def main():
experimentValues = co.OrderedDict()
experimentValues["name"] = 'test'
# experimentValues["name"] = input("Please enter your name:").capitalize()
fullScreen = 0 if experimentValues["name"] == 'test' else 1
mouseVisible = 1 if experimentValues["name"] == 'test' else 0
screenWidth = 600
screenHeight = 600
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
pg.mouse.set_visible(mouseVisible)
gridSize = 15
bounds = [0, 0, gridSize - 1, gridSize - 1]
leaveEdgeSpace = 1 # 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), 'resultsNoTimePressure'))
if not os.path.exists(resultsPath):
os.mkdir(resultsPath)
formalTrialImage = pg.image.load(os.path.join(picturePath, 'NoTimePressureExp.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 maps
condition = namedtuple('condition', 'name decisionSteps initAgent avoidCommitPoint crossPoint targetDisToCrossPoint fixedObstacles')
map1ObsStep0a = condition(name='expCondition1', decisionSteps=0, initAgent=(0, 2), avoidCommitPoint=(1, 2), crossPoint=(3, 3), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep0a = condition(name='expCondition2', decisionSteps=0, initAgent=(0, 2), avoidCommitPoint=(1, 2), crossPoint=(4, 4), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4), (4, 1)])
map1ObsStep0b = condition(name='expCondition1', decisionSteps=0, initAgent=(2, 0), avoidCommitPoint=(2, 1), crossPoint=(3, 3), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep0b = condition(name='expCondition2', decisionSteps=0, initAgent=(2, 0), avoidCommitPoint=(2, 1), crossPoint=(4, 4), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4), (4, 1)])
map1ObsStep1a = condition(name='expCondition1', decisionSteps=1, initAgent=(1, 0), avoidCommitPoint=(2, 1), crossPoint=(3, 3), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep1a = condition(name='expCondition2', decisionSteps=1, initAgent=(1, 0), avoidCommitPoint=(2, 1), crossPoint=(4, 4), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4), (4, 1)])
map1ObsStep1b = condition(name='expCondition1', decisionSteps=1, initAgent=(0, 1), avoidCommitPoint=(1, 2), crossPoint=(3, 3), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep1b = condition(name='expCondition2', decisionSteps=1, initAgent=(0, 1), avoidCommitPoint=(1, 2), crossPoint=(4, 4), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4), (4, 1)])
map1ObsStep2 = condition(name='expCondition1', decisionSteps=2, initAgent=(0, 0), avoidCommitPoint=(2, 2), crossPoint=(3, 3), targetDisToCrossPoint=[4, 5, 6], fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep2 = condition(name='expCondition2', decisionSteps=2, initAgent=(0, 0), avoidCommitPoint=(2, 2), crossPoint=(4, 4), targetDisToCrossPoint=[4, 5, 6], fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4), (4, 1)])
map1ObsStep4 = condition(name='expCondition1', decisionSteps=4, initAgent=(0, 0), avoidCommitPoint=(3, 3), crossPoint=(4, 4), targetDisToCrossPoint=[4, 5, 6], fixedObstacles=[(2, 2), (2, 0), (2, 4), (0, 2), (4, 2)])
map2ObsStep4 = condition(name='expCondition2', decisionSteps=4, initAgent=(0, 0), avoidCommitPoint=(3, 3), crossPoint=(5, 5), targetDisToCrossPoint=[4, 5, 6], fixedObstacles=[(2, 2), (2, 0), (2, 4), (0, 2), (4, 2), (2, 5), (5, 2)])
