_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_500 | The original exception that caused this 500 error. Can be used by frameworks to provide context when handling unexpected errors. | |
doc_501 |
Reconstruct the image from all of its patches. Patches are assumed to overlap and the image is constructed by filling in the patches from left to right, top to bottom, averaging the overlapping regions. Read more in the User Guide. Parameters
patchesndarray of shape (n_patches, patch_height, patch_width) or (n_pa... | |
doc_502 | See Migration guide for more details. tf.compat.v1.raw_ops.ApplyCenteredRMSProp
tf.raw_ops.ApplyCenteredRMSProp(
var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None
)
The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization,... | |
doc_503 | tf.eig
tf.linalg.eig(
tensor, name=None
)
The eigenvalues and eigenvectors for a non-Hermitian matrix in general are complex. The eigenvectors are not guaranteed to be linearly independent. Computes the eigenvalues and right eigenvectors of the innermost N-by-N matrices in tensor such that tensor[...,:,:] * v[..... | |
doc_504 |
Return a normalized rgba array corresponding to x. In the normal case, x is a 1D or 2D sequence of scalars, and the corresponding ndarray of rgba values will be returned, based on the norm and colormap set for this ScalarMappable. There is one special case, for handling images that are already rgb or rgba, such as mi... | |
doc_505 |
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is the number of features. X_test ... | |
doc_506 |
Gram Orthogonal Matching Pursuit (OMP). Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. Read more in the User Guide. Parameters
Gramndarray of shape (n_features, n_features)
Gram matrix of the input data: X.T * X.
Xyndarray of shape (n_features... | |
doc_507 | Exception raised by the Bdb class for quitting the debugger. | |
doc_508 | Exception raised when a reply is received from the server that does not begin with a digit in the range 1–5. | |
doc_509 | tf.experimental.numpy.true_divide(
x1, x2
)
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.true_divide. | |
doc_510 | A string describing the name of the email field on the User model. This value is returned by get_email_field_name(). | |
doc_511 |
Integer number of levels in this MultiIndex. Examples
>>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
>>> mi
MultiIndex([('a', 'b', 'c')],
)
>>> mi.nlevels
3 | |
doc_512 | See Migration guide for more details. tf.compat.v1.raw_ops.SparseConcat
tf.raw_ops.SparseConcat(
indices, values, shapes, concat_dim, name=None
)
Concatenation is with respect to the dense versions of these sparse tensors. It is assumed that each input is a SparseTensor whose elements are ordered along increasin... | |
doc_513 | Create a “child” parser which can be used to parse an external parsed entity referred to by content parsed by the parent parser. The context parameter should be the string passed to the ExternalEntityRefHandler() handler function, described below. The child parser is created with the ordered_attributes and specified_at... | |
doc_514 | The system identifier for the external subset of the document type definition. This will be a URI as a string, or None. | |
doc_515 | Line2D(xdata, ydata[, linewidth, linestyle, ...]) A line - the line can have both a solid linestyle connecting all the vertices, and a marker at each vertex.
