def main():
picturePath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'pictures'))
resultsPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'results'))
machinePolicyPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'machinePolicy'))
dataPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'conditionData'))
df = pd.read_csv(
os.path.join(dataPath, 'DesignConditionForAvoidCommitmentZone.csv'))
df['intentionedDisToTargetMin'] = df.apply(
lambda x: x['minDis'] - x['avoidCommitmentZone'], axis=1)
gridSize = 15
screenWidth = 600
screenHeight = 600
fullScreen = False
renderOn = False
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
pg.mouse.set_visible(False)
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(
os.path.join(picturePath, 'introduction.png'))
finishImage = pg.image.load(os.path.join(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)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
width = [3, 4, 5]
height = [3, 4, 5]
intentionDis = [2, 4, 6]
direction = [45, 135, 225, 315]
distanceDiffList = [0, 2, 4]
minDisList = range(5, 15)
intentionedDisToTargetList = [2, 4, 6]
rectAreaSize = [6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 36]
lineAreaSize = [4, 5, 6, 7, 8, 9, 10]
condition = namedtuple(
'condition',
'name areaType distanceDiff minDis areaSize intentionedDisToTarget')
expCondition = condition(name='expCondition',
areaType='rect',
distanceDiff=[0],
minDis=minDisList,
areaSize=rectAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
rectCondition = condition(
name='controlRect',
areaType='rect',
distanceDiff=[2, 4],
minDis=minDisList,
areaSize=rectAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
straightLineCondition = condition(
name='straightLine',
areaType='straightLine',
distanceDiff=distanceDiffList,
minDis=minDisList,
areaSize=lineAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
midLineCondition = condition(
name='MidLine',
areaType='midLine',
distanceDiff=distanceDiffList,
minDis=minDisList,
areaSize=lineAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
noAreaCondition = condition(
name='noArea',
areaType='none',
distanceDiff=distanceDiffList,
minDis=minDisList,
areaSize=[0],
intentionedDisToTarget=intentionedDisToTargetList)
# Q_dict = pickle.load(open(os.path.join(machinePolicyPath, "noise0.1commitAreaGird15_policy.pkl"), "rb"))
goal_Q_dict = pickle.load(
open(
os.path.join(machinePolicyPath,
"noise0.1commitAreaGoalGird15_policy.pkl"), "rb"))
actionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0)]
checkBoundary = CheckBoundary([0, gridSize - 1], [0, gridSize - 1])
noiseActionSpace = [(0, -2), (0, 2), (-2, 0), (2, 0), (1, 1), (1, -1),
(-1, -1), (-1, 1)]
normalNoise = NormalNoise(noiseActionSpace, gridSize)
sampleToZoneNoise = SampleToZoneNoise(noiseActionSpace)
initPrior = [0.5, 0.5]
# softmaxBeta = 2.5
priorBeta = 5
commitBetaList = np.arange(1, 10, 1)
rewardVarianceList = [50]
softmaxBetaList = np.round(np.arange(0.4, 0.5, 0.01), decimals=2)
softmaxBetaList = [10]
print(softmaxBetaList)
for softmaxBeta in softmaxBetaList:
# goalPolicy = SoftmaxGoalPolicy(goal_Q_dict, softmaxBeta)
# policy = SoftmaxRLPolicy(Q_dict, softmaxBeta)
# for commitBeta in commitBetaList:
for i in range(30):
print(i)
expDesignValues = [[b, h, d] for b in width for h in height
for d in intentionDis]
numExpTrial = len(expDesignValues)
random.shuffle(expDesignValues)
expDesignValues.append(random.choice(expDesignValues))
createExpCondition = CreatExpCondition(direction, gridSize)
samplePositionFromCondition = SamplePositionFromCondition(
df, createExpCondition, expDesignValues)
numExpTrial = len(expDesignValues) - 1
numControlTrial = int(numExpTrial * 2 / 3)
expTrials = [expCondition] * numExpTrial
conditionList = list(expTrials + [rectCondition] * numExpTrial +
[straightLineCondition] * numControlTrial +
