def testdata_siam_desc(num_data=128, desc_dim=8):
rng = np.random.RandomState(0)
network_output = vt.normalize_rows(rng.rand(num_data, desc_dim))
vecs1 = network_output[0::2]
vecs2 = network_output[1::2]
# roll vecs2 so it is essentially translated
vecs2 = np.roll(vecs1, 1, axis=1)
network_output[1::2] = vecs2
# Every other pair is an imposter match
network_output[::4, :] = vt.normalize_rows(rng.rand(32, desc_dim))
#data_per_label = 2
vecs1 = network_output[0::2].astype(np.float32)
vecs2 = network_output[1::2].astype(np.float32)
def true_dist_metric(vecs1, vecs2):
g1_ = np.roll(vecs1, 1, axis=1)
dist = vt.L2(g1_, vecs2)
return dist
#l2dist = vt.L2(vecs1, vecs2)
true_dist = true_dist_metric(vecs1, vecs2)
label = (true_dist > 0).astype(np.float32)
vecs1 = torch.from_numpy(vecs1)
vecs2 = torch.from_numpy(vecs2)
label = torch.from_numpy(label)
return vecs1, vecs2, label
def testdata_dummy_sift(nPts=10, rng=np.random):
r"""
Makes a dummy sift descriptor that has the uint8 * 512 hack
like hesaff returns
Args:
nPts (int): (default = 10)
CommandLine:
python -m vtool.tests.dummy --test-testdata_dummy_sift
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.dummy import * # NOQA
>>> import vtool as vt
>>> nPts = 10
>>> rng = np.random.RandomState(0)
>>> sift = testdata_dummy_sift(nPts, rng)
>>> assert vt.check_sift_validity(sift), 'bad SIFT properties'
>>> #assert np.allclose(((sift / 512) ** 2).sum(axis=1), 1, rtol=.01), 'bad SIFT property'
>>> #assert np.all(sift / 512 < .2), 'bad SIFT property'
"""
import vtool as vt
sift_ = rng.rand(nPts, 128)
# normalize
sift_ = vt.normalize_rows(rng.rand(nPts, 128))
# clip bin values
sift_[sift_ > .2] = .2
# renormalize
sift_ = vt.normalize_rows(rng.rand(nPts, 128))
# compress into uint8
#sift = (sift_ * 512).round().astype(np.uint8)
sift = (sift_ * 512).astype(np.uint8)
return sift
def compute_nonagg_rvecs(invassign, wx, compress=False):
"""
Driver function for nonagg residual computation
Args:
words (ndarray): array of words
idx2_vec (dict): stacked vectors
wx_sublist (list): words of interest
idxs_list (list): list of idxs grouped by wx_sublist
Returns:
tuple : (rvecs_list, flags_list)
"""
# Pick out corresonding lists of residuals and words
vecs = invassign.get_vecs(wx)
word = invassign.vocab.wx2_word[wx]
# Compute nonaggregated normalized residuals
arr_float = np.subtract(word.astype(np.float), vecs.astype(np.float))
vt.normalize_rows(arr_float, out=arr_float)
if compress:
rvecs_list = np.clip(np.round(arr_float * 255.0), -127, 127).astype(np.int8)
else:
rvecs_list = arr_float
# Extract flags (rvecs_list which are all zeros) and rvecs_list
error_flags = ~np.any(rvecs_list, axis=1)
return rvecs_list, error_flags
def compute_nonagg_rvecs(invassign, wx, compress=False):
"""
Driver function for nonagg residual computation
Args:
words (ndarray): array of words
idx2_vec (dict): stacked vectors
wx_sublist (list): words of interest
idxs_list (list): list of idxs grouped by wx_sublist
Returns:
tuple : (rvecs_list, flags_list)
"""
# Pick out corresonding lists of residuals and words
vecs = invassign.get_vecs(wx)
word = invassign.vocab.wx2_word[wx]
# Compute nonaggregated normalized residuals
arr_float = np.subtract(word.astype(np.float), vecs.astype(np.float))
