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 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]
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)
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)
softmaxBeta = 2.5
# policy = pickle.load(open(machinePolicyPath + "noise0.1commitSTNewGird15_policy.pkl", "rb"))
policy = None
goalPolicy = pickle.load(
open(machinePolicyPath + "noise0.1commitSTGoalGird15_policy.pkl",
"rb"))
initPrior = [0.5, 0.5]
commitBetaList = [1, 2, 5, 7]
# for softmaxBeta in softmaxBetaList:
for commitBeta in commitBetaList:
for i in range(30):
print(i)
noiseDesignValues = UpdateWorld.createNoiseDesignValue(
noiseCondition, blockNumber)
shapeDesignValues = UpdateWorld.createShapeDesignValue(
bottom, height)
updateWorld = UpdateWorld.UpdateWorld(direction, dimension)
experimentValues = co.OrderedDict()
experimentValues["name"] = "commitBeta" + str(
commitBeta) + '_' + str(i)
resultsDirPath = os.path.join(resultsPath,
"commitBeta" + str(commitBeta))
if not os.path.exists(resultsDirPath):
os.makedirs(resultsDirPath)
writerPath = os.path.join(resultsDirPath,
experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
# modelController = ModelController(policy, dimension, softmaxBeta)
modelControllerWithGoal = ModelControllerWithGoal(
dimension, softmaxBeta, policy, goalPolicy, commitBeta)
checkBoundary = CheckBoundary([0, dimension - 1],
[0, dimension - 1])
controller = modelControllerWithGoal
normalNoise = NormalNoise(controller)
awayFromTheGoalNoise = AwayFromTheGoalNoise(controller)
# normalTrial = NormalTrial(controller, drawNewState, drawText, normalNoise, checkBoundary)
# specialTrial = SpecialTrial(controller, drawNewState, drawText, awayFromTheGoalNoise, checkBoundary)
inferGoalPosterior = InferGoalPosterior(goalPolicy, commitBeta)
normalTrial = NormalTrialWithGoal(controller, drawNewState,
drawText, normalNoise,
checkBoundary, initPrior,
inferGoalPosterior)
specialTrial = SpecialTrialWithGoal(controller, drawNewState,
drawText, awayFromTheGoalNoise,
checkBoundary, initPrior,
inferGoalPosterior)
experiment = Experiment(normalTrial, specialTrial, writer,
experimentValues, updateWorld, drawImage,
resultsPath, minDistanceBetweenGrids)
# debug
# noiseDesignValues = ['special'] * 10
experiment(noiseDesignValues, shapeDesignValues)
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)
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 = 40
episilonGreedy = 1
temperature = 1
pg.event.set_allowed([pg.KEYDOWN, pg.QUIT])
picturePath = os.path.abspath(os.path.join(os.getcwd(),
"../..")) + '/Pictures/'
resultsPath = os.path.abspath(os.path.join(os.getcwd(),
"../..")) + '/Results/'
policyPath = os.path.abspath(os.path.join(os.getcwd(),
"../..")) + '/machinePolicy/'
selectModelParameter = SelectModelParameter(resultsPath, 'csv')
policyFile = open(policyPath + "SingleWolfTwoSheepsGrid15.pkl", "rb")
policy = pickle.load(policyFile)
softmaxBeta = list(range(35, 45, 1))
crossEntropyResults = dict()
for Beta in softmaxBeta:
modelController = ModelController(policy, dimension, Beta,
episilonGreedy)
boltzmannModelController = BoltzmannModelController(
policy, dimension, temperature)
controller = boltzmannModelController
numberOfMachineRun = 5
for i in range(numberOfMachineRun):
experimentValues = co.OrderedDict()
experimentValues["name"] = 'machineEpisilon' + str(
episilonGreedy) + "Beta" + str(Beta) + "_" + str(i)
experimentValues["condition"] = 'None'
writerPath = resultsPath + experimentValues["name"] + '.csv'
writer = WriteDataFrameToCSV(writerPath)
restImage = pg.image.load(picturePath + 'rest.png')
finishImage = pg.image.load(picturePath + 'finish.png')
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)
experiment(designValues)
crossEntropyResults[Beta] = selectModelParameter("Beta" +
str(Beta))
optimalBeta = min(crossEntropyResults, key=crossEntropyResults.get)
print(optimalBeta)