def extractInstance(filename):
instances =[]
# calculate the sections to distinguish examples providing the treshold
secBeg, secEnd = signSection(filename, 0.3)
f = open(filename, 'r')
xs =[]
ys =[]
t = -1
flag = 0;
ex=0
for line in f:
t+=1
a = line.split('\n')
a = a[0].split('\t')
if (len(a) > 1):
temp = a[0].split(' ')
z=float(temp[2])
# finds the example in the file
if(t > secBeg[ex] and t <secEnd[ex]):
xs.append(float(temp[0]))
ys.append(float(temp[1]))
flag = 1
else:
if(flag == 1):
flag = 0
if(ex < len(secEnd)-1):
ex+=1
pair =(xs,ys)
instances.append(pair)
# returns the sequence for the example
#print instances[0][0]
return instances
def similarity(treshold, instance1, instance2, sign1, sign2):
x1 = arange(0)
y1 = arange(0)
x2 = arange(0)
y2 = arange(0)
b, e = signSection(treshold, sign1, instance1)
t = -1
for line in sign1:
t += 1
if t > b:
if t < e:
a = line.split("\n")
a = a[0].split("\t")
if len(a) > 1:
temp = a[0].split(" ")
x1 = append(x1, float(temp[0]))
y1 = append(y1, float(temp[1]))
else:
break
b, e = signSection(treshold, sign2, instance2)
t = -1
for line in sign2:
t += 1
if t > b:
if t < e:
a = line.split("\n")
a = a[0].split("\t")
if len(a) > 1:
temp = a[0].split(" ")
x2 = append(x2, float(temp[0]))
y2 = append(y2, float(temp[1]))
else:
break
dtw = mlpy.Dtw()
dx = dtw.compute(x1, x2)
dy = dtw.compute(y1, y2)
o.write(str(dx))
o.write("\n")
o.write(str(dy))
o.write("\n")
o.write("====================================================================================")
o.write("\n")
currentIndex = 0
ainstances=[]
signRange={}
allinstances={}
similMatr=[]
o = open('C:\Users\Sonmaz\Desktop\gestures\output.txt','w')#call the following function with the sign file and the example between 0 and 10
leetteers = ['a','b']
#allinstances['ax']=[]
#allinstances['ax'].append(xins)
#allinstances['ay']=[]
#allinstances['ay'].append(yins)
#convert python arrays to numpy arrays:
for letter in leetteers:
xins, yins = signSection('C:\Users\Sonmaz\Desktop\gestures\graffiti-'+letter+'.txt', 0.3)
pair=(currentIndex,currentIndex+len(xins))
currentIndex+=len(xins)
signRange[letter]=pair
print 'sign length'
print len(xins)
print len(yins)
for samp in range(len(xins)):
temp = instance()
temp.sign = letter
temp.x=xins[samp]
temp.y=yins[samp]
ainstances.append(temp)
randflag=False
def svmexperiment():
o = open('C:\Users\Sonmaz\Desktop\gestures\output.txt','a')#call the following function with the sign file and the example between 0 and 10
for letter in letters:
xins, yins = signSection('C:\Users\Sonmaz\Desktop\gestures\graffiti-'+letter+'.txt', 0.3)
for i in [0,1,2]:
rand= random.uniform(0,len(xins))
rand=int(floor(rand))
testD.append([letter,xins.pop(rand),yins.pop(rand)])
for i in range(len(xins)):
trainD.append([letter,xins[i],yins[i]])
#===================================================================end of reading the file==========================================
for td in testD:
tmp = Gesture()
tmp.name = td[0]
l = len(td[1])
ls = (l / (n+1))
for i in range(0, n):
tmp.frame1.append([td[1][i * ls : (i+1) * ls],td[2][i * ls : (i+1) * ls]])
for fr1 in range(len(tmp.frame1)-1):
if len(tmp.frame1[fr1][0]+tmp.frame1[fr1+1][0]) < ls:
break
if len(tmp.frame1[fr1][1]+tmp.frame1[fr1+1][1]) < ls:
break
tmp.frame.append([tmp.frame1[fr1][0]+tmp.frame1[fr1+1][0],tmp.frame1[fr1][1]+tmp.frame1[fr1+1][1]])
for fr in tmp.frame:
trans=[]
trans.append(fft.fft(fr[0]))
trans.append(fft.fft(fr[1]))
sum1= 0
sum2 = 0
tmp.features.append(abs(trans[0][0]))
tmp.features.append(abs(trans[1][0]))
for i in range(len(trans[0])):
sum1 += trans[0][i]*trans[0][i]
for i in range(len(trans[1])):
sum2 += trans [1][i]*trans[1][i]
tmp.features.append(int(abs(sum1)/(2*ls - 1)))
tmp.features.append(int(abs(sum2)/(2*ls - 1)))
while len(tmp.features) < (n+1):
tmp.features.append(0)
print 'faaaaaa'
print len(tmp.features)
gesturesD.append(tmp)
for tr in trainD:
tmp = Gesture()
tmp.name = tr[0]
l = len(tr[1])
ls = l / (n+1)
for i in range(0, n):
tmp.frame1.append([tr[1][i * ls : (i+1) * ls],tr[2][i * ls : (i+1) * ls]])
for fr1 in range(len(tmp.frame1)-1):
if len(tmp.frame1[fr1][0]+tmp.frame1[fr1+1][0]) < ls:
break
if len(tmp.frame1[fr1][1]+tmp.frame1[fr1+1][1]) < ls:
break
tmp.frame.append([tmp.frame1[fr1][0]+tmp.frame1[fr1+1][0],tmp.frame1[fr1][1]+tmp.frame1[fr1+1][1]])
for fr in tmp.frame:
trans=[]
#print fr[0]
trans.append(fft.fft(fr[0]))
trans.append(fft.fft(fr[1]))
sum1= 0
sum2 = 0
tmp.features.append(abs(trans[0][0]))
tmp.features.append(abs(trans[1][0]))
for i in range(len(trans[0])):
sum1 += trans[0][i]*trans[0][i]
for i in range(len(trans[1])):
sum2 += trans[1][i]*trans[1][i]
tmp.features.append(int(abs(sum1)/(2*ls - 1)))
tmp.features.append(int(abs(sum2)/(2*ls - 1)))
print 'taaaa'
while len(tmp.features) < (n+1):
tmp.features.append(0)
print len(tmp.features)
gesturesT.append(tmp)
#============================================================================end of feature extraction
truelabels=[]
attributes=[]
labels=[]
testAtr=[]
for ge in gesturesT:
tempAtr=[]
for fe in ge.features:
tempAtr.append(fe)
attributes.append(tempAtr)
if(ge.name == 'e'):
labels.append(-1)
else:
labels.append(1)
for ge in gesturesD:
tempAtr=[]
for fe in ge.features:
tempAtr.append(fe)
testAtr.append(tempAtr)
if(ge.name == 'e'):
truelabels.append(-1)
else:
truelabels.append(1)
#print attributes
attributes = array(attributes)
print attributes
mysvm = Svm()
#print testAtr
testAtr=array(testAtr)
print 'sep'
labels=array(labels)
#labels = array([-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1])
mysvm.compute(attributes,labels)
print testAtr
predictedlabels = mysvm.predict(testAtr)
o.write('\n'+str(truelabels))
o.write(str(predictedlabels))
correct = 0
for la in range(len(truelabels)):
if truelabels[la] == predictedlabels[la]:
correct += 1
print correct
