def evaluateDataSet(strLabel, strWorkingDir, lsFilePath,
dRectDuration=2.0, dSMDuration=2.0, dSCDuration=0.1,
eng=None, strCoder=None, n=23, k=12, m=1, r=3,
nInterleaving=25,
bSourceEncoding=True, bReconciliation=True,
bOutputData=False, lsOutputData=None,
hKeyOutput = None):
"""
Given the parameter values, this function evaluates performance
on a specific data set.
"""
lsResult = []
for fn in lsFilePath:
lsDataResult = sd.evaluateSingleData(strWorkingDir, fn,
dRectDuration = dRectDuration,
dSCDuration = dSCDuration,
dSMDuration = dSMDuration,
eng=eng, strCoder=strCoder,
n=n, k=k, m=m, r=r, nInterleaving=nInterleaving,
bSourceEncoding=bSourceEncoding,
bReconciliation=bReconciliation,
bOutputaData = bOutputData,
lsOutputData = lsOutputData,
hKeyOutput = hKeyOutput)
lsResult.extend(lsDataResult)
dfResult = pd.DataFrame(lsResult)
srMean = dfResult.mean()
srMean.name = strLabel+"_mean"
srStd = dfResult.std()
srStd.name = strLabel+"_std"
return srMean, srStd, dfResult
def evaluateShapeCodingParams(eng):
"""
This function evaluate the parameter of shape coding
"""
# select data
strWorkingDir = "../../data/evaluation/BER/"
strFileNamePattern= None
lsFilePath = cf.getFileList(strWorkingDir, strFileNamePattern)
# params
lsSCWnd = np.arange(0.05, 0.3, 0.05)
dRectWnd = 2.0
dSMWnd = 2.0
strCoder = ecc.CODER_GOLAY
m = 1
n = 23
k = 12
r = 2
nInterleaving = 25
print "%s: n=%d, k=%d, m=%d, r=%d, interleave=%d" % \
(strCoder, n, k, m, r, nInterleaving)
# test
lsResult = []
for dCodingWnd in lsSCWnd:
print "evalauting SCWnd=%.2f..." % dCodingWnd
for fn in lsFilePath:
lsDataResult = sd.evaluateSingleData(strWorkingDir, fn,
dRectDuration = dRectWnd,
dSMDurction = dSMWnd,
dSCDuration = dCodingWnd,
eng=eng, strCoder=strCoder,
n=n, k=k, m=m, r=r,
nInterleaving=nInterleaving)
lsResult.extend(lsDataResult)
dfResult = pd.DataFrame(lsResult)
gp = dfResult.groupby(dfResult[sd.WND_SC])
dfMean = gp.mean()
return dfMean, dfResult
def evaluateReconciliationParams(eng):
"""
Evaluate the effect of reconciliation parameters, i.e., m, k, on
system performance
Paramters:
---------
lsFileList:
list of data files
eng:
instance of matlab engine
strCoder:
name of coder
lsM:
the possible values of m
lsR:
the possible values of r
Returns:
a pandas.DataFrame consisted of all performance result
"""
# select data
strWorkingDir = "../../data/evaluation/reconciliation/"
strFileNamePattern= None
lsFilePath = cf.getFileList(strWorkingDir, strFileNamePattern)
# parameter
strCoder = ecc.CODER_RS
lsM = [4,]
lsR = range(1, 8)
dRectWnd = 2.0
dSMWnd = 2.0
dSCWnd = 0.15
# evaluate
lsResult = []
if (strCoder == ecc.CODER_RS):
for m in lsM:
for r in lsR:
n = 2**m - 1
k = n - 2*r
if(k<1 or n*m>=500):
break
print "testing m=%d, r=%d..." % (m, r)
for fn in lsFilePath:
lsDataResult = sd.evaluateSingleData(strWorkingDir, fn,
dRectDuration=dRectWnd, dSMDuration=dSMWnd,
dSCDuration=dSCWnd,
eng=eng, strCoder=strCoder, n=n, k=k, m=m, r=r)
lsResult.extend(lsDataResult)
elif strCoder == ecc.CODER_GOLAY:
n = 23
k = 12
m = 1
r = 2
for fn in lsFilePath:
lsDataResult = sd.evaluateSingleData(strWorkingDir, fn,
dRectDuration=dRectWnd, dSMDuration=dSMWnd,
dSCDuration=dSCWnd,
eng=eng, strCoder=strCoder, n=n, k=k, m=m, r=r)
lsResult.extend(lsDataResult)
# result
dfResult = pd.DataFrame(lsResult)
dcMatchingRate = {}
for r in lsR:
nMatchedKey = (dfResult[sd.ERR_USER_EC][ (dfResult[sd.R]==r) & \
(dfResult[sd.ERR_USER_EC]==0) ]).count()
nTotalKey = dfResult[sd.ERR_USER_EC][dfResult[sd.R]==r].count()
dMatchingRate = nMatchedKey * 1.0 / nTotalKey
dcMatchingRate[r] = dMatchingRate
srMatchingRate = pd.Series(dcMatchingRate)
return dfSummary, dfResult, srMatchingRate
