self.agents[-1].beta mean = Best(self.pname, self.connect, self.sn, 1, 'MEAN', self.stepcnt, beta, np.mean(fittest), np.mean(prospective), np.mean(exploration), np.mean(foraging), "None") mean.save() std = Best(self.pname, self.connect, self.sn, 1, 'STD', self.stepcnt, beta, np.std(fittest), np.std(prospective), np.std(exploration), np.std(foraging), "None") std.save() # Compute best agent for each fitness self.best_agents(prospective, beta, "PROSPE") self.best_agents(exploration, beta, "EXPLORE") self.best_agents(foraging, beta, "FORGE") self.best_agents(fittest, beta, "OVERALL") return np.argmax(foraging) def best_agents(self, data, beta, header): """Find the best agents in each category.""" idx = np.argmax(data) # dfitness = self.agents[idx].diversity_fitness ofitness = self.agents[idx].individual[0].fitness ffitness = self.agents[idx].food_collected efitness = self.agents[idx].exploration_fitness() pfitness = self.agents[idx].carrying_fitness() phenotype = self.agents[idx].individual[0].phenotype best_agent = Best(self.pname, self.connect, self.sn, idx, header, self.stepcnt, beta, ofitness, pfitness, efitness, ffitness, phenotype) best_agent.save() def find_higest_performer(self): """Find the best agent.""" fitness = self.agents[0].individual[0].fitness fittest = self.agents[0] for agent in self.agents: if agent.individual[0].fitness > fitness: fittest = agent return fittest def find_higest_food_collector(self): """Find the best agent to collect food.""" fitness = self.agents[0].food_collected fittest = self.agents[0] for agent in self.agents: if agent.food_collected > fitness: fittest = agent return fittest def detect_food_moved(self): """Detect food moved.""" grid = self.grid food_loc = self.site.location neighbours = grid.get_neighborhood(food_loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) # print (food_objects) return food_objects def foraging_percent(self): """Compute the percent of the total food in the hub.""" grid = self.grid hub_loc = self.hub.location neighbours = grid.get_neighborhood(hub_loc, self.hub.radius) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) _, hub_grid = grid.find_grid(hub_loc) for food in self.foods: _, food_grid = grid.find_grid(food.location) if food_grid == hub_grid: food_objects += [food] food_objects = set(food_objects) total_food_weights = sum([food.weight for food in food_objects]) return ((total_food_weights * 1.0) / self.total_food_units) * 100
class EvolModel(Model): """A environemnt to model swarms.""" def __init__(self, N, width, height, grid=10, iter=100000, seed=None, expname='COT', agent='EvolAgent', parm='swarm_mcarry.txt'): """Initialize the attributes.""" if seed is None: super(EvolModel, self).__init__(seed=None) else: super(EvolModel, self).__init__(seed) self.runid = datetime.datetime.now().strftime("%s") + str( self.random.randint(1, 1000, 1)[0]) self.pname = '/'.join(os.getcwd().split('/')[:-2]) + '/results/' \ + self.runid + expname self.stepcnt = 1 self.iter = iter self.top = None # Create db connection connect = Connect('swarm', 'swarm', 'swarm', 'localhost') self.connect = connect.tns_connect() # Fill out the experiment table self.experiment = Experiment(self.connect, self.runid, N, seed, expname, iter, width, height, grid) self.experiment.insert_experiment() self.sn = self.experiment.sn # Create a folder to store results os.mkdir(self.pname) self.num_agents = N self.grid = Grid(width, height, grid) self.schedule = SimultaneousActivation(self) self.agents = [] self.parm = parm # Create agents for i in range(self.num_agents): a = eval(agent)(i, self) self.schedule.add(a) # Add the agent to a random grid cell x = self.random.randint(-self.grid.width / 2, self.grid.width / 2) # x = 0 y = self.random.randint(-self.grid.height / 2, self.grid.height / 2) # y = 0 a.location = (x, y) self.grid.add_object_to_grid((x, y), a) a.operation_threshold = 2 # self.num_agents // 10 self.agents.append(a) def create_environment_object(self, jsondata, obj): """Create env from jsondata.""" name = obj.