Multivariate Pattern Analysis in Python

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# mvpa2.kernels.np.ExponentialKernel

class `mvpa2.kernels.np.``ExponentialKernel`(*args, **kwargs)

  The 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_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.
  `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
  `gradient`(data1, data2) | Compute gradient of the kernel matrix.
  `reset`() |

  Initialize instance of ExponentialKernel

  Parameters:

  length_scale :

  > The characteristic length-scale (or length-scales) of the phenomenon under investigation. (Default: 1.0)

  sigma_f :

  > Signal standard deviation. (Default: 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_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.
  `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
  `gradient`(data1, data2) | Compute gradient of the kernel matrix.
  `reset`() |

  `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) BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS.

  `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. Shorter formula. Allows vector of lengthscales (ARD). BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS.

  `gradient`(data1, data2)

    Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.

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