Hi,
Manoj's tip only works if you are already using the 0.18dev version of
sklearn that brings a total refactoring of the gaussian_process submodule.
If you are still using the latest stable (aka 0.17), then custom kernels
can be specified as callable (simple Python function) using the `corr`
keyword argument.
>From GaussianProcess' docstring (v0.17):
> *corr* : string or callable, optional
>
> A stationary autocorrelation function returning the autocorrelation
> between two points x and x’. Default assumes a squared-exponential
> autocorrelation model. Built-in correlation models are:
>
> 'absolute_exponential', 'squared_exponential','generalized_exponential',
> 'cubic', 'linear'
>
>
Here is the code for the squared_exponential kernel:
def squared_exponential(theta, d):
> """
> Squared exponential correlation model (Radial Basis Function).
> (Infinitely differentiable stochastic process, very smooth)::
> n
> theta, dx --> r(theta, dx) = exp( sum - theta_i * (dx_i)^2 )
> i = 1
> Parameters
> ----------
> theta : array_like
> An array with shape 1 (isotropic) or n (anisotropic) giving the
> autocorrelation parameter(s).
> dx : array_like
> An array with shape (n_eval, n_features) giving the componentwise
> distances between locations x and x' at which the correlation model
> should be evaluated.
> Returns
> -------
> r : array_like
> An array with shape (n_eval, ) containing the values of the
> autocorrelation model.
> """
>
> theta = np.asarray(theta, dtype=np.float)
> d = np.asarray(d, dtype=np.float)
>
> if d.ndim > 1:
> n_features = d.shape[1]
> else:
> n_features = 1
>
> if theta.size == 1:
> return np.exp(-theta[0] * np.sum(d ** 2, axis=1))
> elif theta.size != n_features:
> raise ValueError("Length of theta must be 1 or %s" % n_features)
> else:
> return np.exp(-np.sum(theta.reshape(1, n_features) * d ** 2,
> axis=1))
>
Implement the Pearson kernel like this and pass it to the GaussianProcess
estimator.
Cheers,
Vincent
2016-04-01 1:59 GMT+02:00 Manoj Kumar <manojkumarsivaraj...@gmail.com>:
> Hi,
>
> I remember seeing it somewhere but don't recall where exactly.
>
> You can do it by the following.
>
> 1. Inherit from Kernel (
> http://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.kernels.Kernel.html#sklearn.gaussian_process.kernels.Kernel
> )
> 2. Make your hyperparameters on the kernel an attribute starting with
> `hyperparameter_` and make it an instance of Hyperparameter as done here (
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/gaussian_process/kernels.py#L1711
> )
> 3. Implement a __call__ that computes the kernel matrix and if possible
> the gradient.
>
> If not already present, this might be worth illustrating in an example
>
>
> On Thu, Mar 31, 2016 at 7:45 PM, Sebastian Raschka <se.rasc...@gmail.com>
> wrote:
>
>> Seems like that the GaussianProcess class only has an autocorrelation
>> parameter, but when I understand correctly, the autocorrelation function is
>> just the “normalized” covariance kernel, thus it may be possible to provide
>> custom kernels here as well? If not, it may be interesting to re-factor it
>> a little bit and borrow the code from SVM to accept custom kernels in
>> Gaussian Processes as well?
>>
>> > On Mar 31, 2016, at 5:17 PM, Andreas Mueller <t3k...@gmail.com> wrote:
>> >
>> > Hi.
>> > What do you mean by Gaussian regression?
>> > You can specify your own kernels for SVMs, but it will be a bit slower.
>> >
>> > Cheers,
>> > Andy
>> >
>> > On 03/28/2016 09:40 PM, Amita Misra wrote:
>> >> Hi,
>> >>
>> >> I was using weka earlier for support vector regression and gaussian
>> regression
>> >>
>> >> I am now switching to scikit and was trying to replicate my results
>> using support vector regression.
>> >> I could get similar results for poly and rbf kernel.
>> >>
>> >> Is there a way I can specify Pearson VII function kernel(Puk Kernel)
>> for support vector and Gaussian regression?
>> >>
>> >> Thanks,
>> >> Amita
>> >>
>> >>
>> >>
>> >> --
>> >> Amita Misra
>> >> Graduate Student Researcher
>> >> Natural Language and Dialogue Systems Lab
>> >> Baskin School of Engineering
>> >> University of California Santa Cruz
>> >>
>> >>
>> >>
>> >>
>> ------------------------------------------------------------------------------
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>> >>
>> >> _______________________________________________
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>
>
>
> --
> Manoj,
> http://github.com/MechCoder
>
>
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>
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