Isn't the tanimoto coeff a continuous number between 0 and 1?
> how are the skalars are occring in the X vektor?
You are returning a fraction -> "return float(c)/(a1 + b1 - c)"
instead of the count, maybe that's an misunderstanding?
> On Jan 12, 2016, at 1:24 PM, A neuman <themagenta...@gmail.com> wrote:
>
> The custom metric, ist just calculating the tanimoto coef.
>
> a=x.tolist()
> b=y.tolist()
>
> c=np.count_nonzero(x==y)
> a1=a.count(1.0)
> b1=b.count(1.0)
>
> return float(c)/(a1 + b1 - c)
>
>
> so im Just counting 1's in x and 1's in y
>
> c= are the numer, where 1's are matching ( matching == on the same index
> x=[1,0,0,1] and y=[1,0,1,0] would be only matching the first 1.
>
> So in generall all the data samples are 1's and 0's. how are the skalars are
> occring in the X vektor? There should be no skalar ins the X vector. Or am i
> understanding something wrong?
>
> best,
>
>
>
>
> On 12 January 2016 at 19:18, Sebastian Raschka <se.rasc...@gmail.com
> <mailto:se.rasc...@gmail.com>> wrote:
> Hi, I am not sure how your custom metric works, but would a np.where(x >=
> 0.5, 1., 0.) work in your case?
>
>> On Jan 12, 2016, at 1:08 PM, A neuman <themagenta...@gmail.com
>> <mailto:themagenta...@gmail.com>> wrote:
>>
>> 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
>> <mailto: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
>> <mailto: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
>> <mailto: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
>> > <mailto: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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