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robmcmullen/atrcopy | atrcopy/segments.py | get_style_bits | def get_style_bits(match=False, comment=False, selected=False, data=False, diff=False, user=0):
""" Return an int value that contains the specified style bits set.
Available styles for each byte are:
match: part of the currently matched search
comment: user commented area
selected: selected region... | python | def get_style_bits(match=False, comment=False, selected=False, data=False, diff=False, user=0):
""" Return an int value that contains the specified style bits set.
Available styles for each byte are:
match: part of the currently matched search
comment: user commented area
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robmcmullen/atrcopy | atrcopy/segments.py | get_style_mask | def get_style_mask(**kwargs):
"""Get the bit mask that, when anded with data, will turn off the
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"""
bits = get_style_bits(**kwargs)
if 'user' in kwargs and kwargs['user']:
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bits &= (0xff ^ user_bit_mask)
return 0xff ^ bits | python | def get_style_mask(**kwargs):
"""Get the bit mask that, when anded with data, will turn off the
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"""
bits = get_style_bits(**kwargs)
if 'user' in kwargs and kwargs['user']:
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robmcmullen/atrcopy | atrcopy/segments.py | SegmentData.byte_bounds_offset | def byte_bounds_offset(self):
"""Return start and end offsets of this segment's data into the
base array's data.
This ignores the byte order index. Arrays using the byte order index
will have the entire base array's raw data.
"""
if self.data.base is None:
if... | python | def byte_bounds_offset(self):
"""Return start and end offsets of this segment's data into the
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This ignores the byte order index. Arrays using the byte order index
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"""
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robmcmullen/atrcopy | atrcopy/segments.py | SegmentData.get_raw_index | def get_raw_index(self, i):
"""Get index into base array's raw data, given the index into this
segment
"""
if self.is_indexed:
return int(self.order[i])
if self.data.base is None:
return int(i)
return int(self.data_start - self.base_start + i) | python | def get_raw_index(self, i):
"""Get index into base array's raw data, given the index into this
segment
"""
if self.is_indexed:
return int(self.order[i])
if self.data.base is None:
return int(i)
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robmcmullen/atrcopy | atrcopy/segments.py | SegmentData.get_indexes_from_base | def get_indexes_from_base(self):
"""Get array of indexes from the base array, as if this raw data were
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"""
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return np.copy(self.order[i])
if self.data.base is None:
i = 0
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i = self.get_raw_index(0)
... | python | def get_indexes_from_base(self):
"""Get array of indexes from the base array, as if this raw data were
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"""
if self.is_indexed:
return np.copy(self.order[i])
if self.data.base is None:
i = 0
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robmcmullen/atrcopy | atrcopy/segments.py | SegmentData.reverse_index_mapping | def reverse_index_mapping(self):
"""Get mapping from this segment's indexes to the indexes of
the base array.
If the index is < 0, the index is out of range, meaning that it doesn't
exist in this segment and is not mapped to the base array
"""
if self._reverse_index_mapp... | python | def reverse_index_mapping(self):
"""Get mapping from this segment's indexes to the indexes of
the base array.
If the index is < 0, the index is out of range, meaning that it doesn't
exist in this segment and is not mapped to the base array
"""
if self._reverse_index_mapp... | [
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robmcmullen/atrcopy | atrcopy/segments.py | SegmentData.get_reverse_index | def get_reverse_index(self, base_index):
"""Get index into this segment's data given the index into the base data
Raises IndexError if the base index doesn't map to anything in this
segment's data
"""
r = self.reverse_index_mapping[base_index]
if r < 0:
raise... | python | def get_reverse_index(self, base_index):
"""Get index into this segment's data given the index into the base data
Raises IndexError if the base index doesn't map to anything in this
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r = self.reverse_index_mapping[base_index]
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.resize | def resize(self, newsize, zeros=True):
""" Resize the data arrays.
This can only be performed on the container segment. Child segments
must adjust their rawdata to point to the correct place.
Since segments don't keep references to other segments, it is the
user's responsibilit... | python | def resize(self, newsize, zeros=True):
""" Resize the data arrays.
This can only be performed on the container segment. Child segments
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.restore_renamed_serializable_attributes | def restore_renamed_serializable_attributes(self):
"""Hook for the future if attributes have been renamed. The old
attribute names will have been restored in the __dict__.update in
__setstate__, so this routine should move attribute values to their new
names.
"""
if hasat... | python | def restore_renamed_serializable_attributes(self):
"""Hook for the future if attributes have been renamed. The old
attribute names will have been restored in the __dict__.update in
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.reconstruct_raw | def reconstruct_raw(self, rawdata):
"""Reconstruct the pointers to the parent data arrays
Each segment is a view into the primary segment's data, so those
pointers and the order must be restored in the child segments.
