def __call__(self, oneConditionDf):
allTrajectories = self.getTrajectories(oneConditionDf)
minDistance = readParametersFromDf(
oneConditionDf)['hasWalls'] + 0.05
checkStateOfofBound = CheckStateOfofBound(minDistance)
# allMeasurements = np.array(self.measurementFunction(allTrajectories,minDistance))
# allMeasurements = np.array(self.measurementFunction(allTrajectories,minDistance))s
allMeasurements = np.array([
checkStateOfofBound(state) for traj in allTrajectories
for state in traj
])
# print(allMeasurements)
measurementMean = np.mean(allMeasurements)
measurementStd = np.std(allMeasurements)
return pd.Series({'mean': measurementMean, 'std': measurementStd})
def main():
manipulatedVariables = OrderedDict()
#solimp
manipulatedVariables['dmin'] = [0.9]
manipulatedVariables['dmax'] = [0.9999]
manipulatedVariables['width'] = [0.001]
manipulatedVariables['midpoint'] = [0.5] #useless
manipulatedVariables['power'] = [1] #useless
#solref
manipulatedVariables['timeconst'] = [
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
]
manipulatedVariables['dampratio'] = [0.2, 0.4, 0.6, 0.8, 1.0]
eavluateParmetersList = ['dampratio', 'dmax']
lineParameter = ['timeconst']
prametersToDrop = list(
set(manipulatedVariables.keys()) -
set(eavluateParmetersList + lineParameter))
print(prametersToDrop, manipulatedVariables)
# timeconst： 0.1-0.2
# dmin： 0.5-dimax
# dmax：0.9-0.9999
# dampratio：0.3-0.8
productedValues = it.product(
*[[(key, value) for value in values]
for key, values in manipulatedVariables.items()])
parametersAllCondtion = [
dict(list(specificValueParameter))
for specificValueParameter in productedValues
]
evalNum = 500
randomSeed = 1542
dt = 0.05 #0.05
dirName = os.path.dirname(__file__)
dataFolderName = os.path.join(dirName, '..')
trajectoryDirectory = os.path.join(dataFolderName, 'trajectory',
'variousCollsiondt={}'.format(dt))
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
for parameterOneCondiiton in parametersAllCondtion:
# print(parameterOneCondiiton,evalNum,randomSeed)
simuliateOneParameter(parameterOneCondiiton, evalNum, randomSeed, dt)
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
# save evaluation trajectories
trajectoryExtension = '.pickle'
trajectoryFixedParameters = {
'isMujoco': 1,
'isCylinder': 1,
'evalNum': evalNum,
'randomSeed': randomSeed
}
getTrajectorySavePath = GetSavePath(trajectoryDirectory,
trajectoryExtension,
trajectoryFixedParameters)
getTrajectorySavePathFromDf = lambda df: getTrajectorySavePath(
readParametersFromDf(df))
# compute statistics on the trajectories
fuzzySearchParameterNames = []
loadTrajectories = LoadTrajectories(getTrajectorySavePath, loadFromPickle,
fuzzySearchParameterNames)
loadTrajectoriesFromDf = lambda df: loadTrajectories(
readParametersFromDf(df))
def compareXPos(traj, baselineTraj):
SE = [
np.linalg.norm(s1[0][0][:2] - s2[0][0][:2])**2
for s1, s2 in zip(baselineTraj, traj)
]
return SE
originEnvTrajSavePath = os.path.join(
dirName, '..', 'trajectory', 'variousCollsion',
'collisionMoveOriginBaseLine_evalNum={}_randomSeed={}.pickle'.format(
evalNum, randomSeed))
baselineTrajs = loadFromPickle(originEnvTrajSavePath)
class CompareTrajectories():
def __init__(self, baselineTrajs, calculateStatiscs):
self.baselineTrajs = baselineTrajs
self.calculateStatiscs = calculateStatiscs
def __call__(self, trajectorys):
allMeasurements = np.array([
self.calculateStatiscs(traj, baselineTraj)
for traj, baselineTraj in zip(trajectorys, self.baselineTrajs)
])
# print()
measurementMean = allMeasurements.mean(axis=0)
measurementStd = allMeasurements.std(axis=0)
