def evaluate_pfmce(self, evaluate_emg=True):
print "Evaluating performance...."
# if not self.mdp:
# print "No MDP found"
# self.history = []
# return
#action_classifier = pickle.load(open('../data/action_classifier.pkl'))
history = np.array(self.history)
print "raw history shape:", history.shape
history = history[5:-6:5, :] # downsampling, cut the first half second
print "saved history: ", history.shape
if history.shape[0]> 35:
history = savgol_filter(history, 31, 3, axis=0) # smoothing
np.savetxt('user_data/' + self.user_id, history, delimiter=',')
evaluator = Evaluator()
emg_l = history[:,0:8] / EMG_WEIGHT
emg_u = history[:,18:26] / EMG_WEIGHT
ort_l = history[:,14:18]
ort_u = history[:,-4:]
# plt.figure()
# plt.plot(evaluator.ort_l)
# plt.figure()
# plt.plot(align_signal(evaluator.ort_l, ort_l, has_time=False))
# plt.show(block=True)
(diff_emg_l, cost) = dtw_distance(evaluator.emg_l, emg_l)
(diff_emg_u, _) = dtw_distance(evaluator.emg_u, emg_u)
(diff_ort_l, _) = dtw_distance(evaluator.ort_l, ort_l)
(diff_ort_u, _) = dtw_distance(evaluator.ort_u, ort_u)
print "difference cost, alignment cost"
print (diff_emg_l, cost)
print diff_emg_u
print diff_ort_l
print diff_ort_u
if evaluate_emg:
EMG_FACTOR = 0.08
else:
EMG_FACTOR = 0
ORT_FACTOR = 0.25
TIME_FACTOR = 0.05
performance = 100 * math.exp(-EMG_FACTOR*(diff_emg_l+diff_emg_u) \
- ORT_FACTOR*(diff_ort_l+diff_ort_u) \
- TIME_FACTOR*cost) - 8*self.n_prompts
print performance
self.score_pub.publish(int(performance))
with open(self.logfile, 'a') as f:
f.write('performance score: %f\n' %performance)
def evaluate_pfmce(self, evaluate_emg=True):
print "Evaluating performance...."
# if not self.mdp:
# print "No MDP found"
# self.history = []
# return
#action_classifier = pickle.load(open('../data/action_classifier.pkl'))
history = np.array(self.history)
print "raw history shape:", history.shape
history = history[5:-6:5, :] # downsampling, cut the first half second
print "saved history: ", history.shape
if history.shape[0] > 35:
history = savgol_filter(history, 31, 3, axis=0) # smoothing
np.savetxt('user_data/' + self.user_id, history, delimiter=',')
evaluator = Evaluator()
emg_l = history[:, 0:8] / EMG_WEIGHT
emg_u = history[:, 18:26] / EMG_WEIGHT
ort_l = history[:, 14:18]
ort_u = history[:, -4:]
# plt.figure()
# plt.plot(evaluator.ort_l)
# plt.figure()
# plt.plot(align_signal(evaluator.ort_l, ort_l, has_time=False))
# plt.show(block=True)
(diff_emg_l, cost) = dtw_distance(evaluator.emg_l, emg_l)
(diff_emg_u, _) = dtw_distance(evaluator.emg_u, emg_u)
(diff_ort_l, _) = dtw_distance(evaluator.ort_l, ort_l)
(diff_ort_u, _) = dtw_distance(evaluator.ort_u, ort_u)
print "difference cost, alignment cost"
print(diff_emg_l, cost)
print diff_emg_u
print diff_ort_l
print diff_ort_u
if evaluate_emg:
EMG_FACTOR = 0.08
else:
EMG_FACTOR = 0
ORT_FACTOR = 0.25
TIME_FACTOR = 0.05
performance = 100 * math.exp(-EMG_FACTOR*(diff_emg_l+diff_emg_u) \
- ORT_FACTOR*(diff_ort_l+diff_ort_u) \
- TIME_FACTOR*cost) - 8*self.n_prompts
print performance
self.score_pub.publish(int(performance))
with open(self.logfile, 'a') as f:
f.write('performance score: %f\n' % performance)
def evaluate_pfmce(self, evaluate_emg=True):
print "Evaluating performance...."
history = np.array(self.history)
print "raw history shape:", history.shape
#history = history[25:-6:5, :] # downsampling, cut the first half second
history = history[5:-6:5, :] # downsampling, cut the first half second
print "saved history: ", history.shape
if history.shape[0]> 35:
history = savgol_filter(history, 31, 3, axis=0) # smoothing
np.savetxt('user_data/' + self.user_id, history, delimiter=',')
evaluator = Evaluator()
emg_l = history[:,0:8] / EMG_WEIGHT
emg_u = history[:,18:26] / EMG_WEIGHT
ort_l = history[:,14:18]
ort_u = history[:,-4:]
(diff_emg_l, cost) = dtw_distance(evaluator.emg_l, emg_l)
(diff_emg_u, _) = dtw_distance(evaluator.emg_u, emg_u)
(diff_ort_l, _) = dtw_distance(evaluator.ort_l, ort_l)
(diff_ort_u, _) = dtw_distance(evaluator.ort_u, ort_u)
print "difference cost, alignment cost"
print (diff_emg_l, cost)
print diff_emg_u
print diff_ort_l
print diff_ort_u
if evaluate_emg:
EMG_FACTOR = 0.08
else:
EMG_FACTOR = 0
ORT_FACTOR = 0.25
TIME_FACTOR = 0.05
performance = 100 * math.exp(-EMG_FACTOR*(diff_emg_l+diff_emg_u) \
- ORT_FACTOR*(diff_ort_l+diff_ort_u) \
- TIME_FACTOR*cost) - 8*self.n_prompts
print performance
with open(self.logfile, 'a') as f:
f.write('performance score: %f\n' %performance)
def evaluate_pfmce(self, evaluate_emg=True):
print "Evaluating performance...."
history = np.array(self.history)
print "raw history shape:", history.shape
#history = history[25:-6:5, :] # downsampling, cut the first half second
history = history[5:-6:5, :] # downsampling, cut the first half second
print "saved history: ", history.shape
if history.shape[0] > 35:
history = savgol_filter(history, 31, 3, axis=0) # smoothing
np.savetxt('user_data/' + self.user_id, history, delimiter=',')
evaluator = Evaluator()
emg_l = history[:, 0:8] / EMG_WEIGHT
emg_u = history[:, 18:26] / EMG_WEIGHT
ort_l = history[:, 14:18]
ort_u = history[:, -4:]
(diff_emg_l, cost) = dtw_distance(evaluator.emg_l, emg_l)
(diff_emg_u, _) = dtw_distance(evaluator.emg_u, emg_u)
(diff_ort_l, _) = dtw_distance(evaluator.ort_l, ort_l)
(diff_ort_u, _) = dtw_distance(evaluator.ort_u, ort_u)
print "difference cost, alignment cost"
print(diff_emg_l, cost)
print diff_emg_u
print diff_ort_l
print diff_ort_u
if evaluate_emg:
EMG_FACTOR = 0.08
else:
EMG_FACTOR = 0
ORT_FACTOR = 0.25
TIME_FACTOR = 0.05
performance = 100 * math.exp(-EMG_FACTOR*(diff_emg_l+diff_emg_u) \
- ORT_FACTOR*(diff_ort_l+diff_ort_u) \
- TIME_FACTOR*cost) - 8*self.n_prompts
print performance
with open(self.logfile, 'a') as f:
f.write('performance score: %f\n' % performance)