[midLineCondition] * numControlTrial +
[noAreaCondition] * numControlTrial)
numNormalTrials = len(conditionList)
random.shuffle(conditionList)
conditionList.append(expCondition)
numTrialsPerBlock = 3
noiseCondition = list(permutations([1, 2, 0], numTrialsPerBlock))
noiseCondition.append((1, 1, 1))
blockNumber = int(numNormalTrials / numTrialsPerBlock)
noiseDesignValues = createNoiseDesignValue(noiseCondition,
blockNumber)
# deubg
# conditionList = [expCondition] * 27
# noiseDesignValues = ['special'] * 27
# model simulation
noise = 0.1
gamma = 0.9
goalReward = 10
runVI = RunVI(gridSize, actionSpace, noiseActionSpace, noise,
gamma, goalReward)
intentionInfoScale = [-0.025, 0.025]
runModel = RunIntentionModel(runVI, softmaxBeta,
intentionInfoScale)
controller = SampleSoftmaxAction(softmaxBeta)
renderOn = 0
# controller = AvoidCommitModel(goal_Q_dict, Q_dict, softmaxBeta, actionSpace)
normalTrial = NormalTrialOnline(controller, drawNewState, drawText,
normalNoise, checkBoundary,
renderOn)
specialTrial = SpecialTrialOnline(controller, drawNewState,
drawText, sampleToZoneNoise,
checkBoundary, renderOn)
experimentValues = co.OrderedDict()
experimentValues["name"] = "hide0.025commitModelSoftmaxBeta" + str(
softmaxBeta) + '_' + str(i)
resultsDirPath = os.path.join(
resultsPath,
"hide0.025commitModelSoftmaxBeta" + str(softmaxBeta))
if not os.path.exists(resultsDirPath):
os.makedirs(resultsDirPath)
writerPath = os.path.join(resultsDirPath,
experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
experiment = CommitModelExperiment(runModel, normalTrial,
specialTrial, writer,
experimentValues,
samplePositionFromCondition,
drawImage, resultsPath)
experiment(noiseDesignValues, conditionList)
def main():
picturePath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'pictures'))
resultsPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'results'))
machinePolicyPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'machinePolicy'))
dataPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'conditionData'))
df = pd.read_csv(
os.path.join(dataPath, 'DesignConditionForAvoidCommitmentZone.csv'))
df['intentionedDisToTargetMin'] = df.apply(
lambda x: x['minDis'] - x['avoidCommitmentZone'], axis=1)
gridSize = 15
screenWidth = 600
screenHeight = 600
fullScreen = False
renderOn = False
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
# pg.mouse.set_visible(False)
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)
drawBackground = DrawBackground(screen, gridSize, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
# condition
width = [5]
height = [5]
intentionDis = [2, 3, 4]
decisionSteps = [0, 1, 2, 4, 6, 8]
# decisionSteps = [1]
targetDiffs = [0, 0, 1, 2]
rotateAngles = [0, 90, 180, 270]
obstaclesMapStep0 = [[(1, 4), (1, 5), (1, 6), (3, 2), (4, 2), (5, 2),
(6, 2), (7, 2)]]
obstaclesMapStep1 = [[(1, 2), (1, 4), (1, 5), (1, 6), (3, 2), (4, 2),
(5, 2), (6, 2)]]
obstaclesMapStep2 = [[(2, 2), (2, 4), (3, 5), (3, 6), (4, 2), (5, 3),
(6, 3)],
[(2, 2), (2, 4), (2, 5), (3, 6), (4, 2), (5, 2),
(6, 3)],
[(2, 2), (2, 4), (3, 5), (2, 6), (4, 2), (5, 3),
(6, 2)]]
obstaclesMapStep4 = [[(3, 3), (4, 1), (1, 4), (5, 3), (3, 5), (6, 3),
(3, 6)],
[(3, 3), (5, 1), (1, 5), (5, 3), (3, 5), (6, 3),
(3, 6)],
[(3, 3), (3, 1), (1, 3), (5, 3), (3, 5), (6, 3),
(3, 6)]]
obstaclesMapStep6 = [[(4, 4), (4, 1), (4, 2), (6, 4), (4, 6), (1, 4),
(2, 4)],
[(4, 4), (5, 1), (4, 2), (6, 4), (4, 6), (1, 5),
(2, 4)],
[(4, 4), (3, 1), (4, 2), (6, 4), (4, 6), (1, 3),
(2, 4)]]
obstaclesMapStep8 = [[(5, 5), (4, 2), (2, 4), (6, 1), (6, 2), (6, 3),
(1, 6), (2, 6), (3, 6), (7, 5), (5, 7)]]
obstaclesCondition = [
obstaclesMapStep0, obstaclesMapStep1, obstaclesMapStep2,
obstaclesMapStep4, obstaclesMapStep6, obstaclesMapStep8
]
obstaclesMaps = dict(zip(decisionSteps, obstaclesCondition))
rotatePoint = RotatePoint(gridSize)