"""
start, end = self._rawdata_bounds
r = rawdata[sta... | python | def reconstruct_raw(self, rawdata):
"""Reconstruct the pointers to the parent data arrays
Each segment is a view into the primary segment's data, so those
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start, end = self._rawdata_bounds
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_parallel_raw_data | def get_parallel_raw_data(self, other):
""" Get the raw data that is similar to the specified other segment
"""
start, end = other.byte_bounds_offset()
r = self.rawdata[start:end]
if other.rawdata.is_indexed:
r = r.get_indexed[other.order]
return r | python | def get_parallel_raw_data(self, other):
""" Get the raw data that is similar to the specified other segment
"""
start, end = other.byte_bounds_offset()
r = self.rawdata[start:end]
if other.rawdata.is_indexed:
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return r | [
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.serialize_session | def serialize_session(self, mdict):
"""Save extra metadata to a dict so that it can be serialized
This is not saved by __getstate__ because child segments will point to
the same data and this allows it to only be saved for the base segment.
As well as allowing it to be pulled out of the... | python | def serialize_session(self, mdict):
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_index_from_base_index | def get_index_from_base_index(self, base_index):
"""Get index into this array's data given the index into the base array
"""
r = self.rawdata
try:
index = r.get_reverse_index(base_index)
except IndexError:
raise IndexError("index %d not in this segment" % ... | python | def get_index_from_base_index(self, base_index):
"""Get index into this array's data given the index into the base array
"""
r = self.rawdata
try:
index = r.get_reverse_index(base_index)
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_style_ranges | def get_style_ranges(self, **kwargs):
"""Return a list of start, end pairs that match the specified style
"""
style_bits = self.get_style_bits(**kwargs)
matches = (self.style & style_bits) == style_bits
return self.bool_to_ranges(matches) | python | def get_style_ranges(self, **kwargs):
"""Return a list of start, end pairs that match the specified style
"""
style_bits = self.get_style_bits(**kwargs)
matches = (self.style & style_bits) == style_bits
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.fixup_comments | def fixup_comments(self):
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This happens on the base data, so only need to do this on one segment
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_entire_style_ranges | def get_entire_style_ranges(self, split_comments=None, **kwargs):
"""Find sections of the segment that have the same style value.
The arguments to this function are used as a mask for the style to
determine where to split the styles. Style bits that aren't included in
the list will be i... | python | def get_entire_style_ranges(self, split_comments=None, **kwargs):
"""Find sections of the segment that have the same style value.
The arguments to this function are used as a mask for the style to
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_comments_at_indexes | def get_comments_at_indexes(self, indexes):
"""Get a list of comments at specified indexes"""
s = self.style[indexes]
has_comments = np.where(s & comment_bit_mask > 0)[0]
comments = []
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raw = self.get_raw_index(indexes[where_index])
... | python | def get_comments_at_indexes(self, indexes):
"""Get a list of comments at specified indexes"""
s = self.style[indexes]
has_comments = np.where(s & comment_bit_mask > 0)[0]
comments = []
for where_index in has_comments:
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_comment_restore_data | def get_comment_restore_data(self, ranges):
"""Get a chunk of data (designed to be opaque) containing comments,
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"""
restore_data = []
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restore_data = []
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.restore_comments | def restore_comments(self, restore_data):
"""Restore comment styles and data
"""
for start, end, styles, items in restore_data:
log.debug("range: %d-%d" % (start, end))
self.style[start:end] = styles
for i in range(start, end):
rawindex, commen... | python | def restore_comments(self, restore_data):
"""Restore comment styles and data
"""
for start, end, styles, items in restore_data:
log.debug("range: %d-%d" % (start, end))
self.style[start:end] = styles
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.get_comments_in_range | def get_comments_in_range(self, start, end):
"""Get a list of comments at specified indexes"""
comments = {}
# Naive way, but maybe it's fast enough: loop over all comments
# gathering those within the bounds
for rawindex, comment in self.rawdata.extra.comments.items():
... | python | def get_comments_in_range(self, start, end):
"""Get a list of comments at specified indexes"""
comments = {}
# Naive way, but maybe it's fast enough: loop over all comments
# gathering those within the bounds
for rawindex, comment in self.rawdata.extra.comments.items():
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robmcmullen/atrcopy | atrcopy/segments.py | DefaultSegment.copy_user_data | def copy_user_data(self, source, index_offset=0):
"""Copy comments and other user data from the source segment to this
segment.
The index offset is the offset into self based on the index of source.
"""
for index, comment in source.iter_comments_in_segment():
self.se... | python | def copy_user_data(self, source, index_offset=0):
"""Copy comments and other user data from the source segment to this
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The index offset is the offset into self based on the index of source.
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for index, comment in source.iter_comments_in_segment():
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openstax/cnx-archive | cnxarchive/views/xpath.py | xpath_book | def xpath_book(request, uuid, version, return_json=True):
"""
Given a request, book UUID and version:
returns a JSON object or HTML list of results, each result containing:
module_name,
module_uuid,
xpath_results, an array of strings, each an individual xpath result.
"""
xpath_string =... | python | def xpath_book(request, uuid, version, return_json=True):
"""
Given a request, book UUID and version:
returns a JSON object or HTML list of results, each result containing:
module_name,
module_uuid,
xpath_results, an array of strings, each an individual xpath result.
