def cleanFracs(fracs, plot=False, MAwindowSize=8, MAexp=1.4):
frameSize = math.ceil(np.sqrt(len(fracs)))
cleanedParts = []
originalParts = []
if(plot):
pass
#figOrigin = plt.figure()
#figCleaned = plt.figure()
i=1
for time, values in fracs:
if(plot):
pass
#curr = figOrigin.add_subplot(frameSize, frameSize, i)
#curr.plot(time,values)
#length = int((time[-1] - time[0]) / 30)
time, values = inter.getUniformSampled(time, values)
originalParts.append(values)
cleanesdValues = ma.movingAverage(values, MAwindowSize, MAexp)
if(plot):
plt.figure()
plt.plot(values)
plt.plot(cleanesdValues)
plt.show()
#curr = figCleaned.add_subplot(frameSize, frameSize, i)
#curr.plot(time,values)
cleanedParts.append(cleanesdValues)
i+=1
return cleanedParts, originalParts
def analyzeDataInner(time, data, label):
vec = []
featuresNames = []
v, f = getStats(data, label)
vec += v
featuresNames += f
_, un = inter.getUniformSampled(time, data)
cleaned = ma.movingAverage(un, 20, 1.1)
v, f = getStats(cleaned, label + "after LPF ")
vec += v
featuresNames += f
velocity = np.diff(cleaned)
v, f = getStats(velocity, label + " velocity")
vec += v
featuresNames += f
acceleration = np.diff(velocity)
v, f = getStats(acceleration, label + " acceleration")
vec += v
featuresNames += f
jurk = np.diff(acceleration)
v, f = getStats(jurk, label + " jurk")
vec += v
featuresNames += f
return vec, featuresNames
def getData(fileName, vecSize):
f = open(fileName, 'r')
all = []
map = {}
for line in f:
splited = line.split()
if len(splited) == 0:
continue
jointName = splited[0]
if jointName in joints:
if not jointName in map:#initialization
map[jointName] = []
for _ in splited[1:]:
map[jointName].append([])
for i,l in enumerate(map[jointName]):
l.append(float(splited[1+i]))
#map[jointName][i].append(a)
#except:
# pass
ret = []
for joint,vecs in map.items():
for vec in vecs:
_, uniformlySampled = inter.getUniformSampled(range(len(vec)), vec, vecSize)
"""
maxV = np.max(uniformlySampled)
minV = np.min(uniformlySampled)
amp = maxV -minV
if amp==0:
print 'zeroi input'
else:
uniformlySampled = [2*v/amp -1 for v in uniformlySampled]
"""
ret = ret + uniformlySampled.tolist()
"""
ret = []
for i in xrange(0, len(all), vecSize):
ret.append(all[i:i+vecSize])
"""
return ret
for subject in subjects:
for stride in xrange(1,13):
file = '../AMCs/subjects/' + str(subject) + '/' + str(stride) + '.amc'
features = []
for joint in joints:
try:
input = getAMCperiod(joint, file)
except:
continue
input = alignByMax(input)
sub = fig.add_subplot(frameSize*110 + subjects.index(subject))
sub.plot(range(len(input)), input)
sub_uniform = fig_uniform.add_subplot(frameSize*110 + subjects.index(subject))
specialFeatures = [op(input) for op in specialOperators]
new_time, uniform_input = inter.getUniformSampled(xrange(len(input)), input, lenVec)
features += specialFeatures
features += uniform_input.tolist()
sub_uniform.plot( xrange(len(uniform_input)), uniform_input)
data.append(uniform_input)
tags.append(subject)
plt.xlabel('Time (in frames)')
plt.ylabel(joint + ' angle')
plt.title('subject: ' + str(subject))
if len(features)==0:
continue
for f in features:
out.write(str(f)+',')
out.write(str(subject)+'\n')
"""
cl = KNeighborsClassifier()
plt.ylabel(joint + ' angle')
fig_uniform = plt.figure()
data = []
tags = []
for subject in subjects:
for stride in xrange(1,13):
file = 'AMCs/subjects/' + str(subject) + '/' + str(stride) + '.amc'
try:
input = getAMCperiod(joint, file)
except:
continue
input = alignByMax(input)
sub = fig.add_subplot(frameSize*110 + subjects.index(subject))
sub.plot(range(len(input)), input)
sub_uniform = fig_uniform.add_subplot(frameSize*110 + subjects.index(subject))
new_time, uniform_input = inter.getUniformSampled(xrange(len(input)), input, numOfFeatures)
sub_uniform.plot( xrange(numOfFeatures), uniform_input)
data.append(uniform_input)
tags.append(subject)
plt.xlabel('Time (in frames)')
plt.ylabel(joint + ' angle')
plt.title('subject: ' + str(subject))
cl = KNeighborsClassifier()
cl.n_neighbors = 5
cl.weights = 'distance'
testSize = 35
score = crossValidate(cl, data, tags, testSize)
outFile = 'out.txt'