checkBoundary = CheckBoundary([0, gridSize - 1], [0, gridSize - 1])
noiseActionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (1, -1),
(-1, -1), (-1, 1)]
normalNoise = AimActionWithNoise(noiseActionSpace, gridSize)
specialNoise = backToCrossPointNoise
initPrior = [0.5, 0.5]
# inferGoalPosterior = InferGoalPosterior(goalPolicy)
softmaxBetaList = [5]
noise = 0
gamma = 0.9
goalReward = [50, 50]
actionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0)]
runVI = RunVI(gridSize, actionSpace, noiseActionSpace, noise, gamma,
goalReward)
for softmaxBeta in softmaxBetaList:
# for noise in noiseList:
for i in range(20):
print(i)
numBlocks = 3
expDesignValues = [[b, h, d, m, diff] for b in width
for h in height for d in intentionDis
for m in decisionSteps
for diff in targetDiffs] * numBlocks
random.shuffle(expDesignValues)
numExpTrial = len(expDesignValues)
condition = namedtuple('condition', 'name decisionSteps')
expCondition = condition(name='expCondition',
decisionSteps=decisionSteps)
lineCondition = condition(name='lineCondition',
decisionSteps=decisionSteps)
conditionList = [expCondition
] * numExpTrial # + [lineCondition] * numExpTrial
random.shuffle(conditionList)
numNormalTrials = len(conditionList)
noiseDesignValues = [0] * numNormalTrials
if len(conditionList) != len(noiseDesignValues):
raise Exception("unmatch condition design")
# deubg
# expDesignValues = [specialDesign] * 10
# noiseDesignValues = ['special'] * 10
# conditionList = [expCondition] * 10
# debug
isInBoundary = IsInBoundary([0, gridSize - 1], [0, gridSize - 1])
creatRectMap = CreatRectMap(rotateAngles, gridSize, obstaclesMaps,
rotatePoint)
creatLineMap = CreatLineMap(rotateAngles, gridSize, rotatePoint,
isInBoundary)
samplePositionFromCondition = SamplePositionFromCondition(
creatRectMap, creatLineMap, expDesignValues)
modelController = ModelControllerOnline(softmaxBeta)
# modelController = AvoidCommitModel(softmaxBeta, actionSpace, checkBoundary)
controller = modelController
renderOn = 1
normalTrial = NormalTrial(renderOn, controller, drawNewState,
drawText, normalNoise, checkBoundary)
experimentValues = co.OrderedDict()
experimentValues["name"] = "noise" + str(
noise) + '_' + "softmaxBeta" + str(softmaxBeta) + '_' + str(i)
resultsDirPath = os.path.join(
resultsPath,
"noise" + str(noise) + '_' + "softmaxBeta" + str(softmaxBeta))
if not os.path.exists(resultsDirPath):
os.mkdir(resultsDirPath)
writerPath = os.path.join(resultsDirPath,
experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
experiment = ObstacleModelSimulation(normalTrial, writer,
experimentValues,
samplePositionFromCondition,
drawImage, resultsPath, runVI)
experiment(noiseDesignValues, conditionList)
def main():
gridSize = 15
picturePath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'pictures'))
resultsPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'results'))
machinePolicyPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'machinePolicy'))
dataPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'conditionData'))
df = pd.read_csv(
os.path.join(dataPath, 'DesignConditionForAvoidCommitmentZone.csv'))
df['intentionedDisToTargetMin'] = df.apply(
lambda x: x['minDis'] - x['avoidCommitmentZone'], axis=1)
screenWidth = 600
screenHeight = 600
fullScreen = False
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
pg.mouse.set_visible(False)
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)
testTrialImage = pg.image.load(
os.path.join(picturePath, 'testTrialTime.png'))
formalTrialImage = pg.image.load(
os.path.join(picturePath, 'formalTrialTime.png'))
finishImage = pg.image.load(os.path.join(picturePath, 'finish.png'))
restImage = pg.image.load(os.path.join(picturePath, 'rest.png'))
testTrialImage = pg.transform.scale(testTrialImage,
(screenWidth, screenHeight))
