def lin_pos(clist):
xlist = (x['x'] for x in clist)
elist = [x['dedx'] for x in clist]
x = average( xlist, elist )
ylist = (y['y'] for y in clist)
y = average( ylist, elist )
return (x,y)
def calculSD(li, liref):
"""
Calcul de l'écart-type (quantifier le montant de la dispersion) de deux ensembles de données.
:param list li: une liste de valeurs qu'a été identifie par le logiciel
:param list liref: liste de valeurs de référence pour les valeurs étudiées
:return: l'écart-type des valeurs étudiées et des valeurs de référence respectivement
:rtype: (float, float)
"""
n = len(li)
m = len(liref)
x_li = 0
x_ref = 0
x_a = average(li)
x_aref = average(liref)
#Calcul of SD - formula Wikipédia Ecart-type
for x_i, x_iref in zip(li, liref):
x_li += x_i**2
x_ref += x_iref**2
s = ((x_li / n) - x_a**2)**0.5
sref = ((x_ref / m) - x_aref**2)**0.5
return s, sref
def signal_to_symbol(self, values):
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
avg_value = average([average(sym_values.get(coord))
for coord in sym_values])
keys = sorted(self._inv_thresholds.keys())
ret.append(int(self._inv_thresholds[keys[bsearch(keys, avg_value)]]))
return ret
def signal_to_symbol(self, values):
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
avg_value = average(
[average(sym_values.get(coord)) for coord in sym_values])
keys = sorted(self._inv_thresholds.keys())
ret.append(
int(self._inv_thresholds[keys[bsearch(keys, avg_value)]]))
return ret
def tempero_spatial_pooling(self, learning_enabled=True):
'''
Temporal-Spatial pooling routine
--------------
Takes input and calculates active columns (sparse representation)
This is a representation of input in context of previous input
(same region and regions above -- top-down predictions)
'''
# Phase 1: Overlap
self.overlap = self.do_overlap()
log("%s << Overlap (normalized)" % printarray(self.overlap, continuous=True), level=2)
# Phase 2: Inhibition
activating = self.do_inhibition()
log("%s << Activating (inhibited)" % printarray(activating, continuous=True), level=2)
# Phase 3: Learning
if learning_enabled:
log("%s << Active Duty Cycle" % printarray(self.active_duty_cycle, continuous=True), level=2)
log("%s << Overlap Duty Cycle" % printarray(self.overlap_duty_cycle, continuous=True), level=2)
self.do_learning(activating)
# Phase 4: Calculate new activations
# Save pre-step activations
self.last_activation = [cell.activation for cell in self.cells]
# Update activations
for i, cell in enumerate(self.cells):
if activating[i]:
cell.activation = 1.0 # Max out
else:
cell.activation -= cell.fade_rate
if cell.activation < 0:
cell.activation = 0.0
log("%s << Activations" % self.print_cells(), level=2)
# Phase 5: Calculate Distal Biases (TM?)
self.last_bias = np.copy(self.bias)
self.bias = self.calculate_biases()
log("%s << Bias" % printarray(self.bias, continuous=True), level=2)
if VERBOSITY >= 1:
# Log average synapse permanence in region
permanences = []
n_connected = 0
n_synapses = 0
for cell in self.cells:
for seg in cell.distal_segments:
permanences.append(util.average(seg.syn_permanences))
n_synapses += seg.n_synapses()
n_connected += len(filter(lambda x : x > CONNECTED_PERM, seg.syn_permanences))
ave_permanence = util.average(permanences)
log("R%d - average distal synapse permanence: %.1f (%.1f%% connected of %d)" % (self.index, ave_permanence, (n_connected/float(n_synapses))*100., n_synapses), level=1)
def average_of_values(key):
"""Returns the average of some value from all data.
:param key: The key to the values of interest.
:returns: The averages of some values for HackerNews and DataTau
:rtype: dict
"""
return {
HN_KEY: average(_vals_on_all_datetimes(key, dt=False)),
DT_KEY: average(_vals_on_all_datetimes(key, hn=False))
}
def averages_on_datetimes(key):
"""Returns the averages of some values on every datetime
:param key: The key to the value of interest.
:returns: A list of averages for both HackerNews and DataTau
:rtype: dict
"""
averages = {HN_KEY: [], DT_KEY: []}
for dt in _get_datetimes():
averages[HN_KEY].append(average(_get_data()[dt][HN_KEY][key]))
averages[DT_KEY].append(average(_get_data()[dt][DT_KEY][key]))
return averages
def log_pos(clist,a=4.0):
#return tuple of x,y
bump = find_bump(clist)
E_bump = bump['dedx']
E_cutoff = E_bump*exp(-1*a)
wlist = []
for c in clist:
E = c['dedx']
#this is to avoid log of 0 evaluation
#also there are some event with energy less than 1.0
w = 0 if E < E_cutoff or E_bump < 10.0 else a+log(E/E_bump)
wlist.append(w)
x = average((x['x'] for x in clist), wlist)
y = average((y['y'] for y in clist), wlist)
return (x,y)
def list_resources(resource_type=None, query=None, tag=None):
"""return all resources"""
reliability_values = []
first_run = None
last_run = None
response = {
'total': 0,
'success': {
'number': 0,
'percentage': 0
},
'fail': {
'number': 0,
'percentage': 0
},
'first_run': None,
'last_run': None,
'reliability': 0
}
if resource_type is not None:
response['resources'] = models.Resource.query.filter_by(
resource_type=resource_type).all()
if query is not None:
field, term = get_query_field_term(query)
response['resources'] = models.Resource.query.filter(
field.ilike(term)).all()
if tag is not None:
response['resources'] = models.Resource.query.filter(
models.Resource.tags.any(models.Tag.name.in_([tag]))).all()
if 'resources' not in response:
# No query nor resource_type provided: fetch all resources
response['resources'] = models.Resource.query.all()
response['total'] = len(response['resources'])
for resource in response['resources']:
if resource.first_run < first_run or first_run is None:
first_run = resource.first_run
if resource.last_run < last_run or last_run is None:
last_run = resource.last_run
response['first_run'] = first_run
response['last_run'] = last_run
if resource.last_run.success:
response['success']['number'] += 1
else:
response['fail']['number'] += 1
reliability_values.append(resource.reliability)
response['success']['percentage'] = util.percentage(
response['success']['number'], response['total'])
response['fail']['percentage'] = util.percentage(
response['fail']['number'], response['total'])
response['reliability'] = util.average(reliability_values)
return response
def signal_to_symbol(self, values):
thresholds = { # Well, nine (we add one for "black").