vt.normalize_rows(arr_float, out=arr_float)
if compress:
rvecs_list = np.clip(np.round(arr_float * 255.0), -127,
127).astype(np.int8)
else:
rvecs_list = arr_float
# Extract flags (rvecs_list which are all zeros) and rvecs_list
error_flags = ~np.any(rvecs_list, axis=1)
return rvecs_list, error_flags
def testdata_dummy_sift(nPts=10, rng=np.random):
r"""
Makes a dummy sift descriptor that has the uint8 * 512 hack
like hesaff returns
Args:
nPts (int): (default = 10)
CommandLine:
python -m vtool.tests.dummy --test-testdata_dummy_sift
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.dummy import * # NOQA
>>> import vtool as vt
>>> nPts = 10
>>> rng = np.random.RandomState(0)
>>> sift = testdata_dummy_sift(nPts, rng)
>>> assert vt.check_sift_validity(sift), 'bad SIFT properties'
>>> #assert np.allclose(((sift / 512) ** 2).sum(axis=1), 1, rtol=.01), 'bad SIFT property'
>>> #assert np.all(sift / 512 < .2), 'bad SIFT property'
"""
import vtool as vt
sift_ = rng.rand(nPts, 128)
# normalize
sift_ = vt.normalize_rows(rng.rand(nPts, 128))
# clip bin values
sift_[sift_ > .2] = .2
# renormalize
sift_ = vt.normalize_rows(rng.rand(nPts, 128))
# compress into uint8
#sift = (sift_ * 512).round().astype(np.uint8)
sift = (sift_ * 512).astype(np.uint8)
return sift
def aggregate_rvecs(rvecs, maws):
r"""
helper for compute_agg_rvecs
Args:
rvecs (ndarray): residual vectors
maws (ndarray): multi assign weights
Returns:
rvecs_agg : aggregated residual vectors
CommandLine:
python -m ibeis.algo.hots.smk.smk_residuals --test-aggregate_rvecs
./run_tests.py --exclude-doctest-patterns pipeline neighbor score coverage automated_helpers name automatch chip_match multi_index automated special_query scoring automated nn_weights distinctive match_chips4 query_request devcases hstypes params ibsfuncs smk_core, smk_debug control
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> rng = np.random.RandomState(0)
>>> rvecs = (hstypes.RVEC_MAX * rng.rand(4, 128)).astype(hstypes.RVEC_TYPE)
>>> maws = (rng.rand(rvecs.shape[0])).astype(hstypes.FLOAT_TYPE)
>>> rvecs_agg = aggregate_rvecs(rvecs, maws)
>>> result = ut.numpy_str2(rvecs_agg, linewidth=70)
>>> print(result)
np.array([[28, 27, 32, 16, 16, 16, 12, 31, 27, 29, 19, 27, 21, 24, 15,
21, 17, 37, 13, 40, 38, 33, 17, 30, 13, 23, 9, 25, 19, 15,
20, 17, 19, 18, 13, 25, 37, 29, 21, 16, 20, 21, 34, 11, 28,
19, 17, 12, 14, 24, 21, 11, 27, 11, 24, 10, 23, 20, 28, 12,
16, 14, 30, 22, 18, 26, 21, 20, 18, 9, 29, 20, 25, 19, 23,
20, 7, 13, 22, 22, 15, 20, 22, 16, 27, 10, 16, 20, 25, 25,
26, 28, 22, 38, 24, 16, 14, 19, 24, 14, 22, 19, 19, 33, 21,
22, 18, 22, 25, 25, 22, 23, 32, 16, 25, 15, 29, 21, 25, 20,
22, 31, 29, 24, 24, 25, 20, 14]], dtype=np.int8)
"""
if rvecs.shape[0] == 1:
return rvecs
# Prealloc sum output (do not assign the result of sum)
arr_float = np.empty((1, rvecs.shape[1]), dtype=hstypes.FLOAT_TYPE)
# Take weighted average of multi-assigned vectors
(maws[:, np.newaxis] * rvecs.astype(hstypes.FLOAT_TYPE)).sum(
axis=0, out=arr_float[0])
# Jegou uses mean instead. Sum should be fine because we normalize
#rvecs.mean(axis=0, out=rvecs_agg[0])
vt.normalize_rows(arr_float, out=arr_float)
rvecs_agg = compress_normvec(arr_float)
return rvecs_agg