__name__.lower() temp_list = [] i = 0 for json_object in jsondata[name]: location = (json_object["x"], json_object["y"]) if "q_value" in json_object: temp_obj = obj(i, location, json_object["radius"], q_value=json_object["q_value"]) else: temp_obj = obj(i, location, json_object["radius"]) self.grid.add_object_to_grid(location, temp_obj) temp_list.append(temp_obj) i += 1 return temp_list def build_environment_from_json(self): """Build env from jsondata.""" jsondata = JsonData.load_json_file(filename) # Create a instance of JsonData to store object that # needs to be sent to UI self.render = JsonData() self.render.objects = {} for name in jsondata.keys(): obj = eval(name.capitalize()) self.render.objects[name] = self.create_environment_object( jsondata, obj) self.hub = self.render.objects['hub'][0] try: self.foods = [] self.site = self.render.objects['sites'][0] food_radius = self.random.randint(20, 30) for i in range(self.num_agents): f = Food(i, location=self.site.location, radius=food_radius) f.agent_name = None f.phenotype = {} self.grid.add_object_to_grid(f.location, f) self.foods.append(f) except KeyError: pass def step(self): """Step through the environment.""" # Gather info from all the agents self.top = self.gather_info() # Next step self.schedule.step() # Increment the step count self.stepcnt += 1 def gather_info(self): """Gather information from all the agents.""" diversity = np.ones(len(self.agents)) exploration = np.ones(len(self.agents)) foraging = np.ones(len(self.agents)) fittest = np.ones(len(self.agents)) for id in range(len(self.agents)): diversity[id] = self.agents[id].diversity_fitness exploration[id] = self.agents[id].exploration_fitness() foraging[id] = self.agents[id].food_collected fittest[id] = self.agents[id].individual[0].fitness beta = self.agents[-1].beta """ mean = Best( self.pname, self.connect, self.sn, 1, 'MEAN', self.stepcnt, beta, np.mean(fittest), np.mean(diversity), np.mean(exploration), np.mean(foraging), "None" ) mean.save() std = Best( self.pname, self.connect, self.sn, 1, 'STD', self.stepcnt, beta, np.std(fittest), np.std(diversity), np.std(exploration), np.std(foraging), "None" ) std.save() """ # Compute best agent for each fitness self.best_agents(diversity, beta, "DIVERSE") self.best_agents(exploration, beta, "EXPLORE") self.best_agents(foraging, beta, "FORGE") self.best_agents(fittest, beta, "OVERALL") return np.argmax(foraging) def best_agents(self, data, beta, header): """Find the best agents in each category.""" idx = np.argmax(data) dfitness = self.agents[idx].diversity_fitness ofitness = self.agents[idx].individual[0].fitness ffitness = self.agents[idx].food_collected efitness = self.agents[idx].exploration_fitness() phenotype = self.agents[idx].individual[0].phenotype best_agent = Best(self.pname, self.connect, self.sn, idx, header, self.stepcnt, beta, ofitness, dfitness, efitness, ffitness, phenotype) best_agent.save() def find_higest_performer(self): """Find the best agent.""" fitness = self.agents[0].individual[0].fitness fittest = self.agents[0] for agent in self.agents: if agent.individual[0].fitness > fitness: fittest = agent return fittest def find_higest_food_collector(self): """Find the best agent to collect food.""" fitness = self.agents[0].food_collected fittest = self.agents[0] for agent in self.agents: if agent.food_collected > fitness: fittest = agent return fittest def detect_food_moved(self): """Detect food moved.""" grid = self.grid food_loc = self.site.location neighbours = grid.get_neighborhood(food_loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) # print (food_objects) return food_objects def food_in_loc(self, loc): """Find amount of food in hub.""" grid = self.grid neighbours = grid.get_neighborhood(loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) return food_objects
