Example #1
0
    def RunLinearRegressionShogun(q):
      totalTimer = Timer()

      # Load input dataset.
      # If the dataset contains two files then the second file is the responses
      # file.
      try:
        Log.Info("Loading dataset", self.verbose)
        if len(self.dataset) == 2:
          testSet = np.genfromtxt(self.dataset[1], delimiter=',')

        # Use the last row of the training set as the responses.
        X, y = SplitTrainData(self.dataset)

        with totalTimer:
          # Perform linear regression.
          model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y))
          model.train()
          b = model.get_w()

          if len(self.dataset) == 2:
            pred = classifier.apply(RealFeatures(testSet.T))
            self.predictions = pred.get_labels()

      except Exception as e:
        q.put(-1)
        return -1

      time = totalTimer.ElapsedTime()
      q.put(time)
      return time
Example #2
0
    def RunLinearRegressionShogun():
      totalTimer = Timer()

      # Load input dataset.
      # If the dataset contains two files then the second file is the responses
      # file.
      try:
        Log.Info("Loading dataset", self.verbose)
        if len(self.dataset) == 2:
          testSet = np.genfromtxt(self.dataset[1], delimiter=',')

        # Use the last row of the training set as the responses.
        X, y = SplitTrainData(self.dataset)

        if len(options) > 0:
          Log.Fatal("Unknown parameters: " + str(options))
          raise Exception("unknown parameters")

        with totalTimer:
          # Perform linear regression.
          model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y))
          model.train()
          b = model.get_w()

          if len(self.dataset) == 2:
            pred = classifier.apply(RealFeatures(testSet.T))
            self.predictions = pred.get_labels()

      except Exception as e:
        return -1

      return totalTimer.ElapsedTime()
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6):

	from modshogun import RegressionLabels, RealFeatures
	from modshogun import GaussianKernel
	from modshogun import LeastSquaresRegression

	ls=LeastSquaresRegression(RealFeatures(traindat), RegressionLabels(label_train))
	ls.train()
	out = ls.apply(RealFeatures(fm_test)).get_labels()
	return out,ls
Example #4
0
for i in xrange(p):
    X[:, i] -= np.mean(X[:, i])
    X[:, i] /= np.linalg.norm(X[:, i])
y -= np.mean(y)

# train LASSO
LeastAngleRegression = LeastAngleRegression()
LeastAngleRegression.set_labels(RegressionLabels(y))
LeastAngleRegression.train(RealFeatures(X.T))

# train ordinary LSR
if use_ridge:
    lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y))
    lsr.train()
else:
    lsr = LeastSquaresRegression()
    lsr.set_labels(RegressionLabels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:, i] = LeastAngleRegression.get_w(i)

evaluator = MeanSquaredError()

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T))