sentence1 stringlengths 52 3.87M | sentence2 stringlengths 1 47.2k | label stringclasses 1
value |
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def summary(self, campaign_id=None):
""" Returns the campaign summary """
resource_cls = CampaignSummary
single_resource = False
if not campaign_id:
resource_cls = CampaignSummaries
single_resource = True
return super(API, self).get(
resource... | Returns the campaign summary | entailment |
def results(self, campaign_id):
""" Returns just the results for a given campaign """
return super(API, self).get(
resource_id=campaign_id,
resource_action='results',
resource_cls=CampaignResults) | Returns just the results for a given campaign | entailment |
def set_path(self, file_path):
"""
Set the path of the database.
Create the file if it does not exist.
"""
if not file_path:
self.read_data = self.memory_read
self.write_data = self.memory_write
elif not is_valid(file_path):
self.write_... | Set the path of the database.
Create the file if it does not exist. | entailment |
def delete(self, key):
"""
Removes the specified key from the database.
"""
obj = self._get_content()
obj.pop(key, None)
self.write_data(self.path, obj) | Removes the specified key from the database. | entailment |
def data(self, **kwargs):
"""
If a key is passed in, a corresponding value will be returned.
If a key-value pair is passed in then the corresponding key in
the database will be set to the specified value.
A dictionary can be passed in as well.
If a key does not exist and ... | If a key is passed in, a corresponding value will be returned.
If a key-value pair is passed in then the corresponding key in
the database will be set to the specified value.
A dictionary can be passed in as well.
If a key does not exist and a value is provided then an entry
will... | entailment |
def filter(self, filter_arguments):
"""
Takes a dictionary of filter parameters.
Return a list of objects based on a list of parameters.
"""
results = self._get_content()
# Filter based on a dictionary of search parameters
if isinstance(filter_arguments, dict):
... | Takes a dictionary of filter parameters.
Return a list of objects based on a list of parameters. | entailment |
def drop(self):
"""
Remove the database by deleting the JSON file.
"""
import os
if self.path:
if os.path.exists(self.path):
os.remove(self.path)
else:
# Clear the in-memory data if there is no file path
self._data = {} | Remove the database by deleting the JSON file. | entailment |
def read_data(file_path):
"""
Reads a file and returns a json encoded representation of the file.
"""
if not is_valid(file_path):
write_data(file_path, {})
db = open_file_for_reading(file_path)
content = db.read()
obj = decode(content)
db.close()
return obj | Reads a file and returns a json encoded representation of the file. | entailment |
def write_data(path, obj):
"""
Writes to a file and returns the updated file content.
"""
with open_file_for_writing(path) as db:
db.write(encode(obj))
return obj | Writes to a file and returns the updated file content. | entailment |
def is_valid(file_path):
"""
Check to see if a file exists or is empty.
"""
from os import path, stat
can_open = False
try:
with open(file_path) as fp:
can_open = True
except IOError:
return False
is_file = path.isfile(file_path)
return path.exists(fil... | Check to see if a file exists or is empty. | entailment |
def _refine(node_coords, cells_nodes, edge_nodes, cells_edges):
"""Canonically refine a mesh by inserting nodes at all edge midpoints
and make four triangular elements where there was one.
This is a very crude refinement; don't use for actual applications.
"""
num_nodes = len(node_coords)
num_ne... | Canonically refine a mesh by inserting nodes at all edge midpoints
and make four triangular elements where there was one.
This is a very crude refinement; don't use for actual applications. | entailment |
def create_edges(cells_nodes):
"""Setup edge-node and edge-cell relations. Adapted from voropy.
"""
# Create the idx_hierarchy (nodes->edges->cells), i.e., the value of
# `self.idx_hierarchy[0, 2, 27]` is the index of the node of cell 27, edge
# 2, node 0. The shape of `self.idx_hierarchy` is `(2, 3... | Setup edge-node and edge-cell relations. Adapted from voropy. | entailment |
def plot2d(points, cells, mesh_color="k", show_axes=False):
"""Plot a 2D mesh using matplotlib.
"""
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
fig = plt.figure()
ax = fig.gca()
plt.axis("equal")
if not show_axes:
ax.set_axis_off()
xmin... | Plot a 2D mesh using matplotlib. | entailment |
def put(self, job, result):
"Perform a job by a member in the pool and return the result."
self.job.put(job)
r = result.get()
return r | Perform a job by a member in the pool and return the result. | entailment |
def contract(self, jobs, result):
"""
Perform a contract on a number of jobs and block until a result is
retrieved for each job.
"""
for j in jobs:
WorkerPool.put(self, j)
r = []
for i in xrange(len(jobs)):
r.append(result.get())
... | Perform a contract on a number of jobs and block until a result is
retrieved for each job. | entailment |
def cube(
xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0, zmin=0.0, zmax=1.0, nx=11, ny=11, nz=11
):
"""Canonical tetrahedrization of the cube.
Input:
Edge lenghts of the cube
Number of nodes along the edges.
"""
# Generate suitable ranges for parametrization
x_range = numpy.linspace(xmin, xmax,... | Canonical tetrahedrization of the cube.