def aggregate_rvecs(rvecs, maws):
r"""
helper for compute_agg_rvecs
Args:
rvecs (ndarray): residual vectors
maws (ndarray): multi assign weights
Returns:
rvecs_agg : aggregated residual vectors
CommandLine:
python -m ibeis.algo.hots.smk.smk_residuals --test-aggregate_rvecs
./run_tests.py --exclude-doctest-patterns pipeline neighbor score coverage automated_helpers name automatch chip_match multi_index automated special_query scoring automated nn_weights distinctive match_chips4 query_request devcases hstypes params ibsfuncs smk_core, smk_debug control
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> rng = np.random.RandomState(0)
>>> rvecs = (hstypes.RVEC_MAX * rng.rand(4, 128)).astype(hstypes.RVEC_TYPE)
>>> maws = (rng.rand(rvecs.shape[0])).astype(hstypes.FLOAT_TYPE)
>>> rvecs_agg = aggregate_rvecs(rvecs, maws)
>>> result = ut.numpy_str2(rvecs_agg, linewidth=70)
>>> print(result)
np.array([[28, 27, 32, 16, 16, 16, 12, 31, 27, 29, 19, 27, 21, 24, 15,
21, 17, 37, 13, 40, 38, 33, 17, 30, 13, 23, 9, 25, 19, 15,
20, 17, 19, 18, 13, 25, 37, 29, 21, 16, 20, 21, 34, 11, 28,
19, 17, 12, 14, 24, 21, 11, 27, 11, 24, 10, 23, 20, 28, 12,
16, 14, 30, 22, 18, 26, 21, 20, 18, 9, 29, 20, 25, 19, 23,
20, 7, 13, 22, 22, 15, 20, 22, 16, 27, 10, 16, 20, 25, 25,
26, 28, 22, 38, 24, 16, 14, 19, 24, 14, 22, 19, 19, 33, 21,
22, 18, 22, 25, 25, 22, 23, 32, 16, 25, 15, 29, 21, 25, 20,
22, 31, 29, 24, 24, 25, 20, 14]], dtype=np.int8)
"""
if rvecs.shape[0] == 1:
return rvecs
# Prealloc sum output (do not assign the result of sum)
arr_float = np.empty((1, rvecs.shape[1]), dtype=hstypes.FLOAT_TYPE)
# Take weighted average of multi-assigned vectors
(maws[:, np.newaxis] * rvecs.astype(hstypes.FLOAT_TYPE)).sum(axis=0, out=arr_float[0])
# Jegou uses mean instead. Sum should be fine because we normalize
#rvecs.mean(axis=0, out=rvecs_agg[0])
vt.normalize_rows(arr_float, out=arr_float)
rvecs_agg = compress_normvec(arr_float)
return rvecs_agg
def aggregate_rvecs(rvecs, maws, compress=False):
r"""
helper for compute_agg_rvecs
"""
if rvecs.shape[0] == 0:
rvecs_agg = np.empty((0, rvecs.shape[1]), dtype=np.float)
if rvecs.shape[0] == 1:
rvecs_agg = rvecs
else:
# Prealloc sum output (do not assign the result of sum)
arr_float = np.empty((1, rvecs.shape[1]), dtype=np.float)
out = arr_float[0]
# Take weighted average of multi-assigned vectors
total_weight = maws.sum()
weighted_sum = (maws[:, np.newaxis] * rvecs.astype(np.float)).sum(axis=0, out=out)
np.divide(weighted_sum, total_weight, out=out)
vt.normalize_rows(arr_float, out=arr_float)
if compress:
rvecs_agg = np.clip(np.round(arr_float * 255.0), -127, 127).astype(np.int8)
else:
rvecs_agg = arr_float
return rvecs_agg
def aggregate_rvecs(rvecs, maws, compress=False):
r"""
helper for compute_agg_rvecs
"""
if rvecs.shape[0] == 0:
rvecs_agg = np.empty((0, rvecs.shape[1]), dtype=np.float)
if rvecs.shape[0] == 1:
rvecs_agg = rvecs
else:
# Prealloc sum output (do not assign the result of sum)
arr_float = np.empty((1, rvecs.shape[1]), dtype=np.float)
out = arr_float[0]
# Take weighted average of multi-assigned vectors
total_weight = maws.sum()
weighted_sum = (maws[:, np.newaxis] * rvecs.astype(np.float)).sum(
axis=0, out=out)
np.divide(weighted_sum, total_weight, out=out)