VertexSelector(line) Manage the callbacks to maintain a list of selected vertices for Line2D. Functions
segment_hits(cx, cy, x, y, radius) Return the indi... | |
doc_516 | Returns a combined list of strings representing all file suffixes for modules recognized by the standard import machinery. This is a helper for code which simply needs to know if a filesystem path potentially refers to a module without needing any details on the kind of module (for example, inspect.getmodulename()). N... | |
doc_517 |
Forward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.ffill
Returns Series with minimum number of char in object. DataFrame.ffill
Object with missing values filled or None if inplace... | |
doc_518 |
The Python Tkinter Topic Guide provides a great deal of information on using Tk from Python and links to other sources of information on Tk. TKDocs
Extensive tutorial plus friendlier widget pages for some of the widgets. Tkinter 8.5 reference: a GUI for Python
On-line reference material. Tkinter docs from effbot
... | |
doc_519 | See Migration guide for more details. tf.compat.v1.estimator.CheckpointSaverListener, tf.compat.v1.train.CheckpointSaverListener CheckpointSaverListener triggers only in steps when CheckpointSaverHook is triggered, and provides callbacks at the following points: before using the session before each call to Saver.save... | |
doc_520 | Create and return a SAX XMLReader object. The first parser found will be used. If parser_list is provided, it must be an iterable of strings which name modules that have a function named create_parser(). Modules listed in parser_list will be used before modules in the default list of parsers. Changed in version 3.8: T... | |
doc_521 |
Fit the random classifier. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
selfobject | |
doc_522 |
Build or fetch the effective stop words list. Returns
stop_words: list or None
A list of stop words. | |
doc_523 |
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Parameters
valuesarray
The input v... | |
doc_524 |
Remove the artist from the figure if possible. The effect will not be visible until the figure is redrawn, e.g., with FigureCanvasBase.draw_idle. Call relim to update the axes limits if desired. Note: relim will not see collections even if the collection was added to the axes with autolim = True. Note: there is no su... | |
doc_525 |
Get the matrix for the affine part of this transform. | |
doc_526 |
Bases: mpl_toolkits.axisartist.axislines.AxisArtistHelper._Base get_line(axes)[source]
get_nth_coord()[source] | |
doc_527 |
Make a step plot. Call signatures: step(x, y, [fmt], *, data=None, where='pre', **kwargs)
step(x, y, [fmt], x2, y2, [fmt2], ..., *, where='pre', **kwargs)
This is just a thin wrapper around plot which changes some formatting options. Most of the concepts and parameters of plot can be used here as well. Note This me... | |
doc_528 |
Alias for get_linestyle. | |
doc_529 |
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shap... | |
doc_530 | Returns a tensor with all the dimensions of input of size 1 removed. For example, if input is of shape: (A×1×B×C×1×D)(A \times 1 \times B \times C \times 1 \times D) then the out tensor will be of shape: (A×B×C×D)(A \times B \times C \times D) . When dim is given, a squeeze operation is done only in the given dimensio... | |
doc_531 | skimage.graph.route_through_array(array, …) Simple example of how to use the MCP and MCP_Geometric classes.
skimage.graph.shortest_path(arr[, reach, …]) Find the shortest path through an n-d array from one side to another.
skimage.graph.MCP(costs[, offsets, …]) A class for finding the minimum cost path through a gi... | |
doc_532 |
Connect the callback function func to button click events. Returns a connection id, which can be used to disconnect the callback. | |
doc_533 | Backend gloo mpi nccl
Device CPU GPU CPU GPU CPU GPU
send ✓ ✘ ✓ ? ✘ ✘
recv ✓ ✘ ✓ ? ✘ ✘
broadcast ✓ ✓ ✓ ? ✘ ✓
all_reduce ✓ ✓ ✓ ? ✘ ✓
reduce ✓ ✘ ✓ ? ✘ ✓
all_gather ✓ ✘ ✓ ? ✘ ✓
gather ✓ ✘ ✓ ? ✘ ✘
scatter ✓ ✘ ✓ ? ✘ ✘
reduce_scatter ✘ ✘ ✘ ✘ ✘ ✓
all_to_all ✘ ✘ ✓ ? ✘ ✘
barrier ✓ ✘ ✓ ? ✘ ✓ Backends t... | |
doc_534 |
Return the canvas width and height in display coords. | |
doc_535 | Contains the Python system version, in a form usable by the version_string method and the server_version class variable. For example, 'Python/1.4'. | |
doc_536 | See Migration guide for more details. tf.compat.v1.nest.assert_same_structure
tf.nest.assert_same_structure(
nest1, nest2, check_types=True, expand_composites=False
)
Note that namedtuples with identical name and fields are always considered to have the same shallow structure (even with check_types=True). For in... | |
doc_537 | Returns the currently-set application callable. | |
doc_538 |
Bases: matplotlib.axis.YTick A radial-axis tick. This subclass of YTick provides radial ticks with some small modification to their re-positioning such that ticks are rotated based on axes limits. This results in ticks that are correctly perpendicular to the spine. Labels are also rotated to be perpendicular to the s... | |
doc_539 |
Concatenate two or more Series. Parameters
to_append:Series or list/tuple of Series
Series to append with self.
ignore_index:bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
verify_integrity:bool, default False
If True, raise Exception on creating index with duplicates. ... | |
doc_540 | See Migration guide for more details. tf.compat.v1.distribute.RunOptions
tf.distribute.RunOptions(
experimental_enable_dynamic_batch_size=True,
experimental_bucketizing_dynamic_shape=False
)
This can be used to hold some strategy specific configs.