Input:
Edge lenghts of the cube
Number of nodes along the edges. | entailment |
def grow(self):
"Add another worker to the pool."
t = self.worker_factory(self)
t.start()
self._size += 1 | Add another worker to the pool. | entailment |
def shrink(self):
"Get rid of one worker from the pool. Raises IndexError if empty."
if self._size <= 0:
raise IndexError("pool is already empty")
self._size -= 1
self.put(SuicideJob()) | Get rid of one worker from the pool. Raises IndexError if empty. | entailment |
def map(self, fn, *seq):
"Perform a map operation distributed among the workers. Will "
"block until done."
results = Queue()
args = zip(*seq)
for seq in args:
j = SimpleJob(results, fn, seq)
self.put(j)
# Aggregate results
r = []
... | Perform a map operation distributed among the workers. Will | entailment |
def moebius(
num_twists=1, # How many twists are there in the 'paper'?
nl=60, # Number of nodes along the length of the strip
nw=11, # Number of nodes along the width of the strip (>= 2)
mode="classical",
):
"""Creates a simplistic triangular mesh on a slightly Möbius strip. The
Möbius strip ... | Creates a simplistic triangular mesh on a slightly Möbius strip. The
Möbius strip here deviates slightly from the ordinary geometry in that it
is constructed in such a way that the two halves can be exchanged as to
allow better comparison with the pseudo-Möbius geometry.
The mode is either `'classical'... | entailment |
def run(self):
"Get jobs from the queue and perform them as they arrive."
while 1:
# Sleep until there is a job to perform.
job = self.jobs.get()
# Yawn. Time to get some work done.
try:
job.run()
self.jobs.task_done()
... | Get jobs from the queue and perform them as they arrive. | entailment |
def display(contents, domain=DEFAULT_DOMAIN, force_gist=False):
"""
Open a web browser pointing to geojson.io with the specified content.
If the content is large, an anonymous gist will be created on github and
the URL will instruct geojson.io to download the gist data and then
display. If the cont... | Open a web browser pointing to geojson.io with the specified content.
If the content is large, an anonymous gist will be created on github and
the URL will instruct geojson.io to download the gist data and then
display. If the content is small, this step is not needed as the data can
be included in the... | entailment |
def embed(contents='', width='100%', height=512, *args, **kwargs):
"""
Embed geojson.io in an iframe in Jupyter/IPython notebook.
Parameters
----------
contents - see make_url()
width - string, default '100%' - width of the iframe
height - string / int, default 512 - height of the iframe
... | Embed geojson.io in an iframe in Jupyter/IPython notebook.
Parameters
----------
contents - see make_url()
width - string, default '100%' - width of the iframe
height - string / int, default 512 - height of the iframe
kwargs - additional arguments are passed to `make_url()` | entailment |
def make_url(contents, domain=DEFAULT_DOMAIN, force_gist=False,
size_for_gist=MAX_URL_LEN):
"""
Returns the URL to open given the domain and contents.
If the file contents are large, an anonymous gist will be created.
Parameters
----------
contents
* string - assumed to be... | Returns the URL to open given the domain and contents.
If the file contents are large, an anonymous gist will be created.
Parameters
----------
contents
* string - assumed to be GeoJSON
* an object that implements __geo_interface__
A FeatureCollection will be constructed wi... | entailment |
def make_geojson(contents):
"""
Return a GeoJSON string from a variety of inputs.
See the documentation for make_url for the possible contents
input.
Returns
-------
GeoJSON string
"""
if isinstance(contents, six.string_types):
return contents
if hasattr(contents, '__g... | Return a GeoJSON string from a variety of inputs.
See the documentation for make_url for the possible contents
input.
Returns
-------
GeoJSON string | entailment |
def data_url(contents, domain=DEFAULT_DOMAIN):
"""
Return the URL for embedding the GeoJSON data in the URL hash
Parameters
----------
contents - string of GeoJSON
domain - string, default http://geojson.io
"""
url = (domain + '#data=data:application/json,' +
urllib.parse.qu... | Return the URL for embedding the GeoJSON data in the URL hash
Parameters
----------
contents - string of GeoJSON
domain - string, default http://geojson.io | entailment |
def _make_gist(contents, description='', filename='data.geojson'):
"""
Create and return an anonymous gist with a single file and specified
contents
"""
ghapi = github3.GitHub()
files = {filename: {'content': contents}}
gist = ghapi.create_gist(description, files)
return gist | Create and return an anonymous gist with a single file and specified
contents | entailment |
def clenshaw(a, alpha, beta, t):
"""Clenshaw's algorithm for evaluating
S(t) = \\sum a_k P_k(alpha, beta)(t)
where P_k(alpha, beta) is the kth orthogonal polynomial defined by the
recurrence coefficients alpha, beta.
See <https://en.wikipedia.org/wiki/Clenshaw_algorithm> for details.
"""
... | Clenshaw's algorithm for evaluating
S(t) = \\sum a_k P_k(alpha, beta)(t)
where P_k(alpha, beta) is the kth orthogonal polynomial defined by the
recurrence coefficients alpha, beta.
See <https://en.wikipedia.org/wiki/Clenshaw_algorithm> for details. | entailment |
def tree(X, n, alpha=0, symbolic=False):
"""Recurrence coefficients for generalized Laguerre polynomials. Set
alpha=0 (default) to get classical Laguerre.
