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.
Notes
This base class exists solely to allow CBMA algorithms to check the class of their kernel_transformer parameters.
All extra (non-ijk) parameters for a given kernel should be overrideable as parameters to __init__, so we can access them with get_params() and also apply them to datasets with missing data.
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get_params
(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 (mapping of string to any) – Parameter names mapped to their values.
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