code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def about_html(self):
"""Get the html for the /about page.
Currently unused for any functionality.
:rtype: str
"""
if self._about_html:
return self._about_html
else:
self._about_html = request.get(self.about_url)
return self._about_ht... | Get the html for the /about page.
Currently unused for any functionality.
:rtype: str
| about_html | python | pytube/pytube | pytube/contrib/channel.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/channel.py | Unlicense |
def _extract_videos(raw_json: str) -> Tuple[List[str], Optional[str]]:
"""Extracts videos from a raw json page
:param str raw_json: Input json extracted from the page or the last
server response
:rtype: Tuple[List[str], Optional[str]]
:returns: Tuple containing a list of up ... | Extracts videos from a raw json page
:param str raw_json: Input json extracted from the page or the last
server response
:rtype: Tuple[List[str], Optional[str]]
:returns: Tuple containing a list of up to 100 video watch ids and
a continuation token, if more videos are av... | _extract_videos | python | pytube/pytube | pytube/contrib/channel.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/channel.py | Unlicense |
def playlist_id(self):
"""Get the playlist id.
:rtype: str
"""
if self._playlist_id:
return self._playlist_id
self._playlist_id = extract.playlist_id(self._input_url)
return self._playlist_id | Get the playlist id.
:rtype: str
| playlist_id | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def html(self):
"""Get the playlist page html.
:rtype: str
"""
if self._html:
return self._html
self._html = request.get(self.playlist_url)
return self._html | Get the playlist page html.
:rtype: str
| html | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def ytcfg(self):
"""Extract the ytcfg from the playlist page html.
:rtype: dict
"""
if self._ytcfg:
return self._ytcfg
self._ytcfg = extract.get_ytcfg(self.html)
return self._ytcfg | Extract the ytcfg from the playlist page html.
:rtype: dict
| ytcfg | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def initial_data(self):
"""Extract the initial data from the playlist page html.
:rtype: dict
"""
if self._initial_data:
return self._initial_data
else:
self._initial_data = extract.initial_data(self.html)
return self._initial_data | Extract the initial data from the playlist page html.
:rtype: dict
| initial_data | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def sidebar_info(self):
"""Extract the sidebar info from the playlist page html.
:rtype: dict
"""
if self._sidebar_info:
return self._sidebar_info
else:
self._sidebar_info = self.initial_data['sidebar'][
'playlistSidebarRenderer']['items']... | Extract the sidebar info from the playlist page html.
:rtype: dict
| sidebar_info | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def _paginate(
self, until_watch_id: Optional[str] = None
) -> Iterable[List[str]]:
"""Parse the video links from the page source, yields the /watch?v=
part from video link
:param until_watch_id Optional[str]: YouTube Video watch id until
which the playlist should be rea... | Parse the video links from the page source, yields the /watch?v=
part from video link
:param until_watch_id Optional[str]: YouTube Video watch id until
which the playlist should be read.
:rtype: Iterable[List[str]]
:returns: Iterable of lists of YouTube watch ids
| _paginate | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def _build_continuation_url(self, continuation: str) -> Tuple[str, dict, dict]:
"""Helper method to build the url and headers required to request
the next page of videos
:param str continuation: Continuation extracted from the json response
of the last page
:rtype: Tuple[str... | Helper method to build the url and headers required to request
the next page of videos
:param str continuation: Continuation extracted from the json response
of the last page
:rtype: Tuple[str, dict, dict]
:returns: Tuple of an url and required headers for the next http
... | _build_continuation_url | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def _extract_videos(raw_json: str) -> Tuple[List[str], Optional[str]]:
"""Extracts videos from a raw json page
:param str raw_json: Input json extracted from the page or the last
server response
:rtype: Tuple[List[str], Optional[str]]
:returns: Tuple containing a list of up ... | Extracts videos from a raw json page
:param str raw_json: Input json extracted from the page or the last
server response
:rtype: Tuple[List[str], Optional[str]]
:returns: Tuple containing a list of up to 100 video watch ids and
a continuation token, if more videos are av... | _extract_videos | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def trimmed(self, video_id: str) -> Iterable[str]:
"""Retrieve a list of YouTube video URLs trimmed at the given video ID
i.e. if the playlist has video IDs 1,2,3,4 calling trimmed(3) returns
[1,2]
:type video_id: str
video ID to trim the returned list of playlist URLs at
... | Retrieve a list of YouTube video URLs trimmed at the given video ID
i.e. if the playlist has video IDs 1,2,3,4 calling trimmed(3) returns
[1,2]
:type video_id: str
video ID to trim the returned list of playlist URLs at
:rtype: List[str]
:returns:
List of ... | trimmed | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def url_generator(self):
"""Generator that yields video URLs.
:Yields: Video URLs
"""
for page in self._paginate():
for video in page:
yield self._video_url(video) | Generator that yields video URLs.
:Yields: Video URLs
| url_generator | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def last_updated(self) -> Optional[date]:
"""Extract the date that the playlist was last updated.
For some playlists, this will be a specific date, which is returned as a datetime
object. For other playlists, this is an estimate such as "1 week ago". Due to the
fact that this value is r... | Extract the date that the playlist was last updated.
For some playlists, this will be a specific date, which is returned as a datetime
object. For other playlists, this is an estimate such as "1 week ago". Due to the
fact that this value is returned as a string, pytube does a best-effort parsin... | last_updated | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def length(self):
"""Extract the number of videos in the playlist.
