Multivariate Pattern Analysis in Python

### Navigation

- index
- modules |
- modules |
- PyMVPA Home |
- Sitemap »

# mvpa2.clfs.warehouse.GeneralizedLinearKernel

class `mvpa2.clfs.warehouse.``GeneralizedLinearKernel`(*args, **kwargs)

  The linear kernel class.

  Notes

  Available conditional attributes:

  - `gradients`: Dictionary of gradients per a parameter
  - `gradientslog`: Dictionary of gradients per a parameter in logspace

  (Conditional attributes enabled by default suffixed with `+`)

  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`() |

  Initialize instance of GeneralizedLinearKernel

  Parameters:

  sigma_0 :

  > A simple constant squared value which is broadcasted across kernel. In the case of GPR – standard deviation of the Gaussian prior probability Normal(0, sigma_0**2) of the intercept of the linear regression. (Default: 1.0)

  Sigma_p :

  > A generic scalar or vector, or diagonal matrix to scale all dimensions or associate different scaling to each dimensions while computing te kernel matrix: k(x_A,x_B) = x_A^\top \Sigma_p x_B + \sigma_0^2. In the case of GPR – a scalar or a diagonal of covariance matrix of the Gaussian prior probability Normal(0, Sigma_p) on the weights of the linear regression. (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.
  `computed`(*args, **kwargs) | Compute kernel and return self
  `reset`() |

  `reset`()

### Navigation

- index
- modules |
- modules |
- PyMVPA Home |
- Sitemap »