formalTrialImage = pg.transform.scale(formalTrialImage,
(screenWidth, screenHeight))
restImage = pg.transform.scale(
restImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
finishImage = pg.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
drawBackground = DrawBackground(screen, gridSize, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
# condition
width = [5]
height = [5]
intentionDis = [3, 4, 5]
rotateAngles = [0, 90, 180, 270]
decisionSteps = [2, 4, 6, 10]
targetDiffs = [0, 0, 1, 2]
obstaclesMap1 = [[(2, 2), (2, 4), (2, 5), (2, 6), (4, 2), (5, 2), (6, 2)]]
obstaclesMap2 = [[(3, 3), (4, 1), (1, 4), (5, 3), (3, 5), (6, 3), (3, 6)]]
obstaclesMap3 = [[(4, 4), (4, 1), (4, 2), (6, 4), (4, 6), (1, 4), (2, 4)]]
speicalObstacleMap = [[(4, 1), (4, 2), (6, 3), (6, 4), (1, 4), (2, 4),
(3, 6), (4, 6)]]
obstaclesCondition = [
obstaclesMap1, obstaclesMap2, obstaclesMap3, speicalObstacleMap
]
obstaclesMaps = dict(zip(decisionSteps, obstaclesCondition))
numBlocks = 3
expDesignValues = [[b, h, d, m, diff] for b in width for h in height
for d in intentionDis for m in decisionSteps
for diff in targetDiffs] * numBlocks
random.shuffle(expDesignValues)
numExpTrial = len(expDesignValues)
specialDesign = [5, 5, 4, 10, 0]
expDesignValues.append(specialDesign)
condition = namedtuple('condition', 'name decisionSteps')
expCondition = condition(name='expCondition',
decisionSteps=decisionSteps[:-1])
lineCondition = condition(name='lineCondition',
decisionSteps=decisionSteps[:-1])
specialCondition = condition(name='specialCondition', decisionSteps=[10])
numControlTrial = int(numExpTrial / 2)
conditionList = [expCondition] * numControlTrial + [lineCondition
] * numControlTrial
# conditionList = [lineCondition] * numControlTrial
random.shuffle(conditionList)
conditionList.append(specialCondition)
numNormalTrials = len(conditionList)
numTrialsPerBlock = 3
noiseCondition = list(permutations([1, 2, 0],
numTrialsPerBlock)) + [(1, 1, 1)]
blockNumber = int(numNormalTrials / numTrialsPerBlock)
noiseDesignValues = createNoiseDesignValue(noiseCondition, blockNumber)
noise = 0.067
if noise == 0:
noiseDesignValues = [0] * numNormalTrials
if len(conditionList) != len(noiseDesignValues):
raise Exception("unmatch condition design")
print(len(conditionList))
# deubg
# expDesignValues = [specialDesign] * 10
# noiseDesignValues = ['special'] * 10
# conditionList = [specialCondition] * 10
# debug
experimentValues = co.OrderedDict()
# experimentValues["name"] = input("Please enter your name:").capitalize()
experimentValues["name"] = 'test'
writerPath = os.path.join(resultsPath,
't' + experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
baseLineWriterPath = os.path.join(
resultsPath, 'baseLine' + experimentValues["name"] + '.csv')
baseLineWriter = WriteDataFrameToCSV(baseLineWriterPath)
rotatePoint = RotatePoint(gridSize)
isInBoundary = IsInBoundary([0, gridSize - 1], [0, gridSize - 1])
creatRectMap = CreatRectMap(rotateAngles, gridSize, obstaclesMaps,
rotatePoint)
creatLineMap = CreatLineMap(rotateAngles, gridSize, rotatePoint,
isInBoundary)
samplePositionFromCondition = SamplePositionFromCondition(
creatRectMap, creatLineMap, expDesignValues)
pygameActionDict = {
pg.K_UP: (0, -1),
pg.K_DOWN: (0, 1),
pg.K_LEFT: (-1, 0),
pg.K_RIGHT: (1, 0)
}
humanController = HumanController(pygameActionDict)
controller = humanController
checkBoundary = CheckBoundary([0, gridSize - 1], [0, gridSize - 1])
noiseActionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (1, -1),
(-1, -1), (-1, 1)]
normalNoise = AimActionWithNoise(noiseActionSpace, gridSize)
specialNoise = backToCrossPointNoise
normalTrial = NormalTrial(controller, drawNewState, drawText, normalNoise,
checkBoundary)
specialTrial = SpecialTrial(controller, drawNewState, drawText,
specialNoise, checkBoundary)
experiment = ObstacleExperiment(normalTrial, specialTrial, writer,
