adding nearest neighbor for skel data

1 files changed, 52 insertions, 0 deletions

diff --git a/data/nn-goldman.py b/data/nn-goldman.py
new file mode 100755
index 0000000..0a04e7a
--- /dev/null
+++ b/data/nn-goldman.py
@@ -0,0 +1,52 @@
+#!/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)
+