:return: Playlist video count
:rtype: int
"""
count_text = self.sidebar_info[0]['playlistSidebarPrimaryInfoRenderer'][
'stats'][0]['runs'][0]['text']
count_text = count_text.replace(',','')
... | Extract the number of videos in the playlist.
:return: Playlist video count
:rtype: int
| length | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def views(self):
"""Extract view count for playlist.
:return: Playlist view count
:rtype: int
"""
# "1,234,567 views"
views_text = self.sidebar_info[0]['playlistSidebarPrimaryInfoRenderer'][
'stats'][1]['simpleText']
# "1,234,567"
count_text =... | Extract view count for playlist.
:return: Playlist view count
:rtype: int
| views | python | pytube/pytube | pytube/contrib/playlist.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/playlist.py | Unlicense |
def __init__(self, query):
"""Initialize Search object.
:param str query:
Search query provided by the user.
"""
self.query = query
self._innertube_client = InnerTube(client='WEB')
# The first search, without a continuation, is structured differently
... | Initialize Search object.
:param str query:
Search query provided by the user.
| __init__ | python | pytube/pytube | pytube/contrib/search.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/search.py | Unlicense |
def completion_suggestions(self):
"""Return query autocompletion suggestions for the query.
:rtype: list
:returns:
A list of autocomplete suggestions provided by YouTube for the query.
"""
if self._completion_suggestions:
return self._completion_suggestio... | Return query autocompletion suggestions for the query.
:rtype: list
:returns:
A list of autocomplete suggestions provided by YouTube for the query.
| completion_suggestions | python | pytube/pytube | pytube/contrib/search.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/search.py | Unlicense |
def results(self):
"""Return search results.
On first call, will generate and return the first set of results.
Additional results can be generated using ``.get_next_results()``.
:rtype: list
:returns:
A list of YouTube objects.
"""
if self._results:
... | Return search results.
On first call, will generate and return the first set of results.
Additional results can be generated using ``.get_next_results()``.
:rtype: list
:returns:
A list of YouTube objects.
| results | python | pytube/pytube | pytube/contrib/search.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/search.py | Unlicense |
def get_next_results(self):
"""Use the stored continuation string to fetch the next set of results.
This method does not return the results, but instead updates the results property.
"""
if self._current_continuation:
videos, continuation = self.fetch_and_parse(self._current... | Use the stored continuation string to fetch the next set of results.
This method does not return the results, but instead updates the results property.
| get_next_results | python | pytube/pytube | pytube/contrib/search.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/search.py | Unlicense |
def fetch_and_parse(self, continuation=None):
"""Fetch from the innertube API and parse the results.
:param str continuation:
Continuation string for fetching results.
:rtype: tuple
:returns:
A tuple of a list of YouTube objects and a continuation string.
... | Fetch from the innertube API and parse the results.
:param str continuation:
Continuation string for fetching results.
:rtype: tuple
:returns:
A tuple of a list of YouTube objects and a continuation string.
| fetch_and_parse | python | pytube/pytube | pytube/contrib/search.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/search.py | Unlicense |
def fetch_query(self, continuation=None):
"""Fetch raw results from the innertube API.
:param str continuation:
Continuation string for fetching results.
:rtype: dict
:returns:
The raw json object returned by the innertube API.
"""
query_results =... | Fetch raw results from the innertube API.
:param str continuation:
Continuation string for fetching results.
:rtype: dict
:returns:
The raw json object returned by the innertube API.
| fetch_query | python | pytube/pytube | pytube/contrib/search.py | https://github.com/pytube/pytube/blob/master/pytube/contrib/search.py | Unlicense |
def load_and_init_from_playback_file(filename, mock_urlopen):
"""Load a gzip json playback file and create YouTube instance."""
pb = load_playback_file(filename)
# Mock the responses to YouTube
mock_url_open_object = mock.Mock()
mock_url_open_object.read.side_effect = [
pb['watch_html'].enc... | Load a gzip json playback file and create YouTube instance. | load_and_init_from_playback_file | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def playlist_html():
"""Youtube playlist HTML loaded on 2020-01-25 from
https://www.youtube.com/playlist?list=PLzMcBGfZo4-mP7qA9cagf68V06sko5otr
"""
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"mocks",
"playlist.html.gz",
)
with gzip.open(file_p... | Youtube playlist HTML loaded on 2020-01-25 from
https://www.youtube.com/playlist?list=PLzMcBGfZo4-mP7qA9cagf68V06sko5otr
| playlist_html | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def playlist_long_html():
"""Youtube playlist HTML loaded on 2020-01-25 from
https://www.youtube.com/playlist?list=PLzMcBGfZo4-mP7qA9cagf68V06sko5otr
"""
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"mocks",
"playlist_long.html.gz",
)
with gzip.o... | Youtube playlist HTML loaded on 2020-01-25 from
https://www.youtube.com/playlist?list=PLzMcBGfZo4-mP7qA9cagf68V06sko5otr
| playlist_long_html | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def playlist_submenu_html():
"""Youtube playlist HTML loaded on 2020-01-24 from
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
"""
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"mocks",
"playlist_submenu.html.gz",
)
with ... | Youtube playlist HTML loaded on 2020-01-24 from
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
| playlist_submenu_html | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def stream_dict():
"""Youtube instance initialized with video id WXxV9g7lsFE."""
