-i̧VSSKrSSKJrJr SSKJrJr SSKrSSK J r SSK J r JrJr SSKJrJr SSKJr SSKJrJrJrJr SS KJrJr SS KJr SS KJ r SS K!J"r"J#r# SS K$J%r%J&r&J'r'J(r(J)r)J*r* SSK+J,r- SSK+J.r/ SSK+J0r1 /SQr2Sr3"SS\ \S9r4"SS\\4\S9r5Sr6SSjr7g)N)ABCMetaabstractmethod)IntegralReal) BaseEstimatorClassifierMixin _fit_context)ConvergenceWarningNotFittedError) LabelEncoder) check_arraycheck_random_state column_or_1dcompute_class_weight)Interval StrOptions)safe_sparse_dot) available_if)_ovr_decision_functioncheck_classification_targets)_check_large_sparse_check_sample_weight _num_samplescheck_consistent_lengthcheck_is_fitted validate_data) _liblinear)_libsvm)_libsvm_sparse)c_svcnu_svc one_class epsilon_svrnu_svrc URSS-n/n[R"[R"S/U/55n[ U5HnX%UXVS-2SS24n[ US-U5H\nX%UXXS-2SS24n XS- XVXVS-24n XXXXXS-24n UR [ X5[ X5-5 M^ M U$)zyGenerate primal coefficients from dual coefficients for the one-vs-one multi class LibSVM in the case of a linear kernel.rrN)shapenpcumsumhstackrangeappendr) dual_coef n_supportsupport_vectorsn_classcoefsv_locsclass1sv1class2sv2alpha1alpha2s D/var/www/html/venv/lib/python3.13/site-packages/sklearn/svm/_base.py_one_vs_one_coefr;%sooa 1$G Dii A3 "234G.fo 0CCQFGFQJ0F!&/GQJ4G"G"JKCz7?WaZ=P+PPQFw!9L'LLMF KK4v7SS T1! Kc^\rSrSr%Sr\"1Sk5\/\"\SSSS9/\"SS 15\"\ S SSS9/\"\ SSS S9/\"\ S SS S9/\"\ S SS S9/\"\ S S S S9/\"\ S SSS9/S/S/\"\ SSS S9/\"S15\ S/S/\"\SSSS9/S/S.r \ \ S'/SQr \S5rU4Sjr\"SS9S+Sj5rSrSrSrSrSrS rS!rS"rS#rS$rS%rS&r\S'5r S(r!\S)5r"S*r#U=r$$), BaseLibSVMEzBase class for estimators that use libsvm as backing library. This implements support vector machine classification and regression. Parameter documentation is in the derived `SVC` class. >rbfpolylinearsigmoid precomputedrNleft)closedscaleautoneitherright?booleanbalancedverbose random_statekerneldegreegammacoef0tolCnuepsilon shrinking probability cache_size class_weightrOmax_iterrQ_parameter_constraints)rBrAr@rCrDc$UR[;a"[S[<SUR<S35eXlX lX0lX@lXPlX`lXpl Xl Xl Xl Xl XlXlXlXlg)Nzimpl should be one of z, z was given)_impl LIBSVM_IMPL ValueErrorrSrTrUrVrWrXrYrZr[r\r]r^rOr_rQ)selfrSrTrUrVrWrXrYrZr[r\r]r^rOr_rQs r:__init__BaseLibSVM.__init__jsy& ::[ ([TU]5nURS:HURlURS:gURlU$NrD)super__sklearn_tags__rS input_tagspairwisesparseretags __class__s r:rkBaseLibSVM.__sklearn_tags__s?w')#';;-#? !% != r<T)prefer_skip_nested_validationc V [UR5n[R"U5nU(aURS:Xa [ S5eU=(a [ UR5(+Ul[ UR5(a [X5 O[UUU[RSSSS9upURU5n[R"Uc/OU[RS9n[RUR 5n[#U5nUS:wa6XrR$S :wa$['S S U<S UR$S <S 3-5eURS:XaGXqR$S:wa5['SR)UR$S UR$S55eUR$S S :a;UR$S U:wa(['SUR$<SUR$<S35e[ UR5(aSO URnUS:XaSUlO[-UR.[05(aUR.S:XamU(a3UR3U5R55UR55S-- OUR75n U S :waSUR$SU -- OSUlOXUR.S:XaSUR$S- UlO0[-UR.