def evalParams(m1=MEAN_COEFF, m2=STD_COEFF, epsilon=EPSILON_FACTOR,gap=GAP_FACTOR, overlap=OVERLAP_FACTOR):
sum = 0
for stitch in xrange(stitchesNum):
data = []
tags = []
for subject in subjects:
for index in xrange(8):
try:
input = getAMCInput(joint, subject, index)
except:
continue
parts = st.createParts(input, partsAmount)
stitched = st.stitch(parts, m1, m2, epsilon,gap, overlap)
#plt.figure()
#plt.plot(stitched)
periods = pr.breakToPeriods(stitched)
periods = ut.alignByMaxMany(periods)
periods = inter.getUniformSampledVecs(periods, 100)
data = data + periods
tags = tags + [subject]*len(periods)
#st.plotParts(periods)
cl = KNeighborsClassifier()
cl.n_neighbors = 5
cl.weights = 'distance'
testSize = 1
score = crossValidate(cl, data, tags, testSize, testAmount)
#print str(m2)+' '+ str(score)
sum+=score
score = float(sum)/stitchesNum
scores[m1, m2] = score
return score
def evalParams(partsAmount):
noiseStdFactor = 0.02
stances = {}
swings = {}
for subject in subjects:
for index in xrange(8):
try:
input = getAMCInput(joint, subject, index)
except:
continue
amplitude = np.max(input) - np.min(input)
var = (amplitude*noiseStdFactor)**2
for stitch in xrange(stitchesNum):
try:
noisy_parts = st.createParts(input, False, partsAmount, var)
except:
continue
parts = ma.partsmovingAverage(noisy_parts)
stitched_parts, des = loop.stitch(parts)
if(len(stitched_parts) == len(parts)):
continue
stitched = stitched_parts[-1]
input = stitched
tmpSwings, tmpStances = getSwingsAndStances(input)
key = (stitch, subject)
for stanceLen, swingLen in zip(tmpStances, tmpSwings):
if(stanceLen > 55):
stances[key] = stances.get(key, []) + [stanceLen]
if(swingLen > 35):
swings[key] = swings.get(key, []) + [swingLen]
stancesCV = []
swingsCV = []
strideCV = []
strideLengths = {}
stancesLengths = {}
swingsLengths = {}
for stitch in xrange(stitchesNum):
for subject in subjects:
key = (stitch, subject)
stance = None
if not(key in stances and key in swings):
continue
stance = stances[key]
cv = getCV(stance)
if not math.isnan(cv) and 0 < cv :
stancesCV.append(cv)
stancesLengths[subject] = stancesLengths.get(subject, []) + stance
swing = swings[key]
cv = getCV(swing)
if not math.isnan(cv) and 0 < cv :
swingsCV.append(cv)
swingsLengths[subject] = swingsLengths.get(subject, []) + swing
strideLength = [x+y for x,y in zip(stance, swing)]
cv = getCV(strideLength)
if not math.isnan(cv) and 0 < cv :
strideCV.append(cv)
strideLengths[subject] = strideLengths.get(subject, []) + strideLength
stancesLengths = [stancesLengths[subject] for subject in stancesLengths]
swingsLengths = [swingsLengths[subject] for subject in swingsLengths]
strideLengths = [strideLengths[subject] for subject in strideLengths]
strideReference = [115.61538461538461, 113.66666666666667, 116.11111111111111, 126.375]
strideMeans = [np.mean(seq) for seq in strideLengths]
finalGrade = np.mean([np.abs(y-x)/x for x,y in zip(strideReference, strideMeans)])
stanceCVmean = np.mean(stancesCV)
swingCVmean = np.mean(swingsCV)
strideCVsMean = np.mean(strideCV)
titles = ['Subject: ' + str(x) for x in subjects]
#plotParts(stancesLengths, 'Stance number', 'Stance time(in frames)', titles)
#plotParts(swingsLengths, 'Swing number', 'Swing time(in frames)', titles)
#plotParts(strideLengths, 'Stride number', 'Stride time(in frames)', titles)
print 'partsAmount', partsAmount
print stanceCVmean, [np.mean(seq) for seq in stancesLengths]
print swingCVmean, [np.mean(seq) for seq in swingsLengths]
print strideCVsMean, strideMeans
return finalGrade