return pd.Series({'mean': measurementMean, 'std': measurementStd})
measurementFunction = CompareTrajectories(baselineTrajs, compareXPos)
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf,
measurementFunction)
statisticsDf = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf)
fig = plt.figure(0)
rowName, columnName = eavluateParmetersList
numRows = len(manipulatedVariables[rowName])
numColumns = len(manipulatedVariables[columnName])
plotCounter = 1
selfId = 0
for rowVar, grp in statisticsDf.groupby(rowName):
grp.index = grp.index.droplevel(rowName)
for columVar, group in grp.groupby(columnName):
group.index = group.index.droplevel(columnName)
axForDraw = fig.add_subplot(numRows, numColumns, plotCounter)
if (plotCounter % numColumns == 1) or numColumns == 1:
axForDraw.set_ylabel(rowName + ': {}'.format(rowVar))
if plotCounter <= numColumns:
axForDraw.set_title(columnName + ': {}'.format(columVar))
axForDraw.set_ylim(0, 0.02)
drawPerformanceLine(group, axForDraw, lineParameter,
prametersToDrop)
plotCounter += 1
plt.suptitle('MSEforPos,dt={}'.format(dt))
plt.legend(loc='best')
# plt.show()
def compareV(traj, baselineTraj):
# [print(s1[0][0]-s2[0][0],np.linalg.norm(s1[0][0][2:]-s2[0][0][2:]),np.linalg.norm(s1[0][0][:2]-s2[0][0][:2])) for s1,s2 in zip (baselineTraj,traj) ]
SE = [
np.linalg.norm(s1[0][0][2:] - s2[0][0][2:])**2
for s1, s2 in zip(baselineTraj, traj)
]
return SE
measurementFunction2 = CompareTrajectories(baselineTrajs, compareV)
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf,
measurementFunction2)
statisticsDf2 = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf2)
fig = plt.figure(1)
numRows = len(manipulatedVariables[rowName])
numColumns = len(manipulatedVariables[columnName])
plotCounter = 1
for rowVar, grp in statisticsDf2.groupby(rowName):
grp.index = grp.index.droplevel(rowName)
for columVar, group in grp.groupby(columnName):
group.index = group.index.droplevel(columnName)
axForDraw = fig.add_subplot(numRows, numColumns, plotCounter)
if (plotCounter % numColumns == 1) or numColumns == 1:
axForDraw.set_ylabel(rowName + ': {}'.format(rowVar))
if plotCounter <= numColumns:
axForDraw.set_title(columnName + ': {}'.format(columVar))
axForDraw.set_ylim(0, 0.4)
drawPerformanceLine(group, axForDraw, lineParameter,
prametersToDrop)
plotCounter += 1
plt.suptitle('MSEforSpeed,dt={}'.format(dt))
plt.legend(loc='best')
plt.show()
def main():
manipulatedVariables = OrderedDict()
manipulatedVariables['numWolves'] = [3]
manipulatedVariables['numSheeps'] = [1]
manipulatedVariables['numBlocks'] = [2]
manipulatedVariables['maxTimeStep'] = [25]
manipulatedVariables['sheepSpeedMultiplier'] = [1.0]
manipulatedVariables['individualRewardWolf'] = [0]
manipulatedVariables['timeconst'] = [0.5]
manipulatedVariables['dampratio'] = [0.2]
manipulatedVariables['hasWalls'] = [1.0, 1.5, 2.0]
manipulatedVariables['dt'] = [0.05]
eavluateParmetersList = ['hasWalls', 'dt']
lineParameter = ['sheepSpeedMultiplier']
prametersToDrop = list(
set(manipulatedVariables.keys()) -
set(eavluateParmetersList + lineParameter))
print(prametersToDrop, manipulatedVariables)
productedValues = it.product(
*[[(key, value) for value in values]
for key, values in manipulatedVariables.items()])
parametersAllCondtion = [
dict(list(specificValueParameter))
for specificValueParameter in productedValues
]
evalNum = 500
randomSeed = 1542
dt = 0.05 #0.05
dirName = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(dirName, '..', 'trajectory',
'evluateWall')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
time = []
for parameterOneCondiiton in parametersAllCondtion:
# print(parameterOneCondiiton,evalNum,randomSeed)
sampletime = simuliateOneParameter(parameterOneCondiiton, evalNum,
randomSeed)
time.append(sampletime)
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
# save evaluation trajectories
trajectoryExtension = '.pickle'
trajectoryFixedParameters = {'randomSeed': randomSeed, 'evalNum': evalNum}
getTrajectorySavePath = GetSavePath(trajectoryDirectory,
trajectoryExtension,
trajectoryFixedParameters)
getTrajectorySavePathFromDf = lambda df: getTrajectorySavePath(
readParametersFromDf(df))
# compute statistics on the trajectories
fuzzySearchParameterNames = []
loadTrajectories = LoadTrajectories(getTrajectorySavePath, loadFromPickle,
fuzzySearchParameterNames)
loadTrajectoriesFromDf = lambda df: loadTrajectories(
readParametersFromDf(df))
class CheckStateOfofBound(object):
"""docstring for ClassName"""
def __init__(self, minDistance):
self.minDistance = minDistance
def __call__(self, state):
absloc = np.abs([agent[:2] for agent in state[0][:-2]])
return np.any(absloc > self.minDistance)
class ComputeOutOfBounds:
def __init__(self, getTrajectories, CheckStateOfofBound):
self.getTrajectories = getTrajectories
self.CheckStateOfofBound = CheckStateOfofBound
def __call__(self, oneConditionDf):
allTrajectories = self.getTrajectories(oneConditionDf)
minDistance = readParametersFromDf(
oneConditionDf)['hasWalls'] + 0.05
checkStateOfofBound = CheckStateOfofBound(minDistance)
# allMeasurements = np.array(self.measurementFunction(allTrajectories,minDistance))
# allMeasurements = np.array(self.measurementFunction(allTrajectories,minDistance))s
allMeasurements = np.array([
checkStateOfofBound(state) for traj in allTrajectories
for state in traj
])
# print(allMeasurements)
measurementMean = np.mean(allMeasurements)
measurementStd = np.std(allMeasurements)
return pd.Series({'mean': measurementMean, 'std': measurementStd})
computeStatistics = ComputeOutOfBounds(loadTrajectoriesFromDf,
CheckStateOfofBound)
statisticsDf = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf)
for i, parameterOneCondiiton in enumerate(parametersAllCondtion):
print(parameterOneCondiiton, time[i])
def main():
manipulatedVariables = OrderedDict()
manipulatedVariables['damping'] = [0.0]
manipulatedVariables['frictionloss'] = [0.0]
manipulatedVariables['masterForce']=[0.0]
eavluateParmetersList=['frictionloss','damping']
lineParameter=['masterForce']
prametersToDrop=list(set(manipulatedVariables.keys())-set(eavluateParmetersList+lineParameter))
productedValues = it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])
conditions = [dict(list(specificValueParameter)) for specificValueParameter in productedValues]
evalNum=500
randomSeed=133
for condition in conditions:
print(condition)
generateSingleCondition(condition,evalNum,randomSeed)
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
# save evaluation trajectories
trajectoriesSaveDirectory=os.path.join(dirName,'..', 'trajectory','evluateRopeFixedEnv2')
if not os.path.exists(trajectoriesSaveDirectory):
os.makedirs(trajectoriesSaveDirectory)
trajectoryExtension = '.pickle'
trajectoryFixedParameters = { 'randomSeed':randomSeed,'evalNum':evalNum}
getTrajectorySavePath = GetSavePath(trajectoriesSaveDirectory, trajectoryExtension, trajectoryFixedParameters)
getTrajectorySavePathFromDf = lambda df: getTrajectorySavePath(readParametersFromDf(df))
# compute statistics on the trajectories
fuzzySearchParameterNames = []
loadTrajectories = LoadTrajectories(getTrajectorySavePath, loadFromPickle, fuzzySearchParameterNames)
