Module Expr Str ImportFrom alias ImportFrom alias Import alias identifier ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias alias ImportFrom alias ClassDef bases NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Num Num Num Num Num NameLoad NameLoad NameLoad NameLoad NameLoad body Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword NameLoad Assign TupleStore NameStore NameStore NameStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign TupleStore NameStore NameStore AttributeLoad NameLoad attr Assign NameStore BinOpDiv Num Call AttributeLoad NameLoad attr NameLoad Assign NameStore Num Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign AttributeStore NameLoad attr Call NameLoad Assign NameStore NameLoad Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr NameLoad Assign TupleStore NameStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad keyword NameLoad Assign NameStore BinOpPow NameLoad Num For NameStore Call NameLoad AttributeLoad NameLoad attr body If CompareGt NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr BinOpDiv NameLoad SubscriptLoad BinOpAdd NameLoad BinOpDiv NameLoad NameLoad ExtSlice Slice Index NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad If AttributeLoad NameLoad attr body Assign NameStore UnaryOpUSub Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr BinOpAdd NameLoad BinOpMult NameLoad NameLoad orelse Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr BinOpDiv NameLoad SubscriptLoad BinOpAdd NameLoad BinOpDiv NameLoad NameLoad ExtSlice Index NameLoad Slice AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad If AttributeLoad NameLoad attr body Assign NameStore BinOpMult NameLoad Call AttributeLoad NameLoad attr NameLoad AugAssignAdd SubscriptStore NameLoad Slice NameLoad BinOpMult NameLoad NameLoad Assign NameStore UnaryOpUSub Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr BinOpPow BinOpSub NameLoad Call AttributeLoad NameLoad attr NameLoad NameLoad Num Assign NameStore Call AttributeLoad NameLoad attr BinOpDiv BinOpMult NameLoad NameLoad BinOpAdd NameLoad BinOpMult NameLoad NameLoad Assign NameStore BinOpDiv BinOpAdd NameLoad BinOpMult Num NameLoad BinOpAdd Call AttributeLoad NameLoad attr BinOpPow NameLoad Num BinOpMult Num NameLoad Assign NameStore BinOpDiv BinOpAdd BinOpSub NameLoad NameLoad BinOpMult Num NameLoad BinOpAdd NameLoad BinOpMult Num NameLoad If AttributeLoad NameLoad attr body Assign NameStore BinOpSub BinOpMult NameLoad Call NameLoad NameLoad BinOpMult NameLoad NameLoad AugAssignAdd NameStore BinOpSub BinOpMult NameLoad Call NameLoad NameLoad BinOpMult NameLoad NameLoad AugAssignAdd NameStore BinOpMult Num BinOpSub BinOpSub BinOpSub BinOpSub BinOpAdd BinOpMult NameLoad Call NameLoad NameLoad BinOpMult NameLoad Call NameLoad NameLoad BinOpMult NameLoad NameLoad BinOpMult NameLoad Call AttributeLoad NameLoad attr BinOpPow NameLoad Num NameLoad BinOpMult NameLoad Call NameLoad BinOpMult Num AttributeLoad NameLoad attr Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad If BoolOpAnd CompareNotEq NameLoad Num CompareLt Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr BinOpSub NameLoad NameLoad AttributeLoad NameLoad attr body If NameLoad body Expr Call NameLoad Str Call NameLoad NameLoad Str Break Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad Return NameLoad decorator_list decorator_list ClassDef bases NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Num Num Num Num Num NameLoad Num NameLoad NameLoad NameLoad NameLoad body Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword NameLoad Assign TupleStore NameStore NameStore AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign TupleStore NameStore NameStore NameStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword NameLoad Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore BinOpDiv Num Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign AttributeStore NameLoad attr Call NameLoad Assign NameStore NameLoad For NameStore Call NameLoad AttributeLoad NameLoad attr body Assign NameStore Call NameLoad BinOpAdd BinOpDiv Call AttributeLoad NameLoad attr NameLoad NameLoad Call AttributeLoad NameLoad attr BinOpMult SubscriptLoad NameLoad ExtSlice Slice Index NameLoad Call AttributeLoad NameLoad attr BinOpDiv Num SubscriptLoad NameLoad Index NameLoad ListLoad Num Num AttributeLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad BinOpMult SubscriptLoad NameLoad ExtSlice Slice Index NameLoad Call AttributeLoad NameLoad attr BinOpDiv Num SubscriptLoad NameLoad Index NameLoad ListLoad Num Num Assign NameStore UnaryOpUSub Call AttributeLoad NameLoad attr BinOpMult Call AttributeLoad NameLoad attr BinOpDiv Num SubscriptLoad NameLoad Index NameLoad ListLoad Num Num AttributeLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad attr NameLoad AugAssignAdd SubscriptStore AttributeLoad NameLoad attr Slice BinOpAdd SubscriptLoad AttributeLoad NameLoad attr Index Num Num BinOpDiv Num SubscriptLoad NameLoad Index NameLoad Assign SubscriptStore NameLoad Index NameLoad BinOpMult NameLoad Call AttributeLoad NameLoad attr NameLoad Call AttributeLoad NameLoad attr AttributeLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr BinOpPow BinOpSub NameLoad Call AttributeLoad NameLoad attr NameLoad NameLoad Num Assign NameStore BinOpSub Num BinOpMult SubscriptLoad NameLoad Index NameLoad Call AttributeLoad NameLoad attr NameLoad Assign SubscriptStore NameLoad Index NameLoad BinOpDiv BinOpAdd NameLoad BinOpMult Num NameLoad BinOpAdd BinOpPow SubscriptLoad NameLoad Index NameLoad Num BinOpMult Num NameLoad Assign NameStore BinOpDiv BinOpAdd BinOpSub NameLoad Call AttributeLoad NameLoad attr BinOpMult Num NameLoad BinOpAdd NameLoad BinOpMult Num NameLoad Assign NameStore CompareLt NameLoad AttributeLoad NameLoad attr Assign SubscriptStore NameLoad Index UnaryOpInvert NameLoad Num If AttributeLoad NameLoad attr body Assign NameStore Call AttributeLoad BinOpSub BinOpMult NameLoad Call AttributeLoad NameLoad attr NameLoad BinOpMult NameLoad NameLoad attr AugAssignAdd NameStore BinOpSub BinOpMult NameLoad Call NameLoad NameLoad BinOpMult NameLoad NameLoad AugAssignAdd NameStore BinOpMult Num BinOpAdd BinOpAdd Call NameLoad NameLoad BinOpMult NameLoad Call NameLoad NameLoad Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad AugAssignSub NameStore BinOpMult Num BinOpAdd BinOpMult NameLoad NameLoad Call AttributeLoad BinOpMult NameLoad BinOpPow NameLoad Num attr Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad If BoolOpAnd CompareGt NameLoad Num CompareLt Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr BinOpSub NameLoad NameLoad AttributeLoad NameLoad attr body If NameLoad body Expr Call NameLoad BinOpMod Str NameLoad Break Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad Return NameLoad decorator_list decorator_list
N/A N/A 
Various bayesian regression
 __future__ print_function math log N/A numpy np scipy linalg base LinearModel base RegressorMixin utils.extmath fast_logdet pinvh utils check_X_y BayesianRidge N/A LinearModel RegressorMixin N/A N/A Bayesian ridge regression

