class_docstr: Lasso model fit with Least Angle Regression a.k.a. Lars
  
  It is a Linear Model trained with an L1 prior as regularizer.
  
  The optimization objective for Lasso is::
  
  (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
  
  Read more in the :ref:`User Guide <least_angle_regression>`.
  
  Parameters
  ----------
  alpha : float
      Constant that multiplies the penalty term. Defaults to 1.0.
      ``alpha = 0`` is equivalent to an ordinary least square, solved
      by :class:`LinearRegression`. For numerical reasons, using
      ``alpha = 0`` with the LassoLars object is not advised and you
      should prefer the LinearRegression object.
  
  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.
      Under the positive restriction the model coefficients will not converge
      to the ordinary-least-squares solution for small values of alpha.
      Only coefficients up to the smallest alpha value (``alphas_[alphas_ >
      0.].min()`` when fit_path=True) reached by the stepwise Lars-Lasso
      algorithm are typically in congruence with the solution of the
      coordinate descent Lasso estimator.
  
  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.
  
  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 ``max_iter``, ``n_features``, or the number of         nodes in the path with correlation greater than ``alpha``, 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) or list
      If a list is passed it's expected to be one 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.LassoLars(alpha=0.01)
  >>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1])
  ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
  LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True,
       fit_path=True, max_iter=500, normalize=True, positive=False,
       precompute='auto', verbose=False)
  >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
  [ 0.         -0.963257...]
  
  See also
  --------
  lars_path
  lasso_path
  Lasso
  LassoCV
  LassoLarsCV
  sklearn.decomposition.sparse_encode
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code: def __init__(
      self,
      alpha=1.0,
      fit_intercept=True,
      verbose=False,
      normalize=True,
      precompute="auto",
      max_iter=500,
      eps=np.finfo(np.float).eps,
      copy_X=True,
      fit_path=True,
      positive=False,
  ):
      self.alpha = alpha
      self.fit_intercept = fit_intercept
      self.max_iter = max_iter
      self.verbose = verbose
      self.normalize = normalize
      self.method = "lasso"
      self.positive = positive
      self.precompute = precompute
      self.copy_X = copy_X
      self.eps = eps
      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 LassoLars object from inherited base classes: Lars with example: obj = LassoLars(alpha, fit_intercept, verbose, normalize, precompute, max_iter, eps, copy_X, fit_path, positive)