"""
args = recurrence_coefficients(n, alpha=alpha, symbolic=symbolic)
return line_tree(X, *args) | Recurrence coefficients for generalized Laguerre polynomials. Set
alpha=0 (default) to get classical Laguerre. | entailment |
def recurrence_coefficients(n, alpha, standardization="normal", symbolic=False):
"""Recurrence coefficients for generalized Laguerre polynomials.
vals_k = vals_{k-1} * (t*a_k - b_k) - vals{k-2} * c_k
"""
S = sympy.S if symbolic else lambda x: x
sqrt = sympy.sqrt if symbolic else numpy.sqrt
... | Recurrence coefficients for generalized Laguerre polynomials.
vals_k = vals_{k-1} * (t*a_k - b_k) - vals{k-2} * c_k | entailment |
def jacobi(n, alpha, beta, standardization, symbolic=False):
"""Generate the recurrence coefficients a_k, b_k, c_k in
P_{k+1}(x) = (a_k x - b_k)*P_{k}(x) - c_k P_{k-1}(x)
for the Jacobi polynomials which are orthogonal on [-1, 1]
with respect to the weight w(x)=[(1-x)^alpha]*[(1+x)^beta]; see
<htt... | Generate the recurrence coefficients a_k, b_k, c_k in
P_{k+1}(x) = (a_k x - b_k)*P_{k}(x) - c_k P_{k-1}(x)
for the Jacobi polynomials which are orthogonal on [-1, 1]
with respect to the weight w(x)=[(1-x)^alpha]*[(1+x)^beta]; see
<https://en.wikipedia.org/wiki/Jacobi_polynomials#Recurrence_relations>. | entailment |
def plot(f, lcar=1.0e-1):
"""Plot function over a disk.
"""
import matplotlib
import matplotlib.pyplot as plt
import pygmsh
geom = pygmsh.built_in.Geometry()
geom.add_circle([0.0, 0.0, 0.0], 1.0, lcar, num_sections=4, compound=True)
points, cells, _, _, _ = pygmsh.generate_mesh(geom, ve... | Plot function over a disk. | entailment |
def tree(X, n, symbolic=False):
"""Evaluates the entire tree of orthogonal polynomials for the n-cube
The computation is organized such that tree returns a list of arrays, L={0,
..., dim}, where each level corresponds to the polynomial degree L.
Further, each level is organized like a discrete (dim-1)-... | Evaluates the entire tree of orthogonal polynomials for the n-cube
The computation is organized such that tree returns a list of arrays, L={0,
..., dim}, where each level corresponds to the polynomial degree L.
Further, each level is organized like a discrete (dim-1)-dimensional
simplex. Let's demonstr... | entailment |
def line_evaluate(t, p0, a, b, c):
"""Evaluate the orthogonal polynomial defined by its recurrence coefficients
a, b, and c at the point(s) t.
"""
vals1 = numpy.zeros_like(t, dtype=int)
# The order is important here; see
# <https://github.com/sympy/sympy/issues/13637>.
vals2 = numpy.ones_lik... | Evaluate the orthogonal polynomial defined by its recurrence coefficients
a, b, and c at the point(s) t. | entailment |
def plot(corners, f, n=100):
"""Plot function over a triangle.
"""
import matplotlib.tri
import matplotlib.pyplot as plt
# discretization points
def partition(boxes, balls):
# <https://stackoverflow.com/a/36748940/353337>
def rec(boxes, balls, parent=tuple()):
if box... | Plot function over a triangle. | entailment |
def _iget(key, lookup_dict):
"""
Case-insensitive search for `key` within keys of `lookup_dict`.
"""
for k, v in lookup_dict.items():
if k.lower() == key.lower():
return v
return None | Case-insensitive search for `key` within keys of `lookup_dict`. | entailment |
def getlang_by_name(name):
"""
Try to lookup a Language object by name, e.g. 'English', in internal language list.
Returns None if lookup by language name fails in resources/languagelookup.json.
"""
direct_match = _iget(name, _LANGUAGE_NAME_LOOKUP)
if direct_match:
return direct_match
... | Try to lookup a Language object by name, e.g. 'English', in internal language list.
Returns None if lookup by language name fails in resources/languagelookup.json. | entailment |
def getlang_by_native_name(native_name):
"""
Try to lookup a Language object by native_name, e.g. 'English', in internal language list.
Returns None if lookup by language name fails in resources/languagelookup.json.
"""
direct_match = _iget(native_name, _LANGUAGE_NATIVE_NAME_LOOKUP)
if direct_ma... | Try to lookup a Language object by native_name, e.g. 'English', in internal language list.
Returns None if lookup by language name fails in resources/languagelookup.json. | entailment |
def getlang_by_alpha2(code):
"""
Lookup a Language object for language code `code` based on these strategies:
- Special case rules for Hebrew and Chinese Hans/Hant scripts
- Using `alpha_2` lookup in `pycountry.languages` followed by lookup for a
language with the same `name` in the internal... | Lookup a Language object for language code `code` based on these strategies:
- Special case rules for Hebrew and Chinese Hans/Hant scripts
- Using `alpha_2` lookup in `pycountry.languages` followed by lookup for a
language with the same `name` in the internal representaion
Returns `None` if no m... | entailment |
def write(filename, f):
"""Write a function `f` defined in terms of spherical coordinates to a file.
"""
import meshio
import meshzoo
points, cells = meshzoo.iso_sphere(5)
# get spherical coordinates from points
polar = numpy.arccos(points[:, 2])
azimuthal = numpy.arctan2(points[:, 1], ... | Write a function `f` defined in terms of spherical coordinates to a file. | entailment |
def tree_sph(polar, azimuthal, n, standardization, symbolic=False):
"""Evaluate all spherical harmonics of degree at most `n` at angles `polar`,
`azimuthal`.