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openstax/cnx-archive | cnxarchive/views/xpath.py | xpath_page | def xpath_page(request, uuid, version):
"""Given a page UUID (and optional version), returns a JSON object of
results, as in xpath_book()"""
xpath_string = request.params.get('q')
return execute_xpath(xpath_string, 'xpath-module', uuid, version) | python | def xpath_page(request, uuid, version):
"""Given a page UUID (and optional version), returns a JSON object of
results, as in xpath_book()"""
xpath_string = request.params.get('q')
return execute_xpath(xpath_string, 'xpath-module', uuid, version) | [
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openstax/cnx-archive | cnxarchive/views/xpath.py | execute_xpath | def execute_xpath(xpath_string, sql_function, uuid, version):
"""Executes either xpath or xpath-module SQL function with given input
params."""
settings = get_current_registry().settings
with db_connect() as db_connection:
with db_connection.cursor() as cursor:
try:
... | python | def execute_xpath(xpath_string, sql_function, uuid, version):
"""Executes either xpath or xpath-module SQL function with given input
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settings = get_current_registry().settings
with db_connect() as db_connection:
with db_connection.cursor() as cursor:
try:
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openstax/cnx-archive | cnxarchive/views/xpath.py | xpath | def xpath(request):
"""View for the route. Determines UUID and version from input request
and determines the type of UUID (collection or module) and executes
the corresponding method."""
ident_hash = request.params.get('id')
xpath_string = request.params.get('q')
if not ident_hash or not xpath_... | python | def xpath(request):
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and determines the type of UUID (collection or module) and executes
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ident_hash = request.params.get('id')
xpath_string = request.params.get('q')
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openstax/cnx-archive | cnxarchive/views/content.py | tree_to_html | def tree_to_html(tree):
"""Return html list version of book tree."""
ul = etree.Element('ul')
html_listify([tree], ul)
return HTML_WRAPPER.format(etree.tostring(ul)) | python | def tree_to_html(tree):
"""Return html list version of book tree."""
ul = etree.Element('ul')
html_listify([tree], ul)
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openstax/cnx-archive | cnxarchive/views/content.py | _get_content_json | def _get_content_json(ident_hash=None):
"""Return a content as a dict from its ident-hash (uuid@version)."""
request = get_current_request()
routing_args = request and request.matchdict or {}
if not ident_hash:
ident_hash = routing_args['ident_hash']
as_collated = asbool(request.GET.get('as... | python | def _get_content_json(ident_hash=None):
"""Return a content as a dict from its ident-hash (uuid@version)."""
request = get_current_request()
routing_args = request and request.matchdict or {}
if not ident_hash:
ident_hash = routing_args['ident_hash']
as_collated = asbool(request.GET.get('as... | [
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openstax/cnx-archive | cnxarchive/views/content.py | get_content_json | def get_content_json(request):
"""Retrieve content as JSON using the ident-hash (uuid@version)."""
result = _get_content_json()
resp = request.response
resp.status = "200 OK"
resp.content_type = 'application/json'
resp.body = json.dumps(result)
return result, resp | python | def get_content_json(request):
"""Retrieve content as JSON using the ident-hash (uuid@version)."""
result = _get_content_json()
resp = request.response
resp.status = "200 OK"
resp.content_type = 'application/json'
resp.body = json.dumps(result)
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openstax/cnx-archive | cnxarchive/views/content.py | get_content_html | def get_content_html(request):
"""Retrieve content as HTML using the ident-hash (uuid@version)."""
result = _get_content_json()
media_type = result['mediaType']
if media_type == COLLECTION_MIMETYPE:
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else:
content = result['content']
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"""Retrieve content as HTML using the ident-hash (uuid@version)."""
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content = result['content']
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openstax/cnx-archive | cnxarchive/views/content.py | html_listify | def html_listify(tree, root_ul_element, parent_id=None):
"""Recursively construct HTML nested list version of book tree.
The original caller should not call this function with the
`parent_id` defined.
"""
request = get_current_request()
is_first_node = parent_id is None
if is_first_node:
... | python | def html_listify(tree, root_ul_element, parent_id=None):
"""Recursively construct HTML nested list version of book tree.
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"""
request = get_current_request()
is_first_node = parent_id is None
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openstax/cnx-archive | cnxarchive/views/content.py | get_export_allowable_types | def get_export_allowable_types(cursor, exports_dirs, id, version):
"""Return export types."""
request = get_current_request()
type_settings = request.registry.settings['_type_info']
type_names = [k for k, v in type_settings]
type_infos = [v for k, v in type_settings]
# We took the type_names dir... | python | def get_export_allowable_types(cursor, exports_dirs, id, version):
"""Return export types."""
request = get_current_request()
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openstax/cnx-archive | cnxarchive/views/content.py | get_book_info | def get_book_info(cursor, real_dict_cursor, book_id,
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"""Return information about a given book.
Return the book's title, id, shortId, authors and revised date.
Raise HTTPNotFound if the page is not in the book.
"""
book_ident_hash = join_ident_... | python | def get_book_info(cursor, real_dict_cursor, book_id,
book_version, page_id, page_version):
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Return the book's title, id, shortId, authors and revised date.
