nimare.meta.cbma.kernel
.KernelTransformer¶
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class
KernelTransformer
[source]¶ Base class for modeled activation-generating methods.
Coordinate-based meta-analyses leverage coordinates reported in neuroimaging papers to simulate the thresholded statistical maps from the original analyses. This generally involves convolving each coordinate with a kernel (typically a Gaussian or binary sphere) that may be weighted based on some additional measure, such as statistic value or sample size.
Methods
get_params
(self[, deep])Get parameters for this estimator. load
(filename[, compressed])Load a pickled class instance from file. save
(self, filename[, compress])Pickle the class instance to the provided file. set_params
(self, \*\*params)Set the parameters of this estimator. transform
(self, dataset)Add stuff to transformer. -
get_params
(self, deep=True)[source]¶ 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 – Parameter names mapped to their values. Return type: mapping of string to any
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classmethod
load
(filename, compressed=True)[source]¶ Load a pickled class instance from file.
Parameters: Returns: obj – Loaded class object.
Return type: class object
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save
(self, filename, compress=True)[source]¶ Pickle the class instance to the provided file.
Parameters:
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set_params
(self, **params)[source]¶ 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: Return type: self
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