def doRambamAlgo(time, values, numOfClusters, disFactor=0.2):
atoms = [
[14, 15, 17, 19, 24, 28, 33, 33, 33, 33],
[33, 33, 33, 33, 28, 24, 19, 17, 15, 14],
[15, 14, 12, 12, 14, 15, 16, 17.5, 17.5, 17.5],
[17.5, 17.5, 16, 15, 14, 13, 12, 12, 14, 15]
]
minimalCluster = len(atoms[0])
fracs = ke.clusterByTime(time, values, False, minimalCluster)
#originalFracs = copy.deepcopy(fracs)
prob = 0.05
fracs = ke.filterOutliers(fracs, False, prob)
i=0
parts, kuku = ke.cleanFracs(fracs, False)
vecs, mats = createClustersAndMatchingMatrices(parts, atoms, numOfClusters, disFactor)
strides = createStridesFromAtoms(mats, vecs)
if(numOfClusters > 2):
whole = orderWithCost(strides)
xlabel = 'Frames(each frame is 33 miliseconds)'
ylabel = 'Right knee angle (in degrees)'
st.plotParts(strides, xlabel, ylabel, xrange(len(strides)))
else:
if(numOfClusters == 2) :
byOrderDis = getDistanceBetweenAtoms(strides[0], strides[1])
byOrder = appendAtom(strides[0], strides[1])
inverseDis = getDistanceBetweenAtoms(strides[1], strides[0])
inverse = appendAtom(strides[1], strides[0])
whole = byOrder if byOrderDis < inverseDis else inverse
else:
whole = strides[0]
return whole
def plotStas(periods, title):
st.plotParts(periods)
vec = [len(period) for period in periods]
plt.figure()
mean = np.mean(vec)
std = np.std(vec)
skew =stats.skew(vec)
ku = stats.kurtosis(vec)
plt.title(title +' mean: '+ str(mean)+' std: '+str(std)+' skew: '+str(skew)+' kurtosis: '+str(ku))
plt.scatter(range(len(vec)), vec)
plt.figure()
plt.hist(vec)