experimentValues,
samplePositionFromCondition, drawImage,
resultsPath)
singleGoalTrial = SingleGoalTrial(controller, drawNewState, drawText,
normalNoise, checkBoundary)
creatSingleGoalMap = CreatSingleGoalMap(gridSize)
singleGoalExperiment = SingleGoalExperiment(singleGoalTrial,
baseLineWriter,
experimentValues,
creatSingleGoalMap)
baseLineTrialCondition = [6, 8, 10, 12, 14]
numBaseLineTrialBlock = 2
numBaseLineTrial = len(baseLineTrialCondition) * numBaseLineTrialBlock
baseLineNoiseDesignValues = np.array([
random.choice(noiseCondition) for _ in range(numBaseLineTrial)
]).flatten().tolist()
baseLineConditionList = baseLineTrialCondition * numBaseLineTrialBlock
random.shuffle(baseLineConditionList)
drawImage(testTrialImage)
singleGoalExperiment(baseLineNoiseDesignValues, baseLineConditionList)
drawImage(restImage)
drawImage(formalTrialImage)
experiment(noiseDesignValues, conditionList)
drawImage(finishImage)
def main():
picturePath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'pictures'))
resultsPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'results'))
machinePolicyPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'machinePolicy'))
dataPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'conditionData'))
df = pd.read_csv(
os.path.join(dataPath, 'DesignConditionForAvoidCommitmentZone.csv'))
df['intentionedDisToTargetMin'] = df.apply(
lambda x: x['minDis'] - x['avoidCommitmentZone'], axis=1)
gridSize = 15
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(
os.path.join(picturePath, 'introduction.png'))
finishImage = pg.image.load(os.path.join(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)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
for i in range(20):
print(i)
width = [3, 4, 5]
height = [3, 4, 5]
intentionDis = [2, 4, 6]
direction = [45, 135, 225, 315]
expDesignValues = [[b, h, d] for b in width for h in height
for d in intentionDis]
numExpTrial = len(expDesignValues)
random.shuffle(expDesignValues)
expDesignValues.append(random.choice(expDesignValues))
createExpCondition = CreatExpCondition(direction, gridSize)
samplePositionFromCondition = SamplePositionFromCondition(
df, createExpCondition, expDesignValues)
distanceDiffList = [0, 2, 4]
minDisList = range(5, 15)
intentionedDisToTargetList = [2, 4, 6]
rectAreaSize = [6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 36]
lineAreaSize = [4, 5, 6, 7, 8, 9, 10]
condition = namedtuple(
'condition',
'name areaType distanceDiff minDis areaSize intentionedDisToTarget'
)
expCondition = condition(
name='expCondition',
areaType='rect',
distanceDiff=[0],
minDis=minDisList,
areaSize=rectAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
rectCondition = condition(
name='controlRect',
areaType='rect',
distanceDiff=[2, 4],
minDis=minDisList,
areaSize=rectAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
straightLineCondition = condition(
name='straightLine',
areaType='straightLine',
distanceDiff=distanceDiffList,
minDis=minDisList,
areaSize=lineAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
midLineCondition = condition(
name='MidLine',
areaType='midLine',
distanceDiff=distanceDiffList,
minDis=minDisList,
areaSize=lineAreaSize,
intentionedDisToTarget=intentionedDisToTargetList)
noAreaCondition = condition(
name='noArea',
areaType='none',
distanceDiff=distanceDiffList,
minDis=minDisList,
areaSize=[0],
intentionedDisToTarget=intentionedDisToTargetList)
numControlTrial = int(numExpTrial * 2 / 3)
expTrials = [expCondition] * numExpTrial
conditionList = list(expTrials + [rectCondition] * numExpTrial +
[straightLineCondition] * numControlTrial +
[midLineCondition] * numControlTrial +
[noAreaCondition] * numControlTrial)
random.shuffle(conditionList)
numNormalTrials = len(conditionList)
conditionList.append(expCondition)
noiseCondition = list(permutations([1, 2, 0], 3))