vt.normalize_rows(arr_float, out=arr_float)
if compress:
rvecs_agg = np.clip(np.round(arr_float * 255.0), -127,
127).astype(np.int8)
else:
rvecs_agg = arr_float
return rvecs_agg
def understanding_pseudomax_props(mode=2):
"""
Function showing some properties of distances between normalized pseudomax vectors
CommandLine:
python -m vtool.distance --test-understanding_pseudomax_props
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.distance import * # NOQA
>>> for mode in [0, 1, 2, 3]:
... print('+---')
... print('mode = %r' % (mode,))
... result = understanding_pseudomax_props(mode)
... print('L___')
>>> print(result)
"""
import vtool as vt
pseudo_max = 512
rng = np.random.RandomState(0)
num = 10
if mode == 0:
dim = 2
p1_01 = (vt.normalize_rows(rng.rand(num, dim)))
p2_01 = (vt.normalize_rows(rng.rand(num, dim)))
elif mode == 1:
p1_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
p2_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
elif mode == 2:
# Build theoretically maximally distant normalized vectors (type 1)
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0::2] = 1
p1_01[:, 1::2] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
elif mode == 3:
# Build theoretically maximally distant vectors (type 2)
# This mode will clip if cast to uint8, thus failing the test
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0] = 1
p1_01[:, 1:] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
pass
print('ndims = %r' % (p1_01.shape[1]))
p1_01 = p1_01.astype(TEMP_VEC_DTYPE)
p2_01 = p2_01.astype(TEMP_VEC_DTYPE)
p1_256 = p1_01 * pseudo_max
p2_256 = p2_01 * pseudo_max
dist_sqrd_01 = vt.L2_sqrd(p1_01, p2_01)
dist_sqrd_256 = vt.L2_sqrd(p1_256, p2_256)
dist_01 = np.sqrt(dist_sqrd_01)
dist_256 = np.sqrt(dist_sqrd_256)
print('dist_sqrd_01 = %s' % (ut.numpy_str(dist_sqrd_01, precision=2), ))
print('dist_sqrd_256 = %s' % (ut.numpy_str(dist_sqrd_256, precision=2), ))
print('dist_01 = %s' % (ut.numpy_str(dist_01, precision=2), ))
print('dist_256 = %s' % (ut.numpy_str(dist_256, precision=2), ))
print('--')
print('sqrt(2) = %f' % (np.sqrt(2)))
print('--')
assert np.all(dist_01 == vt.L2(p1_01, p2_01))
assert np.all(dist_256 == vt.L2(p1_256, p2_256))
const_sqrd = dist_sqrd_256 / dist_sqrd_01
const = dist_256 / dist_01
print('const = %r' % (const[0], ))
print('const_sqrd = %r' % (const_sqrd[0], ))
print('1 / const = %r' % (1 / const[0], ))
print('1 / const_sqrd = %r' % (1 / const_sqrd[0], ))
assert ut.allsame(const)
assert ut.allsame(const_sqrd)
assert np.all(const == np.sqrt(const_sqrd))
# Assert that distance conversions work
assert np.all(dist_256 / const == dist_01)
assert np.all(dist_sqrd_256 / const_sqrd == dist_sqrd_01)
print('Conversions work')
print('Maximal L2 distance between any two NON-NEGATIVE L2-NORMALIZED'
' vectors should always be sqrt(2)')
def get_norm_residuals(vecs, word):
"""
computes normalized residuals of vectors with respect to a word
Args:
vecs (ndarray):
word (ndarray):
Returns:
tuple : (rvecs_n, rvec_flag)
CommandLine:
python -m ibeis.algo.hots.smk.smk_residuals --test-get_norm_residuals
Example:
>>> # ENABLE_DOCTEST
>>> # The case where vecs != words
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> rng = np.random.RandomState(0)
