def metric(self):
totalTimer = Timer()
with totalTimer:
model = mlpy.LARS()
model.learn(self.data[0], self.data[1])
output = model.beta()
metric = {}
metric["runtime"] = totalTimer.ElapsedTime()
return metric
def lars_base_main():
x, y = get_input('train')
lars = mlpy.LARS()
lars.learn(x, y)
test_x, test_y = get_input('test')
right_num = 0
for pos in range(0, len(test_x)):
yy = lars.pred(test_x[pos])
#print yy ,test_y[pos]
if abs(yy - test_y[pos]) < 5.0:
right_num += 1
print right_num * 1.0 / len(test_x)
def RunLARSMlpy():
totalTimer = Timer()
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
inputData = np.genfromtxt(self.dataset[0], delimiter=',')
responsesData = np.genfromtxt(self.dataset[1], delimiter=',')
try:
with totalTimer:
# Perform LARS.
model = mlpy.LARS()
model.learn(inputData, responsesData)
out = model.beta()
except Exception as e:
return -1
return totalTimer.ElapsedTime()