Structures | Collection of abstract data structures implemented in Java
kandi X-RAY | Structures Summary
kandi X-RAY | Structures Summary
Structures is a nice compilation of abstract data structures that we could think of! Since its inception, it is growing slowly into a huge collection of ADT journal. We have written the intuition behind developing each structure in its code as introductory comment block with algorithmic analysis and its merits and de-merits. The de-merits of one ADT leads to the motivation of creating another ADT, so you will find it like a chain story to browse :P.
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Top functions reviewed by kandi - BETA
- Inserts a new symbol into symbol table
- Resize keys array
- Resizes the array to a new size
- Adds an item to the end of the deque
- Inserts an element at the front of the queue
- Inserts a new data item into the symbol table
- Deletes the key with the given key
- Test program
- Returns the maximum depth of an expression
- Append item to end of list
- Appends the specified item to the list
- Returns the average degree of the graph
- Reverranges the array
- Adds an item to the top of the stack
- Inserts a string element at the end of the queue
- Removes a key
- Returns the item at the specified position in the list
- Bfs
- Command - line entry point
- Opens the site
- Removes a random item from the queue
- Returns a string representation of the graph
- Removes an item from the stack
- Removes the specified node at the specified position
- Ranges the given array
- Returns a string with the identical numbers
Structures Key Features
Structures Examples and Code Snippets
Lines of Code : 17
License : No License
Copyconst { fromJS } = require('immutable');
const nested = fromJS({ a: { b: { c: [3, 4, 5] } } });
// Map { a: Map { b: Map { c: List [ 3, 4, 5 ] } } }
const { fromJS } = require('immutable');
const nested = fromJS({ a: { b: { c: [3, 4, 5] } } });
co def map_structure(func, *structure, **kwargs):
"""Creates a new structure by applying `func` to each atom in `structure`.
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
for the definition of a structure.
Applies `fun def assert_same_structure(nest1, nest2, check_types=True,
expand_composites=False):
"""Asserts that two structures are nested in the same way.
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
for def is_same_structure(structure1,
structure2,
check_values=False):
"""Check two structures for equality, optionally of types and of values."""
try:
nest.assert_same_structure(structure1, structure2, Community Discussions
Trending Discussions on Structures
QUESTION
Lazy fold uses a lot of RAM. In Data.List, foldl' provides a left fold that uses strict evaluation. For example, the following computes the sum of 10 million zeros with little increase in RAM usage.
ANSWER
Answered 2022-Apr-03 at 01:58foldl' only evaluates the intermediate state to weak head normal form—i.e. up to the first constructor. That's the most a generic function can do, and what functions that are called "strict" generally do. Evaluating (x1, y1) <+> (x2, y2) until it looks like a constructor gives (x1 + x2, y1 + y2), where the parts are still unevaluated (they have been "protected" by the (,)). Through the iteration, foldl' being strict keeps the state in the form (_, _) instead of (_, _) <+> (_, _) <+> ..., but the _s grow into huge unevaluated terms of form _ + _ + _ + ....
Modify <+> to evaluate the additions before it exposes the constructor.
QUESTION
Update: the root issue was a bug which was fixed in Spark 3.2.0.
Input df structures are identic in both runs, but outputs are different. Only the second run returns desired result (df6). I know I can use aliases for dataframes which would return desired result.
The question. What is the underlying Spark mechanics in creating df3? Spark reads df1.c1 == df2.c2 in the join's on clause, but it's evident that it does not pay attention to the dfs provided. What's under the hood there? How to anticipate such behaviour?
First run (incorrect df3 result):
ANSWER
Answered 2021-Sep-24 at 16:19Spark for some reason doesn't distinguish your c1 and c2 columns correctly. This is the fix for df3 to have your expected result:
QUESTION
Suppose that I have implemented two Python types using the C extension API and that the types are identical (same data layouts/C struct) with the exception of their names and a few methods. Assuming that all methods respect the data layout, can you safely change the type of an object from one of these types into the other in a C function?
Notably, as of Python 3.9, there appears to be a function Py_SET_TYPE, but the documentation is not clear as to whether/when this is safe to do. I'm interested in knowing both how to use this function safely and whether types can be safely changed prior to version 3.9.
I'm writing a Python C extension to implement a Persistent Hash Array Mapped Trie (PHAMT); in case it's useful, the source code is here (as of writing, it is at this commit). A feature I would like to add is the ability to create a Transient Hash Array Mapped Trie (THAMT) from a PHAMT. THAMTs can be created from PHAMTs in O(1) time and can be mutated in-place efficiently. Critically, THAMTs have the exact same underlying C data-structure as PHAMTs—the only real difference between a PHAMT and a THAMT is a few methods encapsulated by their Python types. This common structure allows one to very efficiently turn a THAMT back into a PHAMT once one has finished performing a set of edits. (This pattern typically reduces the number of memory allocations when performing a large number of updates to a PHAMT).