'0': 235,
'1': 207,
'2': 179,
'3': 151,
'4': 123,
'5': 95,
'6': 67,
'7': 39,
'8': 11,
}
_inv_thresholds = dict((v,k) for k, v in thresholds.iteritems())
_keys = sorted(_inv_thresholds.keys())
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
_values = []
for coord in sym_values:
_values.append(sym_values.get(coord))
avg_value = average(_values)
_bs = bsearch(_keys, avg_value)
_key = _keys[_bs]
base8 = _inv_thresholds[_key]
ret.append(base8)
return ret
def signal_to_symbol(self, values):
thresholds = { # Well, nine (we add one for "black").
'0': 235,
'1': 207,
'2': 179,
'3': 151,
'4': 123,
'5': 95,
'6': 67,
'7': 39,
'8': 11,
}
_inv_thresholds = dict((v, k) for k, v in thresholds.iteritems())
_keys = sorted(_inv_thresholds.keys())
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
_values = []
for coord in sym_values:
_values.append(sym_values.get(coord))
avg_value = average(_values)
_bs = bsearch(_keys, avg_value)
_key = _keys[_bs]
base8 = _inv_thresholds[_key]
ret.append(base8)
return ret
def do_learning(self):
for col in self._get_active_columns():
for s in col.segment.potential_synapses:
if s.active():
s.permanence += self.permanence_inc
s.permanence = min(1.0, s.permanence)
else:
s.permanence -= self.permanence_dec
s.permanence = max(0.0, s.permanence)
all_field_sizes = []
for c, col in enumerate(self.columns):
min_duty_cycle = 0.01 * self._max_duty_cycle(self.neighbors[c])
column_active = self.active_columns[-1][c]
sufficient_overlap = self.overlap[c] > self.brain.min_overlap
col.update_duty_cycles(active=column_active, overlap=sufficient_overlap)
self.boost[c] = self._boost_function(c, min_duty_cycle) # Updates boost value for column (higher if below min)
# Check if overlap duty cycle less than minimum (note: min is calculated from max *active* not overlap)
if self.overlap_duty_cycle[c] < min_duty_cycle:
self._increase_permanences(c, 0.1 * CONNECTED_PERM)
all_field_sizes.append(self.columns[c].connected_receptive_field_size())
# Update inhibition radius (based on updated active connections in each column)
self.inhibition_radius = util.average(all_field_sizes)
def _host_color(self, host):
def player_colors_component(x):
return [player.worm.color[x] for player in host.players]
r, g, b = tuple([average(player_colors_component(i))/4.0
for i in xrange(3)])
br, bg, bb = config.HUD_HOST_BASE_COLOR
return r+br, g+bg, b+bb
def finalize_attempt_data(attempt, start_time):
t_, e_, d_, s_, _ = attempt['last_line'].split('\t', 4)
s_ = datetime.datetime.strptime(t_, '%m-%d-%y-%H-%M-%S')
s_ = (s_ - epoch).total_seconds()
s_ = s_ - start_time
del(attempt['last_line'])
attempt['end_date'] = t_
attempt['total_time'] = (float(s_) - attempt['start_time']) #/ 60.0
me_total = 0.0
for i in ['tests','submissions','replays']:
me_total += attempt['me_'+i]
if attempt['total_time']:
attempt['r_me_per_minute'] = me_total/(attempt['total_time']/60.0)
for i in ['tests', 'submissions', 'replays']:
if attempt['total_time']:
attempt['r_me_'+i+'_per_minute'] = 1.0*attempt['me_'+i] / (attempt['total_time'] / 60.0)
if me_total:
attempt['r_me_' + i] = 1.0*attempt['me_' + i] / me_total
for i in ['_tests', '_submissions','']:
lst_ = []
for j in attempt['_t_d_component_dragged_me_lst']:
if j[0]==i or not i:
lst_.append(j[1])
if lst_:
attempt['t_d_component_dragged_me' + i + '_avg'] = util.average(lst_)
attempt['t_d_component_dragged_me' + i + '_min'] = min(lst_)
'''
def get_stats_based_on_entities(self,tag_to_compare_against=TaggableContainer.TAG_CHARACTER_SYMBOL):
characters = Entity.filter_characters(self.entities)
characters_uniq = set([i.symbol for i in characters if ',' not in i.symbol])
coref_groups_with_characters = [i for i in self.coreference_groups if i.contains_a_character()]
coref_groups_with_characters_c_gr = [i.number_of_distinct_characters(tag_to_compare_against) for i in coref_groups_with_characters]
coref_groups_per_character_gr_c = [i.number_of_distinct_coref_groups(tag_to_compare_against) for i in characters]
return [len(self.entities),
len(characters),
len(characters_uniq),
sum([len(self.get_coreference_group_by_id(i.id).mentions) for i in self.entities]),
sum([len(self.get_coreference_group_by_id(i.id).mentions) for i in characters]),
sum([len(self.get_coreference_group_by_id(i.id).mentions) for i in characters])/len(characters) if characters else 0,
len(self.coreference_groups),
len(coref_groups_with_characters),
util.average(coref_groups_with_characters_c_gr),
util.average(coref_groups_per_character_gr_c)
]
def single_sensor_accuracy(self):
sensor = []
accuracy = []
std = []
for s in self.dataset.get_sensors:
if s != "activityrecognition":
print(s)
features = self.dataset.get_sensor_features(s)
train = self.dataset.get_train.copy()
test = self.dataset.get_test.copy()
train_features, train_classes, test_features, test_classes = self.__get_sets_for_classification(
train, test, features)
singleAcc = []
for i in range(const.REPEAT):
# build classifier
classifier_forest = RandomForestClassifier(
n_estimators=const.PAR_RF_ESTIMATOR)
classifier_forest.fit(train_features, train_classes)
test_prediction_forest = classifier_forest.predict(
test_features)
acc_forest = accuracy_score(test_classes,
test_prediction_forest)
singleAcc.append(acc_forest)
accM = util.average(singleAcc)
variance = list(map(lambda x: (x - accM)**2, singleAcc))
standard_deviation = math.sqrt(util.average(variance))
print(s, accM, standard_deviation)
accuracy.append(accM)
std.append(standard_deviation)
sensor.append(s)
df_single_sensor_acc = pd.DataFrame({
'sensor': sensor,
'accuracy': accuracy,
'dev_standard': std
})
df_single_sensor_acc = df_single_sensor_acc.sort_values(
by='accuracy', ascending=False)
if not os.path.exists(const.DIR_RESULTS):
os.makedirs(const.DIR_RESULTS)
df_single_sensor_acc.to_csv(const.DIR_RESULTS + "/" +
const.FILE_SINGLE_SENSOR_ANALYSIS,
index=False)
def get_stats(self,tag_to_compare_against=TaggableContainer.TAG_CHARACTER_SYMBOL):
characters = [i for i in self.document.get_all_mentions() if i.is_independent and 'CH' in i.get_taxonomy(TaxonomyContainer.TAXONOMY_NONCHARACTER)]
characters_uniq = set(util.flatten([i.get_tag(TaggableContainer.TAG_CHARACTER_SYMBOL) for i in characters]))
coref_groups_with_characters = [i for i in self.coreference_groups if i.contains_a_character()]
coref_groups_with_characters_c_gr = [i.number_of_distinct_characters(tag_to_compare_against) for i in coref_groups_with_characters]
coref_groups_per_character_gr_c = [self.number_of_distinct_coref_groups(i) for i in characters_uniq]
#coref_groups_per_character_gr_c = [i.number_of_distinct_coref_groups(tag_to_compare_against) for i in characters]
return [len(self.entities),
len(characters),
len(characters_uniq),
sum([len(self.get_coreference_group_by_id(i.id).mentions) for i in self.entities]),
#sum([len(self.get_coreference_group_by_id(i.id).mentions) for i in characters]),
#sum([len(self.get_coreference_group_by_id(i.id).mentions) for i in characters])/len(characters) if characters else 0,
len(self.coreference_groups),
len(coref_groups_with_characters),
util.average(coref_groups_with_characters_c_gr),
util.average(coref_groups_per_character_gr_c)
]
def cleanup(self):
