-i/SrSSKrSSKJrJr SSKrSSKJr SSK J r J r SSK J r SSKJr SS KJrJr SS KJr SS KJrJrJr S S KJr S SKJr /SQr\\S.r"SS\ 5rg)z5 Kernel Density Estimation ------------------------- N)IntegralReal)gammainc) BaseEstimator _fit_context) VALID_METRICS)check_random_state)Interval StrOptions) row_norms)_check_sample_weightcheck_is_fitted validate_data)BallTree)KDTree)gaussiantophat epanechnikov exponentiallinearcosine) ball_treekd_treec\rSrSr%Sr\"\SSSS9\"SS15/\"\"\ R55S 1-5/\"\"\ 55/\"\"\ R"\ R5VVVs/sH n[UPM snnn655/\"\SSS S9/\"\SSS S9/S /\"\S SS S9/S\/S . r\\S'SS SSSSSSSS . SjrSr\"SS9SSj5rSrSSjrSSjrSrgs snnnf) KernelDensity&a Kernel Density Estimation. Read more in the :ref:`User Guide `. Parameters ---------- bandwidth : float or {"scott", "silverman"}, default=1.0 The bandwidth of the kernel. If bandwidth is a float, it defines the bandwidth of the kernel. If bandwidth is a string, one of the estimation methods is implemented. algorithm : {'kd_tree', 'ball_tree', 'auto'}, default='auto' The tree algorithm to use. kernel : {'gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear', 'cosine'}, default='gaussian' The kernel to use. metric : str, default='euclidean' Metric to use for distance computation. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. Not all metrics are valid with all algorithms: refer to the documentation of :class:`BallTree` and :class:`KDTree`. Note that the normalization of the density output is correct only for the Euclidean distance metric. atol : float, default=0 The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. rtol : float, default=0 The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. breadth_first : bool, default=True If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach. leaf_size : int, default=40 Specify the leaf size of the underlying tree. See :class:`BallTree` or :class:`KDTree` for details. metric_params : dict, default=None Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of :class:`BallTree` or :class:`KDTree`. Attributes ---------- n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 tree_ : ``BinaryTree`` instance The tree algorithm for fast generalized N-point problems. 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. bandwidth_ : float Value of the bandwidth, given directly by the bandwidth parameter or estimated using the 'scott' or 'silverman' method. .. versionadded:: 1.0 See Also -------- sklearn.neighbors.KDTree : K-dimensional tree for fast generalized N-point problems. sklearn.neighbors.BallTree : Ball tree for fast generalized N-point problems. Examples -------- Compute a gaussian kernel density estimate with a fixed bandwidth. >>> from sklearn.neighbors import KernelDensity >>> import numpy as np >>> rng = np.random.RandomState(42) >>> X = rng.random_sample((100, 3)) >>> kde = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(X) >>> log_density = kde.score_samples(X[:3]) >>> log_density array([-1.52955942, -1.51462041, -1.60244657]) rNneither)closedscott silvermanautoleftbooleanr) bandwidth algorithmkernelmetricatolrtol breadth_first leaf_size metric_params_parameter_constraints?r euclideanT(c pX lXlX0lX@lXPlX`lXplXlXlgN) r'r&r(r)r*r+r,r-r.) selfr&r'r(r)r*r+r,r-r.s I/var/www/html/venv/lib/python3.13/site-packages/sklearn/neighbors/_kde.py__init__KernelDensity.__init__s3#"    *"*cUS:Xa+U[R;agU[R;aggU[UR;a"[ SR [UU55eU$)Nr#rrzinvalid metric for {0}: '{1}')r valid_metricsr TREE_DICT ValueErrorformat)r5r'r)s r6_choose_algorithmKernelDensity._choose_algorithmss  --- 8111"2Yy1??? 