def __call__(self, state, scope, paramTypes, datum, clusters, *args):
if len(clusters) == 0:
raise PFARuntimeException("no clusters")
if len(args) == 1:
metric, = args
distances = [callfcn(state, scope, metric, [datum, x["center"]]) for x in clusters]
else:
distances = [sum((di - xi)**2 for di, xi in zip(datum, x["center"])) for x in clusters]
index, = argLowestN(distances, 1, lambda a, b: a < b)
return clusters[index]
def __call__(self, state, scope, paramTypes, n, datum, clusters, *args):
if n < 0:
raise PFARuntimeException("n must be nonnegative")
if len(args) == 1:
metric, = args
distances = [callfcn(state, scope, metric, [datum, x["center"]]) for x in clusters]
else:
distances = [sum((di - xi)**2 for di, xi in zip(datum, x["center"])) for x in clusters]
indexes = argLowestN(distances, n, lambda a, b: a < b)
return [clusters[i] for i in indexes]
def __call__(self, state, scope, paramTypes, k, datum, codebook, *args):
if k < 0:
raise PFARuntimeException("k must be nonnegative")
if len(args) == 1:
metric, = args
distances = [callfcn(state, scope, metric, [datum, x]) for x in codebook]
else:
if len(codebook) == 0:
return []
else:
dimensions = len(datum)
for x in codebook:
if len(x) != dimensions:
raise PFARuntimeException("inconsistent dimensionality")
distances = [sum((di - xi)**2 for di, xi in zip(datum, x)) for x in codebook]
indexes = argLowestN(distances, k, lambda a, b: a < b)
return [codebook[i] for i in indexes]