map1ObsStep6 = condition(name='expCondition1', decisionSteps=6, initAgent=(0, 0), avoidCommitPoint=(4, 4), crossPoint=(5, 5), targetDisToCrossPoint=[4, 5, 6], fixedObstacles=[(3, 3), (4, 0), (3, 1), (3, 5), (5, 3), (1, 3), (0, 4)])
map2ObsStep6 = condition(name='expCondition2', decisionSteps=6, initAgent=(0, 0), avoidCommitPoint=(4, 4), crossPoint=(6, 6), targetDisToCrossPoint=[4, 5, 6], fixedObstacles=[(3, 3), (4, 0), (3, 1), (3, 5), (5, 3), (1, 3), (0, 4), (3, 6), (6, 3), ])
specialCondition = condition(name='specialCondition', decisionSteps=0, initAgent=(0, 0), avoidCommitPoint=[-1, -1], crossPoint=(5, 5), targetDisToCrossPoint=[5], fixedObstacles=[(3, 0), (0, 3), (1, 4), (1, 6), (4, 1), (6, 1), (6, 2), (2, 6), (5, 3), (3, 5)])
controlCondition1 = condition(name='controlCondition', decisionSteps=0, initAgent=(0, 0), avoidCommitPoint=[-1, -1], crossPoint=(5, 5), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(3, 0), (0, 3), (1, 4), (1, 6), (4, 1), (6, 1), (6, 2), (2, 6), (5, 3), (3, 5)])
controlCondition2 = condition(name='controlCondition', decisionSteps=0, initAgent=(0, 0), avoidCommitPoint=[-1, -1], crossPoint=(5, 5), targetDisToCrossPoint=[5, 6, 7], fixedObstacles=[(3, 0), (0, 3), (1, 4), (5, 3), (4, 1), (3, 5), (2, 2), (6, 1), (6, 2), (1, 6), (2, 6), (5, 3)])
numOfObstacles = 18
controlDiffList = [0, 1, 2]
minSteps = 8
maxsteps = 13
minDistanceBetweenTargets = 4
minDistanceBetweenGrids = max(controlDiffList) + 1
maxDistanceBetweenGrids = calculateMaxDistanceOfGrid(bounds) - minDistanceBetweenGrids
randomWorld = RandomWorld(bounds, minDistanceBetweenGrids, maxDistanceBetweenGrids, numOfObstacles)
randomCondition = namedtuple('condition', 'name creatMap decisionSteps minSteps maxsteps minDistanceBetweenTargets controlDiffList')
randomMaps = randomCondition(name='randomCondition', creatMap=randomWorld, decisionSteps=0, minSteps=minSteps, maxsteps=maxsteps, minDistanceBetweenTargets=minDistanceBetweenTargets, controlDiffList=controlDiffList)
conditionList = [map1ObsStep0a, map1ObsStep0b, map1ObsStep1a, map1ObsStep1b, controlCondition1] + [map1ObsStep2, map1ObsStep4, map1ObsStep6] * 2 + [map2ObsStep0a, map2ObsStep0b, map2ObsStep1a, map2ObsStep1b, controlCondition2] + [map2ObsStep2, map2ObsStep4, map2ObsStep6] * 2 + [randomMaps] * 2
targetDiffsList = [0, 1, 2, 'controlAvoid']
# conditionList = [randomMaps] * 10
# targetDiffsList = ['controlAvoid']
numBlocks = 3
expDesignValues = [[condition, diff] for condition in conditionList for diff in targetDiffsList] * numBlocks
numExpTrial = len(expDesignValues)
numNormalTrial = len(expDesignValues)
random.shuffle(expDesignValues)
specialDesign = [specialCondition, 0]
expDesignValues.append(specialDesign)
numTrialsPerNoiseBlock = 3
noiseCondition = list(permutations([1, 2, 0], numTrialsPerNoiseBlock)) + [(1, 1, 1)]
blockNumber = int(numNormalTrial / numTrialsPerNoiseBlock)
noiseDesignValues = createNoiseDesignValue(noiseCondition, blockNumber)
# noiseDesignValues = [0] * numNormalTrials
# deubg
# expDesignValues = [specialDesign] * 2
# noiseDesignValues = ['special'] * 2
# debugs
print('trial:', len(expDesignValues))
