o ?Hh/@sdZddlZddlmZmZddlZddlmZddl m Z m Z ddl m Z ddlmZdd lmZmZdd lmZdd lmZmZmZd d lmZd dlmZgdZeedZGddde ZdS)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 @s eZdZUdZeeddddeddhgeee dhBgeee geee j d d e Dgeeddd dgeeddd dgd gee d dd dgdegd Zeed<dddddddddd ddZddZeddd#ddZddZd$dd Zd%d!d"ZdS)& KernelDensitya 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 silvermanautocCsg|]}t|qSr).0algr"r"V/home/air/sanwanet/gpt-api/venv/lib/python3.10/site-packages/sklearn/neighbors/_kde.py szKernelDensity.leftbooleanr) bandwidth algorithmkernelmetricatolrtol breadth_first leaf_size metric_params_parameter_constraints?r euclideanT(c Cs:||_||_||_||_||_||_||_||_| |_dSN) r*r)r+r,r-r.r/r0r1) selfr)r*r+r,r-r.r/r0r1r"r"r%__init__s  zKernelDensity.__init__cCsN|dkr|tjvr dS|tjvrdSdS|t|jvr%tdt|||S)Nr!rrzinvalid metric for {0}: '{1}')r valid_metricsr TREE_DICT ValueErrorformat)r7r*r,r"r"r%_choose_algorithms  zKernelDensity._choose_algorithmF)prefer_skip_nested_validationcCs||j|j}t|jtrA|jdkr#|jdd|jdd|_n"|jdkr@|jd|jdddd|jdd|_n|j|_t||dt j d }|d ur[t ||t j d d }|j }|d urdi}t ||f|j|j|d ||_|S)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. rrrr rC)orderdtypeNT)rCensure_non_negative)r,r0 sample_weight)r=r*r, isinstancer)strshape bandwidth_rnpfloat64rr1r:r0tree_)r7XyrEr*kwargsr"r"r%fits8     zKernelDensity.fitc Cs~t|t||dtjdd}|jjdur|jjjd}n|jj}|j |}|jj ||j |j ||j |jdd}|t|8}|S)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. rAF)rBrCresetNrT)hr+r-r.r/ return_log)rrrJrKrLrEdatarH sum_weightr-kernel_densityrIr+r.r/log)r7rMNatol_N log_densityr"r"r% score_sampless"   zKernelDensity.score_samplescCst||S)a}Compute 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. )rJsumr[)r7rMrNr"r"r%scoreszKernelDensity.scorec Cs,t||jdvr tt|jj}t|}|jdd|d}|jj dur1||j d tj }nt t|jj }|d}t|||}|jdkrXt||||jS|jdkr|j d} |j|| fd} t| d d } td | d | d | |jt| } ||| | ddtjfSdS) 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)sizeNr?rrT)squaredg?r3)rr+NotImplementedErrorrJasarrayrLrTr uniformrErHastypeint64cumsum searchsorted atleast_2dnormalrIr rsqrtnewaxis) r7 n_samples random_staterTrngui cumsum_weightrUdimrMs_sq correctionr"r"r%sample4s2      zKernelDensity.sample)NNr6)rN)__name__ __module__ __qualname____doc__r rr setr:keys VALID_KERNELS itertoolschainrdictr2__annotations__r8r=rrPr[r]rtr"r"r"r%r&sF _    6 &r) rxr|numbersrrnumpyrJ scipy.specialrbaserrneighbors._baser utilsr utils._param_validationr r utils.extmathr utils.validationrrr _ball_treer_kd_treerr{r:rr"r"r"r%s