[85(aUR.UlUR(a UR:O UR<n UR>(a [ASSS9 URC[RD"S5RF5n U "XX6XS9 [IUS5(a UR$OU4Ul%URLRO5Ul(URRUl*UR S;a@[WURX5S:Xa'U=RLS-sl&URR*Ul)UR(aURTRZO URTn [R\"URP5R_5n [R\"U 5R_5nU (aU(d ['S5eUR S;aUR`Ul1U$UR`Re5Ul1U$) aFit the SVM model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. For kernel="precomputed", the expected shape of X is (n_samples, n_samples). y : array-like of shape (n_samples,) Target values (class labels in classification, real numbers in regression). sample_weight : array-like of shape (n_samples,), default=None Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points. Returns ------- self : object Fitted estimator. Notes ----- If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied. If X is a dense array, then the other methods will not support sparse matrices as input. rDz-Sparse precomputed kernels are not supported.rXcsrF)dtypeorder accept_sparseaccept_large_sparservrrz"X and y have incompatible shapes. zX has z samples, but y has .rzDPrecomputed matrix must be a square matrix. Input is a {}x{} matrix.z.sample_weight and X have incompatible shapes: z vs zT Note: Sparse matrices cannot be indexed w/boolean masks (use `indices=True` in CV).rIrGrLrHz[LibSVM]endi) random_seedr(r"r#rPzxThe dual coefficients or intercepts are not finite. The input data may contain large values and need to be preprocessed.)3rrQspissparserS TypeErrorcallable_sparserrr)float64_validate_targetsasarrayrcindexrbrr(rdformat_gamma isinstancerUstrmultiplymeanvarr _sparse_fit _dense_fitrOprintrandintiinfomaxhasattr shape_fit_ intercept_copy _intercept_ dual_coef_ _dual_coef_lenclasses_dataisfiniteall _num_itern_iter_item)reXy sample_weightrndrn solver_type n_samplesrSX_varfitseedr.intercept_finitenessdual_coef_finitenesss r:rBaseLibSVM.fitsD!!2!23Q dkk]2KL L;ht{{&;"; DKK #A ) jj#$)DA  " "1 % 'B]"** "'' 3 !O !  WWQZ 757@!''!*MN  ;;- 'I,C,,2F1771:qwwqz,J    q !A %-*=*=a*@I*M !&& 1 #+4;;"7"7T[[ ] "DK  C ( (zzW$DJA,,.!&&(q@PQPUPUPW>DL  >>..0DL r<cN[USS9R[RSS9$)zhValidation of y and class_weight. Default implementation for SVR and one-class; overridden in BaseSVC. TwarnF)r)rastyper)r)rers r:rBaseLibSVM._validate_targets's% AD)00%0HHr<cURS;deURS:Xa)[R"SUR-[5 gg)NrrrznSolver terminated early (max_iter=%i). Consider pre-processing your data with StandardScaler or MinMaxScaler.) fit_status_warningsrr_r res r:_warn_from_fit_status BaseLibSVM._warn_from_fit_status.sL6)))   q MM359]]C#   !r<c 0[UR5(aBXlURU5nURSURS:wa [ S5e[ R"UR5 [ R"UU40SU_SU_S[US[R"S55_SU_S UR_S UR_S UR_S UR _S UR"_SUR$_SUR&_SUR(_SUR*_SUR,_SUR._SU_6u UlUlUlUlUlUlUlUlUl URC5 g)Nrrz(X.shape[0] should be equal to X.shape[1]svm_typerr^ class_weight_rSrXrYr\rTr[rWr]rVrUrZr_r)"rrS_BaseLibSVM__Xfit_compute_kernelr(rdlibsvmset_verbosity_wraprOrgetattrr)emptyrXrYr\rTr[rWr]rVrrZr_support_support_vectors_ _n_supportrr_probA_probBrrr)rerrrrrSrs r:rBaseLibSVM._dense_fit8s DKK K$$Q'AwwqzQWWQZ' !KLL!!$,,/ JJ  ! (  ! D    ff ww (( ;; nn   ** ++ LL! "]]# $$%  M  ! O O O K K   N, ""$r<c4[R"UR[RSS9UlUR 5 UR R U5n[R"UR5 [R"URSURURURUUUURURUR UR"UR$['US[R("S55UUR*UR,UR.[1UR25[1UR45UR6U5u UlUlnUlUlUl Ul!Ul"Ul#URI5 [KUS5(a[MURN5S- n OSn UR:RSn [RP"[RR"U 5U 5n U (d[TRV"/5Ul,g[RR"SU RZS-U RZU - 5n [TRV"XU 4X45Ul,g)NrXrvrwrrrr).r)rrr sort_indices_sparse_kernelsr libsvm_sparserrOlibsvm_sparse_trainr(indicesindptrrTrrVrWrXrrrYr]rZintr[r\r_rrrrrrrrrrrrtilearanger csr_matrixrsize) rerrrrrSr kernel_typedual_coef_datar1n_SVdual_coef_indicesdual_coef_indptrs r:rBaseLibSVM._sparse_fitgsAFF"**C@ **008 ((6  - - GGAJ FF II HH   KK KK JJ HH FF D/288A; 7  GG OO LL     ! MM +  M  !  