def main():
dimension = 15
bounds = [0, 0, dimension - 1, dimension - 1]
condition = [-5, -3, -1, 0, 1, 3, 5]
minDistanceBetweenGrids = max(condition) + 1
maxDistanceBetweenGrids = UpdateWorld.calculateMaxDistanceOfGrid(bounds) - minDistanceBetweenGrids
block = 15
initialWorld = UpdateWorld.InitialWorld(bounds)
updateWorld = UpdateWorld.UpdateWorld(bounds, condition, minDistanceBetweenGrids, maxDistanceBetweenGrids)
pg.init()
screenWidth = 680
screenHeight = 680
screen = pg.display.set_mode((screenWidth, screenHeight))
leaveEdgeSpace = 2
lineWidth = 1
backgroundColor = [205, 255, 204]
lineColor = [0, 0, 0]
targetColor = [255, 50, 50]
playerColor = [50, 50, 255]
targetRadius = 10
playerRadius = 10
textColorTuple = (255, 50, 50)
softmaxBeta = 2.5
episilonGreedy = 1
pg.event.set_allowed([pg.KEYDOWN, pg.QUIT])
picturePath = os.path.join(os.path.join(DIRNAME, '../..'), '/pictures/')
resultsPath = os.path.join(os.path.join(DIRNAME, '../..'), '/results/')
policyPath = os.path.join(os.path.join(DIRNAME, '../..'), 'machinePolicy/')
humanController = HumanController(dimension)
policyFile = open(policyPath + "noise0commitSnakeGoalGird15_policy.pkl", "rb")
policy = pickle.load(policyFile)
modelController = ModelController(policy, dimension, softmaxBeta, episilonGreedy)
controller = modelController
numberOfMachineRun = 20
for i in range(16, numberOfMachineRun):
experimentValues = co.OrderedDict()
# experimentValues["name"] = input("Please enter your name:").capitalize()
experimentValues["name"] = 'machineEpisilon' + str(episilonGreedy) + "_" + str(i)
experimentValues["condition"] = 'None'
writerPath = resultsPath + experimentValues["name"] + '.csv'
writer = WriteDataFrameToCSV(writerPath)
introductionImage = pg.image.load(picturePath + 'introduction.png')
restImage = pg.image.load(picturePath + 'rest.png')
finishImage = pg.image.load(picturePath + 'finish.png')
introductionImage = pg.transform.scale(introductionImage, (screenWidth, screenHeight))
finishImage = pg.transform.scale(finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
drawBackground = DrawBackground(screen, dimension, leaveEdgeSpace, backgroundColor, lineColor, lineWidth,
textColorTuple)
checkBoundary = CheckBoundary([0, dimension - 1], [0, dimension - 1])
drawNewState = DrawNewState(screen, drawBackground, targetColor, playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
designValues = UpdateWorld.createDesignValues(condition * 3, block)
restTrial = list(range(0, len(designValues), len(condition) * 15))
trial = Trial(controller, drawNewState, checkBoundary)
experiment = Experiment(trial, writer, experimentValues, initialWorld, updateWorld, drawImage, resultsPath,
minDistanceBetweenGrids, maxDistanceBetweenGrids, restImage, finishImage, restTrial)
# drawImage(introductionImage)
experiment(designValues)
def constructButtons(self):
posX = cfg.displaySize[0] / 2
posY = cfg.displaySize[1] / 2
pvp = Button(self.screen, "Person VS Person", (posX, posY - 150), 60)
pvm = Button(self.screen, "Person VS Machine", (posX, posY - 50), 60)
mvp = Button(self.screen, "Machine VS Person", (posX, posY + 50), 60)
mvm = Button(self.screen, "Machine VS Machine", (posX, posY + 150), 60)
pvp.playState = PlayState.PlayState(HumanController(),
HumanController())
pvm.playState = PlayState.PlayState(HumanController(),
MinMaxController())
mvp.playState = PlayState.PlayState(MinMaxController(),
HumanController())
mvm.playState = PlayState.PlayState(MinMaxController(),
MinMaxController())
self.buttons = [pvp, pvm, mvp, mvm]
def constructButtons(self):
posX = cfg.displaySize[0] / 2
posY = cfg.displaySize[1] / 2
pvp = Button(self.screen, "Person VS Person", (posX, posY - 150), 60)
pvm = Button(self.screen, "Person VS Machine", (posX, posY - 50), 60)
mvp = Button(self.screen, "Machine VS Person", (posX, posY + 50), 60)
mvm = Button(self.screen, "Machine VS Machine", (posX, posY + 150), 60)
human = HumanController()
minmax = MinMaxController()
pvp.setOnClickedCallback(target=self.buttonClickedCallback, args=(PlayState.PlayState(human, human),))
pvm.setOnClickedCallback(target=self.buttonClickedCallback, args=(PlayState.PlayState(human, minmax),))
mvp.setOnClickedCallback(target=self.buttonClickedCallback, args=(PlayState.PlayState(minmax, human),))
mvm.setOnClickedCallback(target=self.buttonClickedCallback, args=(PlayState.PlayState(minmax, MinMaxController()),))