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openstax/cnx-archive | cnxarchive/views/content.py | get_portal_type | def get_portal_type(cursor, id, version):
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cursor.execute(sql_statement, vars=(args,))
r... | python | def get_portal_type(cursor, id, version):
"""Return the module's portal_type."""
args = join_ident_hash(id, version)
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with db_connect() as db_connection:
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canon_host = settings.get('canonical-hostname',
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openstax/cnx-archive | cnxarchive/views/content.py | get_content | def get_content(request):
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"""
ext = request.matchdict.get('ext')
accept = request.headers.get('ACCEPT', '')
if not ext:
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... | python | def get_content(request):
"""Retrieve content using the ident-hash (uuid@version).
Depending on extension or HTTP_ACCEPT header return HTML or JSON.
"""
ext = request.matchdict.get('ext')
accept = request.headers.get('ACCEPT', '')
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | map_indices_child2parent | def map_indices_child2parent(child, child_indices):
"""Map child RTDCBase event indices to parent RTDCBase
Parameters
----------
child: RTDC_Hierarchy
hierarchy child with `child_indices`
child_indices: 1d ndarray
child indices to map
Returns
-------
parent_indices: 1d ... | python | def map_indices_child2parent(child, child_indices):
"""Map child RTDCBase event indices to parent RTDCBase
Parameters
----------
child: RTDC_Hierarchy
hierarchy child with `child_indices`
child_indices: 1d ndarray
child indices to map
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | map_indices_child2root | def map_indices_child2root(child, child_indices):
"""Map RTDC_Hierarchy event indices to root RTDCBase
Parameters
----------
child: RTDC_Hierarchy
RTDCBase hierarchy child
child_indices: 1d ndarray
child indices to map
Returns
-------
root_indices: 1d ndarray
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RTDCBase hierarchy child
child_indices: 1d ndarray
child indices to map
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | map_indices_parent2child | def map_indices_parent2child(child, parent_indices):
"""Map parent RTDCBase event indices to RTDC_Hierarchy
Parameters
----------
parent: RTDC_Hierarchy
hierarchy child
parent_indices: 1d ndarray
hierarchy parent (`child.hparent`) indices to map
Returns
-------
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"""Map parent RTDCBase event indices to RTDC_Hierarchy
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parent: RTDC_Hierarchy
hierarchy child
parent_indices: 1d ndarray
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | map_indices_root2child | def map_indices_root2child(child, root_indices):
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Parameters
----------
parent: RTDCBase
hierarchy parent of `child`.
root_indices: 1d ndarray
hierarchy root indices to map
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | HierarchyFilter.apply_manual_indices | def apply_manual_indices(self, manual_indices):
"""Write to `self.manual`
Write `manual_indices` to the boolean array `self.manual`
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Notes
-----
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a filt... | python | def apply_manual_indices(self, manual_indices):
"""Write to `self.manual`
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | HierarchyFilter.retrieve_manual_indices | def retrieve_manual_indices(self):
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... | python | def retrieve_manual_indices(self):
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | RTDC_Hierarchy.apply_filter | def apply_filter(self, *args, **kwargs):
"""Overridden `apply_filter` to perform tasks for hierarchy child"""
if self.filter is not None:
# make sure self.filter knows about root manual indices
self.filter.retrieve_manual_indices()
# Copy event data from hierarchy parent... | python | def apply_filter(self, *args, **kwargs):
"""Overridden `apply_filter` to perform tasks for hierarchy child"""
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_hierarchy.py | RTDC_Hierarchy.hash | def hash(self):
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# Do not apply filters here (speed)
hph = self.hparent.hash
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dhash = hashobj(hph + hpfilt)
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"""Hashes of a hierarchy child changes if the parent changes"""
# Do not apply filters here (speed)
hph = self.hparent.hash
hpfilt = hashobj(self.hparent._filter)
dhash = hashobj(hph + hpfilt)
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_tdms/event_image.py | ImageColumn.find_video_file | def find_video_file(rtdc_dataset):
"""Tries to find a video file that belongs to an RTDC dataset
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"""
video = None
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# Cell images (video)
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ZELLMECHANIK-DRESDEN/dclab | dclab/rtdc_dataset/fmt_tdms/event_image.py | ImageMap._get_image_workaround_seek | def _get_image_workaround_seek(self, idx):
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This is a workaround for an all-zero image returned by `imageio`.
"""
warnings.warn("imageio workaround used!")
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"""Same as __getitem__ but seek through the video beforehand
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mhostetter/nhl | nhl/list.py | List.select | def select(self, attr, default=None):
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robmcmullen/atrcopy | atrcopy/ataridos.py | AtariDosDiskImage.as_new_format | def as_new_format(self, format="ATR"):
""" Create a new disk image in the specified format
"""
first_data = len(self.header)
raw = self.rawdata[first_data:]
data = add_atr_header(raw)
newraw = SegmentData(data)
image = self.__class__(newraw)
return image | python | def as_new_format(self, format="ATR"):
""" Create a new disk image in the specified format
"""
first_data = len(self.header)
raw = self.rawdata[first_data:]
data = add_atr_header(raw)
newraw = SegmentData(data)
image = self.__class__(newraw)
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simse/pymitv | pymitv/tv.py | TV.set_source | def set_source(self, source):
"""Selects and saves source."""
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# Save new source
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openstax/cnx-archive | cnxarchive/__init__.py | find_migrations_directory | def find_migrations_directory():
"""Finds and returns the location of the database migrations directory.
This function is used from a setuptools entry-point for db-migrator.