class SimModel(Model): """A environemnt to model swarms.""" def __init__(self, N, width, height, grid=10, iter=100000, xmlstrings=None, seed=None, viewer=False, pname=None, expname='COTSimulation', agent='SimAgent'): """Initialize the attributes.""" if seed is None: super(SimModel, self).__init__(seed=None) else: super(SimModel, self).__init__(seed) self.runid = datetime.datetime.now().strftime("%s") + str( self.random.randint(1, 1000, 1)[0]) if pname is None: self.pname = os.getcwd() + '/' + self.runid + expname else: self.pname = pname + '/' + self.runid + expname self.width = width self.height = height self.stepcnt = 1 self.iter = iter self.xmlstrings = xmlstrings self.viewer = viewer # Create db connection connect = Connect('swarm', 'swarm', 'swarm', 'localhost') self.connect = connect.tns_connect() # Fill out the experiment table self.experiment = Experiment(self.connect, self.runid, N, seed, expname, iter, width, height, grid, phenotype=xmlstrings[0]) self.experiment.insert_experiment_simulation() self.sn = self.experiment.sn # Create a folder to store results os.mkdir(self.pname) self.num_agents = N self.grid = Grid(width, height, grid) self.schedule = SimultaneousActivation(self) self.agents = [] bound = np.ceil((self.num_agents * 1.0) / len(self.xmlstrings)) j = 0 # Create agents for i in range(self.num_agents): # print (i, j, self.xmlstrings[j]) a = eval(agent)(i, self, xmlstring=self.xmlstrings[j]) self.schedule.add(a) # Add the agent to a random grid cell # x = self.random.randint( # -self.grid.width / 2, self.grid.width / 2) x = 0 # y = self.random.randint( # -self.grid.height / 2, self.grid.height / 2) y = 0 a.location = (x, y) self.grid.add_object_to_grid((x, y), a) a.operation_threshold = 2 # self.num_agents // 10 self.agents.append(a) if (i + 1) % bound == 0: j += 1 def create_environment_object(self, jsondata, obj): """Create env from jsondata.""" name = obj.__name__.lower() temp_list = [] i = 0 for json_object in jsondata[name]: location = (json_object["x"], json_object["y"]) if "q_value" in json_object: temp_obj = obj(i, location, json_object["radius"], q_value=json_object["q_value"]) else: temp_obj = obj(i, location, json_object["radius"]) self.grid.add_object_to_grid(location, temp_obj) temp_list.append(temp_obj) i += 1 return temp_list def build_environment_from_json(self): """Build env from jsondata.""" jsondata = JsonData.load_json_file(filename) # Create a instance of JsonData to store object that # needs to be sent to UI self.render = JsonData() self.render.objects = {} for name in jsondata.keys(): obj = eval(name.capitalize()) self.render.objects[name] = self.create_environment_object( jsondata, obj) self.hub = self.render.objects['hub'][0] try: self.foods = [] self.site = self.render.objects['sites'][0] food_radius = self.random.randint(20, 30) for i in range(self.num_agents): f = Food(i, location=self.site.location, radius=food_radius) f.agent_name = None self.grid.add_object_to_grid(f.location, f) self.foods.append(f) except KeyError: pass if self.viewer: self.ui = UI((self.width, self.height), [self.hub], self.agents, [self.site], food=self.foods) def step(self): """Step through the environment.""" # Gather info from all the agents # self.gather_info() # Next step self.schedule.step() # Increment the step count self.stepcnt += 1 if self.viewer: self.ui.step() def find_higest_performer(self): """Find the best agent.""" fitness = self.agents[0].individual[0].fitness fittest = self.agents[0] for agent in self.agents: if agent.individual[0].fitness > fitness: fittest = agent return fittest def find_higest_food_collector(self): """Find the best agent to collect food.""" fitness = self.agents[0].food_collected fittest = self.agents[0] for agent in self.agents: if agent.food_collected > fitness: fittest = agent return fittest def detect_food_moved(self): """Detect food moved.""" grid = self.grid food_loc = self.site.location neighbours = grid.get_neighborhood(food_loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) # print (food_objects) return food_objects def food_in_hub(self): """Find amount of food in hub.""" grid = self.grid food_loc = self.hub.location neighbours = grid.get_neighborhood(food_loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) return len(food_objects) def food_in_loc(self, loc): """Find amount of food in hub.""" grid = self.grid neighbours = grid.get_neighborhood(loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) return food_objects