"""
cos = numpy.vectorize(sympy.cos) if symbolic else numpy.cos
# Conventions from
# <https://en.wikipedia.org/wiki/Spherical_harmonics#O... | Evaluate all spherical harmonics of degree at most `n` at angles `polar`,
`azimuthal`. | entailment |
def tree_alp(
x, n, standardization, phi=None, with_condon_shortley_phase=True, symbolic=False
):
"""Evaluates the entire tree of associated Legendre polynomials up to depth
n.
There are many recurrence relations that can be used to construct the
associated Legendre polynomials. However, only few ar... | Evaluates the entire tree of associated Legendre polynomials up to depth
n.
There are many recurrence relations that can be used to construct the
associated Legendre polynomials. However, only few are numerically stable.
Many implementations (including this one) use the classical Legendre
recurrence... | entailment |
def tree(X, n, symbolic=False):
"""Evaluates the entire tree of orthogonal polynomials on the unit disk.
The return value is a list of arrays, where `out[k]` hosts the `2*k+1`
values of the `k`th level of the tree
(0, 0)
(0, 1) (1, 1)
(0, 2) (1, 2) (2, 2)
... .... | Evaluates the entire tree of orthogonal polynomials on the unit disk.
The return value is a list of arrays, where `out[k]` hosts the `2*k+1`
values of the `k`th level of the tree
(0, 0)
(0, 1) (1, 1)
(0, 2) (1, 2) (2, 2)
... ... ... | entailment |
def tree(bary, n, standardization, symbolic=False):
"""Evaluates the entire tree of orthogonal triangle polynomials.
The return value is a list of arrays, where `out[k]` hosts the `2*k+1`
values of the `k`th level of the tree
(0, 0)
(0, 1) (1, 1)
(0, 2) (1, 2) (2, 2)
... | Evaluates the entire tree of orthogonal triangle polynomials.
The return value is a list of arrays, where `out[k]` hosts the `2*k+1`
values of the `k`th level of the tree
(0, 0)
(0, 1) (1, 1)
(0, 2) (1, 2) (2, 2)
... ... ...
For reference, see
Abedal... | entailment |
def check_if_this_file_exist(filename):
"""Check if this file exist and if it's a directory
This function will check if the given filename
actually exists and if it's not a Directory
Arguments:
filename {string} -- filename
Return:
True : if it's not a directory and if this file ... | Check if this file exist and if it's a directory
This function will check if the given filename
actually exists and if it's not a Directory
Arguments:
filename {string} -- filename
Return:
True : if it's not a directory and if this file exist
False : If it's not a file and if... | entailment |
def command_line(cmd):
"""Handle the command line call
keyword arguments:
cmd = a list
return
0 if error
or a string for the command line output
"""
try:
s = subprocess.Popen(cmd, stdout=subprocess.PIPE)
s = s.stdout.read()
return s.strip()
except subproce... | Handle the command line call
keyword arguments:
cmd = a list
return
0 if error
or a string for the command line output | entailment |
def information(filename):
"""Returns the file exif"""
check_if_this_file_exist(filename)
filename = os.path.abspath(filename)
result = get_json(filename)
result = result[0]
return result | Returns the file exif | entailment |
def get_json(filename):
""" Return a json value of the exif
Get a filename and return a JSON object
Arguments:
filename {string} -- your filename
Returns:
[JSON] -- Return a JSON object
"""
check_if_this_file_exist(filename)
#Process this function
filename = os.path.a... | Return a json value of the exif
Get a filename and return a JSON object
Arguments:
filename {string} -- your filename
Returns:
[JSON] -- Return a JSON object | entailment |
def get_csv(filename):
""" Return a csv representation of the exif
get a filename and returns a unicode string with a CSV format
Arguments:
filename {string} -- your filename
Returns:
[unicode] -- unicode string
"""
check_if_this_file_exist(filename)
#Process this functio... | Return a csv representation of the exif
get a filename and returns a unicode string with a CSV format
Arguments:
filename {string} -- your filename
Returns:
[unicode] -- unicode string | entailment |
def print_a_header(message="-"):
"""Header
This function will output a message in a header
Keyword Arguments:
message {str} -- [the message string] (default: {"-"})
"""
print("-".center(60,'-'))
print(message.center(60,'-'))
print("-".center(60,'-'))
print() | Header
This function will output a message in a header
Keyword Arguments:
message {str} -- [the message string] (default: {"-"}) | entailment |
def check_if_exiftool_is_already_installed():
"""Requirements
This function will check if Exiftool is installed
on your system
Return: True if Exiftool is Installed
False if not
"""
result = 1;
command = ["exiftool", "-ver"]
with open(os.devnull, "w") as fnull:
res... | Requirements
This function will check if Exiftool is installed
on your system
Return: True if Exiftool is Installed
False if not | entailment |
def render(self, context=None, clean=False):
"""
Render email with provided context
Arguments
---------
context : dict
|context| If not specified then the
:attr:`~mail_templated.EmailMessage.context` property is
used.
Keyword Argument... | Render email with provided context
Arguments
---------
context : dict
|context| If not specified then the
:attr:`~mail_templated.EmailMessage.context` property is
used.
Keyword Arguments
-----------------
clean : bool
If `... | entailment |
def send(self, *args, **kwargs):
"""
Send email message, render if it is not rendered yet.
Note
----
Any extra arguments are passed to
:class:`EmailMultiAlternatives.send() <django.core.mail.EmailMessage>`.
Keyword Arguments
-----------------
cle... | Send email message, render if it is not rendered yet.
