import utils.stitching.stitching.mineByPattern as mbp
from numpy import linalg as LA
import copy
fileName = 'inputs/ran_5_2_14_840.skl'
joint = 'AnkleRight_X'
time, values = ae.getAngleVec(fileName, joint, True)
disFactor=0.2
numOfClusters = 6
minimalCluster = 10
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)
pattern = [14, 15, 17, 19, 24, 28, 33, 33, 33, 33, \
33, 33, 28, 24, 19, 17, 15, 14,\
12, 12, 14, 15, 16, 17.5, 17.5, 17.5, \
16, 15, 14, 13, 12, 12]
i=1
framSize = 4
groupSize = framSize**2
minedParts = []
for part in parts:
index = i%groupSize
retVal = qu.getAtomFromFrac(part, pattern, qu.extractPartialPattern)
if retVal is None:
continue
import utils.MovingAverage as ma import algorithm.partitionizing as prt from tsp_solver.greedy import solve_tsp import algorithm.mineByPattern as mp #Reading from file fileName = 'myKinect/v2RanLong.skl' joint = 'KneeLeft_X' time, frameNumbers, angles= ae.getAngleVec(fileName, joint, True, 'NEW') #Extracting clean fractions minimalCluster=20 fracs = ke.clusterByTime(time, frameNumbers, angles, False, minimalCluster) prob = 0.1 fracs = ke.filterOutliers(fracs, False, prob) i=0 cleanedParts, _ = ke.cleanFracs(fracs, False, 5, 1.5) st.plotParts(cleanedParts) #Creating pattern to mine for lenOfCycle = 35 pattern = mp.createFlippedUpattern(angles, lenOfCycle, 3) #Mining the pattern from the input fig = plt.figure() framSize= np.ceil(np.sqrt(len(cleanedParts))) minedParts = [] minedPartsAsList = [] dises = [] threshold = 0 sizeFactor=2 lengths = []
