def test_set(self):
os.system("python main.py -t 0.2 > test_clock.txt")
data = parseData("test_clock.txt")
self.assertEqual(0.2, data['clock']['total'])
os.system("python main.py -t 0.5 > test_clock.txt")
data = parseData("test_clock.txt")
self.assertEqual(0.5, data['clock']['total'])
def test_set(self):
os.system("python main.py -t 0.2 -r 500 > test_road.txt")
data = parseData("test_road.txt")
# Check the cmd line arg
self.assertEqual(500, data['road_length'])
# Do the math
self.assertEqual(500,
data['road']['east_end'] - data['road']['west_end'])
# Check road sources
for src in data['sources']:
if src['direction'] == "WestBound":
self.assertEqual(500, src['location'])
# Check road users time spent on road
for user in data['road_users']['list']:
if not user['left_on_road']:
if not user['platoon']:
time = user['removed'] - user['created']
self.assertAlmostEqual(time,
500 / abs(user['velocity']),
delta=1,
msg="Failed: Road User ID: " +
str(user['id']))
else:
time = user['removed'] - user['created']
# Add 0.1 to account for rounding
self.assertTrue(time + 0.1 >= 500 / abs(user['velocity']),
msg="Failed: Road User ID: " +
str(user['id']))
def test_constant(self):
os.system("python main.py -t 0.2 -cd 0 -pd 0 -vd 0 > test_speeds.txt")
data = parseData("test_speeds.txt")
clock_step = data['clock']['tick_length']
users = data['road_users']['list']
for user in users:
# Verify distance/tick of road users
self.assertAlmostEqual(user['velocity_tick'],
user['velocity'] * clock_step,
delta=0.1)
# Verify constant speed - equality check
if user['type'] == "Pedestrian":
self.assertAlmostEqual(data['ped_speed'],
abs(user['velocity']),
delta=0.1)
if user['type'] == "Bicycle":
self.assertAlmostEqual(data['bike_speed'],
abs(user['velocity']),
delta=0.1)
if user['type'] == "Motor Vehicle":
self.assertAlmostEqual(data['mv_speed'],
abs(user['velocity']),
delta=0.1)
# Verify speed corresponds to direction
if user['direction'] == "EastBound":
self.assertTrue(user['velocity'] > 0)
else:
self.assertTrue(user['velocity'] < 0)
def test_set(self):
os.system("python main.py -t 0.2 -r 500 > test_IDs.txt")
data = parseData("test_IDs.txt")
users = data['road_users']['list']
last = None
count = 0
# Check user IDs
for user in users:
if last == None:
last = user['id']
else:
self.assertEqual(user['id'], last + 1)
last = user['id']
count += 1
# Test length of users
if user['type'] == "Pedestrian":
self.assertEqual(1, user['length'])
if user['type'] == "Bicycle":
self.assertEqual(2, user['length'])
if user['type'] == "Motor Vehicle":
self.assertEqual(6, user['length'])
self.assertEqual(count, data['road_users']['totals']['total'])
self.assertEqual(count, last + 1)
def test_types(self):
os.system("python main.py -t 1 > test_platoon.txt")
data = parseData("test_platoon.txt")
users = data['road_users']['list']
for user in users:
if user['platoon']:
self.assertTrue(user['type'] == "Motor Vehicle")
def test_users(self):
os.system("python main.py -t 1 > test_critical.txt")
data = parseData("test_critical.txt")
for inter in data['interactions']:
users = set()
for user in inter['road_users']:
users.add(user['id'])
for user in inter['critical_users']:
self.assertTrue(user in users)
def test_set(self):
args = "-r 2000 -t 0.5 -it 3.0 -p 10 -c 15 -v 25 -pp 25 -cp 75 -vp 30 -pa 1 -ca 1 -va 1 -ps 1.2 -cs 6.2 -vs 10.2 -pd 0 -cd 0 -vd 0 -d 1 -sd 1"
os.system("python main.py " + args + " > test_vars.txt")
data = parseData("test_vars.txt")
nums = extract_nums(args)
vals = list(data.values())
for i in range(len(nums)):
self.assertEqual(nums[i], vals[i + 1])
def listAppliancesList(self, name=None, ltype=None):
url = self.reference.createAppendURL(string='appliances')
data = {}
if ltype is not None:
data['type'] = ltype
if name is not None:
data['name'] = name
r = self.auth.get(url, params=data)
return parseData(r.text)
def test_default(self):
os.system("python main.py -t 0.2 > test_road.txt")
data = parseData("test_road.txt")
# Check the cmd line arg
self.assertEqual(1000, data['road_length'])
# Do the math
self.assertEqual(1000, data['road']['east_end'] - data['road']['west_end'])
# Check road sources
for src in data['sources']:
if src['direction'] == "WestBound":
self.assertEqual(1000, src['location'])
def test_loc(self):
os.system("python main.py -t 0.2 > test_locations.txt")
data = parseData("test_locations.txt")
sources = data['sources']
users = data['road_users']['list']
for user in users:
direction = user['direction']
user_type = user['type']
src = None
for source in sources:
if source['direction'] == direction and source['type'] in user_type:
src = source
self.assertAlmostEqual(src['location'], user['create_pos'], delta=1, msg="Failed: Road User ID: " + str(user['id']))
def test_label(self):
os.system("python main.py -t 10 > test_critical.txt")
data = parseData("test_critical.txt")
for inter in data['interactions']:
res = Counter(inter['code'])
if "M" in res:
if res['M'] == 2:
self.assertTrue(inter['critical'])
else:
self.assertFalse(inter['critical'])
else:
self.assertFalse(inter['critical'])
def test_two(self):
os.system("python main.py -t 1 -c 0 -p 0 -vd 0 > test_phase1.txt")
data = parseData("test_phase1.txt")
pred_meetings = ((25/3600) / data['mv_speed'] * data['road_length']) + ((25/3600) * (data['road_length'] / data['mv_speed']))
counts = []
# For each user, count the number of interactions they were involved in
for user in data['road_users']['list']:
count = 0
for event in data['interactions']:
for participant in event['road_users']:
if user['id'] == participant['id']:
count += 1
counts.append(count)
# Take the median of the counts, as that should be close to the prediction
self.assertAlmostEqual(pred_meetings, statistics.median(counts), delta=0.5)
def processData():
#takes data from database and process it to produce analysisInfo with features that can be used for machine learning
children=parse.parseData()
analysis = extract(children)
startT=findStartDate(analysis)
endT=findEndDate(analysis)
print "date diff", dateDiff(startT, endT)
extend(children, analysis, startT, endT)
addSleepChart(children, analysis)
computeRateNtTr(analysis)
compBigImprov(analysis)
computeCompleteDevRate(analysis)
userReport(analysis)
#searchMissingUsers(analysis)
addFeatureForML(children, analysis)
computeCorrCoef(analysis)
return children, analysis, startT, endT
def test_number(self):
os.system("python main.py -t 1 > test_platoon.txt")
data = parseData("test_platoon.txt")
users = data['road_users']['list']
east_platooners = 0
west_platooners = 0
for user in users:
if user['direction'] == "EastBound":
if user['platoon']:
east_platooners += 1
else:
self.assertFalse(east_platooners == 1)
east_platooners = 0
else:
if user['platoon']:
west_platooners += 1
else:
self.assertFalse(west_platooners == 1)
west_platooners = 0
self.assertFalse(east_platooners == 1)
self.assertFalse(west_platooners == 1)
def distributions(self):
os.system("python main.py -c 50 -p 50 > test_speeds.txt")
data = parseData("test_speeds.txt")
ped_speeds = []
bike_speeds = []
veh_speeds = []
for user in data['road_users']['list']:
if user['type'] == 'Pedestrian':
ped_speeds.append(abs(user['velocity']))
elif user['type'] == 'Bicycle':
bike_speeds.append(abs(user['velocity']))
else:
veh_speeds.append(abs(user['velocity']))
# Test pvalue for normal dist
pval = stats.normaltest(ped_speeds).pvalue
self.assertGreater(pval,
0.05,
msg="Testing normal dist for ped speeds")
pval = stats.normaltest(bike_speeds).pvalue
self.assertGreater(pval,
0.05,
msg="Testing normal dist for ped speeds")
pval = stats.normaltest(veh_speeds).pvalue
self.assertGreater(pval,
0.05,
msg="Testing normal dist for ped speeds")
# Check median
self.assertAlmostEqual(data['ped_speed'],
statistics.median(ped_speeds),
delta=2)
self.assertAlmostEqual(data['bike_speed'],
statistics.median(bike_speeds),
delta=2)
self.assertAlmostEqual(data['mv_speed'],
statistics.median(veh_speeds),
delta=2)
def checkVersion(self):
r = self.auth.get(self.versionURL())
return parseData(r.text)
def listAppliances(self, pagesize=10, page=1):
url = self.reference.createAppendURL(string='appliances')
r = self.auth.get(url, params={'pageSize': pagesize, 'page': page})
return parseData(r.text)
#Jackson Zou #SoftDev pd9 #K09 -- Yummy Mongo Py #2020-02-26 from pymongo import MongoClient from parse import parseData, getRestaurantsByBorough, getRestaurantsByZip, getRestaurantsByZipandGrade, getRestaurantsByZipandScore client = MongoClient() db = client["restaurants"] print(db) parseData(db, "primer-dataset.json") getRestaurantsByZipandScore(db, "10024", "3") getRestaurantsByZipandGrade(db, "10024", "B") getRestaurantsByZip(db, "10024") getRestaurantsByBorough(db, "Brooklyn")
# Result output csv file
OUTPUT_CSV_FILENAME = "output/result.csv"
# Nerual Network Parameters
HIDDEN_LAYER = [128] # 1 hidden layer
BPTT_ORDER = 4
LEARNING_RATE = 0.05
EPOCH_NUM = 10 # number of epochs to run before saving the model
BATCH_SIZE = 1
currentEpoch = 0
print 'Parsing training data...'
t0 = time.time()
trainWordIndices = parse.parseData(TRAIN_FILENAME)
t1 = time.time()
print '...costs ', t1 - t0, ' seconds'
NEURON_NUM_LIST = [ HIDDEN_LAYER + [ wordUtil.WORD_VECTOR_SIZE ] ] + HIDDEN_LAYER + [ [ wordUtil.TOTAL_WORDS, wordUtil.WORD_CLASS_NUM ] ]
print 'Generating utils for class-based output layer...'
t0 = time.time()
wordUtil.genWordClassUtils(trainWordIndices)
t1 = time.time()
print '...costs ', t1 - t0, ' seconds'
print 'Training...'
aDNN = dnn.dnn( NEURON_NUM_LIST, BPTT_ORDER, LEARNING_RATE, EPOCH_NUM, BATCH_SIZE, LOAD_MODEL_FILENAME )
while True:
t2 = time.time()
def commitData(self, pagesize=10, page=1):
url = self.reference.createAppendURL(string='commit')
r = self.auth.post(url)
return parseData(r.text)
