def testdata_raw_internals1(**kwargs):
from ibeis.algo.hots.smk import smk_debug
ibs, annots_df, daids, qaids, invindex, qreq_ = smk_debug.testdata_raw_internals0(**kwargs)
qparams = qreq_.qparams
print('[smk_debug] testdata_raw_internals1')
words = invindex.words
wordflann = invindex.wordflann
idx2_vec = invindex.idx2_dvec
nAssign = 1 # 1 for database
massign_sigma = qparams.massign_sigma
massign_alpha = qparams.massign_alpha
massign_equal_weights = qparams.massign_equal_weights
# TODO: Extract args from function via inspect
_dbargs = (wordflann, words, idx2_vec, nAssign, massign_alpha,
massign_sigma, massign_equal_weights)
(wx2_idxs, wx2_dmaws, idx2_wxs) = smk_index.assign_to_words_(*_dbargs)
invindex.wx2_idxs = wx2_idxs
invindex.wx2_dmaws = wx2_dmaws
invindex.idx2_wxs = idx2_wxs
return ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams
def testdata_raw_internals1(**kwargs):
from ibeis.algo.hots.smk import smk_debug
ibs, annots_df, daids, qaids, invindex, qreq_ = smk_debug.testdata_raw_internals0(
**kwargs)
qparams = qreq_.qparams
print('[smk_debug] testdata_raw_internals1')
words = invindex.words
wordflann = invindex.wordflann
idx2_vec = invindex.idx2_dvec
nAssign = 1 # 1 for database
massign_sigma = qparams.massign_sigma
massign_alpha = qparams.massign_alpha
massign_equal_weights = qparams.massign_equal_weights
# TODO: Extract args from function via inspect
_dbargs = (wordflann, words, idx2_vec, nAssign, massign_alpha,
massign_sigma, massign_equal_weights)
(wx2_idxs, wx2_dmaws, idx2_wxs) = smk_index.assign_to_words_(*_dbargs)
invindex.wx2_idxs = wx2_idxs
invindex.wx2_dmaws = wx2_dmaws
invindex.idx2_wxs = idx2_wxs
return ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams
def testdata_sccw_sum(**kwargs):
from ibeis.algo.hots.smk import smk_debug
from ibeis.algo.hots.smk import smk_index
ibs, annots_df, qaid, invindex, qparams = smk_debug.testdata_query_repr(
**kwargs)
aggregate = qparams.aggregate
smk_alpha = qparams.smk_alpha
smk_thresh = qparams.smk_thresh
nAssign = qparams.nAssign
massign_sigma = qparams.massign_sigma
massign_alpha = qparams.massign_alpha
massign_equal_weights = qparams.massign_equal_weights
nAssign = qparams.nAssign
wx2_idf = invindex.wx2_idf
words = invindex.words
wordflann = invindex.wordflann
#qfx2_vec = annots_df['vecs'][qaid].values
qfx2_vec = annots_df['vecs'][qaid]
# Assign query to (multiple) words
_wx2_qfxs, wx2_qmaws, qfx2_wxs = smk_index.assign_to_words_(
wordflann, words, qfx2_vec, nAssign, massign_alpha, massign_sigma,
massign_equal_weights)
# Hack to make implementing asmk easier, very redundant
qfx2_aid = np.array([qaid] * len(qfx2_wxs), dtype=hstypes.INTEGER_TYPE)
qfx2_qfx = np.arange(len(qfx2_vec))
# Compute query residuals
wx2_qrvecs, wx2_qaids, wx2_qfxs, wx2_qmaws, wx2_flags = smk_index.compute_residuals_(
words, _wx2_qfxs, wx2_qmaws, qfx2_vec, qfx2_aid, qfx2_qfx, aggregate)
# Compute query sccw
if ut.VERBOSE:
print('[smk_index] Query TF smk_alpha=%r, smk_thresh=%r' %
(smk_alpha, smk_thresh))
wx_sublist = np.array(wx2_qrvecs.keys(), dtype=hstypes.INDEX_TYPE)
idf_list = [wx2_idf[wx] for wx in wx_sublist]
rvecs_list = [wx2_qrvecs[wx] for wx in wx_sublist]
