def api_test(experiment_id_number):
"""Prepare an experiment already uploaded to the db
to be run against the model"""
clean_experiment_number = int(experiment_id_number)
try:
query = db.session.query(Sensor)
df = pd.read_sql_query(query.statement, query.session.bind)
df2 = df[df['experiment_id'] == clean_experiment_number]
current_app.logger.debug(df2)
current_app.logger.debug('run resolve_acc_gyro')
df2 = resolve_acc_gyro_db(df2)
current_app.logger.debug(df2)
current_app.logger.debug('run create_rm_feature')
df2 = create_rm_feature(df2, TIME_SEQUENCE_LENGTH)
current_app.logger.debug(df2)
test_data = blank_filter(df2)
current_app.logger.debug(test_data)
# TODO: NEED TO MAKE SURE FEATURE NUMBER IS THE SAME
#Xt = df2.drop(['state', 'index'], axis=1)
Xt = polynomial_features.fit_transform(test_data)
current_app.logger.debug(Xt)
return Xt
except Exception as e:
current_app.logger.debug('error: {}'.format(e))
abort(500)
def prep():
"""prepare the raw sensor data"""
#1 Your mount
ymount_td = combine_setState_createFeatures('your_mount_raw_data',
'your_mount')
#2 Your side control
ysc_td = combine_setState_createFeatures('your_side_control_raw_data',
'your_side_control')
#3 Your closed guard
ycg_td = combine_setState_createFeatures('your_closed_guard_raw_data',
'your_closed_guard')
#4 Your back control
ybc_td = combine_setState_createFeatures('your_back_control_raw_data',
'your_back_control')
#5 Opponent mount or opponent side control
omountsc_td = combine_setState_createFeatures(
'opponent_mount_and_opponent_side_control_raw_data',
'opponent_mount_or_sc')
#6 Opponent closed guard
ocg_td = combine_setState_createFeatures('opponent_closed_guard_raw_data',
'opponent_closed_guard')
#7 Opponent back control
obc_td = combine_setState_createFeatures('opponent_back_control_raw_data',
'opponent_back_control')
#8 "Non jiu-jitsu" motion
nonjj_td = combine_setState_createFeatures('non_jj_raw_data', 'non_jj')
training_data = concat_data([
ymount_td, ysc_td, ycg_td, ybc_td, omountsc_td, ocg_td, obc_td,
nonjj_td
])
# remove NaN
training_data = blank_filter(training_data)
return training_data
def api_test(experiment_id_number):
"""Prepare an experiment already uploaded to the db
to be run against the model"""
clean_experiment_number = int(experiment_id_number)
try:
query = db.session.query(Sensor)
df = pd.read_sql_query(query.statement, query.session.bind)
df2 = df[df['experiment_id']==clean_experiment_number]
current_app.logger.debug(df2)
current_app.logger.debug('run resolve_acc_gyro')
df2 = resolve_acc_gyro_db(df2)
current_app.logger.debug(df2)
current_app.logger.debug('run create_rm_feature')
df2 = create_rm_feature(df2, TIME_SEQUENCE_LENGTH)
current_app.logger.debug(df2)
test_data = blank_filter(df2)
current_app.logger.debug(test_data)
# TODO: NEED TO MAKE SURE FEATURE NUMBER IS THE SAME
#Xt = df2.drop(['state', 'index'], axis=1)
Xt = polynomial_features.fit_transform(test_data)
current_app.logger.debug(Xt)
return Xt
except Exception as e:
current_app.logger.debug('error: {}'.format(e))
abort(500)
def prep_test(el_file):
el_file = DIR + '/data/test_cases/' + el_file
df = pd.DataFrame()
df = pd.read_csv(el_file, index_col=None, header=0)
df = resolve_acc_gyro(df)
df = create_rm_feature(df, TIME_SEQUENCE_LENGTH)
test_data = blank_filter(df)
return test_data
def prep_test(el_file):
el_file = DIR + '/data/test_cases/' + el_file
df = pd.DataFrame()
df = pd.read_csv(el_file, index_col=None, header=0)
df = resolve_acc_gyro(df)
df = create_rm_feature(df, TIME_SEQUENCE_LENGTH)
test_data = blank_filter(df)
return test_data
def prep_test(test_file, real_test=False):
""" prepares test data to check for algorithm accuracy
so does not set the state
"""
el_file = 'data/test_cases/' + test_file
df = pd.DataFrame()
df = pd.read_csv(el_file, index_col=None, header=0)
if real_test == True:
df = resolve_acc_gyro_labels(df)
else:
df = resolve_acc_gyro(df)
df = create_features(df, _window=40, test=True, label_test=real_test)
test_data = blank_filter(df)
return test_data
def prep(window=40):
"""prepare the raw sensor data
the argument window determines the size of the sliding selection window
for the time series. Given that data has been collected at a frequency of
25Hz, a sliding window of 40 will give you combined data windows
of 1.6 seconds.
