Multivariate Pattern Analysis in Python

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# mvpa2.kernels.base.NumpyKernel

class `mvpa2.kernels.base.``NumpyKernel`(*args, **kwargs)

  A Kernel object with internal representation as a 2d numpy array

  Methods

  `add_conversion`(typename, methodfull, methodraw) | Adds methods to the Kernel class for new conversions :Parameters: typename : string Describes kernel type methodfull : function Method which converts to the new kernel object class methodraw : function Method which returns a raw kernel ..
  `as_ls`(kernel) |
  `as_np`() | Converts this kernel to a Numpy-based representation
  `as_raw_ls`(kernel) |
  `as_raw_np`() | Directly return this kernel as a numpy array.
  `cleanup`() | Wipe out internal representation
  `compute`(ds1[, ds2]) | Generic computation of any kernel Assumptions: - ds1, ds2 are either datasets or arrays, - presumably 2D (not checked neither enforced here - _compute takes ndarrays.
  `computed`(*args, **kwargs) | Compute kernel and return self
  `reset`() |

  Base Kernel class has no parameters

  Methods

  `add_conversion`(typename, methodfull, methodraw) | Adds methods to the Kernel class for new conversions :Parameters: typename : string Describes kernel type methodfull : function Method which converts to the new kernel object class methodraw : function Method which returns a raw kernel ..
  `as_ls`(kernel) |
  `as_np`() | Converts this kernel to a Numpy-based representation
  `as_raw_ls`(kernel) |
  `as_raw_np`() | Directly return this kernel as a numpy array.
  `cleanup`() | Wipe out internal representation
  `compute`(ds1[, ds2]) | Generic computation of any kernel Assumptions: - ds1, ds2 are either datasets or arrays, - presumably 2D (not checked neither enforced here - _compute takes ndarrays.
  `computed`(*args, **kwargs) | Compute kernel and return self
  `reset`() |

  `as_np`()

    Converts this kernel to a Numpy-based representation

  `as_raw_np`()

    Directly return this kernel as a numpy array.

    For Numpy-based kernels - simply returns stored matrix.

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