qmaws_list = [wx2_qmaws[wx] for wx in wx_sublist]
qflags_list = [wx2_flags[wx] for wx in wx_sublist]
return idf_list, rvecs_list, qflags_list, qmaws_list, smk_alpha, smk_thresh
def testdata_sccw_sum(**kwargs):
from ibeis.algo.hots.smk import smk_debug
from ibeis.algo.hots.smk import smk_index
ibs, annots_df, qaid, invindex, qparams = smk_debug.testdata_query_repr(**kwargs)
aggregate = qparams.aggregate
smk_alpha = qparams.smk_alpha
smk_thresh = qparams.smk_thresh
nAssign = qparams.nAssign
massign_sigma = qparams.massign_sigma
massign_alpha = qparams.massign_alpha
massign_equal_weights = qparams.massign_equal_weights
nAssign = qparams.nAssign
wx2_idf = invindex.wx2_idf
words = invindex.words
wordflann = invindex.wordflann
#qfx2_vec = annots_df['vecs'][qaid].values
qfx2_vec = annots_df['vecs'][qaid]
# Assign query to (multiple) words
_wx2_qfxs, wx2_qmaws, qfx2_wxs = smk_index.assign_to_words_(
wordflann, words, qfx2_vec, nAssign, massign_alpha, massign_sigma, massign_equal_weights)
# Hack to make implementing asmk easier, very redundant
qfx2_aid = np.array([qaid] * len(qfx2_wxs), dtype=hstypes.INTEGER_TYPE)
qfx2_qfx = np.arange(len(qfx2_vec))
# Compute query residuals
wx2_qrvecs, wx2_qaids, wx2_qfxs, wx2_qmaws, wx2_flags = smk_index.compute_residuals_(
words, _wx2_qfxs, wx2_qmaws, qfx2_vec, qfx2_aid, qfx2_qfx, aggregate)
# Compute query sccw
if ut.VERBOSE:
print('[smk_index] Query TF smk_alpha=%r, smk_thresh=%r' % (smk_alpha, smk_thresh))
wx_sublist = np.array(wx2_qrvecs.keys(), dtype=hstypes.INDEX_TYPE)
idf_list = [wx2_idf[wx] for wx in wx_sublist]
rvecs_list = [wx2_qrvecs[wx] for wx in wx_sublist]
qmaws_list = [wx2_qmaws[wx] for wx in wx_sublist]
qflags_list = [wx2_flags[wx] for wx in wx_sublist]
return idf_list, rvecs_list, qflags_list, qmaws_list, smk_alpha, smk_thresh
def compute_data_internals_(invindex, qparams, memtrack=None,
delete_rawvecs=True):
"""
Builds each of the inverted index internals.
invindex (InvertedIndex): object for fast vocab lookup
qparams (QueryParams): hyper-parameters
memtrack (None):
delete_rawvecs (bool):
Returns:
None
Example:
>>> from ibeis.algo.hots.smk.smk_repr import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, qreq_ = smk_debug.testdata_raw_internals0()
>>> compute_data_internals_(invindex, qreq_.qparams)
Ignore:
idx2_vec = idx2_dvec
wx2_maws = _wx2_maws # NOQA
"""
# Get information
#if memtrack is None:
# memtrack = ut.MemoryTracker('[DATA INTERNALS ENTRY]')
#memtrack.report('[DATA INTERNALS1]')
#
aggregate = qparams.aggregate
smk_alpha = qparams.smk_alpha
smk_thresh = qparams.smk_thresh
#
massign_alpha = qparams.massign_alpha
massign_sigma = qparams.massign_sigma
massign_equal_weights = qparams.massign_equal_weights
#
vocab_weighting = qparams.vocab_weighting
#
nAssign = 1 # single assignment for database side
idx2_vec = invindex.idx2_dvec
idx2_dfx = invindex.idx2_dfx
idx2_daid = invindex.idx2_daid
daids = invindex.daids
wordflann = invindex.wordflann
words = invindex.words
daid2_label = invindex.daid2_label
wx_series = np.arange(len(words))
#memtrack.track_obj(idx2_vec, 'idx2_vec')
if not ut.QUIET:
print('[smk_repr] compute_data_internals_')
if ut.VERBOSE:
print('[smk_repr] * len(daids) = %r' % (len(daids),))
print('[smk_repr] * len(words) = %r' % (len(words),))
print('[smk_repr] * len(idx2_vec) = %r' % (len(idx2_vec),))
print('[smk_repr] * aggregate = %r' % (aggregate,))
print('[smk_repr] * smk_alpha = %r' % (smk_alpha,))
print('[smk_repr] * smk_thresh = %r' % (smk_thresh,))
# Try to use the cache
#cfgstr = ut.hashstr_arr(words, 'words') + qparams.feat_cfgstr
#cachekw = dict(
#cfgstr=cfgstr,
#appname='smk_test'
#)
#invindex_cache = ut.Cacher('inverted_index', **cachekw)
#try:
# raise IOError('cache is off')
# #cachetup = invindex_cache.load()
# #(idx2_wxs, wx2_idxs, wx2_idf, wx2_drvecs, wx2_aids, wx2_fxs, wx2_maws, daid2_sccw) = cachetup
# invindex.idx2_dvec = None
#except IOError as ex:
# Database word assignments (perform single assignment on database side)
wx2_idxs, _wx2_maws, idx2_wxs = smk_index.assign_to_words_(
wordflann, words, idx2_vec, nAssign, massign_alpha, massign_sigma,
massign_equal_weights)
if ut.DEBUG2:
assert len(idx2_wxs) == len(idx2_vec)
assert len(wx2_idxs.keys()) == len(_wx2_maws.keys())
assert len(wx2_idxs.keys()) <= len(words)
try:
assert len(wx2_idxs.keys()) == len(words)
except AssertionError as ex:
ut.printex(ex, iswarning=True)
# Database word inverse-document-frequency (idf weights)
wx2_idf = smk_index.compute_word_idf_(
wx_series, wx2_idxs, idx2_daid, daids, daid2_label, vocab_weighting,
verbose=True)
if ut.DEBUG2:
assert len(wx2_idf) == len(wx2_idf.keys())
# Compute (normalized) residual vectors and inverse mappings
wx2_drvecs, wx2_aids, wx2_fxs, wx2_dmaws, wx2_dflags = smk_index.compute_residuals_(
words, wx2_idxs, _wx2_maws, idx2_vec, idx2_daid, idx2_dfx,
aggregate, verbose=True)
if not ut.QUIET:
print('[smk_repr] unloading idx2_vec')
if delete_rawvecs:
# Try to save some memory
del _wx2_maws
invindex.idx2_dvec = None
del idx2_vec
# Compute annotation normalization factor
daid2_sccw = smk_index.compute_data_sccw_(
idx2_daid, wx2_drvecs, wx2_dflags, wx2_aids, wx2_idf, wx2_dmaws, smk_alpha,
smk_thresh, verbose=True)
# Cache save
#cachetup = (idx2_wxs, wx2_idxs, wx2_idf, wx2_drvecs, wx2_aids, wx2_fxs, wx2_dmaws, daid2_sccw)
#invindex_cache.save(cachetup)
# Store information
invindex.idx2_wxs = idx2_wxs # stacked index -> word indexes (might not be needed)
invindex.wx2_idxs = wx2_idxs
invindex.wx2_idf = wx2_idf
invindex.wx2_drvecs = wx2_drvecs
invindex.wx2_dflags = wx2_dflags # flag nan rvecs
invindex.wx2_aids = wx2_aids # needed for asmk
invindex.wx2_fxs = wx2_fxs # needed for asmk
invindex.wx2_dmaws = wx2_dmaws # needed for awx2_mawssmk
invindex.daid2_sccw = daid2_sccw
#memtrack.report('[DATA INTERNALS3]')
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_invindex_wx2(invindex)
def new_qindex(annots_df, qaid, invindex, qparams):
r"""
Gets query read for computations
Args:
annots_df (DataFrameProxy): pandas-like data interface
qaid (int): query annotation id
invindex (InvertedIndex): inverted index object
qparams (QueryParams): query parameters object
Returns:
qindex: named tuple containing query information
CommandLine:
python -m ibeis.algo.hots.smk.smk_repr --test-new_qindex
