cur.Ystd = Ystd_cur
# As above but for the labels
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X=None
cur.Y = {'Y':Yn_cur}
cur.Ytestn = {'Ytest':Ytestn_cur}
cur.Ltest = {'Ltest':Ltest_cur}
fname_cur = fname + '_L' + str(i)
cur.training(model_num_inducing, model_num_iterations, model_init_iterations, fname_cur, save_model, economy_save)
mm.append(cur)
ss = [];
sstest = [];
for i in range(len(Lunique)):
for j in range(len(Lunique)):
ss = mm[i].SAMObject.familiarity(mm[j].Y['Y'])
print('Familiarity of model ' + participantList[i] + ' given label: ' + participantList[j] + ' using training data is: ' + str(ss))
print("")
print("")
print("")
for i in range(len(Lunique)):
for j in range(len(Lunique)):
sstest = mm[i].SAMObject.familiarity(mm[j].Ytestn['Ytest'])
# Specification of model type and training parameters
model_type = 'mrd'
model_num_inducing = 30
model_num_iterations = 150
model_init_iterations = 400
fname = modelPath + '/models/' + 'mActions_' + model_type + '_exp' + str(experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model=True
visualise_output=True
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareData(model_type, Ntr, pose_selection)
mySAMpy.training(model_num_inducing, model_num_iterations, model_init_iterations, fname, save_model)
while( not(yarp.Network.isConnected("/speechInteraction/behaviour:o","/sam/face/interaction:i")) ):
print "Waiting for connection with behaviour port..."
pass
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo=dict()
visualiseInfo['ax']=ax
ytmp = mySAMpy.SAMObject.recall(0)
ytmp = numpy.reshape(ytmp,(mySAMpy.imgHeightNew,mySAMpy.imgWidthNew))
fig_nn = pb.figure()
pb.title('Training NN')
model_init_iterations = 400
fname = modelPath + '/models/' + 'mActions_' + model_type + '_exp' + str(
experiment_number) #+ '.pickle'
# Enable to save the model and visualise GP nearest neighbour matching
save_model = True
economy_save = True # ATTENTION!! This is still BETA!!
visualise_output = True
# Reading face data, preparation of data and training of the model
mySAMpy.readData(root_data_dir, participant_index, pose_index)
mySAMpy.prepareData(model_type,
Ntr,
pose_selection,
randSeed=experiment_number)
mySAMpy.training(model_num_inducing, model_num_iterations,
model_init_iterations, fname, save_model, economy_save)
if yarpRunning:
while (not (yarp.Network.isConnected("/speechInteraction/behaviour:o",
"/sam/face/interaction:i"))):
print "Waiting for connection with behaviour port..."
pass
# This is for visualising the mapping of the test face back to the internal memory
if visualise_output:
ax = mySAMpy.SAMObject.visualise()
visualiseInfo = dict()
visualiseInfo['ax'] = ax
ytmp = mySAMpy.SAMObject.recall(0)
ytmp = numpy.reshape(ytmp, (mySAMpy.imgHeightNew, mySAMpy.imgWidthNew))
fig_nn = pb.figure()
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X = None
cur.Y = {'Y':Yn_cur}
cur.Ytestn = {'Ytest':Ytestn_cur}
cur.Ltest = {'Ltest':Ltest_cur}
print 'training data' + str(cur.Y['Y'].shape)
print 'testing data' + str(cur.Ytestn['Ytest'].shape)
fname_cur = fname + '__L' + str(i)
cur.training(model_num_inducing, model_num_iterations, model_init_iterations, fname_cur, save_model, economy_save, keepIfPresent=False, kernelStr=kernelString)
mm.append(cur)
ss = [];
sstest = [];
print
#Ntest = 10
print Ntest
result = np.zeros([len(participantList),Ntest,len(participantList)])
responseIdx = np.zeros([result.shape[0],result.shape[1]])
responseVal = np.zeros([result.shape[0],result.shape[1]])
confusionMatrix = np.zeros([result.shape[0],result.shape[0]])
for i in range(result.shape[0]):
print
print('Participant ' + str(i) + ': ' + participantList[i])
cur.Ystd = Ystd_cur
# As above but for the labels
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X = None
cur.Y = {'Y': Yn_cur}
cur.Ytestn = {'Ytest': Ytestn_cur}
cur.Ltest = {'Ltest': Ltest_cur}
fname_cur = fname + '_L' + str(i)
cur.training(model_num_inducing, model_num_iterations,
model_init_iterations, fname_cur, save_model, economy_save)
mm.append(cur)
ss = []
sstest = []
for i in range(len(Lunique)):
for j in range(len(Lunique)):
ss = mm[i].SAMObject.familiarity(mm[j].Y['Y'])
print('Familiarity of model ' + participantList[i] + ' given label: ' +
participantList[j] + ' using training data is: ' + str(ss))
print("")
print("")
print("")
for i in range(len(Lunique)):
for j in range(len(Lunique)):