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"mocks",
"yt-video-WXxV9g7lsFE-html.json.gz",
)
with gzip.open(file_path, "rb") as f:
content = json.loads(f.read().deco... | Youtube instance initialized with video id WXxV9g7lsFE. | stream_dict | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def channel_videos_html():
"""Youtube channel HTML loaded on 2021-05-05 from
https://www.youtube.com/c/ProgrammingKnowledge/videos
"""
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"mocks",
"channel-videos.html.gz",
)
with gzip.open(file_path, 'rb... | Youtube channel HTML loaded on 2021-05-05 from
https://www.youtube.com/c/ProgrammingKnowledge/videos
| channel_videos_html | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def base_js():
"""Youtube base.js files retrieved on 2022-02-04 and 2022-04-15
from https://www.youtube.com/watch?v=vmzxpUsN0uA and
https://www.youtube.com/watch?v=Y4-GSFKZmEg respectively
"""
base_js_files = []
for file in ["base.js-2022-02-04.gz", "base.js-2022-04-15.gz"]:
file_path = ... | Youtube base.js files retrieved on 2022-02-04 and 2022-04-15
from https://www.youtube.com/watch?v=vmzxpUsN0uA and
https://www.youtube.com/watch?v=Y4-GSFKZmEg respectively
| base_js | python | pytube/pytube | tests/conftest.py | https://github.com/pytube/pytube/blob/master/tests/conftest.py | Unlicense |
def test_safe_filename():
"""Unsafe characters get stripped from generated filename"""
assert helpers.safe_filename("abc1245$$") == "abc1245"
assert helpers.safe_filename("abc##") == "abc" | Unsafe characters get stripped from generated filename | test_safe_filename | python | pytube/pytube | tests/test_helpers.py | https://github.com/pytube/pytube/blob/master/tests/test_helpers.py | Unlicense |
def test_filters(test_input, expected, cipher_signature):
"""Ensure filters produce the expected results."""
result = [s.itag for s in cipher_signature.streams.filter(**test_input)]
assert result == expected | Ensure filters produce the expected results. | test_filters | python | pytube/pytube | tests/test_query.py | https://github.com/pytube/pytube/blob/master/tests/test_query.py | Unlicense |
def test_empty(test_input, cipher_signature):
"""Ensure :meth:`~pytube.StreamQuery.last` and
:meth:`~pytube.StreamQuery.first` return None if the resultset is
empty.
"""
query = cipher_signature.streams.filter(video_codec="vp20")
fn = getattr(query, test_input)
assert fn() is None | Ensure :meth:`~pytube.StreamQuery.last` and
:meth:`~pytube.StreamQuery.first` return None if the resultset is
empty.
| test_empty | python | pytube/pytube | tests/test_query.py | https://github.com/pytube/pytube/blob/master/tests/test_query.py | Unlicense |
def test_order_by(cipher_signature):
"""Ensure :meth:`~pytube.StreamQuery.order_by` sorts the list of
:class:`Stream <Stream>` instances in the expected order.
"""
itags = [
s.itag
for s in cipher_signature.streams.filter(type="audio").order_by("itag")
]
expected_itags = [
... | Ensure :meth:`~pytube.StreamQuery.order_by` sorts the list of
:class:`Stream <Stream>` instances in the expected order.
| test_order_by | python | pytube/pytube | tests/test_query.py | https://github.com/pytube/pytube/blob/master/tests/test_query.py | Unlicense |
def test_order_by_descending(cipher_signature):
"""Ensure :meth:`~pytube.StreamQuery.desc` sorts the list of
:class:`Stream <Stream>` instances in the reverse order.
"""
# numerical values
itags = [
s.itag
for s in cipher_signature.streams.filter(type="audio")
.order_by("itag... | Ensure :meth:`~pytube.StreamQuery.desc` sorts the list of
:class:`Stream <Stream>` instances in the reverse order.
| test_order_by_descending | python | pytube/pytube | tests/test_query.py | https://github.com/pytube/pytube/blob/master/tests/test_query.py | Unlicense |
def test_order_by_ascending(cipher_signature):
"""Ensure :meth:`~pytube.StreamQuery.desc` sorts the list of
:class:`Stream <Stream>` instances in ascending order.
"""
# numerical values
itags = [
s.itag
for s in cipher_signature.streams.filter(type="audio")
.order_by("itag")
... | Ensure :meth:`~pytube.StreamQuery.desc` sorts the list of
:class:`Stream <Stream>` instances in ascending order.
| test_order_by_ascending | python | pytube/pytube | tests/test_query.py | https://github.com/pytube/pytube/blob/master/tests/test_query.py | Unlicense |
def prepare_docstring(s):
"""
Convert a docstring into lines of parseable reST. Return it as a list of
lines usable for inserting into a docutils ViewList (used as argument
of nested_parse().) An empty line is added to act as a separator between
this docstring and following content.
"""
if... |
Convert a docstring into lines of parseable reST. Return it as a list of
lines usable for inserting into a docutils ViewList (used as argument
of nested_parse().) An empty line is added to act as a separator between
this docstring and following content.
| prepare_docstring | python | pybrain/pybrain | docs/sphinx/autodoc_hack.py | https://github.com/pybrain/pybrain/blob/master/docs/sphinx/autodoc_hack.py | BSD-3-Clause |
def performAction(self, action):
"""Incoming action is an int between 0 and 8. The action we provide to
the environment consists of a torque T in {-2 N, 0, 2 N}, and a
displacement d in {-.02 m, 0, 0.02 m}.