# TODO: decouple printer and connection.
self.printer.terminate()
print("Terminating threads...")
self.printer.join()
if "-d" in sys.argv and self.buff.log:
print("%d high latency events recorded, max=%r, avg=%r" %
(len(self.buff.log), max(
self.buff.log), util.average(self.buff.log)))
def list_resources(resource_type=None, query=None):
"""return all resources"""
reliability_values = []
first_run = None
last_run = None
response = {
'total': 0,
'success': {
'number': 0,
'percentage': 0
},
'fail': {
'number': 0,
'percentage': 0
},
'first_run': None,
'last_run': None,
'reliability': 0
}
if resource_type is not None:
response['resources'] = models.Resource.query.filter_by(
resource_type=resource_type).all()
if query is not None:
field, term = get_query_field_term(query)
response['resources'] = models.Resource.query.filter(
field.ilike(term)).all()
else:
response['resources'] = models.Resource.query.all()
response['total'] = len(response['resources'])
for resource in response['resources']:
if resource.first_run < first_run or first_run is None:
first_run = resource.first_run
if resource.last_run < last_run or last_run is None:
last_run = resource.last_run
response['first_run'] = first_run
response['last_run'] = last_run
if resource.last_run.success:
response['success']['number'] += 1
else:
response['fail']['number'] += 1
reliability_values.append(resource.reliability)
response['success']['percentage'] = util.percentage(
response['success']['number'], response['total'])
response['fail']['percentage'] = util.percentage(
response['fail']['number'], response['total'])
response['reliability'] = util.average(reliability_values)
return response
def calculStandardDeviation(data):
"""
Calculer l'écart-type d'un ensemble de données.
:param list data: liste de valeurs
:return: l'écart-type de l'ensemble de valeurs
:rtype: float
"""
av = average(data)
n = len(data)
s = (sum((val - av)**2 for val in data) / (n - 1))**0.5
return s
def search(f, neg_point, pos_point):
midpoint = average(neg_point, pos_point)
if close_enough(neg_point, pos_point):
return midpoint
test_value = f(midpoint)
if test_value > 0:
return search(f, neg_point, midpoint)
elif test_value < 0:
return search(f, midpoint, pos_point)
else:
return midpoint
def cleanup(self):
# TODO: decouple printer and connection.
self.printer.terminate()
print("Terminating threads...")
self.printer.join()
if "-d" in sys.argv and self.buff.log:
print(
"%d high latency events recorded, max=%r, avg=%r" % (
len(self.buff.log),
max(self.buff.log),
util.average(self.buff.log)
)
)
def analyse(path, filter_fn, field_name, print_csv=False):
data = load_data(path, filter_fn)
occurrences = data['days']
day_of_cycle = data['day_of_cycle']
weekdays = data['weekdays']
day_of_cycle_total = sum([day_of_cycle[x] for x in day_of_cycle])
if len(occurrences) == 0:
print "No tags found. Are you sure '%s' is the correct tag?" % tag
return
deltas = []
for d in xrange(len(occurrences)-1):
delta = occurrences[d+1] - occurrences[d]
if delta.days > 2:
deltas.append(delta.days)
if print_csv:
print "date,%s" % field_name
for d in date_range(occurrences[0], occurrences[len(occurrences)-1]):
if d in occurrences:
print str(d) + ",1"
else:
print str(d) + ",0"
return
print "==============="
print "Day of cycle distribution"
previous = None
for k in sorted(day_of_cycle.keys()):
if previous:
if k - previous > 1:
print ".\n."