noiseCondition.append((1, 1, 1))
blockNumber = int(numNormalTrials / 3)
noiseDesignValues = np.array([
random.choice(noiseCondition) for _ in range(blockNumber)
]).flatten().tolist()
noiseDesignValues.append('special')
policy = pickle.load(
open(
os.path.join(machinePolicyPath,
"noise0.1WolfToTwoSheepGird15_policy.pkl"), "rb"))
softmaxBeta = 2.5
modelController = ModelController(policy, gridSize, softmaxBeta)
controller = modelController
checkBoundary = CheckBoundary([0, gridSize - 1], [0, gridSize - 1])
noiseActionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (1, -1),
(-1, -1), (-1, 1)]
normalNoise = NormalNoise(noiseActionSpace, gridSize)
experimentValues = co.OrderedDict()
experimentValues["name"] = "softmaxModel" + str(i)
baseLineWriterPath = os.path.join(
resultsPath, 'baseLine' + experimentValues["name"] + '.csv')
baseLineWriter = WriteDataFrameToCSV(baseLineWriterPath)
singleGoalTrial = SingleGoalTrial(controller, drawNewState, drawText,
normalNoise, checkBoundary)
creatSingleGoalMap = CreatSingleGoalMap(gridSize)
singleGoalExperiment = SingleGoalExperiment(singleGoalTrial,
baseLineWriter,
experimentValues,
creatSingleGoalMap)
baseLineTrialCondition = [6, 8, 10, 12, 14]
numBaseLineTrialBlock = 5
numBaseLineTrial = len(baseLineTrialCondition) * numBaseLineTrialBlock
baseLineNoiseDesignValues = np.array([
random.choice(noiseCondition) for _ in range(numBaseLineTrial)
]).flatten().tolist()
baseLineConditionList = baseLineTrialCondition * numBaseLineTrialBlock
random.shuffle(baseLineConditionList)
singleGoalExperiment(baseLineNoiseDesignValues, baseLineConditionList)
def main():
experimentValues = co.OrderedDict()
experimentValues["name"] = 'test'
# experimentValues["name"] = input("Please enter your name:").capitalize()
fullScreen = 1
mouseVisible = False
screenWidth = 600
screenHeight = 600
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
pg.mouse.set_visible(mouseVisible)
gridSize = 15
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)
picturePath = os.path.abspath(os.path.join(os.path.join(os.getcwd(), os.pardir), 'pictures'))
resultsPath = os.path.abspath(os.path.join(os.path.join(os.getcwd(), os.pardir), 'results'))
formalTrialImage = pg.image.load(os.path.join(picturePath, 'formalTrial.png'))
finishImage = pg.image.load(os.path.join(picturePath, 'finish.png'))
restImage = pg.image.load(os.path.join(picturePath, 'rest.png'))
formalTrialImage = pg.transform.scale(formalTrialImage, (screenWidth, screenHeight))
restImage = pg.transform.scale(restImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
finishImage = pg.transform.scale(finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
drawBackground = DrawBackground(screen, gridSize, leaveEdgeSpace, backgroundColor, lineColor, lineWidth, textColorTuple)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor, playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
# condition
width = [5]
height = [5]
intentionDis = [2, 3, 4]
decisionSteps = [0, 1, 2, 4, 6]
targetDiffs = [0, 0, 1, 2]
rotateAngles = [0, 90, 180, 270]
# obstaclesMapStep
obstaclesMapStep0 = [[(3, 2), (4, 2), (5, 3), (6, 3), (7, 4), (1, 3), (2, 4), (2, 5), (3, 6)]]
obstaclesMapStep1 = [[(1, 2), (2, 4), (2, 5), (3, 2), (4, 2), (3, 6), (4, 7), (5, 3), (6, 4)]]
obstaclesMapStep2 = [[(2, 2), (2, 4), (2, 5), (3, 6), (4, 7), (4, 2), (5, 2), (6, 3), (7, 3)]]
obstaclesMapStep4 = [[(3, 3), (4, 1), (1, 4), (5, 1), (1, 5), (5, 3), (6, 3), (3, 5), (3, 6)]]
obstaclesMapStep6 = [[(4, 4), (4, 1), (4, 2), (6, 4), (7, 4), (4, 6), (4, 7), (1, 4), (2, 4)]]
speicalObstacleMap = [[(4, 1), (4, 2), (6, 3), (6, 4), (1, 4), (2, 4), (3, 6), (4, 6)],
[(5, 1), (4, 2), (6, 3), (6, 4), (1, 5), (2, 4), (3, 6), (4, 6)],
[(3, 1), (4, 2), (6, 3), (6, 4), (1, 3), (2, 4), (3, 6), (4, 6)]]
obstaclesCondition = [obstaclesMapStep0, obstaclesMapStep1, obstaclesMapStep2, obstaclesMapStep4, obstaclesMapStep6]
