def train(self, training_dataset):
feature_summary = {"class_1": [], "class_2": []}
for cls, feature_matrix in training_dataset.iteritems():
for feature in feature_matrix:
# Append the whole feature vector (all values for the feature in the class for the training data
feature_summary[cls].append((feature, util.stdev(feature)))
return feature_summary
def run_sim():
global t, i
state['series'] = []
t = config.DATE_START - timedelta(days=1)
i = -1
while t < datetime.now() + timedelta(days=365):
t += timedelta(days=1)
if t not in btc.usd_d:
continue
for code in scripts:
exec_script(code)
i += 1
state['series'].append((t, state['base_tether'] + state['base_ntx']))
std = util.stdev(state['series'][100:-365], btc.mcap)
if std < best['stdev']:
best['series'] = state['series']
best['stdev'] = std
best['world'] = copy.deepcopy(world)
print()
print("stdev = ", best['stdev'])
pprint(best['world'])
lines['sim'].set_ydata([a[1] for a in best['series']])
fig.canvas.draw()
fig.canvas.flush_events()
def markerReliabilityCheck(self,
markerCount,
alphaWeight=1,
betaWeight=5,
lengthWeight=2):
markers = {}
for i in range(markerCount):
ms = super(Tobo, self).see_cube()
for m in ms:
mCode = str(m.info.code)
if mCode in markers:
markers[mCode]["marker"].append(m)
markers[mCode]["alpha"].append(m.centre.rot_y)
markers[mCode]["beta"].append(m.orientation.rot_y)
markers[mCode]["length"].append(m.centre.dist)
else:
markers.update({
mCode: {
"marker": [m],
"alpha": [m.centre.rot_y],
"beta": [m.orientation.rot_y],
"length": [m.centre.dist]
}
})
for key in list(markers):
mDict = markers[key]
sqrtN = math.sqrt(len(mDict["marker"]))
alphaXBar = util.mean(mDict["alpha"])
alphaSd = util.stdev(mDict["alpha"]) / sqrtN
betaXBar = util.mean(mDict["beta"])
betaSd = util.stdev(mDict["beta"]) / sqrtN
lengthXBar = util.mean(mDict["length"])
lengthSd = util.stdev(mDict["length"]) / sqrtN
error = (alphaWeight * alphaSd + betaWeight * betaSd +
lengthWeight * lengthSd) / (alphaWeight + betaWeight +
lengthWeight)
for m in mDict["marker"]:
m.error = error
def get_power(tx, mcap0, mcap, ref_mcap):
delta = 0.01
p = 1 - delta
best = (2**256, 0)
while p < 2.4:
p += delta
s = [(a[0], (mcap[i][1] / mcap0) * a[1]**p) for i, a in enumerate(tx)]
stdev = util.stdev(s, ref_mcap)
if stdev < best[0]:
best = (stdev, p)
return best[1]
def train(self, training_dataset):
"""
Calculates the mean and stdev of each feature in each class
:param training_dataset: A n*m matrix of n features and m training items
:return: a dictionary containing lists of the expected value and standard deviation for each feature in each class
"""
feature_summary = {"class_1": [], "class_2": []}
for cls, feature_matrix in training_dataset.iteritems():
for feature in feature_matrix:
feature_summary[cls].append((util.mean(feature), util.stdev(feature)))
self.training_params = feature_summary
return feature_summary
import util
import math
NUM_TESTS = 10
def startBench(coroutines) -> int:
return int(util.run('bench0', [coroutines])[2])
print('--Start go bench--')
util.build_go('go/bench0.go')
time = [startBench('100000') for i in range(NUM_TESTS)]
mean, std = util.stdev(time)
print('Result: {} +/- {}'.format(mean, std))
util.remove('bench0')
import util
import math
NUM_TESTS = 10
def startBench(coroutines) -> int:
return int(util.run('benchSwitch', [coroutines])[7])
print('--Start go bench--')
util.build_go('go/benchSwitch.go')
switchesPerSecond = [startBench('100') for i in range(NUM_TESTS)]
mean, std = util.stdev(switchesPerSecond)
print('Result: {} +/- {}'.format(mean, std))
util.remove('benchSwitch')