previous = k
print ("Day %s:" % k).ljust(10), str(day_of_cycle[k]).ljust(4), round(day_of_cycle[k] / float(day_of_cycle_total), 2)
print "==============="
print "Weekday distribution"
for k in sorted(weekdays.keys()):
print weekday_from_int(k).ljust(5), weekdays[k]
print "==============="
print "Total amount of days with %s: " % field_name, len(occurrences)
print "Average amount of days between %s: " % field_name, average(deltas)
print "Std dev: ", std_dev(deltas)
print "Last day with %s: " % field_name, occurrences[len(occurrences)-1]
print "Days between today and last day with %s: " % field_name, (datetime.datetime.today().date() - occurrences[len(occurrences)-1].date()).days
print "==============="
def list_resources(resource_type=None):
"""return all resources"""
reliability_values = []
response = {
'total': 0,
'success': {
'number': 0,
'percentage': 0
},
'fail': {
'number': 0,
'percentage': 0
},
'reliability': 0
}
if resource_type is not None:
response['resources'] = models.Resource.query.filter_by(
resource_type=resource_type).all()
else:
response['resources'] = models.Resource.query.all()
response['total'] = len(response['resources'])
for resource in response['resources']:
if resource.last_run.success:
response['success']['number'] += 1
else:
response['fail']['number'] += 1
reliability_values.append(resource.reliability)
response['success']['percentage'] = util.percentage(
response['success']['number'], response['total'])
response['fail']['percentage'] = util.percentage(
response['fail']['number'], response['total'])
response['reliability'] = util.average(reliability_values)
return response
def list_resources(resource_type=None):
"""return all resources"""
reliability_values = []
response = {
'total': 0,
'success': {
'number': 0,
'percentage': 0
},
'fail': {
'number': 0,
'percentage': 0
},
'reliability': 0
}
if resource_type is not None:
response['resources'] = models.Resource.query.filter_by(
resource_type=resource_type).all()
else:
response['resources'] = models.Resource.query.all()
response['total'] = len(response['resources'])
for resource in response['resources']:
if resource.last_run.success:
response['success']['number'] += 1
else:
response['fail']['number'] += 1
reliability_values.append(resource.reliability)
response['success']['percentage'] = util.percentage(
response['success']['number'], response['total'])
response['fail']['percentage'] = util.percentage(
response['fail']['number'], response['total'])
response['reliability'] = util.average(reliability_values)
return response
def run(window):
timer = pygame.time.Clock()
width = window.get_width()
height = window.get_height()
font = pygame.freetype.SysFont('Comic Sans MS', 18)
boundary = Rectangle((0, 0), (width, height))
particles = []
using_quadtree = False
for i in range(0, 200):
particles.append(
Particle([random_zero_to_max(width),
random_zero_to_max(height)], 2))
running = True
frame_rate = []
while running:
window.fill((0, 0, 0))
dt = timer.tick()
qtree = Quadtree(boundary, 4)
for particle in particles:
qtree.insert(particle)
for particle in particles:
if not particle.highlight:
if using_quadtree:
region = Circle([particle.x, particle.y],
particle.r * 2 - 1)
others = qtree.query(region)
particle.check_collision(others)
else:
particle.check_collision(particles)
particle.render(window)
particle.move(width, height)
if len(frame_rate) > 10:
frame_rate.pop(0)
frame_rate.append(1000 / dt)
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
if event.type == pygame.MOUSEBUTTONUP:
using_quadtree = not using_quadtree
if using_quadtree:
text_render, _ = font.render('Using QuadTree', (255, 255, 255))
else:
text_render, _ = font.render('Not Using QuadTree', (255, 255, 255))
rect = text_render.get_rect(center=(400 + 100 / 2, 300 + 20 / 2))
window.blit(text_render, rect)
text_render, _ = font.render(
'{} FPS'.format(round(average(frame_rate), 1)), (255, 255, 255))
rect = text_render.get_rect(center=(400 + 100 / 2, 350 + 20 / 2))
window.blit(text_render, rect)
pygame.display.update()
def improve(guess):
return average(guess, (x / guess))
def list_resources(resource_type=None, query=None, tag=None):
"""return all resources"""
reliability_values = []
first_run = None
last_run = None
response = {
'total': 0,
'success': {
'number': 0,
'percentage': 0
},
'fail': {
'number': 0,
'percentage': 0
},
'first_run': None,
'last_run': None,
'reliability': 0
}
if resource_type is not None:
response['resources'] = models.Resource.query.filter_by(
resource_type=resource_type).all()
if query is not None:
field, term = get_query_field_term(query)
response['resources'] = models.Resource.query.filter(
field.ilike(term)).all()
if tag is not None:
response['resources'] = models.Resource.query.filter(
models.Resource.tags.any(models.Tag.name.in_([tag]))).all()
if 'resources' not in response:
# No query nor resource_type provided: fetch all resources
response['resources'] = models.Resource.query.all()
response['total'] = len(response['resources'])
response['success']['percentage'] = 0
response['fail']['percentage'] = 0
response['reliability'] = 0
for resource in response['resources']:
if resource.runs.count() > 0:
# View should work even without Runs
if resource.first_run < first_run or first_run is None:
first_run = resource.first_run
if resource.last_run < last_run or last_run is None:
last_run = resource.last_run
response['first_run'] = first_run
response['last_run'] = last_run
if resource.last_run.success:
response['success']['number'] += 1
else:
response['fail']['number'] += 1
reliability_values.append(resource.reliability)
response['success']['percentage'] = util.percentage(
response['success']['number'], response['total'])
response['fail']['percentage'] = util.percentage(
response['fail']['number'], response['total'])
response['reliability'] = util.average(reliability_values)
return response
def test_empty_list(self):
with self.assertRaises(ValueError):
avg = average([])
data[folder][sub['name']] = []
for data_path in paths:
json_data = util.open_json_result(os.path.join(path, folder, sub['name'], data_path))
if type(json_data) is list:
d = json_data[0]
else:
d = json_data
data[folder][sub['name']].append(d)
#Average and find breakeven points, for final series.
breakeven_points = {}
for folder in folders:
breakeven_points[folder] = []
for sub in sub_folders:
pr_avg = util.average(data[folder][sub['name']], pr_key_y, pr_key_x)
breakeven = util.find_breakeven(pr_avg, samples=4)
breakeven_points[folder].append({"x": sub['value'], "y": breakeven[1]})
print breakeven_points
#series.append({"name": folder, "data": pr_avg})