# debug
# decisionSteps = [6]
# obstaclesCondition = [obstaclesMapStep6]
# debug
obstaclesMaps = dict(zip(decisionSteps, obstaclesCondition))
numBlocks = 3
expDesignValues = [[b, h, d, m, diff] for b in width for h in height for d in intentionDis for m in decisionSteps for diff in targetDiffs] * numBlocks
random.shuffle(expDesignValues)
numExpTrial = len(expDesignValues)
specialDesign = [5, 5, 4, 10, 0]
expDesignValues.append(specialDesign)
condition = namedtuple('condition', 'name decisionSteps')
expCondition = condition(name='expCondition', decisionSteps=decisionSteps)
lineCondition = condition(name='lineCondition', decisionSteps=decisionSteps)
# specialCondition = condition(name='specialCondition', decisionSteps=[10])
conditionList = [expCondition] * numExpTrial # + [lineCondition] * numExpTrial
# conditionList = [lineCondition] * numExpTrial
random.shuffle(conditionList)
numNormalTrials = len(conditionList)
# numTrialsPerBlock = 3
# noiseCondition = list(permutations([1, 2, 0], numTrialsPerBlock)) + [(1, 1, 1)]
# blockNumber = int(numNormalTrials / numTrialsPerBlock)
# noiseDesignValues = createNoiseDesignValue(noiseCondition, blockNumber)
# conditionList.append(specialCondition)
noiseDesignValues = [0] * numNormalTrials
# deubg
# expDesignValues = [specialDesign] * 10
# noiseDesignValues = ['special'] * 10
# conditionList = [specialCondition] * 10
# debug
print('trial:', len(conditionList))
if len(conditionList) != len(noiseDesignValues):
raise Exception("unmatch condition design")
writerPath = os.path.join(resultsPath, experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
rotatePoint = RotatePoint(gridSize)
isInBoundary = IsInBoundary([0, gridSize - 1], [0, gridSize - 1])
creatRectMap = CreatRectMap(rotateAngles, gridSize, obstaclesMaps, rotatePoint)
creatLineMap = CreatLineMap(rotateAngles, gridSize, rotatePoint, isInBoundary)
samplePositionFromCondition = SamplePositionFromCondition(creatRectMap, creatLineMap, expDesignValues)
pygameActionDict = {pg.K_UP: (0, -1), pg.K_DOWN: (0, 1), pg.K_LEFT: (-1, 0), pg.K_RIGHT: (1, 0)}
humanController = HumanController(pygameActionDict)
controller = humanController
checkBoundary = CheckBoundary([0, gridSize - 1], [0, gridSize - 1])
noiseActionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (1, -1), (-1, -1), (-1, 1)]
# noiseActionSpace = [(0, 0)]
normalNoise = AimActionWithNoise(noiseActionSpace, gridSize)
specialNoise = backToCrossPointNoise
normalTrial = NormalTrial(controller, drawNewState, drawText, normalNoise, checkBoundary)
restTrialInterval = math.ceil(numNormalTrials / 4)
restTrial = list(range(restTrialInterval, numNormalTrials, restTrialInterval))
experiment = ObstacleExperiment(normalTrial, writer, experimentValues, samplePositionFromCondition, drawImage, resultsPath, restTrial, restImage)
# start exp
drawImage(formalTrialImage)
experiment(noiseDesignValues, conditionList)
drawImage(finishImage)
def main():
picturePath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'pictures'))
resultsPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'results'))
machinePolicyPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'machinePolicy'))
dataPath = os.path.abspath(
os.path.join(os.path.join(os.getcwd(), os.pardir), 'conditionData'))
df = pd.read_csv(
os.path.join(dataPath, 'DesignConditionForAvoidCommitmentZone.csv'))
df['intentionedDisToTargetMin'] = df.apply(
lambda x: x['minDis'] - x['avoidCommitmentZone'], axis=1)
gridSize = 15
screenWidth = 600
screenHeight = 600
fullScreen = False
renderOn = False
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
pg.mouse.set_visible(False)
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(
os.path.join(picturePath, 'introduction.png'))
finishImage = pg.image.load(os.path.join(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)