self.buttons = [pvp, pvm, mvp, mvm]
def main():
dimension = 21
bounds = [0, 0, dimension - 1, dimension - 1]
minDistanceBetweenGrids = 5
condition = [-5, -3, -1, 0, 1, 3, 5]
initialWorld = UpdateWorld.InitialWorld(bounds)
pg.init()
screenWidth = 720
screenHeight = 720
screen = pg.display.set_mode((screenWidth, screenHeight))
gridSize = 21
leaveEdgeSpace = 2
lineWidth = 1
backgroundColor = [205, 255, 204]
lineColor = [0, 0, 0]
targetColor = [255, 50, 50]
playerColor = [50, 50, 255]
targetRadius = 10
playerRadius = 10
stopwatchUnit = 10
textColorTuple = (255, 50, 50)
stopwatchEvent = pg.USEREVENT + 1
pg.time.set_timer(stopwatchEvent, stopwatchUnit)
pg.event.set_allowed([pg.KEYDOWN, pg.QUIT, stopwatchEvent])
finishTime = 90000
currentStopwatch = 32888
score = 0
drawBackground = DrawBackground(screen, gridSize, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
humanController = HumanController(gridSize, stopwatchEvent, stopwatchUnit,
drawNewState, finishTime)
policy = pickle.load(open("SingleWolfTwoSheepsGrid15.pkl", "rb"))
modelController = ModelController(policy, gridSize, stopwatchEvent,
stopwatchUnit, drawNewState, finishTime)
trial = Trial(modelController, drawNewState, stopwatchEvent, finishTime)
bean1Grid, bean2Grid, playerGrid = initialWorld(minDistanceBetweenGrids)
bean1Grid = (3, 13)
bean2Grid = (5, 0)
playerGrid = (0, 8)
results = trial(bean1Grid, bean2Grid, playerGrid, score, currentStopwatch)
def main():
dimension = 15
minDistanceBetweenGrids = 5
blockNumber = 3
noiseCondition = list(permutations([1, 2, 0], 3))
noiseCondition.append((1, 1, 1))
picturePath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/Pictures/'
resultsPath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/Results/'
machinePolicyPath = os.path.abspath(os.path.join(
os.getcwd(), os.pardir)) + '/machinePolicy/'
bottom = [4, 6, 8]
height = [6, 7, 8]
direction = [0, 90, 180, 270]
noiseDesignValues = UpdateWorld.createNoiseDesignValue(
noiseCondition, blockNumber)
shapeDesignValues = UpdateWorld.createShapeDesignValue(bottom, height)
updateWorld = UpdateWorld.UpdateWorld(direction, dimension)
pg.init()
screenWidth = 600
screenHeight = 600
screen = pg.display.set_mode((screenWidth, screenHeight))
leaveEdgeSpace = 2
lineWidth = 1
backgroundColor = [205, 255, 204]
lineColor = [0, 0, 0]
targetColor = [255, 50, 50]
playerColor = [50, 50, 255]
targetRadius = 10
playerRadius = 10
textColorTuple = (255, 50, 50)
softmaxBeta = -1
experimentValues = co.OrderedDict()
for i in range(50):
experimentValues["name"] = "maxModel" + str(i)
# experimentValues["name"] = input("Please enter your name:").capitalize()
writerPath = resultsPath + experimentValues["name"] + '.csv'
writer = WriteDataFrameToCSV(writerPath)
introductionImage = pg.image.load(picturePath + 'introduction.png')
finishImage = pg.image.load(picturePath + 'finish.png')
introductionImage = pg.transform.scale(introductionImage,
(screenWidth, screenHeight))
finishImage = pg.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
drawBackground = DrawBackground(screen, dimension, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
policy = pickle.load(
open(machinePolicyPath + "noise0.1WolfToTwoSheepGird15_policy.pkl",
"rb"))
modelController = ModelController(policy, dimension, softmaxBeta)
humanController = HumanController(dimension)
checkBoundary = CheckBoundary([0, dimension - 1], [0, dimension - 1])
controller = modelController
normalNoise = NormalNoise(controller)
awayFromTheGoalNoise = AwayFromTheGoalNoise(controller)
normalTrial = NormalTrial(controller, drawNewState, drawText,
normalNoise, checkBoundary)
specialTrial = SpecialTrial(controller, drawNewState, drawText,
awayFromTheGoalNoise, checkBoundary)
experiment = Experiment(normalTrial, specialTrial, writer,
experimentValues, updateWorld, drawImage,
resultsPath, minDistanceBetweenGrids)
# drawImage(introductionImage)
experiment(noiseDesignValues, shapeDesignValues)
def main():
dimension = 15
bounds = [0, 0, dimension - 1, dimension - 1]
condition = [-5, -3, -1, 0, 1, 3, 5]
minDistanceBetweenGrids = max(condition) + 1
maxDistanceBetweenGrids = UpdateWorld.calculateMaxDistanceOfGrid(
bounds) - minDistanceBetweenGrids
block = 1
initialWorld = UpdateWorld.InitialWorld(bounds)
updateWorld = UpdateWorld.UpdateWorld(bounds, condition,
minDistanceBetweenGrids,
maxDistanceBetweenGrids)
pg.init()