if len(expDesignValues) != len(noiseDesignValues):
print(len(noiseDesignValues))
raise Exception("unmatch condition design")
writerPath = os.path.join(resultsPath, experimentValues["name"] + '_noTime' + '.csv')
writer = WriteDataFrameToCSV(writerPath)
rotatePoint = RotatePoint(gridSize)
isInBoundary = IsInBoundary([0, gridSize - 1], [0, gridSize - 1])
rotateAngles = [0, 90, 180, 270]
creatMap = CreatMap(rotateAngles, gridSize, rotatePoint, numOfObstacles)
pygameActionDict = {pg.K_UP: (0, -1), pg.K_DOWN: (0, 1), pg.K_LEFT: (-1, 0), pg.K_RIGHT: (1, 0)}
responseTimeLimits = 8000
humanController = HumanController(pygameActionDict, responseTimeLimits)
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)
specialTrial = SpecialTrial(controller, drawNewState, drawText, specialNoise, checkBoundary)
restTrialInterval = math.ceil(numExpTrial / 4)
restTrialIndex = list(range(restTrialInterval, numExpTrial, restTrialInterval))
experiment = ObstacleExperiment(creatMap, normalTrial, specialTrial, writer, experimentValues, drawImage, restTrialIndex, restImage)
# start exp
drawImage(formalTrialImage)
experiment(noiseDesignValues, expDesignValues)
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'))
gridSize = 15
bounds = [0, 0, gridSize - 1, gridSize - 1]
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 maps
condition = namedtuple(
'condition',
'name decisionSteps initAgent avoidCommitPoint crossPoint targetDisToCrossPoint fixedObstacles'
)
map1ObsStep0a = condition(name='expCondition1',
decisionSteps=0,
initAgent=(0, 2),
avoidCommitPoint=(1, 2),
crossPoint=(3, 3),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep0a = condition(name='expCondition2',
decisionSteps=0,
initAgent=(0, 2),
avoidCommitPoint=(1, 2),
crossPoint=(4, 4),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4),
(4, 1)])
map1ObsStep0b = condition(name='expCondition1',
decisionSteps=0,
initAgent=(2, 0),
avoidCommitPoint=(2, 1),
crossPoint=(3, 3),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep0b = condition(name='expCondition2',
decisionSteps=0,
initAgent=(2, 0),
avoidCommitPoint=(2, 1),
crossPoint=(4, 4),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4),
(4, 1)])
map1ObsStep1a = condition(name='expCondition1',
decisionSteps=1,
initAgent=(1, 0),
avoidCommitPoint=(2, 1),
crossPoint=(3, 3),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep1a = condition(name='expCondition2',
decisionSteps=1,
initAgent=(1, 0),
avoidCommitPoint=(2, 1),
crossPoint=(4, 4),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4),
(4, 1)])
map1ObsStep1b = condition(name='expCondition1',
decisionSteps=1,
initAgent=(0, 1),
avoidCommitPoint=(1, 2),
crossPoint=(3, 3),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep1b = condition(name='expCondition2',
decisionSteps=1,
initAgent=(0, 1),
avoidCommitPoint=(1, 2),
crossPoint=(4, 4),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4),
(4, 1)])
map1ObsStep2 = condition(name='expCondition1',
decisionSteps=2,
initAgent=(0, 0),
avoidCommitPoint=(2, 2),
crossPoint=(3, 3),
targetDisToCrossPoint=[4, 5, 6],
fixedObstacles=[(1, 1), (1, 3), (3, 1)])
map2ObsStep2 = condition(name='expCondition2',
decisionSteps=2,
initAgent=(0, 0),
