_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_10500 |
Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. Note New code should use the permutation method of a default_rng() instance instead; please see the Quick Start. Parameters
xint or array_like
If x is an integer, randomly per... | |
doc_10501 |
Get a list of all estimators from sklearn. This function crawls the module and gets all classes that inherit from BaseEstimator. Classes that are defined in test-modules are not included. Parameters
type_filter{“classifier”, “regressor”, “cluster”, “transformer”} or list of such str, default=None
Which kind of ... | |
doc_10502 |
Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales. Returns
chelsea(300, 451, 3) uint8 ndarray
Chelsea image. Notes No copyright restrictions. CC0 by the photographer (Stefan van der Walt). | |
doc_10503 | Return True if the object is a code. | |
doc_10504 |
Return a copy of the array collapsed into one dimension. Parameters
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order... | |
doc_10505 |
Computes the (weighted) graph of Neighbors for points in X Parameters
Xarray-like of shape (n_samples_transform, n_features)
Sample data. Returns
Xtsparse matrix of shape (n_samples_transform, n_samples_fit)
Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explici... | |
doc_10506 | See Migration guide for more details. tf.compat.v1.raw_ops.Relu
tf.raw_ops.Relu(
features, name=None
)
See: https://en.wikipedia.org/wiki/Rectifier_(neural_networks) Example usage: >>> tf.nn.relu([-2., 0., -0., 3.]).numpy()
array([ 0., 0., -0., 3.], dtype=float32)
Args
features A Tensor. Must be one ... | |
doc_10507 |
Compute the F-beta score. The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0. The beta parameter determines the weight of recall in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 conside... | |
doc_10508 | The minimum number of bytes which can be written without blocking to a pipe when the pipe has been reported as ready for writing by select(), poll() or another interface in this module. This doesn’t apply to other kind of file-like objects such as sockets. This value is guaranteed by POSIX to be at least 512. Availabil... | |
doc_10509 |
Bases: matplotlib.collections.LineCollection A collection of locations along a single axis at which an "event" occurred. The events are given by a 1-dimensional array. They do not have an amplitude and are displayed as parallel lines. Parameters
positions1D array-like
Each value is an event.
orientation{'hori... | |
doc_10510 | sklearn.datasets.make_gaussian_quantiles(*, mean=None, cov=1.0, n_samples=100, n_features=2, n_classes=3, shuffle=True, random_state=None) [source]
Generate isotropic Gaussian and label samples by quantile. This classification dataset is constructed by taking a multi-dimensional standard normal distribution and defin... | |
doc_10511 | A class for generating random numbers using the highest-quality sources provided by the operating system. See random.SystemRandom for additional details. | |
doc_10512 | tf.less_equal Compat aliases for migration See Migration guide for more details. tf.compat.v1.less_equal, tf.compat.v1.math.less_equal
tf.math.less_equal(
x, y, name=None
)
Note: math.less_equal supports broadcasting. More about broadcasting here
Example: x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math... | |
doc_10513 | get the byte order of SDL get_sdl_byteorder() -> int Returns the byte order of the SDL library. It returns 1234 for little endian byte order and 4321 for big endian byte order. New in pygame 1.8. | |
doc_10514 |
Copy properties from other to self. | |
doc_10515 | draw a filled trigon/triangle filled_trigon(surface, x1, y1, x2, y2, x3, y3, color) -> None Draws a filled trigon (triangle) on the given surface. For an unfilled trigon use trigon(). A filled_trigon can also be drawn using filled_polygon() e.g. filled_polygon(surface, ((x1, y1), (x2, y2), (x3, y3)), color)
Para... | |
doc_10516 | tf.optimizers.schedules.InverseTimeDecay Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.optimizers.schedules.InverseTimeDecay
tf.keras.optimizers.schedules.InverseTimeDecay(
initial_learning_rate, decay_steps, decay_rate, staircase=False, name=None
)
When training a model, ... | |
doc_10517 | Optional. This attribute defines the protocol ('http' or 'https') of the URLs in the sitemap. If it isn’t set, the protocol with which the sitemap was requested is used. If the sitemap is built outside the context of a request, the default is 'http'. Deprecated since version 4.0: The default protocol for sitemaps buil... | |
doc_10518 | See Migration guide for more details. tf.compat.v1.check_numerics, tf.compat.v1.debugging.check_numerics
tf.debugging.check_numerics(
tensor, message, name=None
)
When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.
