Daniel

As you have pointed out on the comments section in:
https://gist.github.com/GaelVaroquaux/ead9898bd3c973c40429

That you are getting negative MI. How did you overcome this problem?

--
sp

On Thu, Feb 11, 2016 at 7:27 PM, Shishir Pandey <shishir...@gmail.com>
wrote:

> 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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