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import random

from sklearn.neighbors import NearestNeighbors

import numpy as np

class Smote:

def __init__(self,samples,N=10,k=5):

self.n_samples,self.n_attrs=samples.shape

self.N=N

self.k=k

self.samples=samples

self.newindex=0

# self.synthetic=np.zeros((self.n_samples*N,self.n_attrs))

def over_sampling(self):

N=int(self.N/100)

self.synthetic = np.zeros((self.n_samples * N, self.n_attrs))

neighbors=NearestNeighbors(n_neighbors=self.k).fit(self.samples)

print 'neighbors',neighbors

for i in range(len(self.samples)):

nnarray=neighbors.kneighbors(self.samples[i].reshape(1,-1),return_distance=False)[0]

#print nnarray

self._populate(N,i,nnarray)

return self.synthetic

# for each minority class samples,choose N of the k nearest neighbors and generate N synthetic samples.

def _populate(self,N,i,nnarray):

for j in range(N):

nn=random.randint(0,self.k-1)

dif=self.samples[nnarray[nn]]-self.samples[i]

gap=random.random()

self.synthetic[self.newindex]=self.samples[i]+gap*dif

self.newindex+=1

a=np.array([[1,2,3],[4,5,6],[2,3,1],[2,1,2],[2,3,4],[2,3,4]])

s=Smote(a,N=100)

print s.over_sampling()