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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