sklearn.neighbors.KernelDensity
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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: |
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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 and KDTree 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 or KDTree 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 or KDTree . |
Methods
fit (X[, y, sample_weight]) | 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. |
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__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, sample_weight=None)
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Fit the Kernel Density model on the data.
Parameters: |
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X : array_like, shape (n_samples, n_features) -
List of n_features-dimensional data points. Each row corresponds to a single data point. -
sample_weight : array_like, shape (n_samples,), optional -
List of sample weights attached to the data X. |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
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deep : boolean, optional -
If True, will return the parameters for this estimator and contained subobjects that are estimators. |
Returns: |
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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: |
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n_samples : int, optional -
Number of samples to generate. Defaults to 1. -
random_state : int, RandomState instance or None. default to None -
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random . |
Returns: |
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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: |
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X : array_like, shape (n_samples, n_features) -
List of n_features-dimensional data points. Each row corresponds to a single data point. |
Returns: |
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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: |
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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: |
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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.
Examples using sklearn.neighbors.KernelDensity