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.smk.smk_repr import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, qaid, invindex, qparams = smk_debug.testdata_query_repr(db='PZ_Mothers', nWords=128000)
>>> qindex = new_qindex(annots_df, qaid, invindex, qparams)
>>> assert smk_debug.check_wx2_rvecs(qindex.wx2_qrvecs), 'has nan'
>>> smk_debug.invindex_dbgstr(invindex)
Ignore::
idx2_vec = qfx2_vec
idx2_aid = qfx2_aid
idx2_fx = qfx2_qfx
wx2_idxs = _wx2_qfxs
wx2_maws = _wx2_maws
from ibeis.algo.hots.smk import smk_repr
import utool as ut
ut.rrrr()
print(ut.make_default_docstr(smk_repr.new_qindex))
"""
# TODO: Precompute and lookup residuals and assignments
if not ut.QUIET:
print('[smk_repr] Query Repr qaid=%r' % (qaid,))
#
nAssign = qparams.nAssign
massign_alpha = qparams.massign_alpha
massign_sigma = qparams.massign_sigma
massign_equal_weights = qparams.massign_equal_weights
#
aggregate = qparams.aggregate
smk_alpha = qparams.smk_alpha
smk_thresh = qparams.smk_thresh
#
wx2_idf = invindex.wx2_idf
words = invindex.words
wordflann = invindex.wordflann
#qfx2_vec = annots_df['vecs'][qaid]
# TODO: remove all mention of annot_df and ensure that qparams is passed corectly to config2_
qfx2_vec = annots_df.ibs.get_annot_vecs(qaid, config2_=qparams)
#-------------------
# Assign query to (multiple) words
#-------------------
_wx2_qfxs, _wx2_maws, qfx2_wxs = smk_index.assign_to_words_(
wordflann, words, qfx2_vec, nAssign, massign_alpha,
massign_sigma, massign_equal_weights)
# Hack to make implementing asmk easier, very redundant
qfx2_aid = np.array([qaid] * len(qfx2_wxs), dtype=hstypes.INTEGER_TYPE)
qfx2_qfx = np.arange(len(qfx2_vec))
#-------------------
# Compute query residuals
#-------------------
wx2_qrvecs, wx2_qaids, wx2_qfxs, wx2_maws, wx2_qflags = smk_index.compute_residuals_(
words, _wx2_qfxs, _wx2_maws, qfx2_vec, qfx2_aid, qfx2_qfx, aggregate)
# each value in wx2_ dicts is a list with len equal to the number of rvecs
if ut.VERBOSE:
print('[smk_repr] Query SCCW smk_alpha=%r, smk_thresh=%r' % (smk_alpha, smk_thresh))
#-------------------
# Compute query sccw
#-------------------
wx_sublist = np.array(wx2_qrvecs.keys(), dtype=hstypes.INDEX_TYPE)
idf_list = [wx2_idf[wx] for wx in wx_sublist]
rvecs_list = [wx2_qrvecs[wx] for wx in wx_sublist]
maws_list = [wx2_maws[wx] for wx in wx_sublist]
flags_list = [wx2_qflags[wx] for wx in wx_sublist]
query_sccw = smk_scoring.sccw_summation(rvecs_list, flags_list, idf_list, maws_list, smk_alpha, smk_thresh)
try:
assert query_sccw > 0, 'query_sccw=%r is not positive!' % (query_sccw,)
except Exception as ex:
ut.printex(ex)
raise
#-------------------
# Build query representationm class/tuple
#-------------------
if DEBUG_SMK:
from ibeis.algo.hots.smk import smk_debug
qfx2_vec = annots_df['vecs'][qaid]
assert smk_debug.check_wx2_rvecs2(
invindex, wx2_qrvecs, wx2_qfxs, qfx2_vec), 'bad qindex'
qindex = QueryIndex(wx2_qrvecs, wx2_qflags, wx2_maws, wx2_qaids, wx2_qfxs, query_sccw)
return qindex
def compute_data_internals_(invindex,
qparams,
memtrack=None,
delete_rawvecs=True):
"""
Builds each of the inverted index internals.