Note
----
Any extra arguments are passed to
:class:`EmailMultiAlternatives.send() <django.core.mail.EmailMessage>`.
Keyword Arguments
-----------------
clean : bool
If ``True``, remove any templat... | entailment |
def send_mail(template_name, context, from_email, recipient_list,
fail_silently=False, auth_user=None, auth_password=None,
connection=None, **kwargs):
"""
Easy wrapper for sending a single email message to a recipient list using
django template system.
It works almost the sa... | Easy wrapper for sending a single email message to a recipient list using
django template system.
It works almost the same way as the standard
:func:`send_mail()<django.core.mail.send_mail>` function.
.. |main_difference| replace:: The main
difference is that two first arguments ``subject`` an... | entailment |
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random_state=None, **kwds):
"""Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster
distance (``a``) and the mean nearest-cluster distance (``b... | Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster
distance (``a``) and the mean nearest-cluster distance (``b``) for each
sample. The Silhouette Coefficient for a sample is ``(b - a) / max(a,
b)``. To clarify, ``b`` is the di... | entailment |
def silhouette_samples(X, labels, metric='euclidean', **kwds):
"""Compute the Silhouette Coefficient for each sample.
The Silhouette Coefficient is a measure of how well samples are clustered
with samples that are similar to themselves. Clustering models with a high
Silhouette Coefficient are said to b... | Compute the Silhouette Coefficient for each sample.
The Silhouette Coefficient is a measure of how well samples are clustered
with samples that are similar to themselves. Clustering models with a high
Silhouette Coefficient are said to be dense, where samples in the same
cluster are similar to each oth... | entailment |
def calinski_harabaz_score(X, labels):
"""Compute the Calinski and Harabaz score.
The score is defined as ratio between the within-cluster dispersion and
the between-cluster dispersion.
Read more in the :ref:`User Guide <calinski_harabaz_index>`.
Parameters
----------
X : array-like, shap... | Compute the Calinski and Harabaz score.
The score is defined as ratio between the within-cluster dispersion and
the between-cluster dispersion.
Read more in the :ref:`User Guide <calinski_harabaz_index>`.
Parameters
----------
X : array-like, shape (``n_samples``, ``n_features``)
List... | entailment |
def handle_zeros_in_scale(scale, copy=True):
''' Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
Adapted from sklearn.preprocessing.data'''
# if we are fitting on 1D arrays, scale might be a scalar
if np.isscalar(scale):
... | Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
Adapted from sklearn.preprocessing.data | entailment |
def _joint_probabilities(distances, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances.
Parameters
----------
distances : array, shape (n_samples * (n_samples-1) / 2,)
Distances of samples are stored as condensed matrices, i.e.
we omit the diagonal and dupl... | Compute joint probabilities p_ij from distances.
Parameters
----------
distances : array, shape (n_samples * (n_samples-1) / 2,)
Distances of samples are stored as condensed matrices, i.e.
we omit the diagonal and duplicate entries and store everything
in a one-dimensional array.
... | entailment |
def _joint_probabilities_nn(distances, neighbors, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances using just nearest
neighbors.
This method is approximately equal to _joint_probabilities. The latter
is O(N), but limiting the joint probability to nearest neighbors improv... | Compute joint probabilities p_ij from distances using just nearest
neighbors.
This method is approximately equal to _joint_probabilities. The latter
is O(N), but limiting the joint probability to nearest neighbors improves
this substantially to O(uN).
Parameters
----------
distances : arra... | entailment |
def _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components,
skip_num_points=0):
"""t-SNE objective function: gradient of the KL divergence
of p_ijs and q_ijs and the absolute error.
Parameters
----------
params : array, shape (n_params,)
Unraveled embedding... | t-SNE objective function: gradient of the KL divergence
of p_ijs and q_ijs and the absolute error.
Parameters
----------
params : array, shape (n_params,)
Unraveled embedding.
P : array, shape (n_samples * (n_samples-1) / 2,)
Condensed joint probability matrix.
degrees_of_free... | entailment |
def _kl_divergence_bh(params, P, degrees_of_freedom, n_samples, n_components,
angle=0.5, skip_num_points=0, verbose=False):
"""t-SNE objective function: KL divergence of p_ijs and q_ijs.
Uses Barnes-Hut tree methods to calculate the gradient that
runs in O(NlogN) instead of O(N^2)
... | t-SNE objective function: KL divergence of p_ijs and q_ijs.
Uses Barnes-Hut tree methods to calculate the gradient that
runs in O(NlogN) instead of O(N^2)
Parameters
----------
params : array, shape (n_params,)
Unraveled embedding.
P : csr sparse matrix, shape (n_samples, n_sample)
... | entailment |
def _gradient_descent(objective, p0, it, n_iter,
n_iter_check=1, n_iter_without_progress=300,
momentum=0.8, learning_rate=200.0, min_gain=0.01,
min_grad_norm=1e-7, verbose=0, args=None, kwargs=None):
"""Batch gradient descent with momentum and indivi... | Batch gradient descent with momentum and individual gains.
Parameters
----------
objective : function or callable
Should return a tuple of cost and gradient for a given parameter
vector. When expensive to compute, the cost can optionally
be None and can be computed every n_iter_chec... | entailment |
def trustworthiness(X, X_embedded, n_neighbors=5, precomputed=False):
"""Expresses to what extent the local structure is retained.
