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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