f.write('lr: {0}\n'.format(hp.lr))
f.write('lr_scheduler_step_size: {0}\n'.format(
hp.lr_scheduler_step_size))
f.write('max_epoch: {0}\n'.format(hp.max_epoch))
if hp.optimizer != 'sgdr':
f.write('lr_gamma: {0}\n'.format(hp.lr_gamma))
else:
f.write('sgdr_T_mult: {0}\n'.format(hp.sgdr_T_mult))
f.write('lr_sgd_momentum: {0}\n'.format(hp.lr_sgd_momentum))
f.write('lr_weight_decay: {0}\n'.format(hp.lr_weight_decay))
f.write('bn_momentum: {0}\n'.format(hp.bn_momentum))
f.write('bn_gamma: {0}\n'.format(hp.bn_gamma))
f.write('bn_clip: {0}\n'.format(hp.bn_clip))
f.write('bn_step_size: {0}\n'.format(hp.bn_step_size))
f.write('optimizer: {0}\n'.format(hp.optimizer))
f.write('net: {0}\n'.format(hp.net))
f.write('grid_size: {0}\n'.format(hp.grid_size))
f.write('time_step: {0}\n'.format(hp.time_step))
f.write('pic_flip: {0}\n'.format(hp.pic_flip))
f.write('sigma: {0}\n'.format(hp.sigma))
f.write('manual_seed: {0}\n'.format(hp.manual_seed))
f.write('device: {0}\n'.format(hp.device))
f.write('batch_size: {0}\n'.format(hp.batch_size))
f.write('drop_rate: {0}\n'.format(hp.drop_rate))
f.write('label_smooth: {0}\n'.format(hp.label_smooth))
f.write('penalty: {0}\n'.format(hp.penalty))
f.write('oversampling: {0}\n'.format(hp.oversampling))
f.write('batch_norm: {0}\n'.format(hp.batch_norm))
trainer.train()
"mobileData?access_token=QCOI7AjXi7Is90f9hK0BQsOQuKxoU2ISnBa9HLt6Bmsg0nvQbOqPAbELCzTsl2ww"
sensorUrl = "http://sensing-ms-api.mybluemix.net/api/Patients/0/" \
"sensorData?access_token=QCOI7AjXi7Is90f9hK0BQsOQuKxoU2ISnBa9HLt6Bmsg0nvQbOqPAbELCzTsl2ww"
featuresNames = ["heartrate", "temperature", "steps", "activity"]
allFeaturesNames = [
"heartrate_std", "heartrate_max", "heartrate_avg", "heartrate_min",
"temperature_std", "temperature_max", "temperature_avg", "temperature_min",
"steps", "activity_score", "activity_minutes"
]
mobileData = getData(mobileUrl)
sensorData = getData(sensorUrl)
groups = groupData(mobileData, sensorData)
features, labels = extractFeatures(groups, featuresNames)
clf = Classifier()
clf.train(features, labels)
rules = clf.getDangerousRules(allFeaturesNames)
print rules
maxSeconds = 1
seconds = maxSeconds
previousSensorData = None
while True:
if seconds > 0:
print "\nWaiting for "+str(seconds)+" seconds."
sleep(1)
seconds -= 1
else:
print "\nStarting again..."
latestSensorData = getLatestSensorData(sensorUrl)
if previousSensorData is None or latestSensorData["datetime"] != previousSensorData["datetime"]:
previousSensorData = latestSensorData
mobileUrl = "http://sensing-ms-api.mybluemix.net/api/Patients/0/" \
"mobileData?access_token=QCOI7AjXi7Is90f9hK0BQsOQuKxoU2ISnBa9HLt6Bmsg0nvQbOqPAbELCzTsl2ww"
sensorUrl = "http://sensing-ms-api.mybluemix.net/api/Patients/0/" \
"sensorData?access_token=QCOI7AjXi7Is90f9hK0BQsOQuKxoU2ISnBa9HLt6Bmsg0nvQbOqPAbELCzTsl2ww"
featuresNames = ["heartrate", "temperature", "steps", "activity"]
allFeaturesNames = [
"heartrate_std", "heartrate_max", "heartrate_avg", "heartrate_min",
"temperature_std", "temperature_max", "temperature_avg",
"temperature_min", "steps", "activity_score", "activity_minutes"
]
mobileData = getData(mobileUrl)
sensorData = getData(sensorUrl)
groups = groupData(mobileData, sensorData)
features, labels = extractFeatures(groups, featuresNames)
clf = Classifier()
clf.train(features, labels)
rules = clf.getDangerousRules(allFeaturesNames)
print rules
maxSeconds = 1
seconds = maxSeconds
previousSensorData = None
while True:
if seconds > 0:
print "\nWaiting for " + str(seconds) + " seconds."
sleep(1)
seconds -= 1
else:
print "\nStarting again..."
latestSensorData = getLatestSensorData(sensorUrl)
if previousSensorData is None or latestSensorData[
"datetime"] != previousSensorData["datetime"]: