lap | Linear Assignment Problem solver | Machine Learning library

by gatagat Python Version: 0.4.0 License: BSD-2-Clause

kandi X-RAY | lap Summary

lap is a Python library typically used in Artificial Intelligence, Machine Learning, Example Codes applications. lap has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However lap has 1 bugs. You can install using 'pip install lap' or download it from GitHub, PyPI.

lap is a [linear assignment problem] solver using Jonker-Volgenant algorithm for dense (LAPJV [1]) or sparse (LAPMOD [2]) matrices. Both algorithms are implemented from scratch based solely on the papers [1,2] and the public domain Pascal implementation provided by A. Volgenant [3]. In my tests the LAPMOD implementation seems to be faster than the LAPJV implementation for matrices with a side of more than ~5000 and with less than 50% finite coefficients. [1] R. Jonker and A. Volgenant, "A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems", Computing 38, 325-340 (1987) [2] A. Volgenant, "Linear and Semi-Assignment Problems: A Core Oriented Approach", Computer Ops Res. 23, 917-932 (1996) [3]

Support

Quality

Security

License

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kandi-support Support

lap has a low active ecosystem.
It has 96 star(s) with 26 fork(s). There are 4 watchers for this library.
It had no major release in the last 12 months.
There are 10 open issues and 11 have been closed. On average issues are closed in 60 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of lap is 0.4.0

lap Support

Best in #Machine Learning

Average in #Machine Learning

lap Support

Best in #Machine Learning

Average in #Machine Learning

quality kandi Quality

lap has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 54 code smells.

lap Quality

Best in #Machine Learning

Average in #Machine Learning

lap Quality

Best in #Machine Learning

Average in #Machine Learning

security Security

lap has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
lap code analysis shows 0 unresolved vulnerabilities.
There are 5 security hotspots that need review.

lap Security

Best in #Machine Learning

Average in #Machine Learning

lap Security

Best in #Machine Learning

Average in #Machine Learning

license License

lap is licensed under the BSD-2-Clause License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

lap License

Best in #Machine Learning

Average in #Machine Learning

lap License

Best in #Machine Learning

Average in #Machine Learning

build Reuse

lap releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
It has 1430 lines of code, 122 functions and 15 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.

lap Reuse

Best in #Machine Learning

Average in #Machine Learning

lap Reuse

Best in #Machine Learning

Average in #Machine Learning

Top functions reviewed by kandi - BETA

kandi has reviewed lap and discovered the below as its top functions. This is intended to give you an instant insight into lap implemented functionality, and help decide if they suit your requirements.

Evaluate the C function

Calculate the cost function
Calculates the cost of the cost function
Calculate hard data
Binary search for a given key

Setup the package

Wrapper function for Cythonize
Generate cython
Return the current status of numpy

Return a Configuration object

Evaluate a benchmark

Test the CSCY algorithm

Test the C_C_c_2_2

Test for the MODPY algorithm

Test for the C_C_c_c_c_c_1

Test the C_C

Evaluate the marginal assignment function

Get all kandi verified functions for this library.

lap Key Features

Linear Assignment Problem solver (LAPJV/LAPMOD).

lap Examples and Code Snippets

Community Discussions

Trending Discussions on lap

Construct non-overlapping datetime record (start, end datetime) dataframe
ReactJS taking data from API
Can I implement this kind of profiling code with a macro?
How to save time value in dynamically created li to input box with javascript
Flutter iOS Error Class AMSupportURLConnectionDelegate is implemented in both /usr/lib/libamsupport.dylib (0x203913130)
How to accelerate my written python code: function containing nested functions for classification of points by polygons
R+Tidyverse: Tibbles don't appear to store milliseconds
Memoize multi-dimensional recursive solutions in haskell
Edit part of a Message Embed (Discord.JS)
Wrong type casting in FOR-IN loop wrecks seemingly unrelated variable

Trending Discussions on lap

QUESTION

Construct non-overlapping datetime record (start, end datetime) dataframe

Asked 2022-Mar-22 at 13:56

I need to create a dataframe removing over-lapping start and end datetimes for multiple ids. I will use the start and end datetimes to aggregate values in a high frequency pandas dataframe, so I need to remove those overlapping datetimes in mst_df.

import pandas as pd
 
#Proxy reference dataframe
master = [['site a', '2021-07-08 00:00:00', '2021-07-08 10:56:00'], 
        ['site a', '2021-07-08 06:00:00',   '2021-07-08 12:00:00'], #slightly overlapping
        ['site a', '2021-07-08 17:36:00',   '2021-07-09 11:40:00'],
        ['site a', '2021-07-08 18:00:00',   '2021-07-09 11:40:00'], #overlapping
        ['site a', '2021-07-09 00:00:00',   '2021-07-09 05:40:00'], #overlapping
        ['site b', '2021-07-08 00:00:00',   '2021-07-08 10:24:00'],
        ['site b', '2021-07-08 06:00:00',   '2021-07-08 10:24:00'], #overlapping
        ['site b', '2021-07-08 17:32:00',   '2021-07-09 11:12:00'],
        ['site b', '2021-07-08 18:00:00',   '2021-07-09 11:12:00'], #overlapping
        ['site b', '2021-07-09 00:00:00',   '2021-07-09 13:00:00']] #slightly overlapping

 
mst_df = pd.DataFrame(master, columns = ['id', 'start', 'end'])
mst_df['start'] = pd.to_datetime(mst_df['start'], infer_datetime_format=True)
mst_df['end'] = pd.to_datetime(mst_df['end'], infer_datetime_format=True)

