class_docstr: Cross-validated Least Angle Regression model
  
  Read more in the :ref:`User Guide <least_angle_regression>`.
  
  Parameters
  ----------
  fit_intercept : boolean
      whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (e.g. data is expected to be already centered).
  
  positive : boolean (default=False)
      Restrict coefficients to be >= 0. Be aware that you might want to
      remove fit_intercept which is set True by default.
  
  verbose : boolean or integer, optional
      Sets the verbosity amount
  
  normalize : boolean, optional, default False
      If True, the regressors X will be normalized before regression.
      This parameter is ignored when `fit_intercept` is set to False.
      When the regressors are normalized, note that this makes the
      hyperparameters learnt more robust and almost independent of the number
      of samples. The same property is not valid for standardized data.
      However, if you wish to standardize, please use
      `preprocessing.StandardScaler` before calling `fit` on an estimator
      with `normalize=False`.
  
  copy_X : boolean, optional, default True
      If ``True``, X will be copied; else, it may be overwritten.
  
  precompute : True | False | 'auto' | array-like
      Whether to use a precomputed Gram matrix to speed up
      calculations. If set to ``'auto'`` let us decide. The Gram
      matrix can also be passed as argument.
  
  max_iter: integer, optional
      Maximum number of iterations to perform.
  
  cv : int, cross-validation generator or an iterable, optional
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 3-fold cross-validation,
      - integer, to specify the number of folds.
      - An object to be used as a cross-validation generator.
      - An iterable yielding train/test splits.
  
      For integer/None inputs, :class:`KFold` is used.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
  max_n_alphas : integer, optional
      The maximum number of points on the path used to compute the
      residuals in the cross-validation
  
  n_jobs : integer, optional
      Number of CPUs to use during the cross validation. If ``-1``, use
      all the CPUs
  
  eps : float, optional
      The machine-precision regularization in the computation of the
      Cholesky diagonal factors. Increase this for very ill-conditioned
      systems.
  
  
  Attributes
  ----------
  coef_ : array, shape (n_features,)
      parameter vector (w in the formulation formula)
  
  intercept_ : float
      independent term in decision function
  
  coef_path_ : array, shape (n_features, n_alphas)
      the varying values of the coefficients along the path
  
  alpha_ : float
      the estimated regularization parameter alpha
  
  alphas_ : array, shape (n_alphas,)
      the different values of alpha along the path
  
  cv_alphas_ : array, shape (n_cv_alphas,)
      all the values of alpha along the path for the different folds
  
  cv_mse_path_ : array, shape (n_folds, n_cv_alphas)
      the mean square error on left-out for each fold along the path
      (alpha values given by ``cv_alphas``)
  
  n_iter_ : array-like or int
      the number of iterations run by Lars with the optimal alpha.
  
  See also
  --------
  lars_path, LassoLars, LassoLarsCV
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code: def alpha(self):
      # impedance matching for the above Lars.fit (should not be documented)
      return self.alpha_
  
imports: from __future__ import print_function
  from math import log
  import sys
  import warnings
  from distutils.version import LooseVersion
  import numpy
  from scipy import linalg, interpolate
  from scipy.linalg.lapack import get_lapack_funcs
  from .base import LinearModel
  from ..base import RegressorMixin
  from ..utils import arrayfuncs, as_float_array, check_X_y
  from ..model_selection import check_cv
  from ..exceptions import ConvergenceWarning
  from ..externals.joblib import Parallel, delayed
  from ..externals.six.moves import xrange
  from ..externals.six import string_types
  import scipy
label_desc: Use image node_id 3 for calling the LarsCV obj's underlying member method code with example usage: obj.alpha() and returns: self