"""
here = os.path.abspath(os.path.dirname(__file__))
return os.path.join(here, 'sql/migrations') | python | def find_migrations_directory():
"""Finds and returns the location of the database migrations directory.
This function is used from a setuptools entry-point for db-migrator.
"""
here = os.path.abspath(os.path.dirname(__file__))
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openstax/cnx-archive | cnxarchive/__init__.py | declare_api_routes | def declare_api_routes(config):
"""Declare routes, with a custom pregenerator."""
# The pregenerator makes sure we can generate a path using
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# The pregenerator makes sure we can generate a path using
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openstax/cnx-archive | cnxarchive/__init__.py | declare_type_info | def declare_type_info(config):
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"""Lookup type info from app configuration."""
settings = config.registry.settings
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openstax/cnx-archive | cnxarchive/__init__.py | main | def main(global_config, **settings):
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initialize_sentry_integration()
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declare_api_routes(config)
declare_type_info(config)
# allowing the pyramid templates to render rss and xml
config.include('pyramid_jinja2')
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"""Main WSGI application factory."""
initialize_sentry_integration()
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declare_api_routes(config)
declare_type_info(config)
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lucasmaystre/choix | choix/lsr.py | _init_lsr | def _init_lsr(n_items, alpha, initial_params):
"""Initialize the LSR Markov chain and the weights."""
if initial_params is None:
weights = np.ones(n_items)
else:
weights = exp_transform(initial_params)
chain = alpha * np.ones((n_items, n_items), dtype=float)
return weights, chain | python | def _init_lsr(n_items, alpha, initial_params):
"""Initialize the LSR Markov chain and the weights."""
if initial_params is None:
weights = np.ones(n_items)
else:
weights = exp_transform(initial_params)
chain = alpha * np.ones((n_items, n_items), dtype=float)
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lucasmaystre/choix | choix/lsr.py | _ilsr | def _ilsr(fun, params, max_iter, tol):
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Raises
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Raises
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lucasmaystre/choix | choix/lsr.py | lsr_pairwise | def lsr_pairwise(n_items, data, alpha=0.0, initial_params=None):
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lucasmaystre/choix | choix/lsr.py | ilsr_pairwise | def ilsr_pairwise(
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lucasmaystre/choix | choix/lsr.py | lsr_pairwise_dense | def lsr_pairwise_dense(comp_mat, alpha=0.0, initial_params=None):
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lucasmaystre/choix | choix/lsr.py | ilsr_pairwise_dense | def ilsr_pairwise_dense(
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lucasmaystre/choix | choix/lsr.py | rank_centrality | def rank_centrality(n_items, data, alpha=0.0):
"""Compute the Rank Centrality estimate of model parameters.
This function implements Negahban et al.'s Rank Centrality algorithm
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considers the *ratio* of wins for each pair (instead o... | python | def rank_centrality(n_items, data, alpha=0.0):
"""Compute the Rank Centrality estimate of model parameters.
This function implements Negahban et al.'s Rank Centrality algorithm
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lucasmaystre/choix | choix/lsr.py | lsr_rankings | def lsr_rankings(n_items, data, alpha=0.0, initial_params=None):
"""Compute the LSR estimate of model parameters.
This function implements the Luce Spectral Ranking inference algorithm
[MG15]_ for ranking data (see :ref:`data-rankings`).
The argument ``initial_params`` can be used to iteratively refin... | python | def lsr_rankings(n_items, data, alpha=0.0, initial_params=None):
"""Compute the LSR estimate of model parameters.
This function implements the Luce Spectral Ranking inference algorithm
[MG15]_ for ranking data (see :ref:`data-rankings`).
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lucasmaystre/choix | choix/lsr.py | ilsr_rankings | def ilsr_rankings(
n_items, data, alpha=0.0, initial_params=None, max_iter=100, tol=1e-8):
"""Compute the ML estimate of model parameters using I-LSR.
This function computes the maximum-likelihood (ML) estimate of model
parameters given ranking data (see :ref:`data-rankings`), using the
iterati... | python | def ilsr_rankings(
n_items, data, alpha=0.0, initial_params=None, max_iter=100, tol=1e-8):
"""Compute the ML estimate of model parameters using I-LSR.
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lucasmaystre/choix | choix/lsr.py | lsr_top1 | def lsr_top1(n_items, data, alpha=0.0, initial_params=None):
"""Compute the LSR estimate of model parameters.
This function implements the Luce Spectral Ranking inference algorithm
[MG15]_ for top-1 data (see :ref:`data-top1`).
The argument ``initial_params`` can be used to iteratively refine an
e... | python | def lsr_top1(n_items, data, alpha=0.0, initial_params=None):
"""Compute the LSR estimate of model parameters.
This function implements the Luce Spectral Ranking inference algorithm
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lucasmaystre/choix | choix/lsr.py | ilsr_top1 | def ilsr_top1(
n_items, data, alpha=0.0, initial_params=None, max_iter=100, tol=1e-8):
"""Compute the ML estimate of model parameters using I-LSR.
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lucasmaystre/choix | choix/ep.py | ep_pairwise | def ep_pairwise(n_items, data, alpha, model="logit", max_iter=100,
initial_state=None):
"""Compute a distribution of model parameters using the EP algorithm.