3::9Y;OQWX r9F)prefer_skip_nested_validationcURURUR5n[UR[ 5(aURS:Xa+UR SSUR SS-- -UlObURS:Xa@UR SUR SS--S- SUR SS-- -UlOURUl[XS[RS 9nUb[X1[RS S 9nURnUc0n[U"U4URURUS .UD6UlU$) aFit the Kernel Density model on the data. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : None Ignored. This parameter exists only for compatibility with :class:`~sklearn.pipeline.Pipeline`. sample_weight : array-like of shape (n_samples,), default=None List of sample weights attached to the data X. .. versionadded:: 0.20 Returns ------- self : object Returns the instance itself. r!rrr"rC)orderdtypeT)rGensure_non_negative)r)r- sample_weight)r?r'r) isinstancer&strshape bandwidth_rnpfloat64rr.r<r-tree_)r5XyrIr'kwargss r6fitKernelDensity.fits76**4>>4;;G dnnc * *~~("#''!*qwwqzA~1F"G;.#$771:a#@1#D!''!*q.)##nnDO $BJJ ?  $0 M## >Fy)  ;;nn'      r9c [U5 [XS[RSS9nURR c$URR RSnOURRnURU-nURRUURURUURURSS9nU[R"U5-nU$)aCompute the log-likelihood of each sample under the model. Parameters ---------- X : array-like of shape (n_samples, n_features) An array of points to query. Last dimension should match dimension of training data (n_features). Returns ------- density : ndarray of shape (n_samples,) Log-likelihood of each sample in `X`. These are normalized to be probability densities, so values will be low for high-dimensional data. rEF)rFrGresetrT)hr(r*r+r, return_log)rrrNrOrPrIdatarL sum_weightr*kernel_densityrMr(r+r,log)r5rQNatol_N log_densitys r6 score_samplesKernelDensity.score_sampless  $BJJe L :: # # + %%a(A %%AQjj// oo;;,,0  rvvay r9cL[R"URU55$)aCompute the total log-likelihood under the model. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : None Ignored. This parameter exists only for compatibility with :class:`~sklearn.pipeline.Pipeline`. Returns ------- logprob : float Total log-likelihood of the data in X. This is normalized to be a probability density, so the value will be low for high-dimensional data. )rNsumra)r5rQrRs r6scoreKernelDensity.scores(vvd((+,,r9c[U5 URS;a [5e[R"UR R 5n[U5nURSSUS9nUR Rc/XSRS-R[R5nO\[R"[R"UR R55nUSn[R"XuU-5nURS:Xa2[R"UR!X6UR"55$URS:XaURSn UR!X4S9n [%U S S 9n ['S U -S U -5S U - -UR"-[R("U 5- n X6XSS2[R*4--$g) aGenerate random samples from the model. Currently, this is implemented only for gaussian and tophat kernels. Parameters ---------- n_samples : int, default=1 Number of samples to generate. random_state : int, RandomState instance or None, default=None Determines random number generation used to generate random samples. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. Returns ------- X : array-like of shape (n_samples, n_features) List of samples. )rrrr)sizeNrCrrT)squaredg?r0)rr(NotImplementedErrorrNasarrayrPrZr uniformrIrLastypeint64cumsum searchsorted atleast_2dnormalrMr rsqrtnewaxis) r5 n_samples random_staterZrngui cumsum_weightr[dimrQs_sq corrections r6sampleKernelDensity.sample4s*  ;;4 4%' 'zz$**//* . KK19K - :: # # +ZZ]"**2884AIIbjj1I1I&JKM&r*J :~>A ;;* $==DGT__!EF F [[H $**Q-C  0 1AQ-DsC$J/C#I>//"''$-   7QArzzM!::: :%r9) r'r*r&rMr,r(r-r)r.r+rP)NNr4)rN)__name__ __module__ __qualname____firstlineno____doc__r rr setr<keys VALID_KERNELS itertoolschainr rdictr/__annotations__r7r?rrTrarer~__static_attributes__).0algr s000r6rr&sA[~ T1d9 5 - . !Y^^%5!6&!ABCc-012 IOOINNDT%UDTSmC&8DT%UVW  $478$478#xD@A!$D,  +. &+4 4l$L-,3;O&Vs.C'r) rrnumbersrrnumpyrN scipy.specialrbaserrneighbors._baser utilsr utils._param_validationr r utils.extmathr utils.validationrrr _ball_treer_kd_treerrr<rr9r6rsT"".+&:%SS  #v 6 A;MA;r9