def RunLinearRegressionMlpy(q):
totalTimer = Timer()
# Load input dataset.
# If the dataset contains two files then the second file is the test file.
Log.Info("Loading dataset", self.verbose)
if len(self.dataset) >= 2:
test_data = np.genfromtxt(self.dataset[1], delimiter=',')
# Use the last row of the training set as the responses.
X, y = SplitTrainData(self.dataset[0])
try:
with totalTimer:
# Perform linear regression.
model = mlpy.OLS()
model.learn(X, y)
b = model.beta()
if len(self.dataset) >= 2:
#prediction on the test data.
pred = model.pred(test_data)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
if len(self.dataset) >= 2:
np.savetxt("mlpy_lr_predictions.csv", pred, delimiter="\n")
return time
def RunLinearRegressionMlpy(q):
totalTimer = Timer()
# Load input dataset.
# If the dataset contains two files then the second file is the responses
# file.
Log.Info("Loading dataset", self.verbose)
if len(self.dataset) == 2:
X = np.genfromtxt(self.dataset[0], delimiter=',')
y = np.genfromtxt(self.dataset[1], delimiter=',')
else:
X = np.genfromtxt(self.dataset, delimiter=',')
y = X[:, (X.shape[1] - 1)]
X = X[:,:-1]
try:
with totalTimer:
# Perform linear regression.
model = mlpy.OLS()
model.learn(X, y)
b = model.beta()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def __init__(self, file_name, flag):
if flag == "ridge":
self.lrs = [mlpy.Ridge() for i in range(7)]
else:
self.lrs = [mlpy.OLS() for i in range(7)]
self.file_name = file_name
self.x_weekday = dict()
self.y_weekday = dict()
def regression_linear(x,y):
'''
Estimate a linear regression
'''
# create the model object
ols = ml.OLS()
# estimate the model
ols.learn(x, y)
# and return the fit model
return ols
def BuildModel(self, data, responses):
# Create and train the classifier.
model = mlpy.OLS()
model.learn(data, responses)
return model