... | |
doc_10519 |
Alias for set_zlim3d. | |
doc_10520 | restart music rewind() -> None Resets playback of the current music to the beginning. | |
doc_10521 | Return buffered data without advancing the file position. At least one byte of data will be returned (unless at EOF). The exact number of bytes returned is unspecified. Note While calling peek() does not change the file position of the BZ2File, it may change the position of the underlying file object (e.g. if the BZ2F... | |
doc_10522 | In-place version of mvlgamma() | |
doc_10523 | See Migration guide for more details. tf.compat.v1.assert_proper_iterable, tf.compat.v1.debugging.assert_proper_iterable
tf.debugging.assert_proper_iterable(
values
)
Ops that expect iterables of Tensor can call this to validate input. Useful since Tensor, ndarray, byte/text type are all iterables themselves.
... | |
doc_10524 | See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DPerChannel
tf.raw_ops.QuantizedConv2DPerChannel(
input, filter, min_input, max_input, min_filter, max_filter, strides, padding,
out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1], name=None
)
Args
input A Tensor. Must be one of... | |
doc_10525 |
Return True if x is a not complex type or an array of complex numbers. The type of the input is checked, not the value. So even if the input has an imaginary part equal to zero, isrealobj evaluates to False if the data type is complex. Parameters
xany
The input can be of any type and shape. Returns
ybool
... | |
doc_10526 |
Return whether the artist is pickable. See also
set_picker, get_picker, pick | |
doc_10527 |
Set the norm limits for image scaling. Parameters
vmin, vmaxfloat
The limits. The limits may also be passed as a tuple (vmin, vmax) as a single positional argument. | |
doc_10528 | See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyKerasMomentum
tf.raw_ops.ResourceSparseApplyKerasMomentum(
var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False,
name=None
)
Set use_nesterov = True if you want to use Nesterov momentum. That is for rows w... | |
doc_10529 | The name of the current input file, as initially set at class instantiation time or stacked by later source requests. It may be useful to examine this when constructing error messages. | |
doc_10530 | Calculates the appropriate metaclass and creates the class namespace. The arguments are the components that make up a class definition header: the class name, the base classes (in order) and the keyword arguments (such as metaclass). The return value is a 3-tuple: metaclass, namespace, kwds metaclass is the appropriate... | |
doc_10531 |
Transform data back to its original space. Parameters
Xarray-like of shape (n_samples, n_components)
New data, where n_samples is the number of samples and n_components is the number of pls components. Returns
x_reconstructedarray-like of shape (n_samples, n_features)
Notes This transformation will on... | |
doc_10532 | See Migration guide for more details. tf.compat.v1.keras.experimental.LinearCosineDecay
tf.keras.experimental.LinearCosineDecay(
initial_learning_rate, decay_steps, num_periods=0.5, alpha=0.0, beta=0.001,
name=None
)
See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.074... | |
doc_10533 |
Provide rolling window calculations. Parameters
window:int, offset, or BaseIndexer subclass
Size of the moving window. If an integer, the fixed number of observations used for each window. If an offset, the time period of each window. Each window will be a variable sized based on the observations included in th... | |
doc_10534 |
Copy properties of other into self and return PDF commands needed to transform self into other. | |
doc_10535 | Deserialize fp (a .read()-supporting text file or binary file containing a JSON document) to a Python object using this conversion table. object_hook is an optional function that will be called with the result of any object literal decoded (a dict). The return value of object_hook will be used instead of the dict. This... | |
doc_10536 |
def open(path):
f = builtins.open(path, 'r')
return UpperCaser(f)
class UpperCaser:
'''Wrapper around a file that converts output to upper-case.'''