The trustworthiness is within [0, 1]. It is defined as
.. math::
T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1}
\sum_{j \in U^{(k)}_i} (r(i, j) - k)
... | Expresses to what extent the local structure is retained.
The trustworthiness is within [0, 1]. It is defined as
.. math::
T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1}
\sum_{j \in U^{(k)}_i} (r(i, j) - k)
where :math:`r(i, j)` is the rank of the embedded datapoint j
accordi... | entailment |
def _fit(self, X, skip_num_points=0):
"""Fit the model using X as training data.
Note that sparse arrays can only be handled by method='exact'.
It is recommended that you convert your sparse array to dense
(e.g. `X.toarray()`) if it fits in memory, or otherwise using a
dimension... | Fit the model using X as training data.
Note that sparse arrays can only be handled by method='exact'.
It is recommended that you convert your sparse array to dense
(e.g. `X.toarray()`) if it fits in memory, or otherwise using a
dimensionality reduction technique (e.g. TruncatedSVD).
... | entailment |
def _tsne(self, P, degrees_of_freedom, n_samples, random_state, X_embedded,
neighbors=None, skip_num_points=0):
"""Runs t-SNE."""
# t-SNE minimizes the Kullback-Leiber divergence of the Gaussians P
# and the Student's t-distributions Q. The optimization algorithm that
# we ... | Runs t-SNE. | entailment |
def fit_transform(self, X, y=None):
"""Fit X into an embedded space and return that transformed
output.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
mat... | Fit X into an embedded space and return that transformed
output.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
matrix. Otherwise it contains a sample per row.
... | entailment |
def correct(datasets_full, genes_list, return_dimred=False,
batch_size=BATCH_SIZE, verbose=VERBOSE, ds_names=None,
dimred=DIMRED, approx=APPROX, sigma=SIGMA, alpha=ALPHA, knn=KNN,
return_dense=False, hvg=None, union=False,
geosketch=False, geosketch_max=20000):
"""Int... | Integrate and batch correct a list of data sets.
Parameters
----------
datasets_full : `list` of `scipy.sparse.csr_matrix` or of `numpy.ndarray`
Data sets to integrate and correct.
genes_list: `list` of `list` of `string`
List of genes for each data set.
return_dimred: `bool`, optio... | entailment |
def integrate(datasets_full, genes_list, batch_size=BATCH_SIZE,
verbose=VERBOSE, ds_names=None, dimred=DIMRED, approx=APPROX,
sigma=SIGMA, alpha=ALPHA, knn=KNN, geosketch=False,
geosketch_max=20000, n_iter=1, union=False, hvg=None):
"""Integrate a list of data sets.
Pa... | Integrate a list of data sets.
Parameters
----------
datasets_full : `list` of `scipy.sparse.csr_matrix` or of `numpy.ndarray`
Data sets to integrate and correct.
genes_list: `list` of `list` of `string`
List of genes for each data set.
batch_size: `int`, optional (default: `5000`)
... | entailment |
def correct_scanpy(adatas, **kwargs):
"""Batch correct a list of `scanpy.api.AnnData`.
Parameters
----------
adatas : `list` of `scanpy.api.AnnData`
Data sets to integrate and/or correct.
kwargs : `dict`
See documentation for the `correct()` method for a full list of
paramet... | Batch correct a list of `scanpy.api.AnnData`.
Parameters
----------
adatas : `list` of `scanpy.api.AnnData`
Data sets to integrate and/or correct.
kwargs : `dict`
See documentation for the `correct()` method for a full list of
parameters to use for batch correction.
Returns... | entailment |
def integrate_scanpy(adatas, **kwargs):
"""Integrate a list of `scanpy.api.AnnData`.
Parameters
----------
adatas : `list` of `scanpy.api.AnnData`
Data sets to integrate.
kwargs : `dict`
See documentation for the `integrate()` method for a full list of
parameters to use for ... | Integrate a list of `scanpy.api.AnnData`.
Parameters
----------
adatas : `list` of `scanpy.api.AnnData`
Data sets to integrate.
kwargs : `dict`
See documentation for the `integrate()` method for a full list of
parameters to use for batch correction.
Returns
-------
... | entailment |
def augknt(knots, order):
"""Augment a knot vector.
Parameters:
knots:
Python list or rank-1 array, the original knot vector (without endpoint repeats)
order:
int, >= 0, order of spline
Returns:
list_of_knots:
rank-1 array that has (`order` + 1) copies of ``knots[0]``, then ``k... | Augment a knot vector.
Parameters:
knots:
Python list or rank-1 array, the original knot vector (without endpoint repeats)
order:
int, >= 0, order of spline
Returns:
list_of_knots:
rank-1 array that has (`order` + 1) copies of ``knots[0]``, then ``knots[1:-1]``, and finally (`order... | entailment |
def aveknt(t, k):
"""Compute the running average of `k` successive elements of `t`. Return the averaged array.
Parameters:
t:
Python list or rank-1 array
k:
int, >= 2, how many successive elements to average
Returns:
rank-1 array, averaged data. If k > len(t), returns a zero-length arr... | Compute the running average of `k` successive elements of `t`. Return the averaged array.
Parameters:
t:
Python list or rank-1 array
k:
int, >= 2, how many successive elements to average
Returns:
rank-1 array, averaged data. If k > len(t), returns a zero-length array.
Caveat:
This is ... | entailment |
def aptknt(tau, order):
"""Create an acceptable knot vector.
Minimal emulation of MATLAB's ``aptknt``.
