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
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'
out = open(outFile, 'r')
scores = []
testSizes = []
for line in out:
splited = line.split()
scores.append(splited[0])
testSizes.append(splited[1])
plt.figure()
plt.plot(testSizes, scores)
plt.xlabel('Test set size)')
plt.ylabel('Prediction precision')
plt.title('Prediction precision for 4 subjects, data set size is 43')