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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