Desired DataFrame:

    id      start               end
    site a  2021-07-08 00:00:00 2021-07-08 12:00:00
    site a  2021-07-08 17:36:00 2021-07-09 11:40:00
    site b  2021-07-08 00:00:00 2021-07-08 10:24:00
    site b  2021-07-08 17:32:00 2021-07-09 13:00:00

ANSWER

Answered 2022-Mar-22 at 13:56

I don't know if pandas has special function for this. It has Interval.overlaping() to check if two ranges overlaps (and it works even with datetime) but I don't see function to merge these two ranges so it still needs own code for merging. Fortunately it is easy.

Rows are sorted by start so rows are not overlaping when previous_end < next_start and I use it in for-loop.

But first I group by site to work with every site separatelly.

Next I get first row (as previous) and run loop with other rows (as next) and check previous_end < next_start.

If it is True then I can put previous on list of results and get next as previous to work with rest of rows.

If it is False then I create new range from both rows and use it to work with rest of rows.

Finally I add previous to list.

After processing all groups I convert all to DataFrame.

import pandas as pd
 
#Proxy reference dataframe
master = [
    ['site a', '2021-07-08 00:00:00',   '2021-07-08 10:56:00'], 
    ['site a', '2021-07-08 06:00:00',   '2021-07-08 12:00:00'], # slightly overlapping
    ['site a', '2021-07-08 17:36:00',   '2021-07-09 11:40:00'],
    ['site a', '2021-07-08 18:00:00',   '2021-07-09 11:40:00'], # overlapping
    ['site a', '2021-07-09 00:00:00',   '2021-07-09 05:40:00'], # overlapping
    ['site b', '2021-07-08 00:00:00',   '2021-07-08 10:24:00'],
    ['site b', '2021-07-08 06:00:00',   '2021-07-08 10:24:00'], # overlapping
    ['site b', '2021-07-08 17:32:00',   '2021-07-09 11:12:00'],
    ['site b', '2021-07-08 18:00:00',   '2021-07-09 11:12:00'], # overlapping
    ['site b', '2021-07-09 00:00:00',   '2021-07-09 13:00:00']  # slightly overlapping
]

mst_df = pd.DataFrame(master, columns = ['id', 'start', 'end'])

mst_df['start'] = pd.to_datetime(mst_df['start'], infer_datetime_format=True)
mst_df['end']   = pd.to_datetime(mst_df['end'], infer_datetime_format=True)

result = []

for val, group in mst_df.groupby('id'):
    
    # get first
    prev = group.iloc[0]
    
    for idx, item in group[1:].iterrows():
        if prev['end'] < item['start']:
            # not overlapping - put previous to results and use next as previous
            result.append(prev)
            prev = item
        else:
            # overlappig - create on range start, end
            prev['start'] = min(prev['start'], item['start'])
            prev['end']   = max(prev['end'], item['end'])
    
    # add when there is no next item
    result.append(prev)

print(pd.DataFrame(result))

Result:

       id               start                 end
0  site a 2021-07-08 00:00:00 2021-07-08 12:00:00
2  site a 2021-07-08 17:36:00 2021-07-09 11:40:00
5  site b 2021-07-08 00:00:00 2021-07-08 10:24:00
7  site b 2021-07-08 17:32:00 2021-07-09 13:00:00

Source https://stackoverflow.com/questions/71564490

QUESTION

ReactJS taking data from API

Asked 2022-Mar-20 at 04:35

Hello guys i don't know how to take data from API.
To be more specific, i have a API like this:

{
  "data": {
    "free_courses": [
      {
        "id": 7,
        "title": "Kiến Thức Nhập Môn IT",
        "slug": "lessons-for-newbie",
        "description": "Để có cái nhìn tổng quan về ngành IT - Lập trình web các bạn nên xem các videos tại khóa này trước nhé.",
        "image": "courses/7.png",
      },
    ]
  }
}

In reactjs, i have something like this:

function Contact() { const [products, setProducts] = useState({});

useEffect(() => {
  const getProducts = async () => {
    try {
      const url = "https://api-gateway.fullstack.edu.vn/api/combined-courses";
      const response = await fetch(url);
      const responseJSON = await response.json();
      setProducts(responseJSON);
      //console.log("ResponseJSON:", responseJSON);
    } catch (error) {
      console.log("Failed to fetch products: ", error);
    }
  };
  getProducts();
}, []);
return (
  <>
    {
      <div className="contact-container">
        {products.map((product) => (
          <ul key={product._id}>
            <h1>{product.title}</h1>
            <h2>{product.description}</h2>
          </ul>
        ))}
      </div>
    }
  </>
);

}

When I refresh the page and I got an error like this: Uncaught TypeError: products.map is not a function

I can get the //console.log("ResponseJSON:", responseJSON); and see my data from console.log but when I render it on the website and it announced an error. Thus, anyone can help me to fix my problem.

Thank you for all your answers.

ANSWER

Answered 2022-Mar-20 at 04:35

Make your state as an array :

const [products, setProducts] = useState([])

Also , your response from the fetch should be an array ... or , you have to save it in the form of array , so that map function works properly.

Just add a check before you render elements of a list .

<div className="contact-container">
        {products?.length&&products.map((product) => (
          <ul key={product._id}>
            <h1>{product.title}</h1>
            <h2>{product.description}</h2>
          </ul>
        ))}
      </div>

This must solve your issue .

Source https://stackoverflow.com/questions/71541318

QUESTION

Can I implement this kind of profiling code with a macro?

Asked 2022-Mar-16 at 21:11

Not an expert on preprocessor macro tricks, so if the problem here is just that I'm not familiar with some common macro idiom I'd be happy with just a term to Google. X macros are about as far as I've got before and I'm pretty sure I can't do anything with them.

Right now I do some stuff like this in code:

std::size_t trial = 0;

std::array<std::array<uint64_t, 5>, MAX_TRIALS> results_f;

void f()
{
  unsigned int core;

  results_f[trial][0] = __rdtscp(&core);

  // do stuff

  results_f[trial][1] = __rdtscp(&core);

  // do some more stuff

  results_f[trial][2] = __rdtscp(&core);

  // do yet more stuff

  results_f[trial][3] = __rdtscp(&core);

  // do even more stuff

  results_f[trial][4] = __rdtscp(&core);

  if(++trial == MAX_TRIALS)
  {
    process_timestamps_f(results);
    trial = 0;
  }
}

(Here __rdtscp is an x86 intrinsic that gets a tick number from the CPU.)

I would instead like to be able to write something like so:

STOPWATCH_BOILERPLATE_PRE(f);

void f()
{
  STOPWATCH_BEGIN;

  // do stuff

  STOPWATCH_LAP;  

  // do some more stuff

  STOPWATCH_LAP;

  // do yet more stuff

  STOPWATCH_LAP;

  // do even more stuff

  STOPWATCH_END; 
};

STOPWATCH_BOILERPLATE_POST(f);

So basically, I need to be able to count the number of times that STOPWATCH_LAP appears inside of f and use it to set the size of an array which is visible inside of f.

Bonus: it would be nice if, inside of process_timestamps_f, I can write something like LINE_ID(f, 3) and get the results of the __LINE__ preprocessor macro at the third instance of STOPWATCH_LAP in f.

(I imagine the actual code probably can't look exactly like what I wrote above. Open to whatever modifications are required to make this work. The only real requirement is that I don't have to constantly count how many of these lap points I've put into a function and update the corresponding code to match.)

ANSWER

Answered 2022-Mar-16 at 21:11

I am assuming you do not want to have any dynamic memory management involved? Because otherwise you could simply use a std::vector and do a push_back() for each result...

Otherwise, I do not think this can be achieved easily by just using standard language elements. But MSVC, clang and gcc support __COUNTER__, which is a special macro that is incremented in each use and that can be exploited here. Storing the initial value before the function, then using it in every "LAP", you can compute the number of laps within the function. Moreover, you can declare the result array without needing to specify the first dimension before the function if you use a C-array via extern, and then define it afterwards with the now known number of laps. You can also simply store the __LINE__ result at the same time when you store the __rdtscp() result. See the following example. It is all quite fragile and assumes that the macros are used in that order, but depending on the actual code, it might be sufficient (https://godbolt.org/z/crrGY7n4P):

#include <array>
#include <cstdint>
#include <iostream>

#ifndef _MSC_VER 
    // https://code-examples.net/en/q/e19526
    uint64_t __rdtscp( uint32_t * aux )
    {
        uint64_t rax,rdx;
        asm volatile ( "rdtscp\n" : "=a" (rax), "=d" (rdx), "=c" (*aux) : : );
        return (rdx << 32) + rax;
    }
#else 
    #include <intrin.h>
#endif

constexpr std::size_t MAX_TRIALS = 3;

struct ResultElem
{
    uint64_t timing;
    unsigned line;
};


void process_timestamps(ResultElem results[][MAX_TRIALS], std::size_t numResult, char const * const func)
{
    std::cout << func << ": Num = " << numResult << std::endl;
    for (std::size_t trial = 0; trial < MAX_TRIALS; ++trial) {
        std::cout << "\tTrial=" << trial << std::endl;
        for (std::size_t i = 0; i < numResult; ++i) {
            std::cout << "\t\tLine=" << results[i][trial].line << ", time=" << results[i][trial].timing << std::endl;
        }
    }
}


