def _fingerprint_similarity( self, f1, f2 ):
print "computing similarity for fingerprints: {} {}".format(f1, f2)
f1,f2= DictTree.intersect( f1, f2 )
def feature_map(*features):
f1,f2= features
return f1.similarity(f2)
similarities= DictTree.map( feature_map, f1, f2 )
return self._reducer( similarities )
def visualize_normal_composites( composites, show=True ):
COLORS= ('red', 'orange','yellow','green', 'blue', 'violet')
n= len(composites)
width= 0.9-(0.1*n)
common= DictTree.intersect( *composites )
colors= COLORS[:n]
for i,c,color in zip(range(n),common, colors):
visualize_normal_composite( c, color=color, show=False, offset=i*0.1, width=width)
if show:
pyplot.show()
def _fingerprint_similarity(self, f1, f2):
print "computing similarity for fingerprints: {} {}".format(
f1, f2)
f1, f2 = DictTree.intersect(f1, f2)
def feature_map(*features):
f1, f2 = features
return f1.similarity(f2)
similarities = DictTree.map(feature_map, f1, f2)
return self._reducer(similarities)