This function computes an approximate Bayesian posterior probability
distribution over model parameters, given pairwise-comparison data (see
... | python | def ep_pairwise(n_items, data, alpha, model="logit", max_iter=100,
initial_state=None):
"""Compute a distribution of model parameters using the EP algorithm.
This function computes an approximate Bayesian posterior probability
distribution over model parameters, given pairwise-comparison data (see
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lucasmaystre/choix | choix/ep.py | _ep_pairwise | def _ep_pairwise(
n_items, comparisons, alpha, match_moments, max_iter, initial_state):
"""Compute a distribution of model parameters using the EP algorithm.
Raises
------
RuntimeError
If the algorithm does not converge after ``max_iter`` iterations.
"""
# Static variable that a... | python | def _ep_pairwise(
n_items, comparisons, alpha, match_moments, max_iter, initial_state):
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Raises
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lucasmaystre/choix | choix/ep.py | _log_phi | def _log_phi(z):
"""Stable computation of the log of the Normal CDF and its derivative."""
# Adapted from the GPML function `logphi.m`.
if z * z < 0.0492:
# First case: z close to zero.
coef = -z / SQRT2PI
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res = -... | python | def _log_phi(z):
"""Stable computation of the log of the Normal CDF and its derivative."""
# Adapted from the GPML function `logphi.m`.
if z * z < 0.0492:
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lucasmaystre/choix | choix/ep.py | _init_ws | def _init_ws(n_items, comparisons, prior_inv, tau, nu):
"""Initialize parameters in the weight space."""
prec = np.zeros((n_items, n_items))
xs = np.zeros(n_items)
for i, (a, b) in enumerate(comparisons):
prec[(a, a, b, b), (a, b, a, b)] += tau[i] * MAT_ONE_FLAT
xs[a] += nu[i]
x... | python | def _init_ws(n_items, comparisons, prior_inv, tau, nu):
"""Initialize parameters in the weight space."""
prec = np.zeros((n_items, n_items))
xs = np.zeros(n_items)
for i, (a, b) in enumerate(comparisons):
prec[(a, a, b, b), (a, b, a, b)] += tau[i] * MAT_ONE_FLAT
xs[a] += nu[i]
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lucasmaystre/choix | choix/utils.py | exp_transform | def exp_transform(params):
"""Transform parameters into exp-scale weights."""
weights = np.exp(np.asarray(params) - np.mean(params))
return (len(weights) / weights.sum()) * weights | python | def exp_transform(params):
"""Transform parameters into exp-scale weights."""
weights = np.exp(np.asarray(params) - np.mean(params))
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lucasmaystre/choix | choix/utils.py | softmax | def softmax(xs):
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lucasmaystre/choix | choix/utils.py | inv_posdef | def inv_posdef(mat):
"""Stable inverse of a positive definite matrix."""
# See:
# - http://www.seas.ucla.edu/~vandenbe/103/lectures/chol.pdf
# - http://scicomp.stackexchange.com/questions/3188
chol = np.linalg.cholesky(mat)
ident = np.eye(mat.shape[0])
res = solve_triangular(chol, ident, low... | python | def inv_posdef(mat):
"""Stable inverse of a positive definite matrix."""
# See:
# - http://www.seas.ucla.edu/~vandenbe/103/lectures/chol.pdf
# - http://scicomp.stackexchange.com/questions/3188
chol = np.linalg.cholesky(mat)
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lucasmaystre/choix | choix/utils.py | footrule_dist | def footrule_dist(params1, params2=None):
r"""Compute Spearman's footrule distance between two models.
This function computes Spearman's footrule distance between the rankings
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lucasmaystre/choix | choix/utils.py | kendalltau_dist | def kendalltau_dist(params1, params2=None):
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lucasmaystre/choix | choix/utils.py | rmse | def rmse(params1, params2):
r"""Compute the root-mean-squared error between two models.
Parameters
----------
params1 : array_like
Parameters of the first model.
params2 : array_like
Parameters of the second model.
Returns
-------
error : float
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r"""Compute the root-mean-squared error between two models.
Parameters
----------
params1 : array_like
Parameters of the first model.
params2 : array_like
Parameters of the second model.
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error : float
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lucasmaystre/choix | choix/utils.py | log_likelihood_pairwise | def log_likelihood_pairwise(data, params):
"""Compute the log-likelihood of model parameters."""
loglik = 0
for winner, loser in data:
loglik -= np.logaddexp(0, -(params[winner] - params[loser]))
return loglik | python | def log_likelihood_pairwise(data, params):
"""Compute the log-likelihood of model parameters."""
loglik = 0
for winner, loser in data:
loglik -= np.logaddexp(0, -(params[winner] - params[loser]))
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lucasmaystre/choix | choix/utils.py | log_likelihood_rankings | def log_likelihood_rankings(data, params):
"""Compute the log-likelihood of model parameters."""
loglik = 0
params = np.asarray(params)
for ranking in data:
for i, winner in enumerate(ranking[:-1]):
loglik -= logsumexp(params.take(ranking[i:]) - params[winner])
return loglik | python | def log_likelihood_rankings(data, params):
"""Compute the log-likelihood of model parameters."""