#define STOPWATCH_BOILERPLATE_PRE(f) \
    extern ResultElem results_ ## f[][MAX_TRIALS]; \
    constexpr std::size_t counterStart_ ## f = __COUNTER__; \
    std::size_t trial_ ## f = 0;

#define STOPWATCH_BEGIN(f) uint32_t core; STOPWATCH_LAP(f)

#define STOPWATCH_LAP(f) results_ ## f[__COUNTER__ - counterStart_ ## f - 1][trial_ ## f] = {__rdtscp(&core), __LINE__}

#define STOPWATCH_END(f) \
  STOPWATCH_LAP(f); \
  if(++trial_ ## f == MAX_TRIALS) { \
    process_timestamps(results_ ## f, __COUNTER__ - counterStart_ ## f - 1, #f); \
    trial_ ## f = 0; \
  }

// Needs to be used directly after STOPWATCH_END() because we subtract 2 from __COUNTER__.
#define STOPWATCH_BOILERPLATE_POST(f) \
    constexpr std::size_t numResult_ ## f = __COUNTER__ - counterStart_ ## f - 2; \
    ResultElem results_ ## f[numResult_ ## f][MAX_TRIALS];


STOPWATCH_BOILERPLATE_PRE(f)

void f()
{
  STOPWATCH_BEGIN(f);

  // do stuff

  STOPWATCH_LAP(f);

  // do some more stuff

  STOPWATCH_LAP(f);

  // do even more stuff

  STOPWATCH_END(f);
}

STOPWATCH_BOILERPLATE_POST(f)

Alternatives I could think of:

Without dynamic allocations and staying in the standard, you might build something using BOOST_PP_COUNTER. The STOPWATCH_LAP would then probably turn into some form of #include statement.
I could also imagine that it might be possible to build something without macros using a weird loophole in C++14, but that gets terribly complicated.

Source https://stackoverflow.com/questions/71497660

QUESTION

How to save time value in dynamically created li to input box with javascript

Asked 2022-Mar-05 at 15:41

How to save a time value in dynamically created li to input box with javascript I have a simple timer, that starts, stops, pauses, takes a time snap and resets the time snap. The timesnap in generated and displayed in the webpage inside a li. It all works fine what I am struggling with is trying to click on a displayed time snap and have the value placed in an input box so I can later save a selected value to a database. This is the script I am using to place the clicked on li item into the input box

var items = document.querySelectorAll("#list li");
      for (var i = 0; i < items.length; i++) {
        items[i].onclick = function () {
          document.getElementById("inptSnap").value = this.innerHTML;
        };
      }

This is the html

<div class="container">
      <!--               Different App            -->

      <div class="timeDisplay">00:00:00</div>

      <button id="begin">Start</button>

      <button id="hold">Pause</button>

      <button id="end">Stop</button>

      <button id="timeSnap">Time Snap</button>
      <button id="resetSnap">Reset Time Snap</button>

      <ul id="list" class="laps"></ul>

      <div>
        <input type="text" id="inptSnap" />
      </div>
    </div>

This is the full timer script with the attempted select value onclick

var begin = document.getElementById("begin");
      begin.addEventListener("click", start);

      var end = document.getElementById("end");
      end.addEventListener("click", stop);

      var hold = document.getElementById("hold");
      hold.addEventListener("click", pause);

      var timeSnap = document.getElementById("timeSnap");
      timeSnap.addEventListener("click", snap);

      var timeSnap = document.getElementById("timeSnap");
      timeSnap.addEventListener("click", pause);

      var resetSnap = document.getElementById("resetSnap");
      resetSnap.addEventListener("click", resetSnaps);

      var ms = 0,
        s = 0,
        m = 0;
      var timeCounter;
      var displayEl = document.querySelector(".timeDisplay");
      var lapsContainer = document.querySelector(".laps");

      function start() {
        if (!timeCounter) {
          timeCounter = setInterval(run, 10);
        }
      }

      function run() {
        displayEl.textContent = displayTimeCount();

        ms++;
        if (ms == 100) {
          ms = 0;
          s++;
        }
        if (s == 60) {
          s = 0;
          m++;
        }
      }

      function stop() {
        stopTimer();
        ms = 0;
        s = 0;
        m = 0;
        displayEl.textContent = displayTimeCount();
      }
      function stopTimer() {
        clearInterval(timeCounter);
        timeCounter = false;
      }

      function pause() {
        stopTimer();
      }

      function displayTimeCount() {
        return (
          (m < 10 ? "0" + m : m) +
          ":" +
          (s < 10 ? "0" + s : s) +
          ":" +
          (ms < 10 ? "0" + ms : ms)
        );
      }
      function snap() {
        if (timeCounter) {
          var li = document.createElement("li");
          li.innerText = displayTimeCount();
          lapsContainer.appendChild(li);
        }
      }

      function resetSnaps() {
        lapsContainer.innerHTML = "";
      }

      // Script to put lap into input box
      var items = document.querySelectorAll("#list li");
      for (var i = 0; i < items.length; i++) {
        items[i].onclick = function () {
          document.getElementById("inptSnap").value = this.innerHTML;
        };
      }

