class_docstr: Least Angle Regression model a.k.a. LAR
  
  Read more in the :ref:`User Guide <least_angle_regression>`.
  
  Parameters
  ----------
  n_nonzero_coefs : int, optional
      Target number of non-zero coefficients. Use ``np.inf`` for no limit.
  
  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`.
  
  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.
  
  copy_X : boolean, optional, default True
      If ``True``, X will be copied; else, it may be overwritten.
  
  eps : float, optional
      The machine-precision regularization in the computation of the
      Cholesky diagonal factors. Increase this for very ill-conditioned
      systems. Unlike the ``tol`` parameter in some iterative
      optimization-based algorithms, this parameter does not control
      the tolerance of the optimization.
  
  fit_path : boolean
      If True the full path is stored in the ``coef_path_`` attribute.
      If you compute the solution for a large problem or many targets,
      setting ``fit_path`` to ``False`` will lead to a speedup, especially
      with a small alpha.
  
  Attributes
  ----------
  alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays
      Maximum of covariances (in absolute value) at each iteration.         ``n_alphas`` is either ``n_nonzero_coefs`` or ``n_features``,         whichever is smaller.
  
  active_ : list, length = n_alphas | list of n_targets such lists
      Indices of active variables at the end of the path.
  
  coef_path_ : array, shape (n_features, n_alphas + 1)         | list of n_targets such arrays
      The varying values of the coefficients along the path. It is not
      present if the ``fit_path`` parameter is ``False``.
  
  coef_ : array, shape (n_features,) or (n_targets, n_features)
      Parameter vector (w in the formulation formula).
  
  intercept_ : float | array, shape (n_targets,)
      Independent term in decision function.
  
  n_iter_ : array-like or int
      The number of iterations taken by lars_path to find the
      grid of alphas for each target.
  
  Examples
  --------
  >>> from sklearn import linear_model
  >>> clf = linear_model.Lars(n_nonzero_coefs=1)
  >>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
  ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
  Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True,
     n_nonzero_coefs=1, normalize=True, positive=False, precompute='auto',
     verbose=False)
  >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
  [ 0. -1.11...]
  
  See also
  --------
  lars_path, LarsCV
  sklearn.decomposition.sparse_encode
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code: def __init__(
      self,
      fit_intercept=True,
      verbose=False,
      normalize=True,
      precompute="auto",
      n_nonzero_coefs=500,
      eps=np.finfo(np.float).eps,
      copy_X=True,
      fit_path=True,
      positive=False,
  ):
      self.fit_intercept = fit_intercept
      self.verbose = verbose
      self.normalize = normalize
      self.method = "lar"
      self.precompute = precompute
      self.n_nonzero_coefs = n_nonzero_coefs
      self.positive = positive
      self.eps = eps
      self.copy_X = copy_X
      self.fit_path = fit_path
  
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 1 to create a new Lars object from inherited base classes: LinearModel, RegressorMixin with example: obj = Lars(fit_intercept, verbose, normalize, precompute, n_nonzero_coefs, eps, copy_X, fit_path, positive)