"""
self.t += 1
assert round(action[0]) == action[0]
# ... | Incoming action is an int between 0 and 8. The action we provide to
the environment consists of a torque T in {-2 N, 0, 2 N}, and a
displacement d in {-.02 m, 0, 0.02 m}.
| performAction | python | pybrain/pybrain | examples/rl/environments/linear_fa/bicycle.py | https://github.com/pybrain/pybrain/blob/master/examples/rl/environments/linear_fa/bicycle.py | BSD-3-Clause |
def evalRnnOnSeqDataset(net, DS, verbose = False, silent = False):
""" evaluate the network on all the sequences of a dataset. """
r = 0.
samples = 0.
for seq in DS:
net.reset()
for i, t in seq:
res = net.activate(i)
if verbose:
print(t, res)
... | evaluate the network on all the sequences of a dataset. | evalRnnOnSeqDataset | python | pybrain/pybrain | examples/supervised/backprop/parityrnn.py | https://github.com/pybrain/pybrain/blob/master/examples/supervised/backprop/parityrnn.py | BSD-3-Clause |
def multigaussian(x, mean, stddev):
"""Returns value of uncorrelated Gaussians at given scalar point.
x: scalar
mean: vector
stddev: vector
"""
tmp = -0.5 * ((x-mean)/stddev)**2
return np.exp(tmp) / (np.sqrt(2.*np.pi) * stddev) | Returns value of uncorrelated Gaussians at given scalar point.
x: scalar
mean: vector
stddev: vector
| multigaussian | python | pybrain/pybrain | examples/supervised/neuralnets+svm/example_mixturedensity.py | https://github.com/pybrain/pybrain/blob/master/examples/supervised/neuralnets+svm/example_mixturedensity.py | BSD-3-Clause |
def generateClassificationData(size, nClasses=3):
""" generate a set of points in 2D belonging to two or three different classes """
if nClasses==3:
means = [(-1,0),(2,4),(3,1)]
else:
means = [(-2,0),(2,1),(6,0)]
cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7])]
dataset = Classi... | generate a set of points in 2D belonging to two or three different classes | generateClassificationData | python | pybrain/pybrain | examples/supervised/neuralnets+svm/datasets/datagenerator.py | https://github.com/pybrain/pybrain/blob/master/examples/supervised/neuralnets+svm/datasets/datagenerator.py | BSD-3-Clause |
def generateGridData(x,y, return_ticks=False):
""" Generates a dataset containing a regular grid of points. The x and y arguments
contain start, end, and step each. Returns the dataset and the x and y mesh or ticks."""
x = np.arange(x[0], x[1], x[2])
y = np.arange(y[0], y[1], y[2])
X, Y = np.meshgri... | Generates a dataset containing a regular grid of points. The x and y arguments
contain start, end, and step each. Returns the dataset and the x and y mesh or ticks. | generateGridData | python | pybrain/pybrain | examples/supervised/neuralnets+svm/datasets/datagenerator.py | https://github.com/pybrain/pybrain/blob/master/examples/supervised/neuralnets+svm/datasets/datagenerator.py | BSD-3-Clause |
def generateNoisySines( npoints, nseq, noise=0.3 ):
""" construct a 2-class dataset out of noisy sines """
x = np.arange(npoints)/float(npoints) * 20.
y1 = np.sin(x+rand(1)*3.)
y2 = np.sin(x/2.+rand(1)*3.)
DS = SequenceClassificationDataSet(1,1, nb_classes=2)
for _ in range(nseq):
DS.new... | construct a 2-class dataset out of noisy sines | generateNoisySines | python | pybrain/pybrain | examples/supervised/neuralnets+svm/datasets/datagenerator.py | https://github.com/pybrain/pybrain/blob/master/examples/supervised/neuralnets+svm/datasets/datagenerator.py | BSD-3-Clause |
def makeData(amount = 10000):
"""Return 2D dataset of points in (0, 1) where points in a circle of
radius .4 around the center are blue and all the others are red."""
center = array([0.5, 0.5])
def makePoint():
"""Return a random point and its satellite information.
Satellite is 'blue'... | Return 2D dataset of points in (0, 1) where points in a circle of
radius .4 around the center are blue and all the others are red. | makeData | python | pybrain/pybrain | examples/unsupervised/lsh.py | https://github.com/pybrain/pybrain/blob/master/examples/unsupervised/lsh.py | BSD-3-Clause |
def makePoint():
"""Return a random point and its satellite information.
Satellite is 'blue' if point is in the circle, else 'red'."""
point = random.random((2,)) * 10
vectorLength = lambda x: dot(x.T, x)
return point, 'blue' if vectorLength(point - center) < 25 else 'red' | Return a random point and its satellite information.
Satellite is 'blue' if point is in the circle, else 'red'. | makePoint | python | pybrain/pybrain | examples/unsupervised/lsh.py | https://github.com/pybrain/pybrain/blob/master/examples/unsupervised/lsh.py | BSD-3-Clause |
def drawIndex(probs, tolerant=False):
""" Draws an index given an array of probabilities.
:key tolerant: if set to True, the array is normalized to sum to 1. """
if not sum(probs) < 1.00001 or not sum(probs) > 0.99999:
if tolerant:
probs /= sum(probs)
else:
print((p... | Draws an index given an array of probabilities.
:key tolerant: if set to True, the array is normalized to sum to 1. | drawIndex | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def drawGibbs(vals, temperature=1.):
""" Return the index of the sample drawn by a softmax (Gibbs). """
if temperature == 0:
# randomly pick one of the values with the max value.
m = max(vals)
best = []
for i, v in enumerate(vals):
if v == m:
best.appe... | Return the index of the sample drawn by a softmax (Gibbs). | drawGibbs | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def iterCombinations(tup):
""" all possible of integer tuples of the same dimension than tup, and each component being
positive and strictly inferior to the corresponding entry in tup. """
if len(tup) == 1:
for i in range(tup[0]):
yield (i,)
elif len(tup) > 1:
for prefix in i... | all possible of integer tuples of the same dimension than tup, and each component being
positive and strictly inferior to the corresponding entry in tup. | iterCombinations | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def setAllArgs(obj, argdict):
""" set all those internal variables which have the same name than an entry in the
given object's dictionary.