This is the CodePen Link

I would be very grateful for any pointers and advice, thanks

ANSWER

Answered 2022-Feb-18 at 13:46

So I understand that you need a place value kind of thing.

 var begin = document.getElementById("begin");
      begin.addEventListener("click", start);

      var end = document.getElementById("end");
      end.addEventListener("click", stop);

      var hold = document.getElementById("hold");
      hold.addEventListener("click", pause);

      var timeSnap = document.getElementById("timeSnap");
      timeSnap.addEventListener("click", snap);

      var timeSnap = document.getElementById("timeSnap");
      timeSnap.addEventListener("click", pause);

      var resetSnap = document.getElementById("resetSnap");
      resetSnap.addEventListener("click", resetSnaps);

      var ms = 0,
        s = 0,
        m = 0;
      var timeCounter;
      var displayEl = document.querySelector(".timeDisplay");
      var lapsContainer = document.querySelector(".laps");

      function start() {
        if (!timeCounter) {
          timeCounter = setInterval(run, 10);
        }
      }

      function run() {
        displayEl.textContent = displayTimeCount();

        ms++;
        if (ms == 100) {
          ms = 0;
          s++;
        }
        if (s == 60) {
          s = 0;
          m++;
        }
      }

      function stop() {
        stopTimer();
        ms = 0;
        s = 0;
        m = 0;
        displayEl.textContent = displayTimeCount();
      }
      function stopTimer() {
        clearInterval(timeCounter);
        timeCounter = false;
      }

      function pause() {
        stopTimer();
      }

      function displayTimeCount() {
        return (
          (m < 10 ? "0" + m : m) +
          ":" +
          (s < 10 ? "0" + s : s) +
          ":" +
          (ms < 10 ? "0" + ms : ms)
        );
      }
      function snap() {
        if (timeCounter) {
          var input = document.createElement("input");
          input.value = displayTimeCount();
          lapsContainer.appendChild(input);
        }
      }

      function resetSnaps() {
        lapsContainer.innerHTML = "";
      }

      // Script to put lap into input box
      var items = document.querySelectorAll("#list li");
      for (var i = 0; i < items.length; i++) {
        items[i].onclick = function () {
          document.getElementById("inptSnap").value = this.innerHTML;
        };
      }

 .timeDisplay {
        font-size: 32px;
      }
      ul li {
        list-style: none;
        font-size: 32px;
      }
      .container {
        width: 400px;
        margin: auto;
      }

<div class="container">
      <!--               Different App            -->

      <div class="timeDisplay">00:00:00</div>

      <button id="begin">Start</button>

      <button id="hold">Pause</button>

      <button id="end">Stop</button>

      <button id="timeSnap">Time Snap</button>
      <button id="resetSnap">Reset Time Snap</button>

      <ul id="list" class="laps">
        
      </ul>

      <div>
        <input type="text" id="inptSnap" />
      </div>
    </div>

Source https://stackoverflow.com/questions/71174215

QUESTION

Flutter iOS Error Class AMSupportURLConnectionDelegate is implemented in both /usr/lib/libamsupport.dylib (0x203913130)

Asked 2022-Mar-01 at 05:29

Hi am facing an issue while running flutter project in MacBook Air M1 chip Lap. Tried all possibilities couldn't find where is the exact problem.

All basic solutions like flutter clean, flutter pub get, pod deintegrate & install, flutter build ios, flutter run but still same issue. only on iOS simulator not deploying.

Any solution for this. Thanks in advance.

Error

Launching lib/main.dart on iPhone 13 in debug mode...
Running pod install...                                              5.3s
Running Xcode build...                                                  
Xcode build done.                                           104.1s
Failed to build iOS app
Error output from Xcode build:
↳
    objc[25282]: Class AMSupportURLConnectionDelegate is implemented in both /usr/lib/libamsupport.dylib (0x203913130) and
    /Library/Apple/System/Library/PrivateFrameworks/MobileDevice.framework/Versions/A/MobileDevice (0x103bc02c8). One of the two will be used. Which one is undefined.
    objc[25282]: Class AMSupportURLSession is implemented in both /usr/lib/libamsupport.dylib (0x203913180) and
    /Library/Apple/System/Library/PrivateFrameworks/MobileDevice.framework/Versions/A/MobileDevice (0x103bc0318). One of the two will be used. Which one is undefined.
    ** BUILD FAILED **

flutter doctor -v

[✓] Flutter (Channel stable, 2.8.1, on macOS 12.0.1 21A559 darwin-arm, locale
    en-IN)
    • Flutter version 2.8.1 at
      /Users/macsystem/Documents/developer/flutter
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ANSWER

Answered 2022-Feb-02 at 04:43

I have been facing this same issue for some time now. the same setup is working nicely in a mac with intel chip. But i have even done a resetup of my system, m1 mac still throws the same error.