avoidCommitPoint=(2, 2),
crossPoint=(4, 4),
targetDisToCrossPoint=[4, 5, 6],
fixedObstacles=[(1, 1), (1, 3), (3, 1), (1, 4),
(4, 1)])
map1ObsStep4 = condition(name='expCondition1',
decisionSteps=4,
initAgent=(0, 0),
avoidCommitPoint=(3, 3),
crossPoint=(4, 4),
targetDisToCrossPoint=[4, 5, 6],
fixedObstacles=[(2, 2), (2, 0), (2, 4), (0, 2),
(4, 2)])
map2ObsStep4 = condition(name='expCondition2',
decisionSteps=4,
initAgent=(0, 0),
avoidCommitPoint=(3, 3),
crossPoint=(5, 5),
targetDisToCrossPoint=[4, 5, 6],
fixedObstacles=[(2, 2), (2, 0), (2, 4), (0, 2),
(4, 2), (2, 5), (5, 2)])
map1ObsStep6 = condition(name='expCondition1',
decisionSteps=6,
initAgent=(0, 0),
avoidCommitPoint=(4, 4),
crossPoint=(5, 5),
targetDisToCrossPoint=[4, 5, 6],
fixedObstacles=[(3, 3), (4, 0), (3, 1), (3, 5),
(5, 3), (1, 3), (0, 4)])
map2ObsStep6 = condition(name='expCondition2',
decisionSteps=6,
initAgent=(0, 0),
avoidCommitPoint=(4, 4),
crossPoint=(6, 6),
targetDisToCrossPoint=[4, 5, 6],
fixedObstacles=[
(3, 3),
(4, 0),
(3, 1),
(3, 5),
(5, 3),
(1, 3),
(0, 4),
(3, 6),
(6, 3),
])
specialCondition = condition(name='specialCondition',
decisionSteps=0,
initAgent=(0, 0),
avoidCommitPoint=[-1, -1],
crossPoint=(5, 5),
targetDisToCrossPoint=[5],
fixedObstacles=[
(3, 0), (0, 3), (1, 4), (1, 6), (4, 1),
(6, 1), (6, 2), (2, 6), (5, 3), (3, 5)
])
controlCondition1 = condition(name='controlCondition',
decisionSteps=0,
initAgent=(0, 0),
avoidCommitPoint=[-1, -1],
crossPoint=(5, 5),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[
(3, 0), (0, 3), (1, 4), (1, 6), (4, 1),
(6, 1), (6, 2), (2, 6), (5, 3), (3, 5)
])
controlCondition2 = condition(name='controlCondition',
decisionSteps=0,
initAgent=(0, 0),
avoidCommitPoint=[-1, -1],
crossPoint=(5, 5),
targetDisToCrossPoint=[5, 6, 7],
fixedObstacles=[
(3, 0), (0, 3), (1, 4), (5, 3), (4, 1),
(3, 5), (2, 2), (6, 1), (6, 2), (1, 6),
(2, 6), (5, 3)
])
numOfObstacles = 18
controlDiffList = [0, 1, 2]
minSteps = 8
maxsteps = 13
minDistanceBetweenTargets = 4
minDistanceBetweenGrids = max(controlDiffList) + 1
maxDistanceBetweenGrids = calculateMaxDistanceOfGrid(
bounds) - minDistanceBetweenGrids
randomWorld = RandomWorld(bounds, minDistanceBetweenGrids,
maxDistanceBetweenGrids, numOfObstacles)
randomCondition = namedtuple(
'condition',
'name creatMap decisionSteps minSteps maxsteps minDistanceBetweenTargets controlDiffList'
)
randomMaps = randomCondition(
name='randomCondition',
creatMap=randomWorld,
decisionSteps=0,
minSteps=minSteps,
maxsteps=maxsteps,
minDistanceBetweenTargets=minDistanceBetweenTargets,
controlDiffList=controlDiffList)
# conditionList = [map1ObsStep0a, map1ObsStep0b, map1ObsStep1a, map1ObsStep1b, controlCondition1] + [map1ObsStep2, map1ObsStep4, map1ObsStep6] * 2 + [map2ObsStep0a, map2ObsStep0b, map2ObsStep1a, map2ObsStep1b, controlCondition2] + [map2ObsStep2, map2ObsStep4, map2ObsStep6] * 2 + [randomMaps] * 2
# targetDiffsList = [0, 1, 2, 'controlAvoid']
conditionList = [
map1ObsStep0a, map1ObsStep0b, map1ObsStep1a, map1ObsStep1b,
controlCondition1
] + [map1ObsStep2, map1ObsStep4, map1ObsStep6] * 2 + [
map2ObsStep0a, map2ObsStep0b, map2ObsStep1a, map2ObsStep1b,
controlCondition2