This function can be useful for quick initializations. """
xmlstore = isinstance(obj, XMLBuildable)
for n in list(argdict.keys()):
if hasattr(obj, n):
... | set all those internal variables which have the same name than an entry in the
given object's dictionary.
This function can be useful for quick initializations. | setAllArgs | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def percentError(out, true):
""" return percentage of mismatch between out and target values (lists and arrays accepted) """
arrout = array(out).flatten()
wrong = where(arrout != array(true).flatten())[0].size
return 100. * float(wrong) / float(arrout.size) | return percentage of mismatch between out and target values (lists and arrays accepted) | percentError | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def formatFromExtension(fname):
"""Tries to infer a protocol from the file extension."""
_base, ext = os.path.splitext(fname)
if not ext:
return None
try:
format = known_extensions[ext.replace('.', '')]
except KeyError:
format = None
return format | Tries to infer a protocol from the file extension. | formatFromExtension | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def saveToFileLike(self, flo, format=None, **kwargs):
"""Save the object to a given file like object in the given format.
"""
format = 'pickle' if format is None else format
save = getattr(self, "save_%s" % format, None)
if save is None:
raise ValueError("Unknown form... | Save the object to a given file like object in the given format.
| saveToFileLike | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def loadFromFileLike(cls, flo, format=None):
"""Load the object to a given file like object with the given protocol.
"""
format = 'pickle' if format is None else format
load = getattr(cls, "load_%s" % format, None)
if load is None:
raise ValueError("Unknown format '%s... | Load the object to a given file like object with the given protocol.
| loadFromFileLike | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def saveToFile(self, filename, format=None, **kwargs):
"""Save the object to file given by filename."""
if format is None:
# try to derive protocol from file extension
format = formatFromExtension(filename)
with open(filename, 'wb') as fp:
self.saveToFileLike(... | Save the object to file given by filename. | saveToFile | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def loadFromFile(cls, filename, format=None):
"""Return an instance of the class that is saved in the file with the
given filename in the specified format."""
if format is None:
# try to derive protocol from file extension
format = formatFromExtension(filename)
wi... | Return an instance of the class that is saved in the file with the
given filename in the specified format. | loadFromFile | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def _getName(self):
"""Returns the name, which is generated if it has not been already."""
if self._name is None:
self._name = self._generateName()
return self._name | Returns the name, which is generated if it has not been already. | _getName | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def fListToString(a_list, a_precision=3):
""" Returns a string representing a list of floats with a given precision """
from numpy import around
s_list = ", ".join(("%g" % around(x, a_precision)).ljust(a_precision+3)
for x in a_list)
return "[%s]" % s_list | Returns a string representing a list of floats with a given precision | fListToString | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def tupleRemoveItem(tup, index):
""" remove the item at position index of the tuple and return a new tuple. """
l = list(tup)
return tuple(l[:index] + l[index + 1:]) | remove the item at position index of the tuple and return a new tuple. | tupleRemoveItem | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def confidenceIntervalSize(stdev, nbsamples):
""" Determine the size of the confidence interval, given the standard deviation and the number of samples.
t-test-percentile: 97.5%, infinitely many degrees of freedom,
therefore on the two-sided interval: 95% """
# CHECKME: for better precision, maybe get t... | Determine the size of the confidence interval, given the standard deviation and the number of samples.
t-test-percentile: 97.5%, infinitely many degrees of freedom,
therefore on the two-sided interval: 95% | confidenceIntervalSize | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def threaded(callback=lambda * args, **kwargs: None, daemonic=False):
"""Decorate a function to run in its own thread and report the result
by calling callback with it."""
def innerDecorator(func):
def inner(*args, **kwargs):
target = lambda: callback(func(*args, **kwargs))
... | Decorate a function to run in its own thread and report the result
by calling callback with it. | threaded | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def garbagecollect(func):
"""Decorate a function to invoke the garbage collector after each execution.
"""
def inner(*args, **kwargs):
result = func(*args, **kwargs)
gc.collect()
return result
return inner | Decorate a function to invoke the garbage collector after each execution.
| garbagecollect | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def memoize(func):
"""Decorate a function to 'memoize' results by holding it in a cache that
maps call arguments to returns."""
cache = {}
def inner(*args, **kwargs):
# Dictionaries and lists are unhashable
args = tuple(args)
# Make a set for checking in the cache, since the orde... | Decorate a function to 'memoize' results by holding it in a cache that
maps call arguments to returns. | memoize | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def storeCallResults(obj, verbose=False):
"""Pseudo-decorate an object to store all evaluations of the function in the returned list."""
results = []
oldcall = obj.__class__.__call__
def newcall(*args, **kwargs):
result = oldcall(*args, **kwargs)
results.append(result)
if verbose... | Pseudo-decorate an object to store all evaluations of the function in the returned list. | storeCallResults | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def multiEvaluate(repeat):
"""Decorate a function to evaluate repeatedly with the same arguments, and return the average result """
def decorator(func):
def inner(*args, **kwargs):
result = 0.
for dummy in range(repeat):
result += func(*args, **kwargs)
... | Decorate a function to evaluate repeatedly with the same arguments, and return the average result | multiEvaluate | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def _import(name):
"""Return module from a package.
