def __init__(self,
jvid_filename,
recording_name,
classifier_name=None,
save=True,
test=False):
print_status("Trainer", "Starting")
### Step 1: get in the original skeletons ###
self.skeletons_raw = read_in_skeletons(jvid_filename)
### Step 2: add derivatives to them (fills skeletons_with_derivs) ###
self.skeletons_with_derivs = add_derivatives_to_skeletons(
self.skeletons_raw, self.fd_interval_1, self.fd_interval_2,
self.fd_interval_1, self.fd_interval_2)
### Step 3: put it into training data form ###
(X, Y) = self.get_training_data()
### Step 4: get it in training data form ###
all_data = zip(X, Y)
print " ### Number of examples: ", len(all_data)
random.shuffle(all_data)
training = all_data
# training = all_data[:5000]
# testing = all_data[5000:]
#--- training ---
X_train = [t[0] for t in training]
Y_train = [t[1] for t in training]
#--- testing ---
# X_test = [t[0] for t in testing]
# Y_test = [t[1] for t in testing]
#--- get the classifier ---
print_status("Trainer", "Training the classifier")
self.classifier = LogisticRegression().fit(X_train, Y_train)
#--- saving data ----
if save:
if classifier_name == None:
classifier_name = recording_name.split('.')[0] + '.obj'
classifier_name = os.path.join(os.getcwd(),
'python_backend/classifiers',
classifier_name)
print_status("Trainer",
"Pickling the classifier at " + classifier_name)
self.save_classifier(classifier_name)
def __init__ (self, jvid_filename, recording_name, classifier_name=None, save=True, test=False):
print_status ("Trainer", "Starting")
### Step 1: get in the original skeletons ###
self.skeletons_raw = read_in_skeletons (jvid_filename)
### Step 2: add derivatives to them (fills skeletons_with_derivs) ###
self.skeletons_with_derivs = add_derivatives_to_skeletons (self.skeletons_raw, self.fd_interval_1, self.fd_interval_2, self.fd_interval_1, self.fd_interval_2)
### Step 3: put it into training data form ###
(X, Y) = self.get_training_data ()
### Step 4: get it in training data form ###
all_data = zip(X, Y)
print " ### Number of examples: ", len(all_data)
random.shuffle (all_data);
training = all_data
# training = all_data[:5000]
# testing = all_data[5000:]
#--- training ---
X_train = [t[0] for t in training]
Y_train = [t[1] for t in training]
#--- testing ---
# X_test = [t[0] for t in testing]
# Y_test = [t[1] for t in testing]
#--- get the classifier ---
print_status ("Trainer", "Training the classifier")
self.classifier = LogisticRegression().fit(X_train, Y_train)
#--- saving data ----
if save:
if classifier_name == None:
classifier_name = recording_name.split('.')[0] + '.obj'
classifier_name = os.path.join (os.getcwd (), 'python_backend/classifiers', classifier_name)
print_status ("Trainer", "Pickling the classifier at " + classifier_name)
self.save_classifier (classifier_name)
save = True
else:
save = False
if 'test' in sys.argv:
test = True
else:
test = False
### Step 2: read in the skeleton timeseries and add derivatives to them ###
skeletons = read_in_skeletons(infile_name)
fd_interval_1 = 5
fd_interval_2 = 10
sd_interval_1 = 5
sd_interval_2 = 10
skeletons = add_derivatives_to_skeletons(skeletons, fd_interval_1,
fd_interval_2, sd_interval_1,
sd_interval_2)
### Step 3: put it into training data form ###
(X, Y) = get_training_data(skeletons)
### Step 4: get it in training data form ###
all_data = zip(X, Y)
print " ### Number of examples: ", len(all_data)
random.shuffle(all_data)
training = all_data
# training = all_data[:5000]
# testing = all_data[5000:]
#--- training ---
X_train = [t[0] for t in training]
save = True else: save = False if 'test' in sys.argv: test = True else: test = False ### Step 2: read in the skeleton timeseries and add derivatives to them ### skeletons = read_in_skeletons (infile_name) fd_interval_1 = 5 fd_interval_2 = 10 sd_interval_1 = 5 sd_interval_2 = 10 skeletons = add_derivatives_to_skeletons (skeletons, fd_interval_1, fd_interval_2, sd_interval_1, sd_interval_2) ### Step 3: put it into training data form ### (X, Y) = get_training_data (skeletons) ### Step 4: get it in training data form ### all_data = zip(X, Y) print " ### Number of examples: ", len(all_data) random.shuffle (all_data); training = all_data # training = all_data[:5000] # testing = all_data[5000:] #--- training --- X_train = [t[0] for t in training] Y_train = [t[1] for t in training]
