Ah, that helped me a lot!!!
So i just write my own function that returns an skalar. This function is
used in the metric parameter of the kNN function.
Thank you!!!
On 9 January 2016 at 03:41, Sebastian Raschka <se.rasc...@gmail.com> wrote:
> You could just need “regular" Python function that outputs a scalar. For
> example, consider the following example:
>
> >>> from sklearn.neighbors import NearestNeighbors
> >>> import numpy as np
> >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
> >>> nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)
> >>> distances, indices = nbrs.kneighbors(X)
> >>> distances
> array([[ 0. , 1. ],
> [ 0. , 1. ],
> [ 0. , 1.41421356],
> [ 0. , 1. ],
> [ 0. , 1. ],
> [ 0. , 1.41421356]])
>
> (note that I am using the NearestNeighbors class here, but the same
> applies to the KNeighborsClassifier)
>
> For example, to compute the distances between samples as Euclidean
> distance (the default) you could just define a Python function
>
> >>> def eucldist(x, y):
> ... return np.sqrt(np.sum((x-y)**2))
> >>> nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree',
> metric=eucldist).fit(X)
> >>> distances, indices = nbrs.kneighbors(X)
> >>> distances
> array([[ 0. , 1. ],
> [ 0. , 1. ],
> [ 0. , 1.41421356],
> [ 0. , 1. ],
> [ 0. , 1. ],
> [ 0. , 1.41421356]])
>
> (alt. you could provide it as lambda function)
>
> Best,
> Sebastian
>
> > On Jan 8, 2016, at 9:19 PM, A neuman <themagenta...@gmail.com> wrote:
> >
> > Hello everyone,
> >
> > I actually want to use the KNeighboursClassifier, with my own distances.
> >
> > in the Documentation stands the following:
> >
> > [callable] : a user-defined function which accepts an array of
> distances, and returns an array of the same shape containing the weights.
> >
> > I just dont know, how should the array looks like?
> >
> > For example, if I have 100 Samples, the array has a size 100*100?
> > So for every samples there is a distance to the other 99 samples.
> >
> > [[0.4, 0.2, ...],[0.3,0.1,...]........[0.9,0.6,...]] something like
> this?
> >
> > I would appreciate your help.
> >
> > best,
> >
> >
> >
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