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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