>>> vecs = (hstypes.VEC_MAX * rng.rand(4, 128)).astype(hstypes.VEC_TYPE)
>>> word = (hstypes.VEC_MAX * rng.rand(1, 128)).astype(hstypes.VEC_TYPE)
>>> rvecs_n = get_norm_residuals(vecs, word)
>>> result = ut.numpy_str2(rvecs_n)
>>> print(result)
Example:
>>> # ENABLE_DOCTEST
>>> # The case where vecs == words
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> rng = np.random.RandomState(0)
>>> vecs = (hstypes.VEC_MAX * rng.rand(4, 128)).astype(hstypes.VEC_TYPE)
>>> word = vecs[1]
>>> rvecs_n = get_norm_residuals(vecs, word)
>>> result = ut.numpy_str2(rvecs_n)
>>> print(result)
IGNORE
rvecs_agg8 = compress_normvec_uint8(arr_float)
rvecs_agg16 = compress_normvec_float16(arr_float)
ut.print_object_size(rvecs_agg16, 'rvecs_agg16: ')
ut.print_object_size(rvecs_agg8, 'rvecs_agg8: ')
ut.print_object_size(rvec_flag, 'rvec_flag: ')
%timeit np.isnan(_rvec_sums)
%timeit _rvec_sums == 0
%timeit np.equal(rvec_sums, 0)
%timeit rvec_sums == 0
%timeit np.logical_or(np.isnan(_rvec_sums), _rvec_sums == 0)
"""
# Compute residuals of assigned vectors
#rvecs_n = word.astype(dtype=FLOAT_TYPE) - vecs.astype(dtype=FLOAT_TYPE)
arr_float = np.subtract(word.astype(hstypes.FLOAT_TYPE), vecs.astype(hstypes.FLOAT_TYPE))
# Faster, but doesnt work with np.norm
#rvecs_n = np.subtract(word.view(hstypes.FLOAT_TYPE), vecs.view(hstypes.FLOAT_TYPE))
vt.normalize_rows(arr_float, out=arr_float)
# Mark null residuals
#_rvec_sums = arr_float.sum(axis=1)
#rvec_flag = np.isnan(_rvec_sums)
# Converts normvec to a smaller type like float16 or int8
rvecs_n = compress_normvec(arr_float)
# IF FLOAT16 WE NEED TO FILL NANS
# (but we should use int8, and in that case it is implicit)
# rvecs_n = np.nan_to_num(rvecs_n)
return rvecs_n
def understanding_pseudomax_props(mode=2):
"""
Function showing some properties of distances between normalized pseudomax vectors
CommandLine:
python -m vtool.distance --test-understanding_pseudomax_props
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.distance import * # NOQA
>>> for mode in [0, 1, 2, 3]:
... print('+---')
... print('mode = %r' % (mode,))
... result = understanding_pseudomax_props(mode)
... print('L___')
>>> print(result)
"""
import vtool as vt
pseudo_max = 512
rng = np.random.RandomState(0)
num = 10
if mode == 0:
dim = 2
p1_01 = (vt.normalize_rows(rng.rand(num, dim)))
p2_01 = (vt.normalize_rows(rng.rand(num, dim)))
elif mode == 1:
p1_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
p2_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
elif mode == 2:
# Build theoretically maximally distant normalized vectors (type 1)
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0::2] = 1
p1_01[:, 1::2] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
elif mode == 3:
# Build theoretically maximally distant vectors (type 2)
# This mode will clip if cast to uint8, thus failing the test
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0] = 1
p1_01[:, 1:] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
pass
print('ndims = %r' % (p1_01.shape[1]))
p1_01 = p1_01.astype(TEMP_VEC_DTYPE)
p2_01 = p2_01.astype(TEMP_VEC_DTYPE)
p1_256 = p1_01 * pseudo_max
p2_256 = p2_01 * pseudo_max
dist_sqrd_01 = vt.L2_sqrd(p1_01, p2_01)
dist_sqrd_256 = vt.L2_sqrd(p1_256, p2_256)
