/Цiq=@SrSSKrSSKJr SSKJrJr SSKrSSK J r SSK J r SSK Jr SSKJr SS KJrJr SS KJr SS KJr SS KJr SS KJr SSKJrJr SSKJ r \RB"\RD5RFr$Sr%SSjr&Sr'Sr("SS\\5r)g)z< A Theil-Sen Estimator for Multiple Linear Regression Model N) combinations)IntegralReal)effective_n_jobs)linalg)get_lapack_funcs)binom)RegressorMixin _fit_context)ConvergenceWarning) LinearModel)check_random_state)Interval)Paralleldelayed) validate_datac.X- n[R"[R"US-SS95nU[:n[ UR5UR S:5nX$nX4SS2[R 4n[R"[R"X#- SS95nU[:a8[R"XSS24U- SS9[R"SU- SS9- nOSnSn[SSXV- - 5U-[SXV- 5U--$)uModified Weiszfeld step. This function defines one iteration step in order to approximate the spatial median (L1 median). It is a form of an iteratively re-weighted least squares method. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. x_old : ndarray of shape = (n_features,) Current start vector. Returns ------- x_new : ndarray of shape (n_features,) New iteration step. References ---------- - On Computation of Spatial Median for Robust Data Mining, 2005 T. Kärkkäinen and S. Äyrämö http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf axisrNg?) npsqrtsum_EPSILONintshapenewaxisrnormmaxmin)Xx_olddiff diff_normmask is_x_old_in_X quotient_norm new_directions ^/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/sklearn/linear_model/_theil_sen.py_modified_weiszfeld_stepr,s6 9DtQwQ/0I  D QWWQZ/0M :D2:: .IKKt'7a @AMxqqzI5A> MB     C}445 E c=0 1E 9 :cURSS:Xa%S[R"UR5SS94$US-n[R"USS9n[ U5H3n[ X5n[R"X5- S-5U:a XE4$UnM5 [R"SRUS9[5 WW4$) uSpatial median (L1 median). The spatial median is member of a class of so-called M-estimators which are defined by an optimization problem. Given a number of p points in an n-dimensional space, the point x minimizing the sum of all distances to the p other points is called spatial median. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. max_iter : int, default=300 Maximum number of iterations. tol : float, default=1.e-3 Stop the algorithm if spatial_median has converged. Returns ------- spatial_median : ndarray of shape = (n_features,) Spatial median. n_iter : int Number of iterations needed. References ---------- - On Computation of Spatial Median for Robust Data Mining, 2005 T. Kärkkäinen and S. Äyrämö http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf rT)keepdimsrrrzYMaximum number of iterations {max_iter} reached in spatial median for TheilSen regressor.)max_iter) rrmedianravelmeanranger,rwarningswarnformatr )r#r0tolspatial_median_oldn_iterspatial_medians r+_spatial_medianr<PsD wwqzQ"))AGGI555AIC+/1!H 66%61< = C   !!"0  "   vxv(   > !!r-c:SSSU- -X- S--U-S- U- - $)zApproximation of the breakdown point. Parameters ---------- n_samples : int Number of samples. n_subsamples : int Number of subsamples to consider. Returns ------- breakdown_point : float Approximation of breakdown point. rg?) n_samples n_subsampless r+_breakdown_pointrAsE" A $ %)AA)E F      r-c[U5nURSU-nURSn[R"URSU45n[R"XT45n[R "[ XT55n[SXx45un [U5H-upX SS24USS2US24'XUSU&U "Xx5SSUXj'M/ U$)aQLeast Squares Estimator for TheilSenRegressor class. This function calculates the least squares method on a subset of rows of X and y defined by the indices array. Optionally, an intercept column is added if intercept is set to true. Parameters ---------- X : array-like of shape (n_samples, n_features) Design matrix, where `n_samples` is the number of samples and `n_features` is the number of features. y : ndarray of shape (n_samples,) Target vector, where `n_samples` is the number of samples. indices : ndarray of shape (n_subpopulation, n_subsamples) Indices of all subsamples with respect to the chosen subpopulation. fit_intercept : bool Fit intercept or not. Returns ------- weights : ndarray of shape (n_subpopulation, n_features + intercept) Solution matrix of n_subpopulation solved least square problems. rr)gelssN) rrremptyoneszerosr!r enumerate) r#yindices fit_intercept n_featuresr@weightsX_subpopulationy_subpopulationlstsqindexsubsets r+_lstsqrRs6 &Mm+J==#Lhh a(*56Ggg|89OhhL =?O _,NOHU"7+ -.qy\=>)*)* &@CKZP, Nr-c \rSrSr%SrS/\"\SSSS9/S\/\"\SSSS9/\"\S SSS9/S /S\/S /S .r\ \ S 'SSSSSSSSS .Sjr Sr \ "SS9S5rSrg)TheilSenRegressoraTheil-Sen Estimator: robust multivariate regression model. The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and efficiency. Since the number of least square solutions is "n_samples choose n_subsamples", it can be extremely large and can therefore be limited with max_subpopulation. If this limit is reached, the subsets are chosen randomly. In a final step, the spatial median (or L1 median) is calculated of all least square solutions. Read more in the :ref:`User Guide `. Parameters ---------- fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations. max_subpopulation : int, default=1e4 Instead of computing with a set of cardinality 'n choose k', where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if 'n choose k' is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed. Note that the data type should be int but floats such as 1e4 can be accepted too. n_subsamples : int, default=None Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=True) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If None, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, Theil-Sen is identical to least squares. max_iter : int, default=300 Maximum number of iterations for the calculation of spatial median. tol : float, default=1e-3 Tolerance when calculating spatial median. random_state : int, RandomState instance or None, default=None A random number generator instance to define the state of the random permutations generator. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. n_jobs : int, default=None Number of CPUs to use during the cross validation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : bool, default=False Verbose mode when fitting the model. Attributes ---------- coef_ : ndarray of shape (n_features,) Coefficients of the regression model (median of distribution). intercept_ : float Estimated intercept of regression model. breakdown_ : float Approximated breakdown point. n_iter_ : int Number of iterations needed for the spatial median. n_subpopulation_ : int Number of combinations taken into account from 'n choose k', where n is the number of samples and k is the number of subsamples. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- HuberRegressor : Linear regression model that is robust to outliers. RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm. SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD. References ---------- - Theil-Sen Estimators in a Multiple Linear Regression Model, 2009 Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang http://home.olemiss.edu/~xdang/papers/MTSE.pdf Examples -------- >>> from sklearn.linear_model import TheilSenRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression( ... n_samples=200, n_features=2, noise=4.0, random_state=0) >>> reg = TheilSenRegressor(random_state=0).fit(X, y) >>> reg.score(X, y) 0.9884 >>> reg.predict(X[:1,]) array([-31.5871]) booleanrNleft)closedrr random_stateverboserJmax_subpopulationr@r0r8rYn_jobsrZ_parameter_constraintsTg@,MbP?FcdXlX lX0lX@lXPlX`lXplXlgNr[) selfrJr\r@r0r8rYr]rZs r+__init__TheilSenRegressor.__init__Ms0+!2( (  r-c URnUR(aUS-nOUnUbzX1:a[SRX155eX:a6XC:a0UR(aSOSn[SRXTU55eO+X1:wa[SRX155eO [ XA5n[ S[ R"[X555n[[ URU55nX74$)Nrz=Invalid parameter since n_subsamples > n_samples ({0} > {1}).z+1zAInvalid parameter since n_features{0} > n_subsamples ({1} > {2}).z\Invalid parameter since n_subsamples != n_samples ({0} != {1}) while n_samples < n_features.) r@rJ ValueErrorr7r"r!rrintr rr\)rcr?rKr@n_dimplus_1all_combinationsn_subpopulations r+_check_subparams"TheilSenRegressor._check_subparamsbs((   NEE  #' --3VL-L&'%)%7%7TRF$!6&>( ,$((.|(G-u0Lq"''% *H"IJc$"8"8:JKL,,r-)prefer_skip_nested_validationc ^^^^ [TR5n[TTTSS9ummTRupETR XE5unTl[ XF5TlTR(a[SRTR55 [SRU55 [TRU-5n[SRU55 [SRTR 55 [R"[XF55TR::a[![#[%U5U55nO3[%TR 5V s/sHn UR'XFSS9PM nn [)TR*5n [R,"X5m [/U TRS 9"UU UU4S j[%U 555n [R0"U 5n [3U TR4TR6S 9uTln TR:(aU S TlU S STlT$STlU TlT$s sn f)zFit linear model. Parameters ---------- X : ndarray of shape (n_samples, n_features) Training data. y : ndarray of shape (n_samples,) Target values. Returns ------- self : returns an instance of self. Fitted `TheilSenRegressor` estimator. T) y_numericzBreakdown point: {0}zNumber of samples: {0}zTolerable outliers: {0}zNumber of subpopulations: {0}F)sizereplace)r]rZc3n># UH*n[[5"TTTUTR5v M, g7frb)rrRrJ).0jobr# index_listrcrHs r+ (TheilSenRegressor.fit..s5@ $ FOAq*S/43E3E F F$s25)r0r8rrNr) rrYrrrnn_subpopulation_rA breakdown_rZprintr7rrrir r\listrr4choicerr] array_splitrvstackr<r0r8n_iter_rJ intercept_coef_)rcr#rHrYr?rKr@ tol_outliersrI_r]rLcoefsrxs``` @r+fitTheilSenRegressor.fits *$*;*;< T1a481 ! .2.C.C / + d++9C << (//@ A *11)< =t:;L +22<@ A 1889N9NO P 7751 2d6L6L L<i(8,GHGt4455A##I%#P5  "$++.^^G4 &$,,?@ V}@  ))G$- dmm  e   #AhDOqrDJ  "DODJ /s(I) r|rrJrr0r\rr]r{r@rYr8rZ)__name__ __module__ __qualname____firstlineno____doc__rrrr^dict__annotations__rdrnr r__static_attributes__r>r-r+rTrTsod$&tQVDEx(h4?@sD89'("; $D  *#-J5969r-rT)r_r`)*rr5 itertoolsrnumbersrrnumpyrjoblibrscipyrscipy.linalg.lapackr scipy.specialr sklearn.baser r sklearn.exceptionsr sklearn.linear_model._baser sklearn.utilsrsklearn.utils._param_validationrsklearn.utils.parallelrrsklearn.utils.validationrfinfodoubleepsrr,r<rArRrTr>r-r+rsx""#0512,442 88BII  " "0f5"p6)Xr rr-