invindex (InvertedIndex): object for fast vocab lookup
qparams (QueryParams): hyper-parameters
memtrack (None):
delete_rawvecs (bool):
Returns:
None
Example:
>>> from ibeis.algo.hots.smk.smk_repr import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, qreq_ = smk_debug.testdata_raw_internals0()
>>> compute_data_internals_(invindex, qreq_.qparams)
Ignore:
idx2_vec = idx2_dvec
wx2_maws = _wx2_maws # NOQA
"""
# Get information
#if memtrack is None:
# memtrack = ut.MemoryTracker('[DATA INTERNALS ENTRY]')
#memtrack.report('[DATA INTERNALS1]')
#
aggregate = qparams.aggregate
smk_alpha = qparams.smk_alpha
smk_thresh = qparams.smk_thresh
#
massign_alpha = qparams.massign_alpha
massign_sigma = qparams.massign_sigma
massign_equal_weights = qparams.massign_equal_weights
#
vocab_weighting = qparams.vocab_weighting
#
nAssign = 1 # single assignment for database side
idx2_vec = invindex.idx2_dvec
idx2_dfx = invindex.idx2_dfx
idx2_daid = invindex.idx2_daid
daids = invindex.daids
wordflann = invindex.wordflann
words = invindex.words
daid2_label = invindex.daid2_label
wx_series = np.arange(len(words))
#memtrack.track_obj(idx2_vec, 'idx2_vec')
if not ut.QUIET:
print('[smk_repr] compute_data_internals_')
if ut.VERBOSE:
print('[smk_repr] * len(daids) = %r' % (len(daids), ))
print('[smk_repr] * len(words) = %r' % (len(words), ))
print('[smk_repr] * len(idx2_vec) = %r' % (len(idx2_vec), ))
print('[smk_repr] * aggregate = %r' % (aggregate, ))
print('[smk_repr] * smk_alpha = %r' % (smk_alpha, ))
print('[smk_repr] * smk_thresh = %r' % (smk_thresh, ))
# Try to use the cache
#cfgstr = ut.hashstr_arr(words, 'words') + qparams.feat_cfgstr
#cachekw = dict(
#cfgstr=cfgstr,
#appname='smk_test'
#)
#invindex_cache = ut.Cacher('inverted_index', **cachekw)
#try:
# raise IOError('cache is off')
# #cachetup = invindex_cache.load()
# #(idx2_wxs, wx2_idxs, wx2_idf, wx2_drvecs, wx2_aids, wx2_fxs, wx2_maws, daid2_sccw) = cachetup
# invindex.idx2_dvec = None
#except IOError as ex:
# Database word assignments (perform single assignment on database side)
wx2_idxs, _wx2_maws, idx2_wxs = smk_index.assign_to_words_(
wordflann, words, idx2_vec, nAssign, massign_alpha, massign_sigma,
massign_equal_weights)
if ut.DEBUG2:
assert len(idx2_wxs) == len(idx2_vec)
assert len(wx2_idxs.keys()) == len(_wx2_maws.keys())
assert len(wx2_idxs.keys()) <= len(words)
try:
assert len(wx2_idxs.keys()) == len(words)
except AssertionError as ex:
ut.printex(ex, iswarning=True)
# Database word inverse-document-frequency (idf weights)
wx2_idf = smk_index.compute_word_idf_(wx_series,
wx2_idxs,
idx2_daid,
daids,
daid2_label,
vocab_weighting,
verbose=True)
if ut.DEBUG2:
assert len(wx2_idf) == len(wx2_idf.keys())
# Compute (normalized) residual vectors and inverse mappings
wx2_drvecs, wx2_aids, wx2_fxs, wx2_dmaws, wx2_dflags = smk_index.compute_residuals_(
words,
wx2_idxs,
_wx2_maws,
idx2_vec,
idx2_daid,
idx2_dfx,
aggregate,
verbose=True)
if not ut.QUIET:
print('[smk_repr] unloading idx2_vec')
if delete_rawvecs:
# Try to save some memory
del _wx2_maws
invindex.idx2_dvec = None
del idx2_vec
# Compute annotation normalization factor
daid2_sccw = smk_index.compute_data_sccw_(idx2_daid,
wx2_drvecs,
wx2_dflags,
wx2_aids,
wx2_idf,
wx2_dmaws,
smk_alpha,
smk_thresh,
verbose=True)
# Cache save
#cachetup = (idx2_wxs, wx2_idxs, wx2_idf, wx2_drvecs, wx2_aids, wx2_fxs, wx2_dmaws, daid2_sccw)
#invindex_cache.save(cachetup)
# Store information
invindex.idx2_wxs = idx2_wxs # stacked index -> word indexes (might not be needed)
invindex.wx2_idxs = wx2_idxs
invindex.wx2_idf = wx2_idf
invindex.wx2_drvecs = wx2_drvecs
invindex.wx2_dflags = wx2_dflags # flag nan rvecs
