def predict(self, window):
if self.m:
y=signal.filtfilt(self.b, self.a, window)
p = pburg(y, self.order, sampling=self.sampling/self.downsample)
p.run()
power = eeg.bin_power(abs(p.psd),p.frequencies(), self.band, self.sampling/self.downsample)
p_labs, p_acc, p_vals = svm_predict([0], [(10*log10(power*2./(2.*pi))).tolist()], self.m, '-q')
print p_labs
return p_labs
return None
def train(self, log):
f = open(log,'r')
raw=[]
label=[]
while 1:
line=f.readline()
if not line:
break
label.append(int(line))
raw.append(map(float, f.readline().split(',')))
print 'Trials : {0}'.format(len(raw))
f.close()
#downsample to 128Hz
for i in range(len(raw)):
raw[i]=raw[i][::self.downsample]
#windowing and calculating PSD of each window
psd=[[],[]]
for i in range(len(raw)):
while True:
y=signal.filtfilt(self.b, self.a, raw[i][:self.window])
p = pburg(y, self.order, sampling=self.sampling/self.downsample)
p.run()
power = eeg.bin_power(abs(p.psd),p.frequencies(), self.band, self.sampling/self.downsample)
psd[label[i]].append(10*log10(power*2./(2.*pi)))#logPSD
raw[i]=raw[i][self.increment:]
if len(raw[i])<self.window:
break
#averaging psd
self.avg_psd=np.mean(psd,axis=0)
#training svm using svm
y = [0] * len(psd[0])+[1]*len(psd[1])
x = map(list,psd[0])+map(list,psd[1])
print 'n_sample : {0}'.format(len(y))
prob = svm_problem(y, x)
param = '-t 0 -c 4'
svm_train(prob, svm_parameter(param+' -v 10 -q'))#just calulating ross-validation accuracy for classification
self.m = svm_train(prob, svm_parameter(param))
