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, sync_directory, classifier_name, mode):
### Step 1: filename management ###
print_inner_status("Initialization",
"Looking at file " + sync_directory)
self.jvid_filename_raw = os.path.join(sync_directory, 'Raw/video.jvid')
self.jvid_filename_pops_marked = os.path.join(sync_directory,
'Marked/video.jvid')
self.jvid_filename_synchronized = os.path.join(sync_directory,
'Synced/video.jvid')
### Step 2: get the original skeletons ###
print_inner_status("Initialization", "Getting input skeletons")
self.original_skeletons = read_in_skeletons(self.jvid_filename_raw)
### Step 3: add derivatives to them ###
print_inner_status("Initialization", "Adding derivatives to skeletons")
self.original_skeletons = add_derivatives_to_skeletons(
self.original_skeletons, 5, 10, 5, 10)
### Step 4: set up the classifier filename ###
self.classifier_filename = os.path.join(
os.getcwd(), 'python_backend/classifiers/' + classifier_name)
### Step 5.0: if synchronize mode, load the classifier and mark probabilities ###
if mode == 'synchronize':
print_inner_status("Initialization", "Loading the classifier")
self.load_classifier(
'/Users/jhack/Programming/NI/ni_template/python_backend/classifiers/toprock_front_training.obj'
)
print_inner_status("Initialization",
"Adding pop probabilities to skeletons")
self.original_skeletons = mark_pop_probabilities(
self.original_skeletons, self.classifier)
### Step 5.1: if train mode, train the classifier and save it ###
elif mode == 'train':
print_inner_status("Initialization", "Training the classifier")
self.train_classifier()
print_inner_status("Initialization", "Saving the classifier")
self.save_classifier()
return
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, sync_directory, classifier_name, mode):
### Step 1: filename management ###
print_inner_status ("Initialization", "Looking at file " + sync_directory)
self.jvid_filename_raw = os.path.join(sync_directory, 'Raw/video.jvid')
self.jvid_filename_pops_marked = os.path.join(sync_directory, 'Marked/video.jvid')
self.jvid_filename_synchronized = os.path.join(sync_directory, 'Synced/video.jvid')
### Step 2: get the original skeletons ###
print_inner_status ("Initialization", "Getting input skeletons")
self.original_skeletons = read_in_skeletons (self.jvid_filename_raw)
### Step 3: add derivatives to them ###
print_inner_status ("Initialization", "Adding derivatives to skeletons")
self.original_skeletons = add_derivatives_to_skeletons(self.original_skeletons, 5, 10, 5, 10)
### Step 4: set up the classifier filename ###
self.classifier_filename = os.path.join (os.getcwd(), 'python_backend/classifiers/' + classifier_name)
### Step 5.0: if synchronize mode, load the classifier and mark probabilities ###
if mode == 'synchronize':
print_inner_status ("Initialization", "Loading the classifier")
self.load_classifier ('/Users/jhack/Programming/NI/ni_template/python_backend/classifiers/toprock_front_training.obj')
print_inner_status ("Initialization", "Adding pop probabilities to skeletons")
self.original_skeletons = mark_pop_probabilities (self.original_skeletons, self.classifier)
### Step 5.1: if train mode, train the classifier and save it ###
elif mode == 'train':
print_inner_status ("Initialization", "Training the classifier")
self.train_classifier ()
print_inner_status ("Initialization", "Saving the classifier")
self.save_classifier ()
return
def __init__ (self, sync_directory, classifier_name):
### Step 1: filename management ###
print_inner_status ("Initialization", "Looking at file " + sync_directory)
self.jvid_filename_raw = os.path.join(sync_directory, 'Raw/video.jvid')
self.jvid_filename_pops_marked = os.path.join(sync_directory, 'Marked/video.jvid')
self.jvid_filename_synchronized = os.path.join(sync_directory, 'Synced/video.jvid')
### Step 2: load the classifier ###
print_inner_status ("Initialization", "Unpickling the classifier")
self.classifier = pickle.load (open('/Users/jhack/Programming/NI/ni_template/python_backend/classifiers/toprock_front_training.obj', 'r'))
### Step 2: get the original skeletons ###
print_inner_status ("Initialization", "Getting input skeletons")
self.original_skeletons = read_in_skeletons (self.jvid_filename_raw)
### Step 3: add derivatives to them ###
print_inner_status ("Initialization", "Adding derivatives to skeletons")
self.original_skeletons = add_derivatives_to_skeletons(self.original_skeletons, 5, 10, 5, 10)
### Step 4: add pop probabilities to them ###
print_inner_status ("Initialization", "Adding pop probabilities to skeletons")
self.original_skeletons = mark_pop_probabilities (self.original_skeletons, self.classifier)
classifier_name = sys.argv[2]
if 'load' in sys.argv:
load = True
else:
load = False
if 'save' in sys.argv:
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)
if 'load' in sys.argv: load = True else: load = False if 'save' in sys.argv: 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