Sorry, thats not right what I wrote:
X:
[ 0.6371319 0.54557285 0.30214217 0.14690307 0.49778446 0.89183238
0.52445514 0.63379164 0.71873681 0.55008567]
Y:
[ 0.6371319 0.54557285 0.30214217 0.14690307 0.49778446 0.89183238
0.52445514 0.63379164 0.71873681 0.55008567]
X:
[ 0. 0. 0. 0.02358491 0.00471698 0. 0.
0. 0. 0.00471698 0.00471698 0.00471698 0.02830189
0.00943396 0. .............................52358491 0.53773585
0.63207547 0.51886792 0.66037736 0.75 0.57075472 0.59433962
0.63679245 0.8490566 0.71698113 0.02358491]
Y:
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.
1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.
0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0.
1. 1. 1. 1. 0.]
and so on..
but X should be also containing 1's and 0's.
best,
On 12 January 2016 at 19:04, A neuman <themagenta...@gmail.com> wrote:
> Hey,
>
> I Have an another problem,
>
> if I'm using my own metric, there are not only the samples in x and y.
> I'm using a 10 fold cv with k-NN Classifier.
> My Attributes are only 1's and 0's, but if im printing them out, I'll get:
>
> KNeighborsClassifier(metric=myFunc)
>
> def myFunc(x,y):
>
> print x,'\n'
> print y,'\n'
>
> I Cutted some values due to the size:
>
> Thats for x:
>
> [ 0.6371319 0.54557285 0.30214217 0.14690307 0.49778446 0.89183238
> 0.52445514 0.63379164 0.71873681 0.55008567]
>
> [ 0.6371319 0.54557285 0.30214217 0.14690307 0.49778446 0.89183238
> 0.52445514 0.63379164 0.71873681 0.55008567]
>
> [ 0. 0. 0. 0.02358491 0.00471698 0.
> 0.
> 0. 0. 0.00471698 0.00471698 0.00471698 0.02830189
> 0.00943396 0. .............................52358491 0.53773585
> 0.63207547 0.51886792 0.66037736 0.75 0.57075472 0.59433962
> 0.63679245 0.8490566 0.71698113 0.02358491]
>
> [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.
> 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0.
> 1. 1. 1. 1. 0.]
>
>
>
> and for y
>
> [ 0. 0. 0. 0.02358491 0.00471698 0.
> 0.
> 0. 0. 0.00471698 0.00471698 0.00471698 0.02830189
> 0. ..........
> 0.63207547 0.51886792 0.66037736 0.75 0.57075472 0.59433962
> 0.63679245 0.8490566 0.71698113 0.02358491]
>
> [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.
> 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0.
> 0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0.
> 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 1.
> 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0.
> 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0.
> 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 1.
> 1. 1. 1. 1. 0.]
>
>
> The problem is, I have to count the occurences from 0's and 1's in x and
> y. And if there are some other arrays
> lik 0.636..... I dont get the right solution. So in general, i only want
> the array with 1 and 0
>
> best,
>
>
>
>
>
>
>
>
>
>
>
>
>
> On 9 January 2016 at 03:58, A neuman <themagenta...@gmail.com> wrote:
>
>> 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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>>>
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>>
>>
>
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