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class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None)
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Kernel Density Estimation
Read more in the User Guide.
Parameters: bandwidth : float
The bandwidth of the kernel.
algorithm : string
The tree algorithm to use. Valid options are [?kd_tree?|?ball_tree?|?auto?]. Default is ?auto?.
kernel : string
The kernel to use. Valid kernels are [?gaussian?|?tophat?|?epanechnikov?|?exponential?|?linear?|?cosine?] Default is ?gaussian?.
metric : string
The distance metric to use. Note that not all metrics are valid with all algorithms. Refer to the documentation of
BallTree
andKDTree
for a description of available algorithms. Note that the normalization of the density output is correct only for the Euclidean distance metric. Default is ?euclidean?.atol : float
The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 0.
rtol : float
The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 1E-8.
breadth_first : boolean
If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach.
leaf_size : int
Specify the leaf size of the underlying tree. See
BallTree
orKDTree
for details. Default is 40.metric_params : dict
Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of
BallTree
orKDTree
.Methods
fit
(X[, y])Fit the Kernel Density model on the data. get_params
([deep])Get parameters for this estimator. sample
([n_samples, random_state])Generate random samples from the model. score
(X[, y])Compute the total log probability under the model. score_samples
(X)Evaluate the density model on the data. set_params
(\*\*params)Set the parameters of this estimator. -
__init__(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None)
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fit(X, y=None)
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Fit the Kernel Density model on the data.
Parameters: X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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sample(n_samples=1, random_state=None)
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Generate random samples from the model.
Currently, this is implemented only for gaussian and tophat kernels.
Parameters: n_samples : int, optional
Number of samples to generate. Defaults to 1.
random_state : RandomState or an int seed (0 by default)
A random number generator instance.
Returns: X : array_like, shape (n_samples, n_features)
List of samples.
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score(X, y=None)
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Compute the total log probability under the model.
Parameters: X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
Returns: logprob : float
Total log-likelihood of the data in X.
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score_samples(X)
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Evaluate the density model on the data.
Parameters: X : array_like, shape (n_samples, n_features)
An array of points to query. Last dimension should match dimension of training data (n_features).
Returns: density : ndarray, shape (n_samples,)
The array of log(density) evaluations.
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it?s possible to update each component of a nested object.Returns: self :
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neighbors.KernelDensity()
Examples using
2017-01-15 04:24:36
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