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 getSwingsAndStances(input):
periods = par.breakToPeriods(input)
periods = ut.alignByMaxMany(periods)
stances = []
swings = []
for period in periods:
a = np.array(period)
localMax = argrelextrema(a, np.greater, 0, MAXIMA_ORDER)
#localMax is a tuple
for m in localMax:
if(len(m) != 1):
continue
max = m[0]
swings.append(max)
stances.append(int(len(period)-max))
break
return swings, stances