The returned knot vector can be used to generate splines of desired `order`
that are suitable for interpolation to the collocation sites `tau`.
Note that this is only possible when ``len(tau)`` >= `order` + 1.
... | Create an acceptable knot vector.
Minimal emulation of MATLAB's ``aptknt``.
The returned knot vector can be used to generate splines of desired `order`
that are suitable for interpolation to the collocation sites `tau`.
Note that this is only possible when ``len(tau)`` >= `order` + 1.
When this condition does not h... | entailment |
def knt2mlt(t):
"""Count multiplicities of elements in a sorted list or rank-1 array.
Minimal emulation of MATLAB's ``knt2mlt``.
Parameters:
t:
Python list or rank-1 array. Must be sorted!
Returns:
out
rank-1 array such that
out[k] = #{ t[i] == t[k] for i < k }
Example:
If ``... | Count multiplicities of elements in a sorted list or rank-1 array.
Minimal emulation of MATLAB's ``knt2mlt``.
Parameters:
t:
Python list or rank-1 array. Must be sorted!
Returns:
out
rank-1 array such that
out[k] = #{ t[i] == t[k] for i < k }
Example:
If ``t = [1, 1, 2, 3, 3, 3]`... | entailment |
def spcol(knots, order, tau):
"""Return collocation matrix.
Minimal emulation of MATLAB's ``spcol``.
Parameters:
knots:
rank-1 array, knot vector (with appropriately repeated endpoints; see `augknt`, `aptknt`)
order:
int, >= 0, order of spline
tau:
rank-1 array, collocation sit... | Return collocation matrix.
Minimal emulation of MATLAB's ``spcol``.
Parameters:
knots:
rank-1 array, knot vector (with appropriately repeated endpoints; see `augknt`, `aptknt`)
order:
int, >= 0, order of spline
tau:
rank-1 array, collocation sites
Returns:
rank-2 array A such ... | entailment |
def __basis0(self, xi):
"""Order zero basis (for internal use)."""
return np.where(np.all([self.knot_vector[:-1] <= xi,
xi < self.knot_vector[1:]],axis=0), 1.0, 0.0) | Order zero basis (for internal use). | entailment |
def __basis(self, xi, p, compute_derivatives=False):
"""Recursive Cox - de Boor function (for internal use).
Compute basis functions and optionally their first derivatives.
"""
if p == 0:
return self.__basis0(xi)
else:
basis_p_minus_1 = self.__basis(xi, ... | Recursive Cox - de Boor function (for internal use).
Compute basis functions and optionally their first derivatives. | entailment |
def d(self, xi):
"""Convenience function to compute first derivative of basis functions. 'Memoized' for speed."""
return self.__basis(xi, self.p, compute_derivatives=True) | Convenience function to compute first derivative of basis functions. 'Memoized' for speed. | entailment |
def plot(self):
"""Plot basis functions over full range of knots.
Convenience function. Requires matplotlib.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
from sys import stderr
print("ERROR: matplotlib.pyplot not found, matplo... | Plot basis functions over full range of knots.
Convenience function. Requires matplotlib. | entailment |
def __diff_internal(self):
"""Differentiate a B-spline once, and return the resulting coefficients and Bspline objects.
This preserves the Bspline object nature of the data, enabling recursive implementation
of higher-order differentiation (see `diff`).
The value of the first derivative of `B` at a point `x` ... | Differentiate a B-spline once, and return the resulting coefficients and Bspline objects.
This preserves the Bspline object nature of the data, enabling recursive implementation
of higher-order differentiation (see `diff`).
The value of the first derivative of `B` at a point `x` can be obtained as::
def diff1(B,... | entailment |
def diff(self, order=1):
"""Differentiate a B-spline `order` number of times.
Parameters:
order:
int, >= 0
Returns:
**lambda** `x`: ... that evaluates the `order`-th derivative of `B` at the point `x`.
The returned function internally uses __call__, which is 'memoized' for ... | Differentiate a B-spline `order` number of times.
Parameters:
order:
int, >= 0
Returns:
**lambda** `x`: ... that evaluates the `order`-th derivative of `B` at the point `x`.
The returned function internally uses __call__, which is 'memoized' for speed. | entailment |
def collmat(self, tau, deriv_order=0):
"""Compute collocation matrix.
Parameters:
tau:
Python list or rank-1 array, collocation sites
deriv_order:
int, >=0, order of derivative for which to compute the collocation matrix.
The default is 0, which means the function value itself.
... | Compute collocation matrix.
Parameters:
tau:
Python list or rank-1 array, collocation sites
deriv_order:
int, >=0, order of derivative for which to compute the collocation matrix.
The default is 0, which means the function value itself.