] + [map2ObsStep2, map2ObsStep4, map2ObsStep6] * 2
targetDiffsList = [0]
# conditionList = [map1ObsStep0a] + [randomMaps] * 2
n = 1
numBlocks = 3 * n
expDesignValues = [[condition, diff] for condition in conditionList
for diff in targetDiffsList] * numBlocks
numExpTrial = len(expDesignValues)
# conditionList = [condition2]
specialDesign = [specialCondition, 0]
numTrialsPerNoiseBlock = 3
noiseCondition = list(permutations([1, 2, 0],
numTrialsPerNoiseBlock)) + [(1, 1, 1)]
blockNumber = int(numExpTrial / numTrialsPerNoiseBlock)
noiseDesignValues = createNoiseDesignValue(noiseCondition, blockNumber)
rotatePoint = RotatePoint(gridSize)
isInBoundary = IsInBoundary([0, gridSize - 1], [0, gridSize - 1])
rotateAngles = [0, 90, 180, 270]
creatMap = CreatMap(rotateAngles, gridSize, rotatePoint, numOfObstacles)
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
# inferGoalPosterior = InferGoalPosterior(goalPolicy)
softmaxBetaList = [2.5]
noise = 0.067
gamma = 0.9
goalReward = [30, 30]
actionSpace = [(0, -1), (0, 1), (-1, 0), (1, 0)]
runVI = RunVI(gridSize, actionSpace, noiseActionSpace, noise, gamma,
goalReward)
intentionInfoScale = [-0.5, 0.5]
for softmaxBeta in softmaxBetaList:
for i in range(20):
print(i)
expDesignValues = [[condition, diff] for condition in conditionList
for diff in targetDiffsList] * numBlocks
random.shuffle(expDesignValues)
expDesignValues.append(specialDesign)
runModel = RunIntentionModel(runVI, softmaxBeta,
intentionInfoScale)
# modelController = ModelControllerOnline(softmaxBeta)
# modelController = AvoidCommitModel(softmaxBeta, actionSpace, checkBoundary)
controller = SampleSoftmaxAction(softmaxBeta)
renderOn = 1
normalTrial = NormalTrialOnline(renderOn, controller, drawNewState,
drawText, normalNoise,
checkBoundary)
specialTrial = SpecialTrialOnline(renderOn, controller,
drawNewState, drawText,
specialNoise, checkBoundary)
experimentValues = co.OrderedDict()
experimentValues["name"] = "noise" + str(
noise) + '_' + "intentionInfoScale" + str(
intentionInfoScale[1]) + '_' + str(i)
# resultsDirPath = os.path.join(resultsPath, "Intention-" + "noise" + str(noise) + '_' + "softmaxBeta" + str(softmaxBeta))
resultsDirPath = os.path.join(resultsPath, "showIntention3")
if not os.path.exists(resultsDirPath):
os.mkdir(resultsDirPath)
writerPath = os.path.join(resultsDirPath,
experimentValues["name"] + '.csv')
writer = WriteDataFrameToCSV(writerPath)
experiment = IntentionModelSimulation(creatMap, normalTrial,
specialTrial, writer,
experimentValues, drawImage,
resultsPath, runModel)
experiment(noiseDesignValues, expDesignValues)
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():
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
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():
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():
gridSize = 60
bounds = [0, 0, gridSize - 1, gridSize - 1]
minDistanceForReborn = 5
condition = [-5, -3, -1, 0, 1, 3, 5]
counter = [0] * len(condition)
numPlayers = 2
initialWorld = InitialWorld(bounds, numPlayers, minDistanceForReborn)
updateWorld = UpdateWorld(bounds, condition, counter, minDistanceForReborn)
screenWidth = 800
screenHeight = 800
screenCenter = [screenWidth / 2, screenHeight / 2]
