EXPERIMENT_FILE_PATH = join(
KINECT_EXPERIMENT_DIR,
OPTION_INFO['dir_name'],
OPTION_INFO['file_name']
)
## Open file and extract JSON frames data.
FRAMES = normalize_scale(normalize_origin(load_skeleton_data(EXPERIMENT_FILE_PATH), 'HipCenter'),
'HipCenter', 'Head')
## Get the joint distances from the frame data.
DISTANCES = map(generate_distances, FRAMES)
## Split the distance data. Use one half for
## training and the other half for testing.
seed(OPTION_INFO['shuffle_seed'])
shuffle(DISTANCES)
TRAINING_DISTANCES, TESTING_DISTANCES = split_items(DISTANCES, 0.5)
seed(OPTION_INFO['training_seed'])
## Train the weights.
WEIGHTS, _ = KinectWeightsProblem(TRAINING_DISTANCES).anneal()
## Test the weights and display info on how well they work.
SCORES = score_weights(TESTING_DISTANCES, WEIGHTS)
print 'Good:\t%d' % SCORES['good']
print 'Bad:\t%d' % SCORES['bad']
print 'Error:\t%d' % SCORES['error']
print 'Score:\t%.4f%%' % (SCORES['accuracy'] * 100)
print json.dumps(WEIGHTS)
score = self.fitness(skeleton)
if frame['label'] == 1 and score < pivot_score:
correct_count += 1
elif frame['label'] == 0 and score > pivot_score:
correct_count += 1
elif frame['label'] == 1 and score > pivot_score:
incorrect_count += 1
elif frame['label'] == 0 and score < pivot_score:
incorrect_count += 1
total_count = correct_count + incorrect_count + 1 #TODO remove + 1
return float(correct_count) / total_count
## Split the distance data. Use one half for
## training and the other half for testing.
random.seed(OPTION_INFO['shuffle_seed'])
random.shuffle(FRAMES)
TRAINING_FRAMES, TESTING_FRAMES = split_items(FRAMES, 0.5)
random.seed(OPTION_INFO['training_seed'])
## Train the weights.
WEIGHTS, _ = KinectWeightsProblem(TRAINING_FRAMES).anneal()
## Test the weights and display info on how well they work.
SCORES = score_weights(TESTING_DISTANCES, WEIGHTS)
print 'Good:\t%d' % SCORES['good']
print 'Bad:\t%d' % SCORES['bad']
print 'Error:\t%d' % SCORES['error']
print 'Score:\t%.4f%%' % (SCORES['accuracy'] * 100)
print json.dumps(WEIGHTS)
for frame in self.frames:
score = self.fitness(skeleton)
if frame['label'] == 1 and score < pivot_score:
correct_count += 1
elif frame['label'] == 0 and score > pivot_score:
correct_count += 1
elif frame['label'] == 1 and score > pivot_score:
incorrect_count += 1
elif frame['label'] == 0 and score < pivot_score:
incorrect_count += 1
total_count = correct_count + incorrect_count + 1 #TODO remove + 1
return float(correct_count) / total_count
## Split the distance data. Use one half for
## training and the other half for testing.
random.seed(OPTION_INFO['shuffle_seed'])
random.shuffle(FRAMES)
TRAINING_FRAMES, TESTING_FRAMES = split_items(FRAMES, 0.5)
random.seed(OPTION_INFO['training_seed'])
## Train the weights.
WEIGHTS, _ = KinectWeightsProblem(TRAINING_FRAMES).anneal()
## Test the weights and display info on how well they work.
SCORES = score_weights(TESTING_DISTANCES, WEIGHTS)
print 'Good:\t%d' % SCORES['good']
print 'Bad:\t%d' % SCORES['bad']
print 'Error:\t%d' % SCORES['error']
print 'Score:\t%.4f%%' % (SCORES['accuracy'] * 100)
print json.dumps(WEIGHTS)