loadTrajectoriesFromDf = lambda df: loadTrajectories(readParametersFromDf(df))
def computeV(traj):
# [print(s1[0][0]-s2[0][0],np.linalg.norm(s1[0][0][2:]-s2[0][0][2:]),np.linalg.norm(s1[0][0][:2]-s2[0][0][:2])) for s1,s2 in zip (baselineTraj,traj) ]
vel=[np.linalg.norm(agentState[2:]) for state in traj for agentState in state[0]]
# print (vel)
return np.mean(vel)
measurementFunction = computeV
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf, measurementFunction)
statisticsDf = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf)
darwStatisticsDf=DarwStatisticsDf(manipulatedVariables,eavluateParmetersList,lineParameter)
subtitle='velocity'
figIndex=0
ylimMax=1.2
darwStatisticsDf(statisticsDf,subtitle,figIndex,ylimMax)
class fliterMeasurement():
"""docstring for fliterMeasurement"""
def __init__(self, splitLength,splitMeasurement):
self.splitLength = splitLength
self.splitMeasurement = splitMeasurement
def __call__(self,traj):
[splitMeasurement(traj[i:i+self.splitLength]) for i in range(len(traj)-self.splitLength) ]
getWolfPos=lambda state :getPosFromAgentState(state[0][0])
getSheepfPos=lambda state :getPosFromAgentState(state[0][1])
minDistance=0.35
isCaught=IsTerminal(minDistance,getWolfPos,getSheepfPos)
measurementFunction2 =lambda traj: np.mean([isCaught(state) for state in traj])
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf, measurementFunction2)
statisticsDf2 = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf2)
subtitle='caughtRatio(minDistance={})'.format(minDistance)
figIndex=figIndex+1
ylimMax=0.2
darwStatisticsDf(statisticsDf2,subtitle,figIndex,ylimMax)
getWolfPos=lambda state :getPosFromAgentState(state[0][0])
getSheepfPos=lambda state :getPosFromAgentState(state[0][1])
calculateWolfSheepDistance=lambda state:np.linalg.norm(getWolfPos(state)-getSheepfPos(state))
measurementFunction3 =lambda traj: np.mean([calculateWolfSheepDistance(state) for state in traj])
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf, measurementFunction3)
statisticsDf3 = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf3)
subtitle='WolfSheepDistance'
figIndex=figIndex+1
ylimMax=1.6
darwStatisticsDf(statisticsDf3,subtitle,figIndex,ylimMax)
getWolfPos=lambda state :getPosFromAgentState(state[0][0])
getMasterfPos=lambda state :getPosFromAgentState(state[0][2])
calculateWolfMasterDistance=lambda state:np.linalg.norm(getWolfPos(state)-getMasterfPos(state))
measurementFunction3 =lambda traj: np.mean([calculateWolfMasterDistance(state) for state in traj])
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf, measurementFunction3)
statisticsDf3 = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf3)
subtitle='WolfMasterDistance'
figIndex=figIndex+1
ylimMax=0.6
darwStatisticsDf(statisticsDf3,subtitle,figIndex,ylimMax)
getWolfPos=lambda state :getPosFromAgentState(state[0][0])
getSheepfPos=lambda state :getPosFromAgentState(state[0][1])
getWolfVel=lambda state :getVelFromAgentState(state[0][0])
def calculateCrossAngle(vel1,vel2):
vel1complex=complex(vel1[0],vel1[1])
vel2complex=complex(vel2[0],vel2[1])
return np.abs(np.angle(vel2complex/vel1complex))/np.pi*180
calculateDevation= lambda state:calculateCrossAngle(getWolfPos(state)-getSheepfPos(state),getWolfVel(state))
measurementFunction3 =lambda traj: np.mean([calculateDevation(state) for state in traj])
computeStatistics = ComputeStatistics(loadTrajectoriesFromDf, measurementFunction3)
statisticsDf = toSplitFrame.groupby(levelNames).apply(computeStatistics)
print(statisticsDf)
subtitle='Devation'
figIndex=figIndex+1
darwStatisticsDf(statisticsDf,subtitle,figIndex)
plt.show()