#
#Summary figure for MSE loss
loss_points = {}
for folder in folders:
loss_points[folder] = []
for sub in sub_folders:
loss_avg = util.average(data[folder][sub['name']], 'events', lc_key_x)
last_loss = loss_avg[-1][lc_key_y]
loss_points[folder].append({"x": sub['value'], "y": last_loss})
def list_resources(resource_type=None, query=None, tag=None):
"""return all resources"""
reliability_values = []
first_run = None
last_run = None
response = {
'total': 0,
'success': {
'number': 0,
'percentage': 0
},
'fail': {
'number': 0,
'percentage': 0
},
'first_run': None,
'last_run': None,
'reliability': 0
}
filters = ()
if resource_type is not None:
filters = filters + ("resource_type = '%s'" % resource_type,)
if query is not None:
field, term = get_query_field_term(query)
filters = filters + (field.ilike(term),)
if tag is not None:
tag_filter = (models.Resource.tags.any(models.Tag.name.in_([tag])),)
filters = filters + tag_filter
response['resources'] = models.Resource.query.filter(*filters).all()
response['total'] = len(response['resources'])
response['success']['percentage'] = 0
response['fail']['percentage'] = 0
response['reliability'] = 0
for resource in response['resources']:
if resource.runs.count() > 0:
# View should work even without Runs
if resource.first_run < first_run or first_run is None:
first_run = resource.first_run
if resource.last_run < last_run or last_run is None:
last_run = resource.last_run
response['first_run'] = first_run
response['last_run'] = last_run
if resource.last_run.success:
response['success']['number'] += 1
else:
response['fail']['number'] += 1
reliability_values.append(resource.reliability)
response['success']['percentage'] = int(round(util.percentage(
response['success']['number'], response['total'])))
response['fail']['percentage'] = 100 - response['success']['percentage']
response['reliability'] = round(util.average(reliability_values), 1)
return response
def average_response_time(self):
result = 0
if self.runs.count() > 0:
query = [run.response_time for run in self.runs]
result = util.average(query)
return result
else:
d = json_data
data[folders[t]].append(d)
series = []
manual_breakeven = [
[0.513, 0.595, 0.687, 0.712],
[0.337, 0.411, 0.462, 0.524],
] # Very uneven curves is hard to approximate by polyfit. (finds breakeven automatically)
compare_series = []
for j, pr in enumerate(folders[1:]):
pr_per_epoch = []
for i, curve in enumerate(data[pr]):
samples = 10
if i < len(manual_breakeven[j]):
breakeven_points = [0, manual_breakeven[j][i]]
else:
breakeven_points = util.find_breakeven(curve[pr_key_y], samples=samples)
print(folder, breakeven_points)
name = (pr_epoch * i) + 5
series.append({"name": "Epoch " + str(name), "data": curve[pr_key_y], "breakeven": breakeven_points})
pr_per_epoch.append({"epoch": name, "breakeven": breakeven_points[-1]})
compare_series.append({"name": pr, "data": pr_per_epoch, "y_key": "breakeven"})
util.display_precision_recall_plot(series)
series = []
loss_avg = util.average(data[folders[0]], "events", lc_key_x)
series.append({"name": folders[0], "data": loss_avg, "y_key": lc_key_y})
util.display_two_axis_plot(series, compare_series)
[0.513, 0.595, 0.687, 0.712], [0.337, 0.411, 0.462, 0.524]
] #Very uneven curves is hard to approximate by polyfit. (finds breakeven automatically)
compare_series = []
for j, pr in enumerate(folders[1:]):
pr_per_epoch = []
for i, curve in enumerate(data[pr]):
samples = 10
if i < len(manual_breakeven[j]):
breakeven_points = [0, manual_breakeven[j][i]]
else:
breakeven_points = util.find_breakeven(curve[pr_key_y],
samples=samples)
print(folder, breakeven_points)
name = (pr_epoch * i) + 5
series.append({
"name": "Epoch " + str(name),
"data": curve[pr_key_y],
"breakeven": breakeven_points
})
pr_per_epoch.append({'epoch': name, 'breakeven': breakeven_points[-1]})
compare_series.append({
'name': pr,
"data": pr_per_epoch,
'y_key': 'breakeven'
})
util.display_precision_recall_plot(series)
series = []
loss_avg = util.average(data[folders[0]], 'events', lc_key_x)
series.append({"name": folders[0], "data": loss_avg, "y_key": lc_key_y})
util.display_two_axis_plot(series, compare_series)
def main(args):
# Determine which algorithms to perform
algorithms = []
if args.bf:
algorithms.append(wrp.AlgorithmWrapper(bf.CONTENT))
if args.nn:
algorithms.append(wrp.AlgorithmWrapper(nn.CONTENT))
if args.ni:
algorithms.append(wrp.AlgorithmWrapper(ni.CONTENT))
if args.mst:
algorithms.append(wrp.AlgorithmWrapper(mst.CONTENT))
if args.ci:
algorithms.append(wrp.AlgorithmWrapper(ci.CONTENT))
# Initialize plots
fig_correct, fig_complex, plot_correct, plot_complex = init_plots(
algorithms)
# Execute correct command
if args.cmd == 'read':
datasets = dataset.read(args.path)
for ds in datasets:
for algorithm in algorithms:
y1, y2 = analyse_algorithm(ds.adj, ds.order, algorithm,
args.repeat)
plot_correct.scatter(ds.order,
y2,
color=algorithm.color,
alpha=0.5,
s=0.5)
plot_complex.scatter(ds.order,
y1,
color=algorithm.color,
alpha=0.5,
s=0.5)
elif args.cmd == 'random':
if args.write:
if not os.path.exists('datasets'):
os.makedirs('datasets')
order = args.order # reset n
while order <= args.max:
for i in range(args.trials):
path = None
if args.write:
path = "datasets/order_{}_trial_{}.dat".format(order, i)
adj = dataset.generate(order, args.spread, path)
for algorithm in algorithms:
y1, y2 = analyse_algorithm(adj, order, algorithm,
args.repeat)
algorithm.x.append(order)
algorithm.complex.append(y1)
algorithm.working_complex.append(y1)
algorithm.correct.append(y2)
algorithm.working_correct.append(y2)
for algorithm in algorithms:
algorithm.avg_correct.append(
util.average(algorithm.working_correct))
algorithm.avg_complex.append(
util.average(algorithm.working_complex))
algorithm.avg_x.append(order)
algorithm.working_correct.clear()
algorithm.working_complex.clear()
order += 1
if args.plot:
for algorithm in algorithms:
# Plot correctness measure
plot_correct.scatter(algorithm.x,
algorithm.correct,
color=algorithm.color,
alpha=0.5,
s=0.5)
plot_correct.plot(algorithm.avg_x,
algorithm.avg_correct,
'-',
color=algorithm.color,
linewidth=0.5)
fig_correct.savefig('Correctness',
dpi=300,
bbox_inches='tight')
# Plot complexity measure
plot_complex.scatter(algorithm.x,
algorithm.complex,
color=algorithm.color,
alpha=0.5,
s=0.5)
plot_complex.plot(algorithm.avg_x,
algorithm.avg_complex,
'-',
color=algorithm.color,
linewidth=0.5)
fig_complex.savefig('Complexity', dpi=300, bbox_inches='tight')
'''
This tool creates a loss figure containing, training, test and validation error. It averages experiment runs before
constructing a figure.