def __init__(self, f):
self._f = f
def read(self, count=-1):
return self._f.read(count).upper()
# ...
As an implementation det... | |
doc_10537 |
Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. Parameters
Xarray-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None
The query point or points. If not provided, neighbors of each indexed point are returned. I... | |
doc_10538 | In-place version of hypot() | |
doc_10539 | Reference pixel green into a 2d array. pixels_green (Surface) -> array Create a new 2D array that directly references the green values in a Surface. Any changes to the array will affect the pixels in the Surface. This is a fast operation since no data is copied. This can only work on 24-bit or 32-bit Surfaces. The Su... | |
doc_10540 | class collections.abc.MutableSequence
class collections.abc.ByteString
ABCs for read-only and mutable sequences. Implementation note: Some of the mixin methods, such as __iter__(), __reversed__() and index(), make repeated calls to the underlying __getitem__() method. Consequently, if __getitem__() is implemented w... | |
doc_10541 | Combines the STANDARD_RIGHTS_REQUIRED, KEY_QUERY_VALUE, KEY_SET_VALUE, KEY_CREATE_SUB_KEY, KEY_ENUMERATE_SUB_KEYS, KEY_NOTIFY, and KEY_CREATE_LINK access rights. | |
doc_10542 |
Return the mean of the values over the requested axis. Parameters
axis:{index (0), columns (1)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null values when computing the result.
level:int or level name, default None
If the axis is a MultiIndex (hierarchical), count alon... | |
doc_10543 | Start monitoring the fd file descriptor for read availability and invoke callback with the specified arguments once fd is available for reading. | |
doc_10544 |
For pickling. | |
doc_10545 |
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4] | |
doc_10546 | If a formatter is specified, it is used to format the record. The record is then written to the stream followed by terminator. If exception information is present, it is formatted using traceback.print_exception() and appended to the stream. | |
doc_10547 | See Migration guide for more details. tf.compat.v1.raw_ops.ShutdownDistributedTPU
tf.raw_ops.ShutdownDistributedTPU(
name=None
)
The op returns an error if no system is running.
Args
name A name for the operation (optional).
Returns The created Operation. | |
doc_10548 | The mathematical constant e = 2.718281…, to available precision. | |
doc_10549 | Return the number of memory blocks currently allocated by the interpreter, regardless of their size. This function is mainly useful for tracking and debugging memory leaks. Because of the interpreter’s internal caches, the result can vary from call to call; you may have to call _clear_type_cache() and gc.collect() to g... | |
doc_10550 | Simple test application that dumps the environment. You can use it to check if Werkzeug is working properly: >>> from werkzeug.serving import run_simple
>>> from werkzeug.testapp import test_app
>>> run_simple('localhost', 3000, test_app)
* Running on http://localhost:3000/
The application displays important informat... | |
doc_10551 | See Migration guide for more details. tf.compat.v1.manip.space_to_batch_nd, tf.compat.v1.space_to_batch_nd
tf.space_to_batch_nd(
input, block_shape, paddings, name=None
)
This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape block_shape, and interleaves these blocks ... | |
doc_10552 | tf.keras.layers.LSTM(
units, activation='tanh', recurrent_activation='sigmoid',
use_bias=True, kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros', unit_forget_bias=True,
kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None,
act... | |
doc_10553 |
Call self as a function. | |
doc_10554 | Clear some cookies. If invoked without arguments, clear all cookies. If given a single argument, only cookies belonging to that domain will be removed. If given two arguments, cookies belonging to the specified domain and URL path are removed. If given three arguments, then the cookie with the specified domain, path an... | |
doc_10555 |
Orthogonal Matching Pursuit model (OMP). Read more in the User Guide. Parameters
n_nonzero_coefsint, default=None
Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.
tolfloat, default=None
Maximum norm of the residual. If not None, overrides n_non... | |
doc_10556 | Default widget: URLInput
Empty value: Whatever you’ve given as empty_value. Normalizes to: A string. Uses URLValidator to validate that the given value is a valid URL. Error message keys: required, invalid
Has three optional arguments max_length, min_length, and empty_value which work just as they do for CharField. | |
doc_10557 |
The week ordinal of the year. Deprecated since version 1.1.0. weekofyear and week have been deprecated. Please use DatetimeIndex.isocalendar().week instead. | |
doc_10558 | In range(1000000). | |
doc_10559 |
Update this artist's properties from the dict props. Parameters
propsdict | |
doc_10560 | Return the height of the turtle window. >>> screen.window_height()
480 | |
doc_10561 |
Return the number of nanoseconds (n), where 0 <= n < 1 microsecond. Returns
int
Number of nanoseconds. See also Timedelta.components
Return all attributes with assigned values (i.e. days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds). Examples Using string input
>>> td = pd.Timedelt... | |
doc_10562 |
Return how much space should be reserved for text above and below the axes, as a pair of floats. | |
doc_10563 | Return a list of the nested symbol tables. | |
doc_10564 | Return a readable and writable view over the contents of the buffer without copying them. Also, mutating the view will transparently update the contents of the buffer: >>> b = io.BytesIO(b"abcdef")
>>> view = b.getbuffer()
>>> view[2:4] = b"56"
>>> b.getvalue()
b'ab56ef'
Note As long as the view exists, the BytesIO o... | |
doc_10565 | The corresponding error message, as provided by the operating system. It is formatted by the C functions perror() under POSIX, and FormatMessage() under Windows. | |
doc_10566 |
Function that computes the dot product between the Hessian of a given scalar function and a vector v at the point given by the inputs. Parameters
func (function) – a Python function that takes Tensor inputs and returns a Tensor with a single element.