loglik = 0
params = np.asarray(params)
for ranking in data:
for i, winner in enumerate(ranking[:-1]):
loglik -= logsumexp(params.take(ranking[i:]) - params[winner])
return loglik | [
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lucasmaystre/choix | choix/utils.py | log_likelihood_top1 | def log_likelihood_top1(data, params):
"""Compute the log-likelihood of model parameters."""
loglik = 0
params = np.asarray(params)
for winner, losers in data:
idx = np.append(winner, losers)
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return loglik | python | def log_likelihood_top1(data, params):
"""Compute the log-likelihood of model parameters."""
loglik = 0
params = np.asarray(params)
for winner, losers in data:
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lucasmaystre/choix | choix/utils.py | log_likelihood_network | def log_likelihood_network(
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Compute the log-likelihood of model parameters.
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"""
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lucasmaystre/choix | choix/utils.py | statdist | def statdist(generator):
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Parameters
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generator : array_like
Infinitesimal generator matrix of the Markov chain.
Returns
-------
dist : numpy.ndarray
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"""Compute the stationary distribution of a Markov chain.
Parameters
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generator : array_like
Infinitesimal generator matrix of the Markov chain.
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dist : numpy.ndarray
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lucasmaystre/choix | choix/utils.py | generate_params | def generate_params(n_items, interval=5.0, ordered=False):
r"""Generate random model parameters.
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Parameters
----------
n_items : int
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r"""Generate random model parameters.
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lucasmaystre/choix | choix/utils.py | generate_pairwise | def generate_pairwise(params, n_comparisons=10):
"""Generate pairwise comparisons from a Bradley--Terry model.
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corresponding comparison outcomes from a Bradley-... | python | def generate_pairwise(params, n_comparisons=10):
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lucasmaystre/choix | choix/utils.py | generate_rankings | def generate_rankings(params, n_rankings, size=3):
"""Generate rankings according to a Plackett--Luce model.
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uniformly at random, and samples the correspoding partial ranking from a
Plackett--Luce model parametrized by ``params... | python | def generate_rankings(params, n_rankings, size=3):
"""Generate rankings according to a Plackett--Luce model.
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uniformly at random, and samples the correspoding partial ranking from a
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lucasmaystre/choix | choix/utils.py | compare | def compare(items, params, rank=False):
"""Generate a comparison outcome that follows Luce's axiom.
This function samples an outcome for the comparison of a subset of items,
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"""Generate a comparison outcome that follows Luce's axiom.
This function samples an outcome for the comparison of a subset of items,
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lucasmaystre/choix | choix/utils.py | probabilities | def probabilities(items, params):
"""Compute the comparison outcome probabilities given a subset of items.
This function computes, for each item in ``items``, the probability that it
would win (i.e., be chosen) in a comparison involving the items, given
model parameters.
Parameters
----------
... | python | def probabilities(items, params):
"""Compute the comparison outcome probabilities given a subset of items.
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lucasmaystre/choix | choix/opt.py | opt_pairwise | def opt_pairwise(n_items, data, alpha=1e-6, method="Newton-CG",
initial_params=None, max_iter=None, tol=1e-5):
"""Compute the ML estimate of model parameters using ``scipy.optimize``.
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lucasmaystre/choix | choix/opt.py | opt_rankings | def opt_rankings(n_items, data, alpha=1e-6, method="Newton-CG",
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initial_params=None, max_iter=None, tol=1e-5):
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lucasmaystre/choix | choix/opt.py | opt_top1 | def opt_top1(n_items, data, alpha=1e-6, method="Newton-CG",
initial_params=None, max_iter=None, tol=1e-5):
"""Compute the ML estimate of model parameters using ``scipy.optimize``.
This function computes the maximum-likelihood estimate of model parameters
given top-1 data (see :ref:`data-top1`), usi... | python | def opt_top1(n_items, data, alpha=1e-6, method="Newton-CG",
initial_params=None, max_iter=None, tol=1e-5):
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lucasmaystre/choix | choix/opt.py | PairwiseFcts.objective | def objective(self, params):
"""Compute the negative penalized log-likelihood."""
val = self._penalty * np.sum(params**2)
for win, los in self._data:
val += np.logaddexp(0, -(params[win] - params[los]))
return val | python | def objective(self, params):
"""Compute the negative penalized log-likelihood."""
val = self._penalty * np.sum(params**2)
for win, los in self._data:
val += np.logaddexp(0, -(params[win] - params[los]))
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lucasmaystre/choix | choix/opt.py | Top1Fcts.from_rankings | def from_rankings(cls, data, penalty):
"""Alternative constructor for ranking data."""
top1 = list()
for ranking in data:
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top1.append((winner, ranking[i+1:]))
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"""Alternative constructor for ranking data."""
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lucasmaystre/choix | choix/opt.py | Top1Fcts.objective | def objective(self, params):
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lucasmaystre/choix | choix/mm.py | _mm | def _mm(n_items, data, initial_params, alpha, max_iter, tol, mm_fun):
"""
Iteratively refine MM estimates until convergence.
Raises
------
RuntimeError
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"""
if initial_params is None:
params = np.zeros(n_items)
... | python | def _mm(n_items, data, initial_params, alpha, max_iter, tol, mm_fun):
"""
Iteratively refine MM estimates until convergence.
