def SI():
global svc_clf, P300_clf
ind = random.randint(0, 25)
print("num =", ind)
Series = np.load("../npSave/Pavarisa280219R06.npy")[ind, 0, :, :]
print(np.asarray(Series).shape)
bb, a = pre.butter_bandpass(0.5, 30, 500, order=5)
bandpassData = pre.lfilter(bb, a, Series)
print(bandpassData.shape)
KaiserData = []
for i in range(8):
tmp = pre.KaiserFil(bandpassData[i])
KaiserData.append(tmp)
phaseData = np.array(
[np.unwrap(np.angle(hilbert(i))) for i in bandpassData])
powerData = np.array([np.abs(hilbert(i)) for i in bandpassData])
aaa = np.ravel((phaseData, powerData))
A = np.reshape(aaa, (1, -1))
Seq = []
output = svc_clf.decision_function(A) # np.array([FeaturedData]))[0]
for j in range(26):
Seq.append([-output[0][j], j])
Seq.sort() # sort percentage
SI_result = ''
for t in Seq[0:9]: # เลือก 9 ตัวที่มี percent มากสุด
SI_result = SI_result + 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'[
t[1]] # แปรผลเป็นตัวอักษรimport socket
print('Result:' + SI_result)
return SI_result
else:
ind = random.randint(0,25)
print(ind)
Series = np.load("../npSave/Pavarisa280219R06.npy")[ind,0,:,:]
print(np.asarray(Series).shape)
#########Collecting Speech Imagery Data##################
#########Preprocess Data##################
bb, a = pre.butter_bandpass(0.5, 30, 500, order=5)
bandpassData = pre.lfilter(bb, a, Series)
print(bandpassData.shape)
KaiserData = []
for i in range(8):
tmp = pre.KaiserFil(bandpassData[i])
KaiserData.append(tmp)
#FeaturedData = multibandfeat(KaiserData)
#print(np.asarray(KaiserData).shape)
#print("FeaturedData",len(FeaturedData))
#KaiserData = np.array(KaiserData)
phaseData = np.array([np.unwrap(np.angle(hilbert(i))) for i in bandpassData])
powerData = np.array([np.abs(hilbert(i)) for i in bandpassData])
print(phaseData.shape)
print(powerData.shape)
aaa=np.ravel((phaseData,powerData))
print("aaa",aaa.shape)
#shape=(8,4,1000)
#########Preprocess Data##################
A = np.reshape(aaa, (1, -1))
