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#!/usr/bin/python
import sys
import random
import numpy as np
def quadratic_knn_search(data, lidx, ldata, K):
""" find K nearest neighbours of data among ldata """
ndata = ldata.shape[1]
param = ldata.shape[0]
K = K if K < ndata else ndata
retval = []
sqd = ((ldata - data[:,:ndata])**2).sum(axis=0) # data.reshape((param,1)).repeat(ndata, axis=1);
idx = np.argsort(sqd, kind='mergesort')
idx = idx[:K]
return zip(sqd[idx], lidx[idx])
def normalize(a,weights=None):
if weights == None:
weights= {}
cols = a.shape[1]
for i in range(cols):
weights[i] = None
for i in weights.keys():
column = a[:,i]
if weights[i] == None:
weights[i] = np.mean(column), np.std(column)
a[:,i] = (column-weights[i][0])/weights[i][1]
return a
def knn_search( data1, data2, K ):
""" find the K nearest neighbours for data points in data,
using O(n**2) search """
ndata = data1.shape[1]
knn = []
idx = np.arange(ndata)
for i in np.arange(ndata):
_knn = quadratic_knn_search(data1[:,i], idx, data2, K+1) # see above
knn.append( _knn[1:] )
return knn
if __name__ == "__main__":
random.seed()
sk_data = normalize(np.loadtxt(sys.argv[1],comments="#",delimiter=",",usecols=range(1,7,1)).T)
gaussify = np.vectorize(lambda x: x+random.gauss(0,float(sys.argv[2])))
sk1 = gaussify(sk_data)
sk2 = gaussify(sk_data)
print sk1
print sk2
print knn_search(sk1,sk2,1)
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