dist_01 = np.sqrt(dist_sqrd_01)
dist_256 = np.sqrt(dist_sqrd_256)
print('dist_sqrd_01 = %s' % (ut.numpy_str(dist_sqrd_01, precision=2),))
print('dist_sqrd_256 = %s' % (ut.numpy_str(dist_sqrd_256, precision=2),))
print('dist_01 = %s' % (ut.numpy_str(dist_01, precision=2),))
print('dist_256 = %s' % (ut.numpy_str(dist_256, precision=2),))
print('--')
print('sqrt(2) = %f' % (np.sqrt(2)))
print('--')
assert np.all(dist_01 == vt.L2(p1_01, p2_01))
assert np.all(dist_256 == vt.L2(p1_256, p2_256))
const_sqrd = dist_sqrd_256 / dist_sqrd_01
const = dist_256 / dist_01
print('const = %r' % (const[0],))
print('const_sqrd = %r' % (const_sqrd[0],))
print('1 / const = %r' % (1 / const[0],))
print('1 / const_sqrd = %r' % (1 / const_sqrd[0],))
assert ut.allsame(const)
assert ut.allsame(const_sqrd)
assert np.all(const == np.sqrt(const_sqrd))
# Assert that distance conversions work
assert np.all(dist_256 / const == dist_01)
assert np.all(dist_sqrd_256 / const_sqrd == dist_sqrd_01)
print('Conversions work')
print('Maximal L2 distance between any two NON-NEGATIVE L2-NORMALIZED'
' vectors should always be sqrt(2)')
def get_norm_residuals(vecs, word):
"""
computes normalized residuals of vectors with respect to a word
Args:
vecs (ndarray):
word (ndarray):
Returns:
tuple : (rvecs_n, rvec_flag)
CommandLine:
python -m ibeis.algo.hots.smk.smk_residuals --test-get_norm_residuals
Example:
>>> # ENABLE_DOCTEST
>>> # The case where vecs != words
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> rng = np.random.RandomState(0)
>>> vecs = (hstypes.VEC_MAX * rng.rand(4, 128)).astype(hstypes.VEC_TYPE)
>>> word = (hstypes.VEC_MAX * rng.rand(1, 128)).astype(hstypes.VEC_TYPE)
>>> rvecs_n = get_norm_residuals(vecs, word)
>>> result = ut.numpy_str2(rvecs_n)
>>> print(result)
Example:
>>> # ENABLE_DOCTEST
>>> # The case where vecs == words
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> rng = np.random.RandomState(0)
>>> vecs = (hstypes.VEC_MAX * rng.rand(4, 128)).astype(hstypes.VEC_TYPE)
>>> word = vecs[1]
>>> rvecs_n = get_norm_residuals(vecs, word)
>>> result = ut.numpy_str2(rvecs_n)
>>> print(result)
IGNORE
rvecs_agg8 = compress_normvec_uint8(arr_float)
rvecs_agg16 = compress_normvec_float16(arr_float)
ut.print_object_size(rvecs_agg16, 'rvecs_agg16: ')
ut.print_object_size(rvecs_agg8, 'rvecs_agg8: ')
ut.print_object_size(rvec_flag, 'rvec_flag: ')
%timeit np.isnan(_rvec_sums)
%timeit _rvec_sums == 0
%timeit np.equal(rvec_sums, 0)
%timeit rvec_sums == 0
%timeit np.logical_or(np.isnan(_rvec_sums), _rvec_sums == 0)
"""
# Compute residuals of assigned vectors
#rvecs_n = word.astype(dtype=FLOAT_TYPE) - vecs.astype(dtype=FLOAT_TYPE)
arr_float = np.subtract(word.astype(hstypes.FLOAT_TYPE),
vecs.astype(hstypes.FLOAT_TYPE))
# Faster, but doesnt work with np.norm
#rvecs_n = np.subtract(word.view(hstypes.FLOAT_TYPE), vecs.view(hstypes.FLOAT_TYPE))
vt.normalize_rows(arr_float, out=arr_float)
# Mark null residuals
#_rvec_sums = arr_float.sum(axis=1)
#rvec_flag = np.isnan(_rvec_sums)
# Converts normvec to a smaller type like float16 or int8
rvecs_n = compress_normvec(arr_float)
# IF FLOAT16 WE NEED TO FILL NANS
# (but we should use int8, and in that case it is implicit)
# rvecs_n = np.nan_to_num(rvecs_n)
return rvecs_n