These two are equivalent:
> from package import module as bar
> bar = _import('package.module')
"""
mod = __import__(name)
components = name.split('.')
for comp in components[1:]:
try:
mod = getattr(mod, c... | Return module from a package.
These two are equivalent:
> from package import module as bar
> bar = _import('package.module')
| _import | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def gray2int(g, size):
""" Transforms a Gray code back into an integer. """
res = 0
for i in reversed(list(range(size))):
gi = (g >> i) % 2
if i == size - 1:
bi = gi
else:
bi = bi ^ gi
res += bi * 2 ** i
return res | Transforms a Gray code back into an integer. | gray2int | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def asBinary(i):
""" Produces a string from an integer's binary representation.
(preceding zeros removed). """
if i > 1:
if i % 2 == 1:
return asBinary(i >> 1) + '1'
else:
return asBinary(i >> 1) + '0'
else:
return str(i) | Produces a string from an integer's binary representation.
(preceding zeros removed). | asBinary | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def one_to_n(val, maxval):
""" Returns a 1-in-n binary encoding of a non-negative integer. """
a = zeros(maxval, float)
a[val] = 1.
return a | Returns a 1-in-n binary encoding of a non-negative integer. | one_to_n | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def canonicClassString(x):
""" the __class__ attribute changed from old-style to new-style classes... """
if isinstance(x, object):
return repr(x.__class__).split("'")[1]
else:
return repr(x.__class__) | the __class__ attribute changed from old-style to new-style classes... | canonicClassString | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def decrementAny(tup):
""" the closest tuples to tup: decrementing by 1 along any dimension.
Never go into negatives though. """
res = []
for i, x in enumerate(tup):
if x > 0:
res.append(tuple(list(tup[:i]) + [x - 1] + list(tup[i + 1:])))
return res | the closest tuples to tup: decrementing by 1 along any dimension.
Never go into negatives though. | decrementAny | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def reachable(stepFunction, start, destinations, _alreadyseen=None):
""" Determines the subset of destinations that can be reached from a set of starting positions,
while using stepFunction (which produces a list of neighbor states) to navigate.
Uses breadth-first search.
Returns a dictionary with reach... | Determines the subset of destinations that can be reached from a set of starting positions,
while using stepFunction (which produces a list of neighbor states) to navigate.
Uses breadth-first search.
Returns a dictionary with reachable destinations and their distances.
| reachable | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def flood(stepFunction, fullSet, initSet, relevant=None):
""" Returns a list of elements of fullSet linked to some element of initSet
through the neighborhood-setFunction (which must be defined on all elements of fullSet).
:key relevant: (optional) list of relevant elements: stop once all relevant elements... | Returns a list of elements of fullSet linked to some element of initSet
through the neighborhood-setFunction (which must be defined on all elements of fullSet).
:key relevant: (optional) list of relevant elements: stop once all relevant elements are found.
| flood | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def crossproduct(ss, row=None, level=0):
"""Returns the cross-product of the sets given in `ss`."""
if row is None:
row = []
if len(ss) > 1:
return reduce(operator.add,
[crossproduct(ss[1:], row + [i], level + 1) for i in ss[0]])
else:
return [row + [i] for ... | Returns the cross-product of the sets given in `ss`. | crossproduct | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def permuteToBlocks(arr, blockshape):
"""Permute an array so that it consists of linearized blocks.
Example: A two-dimensional array of the form
0 1 2 3
4 5 6 7
8 9 10 11
12 13 14 15
would be turned into an array like this with (2, 2) blocks:
0 1 4 5 2 3 6... | Permute an array so that it consists of linearized blocks.
Example: A two-dimensional array of the form
0 1 2 3
4 5 6 7
8 9 10 11
12 13 14 15
would be turned into an array like this with (2, 2) blocks:
0 1 4 5 2 3 6 7 8 9 12 13 10 11 14 15
| permuteToBlocks | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def triu2flat(m):
""" Flattens an upper triangular matrix, returning a vector of the
non-zero elements. """
dim = m.shape[0]
res = zeros(dim * (dim + 1) / 2)
index = 0
for row in range(dim):
res[index:index + dim - row] = m[row, row:]
index += dim - row
return res | Flattens an upper triangular matrix, returning a vector of the
non-zero elements. | triu2flat | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def flat2triu(a, dim):
""" Produces an upper triangular matrix of dimension dim from the elements of the given vector. """
res = zeros((dim, dim))
index = 0
for row in range(dim):
res[row, row:] = a[index:index + dim - row]
index += dim - row
return res | Produces an upper triangular matrix of dimension dim from the elements of the given vector. | flat2triu | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def blockList2Matrix(l):
""" Converts a list of matrices into a corresponding big block-diagonal one. """
dims = [m.shape[0] for m in l]
s = sum(dims)
res = zeros((s, s))
index = 0
for i in range(len(l)):
d = dims[i]
m = l[i]
res[index:index + d, index:index + d] = m
... | Converts a list of matrices into a corresponding big block-diagonal one. | blockList2Matrix | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def blockCombine(l):
""" Produce a matrix from a list of lists of its components. """
l = [list(map(mat, row)) for row in l]
hdims = [m.shape[1] for m in l[0]]
hs = sum(hdims)
vdims = [row[0].shape[0] for row in l]
vs = sum(vdims)
res = zeros((hs, vs))
vindex = 0
for i, row in enumer... | Produce a matrix from a list of lists of its components. | blockCombine | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def avgFoundAfter(decreasingTargetValues, listsOfActualValues, batchSize=1, useMedian=False):
""" Determine the average number of steps to reach a certain value (for the first time),
given a list of value sequences.
