Home | Trees | Indices | Help |
|
---|
|
Expand input space with Gaussian Radial Basis Functions (RBFs). The input data is filtered through a set of unnormalized Gaussian filters, i.e.:: y_j = exp(-0.5/s_j * ||x - c_j||^2) for isotropic RBFs, or more in general:: y_j = exp(-0.5 * (x-c_j)^T S^-1 (x-c_j)) for anisotropic RBFs.
|
|||
|
|||
|
|||
|
|||
|
|||
Inherited from Inherited from |
|||
Inherited from Node | |||
---|---|---|---|
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|
|||
|
|||
|
|
|||
Inherited from |
|||
Inherited from Node | |||
---|---|---|---|
_train_seq List of tuples:: |
|||
dtype dtype |
|||
input_dim Input dimensions |
|||
output_dim Output dimensions |
|||
supported_dtypes Supported dtypes |
|
:Arguments: centers Centers of the RBFs. The dimensionality of the centers determines the input dimensionality; the number of centers determines the output dimensionalities sizes Radius of the RBFs. ``sizes`` is a list with one element for each RBF, either a scalar (the variance of the RBFs for isotropic RBFs) or a covariance matrix (for anisotropic RBFs). If ``sizes`` is not a list, the same variance/covariance is used for all RBFs.
|
|
|
Process the data contained in `x`. If the object is still in the training phase, the function `stop_training` will be called. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_execute` to implement their execution phase. The docstring of the `_execute` method overwrites this docstring.
|
Return True if the node can be inverted, False otherwise.
|
Return True if the node can be trained, False otherwise.
|
Home | Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Thu Mar 10 15:27:45 2016 | http://epydoc.sourceforge.net |