Multivariate Pattern Analysis in Python

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# mvpa2.clfs.gpr.SquaredExponentialKernel

class `mvpa2.clfs.gpr.``SquaredExponentialKernel`(length_scale=1.0, sigma_f=1.0, **kwargs)

  The Squared Exponential kernel class.

  Note that it can handle a length scale for each dimension for Automtic Relevance Determination.

  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_np`() | Converts this kernel to a Numpy-based representation
  `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.
  `compute_lml_gradient`(alphaalphaT_Kinv, data) | Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
  `compute_lml_gradient_logscale`(...) | Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
  `computed`(*args, **kwargs) | Compute kernel and return self
  `reset`() |
  `set_hyperparameters`(hyperparameter) | Set hyperaparmeters from a vector.

  Initialize a Squared Exponential kernel instance.

  Parameters:

  length_scale : float or numpy.ndarray, optional

  > the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0)

  sigma_f : float, optional

  > Signal standard deviation. (Defaults to 1.0)

  enable_ca : None or list of str

  > Names of the conditional attributes which should be enabled in addition to the default ones

  disable_ca : None or list of str

  > Names of the conditional attributes which should be disabled

  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_np`() | Converts this kernel to a Numpy-based representation
  `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.
  `compute_lml_gradient`(alphaalphaT_Kinv, data) | Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
  `compute_lml_gradient_logscale`(...) | Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel.
  `computed`(*args, **kwargs) | Compute kernel and return self
  `reset`() |
  `set_hyperparameters`(hyperparameter) | Set hyperaparmeters from a vector.

  `compute_lml_gradient`(alphaalphaT_Kinv, data)

    Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD).

  `compute_lml_gradient_logscale`(alphaalphaT_Kinv, data)

    Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Hyperparameters are in log scale which is sometimes more stable. Shorter formula. Allows vector of lengthscales (ARD).

  `length_scale`
  `reset`()
  `set_hyperparameters`(hyperparameter)

    Set hyperaparmeters from a vector.

    Used by model selection.

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