If a value is not always encountered, the length of the longest sequence is used.
Return... | Determine the average number of steps to reach a certain value (for the first time),
given a list of value sequences.
If a value is not always encountered, the length of the longest sequence is used.
Returns an array. | avgFoundAfter | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def matchingDict(d, selection, require_existence=False):
""" Determines if the dictionary d conforms to the specified selection,
i.e. if a (key, x) is in the selection, then if key is in d as well it must be x
or contained in x (if x is a list). """
for k, v in list(selection.items()):
if k in d... | Determines if the dictionary d conforms to the specified selection,
i.e. if a (key, x) is in the selection, then if key is in d as well it must be x
or contained in x (if x is a list). | matchingDict | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def subDict(d, allowedkeys, flip=False):
""" Returns a new dictionary with a subset of the entries of d
that have on of the (dis-)allowed keys."""
res = {}
for k, v in list(d.items()):
if (k in allowedkeys) ^ flip:
res[k] = v
return res | Returns a new dictionary with a subset of the entries of d
that have on of the (dis-)allowed keys. | subDict | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def dictCombinations(listdict):
""" Iterates over dictionaries that go through every possible combination
of key-value pairs as specified in the lists of values for each key in listdict."""
listdict = listdict.copy()
if len(listdict) == 0:
return [{}]
k, vs = listdict.popitem()
res = dic... | Iterates over dictionaries that go through every possible combination
of key-value pairs as specified in the lists of values for each key in listdict. | dictCombinations | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def r_argmax(v):
""" Acts like scipy argmax, but break ties randomly. """
if len(v) == 1:
return 0
maxbid = max(v)
maxbidders = [i for (i, b) in enumerate(v) if b==maxbid]
return choice(maxbidders) | Acts like scipy argmax, but break ties randomly. | r_argmax | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def all_argmax(x):
""" Return the indices of all values that are equal to the maximum: no breaking ties. """
m = max(x)
return [i for i, v in enumerate(x) if v == m] | Return the indices of all values that are equal to the maximum: no breaking ties. | all_argmax | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def sparse_orth(d):
""" Constructs a sparse orthogonal matrix.
The method is described in:
Gi-Sang Cheon et al., Constructions for the sparsest orthogonal matrices,
Bull. Korean Math. Soc 36 (1999) No.1 pp.199-129
"""
from scipy.sparse import eye
from scipy import r_, pi, sin, cos
i... | Constructs a sparse orthogonal matrix.
The method is described in:
Gi-Sang Cheon et al., Constructions for the sparsest orthogonal matrices,
Bull. Korean Math. Soc 36 (1999) No.1 pp.199-129
| sparse_orth | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def binArr2int(arr):
""" Convert a binary array into its (long) integer representation. """
from numpy import packbits
tmp2 = packbits(arr.astype(int))
return sum(val * 256 ** i for i, val in enumerate(tmp2[::-1])) | Convert a binary array into its (long) integer representation. | binArr2int | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def seedit(seed=0):
""" Fixed seed makes for repeatability, but there may be two different
random number generators involved. """
import random
import numpy
random.seed(seed)
numpy.random.seed(seed) | Fixed seed makes for repeatability, but there may be two different
random number generators involved. | seedit | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def weightedUtest(g1, w1, g2, w2):
""" Determines the confidence level of the assertion:
'The values of g2 are higher than those of g1'.
(adapted from the scipy.stats version)
Twist: here the elements of each group have associated weights,
corresponding to how often they are present (i.e. tw... | Determines the confidence level of the assertion:
'The values of g2 are higher than those of g1'.
(adapted from the scipy.stats version)
Twist: here the elements of each group have associated weights,
corresponding to how often they are present (i.e. two identical entries with
weight w are... | weightedUtest | python | pybrain/pybrain | pybrain/utilities.py | https://github.com/pybrain/pybrain/blob/master/pybrain/utilities.py | BSD-3-Clause |
def __init__(self, indim, start=0, stop=1, step=0.1):
""" initializes the gaussian process object.
:arg indim: input dimension
:key start: start of interval for sampling the GP.
:key stop: stop of interval for sampling the GP.
:key step: stepsize for sampling int... | initializes the gaussian process object.
:arg indim: input dimension
:key start: start of interval for sampling the GP.
:key stop: stop of interval for sampling the GP.
:key step: stepsize for sampling interval.