A very convenient way to implement the conversion from THAMT to PHAMT would be to simply change the type pointers of the THAMT objects from the THAMT type to the PHAMT type. I am confident that I can write code that safely navigates this change, but I can imagine that doing so might, for example, break the Python garbage collector.
(To be clear: the motivation is just context as to how the question arose. I'm not looking for help implementing the structures described in the Motivation, I'm looking for an answer to the Question, above.)
...ANSWER
Answered 2022-Mar-02 at 01:13According to the language reference, chapter 3 "Data model" (see here):
An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The type() function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable.[1]
which, to my mind states that the type must never change, and changing it would be illegal as it would break the language specification. The footnote however states that
[1] It is possible in some cases to change an object’s type, under certain controlled conditions. It generally isn’t a good idea though, since it can lead to some very strange behaviour if it is handled incorrectly.
I don't know of any method to change the type of an object from within python itself, so the "possible" may indeed refer to the CPython function.
As far as I can see a PyObject is defined internally as a
QUESTION
I have a simple structure like:
...ANSWER
Answered 2021-Dec-08 at 11:27For number 2 you can try to check out my pod_reflection library:
QUESTION
I'm trying to adapt some layers of existing C++ code to be used by Rust and apparently the way is through a C API.
For example, one function might return a struct as an object
...ANSWER
Answered 2022-Jan-21 at 01:15extern "C" on both sides + #[repr(C)] on the Rust side + only using C-compatible types for interfacing between C++ and Rust, should work.
QUESTION
I am trying to write some APL code that will take a matrix and give back the matrix and all its 90 degree rotations.
As a rotation function I have: {(⌽∘⍉)⍣⍺⊢⍵} , which takes a matrix on the right and number of CW rotations on the left, and it seems to work fine on its own.
To generate all 4 output arrays, I have tried to use the outer product like this:
...ANSWER
Answered 2022-Jan-17 at 05:40What you need is
QUESTION
I understand there are a variety of techniques for sharing memory and data structures between processes in python. This question is specifically about this inherently shared memory in python scripts that existed in python 3.6 but seems to no longer exist in 3.10. Does anyone know why and if it's possible to bring this back in 3.10? Or what this change that I'm observing is? I've upgraded my Mac to Monterey and it no longer supports python 3.6, so I'm forced to upgrade to either 3.9 or 3.10+.
Note: I tend to develop on Mac and run production on Ubuntu. Not sure if that factors in here. Historically with 3.6, everything behaved the same regardless of OS.
Make a simple project with the following python files
myLibrary.py
...ANSWER
Answered 2022-Jan-03 at 23:30In short, since 3.8, CPython uses the spawn start method on MacOs. Before it used the fork method.
On UNIX platforms, the fork start method is used which means that every new multiprocessing process is an exact copy of the parent at the time of the fork.
The spawn method means that it starts a new Python interpreter for each new multiprocessing process. According to the documentation:
The child process will only inherit those resources necessary to run the process object’s
run()method.
It will import your program into this new interpreter, so starting processes et cetera sould only be done from within the if __name__ == '__main__':-block!
This means you cannot count on variables from the parent process being available in the children, unless they are module level constants which would be imported.
So the change is significant.
What can be done?
If the required information could be a module-level constant, that would solve the problem in the simplest way.
If that is not possible (e.g. because the data needs to be generated at runtime) you could have the parent write the information to be shared to a file. E.g. in JSON format and before it starts other processes. Then the children could simply read this. That is probably the next simplest solution.
Using a multiprocessing.Manager would allow you to share a dict between processes. There is however a certain amount of overhead associated with this.
Or you could try calling multiprocessing.set_start_method("fork") before creating processes or pools and see if it doesn't crash in your case. That would revert to the pre-3.8 method on MacOs. But as documented in this bug, there are real problems with using the fork method on MacOs.
Reading the issue indicates that fork might be OK as long as you don't use threads.
QUESTION
For the sake of example let's define a toy automaton type:
...ANSWER
Answered 2021-Dec-24 at 00:37Laziness to the rescue. We can recursively define a list of all the sub-automata, such that their transitions index into that same list:
QUESTION
I'm using C# (.NET 5). Imagine I have a class that stores an array of structures (say, floats):
...ANSWER
Answered 2021-Dec-10 at 19:57To write you can do something like this:
QUESTION
After a career diversion, I am trying to get up to speed with std::views (and functional programming in general). I am using the '|' (pipe) operator with std::views::filter on a vector, and I am puzzled why some code structures compile and others don't.
This code creates a vector of vectors of int, then filters them by sum. I've commented the three statements that are confusing me, the first two of which compile and the third doesn't.
Compilation error is:
...ANSWER
Answered 2021-Dec-01 at 22:04This is passing an lvalue vector into filter:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install Structures
You can use Structures like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Structures component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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