def test_rand_large(self):
n = 1024
for i in xrange(1000):
a = gen(n)
b = gen(n)
actual = bin(a ^ b).count('1')
ntools.assert_equal(df.hamming_distance(a, b), actual)
def nn(self, h, n=1):
"""
Return the nearest `N` neighbors to the given hash code.
Distances are in the range [0,1] and are the percent different each
neighbor hash is from the query, based on the number of bits contained
in the query.
:param h: Hash code to compute the neighbors of. Should be the same bit
length as indexed hash codes.
:type h: numpy.ndarray[bool] | list[bool]
:param n: Number of nearest neighbors to find.
:type n: int
:raises ValueError: No index to query from.
:return: Tuple of nearest N hash codes and a tuple of the distance
values to those neighbors.
:rtype: (tuple[numpy.ndarray[bool], tuple[float])
"""
super(LinearHashIndex, self).nn(h, n)
h_int = bit_vector_to_int_large(h)
bits = len(h)
#: :type: list[int|long]
near_codes = \
heapq.nsmallest(n, self.index,
lambda e: hamming_distance(h_int, e)
)
distances = list(map(hamming_distance, near_codes,
[h_int] * len(near_codes)))
return [int_to_bit_vector_large(c, bits) for c in near_codes], \
[d / float(bits) for d in distances]
def test_rand(self):
n = 64
for i in range(1000):
a = gen(n)
b = gen(n)
actual = bin(a ^ b).count('1')
self.assertEqual(df.hamming_distance(a, b), actual)
def _nn(self, h, n=1):
"""
Internal method to be implemented by sub-classes to return the nearest
`N` neighbor hash codes as bit-vectors to the given hash code
bit-vector.
Distances are in the range [0,1] and are the percent different each
neighbor hash is from the query, based on the number of bits contained
in the query (normalized hamming distance).
When this internal method is called, we have already checked that our
index is not empty.
:param h: Hash code to compute the neighbors of. Should be the same bit
length as indexed hash codes.
:type h: numpy.ndarray[bool]
:param n: Number of nearest neighbors to find.
:type n: int
:return: Tuple of nearest N hash codes and a tuple of the distance
values to those neighbors.
:rtype: (tuple[numpy.ndarray[bool]], tuple[float])
"""
with self._model_lock:
h_int = bit_vector_to_int_large(h)
bits = len(h)
#: :type: list[int|long]
near_codes = \
heapq.nsmallest(n, self.index,
lambda e: hamming_distance(h_int, e)
)
distances = map(hamming_distance, near_codes,
[h_int] * len(near_codes))
return [int_to_bit_vector_large(c, bits) for c in near_codes], \
[d / float(bits) for d in distances]
def _nn(self, h, n=1):
"""
Internal method to be implemented by sub-classes to return the nearest
`N` neighbor hash codes as bit-vectors to the given hash code
bit-vector.
Distances are in the range [0,1] and are the percent different each
neighbor hash is from the query, based on the number of bits contained
in the query (normalized hamming distance).
When this internal method is called, we have already checked that our
index is not empty.
:param h: Hash code to compute the neighbors of. Should be the same bit
length as indexed hash codes.
:type h: numpy.ndarray[bool]
:param n: Number of nearest neighbors to find.
:type n: int
:return: Tuple of nearest N hash codes and a tuple of the distance
values to those neighbors.
:rtype: (tuple[numpy.ndarray[bool]], tuple[float])
"""
with self._model_lock:
h_int = bit_vector_to_int_large(h)
bits = len(h)
#: :type: list[int|long]
near_codes = \
heapq.nsmallest(n, self.index,
lambda e: hamming_distance(h_int, e)
)
distances = map(hamming_distance, near_codes,
[h_int] * len(near_codes))
return [int_to_bit_vector_large(c, bits) for c in near_codes], \
[d / float(bits) for d in distances]
def test_hd_0(self):
ntools.assert_equal(df.hamming_distance(0, 0), 0)
def test_hd_0(self):
self.assertEqual(df.hamming_distance(0, 0), 0)