Source https://stackoverflow.com/questions/70936334

QUESTION

How to accelerate my written python code: function containing nested functions for classification of points by polygons

Asked 2022-Feb-12 at 09:11

I have written the following NumPy code by Python:

def inbox_(points, polygon):
    """ Finding points in a region """

    ll = np.amin(polygon, axis=0)                        # lower limit
    ur = np.amax(polygon, axis=0)                        # upper limit

    in_idx = np.all(np.logical_and(ll <= points, points < ur), axis=1)        # points in the range [boolean]

    return in_idx


def operation_(r, gap, ends_ind):
    """ calculation formula which is applied on the points specified by inbox_ function """

    r_active = np.take(r, ends_ind)                       # taking values from "r" based on indices and shape (paired_values) of "ends_ind"
    r_sub = np.subtract.reduce(r_active, axis=1)          # subtracting each paired "r" determined by "ends_ind" [this line will be used in the final return formula]
    r_add = np.add.reduce(r_active, axis=1)               # adding each paired "r" determined by "ends_ind" [this line will be used in the final return formula]
    paired_cent_dis = np.sum((r_add, gap), axis=0)        # distance of the each two paired points

    return (np.power(gap, 2) * (np.power(paired_cent_dis, 2) + 5 * paired_cent_dis * r_add - 7 * np.power(r_sub, 2))) / (3 * paired_cent_dis)      # Formula



def elapses(r, pos, gap, ends_ind, elem_vert, contact_poss):
    if len(gap) > 0:
        elaps = np.empty([len(elem_vert), ], dtype=object)
        operate_ = operation_(r, gap, ends_ind)

        #elbav = np.empty([len(elem_vert), ], dtype=object)
        #con_num = 0

        for i, j in enumerate(elem_vert):                        # loop for each section (cell or region) of a mesh
            in_bool = inbox_(contact_poss, j)                    # getting boolean array for points within that section
            elaps[i] = np.sum(operate_[in_bool])                 # performing some calculations on that points and get the sum of them for each section
            operate_ = operate_[np.invert(in_bool)]              # slicing the arrays by deleting the points on which the calculations were performed to speed-up the code in next loops                        
            contact_poss = contact_poss[np.invert(in_bool)]      # as above

            #con_num += sum(inbox_(contact_poss, j))
            #inba_bool = inbox_(pos, j)
            #elbav[i] = 4 * np.pi * np.sum(np.power(r[inba_bool], 3)) / 3
            #pos = pos[np.invert(inba_bool)]
            #r = r[np.invert(inba_bool)]

        return elaps


r = np.load('a.npy')
pos = np.load('b.npy')
gap = np.load('c.npy')
ends_ind = np.load('d.npy')
elem_vert = np.load('e.npy')
contact_poss = np.load('f.npy')

elapses(r, pos, gap, ends_ind, elem_vert, contact_poss)

# a --------r-------> parameter corresponding to each coordinate (point); here radius                                  (23605,)     <class 'numpy.ndarray'> <class 'numpy.float64'>
# b -------pos------> coordinates of the points                                                                        (23605, 3)   <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.float64'>
# c -------gap------> if we consider points as spheres by that radii [r], it is maximum length for spheres' over-lap   (103832,)    <class 'numpy.ndarray'> <class 'numpy.float64'>
# d ----ends_ind----> indices for each over-laped spheres                                                              (103832, 2)  <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.int64'>
# e ---elem_vert----> vertices of the mesh's sections or cells                                                         (2000, 8, 3) <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.float64'>
# f --contact_poss--> a coordinate between the paired spheres                                                          (103832, 3)  <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.float64'>

This code will be called frequently from another code with big-data inputs. So, speeding up this code is essential. I have tried to use jit decorator from JAX and Numba libraries to accelerate the code, but I could not work with that properly to make the code better. I have tested the code on Colab (for 3 data sets with loops number of 20, 250, and 2000) for speed and the results were:

11  (ms), 47   (ms), 6.62 (s)       (per loop) <-- without the commented code lines in the code
137 (ms), 1.66 (s) , 4    (m)       (per loop) <-- with activating the commented code lines in the code

What this code does is finding some coordinates in a range and then performing some calculations on them.
I will be very appreciated for any answers that can speed up this code significantly (I believe it could). Also, I will be grateful for any experienced recommendations about speeding up the code by changing (substituting) the used NumPy methods and … or writing method for the math operations.

Notes:

The proposed answers must be executable by python version 2 (being applicable in both versions 2 and 3 is very excellent)
The commented code lines in the code are unnecessary for the main aim and are written just for further evaluations. Any recommendations to handle these lines with the proposed answers is appreciated (is not needed).

Data sets for test:
small data set: https://drive.google.com/file/d/1CswjyoqS8ogLmLQa_oNTOj221chDcbK8/view?usp=sharing
medium data set: https://drive.google.com/file/d/14RJ0Ackx88NzQWloops5FagzuNQYDSrh/view?usp=sharing
large data set: https://drive.google.com/file/d/1dJnXpb3HiAGcRC9PPTwui9joNcij4E_E/view?usp=sharing

ANSWER

Answered 2021-Dec-28 at 14:11

First of all, the algorithm can be improved to be much more efficient. Indeed, a polygon can be directly assigned to each point. This is like a classification of points by polygons. Once the classification is done, you can perform one/many reductions by key where the key is the polygon ID.

