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 <mailto: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 <mailto:shishir...@gmail.com>> wrote:

        Thanks.

        --
        sp

        On Thu, Feb 11, 2016 at 6:41 AM, Daniel Homola
        <daniel.homol...@imperial.ac.uk
        <mailto: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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