fullScreen = False
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
leaveEdgeSpace = 6
lineWidth = 1
backgroundColor = THECOLORS['grey'] # [205, 255, 204]
lineColor = [0, 0, 0]
targetColor = [
THECOLORS['blue'], (0, 0, 128), (0, 168, 107), (0, 168, 107)
] # [255, 50, 50]
playerColors = [THECOLORS['orange'], THECOLORS['red']]
targetRadius = 10
playerRadius = 10
totalBarLength = 100
barHeight = 20
stopwatchUnit = 100
finishTime = 1000 * 60 * 2
block = 1
softmaxBeita = -1
textColorTuple = THECOLORS['green']
stopwatchEvent = pg.USEREVENT + 1
saveImage = False
killzone = 2
wolfSpeedRatio = 1
pg.time.set_timer(stopwatchEvent, stopwatchUnit)
pg.event.set_allowed([pg.KEYDOWN, pg.QUIT, stopwatchEvent])
pg.key.set_repeat(120, 120)
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'))
experimentValues = co.OrderedDict()
# experimentValues["name"] = input("Please enter your name:").capitalize()
experimentValues["name"] = 'kill' + str(killzone)
experimentValues["condition"] = 'all'
writerPath = os.path.join(resultsPath, experimentValues["name"]) + '.csv'
writer = WriteDataFrameToCSV(writerPath)
introductionImage = pg.image.load(
os.path.join(picturePath, 'introduction.png'))
restImage = pg.image.load(os.path.join(picturePath, 'rest.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, playerColors)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColors, targetRadius, playerRadius)
drawImage = DrawImage(screen)
drawAttributionTrail = DrawAttributionTrail(screen, playerColors,
totalBarLength, barHeight,
screenCenter)
saveImageDir = os.path.join(
os.path.join(os.path.abspath(os.path.join(os.getcwd(), os.pardir)),
'data'), experimentValues["name"])
xBoundary = [bounds[0], bounds[2]]
yBoundary = [bounds[1], bounds[3]]
stayInBoundary = StayInBoundary(xBoundary, yBoundary)
############
actionSpace = [(10, 0), (7, 7), (0, 10), (-7, 7), (-10, 0), (-7, -7),
(0, -10), (7, -7)]
preyPowerRatio = 3
sheepActionSpace = list(map(tuple, np.array(actionSpace) * preyPowerRatio))
sheepActionSpace.append((0, 0))
numActionSpace = len(sheepActionSpace)
actionSpaceStill = [(10, 0), (7, 7), (0, 10), (-7, 7), (-10, 0), (-7, -7),
(0, -10), (7, -7)]
sheepActionSpaceStill = list(
map(tuple,
np.array(actionSpaceStill) * preyPowerRatio))
sheepActionSpaceStill.append((0, 0))
numActionSpaceStill = len(sheepActionSpaceStill)
regularizationFactor = 1e-4
sharedWidths = [128]
actionLayerWidths = [128]
valueLayerWidths = [128]
resBlockSize = 2
dropoutRate = 0.0
initializationMethod = 'uniform'
depth = 5
generateSheepModelSingle = GenerateModel(4, numActionSpace,
regularizationFactor)
generateSheepModelMulti = GenerateModel(6, numActionSpaceStill,
regularizationFactor)
initSheepNNModelSingle = generateSheepModelSingle(
sharedWidths * depth, actionLayerWidths, valueLayerWidths,
resBlockSize, initializationMethod, dropoutRate)
initSheepNNModelMulti = generateSheepModelMulti(
sharedWidths * depth, actionLayerWidths, valueLayerWidths,
resBlockSize, initializationMethod, dropoutRate)
sheepPreTrainModelPathSingle = os.path.join(
'..', 'trainedModelsSingle',
'agentId=0_dataSize=5000_depth=5_learningRate=0.0001_maxRunningSteps=150_miniBatchSize=256_numSimulations=100_sampleOneStepPerTraj=0_trainSteps=50000'