inputs (tuple of Tensors or Tensor) – inputs to the function f... | |
doc_10567 | ABC for classes that provide __aiter__ and __anext__ methods. See also the definition of asynchronous iterator. New in version 3.5. | |
doc_10568 |
Draw a unstructured triangular grid as lines and/or markers. The triangulation to plot can be specified in one of two ways; either: triplot(triangulation, ...)
where triangulation is a Triangulation object, or triplot(x, y, ...)
triplot(x, y, triangles, ...)
triplot(x, y, triangles=triangles, ...)
triplot(x, y, mask... | |
doc_10569 |
Registers a backward hook on the module. The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature: hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The grad_input and grad_output are tuples that contain the gradients with ... | |
doc_10570 | sklearn.datasets.make_sparse_coded_signal(n_samples, *, n_components, n_features, n_nonzero_coefs, random_state=None) [source]
Generate a signal as a sparse combination of dictionary elements. Returns a matrix Y = DX, such as D is (n_features, n_components), X is (n_components, n_samples) and each column of X has exa... | |
doc_10571 | Abstract. Retrieves the real component of this number. | |
doc_10572 |
Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Parameters
module (nn.Module) – module containing the tensor to prune
name (str) – parameter name within module on which pruning will act.
amount (int or float) –... | |
doc_10573 | Represents from x import y. module is a raw string of the ‘from’ name, without any leading dots, or None for statements such as from . import foo. level is an integer holding the level of the relative import (0 means absolute import). >>> print(ast.dump(ast.parse('from y import x,y,z'), indent=4))
Module(
body=[
... | |
doc_10574 |
Apply the approximate feature map to X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
New data, where n_samples in the number of samples and n_features is the number of features. Returns
X_newarray-like, shape (n_samples, n_components) | |
doc_10575 |
Return the submatrix corresponding to bicluster i. Parameters
iint
The index of the cluster.
dataarray-like of shape (n_samples, n_features)
The data. Returns
submatrixndarray of shape (n_rows, n_cols)
The submatrix corresponding to bicluster i. Notes Works with sparse matrices. Only works if ro... | |
doc_10576 | Release the lock. When the lock is locked, reset it to unlocked and return. If the lock is unlocked, a RuntimeError is raised. | |
doc_10577 | tf.compat.v1.layers.average_pooling1d(
inputs, pool_size, strides, padding='valid',
data_format='channels_last', name=None
)
Arguments
inputs The tensor over which to pool. Must have rank 3.
pool_size An integer or tuple/list of a single integer, representing the size of the pooling window.
... | |
doc_10578 |
Get Exponential power of dataframe and other, element-wise (binary operator rpow). Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow. Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /,... | |
doc_10579 | tf.compat.v1.estimator.classifier_parse_example_spec(
feature_columns, label_key, label_dtype=tf.dtypes.int64, label_default=None,
weight_column=None
)
If users keep data in tf.Example format, they need to call tf.parse_example with a proper feature spec. There are two main things that this utility helps: Use... | |
doc_10580 | A generic version of collections.abc.Awaitable. New in version 3.5.2. Deprecated since version 3.9: collections.abc.Awaitable now supports []. See PEP 585 and Generic Alias Type. | |
doc_10581 | See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeueUpToV2
tf.raw_ops.QueueDequeueUpToV2(
handle, n, component_types, timeout_ms=-1, name=None
)
This operation is not supported by all queues. If a queue does not support DequeueUpTo, then an Unimplemented error is returned. If the queue is clos... | |
doc_10582 |
Registry to link constraints to transforms.
register(constraint, factory=None) [source]
Registers a Constraint subclass in this registry. Usage: @my_registry.register(MyConstraintClass)
def construct_transform(constraint):
assert isinstance(constraint, MyConstraint)
return MyTransform(constraint.arg_const... | |
doc_10583 |
Apply the dimensionality reduction. Parameters
Xarray-like of shape (n_samples, n_features)
Samples to be transformed.