Raises
------
RuntimeError
If the algorithm does not converge after `max_iter` iterations.
"""
if initial_params is None:
params = np.zeros(n_items)
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lucasmaystre/choix | choix/mm.py | _mm_pairwise | def _mm_pairwise(n_items, data, params):
"""Inner loop of MM algorithm for pairwise data."""
weights = exp_transform(params)
wins = np.zeros(n_items, dtype=float)
denoms = np.zeros(n_items, dtype=float)
for winner, loser in data:
wins[winner] += 1.0
val = 1.0 / (weights[winner] + wei... | python | def _mm_pairwise(n_items, data, params):
"""Inner loop of MM algorithm for pairwise data."""
weights = exp_transform(params)
wins = np.zeros(n_items, dtype=float)
denoms = np.zeros(n_items, dtype=float)
for winner, loser in data:
wins[winner] += 1.0
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lucasmaystre/choix | choix/mm.py | mm_pairwise | def mm_pairwise(
n_items, data, initial_params=None, alpha=0.0,
max_iter=10000, tol=1e-8):
"""Compute the ML estimate of model parameters using the MM algorithm.
This function computes the maximum-likelihood (ML) estimate of model
parameters given pairwise-comparison data (see :ref:`data-pa... | python | def mm_pairwise(
n_items, data, initial_params=None, alpha=0.0,
max_iter=10000, tol=1e-8):
"""Compute the ML estimate of model parameters using the MM algorithm.
This function computes the maximum-likelihood (ML) estimate of model
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lucasmaystre/choix | choix/mm.py | _mm_rankings | def _mm_rankings(n_items, data, params):
"""Inner loop of MM algorithm for ranking data."""
weights = exp_transform(params)
wins = np.zeros(n_items, dtype=float)
denoms = np.zeros(n_items, dtype=float)
for ranking in data:
sum_ = weights.take(ranking).sum()
for i, winner in enumerate... | python | def _mm_rankings(n_items, data, params):
"""Inner loop of MM algorithm for ranking data."""
weights = exp_transform(params)
wins = np.zeros(n_items, dtype=float)
denoms = np.zeros(n_items, dtype=float)
for ranking in data:
sum_ = weights.take(ranking).sum()
for i, winner in enumerate... | [
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lucasmaystre/choix | choix/mm.py | mm_rankings | def mm_rankings(n_items, data, initial_params=None, alpha=0.0,
max_iter=10000, tol=1e-8):
"""Compute the ML estimate of model parameters using the MM algorithm.
This function computes the maximum-likelihood (ML) estimate of model
parameters given ranking data (see :ref:`data-rankings`), using the
... | python | def mm_rankings(n_items, data, initial_params=None, alpha=0.0,
max_iter=10000, tol=1e-8):
"""Compute the ML estimate of model parameters using the MM algorithm.
This function computes the maximum-likelihood (ML) estimate of model
parameters given ranking data (see :ref:`data-rankings`), using the
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lucasmaystre/choix | choix/mm.py | _mm_top1 | def _mm_top1(n_items, data, params):
"""Inner loop of MM algorithm for top1 data."""
weights = exp_transform(params)
wins = np.zeros(n_items, dtype=float)
denoms = np.zeros(n_items, dtype=float)
for winner, losers in data:
wins[winner] += 1
val = 1 / (weights.take(losers).sum() + wei... | python | def _mm_top1(n_items, data, params):
"""Inner loop of MM algorithm for top1 data."""
weights = exp_transform(params)
wins = np.zeros(n_items, dtype=float)
denoms = np.zeros(n_items, dtype=float)
for winner, losers in data:
wins[winner] += 1
val = 1 / (weights.take(losers).sum() + wei... | [
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lucasmaystre/choix | choix/mm.py | mm_top1 | def mm_top1(
n_items, data, initial_params=None, alpha=0.0,
max_iter=10000, tol=1e-8):
"""Compute the ML estimate of model parameters using the MM algorithm.
This function computes the maximum-likelihood (ML) estimate of model
parameters given top-1 data (see :ref:`data-top1`), using the
... | python | def mm_top1(
n_items, data, initial_params=None, alpha=0.0,
max_iter=10000, tol=1e-8):
"""Compute the ML estimate of model parameters using the MM algorithm.
This function computes the maximum-likelihood (ML) estimate of model
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minorization-maximization (MM) algorithm [Hun04]_, [CD12]_.
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lucasmaystre/choix | choix/mm.py | _choicerank | def _choicerank(n_items, data, params):
"""Inner loop of ChoiceRank algorithm."""
weights = exp_transform(params)
adj, adj_t, traffic_in, traffic_out = data
# First phase of message passing.
zs = adj.dot(weights)
# Second phase of message passing.
with np.errstate(invalid="ignore"):
... | python | def _choicerank(n_items, data, params):
"""Inner loop of ChoiceRank algorithm."""
weights = exp_transform(params)
adj, adj_t, traffic_in, traffic_out = data
# First phase of message passing.
zs = adj.dot(weights)
# Second phase of message passing.
with np.errstate(invalid="ignore"):
... | [
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