def main():
sd = SynthData()
ca = coordinateAscent()
cal = CoordinateAscentLasso()
st = Standardize()
beta0Seperate = True
lam = 0.1
X, y, b = sd.generateData(noise=False,
w=np.array([1, 1, 1, 1, 1, 0, 0, 0, 0,
0])[np.newaxis].T)
#if beta0Seperate:
# beta = np.array([1, 1, 1, 1, 0, 0, 0, 0])[np.newaxis].T
#else:
# beta = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0])[np.newaxis].T
#if beta0Seperate:
# y = 1 + np.dot(X, beta)
#else:
# X = np.append(np.ones((X.shape[0], 1)), X, 1)
# y = np.dot(X, beta)
print('Fitting the model with Lasso:')
print('Lambda = ' + str(lam))
print('beta0, array of betas:')
t0 = time()
print(cal.coordinateAscentLasso(y, X, lam, [], False, beta0Seperate))
dt = time() - t0
print('done in %.4fs.' % dt)
print()
print('Fitting the model with plain \'ol Coordinate Ascent')
print('beta0, array of betas:')
t0 = time()
print(ca.coordinateAscent(y, X, [], False))
dt = time() - t0
print('done in %.4fs.' % dt)
print()
#print('Dictionary Learning')
#dl = decomposition.DictionaryLearning(fit_algorithm='cd')
#print(dl.fit(X))
#print(np.shape(dl.components_))
#print(dl.components_)
print('Fitting the model with LARS (from the scikit library)')
clf = linear_model.LassoLars(alpha=0.01)
t0 = time()
print(clf.fit(X, y))
dt = time() - t0
print('done in %.4fs.' % dt)
print('array of betas:')
print(clf.coef_)
return 1
def __init__(self, args):
# create logger
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
self.logger.addHandler(ch)
self.logger.debug("Starting Collector process in %s" % os.getcwd())
self.logger.debug("Gevent Version %s" % gevent.__version__)
#TODO: move output file name to config
#fname = "./NetFlow.%s.bin"%str(time.time()*100000)
#WARN: might want to remove this after testing
#self.out = open(fname,"wb")
#create tool instances
self.interface = Interface()
self.parse = Parse()
self.describe = Describe()
self.standardize = Standardize()
self.transform = Transform()
self.partition = Partition()
self.q = Queue()
self.inWindow = False
self.score = Score()
#TODO: move csv name to config
self.csv = CSV("output.csv")
return super(Collector, self).__init__(args)