invindex.wx2_aids = wx2_aids # needed for asmk
invindex.wx2_fxs = wx2_fxs # needed for asmk
invindex.wx2_dmaws = wx2_dmaws # needed for awx2_mawssmk
invindex.daid2_sccw = daid2_sccw
#memtrack.report('[DATA INTERNALS3]')
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_invindex_wx2(invindex)
def new_qindex(annots_df, qaid, invindex, qparams):
r"""
Gets query read for computations
Args:
annots_df (DataFrameProxy): pandas-like data interface
qaid (int): query annotation id
invindex (InvertedIndex): inverted index object
qparams (QueryParams): query parameters object
Returns:
qindex: named tuple containing query information
CommandLine:
python -m ibeis.algo.hots.smk.smk_repr --test-new_qindex
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.smk.smk_repr import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, qaid, invindex, qparams = smk_debug.testdata_query_repr(db='PZ_Mothers', nWords=128000)
>>> qindex = new_qindex(annots_df, qaid, invindex, qparams)
>>> assert smk_debug.check_wx2_rvecs(qindex.wx2_qrvecs), 'has nan'
>>> smk_debug.invindex_dbgstr(invindex)
Ignore::
idx2_vec = qfx2_vec
idx2_aid = qfx2_aid
idx2_fx = qfx2_qfx
wx2_idxs = _wx2_qfxs
wx2_maws = _wx2_maws
from ibeis.algo.hots.smk import smk_repr
import utool as ut
ut.rrrr()
print(ut.make_default_docstr(smk_repr.new_qindex))
"""
# TODO: Precompute and lookup residuals and assignments
if not ut.QUIET:
print('[smk_repr] Query Repr qaid=%r' % (qaid, ))
#
nAssign = qparams.nAssign
massign_alpha = qparams.massign_alpha
massign_sigma = qparams.massign_sigma
massign_equal_weights = qparams.massign_equal_weights
#
aggregate = qparams.aggregate
smk_alpha = qparams.smk_alpha
smk_thresh = qparams.smk_thresh
#
wx2_idf = invindex.wx2_idf
words = invindex.words
wordflann = invindex.wordflann
#qfx2_vec = annots_df['vecs'][qaid]
# TODO: remove all mention of annot_df and ensure that qparams is passed corectly to config2_
qfx2_vec = annots_df.ibs.get_annot_vecs(qaid, config2_=qparams)
#-------------------
# Assign query to (multiple) words
#-------------------
_wx2_qfxs, _wx2_maws, qfx2_wxs = smk_index.assign_to_words_(
wordflann, words, qfx2_vec, nAssign, massign_alpha, massign_sigma,
massign_equal_weights)
# Hack to make implementing asmk easier, very redundant
qfx2_aid = np.array([qaid] * len(qfx2_wxs), dtype=hstypes.INTEGER_TYPE)
qfx2_qfx = np.arange(len(qfx2_vec))
#-------------------
# Compute query residuals
#-------------------
wx2_qrvecs, wx2_qaids, wx2_qfxs, wx2_maws, wx2_qflags = smk_index.compute_residuals_(
words, _wx2_qfxs, _wx2_maws, qfx2_vec, qfx2_aid, qfx2_qfx, aggregate)
# each value in wx2_ dicts is a list with len equal to the number of rvecs
if ut.VERBOSE:
print('[smk_repr] Query SCCW smk_alpha=%r, smk_thresh=%r' %
(smk_alpha, smk_thresh))
#-------------------
# Compute query sccw
#-------------------
wx_sublist = np.array(wx2_qrvecs.keys(), dtype=hstypes.INDEX_TYPE)
idf_list = [wx2_idf[wx] for wx in wx_sublist]
rvecs_list = [wx2_qrvecs[wx] for wx in wx_sublist]
maws_list = [wx2_maws[wx] for wx in wx_sublist]
flags_list = [wx2_qflags[wx] for wx in wx_sublist]
query_sccw = smk_scoring.sccw_summation(rvecs_list, flags_list, idf_list,
maws_list, smk_alpha, smk_thresh)
try:
assert query_sccw > 0, 'query_sccw=%r is not positive!' % (
query_sccw, )
except Exception as ex:
ut.printex(ex)
raise
#-------------------
# Build query representationm class/tuple
#-------------------
if DEBUG_SMK:
from ibeis.algo.hots.smk import smk_debug
qfx2_vec = annots_df['vecs'][qaid]
assert smk_debug.check_wx2_rvecs2(invindex, wx2_qrvecs, wx2_qfxs,
qfx2_vec), 'bad qindex'
qindex = QueryIndex(wx2_qrvecs, wx2_qflags, wx2_maws, wx2_qaids, wx2_qfxs,
query_sccw)
return qindex