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#!/usr/bin/env python | ||
import numpy as np | ||
import csv | ||
import os | ||
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PROTOCOLS = { | ||
'proto1': {'train': (0, 0.6), 'validation': (0.6, 0.8), 'test': (0.8, 1)}, | ||
'proto2': {'train': (0.4, 1), 'validation': (0, 0.2), 'test': (0.2, 0.4)}, | ||
} | ||
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SUBSETS = [ | ||
'train', | ||
'validation', | ||
'test' | ||
] | ||
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CLASSES = [ | ||
'Other_Activity', | ||
'Watch_TV', | ||
'Sleep_Out_Of_Bed', | ||
'Bathe', | ||
'Cook_Breakfast', | ||
'Dress', | ||
'Toilet', | ||
'Personal_Hygiene', | ||
'Sleep', | ||
'Read', | ||
'Relax', | ||
'Cook_Dinner', | ||
'Drink', | ||
'Eat_Breakfast', | ||
'Morning_Meds', | ||
'Evening_Meds', | ||
'Wash_Breakfast_Dishes', | ||
'Cook_Lunch', | ||
'Wash_Dishes', | ||
'Leave_Home', | ||
'Cook', | ||
'Enter_Home', | ||
'Entertain_Guests', | ||
'Wash_Dinner_Dishes', | ||
'Phone', | ||
'Groom', | ||
'Step_Out', | ||
'Eat_Dinner', | ||
'Eat_Lunch', | ||
'Wash_Lunch_Dishes', | ||
'Bed_Toilet_Transition', | ||
'Eat', | ||
'Go_To_Sleep', | ||
'Wake_Up', | ||
'Work_At_Table' | ||
] | ||
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VARIABLES = [ | ||
'lastSensorEventHours', | ||
'lastSensorEventSeconds', | ||
'lastSensorDayOfWeek', | ||
'windowDuration', | ||
'timeSinceLastSensorEvent', | ||
'prevDominantSensor1', | ||
'prevDominantSensor2', | ||
'lastSensorID', | ||
'lastSensorLocation', | ||
'lastMotionLocation', | ||
'complexity', | ||
'activityChange', | ||
'areaTransitions', | ||
'numDistinctSensors', | ||
'sensorCount-Bathroom', | ||
'sensorCount-Bedroom', | ||
'sensorCount-Chair', | ||
'sensorCount-DiningRoom', | ||
'sensorCount-Hall', | ||
'sensorCount-Ignore', | ||
'sensorCount-Kitchen', | ||
'sensorCount-LivingRoom', | ||
'sensorCount-Office', | ||
'sensorCount-OutsideDoor', | ||
'sensorCount-WorkArea', | ||
'sensorElTime-Bathroom', | ||
'sensorElTime-Bedroom', | ||
'sensorElTime-Chair', | ||
'sensorElTime-DiningRoom', | ||
'sensorElTime-Hall', | ||
'sensorElTime-Ignore', | ||
'sensorElTime-Kitchen', | ||
'sensorElTime-LivingRoom', | ||
'sensorElTime-Office', | ||
'sensorElTime-OutsideDoor', | ||
'sensorElTime-WorkArea' | ||
] | ||
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def load(setname='csh101'): | ||
data = dict([(k, []) for k in CLASSES]) | ||
with open(os.path.join('../data', setname, '{}.ann.features.csv'.format(setname)), 'rt') as f: | ||
reader = csv.reader(f, delimiter=',') | ||
for k, row in enumerate(reader): | ||
if not k: continue | ||
data[row[-1]].append(np.array([z for z in row[:-1]])) | ||
for k in CLASSES: | ||
data[k] = np.vstack(data[k]) | ||
return data | ||
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def split_data(data, subset, splits): | ||
return dict([(k, data[k][range(int(splits[subset][0] * data[k].shape[0]), | ||
int(splits[subset][1] * data[k].shape[0]))]) for k in data]) | ||
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def get(protocol, subset, classes=CLASSES, variables=VARIABLES, setname='csh101'): | ||
retval = split_data(load(setname), subset, PROTOCOLS[protocol]) | ||
varindex = [VARIABLES.index(k) for k in variables] | ||
retval = dict([(k, retval[k][:, varindex]) for k in classes]) | ||
return np.array([retval[k] for k in classes]) |