'''
path = '/home/olav/Documents/Results/E7_road_detection_performance'
folder = 'Network M1'
lc_key_x = 'epoch'
print("Creating averaged loss figures")
paths = os.listdir(os.path.join(path, folder))
data = []
for data_path in paths:
json_data = util.open_json_result(os.path.join(path, folder,data_path))
if type(json_data) is list:
d = json_data[0]
else:
d = json_data
data.append(d)
loss_avg = util.average(data, 'events', lc_key_x)
series = [
{"name": "Training loss", "data": loss_avg, "y_key": "training_loss"},
#{"name": "Validation loss", "data": loss_avg, "y_key": "validation_loss"},
{"name": "Test loss", "data": loss_avg, "y_key": "test_loss"}
]
util.display_loss_curve_plot(series)
def average_link_overlap():
return average(link_overlap_on_datetimes())
def midpoint_segment(segment):
return cons(
average(x_point(start_segment(segment)),
x_point(end_segment(segment))),
average(y_point(start_segment(segment)),
y_point(end_segment(segment))))
def sqrt(x):
return fixed_point(lambda y: average(y, x / y), 1.0)
def average_response_time(self):
query = [run.response_time for run in self.runs]
return util.average(query)
def do_learning(self, activating):
'''
Update permanences
On activating cells, increase permenences for each excitatory synapse above a min. contribution
On non-activating cells, increase permenences for each inhibitory synapse above a min. contribution
'''
n_increased_prox = n_decreased_prox = n_increased_dist = n_decreased_dist = n_conn_prox = n_discon_prox = n_conn_dist = n_discon_dist = 0
for i, is_activating in enumerate(activating):
cell = self.cells[i]
# Proximal
# TODO: Also hold pre-learn segment activation for proximal
if is_activating:
for seg in cell.proximal_segments:
ni, nd, nc, ndc = self.learn_segment(seg, is_activating=is_activating)
n_increased_prox += ni
n_decreased_prox += nd
n_conn_prox += nc
n_discon_prox += ndc
# Distal
any_active = any([seg.active() for seg in cell.distal_segments])
most_active = None
if not any_active:
most_active = cell.most_active_distal_segment()
for seg in cell.distal_segments:
active = seg.active_before_learning
do_learn = is_activating and (active or not any_active)
if do_learn:
ni, nd, nc, ndc = self.learn_segment(seg, is_activating=is_activating, distal=True)
n_increased_dist += ni
n_decreased_dist += nd
n_conn_dist += nc
n_discon_dist += ndc
else:
# Re-initialize change state
seg.decay_permanences()
seg.syn_last_change = [0 for x in seg.syn_last_change]
log("Distal: +%d/-%d (%d connected, %d disconnected)" % (n_increased_dist, n_decreased_dist, n_conn_dist, n_discon_dist))
n_boosted = 0
all_field_sizes = []
for i, cell in enumerate(self.cells):
neighbors = self._neighbors_of(cell)
min_duty_cycle = 0.01 * self._max_duty_cycle(neighbors) # Based on active duty
cell_active = activating[i]
sufficient_overlap = self.overlap[i] > self.brain.min_overlap
cell.update_duty_cycles(active=cell_active, overlap=sufficient_overlap)
if DO_BOOSTING and self.brain.t > T_START_BOOSTING:
self.boost[i] = self._boost_function(i, min_duty_cycle) # Updates boost value for cell (higher if below min)
# Check if overlap duty cycle less than minimum (note: min is calculated from max *active* not overlap)
if self.overlap_duty_cycle[i] < min_duty_cycle:
# log("Increasing permanences for cell %s in region %d due to overlap duty cycle below min: %s" % (i, self.index, min_duty_cycle))
self._increase_permanences(i, 0.1 * CONNECTED_PERM, type="proximal")
n_boosted += 1
# TODO: Boost distal here if active_duty_cycle low?
all_field_sizes.append(self.cells[i].connected_receptive_field_size())
if n_boosted:
log("Boosting %d due to low overlap duty cycle" % n_boosted)
# Update inhibition radius (based on updated active connections in each column)
self.inhibition_radius = util.average(all_field_sizes) * INHIBITION_RADIUS_DISCOUNT
min_positive_radius = 1.0
if self.inhibition_radius and self.inhibition_radius < min_positive_radius:
self.inhibition_radius = min_positive_radius
exact_x = np.array([39.0 / 40, -13.0 / 40, 12.0 / 40])
for i in range(100):
u = np.random.uniform(-10, 10, 3)
gs_x_n, gs_iteration, gs_error = iterative_methods.gauss_seidel(A_b_aug, u, e, m)
j_x_n, j_iteration, j_error = iterative_methods.jacobi(A_b_aug, u, e, m)
gs_results.append(gs_x_n)
gs_iterations.append(gs_iteration)
initial_errors.append(util.norm_inf(u - exact_x))
jacobi_results.append(j_x_n)
jacobi_iterations.append(j_iteration)
gs_approx_x = util.average(gs_results)
jacobi_approx_x = util.average(jacobi_results)
gs_error = util.norm_inf(gs_approx_x - exact_x)
jacobi_error = util.norm_inf(jacobi_approx_x - exact_x)
jacobi_gs_ratios = [float(j) / gs for j, gs in zip(gs_iterations, jacobi_iterations)]
average_ratio = sum(jacobi_gs_ratios) / len(jacobi_gs_ratios)
for error, gs_iteration, j_iteration in zip(initial_errors, gs_iterations, jacobi_iterations):
print('%f, %d, %d' % (error, gs_iteration, j_iteration))
print('')
print('Average Gauss-Seidel Error: %f' % gs_error)
print('Average Jacobi Error: %f' % jacobi_error)
def average_response_time(self):
result = 0
if self.runs.count() > 0:
query = [run.response_time for run in self.runs]
result = util.average(query)
return result
def run(window):
timer = pygame.time.Clock()
width = window.get_width()
height = window.get_height()
font = pygame.freetype.SysFont('Comic Sans MS', 18)
boundary = Rectangle((0, 0), (width, height))
bodies = []
using_quadtree = False
for _ in range(0, 10):
bodies.append(
Body([random_zero_to_max(width),
random_zero_to_max(height)], random(2, 10)))
_, _, total_energy = calculate_energy(bodies)
running = True
frame_rate = []
while running:
window.fill((0, 0, 0))
dt = timer.tick()
qtree = Quadtree(boundary, 4)
for body in bodies:
qtree.insert(Point([body.x[0], body.x[1]]))
for body in bodies:
if not body.highlight:
if using_quadtree:
region = Circle([body.x[0], body.x[1]], body.r * 2 - 1)
others = qtree.query(region)
body.physics(others)
else:
body.physics(bodies)
body.render(window)
body.move(dt)
ajust_speeds(bodies, total_energy, 0.001, 1)
if len(frame_rate) > 10:
frame_rate.pop(0)
frame_rate.append(1000 / dt)
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
if event.type == pygame.MOUSEBUTTONUP:
using_quadtree = not using_quadtree
if using_quadtree:
text_render, _ = font.render('Using QuadTree', (255, 255, 255))
else:
text_render, _ = font.render('Not Using QuadTree', (255, 255, 255))
rect = text_render.get_rect(center=(400 + 100 / 2, 300 + 20 / 2))
window.blit(text_render, rect)
text_render, _ = font.render(
'{} FPS'.format(round(average(frame_rate), 1)), (255, 255, 255))
rect = text_render.get_rect(center=(400 + 100 / 2, 350 + 20 / 2))
window.blit(text_render, rect)
pygame.display.update()
def get_features(interval_data, files, persistent_data, path):
# Given the telemetry logs in a time window, get features
data = {'levels': 0, 'me': 0, 'uploaded': 0,'seq':[], 'feature_vectors':[] }
feature_vector = dict( [(i, 0) for i in get_feature_labels()] )
ir_coord = ( ) # BeginReposition
fr_coord = ( ) # EndReposition
il_coord = ( ) # BeginLink
fl_coord = ( ) # LinkTo
id_coord = ( ) # startDrag
fd_coord = ( ) # endDrag
#feature_vector['level'] = "slice"
feature_vector['tutorial_time'] = 0.0
if len(interval_data) == 0:
return feature_vector
date_, name_, data_, time_, x_, y_ = interval_data[0].split("\t")
s_ = datetime.datetime.strptime(date_,'%m-%d-%y-%H-%M-%S')
s_ = (s_ - epoch).total_seconds()
if not persistent_data['start_time']:
persistent_data['start_time'] = s_
feature_vector['start_date'] = date_
feature_vector['start_time'] = float(s_ - persistent_data['start_time'])
feature_vector['_t_d_component_dragged_me_lst'] = []
feature_vector['_timestamp_last_dragged_component'] = 0.0
start_time = None
missing_files = []
for int_data in interval_data:
date_, name_, data_, time_, x_, y_ = int_data.split("\t")
s_ = datetime.datetime.strptime(date_,'%m-%d-%y-%H-%M-%S')
s_ = (s_ - epoch).total_seconds()
s_ = s_ - persistent_data['start_time']
if name_ == 'TriggerLoadLevel':
trackUsed = -1
if data_:
if data_.startswith('l'):
#print "Level (doesn't have "/data" associated with it): " , d_
level_num = data_.replace('level', '').replace('-', '')
if level_num: data['seq'].append(level_num)
data['levels'] += 1
else:
# Read the data file, get all the components. Reset only when we start a new level (excluding reset).
persistent_data["comp_color_map"] = {}
persistent_data["comp_link_map"] = {}
persistent_data["comp_loc_map"] = {}
persistent_data["direction_layout"] = []
persistent_data["color_layout"] = []
persistent_data["cur_track"] = -1
persistent_data["cur_mouse_comp"] = ""
persistent_data["cur_mouse_time"] = 0.0
persistent_data['linking'] = False
data['me'] += 1
if data_ in files:
dfile = open(path + '/data' + "/" + files[data_][0],'r')
dfiledata = [ d.strip("\n") for d in dfile.readlines() ]
# print "File, number of files (should be 1): {0},{1}".format(files[d_][0],len(files[d_]))
# Find components section, and get components
startParse = False
height = 0
width = 0
incr = 0
for line in dfiledata:
if line.startswith("board_width"):
width = int(line.split("\t")[1])
if line.startswith("board_height"):
height = int(line.split("\t")[1])
if line.startswith('DIRECTIONS'):
for i in range(incr+1,incr+height+1):
persistent_data['direction_layout'].append(dfiledata[i])
if line.startswith('COLORS'):
for i in range(incr+1,incr+height+1):
persistent_data['color_layout'].append(dfiledata[i])
if line.startswith('COMPONENTS'):
startParse = True
else:
if startParse:
if len(line) == 0: # End of components section
startParse = False
else:
tmp = line.split("\t")
compData = ast.literal_eval(tmp[6])
if "color" in compData.keys():
# CompID -> color
persistent_data['comp_color_map'][tmp[0]] = int(compData["color"])
if "link" in compData.keys():
# CompID -> LinkedID
persistent_data['comp_link_map'][tmp[0]] = str(compData["link"])
incr += 1
dfile.close()
data['uploaded'] += 1
else:
missing_files.append(d_)
else:
data['seq'].append('R')
feature_vector['me_replays'] += 1
elif name_ == 'endTutorial':
feature_vector['tutorial_time'] = (float(s_) - feature_vector['start_time']) / 60.0
elif name_ == 'SubmitCurrentLevelPlay':
data['seq'].append('T')
feature_vector['me_tests']+=1
if feature_vector['_timestamp_last_dragged_component']:
feature_vector['_t_d_component_dragged_me_lst'].append(('tests',float(s_)-feature_vector['_timestamp_last_dragged_component']))
elif name_ =='SubmitCurrentLevelME':
data['seq'].append('S')
feature_vector['me_submissions'] += 1
if feature_vector['_timestamp_last_dragged_component']:
feature_vector['_t_d_component_dragged_me_lst'].append(('submissions',float(s_)-feature_vector['_timestamp_last_dragged_component']))
elif name_ == 'tooltip':
feature_vector['tooltips'] += 1
feature_vector['tooltip_'+data_] = feature_vector.get('tooltip_'+data_,0)+1
elif name_ == 'startDrag':
feature_vector['dragged'] += 1
feature_vector['dragged_'+data_] = feature_vector.get('dragged_'+data_,0)+1
feature_vector['_timestamp_last_dragged_component'] = float(s_)
# Coordinates for Start Drag
id_coord = ( float(x_), float(y_) )
elif name_ == "endDrag":
# Coordinates for End Drag
fd_coord = ( float(x_), float(y_) )
# Euclidean distance
dist = 0.0
if ( len(id_coord) != 0 ):
dist = math.sqrt( math.pow((fd_coord[0]-id_coord[0]),2.0) + math.pow((fd_coord[1]-id_coord[1]),2.0) )
else:
dist = 0.0
id_coord = ()
fd_coord = ()
feature_vector['total_dragged_components_dist'] += dist
elif name_ == 'Destroying':
feature_vector['trashed'] += 1
feature_vector['trashed_'+data_] = feature_vector.get('trashed_'+data_,0)+1
elif name_ == 'OnHoverBehavior':
# This implies that we are on a track...but we hover on an object though..?
feature_vector['hover'] += 1
feature_vector['hover_'+data_] = feature_vector.get('hover_'+data_,0)+1
elif name_ == "BeginReposition":
ir_coord = ( float(x_), float(y_) )
elif name_ == "EndReposition":
# Check to see if we swap threads.
if len(persistent_data['cur_mouse_comp']) == 0:
continue;
tmp = persistent_data['cur_mouse_comp'].split("/")
compID = tmp[1]
if tmp[1] not in persistent_data['comp_color_map'].keys():
# Component was thrown away.
continue
feature_vector['num_tracks_used'] += 1
color = persistent_data['comp_color_map'][tmp[1]]
for c in persistent_data['comp_color_map'].keys():
if color == persistent_data['comp_color_map'][c]:
feature_vector['total_components'] += 1
fr_coord = ( float(x_), float(y_) )
# Euclidean distance ( don't actually use this, but keeping this in here for future use )
#dist = 0.0
#if ( len(ir_coord) != 0 ):
# dist = math.sqrt( math.pow((fr_coord[0]-ir_coord[0]),2.0) + math.pow((fr_coord[1]-ir_coord[1]),2.0) )
#else:
# dist = 0.0
ir_coord = ( )
fr_coord = ( )
elif name_ == "BeginLink":
# Before we even link, we need to be over a component
if len(persistent_data['cur_mouse_comp']) == 0:
continue;
tmp = persistent_data['cur_mouse_comp'].split("/")
compID = tmp[1]
if tmp[1] not in persistent_data['comp_color_map'].keys():
