def run():
ptA = HeadlessPetriDish(
size=(400, 300),
nametag="A",
population_size=50,
robot_data={
'max_neighbours': 5,
'max_velocity': 4.0,
'sense_radius': 50.0,
'diffuse_rate': 10,
'act_rate': 1,
'nn_info': {
'hidden_layers': [],
'activation': lambda x: x,
# RELU: lambda x : np.maximum(x, 0.0)
# TANH: np.tanh
# LINEAR: lambda x: x
'modular':
True, #TODO: discuss with group why weights should be modular.
'integerWeights': True
}
},
htg_method=lambda x: selectGSCrandLength(x, min_length=2, max_length=4
),
task=TaskMaxNeighbors(min_fitness=0, max_fitness=5),
fitness_selection_method=lambda x: selectProportional(x),
mutate_method=lambda x: intMutate(x, 0.1),
save_interval=10,
save_filename="./outputs/headless1.csv",
max_iters=10000)
#ptA = PetriDish()
ptA.run()
def run():
ptA = PetriDish(
size = (400, 300),
nametag = "A",
population_size=50,
robot_data = {
'max_neighbours' : 5,
'max_velocity' : 4.0,
'sense_radius': 50.0,
'diffuse_rate': 10,
'act_rate': 1,
'nn_info': {
'hidden_layers' : [],
'activation' : lambda x : x,
# RELU: lambda x : np.maximum(x, 0.0)
# TANH: np.tanh
# LINEAR: lambda x: x
'modular' : True #TODO: discuss with group why weights should be modular.
}
},
htg_method = lambda x: selectGSCrandLength(x, min_length=2, max_length=4),
task = TaskMaxNeighbors(min_fitness=0, max_fitness=5),
fitness_selection_method = lambda x: selectProportional(x),
mutate_method = lambda x: flatMutate(x, 0.1),
visuals = {
'colour_A' : [100.0, 100.0, 100.0],
'colour_B' : [0.0, 225.0, 0.0],
'background' : [250.0, 220.0, 180.0],
'draw_radius' : False,
'draw_transfer' : True
},
save_interval=1000,
record_interval = 10,
interaction_mode = False,
)
#ptA = PetriDish()
ptA.run()
from matplotlib import animation
from IPython.display import HTML
import numpy as np
import pygame
pygame.init()
size = (400, 300)
screen = pygame.display.set_mode(size)
pygame.display.set_caption("Petri Dish - A")
num_neighbours = 5
mng = HtgManager(
pop_size=100,
htg_method=lambda x: selectGSCrandLength(x, min_length=2, max_length=4),
boundary_x=size[0],
boundary_y=size[1],
max_vel=4.0,
sense_radius=50.0,
num_neighbours=num_neighbours,
diffuse_rate=10,
act_rate=1)
preset_colors = []
preset_colors.append([100.0, 100.0, 100.0])
rt = 255.0 / num_neighbours
if num_neighbours > 1:
for i in range(num_neighbours):
preset_colors.append([0.0, rt + rt * i, 0.0])
print(preset_colors)
def __init__(
self,
size=(400, 300),
nametag="A",
population_size=50,
robot_data={
'max_neighbours': 5,
'max_velocity': 4.0,
'sense_radius': 50.0,
'diffuse_rate': 10, # TODO convert from timesteps to secs.
'act_rate': 1, # TODO convert from timesteps to secs.
'nn_info': {
'hidden_layers': [],
'activation': lambda x: x,
'modular': True
}
},
htg_method=lambda x: selectGSCrandLength(x, min_length=2, max_length=4
),
task=TaskClump(0, 100),
fitness_selection_method=selectAll,
mutate_method=lambda x: flatMutate(x, 0.1),
visuals={
'colour_A': [100.0, 100.0, 100.0],
'colour_B': [0.0, 225.0, 0.0],
'background': [250.0, 220.0, 180.0],
'draw_radius': False,
'draw_transfer': True
},
save_interval=-1,
record_interval=10,
interaction_mode=False,
):
"""
size -- dimension of the screen: (float, float) (default: (400, 300))
nametag -- name of the experiment: string (default: "A")
population_size -- number of individuals: int (default: 50)
robot_data -- dict. of robot params: dictionary (default:
{
'max_neighbours' : 5, -- max. number of neighbours a robot can see.
'max_velocity' : 4.0, -- max. velocity a robot can reach.
'sense_radius': 50.0, -- radius of sensory detection.
'diffuse_rate': 10, -- ticks per gene diffusion.
'act_rate': 1, -- ticks per robot action.
nn_info : dict
default: {
'hidden_layers' : [], -- number of hidden layers (input and output are defined by default).
'activation' : lambda x : x, -- activation function (e.g. np.tanh, ReLU, or just linear).
'modular' : True -- whether the weights are the same for each robots sensor channel.
}
are the same. Generally, this makes sense to
keep true.
})
htg_method -- method describing how genes are selected.
visuals -- colours of graphics: dictionary (default:
{
'colour_A' : [100.0, 100.0, 100.0], -- colour when there are no neighbours.
'colour_B' : [0.0, 225.0, 0.0], -- colour when there are max. neighbours.
'background' : [250.0, 220.0, 180.0] -- colour of background.
'draw_radius' : False -- whether to draw the visual radius of a robot.
'draw_transfer' : True -- whether to draw the gene transfer lines.
})
save_interval -- how often (in frames) should the data of the population be saved. -1 means never: int (default: -1)
record_interval -- how ofter (in frames) to record experiment data. If save_interval is -1, nothing is recorded: int (default: 10)
interaction_mode -- whether the user can hover over a robot to have its gene data output on the console: bool (default: False)
"""
pygame.init()
self.screen = pygame.display.set_mode(size)
pygame.display.set_caption("Petri Dish - " + nametag)
self.save_filename = 'outputs/' + 'population_data_' + nametag + '.txt'
self.save_interval = save_interval
self.record_interval = record_interval
self.interaction_mode = interaction_mode
self.mng = HtgManager(
pop_size=population_size,
htg_method=htg_method,
task=task,
fitness_selection_method=fitness_selection_method,
mutate_method=lambda x: flatMutate(x, 0.1),
boundary_x=size[0],
boundary_y=size[1],
max_vel=robot_data['max_velocity'],
sense_radius=robot_data['sense_radius'],
num_neighbours=robot_data['max_neighbours'],
diffuse_rate=robot_data['diffuse_rate'],
act_rate=robot_data['act_rate'],
nn_info=robot_data['nn_info'])
self.visuals = visuals
self.preset_colors = []
self.preset_colors.append(visuals['colour_A'])
#rt = 225.0 / robot_data['max_neighbours'] + 100.0
rt = [(b - a) / robot_data['max_neighbours']
for a, b in zip(visuals['colour_A'], visuals['colour_B'])]
if robot_data['max_neighbours'] > 1:
for i in range(1, robot_data['max_neighbours'] + 1):
#self.preset_colors.append([0.0, rt + rt*i, 0.0])
self.preset_colors.append([
visuals['colour_A'][0] + rt[0] * i,
visuals['colour_A'][1] + rt[1] * i,
visuals['colour_A'][2] + rt[2] * i
])
print(self.preset_colors)
self.mng.update()
x, y, a, c = self.mng.getPopState()
self.fps = 60
self.clock = pygame.time.Clock()
### robot statistics display
self.robot_data = DataDisplay(
{
'ID': 10,
'POS': (10, 10),
#'GENE' : [1, 2, 3, 4, 5]
},
position=[5.0, 5.0],
color=[0.0, 0.0, 0.0],
size=12,
)
self.pause_button = Button('||', [15.0, size[1] - 15.0],
[100.0, 100.0, 100.0], 10)
self.up_fps = Button('>', [30.0, size[1] - 15.0],
[100.0, 100.0, 100.0],
10,
toggle=False)
self.down_fps = Button('<', [2.0, size[1] - 15.0],
[100.0, 100.0, 100.0],
10,
toggle=False)
self.color_button = Button('=', [size[0] - 30.0, 7.0],
[100.0, 100.0, 0.0], 15)
self.color_data = DataDisplay({'VIS': 'neighb.'},
position=[size[0] - 55.0, 0.0],
color=[100.0, 100.0, 0.0],
size=14)
self.fps_data = DataDisplay({
'FPS': 60,
'PAUSE': 'OFF'
},
position=[45.0, size[1] - 22.0],
color=[100.0, 100.0, 100.0],
size=12)
#self.pause_button = DataDisplay(
# {
# '|' : '|'
# },
# position=[0.0, size[1] - 20],
# color=[255.0, 255.0, 255.0],
# size = 20
# )
# data recorder
self.data_recorder = DataRecorder(
gene_size=np.shape(self.mng.pop[0].getGenotype())[0])
self.iters = 0
def __init__(
self,
size=(400, 300),
nametag="A",
population_size=50,
robot_data={
'max_neighbours': 5,
'max_velocity': 4.0,
'sense_radius': 50.0,
'diffuse_rate': 10, # TODO convert from timesteps to secs.