    Fit a Bayesian ridge model and optimize the regularization parameters
    lambda (precision of the weights) and alpha (precision of the noise).

    Read more in the :ref:`User Guide <bayesian_regression>`.

    Parameters
    ----------
    n_iter : int, optional
        Maximum number of iterations.  Default is 300.

    tol : float, optional
        Stop the algorithm if w has converged. Default is 1.e-3.

    alpha_1 : float, optional
        Hyper-parameter : shape parameter for the Gamma distribution prior
        over the alpha parameter. Default is 1.e-6

    alpha_2 : float, optional
        Hyper-parameter : inverse scale parameter (rate parameter) for the
        Gamma distribution prior over the alpha parameter.
        Default is 1.e-6.

    lambda_1 : float, optional
        Hyper-parameter : shape parameter for the Gamma distribution prior
        over the lambda parameter. Default is 1.e-6.

    lambda_2 : float, optional
        Hyper-parameter : inverse scale parameter (rate parameter) for the
        Gamma distribution prior over the lambda parameter.
        Default is 1.e-6

    compute_score : boolean, optional
        If True, compute the objective function at each step of the model.
        Default is False

    fit_intercept : boolean, optional
        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).
        Default is True.

    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.

    verbose : boolean, optional, default False
        Verbose mode when fitting the model.


    Attributes
    ----------
    coef_ : array, shape = (n_features)
        Coefficients of the regression model (mean of distribution)

    alpha_ : float
       estimated precision of the noise.

    lambda_ : float
       estimated precision of the weights.

    scores_ : float
        if computed, value of the objective function (to be maximized)

    Examples
    --------
    >>> from sklearn import linear_model
    >>> clf = linear_model.BayesianRidge()
    >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
    ... # doctest: +NORMALIZE_WHITESPACE
    BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
            copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06,
            n_iter=300, normalize=False, tol=0.001, verbose=False)
    >>> clf.predict([[1, 1]])
    array([ 1.])

    Notes
    -----
    See examples/linear_model/plot_bayesian_ridge.py for an example.
     __init__ N/A N/A self n_iter tol alpha_1 alpha_2 lambda_1 lambda_2 compute_score fit_intercept normalize copy_X verbose N/A 300 0.001 1e-06 1e-06 1e-06 1e-06 False True False True False N/A N/A N/A self n_iter n_iter N/A N/A self tol tol N/A N/A self alpha_1 alpha_1 N/A N/A self alpha_2 alpha_2 N/A N/A self lambda_1 lambda_1 N/A N/A self lambda_2 lambda_2 N/A N/A self compute_score compute_score N/A N/A self fit_intercept fit_intercept N/A N/A self normalize normalize N/A N/A self copy_X copy_X N/A N/A self verbose verbose N/A fit N/A N/A self X y N/A N/A N/A Fit the model

        Parameters
        ----------
        X : numpy array of shape [n_samples,n_features]
            Training data
        y : numpy array of shape [n_samples]
            Target values

        Returns
        -------
        self : returns an instance of self.
         N/A N/A X y N/A check_X_y X y dtype N/A np float64 y_numeric True N/A N/A X y X_offset y_offset X_scale N/A N/A self _preprocess_data X y N/A self fit_intercept N/A self normalize N/A self copy_X N/A N/A n_samples n_features N/A X shape N/A alpha_ N/A 1.0 N/A N/A np var y N/A lambda_ 1.0 N/A verbose N/A self verbose N/A lambda_1 N/A self lambda_1 N/A lambda_2 N/A self lambda_2 N/A alpha_1 N/A self alpha_1 N/A alpha_2 N/A self alpha_2 N/A N/A self scores_ N/A list N/A coef_old_ None N/A XT_y N/A N/A np dot N/A X T y N/A N/A U S Vh N/A N/A linalg svd X full_matrices False N/A eigen_vals_ N/A S 2 N/A iter_ N/A range N/A self n_iter N/A N/A N/A n_samples n_features N/A N/A coef_ N/A N/A np dot N/A Vh T N/A Vh N/A N/A eigen_vals_ N/A lambda_ alpha_ N/A N/A N/A None N/A coef_ N/A N/A np dot coef_ XT_y N/A N/A self compute_score N/A N/A logdet_sigma_ N/A N/A N/A np sum N/A N/A np log N/A lambda_ N/A alpha_ eigen_vals_ N/A N/A coef_ N/A N/A np dot N/A X T N/A N/A np dot N/A U N/A N/A eigen_vals_ N/A lambda_ alpha_ N/A N/A None N/A N/A U T N/A coef_ N/A N/A np dot coef_ y N/A N/A self compute_score N/A N/A logdet_sigma_ N/A lambda_ N/A N/A np ones n_features N/A N/A logdet_sigma_ N/A n_samples N/A alpha_ eigen_vals_ N/A logdet_sigma_ N/A N/A N/A np sum N/A N/A np log logdet_sigma_ N/A rmse_ N/A N/A np sum N/A N/A y N/A N/A np dot X coef_ 2 N/A gamma_ N/A N/A np sum N/A N/A alpha_ eigen_vals_ N/A lambda_ N/A alpha_ eigen_vals_ N/A lambda_ N/A N/A gamma_ N/A 2 lambda_1 N/A N/A N/A np sum N/A coef_ 2 N/A 2 lambda_2 N/A alpha_ N/A N/A N/A n_samples gamma_ N/A 2 alpha_1 N/A rmse_ N/A 2 alpha_2 N/A N/A self compute_score N/A N/A s N/A N/A lambda_1 N/A log lambda_ N/A lambda_2 lambda_ N/A s N/A N/A alpha_1 N/A log alpha_ N/A alpha_2 alpha_ N/A s N/A 0.5 N/A N/A N/A N/A N/A N/A n_features N/A log lambda_ N/A n_samples N/A log alpha_ N/A alpha_ rmse_ N/A lambda_ N/A N/A np sum N/A coef_ 2 logdet_sigma_ N/A n_samples N/A log N/A 2 N/A np pi N/A N/A N/A N/A self scores_ append s N/A N/A N/A iter_ 0 N/A N/A N/A np sum N/A N/A np abs N/A coef_old_ coef_ N/A self tol N/A N/A verbose N/A N/A N/A print Convergence after  N/A str iter_  iterations N/A N/A coef_old_ N/A N/A np copy coef_ N/A N/A self alpha_ alpha_ N/A N/A self lambda_ lambda_ N/A N/A self coef_ coef_ N/A N/A N/A self _set_intercept X_offset y_offset X_scale N/A self N/A N/A ARDRegression N/A LinearModel RegressorMixin N/A N/A Bayesian ARD regression.