# Component was thrown away.
continue
feature_vector['num_tracks_used'] += 1
color = persistent_data['comp_color_map'][tmp[1]]
for c in persistent_data['comp_color_map'].keys():
if color == persistent_data['comp_color_map'][c]:
feature_vector['total_components'] += 1
il_coord = ( float(x_), float(y_) )
elif name_ == "LinkTo":
persistent_data['linking'] = True
fl_coord = ( float(x_), float(y_) )
dist = 0.0
if ( len(il_coord) != 0 ):
dist = math.sqrt( math.pow((fl_coord[0]-il_coord[0]),2.0) + math.pow((fl_coord[1]-il_coord[1]),2.0) )
else:
dist = 0.0
il_coord = ( )
fl_coord = ( )
elif name_ == "OnMouseComponent":
feature_vector['num_mouse_on_comp'] += 1
persistent_data['cur_mouse_comp'] = data_
# Get current time..
persistent_data['cur_mouse_time'] = float(time_)
# Keep a list of components IDs and their corresponding locations
y_ = y_.strip("\n")
tmp = data_.split("/")
persistent_data['comp_loc_map'][tmp[1]] = (float(x_),float(y_))
if ( len(persistent_data['comp_color_map'].keys()) != 0 and tmp[1] in persistent_data['comp_color_map'] ):
if persistent_data['cur_track'] == -1:
persistent_data['cur_track'] = persistent_data['comp_color_map'][tmp[1]]
else:
# Check to see if we changed tracks here
if persistent_data['cur_track'] != persistent_data['comp_color_map'][tmp[1]]:
feature_vector['num_track_changes'] += 1
persistent_data['cur_track'] = persistent_data['comp_color_map'][tmp[1]]
else:
# Staying on the same track
feature_vector['num_track_no_changes'] += 1
if persistent_data['linking']:
if ( len(persistent_data['comp_color_map'].keys()) != 0 ):
tmp = data_.split("/")
if ( tmp[1] in persistent_data['comp_color_map'] ):
feature_vector['num_tracks_used'] += 1
color = persistent_data['comp_color_map'][tmp[1]]
for c in persistent_data['comp_color_map'].keys():
if color == persistent_data['comp_color_map'][c]:
feature_vector['total_components'] += 1
persistent_data['linking'] = False
elif name_ == "OutMouseComponent":
endTime = float(time_)
feature_vector['total_time_on_component'] += (endTime - persistent_data['cur_mouse_time'])
elif name_ == 'ToggleConnectionVisibility' and data_ == 'True':
feature_vector['connection_visibility'] += 1
elif name_ == 'ToggleFlowVisibility' and data_ == 'True':
feature_vector['flow_visibility'] += 1
elif name_ == 'LockFlowVisibility' and data_ == 'True':
feature_vector['flow_tooltip'] += 1
elif name_ == 'TriggerGoalPopUp':
if 'Successfully' in data_:
feature_vector['popup_success'] += 1
elif 'starvation' in data_:
feature_vector['popup_error_starvation'] += 1
feature_vector['popup_error'] += 1
elif 'dead' in data_:
feature_vector['popup_error_deadend'] += 1
feature_vector['popup_error'] += 1
elif 'deliveries' in data_:
feature_vector['popup_error_delivery'] += 1
feature_vector['popup_error'] += 1
else:
feature_vector['popup_error_badgoals'] += 1
feature_vector['popup_error'] += 1
# Once we have searched this, finalize feature_vector
last_line = interval_data[-1]
t_, e_, d_, s_, _ = last_line.split('\t', 4)
s_ = datetime.datetime.strptime(t_, '%m-%d-%y-%H-%M-%S')
s_ = (s_ - epoch).total_seconds()
s_ = s_ - persistent_data['start_time']
feature_vector['end_date'] = t_
feature_vector['total_time'] = (float(s_) - feature_vector['start_time']) #/ 60.0
# NOTE: This should never occur
if feature_vector['total_time'] < 0:
print "start_time, s_: {0},{1}".format(feature_vector['start_time'],s_)
print interval_data
me_total = 0.0
for i in ['tests','submissions','replays']:
me_total += feature_vector['me_'+i]
if feature_vector['total_time']:
feature_vector['r_me_per_minute'] = me_total/(feature_vector['total_time']/60.0)
for i in ['tests', 'submissions', 'replays']:
if feature_vector['total_time']:
feature_vector['r_me_'+i+'_per_minute'] = 1.0*feature_vector['me_'+i] / (feature_vector['total_time'] / 60.0)
feature_vector['r_num_tracks_used_per_minute'] = 1.0*feature_vector['num_tracks_used'] / (feature_vector['total_time'] / 60.0)
feature_vector['r_num_track_no_changes_per_minute'] = 1.0*feature_vector['num_track_no_changes'] / (feature_vector['total_time'] / 60.0)
feature_vector['r_num_track_changes_per_minute'] = 1.0*feature_vector['num_track_changes'] / (feature_vector['total_time'] / 60.0)
feature_vector['r_mouse_on_comp_per_minute'] = 1.0*feature_vector['num_mouse_on_comp'] / (feature_vector['total_time'] / 60.0)
feature_vector['r_dragged_components_per_minute'] = 1.0*feature_vector['dragged'] / ( feature_vector['total_time'] / 60.0 )
feature_vector['r_hover_components_per_minute'] = 1.0*feature_vector['hover'] / ( feature_vector['total_time'] / 60.0 )
if feature_vector['num_mouse_on_comp'] != 0:
feature_vector['avg_time_on_component'] = 1.0*feature_vector['total_time_on_component'] / ( 1.0*feature_vector['num_mouse_on_comp'] )
if feature_vector['dragged'] != 0:
feature_vector['avg_dragged_components_dist'] = 1.0*feature_vector['total_dragged_components_dist'] / ( 1.0*feature_vector['dragged'] )
if feature_vector['num_tracks_used'] != 0:
feature_vector['tracks_used_components_per_track_avg'] = 1.0*feature_vector['total_components'] / ( 1.0*feature_vector["num_tracks_used"] )
if me_total:
feature_vector['r_me_' + i] = 1.0*feature_vector['me_' + i] / me_total
for i in ['_tests', '_submissions','']:
lst_ = []
for j in feature_vector['_t_d_component_dragged_me_lst']:
if j[0]==i or not i:
lst_.append(j[1])
if lst_:
feature_vector['t_d_component_dragged_me' + i + '_avg'] = util.average(lst_)
feature_vector['t_d_component_dragged_me' + i + '_min'] = min(lst_)
return feature_vector
def test_single_list(self):
self.assertEqual(5, average([5]))
self.assertEqual(0, average([0]))