Yarray-like of shape (n_samples,) or (n_samples, n_targets), default=None
Targets. Returns
outarray-like or tuple of array-like
The transformed data X_tranformed if Y is not None, (... | |
doc_10584 |
set_transform is ignored. | |
doc_10585 |
Return the Transform instance used by this artist offset. | |
doc_10586 | New in Django 3.2. Optional. A boolean attribute. When used in conjunction with i18n generated URLs will each have a list of alternate links pointing to other language versions using the hreflang attribute. The default is False. | |
doc_10587 |
Bases: object An object for positioning an inset axes. This is created by specifying the normalized coordinates in the axes, instead of the figure. Parameters
parentmatplotlib.axes.Axes
Axes to use for normalizing coordinates.
lbwhiterable of four floats
The left edge, bottom edge, width, and height of the ... | |
doc_10588 | Sets the store’s default timeout. This timeout is used during initialization and in wait() and get(). Parameters
timeout (timedelta) – timeout to be set in the store. Example::
>>> import torch.distributed as dist
>>> from datetime import timedelta
>>> # Using TCPStore as an example, other store types can also be... | |
doc_10589 |
Create a stem plot. A stem plot draws lines perpendicular to a baseline at each location locs from the baseline to heads, and places a marker there. For vertical stem plots (the default), the locs are x positions, and the heads are y values. For horizontal stem plots, the locs are y positions, and the heads are x val... | |
doc_10590 |
Returns True if input arrays are shape consistent and all elements equal. Shape consistent means they are either the same shape, or one input array can be broadcasted to create the same shape as the other one. Parameters
a1, a2array_like
Input arrays. Returns
outbool
True if equivalent, False otherwise.... | |
doc_10591 | namedtuple() factory function for creating tuple subclasses with named fields
deque list-like container with fast appends and pops on either end
ChainMap dict-like class for creating a single view of multiple mappings
Counter dict subclass for counting hashable objects
OrderedDict dict subclass that remembers t... | |
doc_10592 | Divide the normal distribution into n continuous intervals with equal probability. Returns a list of (n - 1) cut points separating the intervals. Set n to 4 for quartiles (the default). Set n to 10 for deciles. Set n to 100 for percentiles which gives the 99 cuts points that separate the normal distribution into 100 eq... | |
doc_10593 | The file may be archived. | |
doc_10594 | See Migration guide for more details. tf.compat.v1.raw_ops.Spence
tf.raw_ops.Spence(
x, name=None
)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | |
doc_10595 |
Approximate a kernel map using a subset of the training data. Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis. Read more in the User Guide. New in version 0.13. Parameters
kernelstring or callable, default=’rbf’
Kernel map to be approximated. A callable should... | |
doc_10596 |
Return coordinate matrices from coordinate vectors. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn. Changed in version 1.9: 1-D and 0-D cases are allowed. Parameters
x1, x2,…, xnarray_like
1-D arrays repres... | |
doc_10597 |
Return an array with the elements of self converted to uppercase. See also char.upper | |
doc_10598 | Convert a URI to an IRI. All valid UTF-8 characters are unquoted, leaving all reserved and invalid characters quoted. If the URL has a domain, it is decoded from Punycode. >>> uri_to_iri("http://xn--n3h.net/p%C3%A5th?q=%C3%A8ry%DF")
'http://\u2603.net/p\xe5th?q=\xe8ry%DF'
Parameters
uri (Union[str, Tuple[str, str,... | |
doc_10599 | Computes the error function of each element. The error function is defined as follows: erf(x)=2π∫0xe−t2dt\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
Parameters
input (Tensor) – the input tensor. Keyword Arguments
out (Tensor, optional) – the output tensor. Example: >>> torch.erf(torch.tenso... |
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