    Fit the weights of a regression model, using an ARD prior. The weights of
    the regression model are assumed to be in Gaussian distributions.
    Also estimate the parameters lambda (precisions of the distributions of the
    weights) and alpha (precision of the distribution of the noise).
    The estimation is done by an iterative procedures (Evidence Maximization)

    Read more in the :ref:`User Guide <bayesian_regression>`.

    Parameters
    ----------
    n_iter : int, optional
        Maximum number of iterations. Default is 300

    tol : float, optional
        Stop the algorithm if w has converged. Default is 1.e-3.

    alpha_1 : float, optional
        Hyper-parameter : shape parameter for the Gamma distribution prior
        over the alpha parameter. Default is 1.e-6.

    alpha_2 : float, optional
        Hyper-parameter : inverse scale parameter (rate parameter) for the
        Gamma distribution prior over the alpha parameter. Default is 1.e-6.

    lambda_1 : float, optional
        Hyper-parameter : shape parameter for the Gamma distribution prior
        over the lambda parameter. Default is 1.e-6.

    lambda_2 : float, optional
        Hyper-parameter : inverse scale parameter (rate parameter) for the
        Gamma distribution prior over the lambda parameter. Default is 1.e-6.

    compute_score : boolean, optional
        If True, compute the objective function at each step of the model.
        Default is False.

    threshold_lambda : float, optional
        threshold for removing (pruning) weights with high precision from
        the computation. Default is 1.e+4.

    fit_intercept : boolean, optional
        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).
        Default is True.

    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.

    verbose : boolean, optional, default False
        Verbose mode when fitting the model.

    Attributes
    ----------
    coef_ : array, shape = (n_features)
        Coefficients of the regression model (mean of distribution)

    alpha_ : float
       estimated precision of the noise.

    lambda_ : array, shape = (n_features)
       estimated precisions of the weights.

    sigma_ : array, shape = (n_features, n_features)
        estimated variance-covariance matrix of the weights

    scores_ : float
        if computed, value of the objective function (to be maximized)

    Examples
    --------
    >>> from sklearn import linear_model
    >>> clf = linear_model.ARDRegression()
    >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
    ... # doctest: +NORMALIZE_WHITESPACE
    ARDRegression(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
            copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06,
            n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001,
            verbose=False)
    >>> clf.predict([[1, 1]])
    array([ 1.])

    Notes
    --------
    See examples/linear_model/plot_ard.py for an example.
     __init__ N/A N/A self n_iter tol alpha_1 alpha_2 lambda_1 lambda_2 compute_score threshold_lambda fit_intercept normalize copy_X verbose N/A 300 0.001 1e-06 1e-06 1e-06 1e-06 False 10000.0 True False True False N/A N/A N/A self n_iter n_iter N/A N/A self tol tol N/A N/A self fit_intercept fit_intercept N/A N/A self normalize normalize N/A N/A self alpha_1 alpha_1 N/A N/A self alpha_2 alpha_2 N/A N/A self lambda_1 lambda_1 N/A N/A self lambda_2 lambda_2 N/A N/A self compute_score compute_score N/A N/A self threshold_lambda threshold_lambda N/A N/A self copy_X copy_X N/A N/A self verbose verbose N/A fit N/A N/A self X y N/A N/A N/A Fit the ARDRegression model according to the given training data
        and parameters.