This new algorithm consists in:

computing all the bounding boxes of the polygons;
classifying the points by polygons;
performing the reduction by key (where the key is the polygon ID).

This approach is much more efficient than iterating over all the points for each polygons and filtering the attributes arrays (eg. operate_ and contact_poss). Indeed, a filtering is an expensive operation since it requires the target array (that may not fit in the CPU caches) to be fully read and then written back. Not to mention this operation requires a temporary array to be allocated/deleted if it is not performed in-place and the operation cannot benefit from being implemented with SIMD instructions on most x86/x86-64 platforms (as it requires the new AVX-512 instruction set). It is also harder to parallelize since the filtering steps are too fast for threads to be useful but steps need to be done sequentially.

Regarding the implementation of the algorithm, Numba can be used to speed up a lot the overall computation. The main benefit of using Numba is to drastically reduce the number of expensive temporary arrays created by Numpy in your current implementation. Note that you can specify the function types to Numba so it can compile functions when it is defined. Assertions can be used to make the code more robust and help the compiler to know the size of a given dimension so to generate a significantly faster code (the JIT compiler of Numba can unroll the loops). Ternaries operators can help a bit the JIT compiler to generate a faster branch-less program.

Note the classification can be easily parallelized using multiple threads. However, one needs to be very careful about constant propagation since some critical constants (like the shape of the working arrays and assertions) tends not to be propagated to the code executed by threads while the propagation is critical to optimize the hot loops (eg. vectorization, unrolling). Note also that creating of many threads can be expensive on machines with many cores (from 10 ms to 0.1 ms). Thus, this is often better to use a parallel implementation only on big input data.

Here is the resulting implementation (working with both Python2 and Python3):

@nb.njit('float64[::1](float64[::1], float64[::1], int64[:,::1])')
def operation_(r, gap, ends_ind):
    """ calculation formula which is applied on the points specified by findMatchingPolygons_ function """

    nPoints = ends_ind.shape[0]
    assert ends_ind.shape[1] == 2
    assert gap.size == nPoints

    formula = np.empty(nPoints, dtype=np.float64)

    for i in range(nPoints):
        ind0, ind1 = ends_ind[i]
        r0, r1 = r[ind0], r[ind1]
        r_sub = r0 - r1
        r_add = r0 + r1
        cur_gap = gap[i]
        paired_cent_dis = r_add + cur_gap
        formula[i] = (cur_gap**2 * (paired_cent_dis**2 + 5 * paired_cent_dis * r_add - 7 * r_sub**2)) / (3 * paired_cent_dis)

    return formula


# Use `parallel=True` for a parallel implementation
@nb.njit('int32[::1](float64[:,::1], float64[:,:,::1])')
def findMatchingPolygons_(points, polygons):
    """ Attribute to all point a region """

    nPolygons = polygons.shape[0]
    nPolygonPoints = polygons.shape[1]
    nPoints = points.shape[0]
    assert points.shape[1] == 3
    assert polygons.shape[2] == 3

    # Compute the bounding boxes of all polygons

    ll = np.empty((nPolygons, 3), dtype=np.float64)
    ur = np.empty((nPolygons, 3), dtype=np.float64)

    for i in range(nPolygons):
        ll_x, ll_y, ll_z = polygons[i, 0]
        ur_x, ur_y, ur_z = polygons[i, 0]

        for j in range(1, nPolygonPoints):
            x, y, z = polygons[i, j]
            ll_x = x if x<ll_x else ll_x
            ll_y = y if y<ll_y else ll_y
            ll_z = z if z<ll_z else ll_z
            ur_x = x if x>ur_x else ur_x
            ur_y = y if y>ur_y else ur_y
            ur_z = z if z>ur_z else ur_z

        ll[i] = ll_x, ll_y, ll_z
        ur[i] = ur_x, ur_y, ur_z

    # Find for each point its corresponding polygon

    pointPolygonId = np.empty(nPoints, dtype=np.int32)

    # Use `nb.prange(nPoints)` for a parallel implementation
    for i in range(nPoints):
        x, y, z = points[i, 0], points[i, 1], points[i, 2]
        pointPolygonId[i] = -1

        for j in range(polygons.shape[0]):
            if ll[j, 0] <= x < ur[j, 0] and ll[j, 1] <= y < ur[j, 1] and ll[j, 2] <= z < ur[j, 2]:
                pointPolygonId[i] = j
                break

    return pointPolygonId


@nb.njit('float64[::1](float64[:,:,::1], float64[:,::1], float64[::1])')
def computeSections_(elem_vert, contact_poss, operate_):
    nPolygons = elem_vert.shape[0]
    elaps = np.zeros(nPolygons, dtype=np.float64)

    pointPolygonId = findMatchingPolygons_(contact_poss, elem_vert)

    for i, polygonId in enumerate(pointPolygonId):
        if polygonId >= 0:
            elaps[polygonId] += operate_[i]

    return elaps


def elapses(r, pos, gap, ends_ind, elem_vert, contact_poss):
    if len(gap) > 0:
        operate_ = operation_(r, gap, ends_ind)
        return computeSections_(elem_vert, contact_poss, operate_)


r = np.load('a.npy')
pos = np.load('b.npy')
gap = np.load('c.npy')
ends_ind = np.load('d.npy')
elem_vert = np.load('e.npy')
contact_poss = np.load('f.npy')

elapses(r, pos, gap, ends_ind, elem_vert, contact_poss)

