Just to get an idea. Do any of the papers point out any problem as to why
the mutual information cannot be calculated for discrete valued Y matrix?
--
sp
On Thu, Feb 11, 2016 at 5:09 PM, Daniel Homola <
daniel.homol...@imperial.ac.uk> wrote:
> Hi,
>
> if your Y is continuous you can use Gael's code (mutual_information(X,Y),
> k=3) here:
> https://gist.github.com/GaelVaroquaux/ead9898bd3c973c40429
> to estimate MI between two matrices.
>
> if your Y is discrete, i.e. multiple class labellings for X, you can only
> do this column by column separately. At least in my understanding.
>
> Cheers,
> d
>
>
> On 11/02/16 11:12, Shishir Pandey wrote:
>
> 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>
>> 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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>>
>>
>> --
>> Manoj,
>> http://github.com/MechCoder
>>
>>
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