'act_rate': 1, # TODO convert from timesteps to secs.
'nn_info': {
'hidden_layers': [],
'activation': lambda x: x,
'modular': True
}
},
htg_method=lambda x: selectGSCrandLength(x, min_length=2, max_length=4
),
task=TaskClump(0, 100),
fitness_selection_method=selectAll,
mutate_method=lambda x: flatMutate(x, 0.1),
visuals={
'colour_A': [100.0, 100.0, 100.0],
'colour_B': [0.0, 225.0, 0.0],
'background': [250.0, 220.0, 180.0],
'draw_radius': False,
'draw_transfer': True
},
save_interval=-1,
record_interval=10,
max_iters=1000,
save_filename='outputs/' + 'headless_run.csv',
):
"""
size -- dimension of the screen: (float, float) (default: (400, 300))
nametag -- name of the experiment: string (default: "A")
population_size -- number of individuals: int (default: 50)
robot_data -- dict. of robot params: dictionary (default:
{
'max_neighbours' : 5, -- max. number of neighbours a robot can see.
'max_velocity' : 4.0, -- max. velocity a robot can reach.
'sense_radius': 50.0, -- radius of sensory detection.
'diffuse_rate': 10, -- ticks per gene diffusion.
'act_rate': 1, -- ticks per robot action.
nn_info : dict
default: {
'hidden_layers' : [], -- number of hidden layers (input and output are defined by default).
'activation' : lambda x : x, -- activation function (e.g. np.tanh, ReLU, or just linear).
'modular' : True -- whether the weights are the same for each robots sensor channel.
}
are the same. Generally, this makes sense to
keep true.
})
htg_method -- method describing how genes are selected.
visuals -- colours of graphics: dictionary (default:
{
'colour_A' : [100.0, 100.0, 100.0], -- colour when there are no neighbours.
'colour_B' : [0.0, 225.0, 0.0], -- colour when there are max. neighbours.
'background' : [250.0, 220.0, 180.0] -- colour of background.
'draw_radius' : False -- whether to draw the visual radius of a robot.
'draw_transfer' : True -- whether to draw the gene transfer lines.
})
save_interval -- how often (in frames) should the data of the population be saved. -1 means never: int (default: -1)
record_interval -- how ofter (in frames) to record experiment data. If save_interval is -1, nothing is recorded: int (default: 10)
interaction_mode -- whether the user can hover over a robot to have its gene data output on the console: bool (default: False)
"""
self.save_filename = save_filename
self.save_interval = save_interval
self.max_iters = max_iters
self.mng = HtgManager(
pop_size=population_size,
htg_method=htg_method,
task=task,
fitness_selection_method=fitness_selection_method,
mutate_method=lambda x: flatMutate(x, 0.1),
boundary_x=size[0],
boundary_y=size[1],
max_vel=robot_data['max_velocity'],
sense_radius=robot_data['sense_radius'],
num_neighbours=robot_data['max_neighbours'],
diffuse_rate=robot_data['diffuse_rate'],
act_rate=robot_data['act_rate'],
nn_info=robot_data['nn_info'])
self.mng.update()
x, y, a, c = self.mng.getPopState()
self.iters = 0