)
sheepPreTrainModelPathMulti = os.path.join(
'..', 'trainedModelsMulti',
'agentId=0_depth=5_learningRate=0.0001_maxRunningSteps=150_miniBatchSize=256_numSimulations=200_trainSteps=50000'
)
sheepPreTrainModelSingle = restoreVariables(initSheepNNModelSingle,
sheepPreTrainModelPathSingle)
sheepPreTrainModelMulti = restoreVariables(initSheepNNModelMulti,
sheepPreTrainModelPathMulti)
sheepPolicySingleModel = ApproximatePolicy(sheepPreTrainModelSingle,
sheepActionSpace)
sheepPolicyMulti = ApproximatePolicy(sheepPreTrainModelMulti,
sheepActionSpaceStill)
from src.sheepPolicy import SingleChasingPolicy, inferNearestWolf, ComputeLikelihoodByHeatSeeking, InferCurrentWolf, BeliefPolicy
sheepPolicySingle = SingleChasingPolicy(sheepPolicySingleModel,
inferNearestWolf)
baseProb = 0.5
assumePrecision = 50
computeLikelihoodByHeatSeeking = ComputeLikelihoodByHeatSeeking(
baseProb, assumePrecision)
inferCurrentWolf = InferCurrentWolf(computeLikelihoodByHeatSeeking)
beliefPolicy = BeliefPolicy(sheepPolicySingle, sheepPolicySingleModel,
sheepPolicyMulti, inferCurrentWolf)
# sheepPolicySingle = sheepPolicySingleModel
# ## mcts sheep
# from mcts import sheepMCTS
# sheepPolicy = sheepMCTS()
checkTerminationOfTrial = CheckTerminationOfTrial(finishTime)
checkEaten = CheckEaten(killzone, isAnyKilled)
totalScore = 10
attributionTrail = AttributionTrail(totalScore, saveImageDir, saveImage,
drawAttributionTrail)
# sheepPolicy = [sheepPolicySingle,sheepPolicyMulti]
softMaxBeta = 30
softmaxAction = SoftmaxAction(softMaxBeta)
humanController = HumanController(writer, gridSize, stopwatchEvent,
stopwatchUnit, wolfSpeedRatio,
drawNewState, finishTime, stayInBoundary,
saveImage, saveImageDir, beliefPolicy,
chooseGreedyAction)
# policy = pickle.load(open("SingleWolfTwoSheepsGrid15.pkl","rb"))
# modelController = ModelController(policy, gridSize, stopwatchEvent, stopwatchUnit, drawNewState, finishTime, softmaxBeita)
actionSpace = list(it.product([0, 1, -1], repeat=2))
trial = Trial(actionSpace, killzone, stopwatchEvent, drawNewState,
checkTerminationOfTrial, checkEaten, attributionTrail,
humanController)
experiment = Experiment(trial, writer, experimentValues, initialWorld,
updateWorld, drawImage, resultsPath)
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)
screen = pygame.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)
drawBackground = DrawBackground(screen, dimension, leaveEdgeSpace,
backgroundColor, lineColor, lineWidth,
textColorTuple)
drawNewState = DrawNewState(screen, drawBackground, targetColor,
playerColor, targetRadius, playerRadius)
creatStraightLineCondition = CreatStraightLineCondition(
direction, dimension)
width, height, distance, distanceDiff = [2, 2, 2, 0]
target1, target2, playerGrid = creatStraightLineCondition(
width, height, distance, distanceDiff)
import pandas as pd
df = pd.DataFrame(columns=('playerGrid', 'target1', 'target2'))
coordinations = tuple(
it.product(range(1, dimension - 1), range(1, dimension - 1)))
stateAll = list(it.combinations(coordinations, 3))
# print(len(stateAll))
# condition = []
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)