class RunEnvironmentModel(Model): """A environemnt to model swarms.""" def __init__(self, N, width, height, grid=10, iter=100000, xmlstring=None, seed=None): """Initialize the attributes.""" if seed is None: super(RunEnvironmentModel, self).__init__(seed=None) else: super(RunEnvironmentModel, self).__init__(seed) self.runid = datetime.datetime.now().strftime("%s") + str( self.random.randint(1, 1000, 1)[0]) self.pname = os.getcwd() + '/' + self.runid + "SFCommSimulation" self.stepcnt = 1 self.iter = iter self.xmlstring = xmlstring # Create db connection connect = Connect('swarm', 'swarm', 'swarm', 'localhost') self.connect = connect.tns_connect() # Fill out the experiment table self.experiment = Experiment(self.connect, self.runid, N, seed, 'Simuation SFComm', iter, width, height, grid, phenotype=xmlstring) self.experiment.insert_experiment_simulation() self.sn = self.experiment.sn # Create a folder to store results os.mkdir(self.pname) self.num_agents = N self.grid = Grid(width, height, grid) self.schedule = SimultaneousActivation(self) # self.site = Sites(id=1, location=(5, 5), radius=11, q_value=0.5) # self.grid.add_object_to_grid(self.site.location, self.site) # self.hub = Hub(id=1, location=(0, 0), radius=11) # self.grid.add_object_to_grid(self.hub.location, self.hub) self.agents = [] # Create agents for i in range(self.num_agents): a = RunSwarmAgent(i, self) self.schedule.add(a) # Add the agent to a random grid cell x = self.random.randint(-self.grid.width / 2, self.grid.width / 2) # x = 0 y = self.random.randint(-self.grid.height / 2, self.grid.height / 2) # y = 0 a.location = (x, y) self.grid.add_object_to_grid((x, y), a) a.operation_threshold = 2 # self.num_agents // 10 self.agents.append(a) # Add equal number of food source # for i in range(20): # f = Food(i, location=(-29, -29), radius=5) # self.grid.add_object_to_grid(f.location, f) # print (i,x,y) def create_environment_object(self, jsondata, obj): """Create env from jsondata.""" name = obj.__name__.lower() temp_list = [] i = 0 for json_object in jsondata[name]: location = (json_object["x"], json_object["y"]) if "q_value" in json_object: temp_obj = obj(i, location, json_object["radius"], q_value=json_object["q_value"]) else: temp_obj = obj(i, location, json_object["radius"]) self.grid.add_object_to_grid(location, temp_obj) temp_list.append(temp_obj) i += 1 return temp_list def build_environment_from_json(self): """Build env from jsondata.""" jsondata = JsonData.load_json_file(filename) # Create a instance of JsonData to store object that # needs to be sent to UI self.render = JsonData() self.render.objects = {} for name in jsondata.keys(): obj = eval(name.capitalize()) self.render.objects[name] = self.create_environment_object( jsondata, obj) self.hub = self.render.objects['hub'][0] try: self.site = self.render.objects['sites'][0] for i in range(self.num_agents * 2): f = Food(i, location=self.site.location, radius=self.site.radius) f.agent_name = None self.grid.add_object_to_grid(f.location, f) except KeyError: pass def step(self): """Step through the environment.""" # Gather info from all the agents # self.gather_info() # Next step self.schedule.step() # Increment the step count self.stepcnt += 1 def find_higest_performer(self): """Find the best agent.""" fitness = self.agents[0].individual[0].fitness fittest = self.agents[0] for agent in self.agents: if agent.individual[0].fitness > fitness: fittest = agent return fittest def find_higest_food_collector(self): """Find the best agent to collect food.""" fitness = self.agents[0].food_collected fittest = self.agents[0] for agent in self.agents: if agent.food_collected > fitness: fittest = agent return fittest def detect_food_moved(self): """Detect food moved.""" grid = self.grid food_loc = self.site.location neighbours = grid.get_neighborhood(food_loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) # print (food_objects) return food_objects def food_in_hub(self): """Find amount of food in hub.""" grid = self.grid food_loc = self.hub.location neighbours = grid.get_neighborhood(food_loc, 10) food_objects = grid.get_objects_from_list_of_grid('Food', neighbours) return len(food_objects)