:note: start, stop, step can either be scalars... | __init__ | python | pybrain/pybrain | pybrain/auxiliary/gaussprocess.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/gaussprocess.py | BSD-3-Clause |
def trainOnDataset(self, dataset):
""" takes a SequentialDataSet with indim input dimension and scalar target """
assert (dataset.getDimension('input') == self.indim)
assert (dataset.getDimension('target') == 1)
self.trainx = dataset.getField('input')
self.trainy = ravel(dataset... | takes a SequentialDataSet with indim input dimension and scalar target | trainOnDataset | python | pybrain/pybrain | pybrain/auxiliary/gaussprocess.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/gaussprocess.py | BSD-3-Clause |
def addDataset(self, dataset):
""" adds the points from the dataset to the training set """
assert (dataset.getDimension('input') == self.indim)
assert (dataset.getDimension('target') == 1)
self.trainx = r_[self.trainx, dataset.getField('input')]
self.trainy = r_[self.trainy, ra... | adds the points from the dataset to the training set | addDataset | python | pybrain/pybrain | pybrain/auxiliary/gaussprocess.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/gaussprocess.py | BSD-3-Clause |
def __init__(self):
""" initialize algorithms with standard parameters (typical values given in parentheses)"""
# --- BackProp parameters ---
# learning rate (0.1-0.001, down to 1e-7 for RNNs)
self.alpha = 0.1
# alpha decay (0.999; 1.0 = disabled)
self.alphadecay = 1.0
... | initialize algorithms with standard parameters (typical values given in parentheses) | __init__ | python | pybrain/pybrain | pybrain/auxiliary/gradientdescent.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/gradientdescent.py | BSD-3-Clause |
def init(self, values):
""" call this to initialize data structures *after* algorithm to use
has been selected
:arg values: the list (or array) of parameters to perform gradient descent on
(will be copied, original not modified)
"""
assert isinstance(value... | call this to initialize data structures *after* algorithm to use
has been selected
:arg values: the list (or array) of parameters to perform gradient descent on
(will be copied, original not modified)
| init | python | pybrain/pybrain | pybrain/auxiliary/gradientdescent.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/gradientdescent.py | BSD-3-Clause |
def __call__(self, gradient, error=None):
""" calculates parameter change based on given gradient and returns updated parameters """
# check if gradient has correct dimensionality, then make array """
assert len(gradient) == len(self.values)
gradient_arr = asarray(gradient)
if s... | calculates parameter change based on given gradient and returns updated parameters | __call__ | python | pybrain/pybrain | pybrain/auxiliary/gradientdescent.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/gradientdescent.py | BSD-3-Clause |
def importanceMixing(oldpoints, oldpdf, newpdf, newdistr, forcedRefresh = 0.01):
""" Implements importance mixing. Given a set of points, an old and a new pdf-function for them
and a generator function for new points, it produces a list of indices of the old points to be reused and a list of new points.
Par... | Implements importance mixing. Given a set of points, an old and a new pdf-function for them
and a generator function for new points, it produces a list of indices of the old points to be reused and a list of new points.
Parameter (optional): forced refresh rate.
| importanceMixing | python | pybrain/pybrain | pybrain/auxiliary/importancemixing.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/importancemixing.py | BSD-3-Clause |
def reduceDim(data, dim, func='pca'):
"""Reduce the dimension of datapoints to dim via principal component
analysis.
A matrix of shape (n, d) specifies n points of dimension d.
"""
try:
pcaFunc = globals()[func]
except KeyError:
raise ValueError('Unknown function to calc princip... | Reduce the dimension of datapoints to dim via principal component
analysis.
A matrix of shape (n, d) specifies n points of dimension d.
| reduceDim | python | pybrain/pybrain | pybrain/auxiliary/pca.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/pca.py | BSD-3-Clause |
def pca(data, dim):
""" Return the first dim principal components as colums of a matrix.
Every row of the matrix resembles a point in the data space.
"""
assert dim <= data.shape[1], \
"dim must be less or equal than the original dimension"
# We have to make a copy of the original data an... | Return the first dim principal components as colums of a matrix.
Every row of the matrix resembles a point in the data space.
| pca | python | pybrain/pybrain | pybrain/auxiliary/pca.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/pca.py | BSD-3-Clause |
def pPca(data, dim):
"""Return a matrix which contains the first `dim` dimensions principal
components of data.
data is a matrix which's rows correspond to datapoints. Implementation of
the 'probabilistic PCA' algorithm.
"""
num = data.shape[1]
data = asmatrix(makeCentered(data))
# Pick... | Return a matrix which contains the first `dim` dimensions principal
components of data.
data is a matrix which's rows correspond to datapoints. Implementation of
the 'probabilistic PCA' algorithm.
| pPca | python | pybrain/pybrain | pybrain/auxiliary/pca.py | https://github.com/pybrain/pybrain/blob/master/pybrain/auxiliary/pca.py | BSD-3-Clause |
def __init__(self, inp, target=1, nb_classes=0, class_labels=None):
"""Initialize an empty dataset.
`inp` is used to specify the dimensionality of the input. While the
number of targets is given by implicitly by the training samples, it can
also be set explicity by `nb_classes`. To give... | Initialize an empty dataset.
`inp` is used to specify the dimensionality of the input. While the
number of targets is given by implicitly by the training samples, it can
also be set explicity by `nb_classes`. To give the classes names, supply
an iterable of strings as `class_labels`. | __init__ | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def load_matlab(cls, fname):
"""Create a dataset by reading a Matlab file containing one variable
called 'data' which is an array of nSamples * nFeatures + 1 and
contains the class in the first column."""
from mlabwrap import mlab #@UnresolvedImport
d = mlab.load(fname)
r... | Create a dataset by reading a Matlab file containing one variable
called 'data' which is an array of nSamples * nFeatures + 1 and
contains the class in the first column. | load_matlab | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def load_libsvm(cls, f):
"""Create a dataset by reading a sparse LIBSVM/SVMlight format file
(with labels only)."""
nFeat = 0
# find max. number of features
for line in f:
n = int(line.split()[-1].split(':')[0])
if n > nFeat:
nFeat = n
... | Create a dataset by reading a sparse LIBSVM/SVMlight format file
(with labels only). | load_libsvm | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
def __add__(self, other):
"""Adds the patterns of two datasets, if dimensions and type match."""
if type(self) != type(other):
raise TypeError('DataSets to be added must agree in type')
elif self.indim != other.indim:
raise TypeError('DataSets to be added must agree in in... | Adds the patterns of two datasets, if dimensions and type match. | __add__ | python | pybrain/pybrain | pybrain/datasets/classification.py | https://github.com/pybrain/pybrain/blob/master/pybrain/datasets/classification.py | BSD-3-Clause |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.