O O K K   N2 ""$ 4 $ $$--(1,GG$$**1-GGBIIdOW= mmB/DO!yy$))A-/@/E/E/O  !mm4DEDOr<cURU5nUR(a URO URnU"U5$)apPerform regression on samples in X. For an one-class model, +1 (inlier) or -1 (outlier) is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- y_pred : ndarray of shape (n_samples,) The predicted values. )_validate_for_predictr_sparse_predict_dense_predict)rerpredicts r:rBaseLibSVM.predicts7  & &q )*.,,$&&D>$,,x 7L7LEt*%%&  ;;- 'wwqzT__Q// =wwqz4??1#567 " "||! 0C0C0ERS0T1$..2I2I1J,W r<cURS:wa [S5eUR5n[R"U5(aSUR R lU$SUR lU$)zsWeights assigned to the features when `kernel="linear"`. Returns ------- ndarray of shape (n_features, n_classes) rBz2coef_ is only available when using a linear kernelF)rSAttributeError _get_coefrrrflags writeablerer2s r:coef_BaseLibSVM.coef_sc ;;( " !UV V~~ ;;t  (-DIIOO % $)DJJ  r<cB[URUR5$N)rrrrs r:r BaseLibSVM._get_coefst//1F1FGGr<c[U5 [R UR 5nUS;a UR $[R"UR S/5$![a [ef=f)z)Number of support vectors for each class.rr) rr r rcrrbrr)array)rers r:rBaseLibSVM.n_support_sk ! D !$$TZZ0 v ?? "88T__Q/01 1 !  !s A""A3)rX__Xfitrrrrrrrrr]r^rVrTrrZrrUrrSr_rrYr\rQrr[rrrWrOr)%r __module__ __qualname____firstlineno____doc__rrrrrdictr`__annotations__rrrfrkr rrrrrrrrrrrrrpropertyrr r__static_attributes__ __classcell__rqs@r:r>r>Es J K  Haf=> ( ) T3V 4 4tI>?sD;<tS$w7 8c3w78T3V<=[!{ai@A#ZL14>;hD@A'(+$D6JO%)%)N5K6KZI-%^;z(  D" H < 0! F'R,H 2 2r<r>) metaclasscD^\rSrSr%Sr0\R E\"SS15/S/S.Er\\ S'SHr \R\ 5 M \ U4S j5r S rS rU4S jrS r\"\5S5r\"\5S5rSrSrSr\S5r\S5rU4SjrSrU=r$)BaseSVCiz!ABC for LibSVM-based classifiers.ovrovorM)decision_function_shape break_tiesr`)rZrYcV>XlUUl[TU] UUUUUUUSUU U U U U US9 g)NrIrR)r(r)rjrf)rerSrTrUrVrWrXrYr[r\r]r^rOr_r(rQr)rqs r:rfBaseSVC.__init__sQ((?$$ #!%%  r<c,[USS9n[U5 [R"USS9up1[ UR X2S9Ul[U5S:a[S[U5-5eX0l [R"U[RSS9$) NTr)return_inverse)classesrrz>The number of classes has to be greater than one; got %d classrXr) rrr)uniquerr^rrrdrrr)rery_clss r:rBaseSVC._validate_targetss !$ '$Q'2d31$2C2CSW s8a<Pc(   zz!2::S99r<cURU5nURS:Xa=[UR5S:a$[ US:U*[UR55$U$)aEvaluate the decision function for the samples in X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2) Returns the decision function of the sample for each class in the model. If decision_function_shape='ovr', the shape is (n_samples, n_classes). Notes ----- If decision_function_shape='ovo', the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (``coef_``). See also `this question `_ for further details. If decision_function_shape='ovr', the decision function is a monotonic transformation of ovo decision function. r&rr)rr(rrr)rerdecs r:rBaseSVC.decision_functionsU6%%a(  ' '5 0S5G!5K)#'C4T]]9KL L r<c>[U5 UR(aURS:Xa [S5eUR(aNURS:Xa>[ UR 5S:a%[ R"URU5SS9nO[TU])U5nUR R[ R"U[ RS95$)aPerform classification on samples in X. For an one-class model, +1 or -1 is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- y_pred : ndarray of shape (n_samples,) Class labels for samples in X. r'z>break_ties must be False when decision_function_shape is 