screenWidth = 680
screenHeight = 680
screen = pg.display.set_mode((screenWidth, screenHeight))
leaveEdgeSpace = 2
lineWidth = 1
backgroundColor = [205, 255, 204]
lineColor = [0, 0, 0]
targetColor = [255, 50, 50]
playerColor = [50, 50, 255]
targetRadius = 10
playerRadius = 10
textColorTuple = (255, 50, 50)
softmaxBeta = -1
pg.event.set_allowed([pg.KEYDOWN, pg.QUIT])
picturePath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/Pictures/'
resultsPath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/Results/'
policyPath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/machinePolicy/'
experimentValues = co.OrderedDict()
experimentValues["name"] = input("Please enter your name:").capitalize()
experimentValues["condition"] = 'None'
writerPath = resultsPath + experimentValues["name"] + '.csv'
writer = WriteDataFrameToCSV(writerPath)
introductionImage = pg.image.load(picturePath + 'introduction.png')
restImage = pg.image.load(picturePath + 'rest.png')
finishImage = pg.image.load(picturePath + 'finish.png')
introductionImage = pg.transform.scale(introductionImage,
(screenWidth, screenHeight))
finishImage = pg.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
drawBackground = DrawBackground(screen, dimension, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
checkBoundary = CheckBoundary([0, dimension - 1], [0, dimension - 1])
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
humanController = HumanController(dimension)
# policy = pickle.load(open(policyPath+"SingleWolfTwoSheepsGrid15.pkl","rb"))
# modelController = ModelController(policy, dimension,softmaxBeta)
designValues = UpdateWorld.createDesignValues(condition * 3, block)
restTrial = list(range(0, len(designValues), len(condition) * 15))
trial = Trial(humanController, drawNewState, checkBoundary)
experiment = Experiment(trial, writer, experimentValues, initialWorld,
updateWorld, drawImage, resultsPath,
minDistanceBetweenGrids, maxDistanceBetweenGrids,
restImage, finishImage, restTrial)
drawImage(introductionImage)
experiment(designValues)
def main():
gridSize = 15
bounds = [1, 1, gridSize - 2, gridSize - 2]
minDistanceBetweenGrids = 5
condition = [-5, -3, -1, 0, 1, 3, 5]
counter = [0] * len(condition)
initialWorld = UpdateWorld.InitialWorld(bounds)
updateWorld = UpdateWorld.UpdateWorld(bounds, condition, counter)
pg.init()
screenWidth = 680
screenHeight = 680
screen = pg.display.set_mode((screenWidth, screenHeight))
leaveEdgeSpace = 2
lineWidth = 1
backgroundColor = [205, 255, 204]
lineColor = [0, 0, 0]
targetColor = [255, 50, 50]
playerColor = [50, 50, 255]
targetRadius = 10
playerRadius = 10
stopwatchUnit = 100
finishTime = 1000 * 30
block = 1
textColorTuple = (255, 50, 50)
stopwatchEvent = pg.USEREVENT + 1
pg.time.set_timer(stopwatchEvent, stopwatchUnit)
pg.event.set_allowed([pg.KEYDOWN, pg.QUIT, stopwatchEvent])
picturePath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/Pictures/'
resultsPath = os.path.abspath(os.path.join(os.getcwd(),
os.pardir)) + '/Results/'
experimentValues = co.OrderedDict()
experimentValues["name"] = input("Please enter your name:").capitalize()
experimentValues["condition"] = 'None'
writerPath = resultsPath + experimentValues["name"] + '.csv'
writer = WriteDataFrameToCSV(writerPath)
introductionImage = pg.image.load(picturePath + 'introduction.png')
restImage = pg.image.load(picturePath + 'rest.png')
finishImage = pg.image.load(picturePath + 'finish.png')
introductionImage = pg.transform.scale(introductionImage,
(screenWidth, screenHeight))
finishImage = pg.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
drawBackground = DrawBackground(screen, gridSize, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
humanController = HumanController(gridSize, stopwatchEvent, stopwatchUnit,
drawNewState, finishTime)
trial = Trial(humanController, drawNewState, stopwatchEvent, finishTime)
experiment = Experiment(trial, writer, experimentValues, initialWorld,
updateWorld, drawImage, resultsPath,
minDistanceBetweenGrids)
giveExperimentFeedback = GiveExperimentFeedback(screen, textColorTuple,
screenWidth, screenHeight)
drawImage(introductionImage)
score = [0] * block
for i in range(block):
score[i] = experiment(finishTime)
giveExperimentFeedback(i, score)
if i == block - 1:
drawImage(finishImage)
else:
drawImage(restImage)
participantsScore = np.sum(np.array(score))
print(participantsScore)