code: def generateLinearInput(intercept, weights, xMean, xVariance,
                              nPoints, seed, eps):
          
          weights = [float(weight) for weight in weights]
          xMean = [float(mean) for mean in xMean]
          xVariance = [float(var) for var in xVariance]
          return list(callMLlibFunc(
              "generateLinearInputWrapper", float(intercept), weights, xMean,
              xVariance, int(nPoints), int(seed), float(eps)))
docs: :param: intercept bias factor, the term c in X'w + c
          :param: weights   feature vector, the term w in X'w + c
          :param: xMean     Point around which the data X is centered.
          :param: xVariance Variance of the given data
          :param: nPoints   Number of points to be generated
          :param: seed      Random Seed
          :param: eps       Used to scale the noise. If eps is set high,
                            the amount of gaussian noise added is more.
  
          Returns a list of LabeledPoints of length nPoints