Ahh ok,
For future reference, you call a 2D function a function that is defined
over the 2 dimensional space e.g. has 2 variables, hence the example in the
page is a 2D example. What you need would be a 3D function. In Machine
Learning, we usually call these independent dimensions features. So you
would say that a single training example will have 2,3,4 or 5 features.
Then you need to construct your matrix X as a matrix DxF, where D is each
data point and F is each feature.
For example
X=np.random.rand(20,2) #Creates a skeleton structure with 2 features and 20
training points.
Cheers
Leon
On Wed, Jan 23, 2013 at 2:58 PM, Fernando Paolo <[email protected]> wrote:
> If I'm not wrong, this is fitting a model of the form f(x) = y (univariate
> regression, 1d). I want to fit a model of the form f(x,y) = z (bivariate
> regression, 2d).
>
> Cheers,
> -fernando
>
>
> Sent from my iPhone
>
> On Jan 22, 2013, at 9:31 PM, Leon Palafox <[email protected]> wrote:
>
> This example is a 2D regression isn't it?
>
>
> http://scikit-learn.org/dev/modules/gaussian_process.html#an-introductory-regression-example
>
>
>
>
> On Wed, Jan 23, 2013 at 2:25 PM, Fernando Paolo <[email protected]> wrote:
>
>> Hello,
>>
>> I'm new to scikit-learn, and wonder if it is possible to perform 2D
>> Gaussian Process Regression? In other words, I would like to perform a 2d
>> kriging interpolation of spatial data (e.g., some geophysical field), where
>> each data point is represented as (x,y,z) = (lon,lat,var). I can't figure
>> this out from the example given in the website.
>>
>> Thanks so much,
>> -fernando
>>
>>
>> --
>> Fernando Paolo
>> Institute of Geophysics & Planetary Physics
>> Scripps Institution of Oceanography
>> University of California, San Diego
>>
>> web: fspaolo.net
>>
>>
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>
>
> --
> Leon Palafox, M.Sc
> PhD Candidate
> Iba Laboratory
> +81-3-5841-8436
> University of Tokyo
> Tokyo, Japan.
>
>
> ------------------------------------------------------------------------------
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--
Leon Palafox, M.Sc
PhD Candidate
Iba Laboratory
+81-3-5841-8436
University of Tokyo
Tokyo, Japan.
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