'ovo'r&rr)axisrz)rr)r(rdrrr)argmaxrrjrtakerintp)rerrrqs r:rBaseSVC.predicts"  ??t;;uDP  OO,,5DMM"Q& $003!??r<crUR(d [S5eURS;a [S5eg)Nz5predict_proba is not available when probability=Falserz0predict_proba only implemented for SVC and NuSVCT)r\r rbrs r: _check_probaBaseSVC._check_proba=s9 G  ::0 0 !ST Tr<cURU5nURRS:XdURRS:Xa [ S5eUR (a UR O URnU"U5$)a$Compute probabilities of possible outcomes for samples in X. The model needs to have probability information computed at training time: fit with attribute `probability` set to True. Parameters ---------- X : array-like of shape (n_samples, n_features) For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train). Returns ------- T : ndarray of shape (n_samples, n_classes) Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`. Notes ----- The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets. rzApredict_proba is not available when fitted with probability=False)rprobA_rprobB_r r_sparse_predict_proba_dense_predict_proba)rer pred_probas r: predict_probaBaseSVC.predict_probaFsq6  & &q ) ;;  q DKK$4$4$9 S +/,,D & &D[TU]5nURS:gURlU$ri)rjrkrSrlrnros r:rkBaseSVC.__sklearn_tags__s*w')!% != r<)r)rrr()rrrrrr>r`rrr unused_parampoprrfrrrr=rrErIrCrBr rr@rArkr r!r"s@r:r%r%s+$  + +$$.u~$>#? k$D * ""<0*% % N :@@J," "H,- -:6! F r<r%c SS0SSS.S.SSS 00SS 0S S S.S.SSS 00SSSS.0SS.nUS:XaX@$US:wa[SU-5eURUS5nUcSU-nOGURUS5nUc SU<SU<S3nO&URUS5nUcSU<SU<SU<3nOU$[SU<SU<SU<SU<35e)zFind the liblinear magic number for the solver. This number depends on the values of the following attributes: - multi_class - penalty - loss - dual The same number is also internally used by LibLinear to determine which solver to use. Fr)FT)l1l2rcTrr )logistic_regressionhinge squared_hingeepsilon_insensitivesquared_epsilon_insensitivecrammer_singerror&z<`multi_class` must be one of `ovr`, `crammer_singer`, got %rNzloss='%s' is not supportedzThe combination of penalty='z ' and loss='z' is not supportedz' are not supported when dual=zUnsupported set of arguments: z, Parameters: penalty=z, loss=z, dual=)rdget) multi_classpenaltylossdual_solver_type_dict _solver_pen error_string _solver_dual solver_nums r:_get_liblinear_solver_typerzs&"(-aj8KLq "!& !12EF $tRj1(,b.C'D && --   J[ X  $''d3K3d: "w5  D"  &))$5J!CJ4QUW "!  $ . r<cU S;aS[5nURU5nURn[U5S:a[ SUS-5e[ UUXS9nO%[ R"S[ RS9nUn[R"U5 [U 5nU(a [SSS 9 S nU(aUS::a[ S U-5eUn[R"U5 [R"U5 [R"U5 [R "U5(a [#U5 [ R$"U[ RS9R'5n[ R("US S 9n[+X[ RS9n[-XX5n[R."UU[R "U5UU UUUU UR1[ R2"S5R45UU5 unn[5U5nUU :a8"S[:5 U(aUSS2SS24nUUSS2S4-nOUnSnUUU4$)aUsed by Logistic Regression (and CV) and LinearSVC/LinearSVR. Preprocessing is done in this function before supplying it to liblinear. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target vector relative to X C : float Inverse of cross-validation parameter. The lower the C, the higher the penalization. fit_intercept : bool Whether or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: ``[x_1, ..., x_n, 1]``, where 1 corresponds to the intercept. If set to False, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float Liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the `intercept_scaling` parameter can be set to a value greater than 1; the higher the value of `intercept_scaling`, the lower the impact of regularization on it. Then, the weights become `[w_x_1, ..., w_x_n, w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent the feature weights and the intercept weight is scaled by `intercept_scaling`. This scaling allows the intercept term to have a different regularization behavior compared to the other features. class_weight : dict or 'balanced', default=None Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` penalty : {'l1', 'l2'} The norm of the penalty used in regularization. dual : bool Dual or primal formulation, verbose : int Set verbose to any positive number for verbosity. max_iter : int Number of iterations. tol : float Stopping condition. random_state : int, RandomState instance or None, default=None Controls the pseudo random number generation for shuffling the data. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. multi_class : {'ovr', 'crammer_singer'}, default='ovr' `ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer` optimizes a joint objective over all classes. While `crammer_singer` is interesting from an theoretical perspective as it is consistent it is seldom used in practice and rarely leads to better accuracy and is more expensive to compute. If `crammer_singer` is chosen, the options loss, penalty and dual will be ignored. loss : {'logistic_regression', 'hinge', 'squared_hinge', 'epsilon_insensitive', 'squared_epsilon_insensitive}, default='logistic_regression' The loss function used to fit the model. epsilon : float, default=0.1 Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set epsilon=0. sample_weight : array-like of shape (n_samples,), default=None Weights assigned to each sample. Returns ------- coef_ : ndarray of shape (n_features, n_features + 1) The coefficient vector got by minimizing the objective function. intercept_ : float The intercept term added to the vector. n_iter_ : array of int Number of iterations run across for each class. )rmrnrzeThis solver needs samples of at least 2 classes in the data, but the data contains only one class: %rr)r.rrrzz [LibLinear]r|r}gzqIntercept scaling is %r but needs to be greater than 0. To disable fitting an intercept, set fit_intercept=False.W) requirementsrz@Liblinear failed to converge, increase the number of iterations.NrPrI)r fit_transformrrrdrr)rr liblinearrrrrrrrrrrrequirerrz train_wraprrrrrr )rrrX fit_interceptintercept_scalingr^rrrtrOr_rWrQrqrsrZrency_indrrrbiasr raw_coef_r n_iter_maxrrs r:_fit_liblinearr*s)h IIn!!!$<< x=1 '{+  - (a "**5    ) \ *C m$ D  !,.?@  %D g&$$W-   ) {{1~~A JJuBJJ / 5 5 7E JJu3 /E(LM,[4NK"--   A    BHHSM%%& Iw$WJX N  !SbS&!&1b5)99  *g %%r<)Nr&rjg?N)8rabcrrnumbersrrnumpyr) scipy.sparsernrbaserr r exceptionsr r preprocessingr utilsrrrrutils._param_validationrr utils.extmathrutils.metaestimatorsrutils.multiclassrrutils.validationrrrrrrr|rrr rr!rrcr;r>r%rzrr<r:rs'"??;(WW:+/S& -G @n 2'n 2bxozWxv 6J  !D&r<