Attributes
experimental_enable_dynamic_batch_size Bo... | |
doc_541 | Return True if s is a Python keyword. | |
doc_542 | The reset_mock method resets all the call attributes on a mock object: >>> mock = Mock(return_value=None)
>>> mock('hello')
>>> mock.called
True
>>> mock.reset_mock()
>>> mock.called
False
Changed in version 3.6: Added two keyword only argument to the reset_mock function. This can be useful where you want to make a ... | |
doc_543 |
Alias for set_markerfacecolor. | |
doc_544 |
Set the keymap to associate with the specified tool. Parameters
namestr
Name of the Tool.
keystr or list of str
Keys to associate with the tool. | |
doc_545 | tf.metrics.MeanIoU Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.metrics.MeanIoU
tf.keras.metrics.MeanIoU(
num_classes, name=None, dtype=None
)
Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each... | |
doc_546 | See Migration guide for more details. tf.compat.v1.data.experimental.dense_to_sparse_batch
tf.data.experimental.dense_to_sparse_batch(
batch_size, row_shape
)
Like Dataset.padded_batch(), this transformation combines multiple consecutive elements of the dataset, which might have different shapes, into a single e... | |
doc_547 | Gets or sets the HSVA representation of the Color. hsva -> tuple The HSVA representation of the Color. The HSVA components are in the ranges H = [0, 360], S = [0, 100], V = [0, 100], A = [0, 100]. Note that this will not return the absolutely exact HSV values for the set RGB values in all cases. Due to the RGB mappin... | |
doc_548 | (Only supported on Linux 2.5.44 and newer.) Return an edge polling object, which can be used as Edge or Level Triggered interface for I/O events. sizehint informs epoll about the expected number of events to be registered. It must be positive, or -1 to use the default. It is only used on older systems where epoll_creat... | |
doc_549 | Returns the average of the dependent variable (sum(y)/N) as a float, or default if there aren’t any matching rows. | |
doc_550 |
Launch a subplot tool window for a figure. Returns
matplotlib.widgets.SubplotTool | |
doc_551 | tf.estimator.BaselineRegressor(
model_dir=None, label_dimension=1, weight_column=None,
optimizer='Ftrl', config=None,
loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
)
This regressor ignores feature values and will learn to predict the average value of each label. Example:
# Build BaselineRegr... | |
doc_552 | A variable annotated with C may accept a value of type C. In contrast, a variable annotated with Type[C] may accept values that are classes themselves – specifically, it will accept the class object of C. For example: a = 3 # Has type 'int'
b = int # Has type 'Type[int]'
c = type(a) # Also has type 'Typ... | |
doc_553 | class sklearn.base.TransformerMixin [source]
Mixin class for all transformers in scikit-learn. Methods
fit_transform(X[, y]) Fit to data, then transform it.
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and retur... | |
doc_554 |
Bases: matplotlib.offsetbox.OffsetBox Offset Box with the aux_transform. Its children will be transformed with the aux_transform first then will be offsetted. The absolute coordinate of the aux_transform is meaning as it will be automatically adjust so that the left-lower corner of the bounding box of children will b... | |
doc_555 | Casts all floating point parameters and buffers to float datatype. Returns
self Return type
Module | |
doc_556 |
Load data from a text file. Each row in the text file must have the same number of values. Parameters
fnamefile, str, pathlib.Path, list of str, generator
File, filename, list, or generator to read. If the filename extension is .gz or .bz2, the file is first decompressed. Note that generators must return bytes ... | |
doc_557 |
Create an array. Parameters
data:Sequence of objects
The scalars inside data should be instances of the scalar type for dtype. It’s expected that data represents a 1-dimensional array of data. When data is an Index or Series, the underlying array will be extracted from data.
