Hi
I would like to know if :
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/mutual_info_.py
supports Y to be a matrix. From what I see it seems like Y can only be a
column vector.
--
sp
On Thu, Feb 11, 2016 at 9:18 AM, Manoj Kumar <manojkumarsivaraj...@gmail.com
> wrote:
> Hi,
>
> In any case you can just supply metric='chebyshev' to do that for you in
> NearestNeighbors.
>
> On Wed, Feb 10, 2016 at 10:10 PM, Shishir Pandey <shishir...@gmail.com>
> wrote:
>
>> Thanks.
>>
>> --
>> sp
>>
>> On Thu, Feb 11, 2016 at 6:41 AM, Daniel Homola <
>> daniel.homol...@imperial.ac.uk> wrote:
>>
>>> Hi,
>>>
>>> Mr Mayorov has done a great job and coded this up already:
>>>
>>> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/mutual_info_.py
>>>
>>> If you want to do feature selection based on MI, check out the JMI
>>> method:
>>> https://github.com/danielhomola/mifs
>>>
>>> Cheers,
>>> d
>>>
>>>
>>> On 02/11/2016 01:07 AM, Shishir Pandey wrote:
>>>
>>> Hi
>>>
>>> I want to estimate the mutual information based on nearest neighbor
>>> method:
>>> http://arxiv.org/pdf/cond-mat/0305641.pdf
>>>
>>>
>>> This requires me to use the max norm. For which I have defined a
>>> function norm. Not I want Nearest neighbors to fit according to this norm
>>> and when I find the kneighbors I want it to give me kneighbors based on
>>> this max norm but instead I am getting results in Euclidean distances. How
>>> do I fix this? Here is the class that I have created.
>>>
>>>
>>> class MaxNormNN:
>>> """
>>> Nearest neighbors based on max norm
>>> """
>>>
>>> def __init__(self, x_dim, y_dim, x, y):
>>> self.x_dim = x_dim
>>> self.y_dim = y_dim
>>> self.x = x
>>> self.y = y
>>> self. z = np.c_[x,y]
>>>
>>>
>>> def max_norm(self, z1, z2, ord = 1):
>>> x_dist = np.linalg.norm(np.array(z1[:self.x_dim]) - \
>>> np.array(z2[:self.x_dim]), ord = ord)
>>> y_dist = np.linalg.norm(np.array(z1[self.x_dim:]) - \
>>> np.array(z2[self.x_dim:]), ord = ord)
>>> return np.max([x_dist, y_dist])
>>>
>>> def NNs(self):
>>> nn = NearestNeighbors(n_neighbors = 2, func = max_norm)
>>> nn.fit(self.z)
>>> # print nn.kneighbors(self.z)
>>>
>>>
>>>
>>> --
>>> sp
>>>
>>>
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>>
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>
>
> --
> Manoj,
> http://github.com/MechCoder
>
>
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