frameSize = math.ceil(np.sqrt(len(subjects)))
fig = plt.figure()
joint = 'rtibia'
plt.xlabel('Time (in frames)')
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
from operator import add, sub
import utils.LPF as LPF
import numpy.random as rnd
import matplotlib.pyplot as plt
from utils.vicon.amcParser import getAMCperiod
from utils.MovingAverage import movingAverage
def mse(A, B):
return ((np.array(A) - np.array(B)) ** 2).mean(axis=0)
def me(A, B):
return np.mean([np.abs(x-y) for x, y in zip(A,B)])
file = 'AMCs/598.amc'
joint = 'rtibia'
list = getAMCperiod(joint, file)
stride = list[112:251]
list = ut.alignByMax(stride)
amplitude = np.max(list) - np.min(list)
coeffs_num = 16
window_size = 16
maFactor = 1.2
msesMA = []
msesLPF = []
noises = []
noisesFactors = xrange(2,20)
for noiseFactor in noisesFactors:
sumsLPF = []
sumsMA = []
sumsN = []
for i in xrange(30):
noise = map(add, np.random.normal(0,(amplitude/noiseFactor)**2,len(list)), list)
clean = LPF.clean(noise, coeffs_num)