dtype:str, np.dtype, or ExtensionD... | |
doc_558 |
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | |
doc_559 | Write all items (as machine values) to the file object f. | |
doc_560 | A dictionary mapping endpoint names to view functions. To register a view function, use the route() decorator. This data structure is internal. It should not be modified directly and its format may change at any time. | |
doc_561 | A 32-bit number in big-endian format. | |
doc_562 |
One-dimensional ndarray with axis labels (including time series). Labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to aut... | |
doc_563 |
The day of the week with Monday=0, Sunday=6. | |
doc_564 | A shlex instance or subclass instance is a lexical analyzer object. The initialization argument, if present, specifies where to read characters from. It must be a file-/stream-like object with read() and readline() methods, or a string. If no argument is given, input will be taken from sys.stdin. The second optional ar... | |
doc_565 | Computes and returns a pruned version of input tensor t according to the pruning rule specified in compute_mask(). Parameters
t (torch.Tensor) – tensor to prune (of same dimensions as default_mask).
importance_scores (torch.Tensor) – tensor of importance scores (of same shape as t) used to compute mask for pruning... | |
doc_566 | tf.compat.v1.nn.rnn_cell.DeviceWrapper(
*args, **kwargs
)
Args
cell An instance of RNNCell.
device A device string or function, for passing to tf.device.
**kwargs dict of keyword arguments for base layer.
Attributes
graph
output_size
scope_name
state_size
Metho... | |
doc_567 | tf.metrics.deserialize Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.metrics.deserialize
tf.keras.metrics.deserialize(
config, custom_objects=None
)
Arguments
config Metric configuration.
custom_objects Optional dictionary mapping names (strings) to custom o... | |
doc_568 |
Compute the medial axis transform of a binary image Parameters
imagebinary ndarray, shape (M, N)
The image of the shape to be skeletonized.
maskbinary ndarray, shape (M, N), optional
If a mask is given, only those elements in image with a true value in mask are used for computing the medial axis.
return_d... | |
doc_569 | Send normal and ancillary data to the socket, gathering the non-ancillary data from a series of buffers and concatenating it into a single message. The buffers argument specifies the non-ancillary data as an iterable of bytes-like objects (e.g. bytes objects); the operating system may set a limit (sysconf() value SC_IO... | |
doc_570 | See Migration guide for more details. tf.compat.v1.raw_ops.SparseApplyFtrl
tf.raw_ops.SparseApplyFtrl(
var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False,
multiply_linear_by_lr=False, name=None
)
That is for rows we have grad for, we update var, accum and linear as follows: $$accum_n... | |
doc_571 | Subtypes Real and adds numerator and denominator properties, which should be in lowest terms. With these, it provides a default for float().
numerator
Abstract.
denominator
Abstract. | |
doc_572 | New in Django 3.2. The database collation name of the field. Note Collation names are not standardized. As such, this will not be portable across multiple database backends. Oracle Oracle does not support collations for a TextField. | |
doc_573 |
Diff two sets of counts. One common reason to collect instruction counts is to determine the the effect that a particular change will have on the number of instructions needed to perform some unit of work. If a change increases that number, the next logical question is “why”. This generally involves looking at what p... | |
doc_574 | Set prepopulated_fields to a dictionary mapping field names to the fields it should prepopulate from: class ArticleAdmin(admin.ModelAdmin):
prepopulated_fields = {"slug": ("title",)}
When set, the given fields will use a bit of JavaScript to populate from the fields assigned. The main use for this functionality is... | |
doc_575 | begin sound playback play(loops=0, maxtime=0, fade_ms=0) -> Channel Begin playback of the Sound (i.e., on the computer's speakers) on an available Channel. This will forcibly select a Channel, so playback may cut off a currently playing sound if necessary. The loops argument controls how many times the sample will be... | |
doc_576 | Return the entire message flattened as a string. When optional unixfrom is true, the envelope header is included in the returned string. unixfrom defaults to False. For backward compatibility reasons, maxheaderlen defaults to 0, so if you want a different value you must override it explicitly (the value specified for m... | |
doc_577 | Alias for torch.le(). | |
doc_578 | See Migration guide for more details. tf.compat.v1.raw_ops.GroupByReducerDataset
tf.raw_ops.GroupByReducerDataset(
input_dataset, key_func_other_arguments, init_func_other_arguments,
reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func,
reduce_func, finalize_func, output_types, ... | |
doc_579 | class sklearn.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False) [source]
Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine sev... | |
doc_580 |
Return POSIX timestamp as float. Examples
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
>>> ts.timestamp()
1584199972.192548 | |
doc_581 | The OptionMenu creates a menu button of options. | |
doc_582 | sklearn.datasets.fetch_covtype(*, data_home=None, download_if_missing=True, random_state=None, shuffle=False, return_X_y=False, as_frame=False) [source]
Load the covertype dataset (classification). Download it if necessary.