Returns:
A:
if len(tau) > 1, rank-2 a... | entailment |
def build_interfaces_by_method(interfaces):
"""
Create new dictionary from INTERFACES hashed by method then
the endpoints name. For use when using the disqusapi by the
method interface instead of the endpoint interface. For
instance:
'blacklists': {
'add': {
'formats': ['jso... | Create new dictionary from INTERFACES hashed by method then
the endpoints name. For use when using the disqusapi by the
method interface instead of the endpoint interface. For
instance:
'blacklists': {
'add': {
'formats': ['json', 'jsonp'],
'method': 'POST',
... | entailment |
def get_normalized_request_string(method, url, nonce, params, ext='', body_hash=None):
"""
Returns a normalized request string as described iN OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.3.1
"""
urlparts = urlparse.urlparse(url)
if urlparts.query:
... | Returns a normalized request string as described iN OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.3.1 | entailment |
def get_body_hash(params):
"""
Returns BASE64 ( HASH (text) ) as described in OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.2
"""
norm_params = get_normalized_params(params)
return binascii.b2a_base64(hashlib.sha1(norm_params).digest())[:-1] | Returns BASE64 ( HASH (text) ) as described in OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.2 | entailment |
def get_mac_signature(api_secret, norm_request_string):
"""
Returns HMAC-SHA1 (api secret, normalized request string)
"""
hashed = hmac.new(str(api_secret), norm_request_string, hashlib.sha1)
return binascii.b2a_base64(hashed.digest())[:-1] | Returns HMAC-SHA1 (api secret, normalized request string) | entailment |
def open_file(self, access_mode="r"):
"""
input: filename and path.
output: file contents.
"""
try:
with open(self, access_mode, encoding='utf-8') as file:
return file.read()
except IOError:
print(self + " File not found."... | input: filename and path.
output: file contents. | entailment |
def _forward_backward(self, itraj):
"""
Estimation step: Runs the forward-back algorithm on trajectory with index itraj
Parameters
----------
itraj : int
index of the observation trajectory to process
Results
-------
logprob : float
... | Estimation step: Runs the forward-back algorithm on trajectory with index itraj
Parameters
----------
itraj : int
index of the observation trajectory to process
Results
-------
logprob : float
The probability to observe the observation sequence g... | entailment |
def _update_model(self, gammas, count_matrices, maxiter=10000000):
"""
Maximization step: Updates the HMM model given the hidden state assignment and count matrices
Parameters
----------
gamma : [ ndarray(T,N, dtype=float) ]
list of state probabilities for each traje... | Maximization step: Updates the HMM model given the hidden state assignment and count matrices
Parameters
----------
gamma : [ ndarray(T,N, dtype=float) ]
list of state probabilities for each trajectory
count_matrix : [ ndarray(N,N, dtype=float) ]
list of the Baum... | entailment |
def compute_viterbi_paths(self):
"""
Computes the viterbi paths using the current HMM model
"""
# get parameters
K = len(self._observations)
A = self._hmm.transition_matrix
pi = self._hmm.initial_distribution
# compute viterbi path for each trajectory
... | Computes the viterbi paths using the current HMM model | entailment |
def fit(self):
"""
Maximum-likelihood estimation of the HMM using the Baum-Welch algorithm
Returns
-------
model : HMM
The maximum likelihood HMM model.
"""
logger().info("=================================================================")
lo... | Maximum-likelihood estimation of the HMM using the Baum-Welch algorithm
Returns
-------
model : HMM
The maximum likelihood HMM model. | entailment |
def sample_gaussian(mean, covar, covariance_type='diag', n_samples=1,
random_state=None):
"""Generate random samples from a Gaussian distribution.
Parameters
----------
mean : array_like, shape (n_features,)
Mean of the distribution.
covar : array_like, optional
... | Generate random samples from a Gaussian distribution.
Parameters
----------
mean : array_like, shape (n_features,)
Mean of the distribution.
covar : array_like, optional
Covariance of the distribution. The shape depends on `covariance_type`:
scalar if 'spherical',
... | entailment |
def _covar_mstep_diag(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for diagonal cases"""
avg_X2 = np.dot(responsibilities.T, X * X) * norm
avg_means2 = gmm.means_ ** 2
avg_X_means = gmm.means_ * weighted_X_sum * norm
return avg... | Performing the covariance M step for diagonal cases | entailment |
def _covar_mstep_spherical(*args):
"""Performing the covariance M step for spherical cases"""
cv = _covar_mstep_diag(*args)
return np.tile(cv.mean(axis=1)[:, np.newaxis], (1, cv.shape[1])) | Performing the covariance M step for spherical cases | entailment |
def _covar_mstep_full(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for full cases"""
# Eq. 12 from K. Murphy, "Fitting a Conditional Linear Gaussian
# Distribution"
n_features = X.shape[1]
cv = np.empty((gmm.n_components, n_fea... | Performing the covariance M step for full cases | entailment |
def _get_covars(self):
"""Covariance parameters for each mixture component.
The shape depends on `cvtype`::
(`n_states`, 'n_features') if 'spherical',
(`n_features`, `n_features`) if 'tied',
(`n_states`, `n_features`) if 'di... | Covariance parameters for each mixture component.
The shape depends on `cvtype`::
(`n_states`, 'n_features') if 'spherical',
(`n_features`, `n_features`) if 'tied',
(`n_states`, `n_features`) if 'diag',
(`n_states`, `n_f... | entailment |
def _set_covars(self, covars):
"""Provide values for covariance"""
covars = np.asarray(covars)
_validate_covars(covars, self.covariance_type, self.n_components)
self.covars_ = covars | Provide values for covariance | entailment |
def score_samples(self, X):
"""Return the per-sample likelihood of the data under the model.
Compute the log probability of X under the model and
return the posterior distribution (responsibilities) of each
mixture component for each element of X.
Parameters
----------
... | Return the per-sample likelihood of the data under the model.
Compute the log probability of X under the model and
return the posterior distribution (responsibilities) of each
mixture component for each element of X.
Parameters
----------
X: array_like, shape (n_samples... | entailment |
def score(self, X, y=None):
"""Compute the log probability under the model.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
----... | Compute the log probability under the model.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
logprob : array_like, shape... | entailment |
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