        Iterative procedure to maximize the evidence

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Training vector, where n_samples in the number of samples and
            n_features is the number of features.
        y : array, shape = [n_samples]
            Target values (integers)

        Returns
        -------
        self : returns an instance of self.
         N/A N/A X y N/A check_X_y X y dtype N/A np float64 y_numeric True N/A N/A n_samples n_features N/A X shape N/A coef_ N/A N/A np zeros n_features N/A N/A X y X_offset y_offset X_scale N/A N/A self _preprocess_data X y N/A self fit_intercept N/A self normalize N/A self copy_X N/A keep_lambda N/A N/A np ones n_features dtype bool N/A lambda_1 N/A self lambda_1 N/A lambda_2 N/A self lambda_2 N/A alpha_1 N/A self alpha_1 N/A alpha_2 N/A self alpha_2 N/A verbose N/A self verbose N/A alpha_ N/A 1.0 N/A N/A np var y N/A lambda_ N/A N/A np ones n_features N/A N/A self scores_ N/A list N/A coef_old_ None N/A iter_ N/A range N/A self n_iter N/A N/A sigma_ N/A pinvh N/A N/A N/A N/A np eye n_samples alpha_ N/A N/A np dot N/A N/A X N/A N/A N/A keep_lambda N/A N/A np reshape N/A 1.0 N/A lambda_ N/A keep_lambda N/A 1 -1 N/A N/A X N/A N/A N/A keep_lambda T N/A sigma_ N/A N/A np dot sigma_ N/A N/A X N/A N/A N/A keep_lambda N/A N/A np reshape N/A 1.0 N/A lambda_ N/A keep_lambda N/A 1 -1 N/A sigma_ N/A N/A N/A np dot N/A N/A N/A np reshape N/A 1.0 N/A lambda_ N/A keep_lambda N/A -1 1 N/A N/A X N/A N/A N/A keep_lambda T sigma_ N/A N/A N/A sigma_ flat N/A N/A N/A N/A sigma_ shape N/A 1 1 N/A 1.0 N/A lambda_ N/A keep_lambda N/A N/A coef_ N/A keep_lambda N/A alpha_ N/A N/A np dot sigma_ N/A N/A np dot N/A N/A X N/A N/A N/A keep_lambda T y N/A rmse_ N/A N/A np sum N/A N/A y N/A N/A np dot X coef_ 2 N/A gamma_ N/A 1.0 N/A N/A lambda_ N/A keep_lambda N/A N/A np diag sigma_ N/A N/A lambda_ N/A keep_lambda N/A N/A gamma_ N/A 2.0 lambda_1 N/A N/A N/A coef_ N/A keep_lambda 2 N/A 2.0 lambda_2 N/A alpha_ N/A N/A N/A n_samples N/A N/A gamma_ sum N/A 2.0 alpha_1 N/A rmse_ N/A 2.0 alpha_2 N/A keep_lambda N/A lambda_ N/A self threshold_lambda N/A N/A coef_ N/A N/A keep_lambda 0 N/A N/A self compute_score N/A N/A s N/A N/A N/A N/A lambda_1 N/A N/A np log lambda_ N/A lambda_2 lambda_ sum N/A s N/A N/A alpha_1 N/A log alpha_ N/A alpha_2 alpha_ N/A s N/A 0.5 N/A N/A N/A fast_logdet sigma_ N/A n_samples N/A log alpha_ N/A N/A np sum N/A N/A np log lambda_ N/A s N/A 0.5 N/A N/A alpha_ rmse_ N/A N/A N/A lambda_ N/A coef_ 2 sum N/A N/A N/A N/A self scores_ append s N/A N/A N/A iter_ 0 N/A N/A N/A np sum N/A N/A np abs N/A coef_old_ coef_ N/A self tol N/A N/A verbose N/A N/A N/A print N/A Converged after %s iterations iter_ N/A N/A coef_old_ N/A N/A np copy coef_ N/A N/A self coef_ coef_ N/A N/A self alpha_ alpha_ N/A N/A self sigma_ sigma_ N/A N/A self lambda_ lambda_ N/A N/A N/A self _set_intercept X_offset y_offset X_scale N/A self N/A N/A