Classes 7
Samples total 581012
Dimensionality 54
Features int Read more in the User ... | |
doc_583 |
Generate Kernel Density Estimate plot using Gaussian kernels. In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function (PDF) of a random variable. This function uses Gaussian kernels and includes automatic bandwidth determination. Parameters
bw_method:str... | |
doc_584 | os.O_DIRECT
os.O_DIRECTORY
os.O_NOFOLLOW
os.O_NOATIME
os.O_PATH
os.O_TMPFILE
os.O_SHLOCK
os.O_EXLOCK
The above constants are extensions and not present if they are not defined by the C library. Changed in version 3.4: Add O_PATH on systems that support it. Add O_TMPFILE, only available on Linux Kernel ... | |
doc_585 | Replace %xx escapes with their single-octet equivalent, and return a bytes object. string may be either a str or a bytes object. If it is a str, unescaped non-ASCII characters in string are encoded into UTF-8 bytes. Example: unquote_to_bytes('a%26%EF') yields b'a&\xef'. | |
doc_586 | If flag is True, curses will try and use hardware line editing facilities. Otherwise, line insertion/deletion are disabled. | |
doc_587 |
Set the outer radial limit. Parameters
rmaxfloat | |
doc_588 | Round to nearest with ties going away from zero. | |
doc_589 | The plural name for the object: verbose_name_plural = "stories"
If this isn’t given, Django will use verbose_name + "s". | |
doc_590 | In [1]: df = pd.DataFrame(
...: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]}
...: )
...:
In [2]: df
Out[2]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
if-then… An if-then on one column
In [3]: df.loc[df.AAA >= 5, "BBB"] = -1
In... | |
doc_591 | The browser version, if it could be parsed from the string. | |
doc_592 | Token value for "//". | |
doc_593 |
Call transform on the estimator with the best found parameters. Only available if the underlying estimator supports transform and refit=True. Parameters
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator. | |
doc_594 |
Constant kernel. Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, where it modifies the mean of the Gaussian process. \[k(x_1, x_2) = constant\_value \;\forall\; x_1, x_2\] Adding a constant kernel is equivalent to adding a constant: kerne... | |
doc_595 |
Immutable Index implementing a monotonic integer range. RangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Using RangeIndex may in some instances improve computing speed. This is the default index type used by DataFrame and Series when no explicit index is provided by t... | |
doc_596 | Subclass of RawTurtle, has the same interface but draws on a default Screen object created automatically when needed for the first time. | |
doc_597 |
Compute elastic net path with coordinate descent. The elastic net optimization function varies for mono and multi-outputs. For mono-output tasks it is: 1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
For multi-output tasks it is: (1 / (2 * n_samples)) * ||... | |
doc_598 |
Compute cluster centers and predict cluster index for each sample. Convenience method; equivalent to calling fit(X) followed by predict(X). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data to transform.
yIgnored
Not used, present here for API consistency by convention.
sam... | |
doc_599 |
Transform array or sparse matrix X back to feature mappings. X must have been produced by this DictVectorizer’s transform or fit_transform method; it may only have passed through transformers that preserve the number of features and their order. In the case of one-hot/one-of-K coding, the constructed feature names an... |
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