drawText = DrawText(screen, drawBackground)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
drawImage = DrawImage(screen)
# condition
width = [5]
height = [5]
intentionDis = [3, 4, 5]
decisionSteps = [2, 4, 6, 10]
targetDiffs = [0, 2, 4]
rotateAngles = [0, 90, 180, 270]
obstaclesMap1 = [(2, 2), (2, 4), (2, 5), (2, 6), (4, 2), (5, 2), (6, 2)]
obstaclesMap2 = [(3, 3), (4, 1), (1, 4), (5, 3), (3, 5), (6, 3), (3, 6)]
obstaclesMap3 = [(4, 4), (4, 1), (4, 2), (6, 4), (4, 6), (1, 4), (2, 4)]
speicalObstacleMap = [(4, 1), (4, 2), (6, 3), (6, 4), (1, 4), (2, 4),
(3, 6), (4, 6)]
obstaclesCondition = [
obstaclesMap1, obstaclesMap2, obstaclesMap3, speicalObstacleMap
]
obstaclesMaps = dict(zip(decisionSteps, obstaclesCondition))
rotatePoint = RotatePoint(gridSize)
checkBoundary = CheckBoundary([0, gridSize - 1], [0, gridSize - 1])
noiseActionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (1, -1),
(-1, -1), (-1, 1)]
normalNoise = AimActionWithNoise(noiseActionSpace, gridSize)
specialNoise = backToCrossPointNoise
initPrior = [0.5, 0.5]
# inferGoalPosterior = InferGoalPosterior(goalPolicy)
softmaxBetaList = [6]
noiseList = [0]
# noise = 0.067
# for softmaxBeta in softmaxBetaList:
softmaxBeta = 6
for noise in noiseList:
for i in range(20):
print(i)
numBlocks = 5
expDesignValues = [[b, h, d, m, diff] for b in width
for h in height for d in intentionDis
for m in decisionSteps
for diff in targetDiffs] * numBlocks
random.shuffle(expDesignValues)
numExpTrial = len(expDesignValues)
specialDesign = [5, 5, 4, 10, 0]
expDesignValues.append(specialDesign)
condition = namedtuple('condition', 'name decisionSteps')
expCondition = condition(name='expCondition',
decisionSteps=decisionSteps[:-1])
lineCondition = condition(name='lineCondition',
decisionSteps=decisionSteps[:-1])
specialCondition = condition(name='specialCondition',
decisionSteps=[10])
numControlTrial = int(numExpTrial / 2)
conditionList = [expCondition] * numControlTrial + [
lineCondition
] * numControlTrial
conditionList = [lineCondition] * numControlTrial
random.shuffle(conditionList)
numNormalTrials = len(conditionList)
numTrialsPerBlock = 3
noiseCondition = list(permutations([1, 2, 0], numTrialsPerBlock))
noiseCondition.append((1, 1, 1))
blockNumber = int(numNormalTrials / numTrialsPerBlock)
noiseDesignValues = createNoiseDesignValue(noiseCondition,
blockNumber)
conditionList.append(specialCondition)
if noise == 0:
noiseDesignValues = [0] * len(conditionList)
if len(conditionList) != len(noiseDesignValues):
raise Exception("unmatch condition design")
# deubg
# expDesignValues = [specialDesign] * 10
# noiseDesignValues = ['special'] * 10
# conditionList = [expCondition] * 10
# debug
isInBoundary = IsInBoundary([0, gridSize - 1], [0, gridSize - 1])
creatRectMap = CreatRectMap(rotateAngles, gridSize, obstaclesMaps,
rotatePoint)
creatLineMap = CreatLineMap(rotateAngles, gridSize, rotatePoint,
isInBoundary)
samplePositionFromCondition = SamplePositionFromCondition(
creatRectMap, creatLineMap, expDesignValues)
runVI = RunVI(gridSize, noise, noiseActionSpace)
modelController = ModelControllerOnline(softmaxBeta, runVI)
controller = modelController
renderOn = 0
normalTrial = NormalTrial(renderOn, controller, drawNewState,
drawText, normalNoise, checkBoundary)
specialTrial = SpecialTrial(renderOn, controller, drawNewState,
drawText, specialNoise, checkBoundary)
experimentValues = co.OrderedDict()
experimentValues["name"] = "noise" + str(
noise) + '_' + "softmaxBeta" + str(softmaxBeta) + '_' + str(i)
writerPath = os.path.join(resultsPath,
experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
experiment = ObstacleExperiment(normalTrial, specialTrial, writer,
experimentValues,
samplePositionFromCondition,
drawImage, resultsPath)
experiment(noiseDesignValues, conditionList)