"""
#1 Your mount
ymount_td = combine_state_features('your_mount_raw_data', 'your_mount',
window, 0)
#2 Your side control
ysc_td = combine_state_features('your_side_control_raw_data',
'your_side_control', window, 0)
#3 Your closed guard
ycg_td = combine_state_features('your_closed_guard_raw_data',
'your_closed_guard', window, 0)
#4 Your back control
ybc_td = combine_state_features('your_back_control_raw_data',
'your_back_control', window, 0)
#5 Opponent mount or opponent side control
omountsc_td = combine_state_features(
'opponent_mount_and_opponent_side_control_raw_data',
'opponent_mount_or_sc', window, 0)
#6 Opponent closed guard
ocg_td = combine_state_features('opponent_closed_guard_raw_data',
'opponent_closed_guard', window, 0)
#7 Opponent back control
obc_td = combine_state_features('opponent_back_control_raw_data',
'opponent_back_control', window, 0)
#8 "Non jiu-jitsu" motion
nonjj_td = combine_state_features('non_jj_raw_data', 'non_jj', window, 0)
#9 "stand up" motion
stand_up_td = combine_state_features('standing_up_raw_data',
'opponent_closed_guard', window, 1)
training_data = concat_data([
ymount_td, ysc_td, ycg_td, ybc_td, omountsc_td, ocg_td, obc_td,
nonjj_td, stand_up_td
])
# remove NaN
training_data = blank_filter(training_data)
return training_data
def prep():
"""prepare the raw sensor data"""
#1 Your mount
ymount_td = combine_setState_createFeatures('your_mount_raw_data', 'your_mount')
#2 Your side control
ysc_td = combine_setState_createFeatures('your_side_control_raw_data', 'your_side_control')
#3 Your closed guard
ycg_td = combine_setState_createFeatures('your_closed_guard_raw_data', 'your_closed_guard')
#4 Your back control
ybc_td = combine_setState_createFeatures('your_back_control_raw_data', 'your_back_control')
#5 Opponent mount or opponent side control
omountsc_td = combine_setState_createFeatures('opponent_mount_and_opponent_side_control_raw_data', 'opponent_mount_or_sc')
#6 Opponent closed guard
ocg_td = combine_setState_createFeatures('opponent_closed_guard_raw_data', 'opponent_closed_guard')
#7 Opponent back control
obc_td = combine_setState_createFeatures('opponent_back_control_raw_data', 'opponent_back_control')
#8 "Non jiu-jitsu" motion
nonjj_td = combine_setState_createFeatures('non_jj_raw_data', 'non_jj')
training_data = concat_data([ymount_td, ysc_td, ycg_td, ybc_td, omountsc_td, ocg_td, obc_td, nonjj_td])
# remove NaN
training_data = blank_filter(training_data)
return training_data