Here are the results on a old 2-core machine (i7-3520M):

Small dataset:
 - Original version:                5.53 ms
 - Proposed version (sequential):   0.22 ms  (x25)
 - Proposed version (parallel):     0.20 ms  (x27)

Medium dataset:
 - Original version:               53.40 ms
 - Proposed version (sequential):   1.24 ms  (x43)
 - Proposed version (parallel):     0.62 ms  (x86)

Big dataset:
 - Original version:               5742 ms
 - Proposed version (sequential):   144 ms   (x40)
 - Proposed version (parallel):      67 ms   (x86)

Thus, the proposed implementation is up to 86 times faster than the original one.

Source https://stackoverflow.com/questions/70469480

QUESTION

R+Tidyverse: Tibbles don't appear to store milliseconds

Asked 2022-Feb-11 at 18:59

I have a CSV with many values. Among them are times stored like this:

1:34.434

Using readr, I form them into a tibble, and use dplyr to cut out all the unnecessary rows and columns.

lapData <- read_csv("sampledata/sampledata.csv")
lapData <- dplyr::select(lapData, 
                         Driver, Lap, Penalty, 'Lap Time', 'Lap type', 
                         'Pressure FR', 'Pressure FL', 'Pressure RR', 'Pressure RL', 
                         'Temp FR', 'Temp FL', 'Temp RR', 'Temp RL',
                         'Road Temp')
lapData <- dplyr::filter(lapData, Driver == "[personal info]")
lapData <- dplyr::filter(lapData, is.na(Penalty))
lapData <- dplyr::filter(lapData, Lap > 1)

I then use print(ggplot(data = lapData, mapping = aes(Lap, 'Lap Time')) + geom_line()) to print the data, and it looks like this:
plot showing loss of precision
If it isn't clear, the vertices of the graph line are rounded to the seconds value instead of using the full milisecond precision, which I would like to have.
If I then print(lapData), I see the following entries under 'Lap Time':
printed tibble
I did some research, and found this, which seems to indicate that the loss of precision in the printout from the tibble is inconsequential, and the full data is still stored in the tibble. However, the plot contradicts this statement.

How do I get the plot to show the full millisecond resolution?

ANSWER

Answered 2022-Feb-11 at 18:59

Your time format doesn't agree with the time format that readr expects by default. Specifically, readr interprets 1:34.434 as 1 hour and 34 minutes, rather than as 1 minute and 34.434 seconds.

text <- "id,time
1,1:23.456
2,2:34.567
3,3:45.678
"
cat(text, file = "foo.csv")

library("readr")
tt1 <- read_csv("foo.csv")
tt1
## # A tibble: 3 × 2
##      id time  
##   <dbl> <time>
## 1     1 01:23 
## 2     2 02:34 
## 3     3 03:45

as.double(tt1$time) / 60 # numbers of minutes
## [1]  83 154 225

The documentation suggests to use the col_types argument of read_csv to specify the time format. Normally, this would work:

col_types <- list(id = col_integer(), time = col_time(format = "%M:%OS"))

But it doesn't:

tt2 <- read_csv("foo.csv", col_types = col_types)
## Warning message:                                                                                                   
## One or more parsing issues, see `problems()` for details

tt2
## # A tibble: 3 × 2
##      id time  
##   <int> <time>
## 1     1    NA 
## 2     2    NA 
## 3     3    NA

The problem is that readr's parser requires minutes to be padded with zeros:

parse_time("01:23.456", format = "%M:%OS")
## 00:01:23.456

parse_time("1:23.456", format = "%M:%OS")
## Warning: 1 parsing failure.
## row col         expected   actual
##   1  -- time like %M:%OS 1:23.456
## 
## NA

That surprised me, because base R's parser doesn't have that constraint:

strptime("1:23.456", format = "%M:%OS")
## [1] "2022-02-11 00:01:23 EST"

A workaround is to read the offending column as a character vector and coerce it to numeric via strptime after:

tt3 <- read_csv("foo.csv", col_types = list(id = col_integer(), time = col_character()))
tt3
## # A tibble: 3 × 2
##      id time    
##   <int> <chr>   
## 1     1 1:23.456
## 2     2 2:34.567
## 3     3 3:45.678

library("dplyr")
tt4 <- tt3 %>% mutate(seconds = with(strptime(time, format = "%M:%OS"), 60 * min + sec))
options(pillar.sigfig = 10L)
tt4
## # A tibble: 3 × 3
##      id time     seconds
##   <int> <chr>      <dbl>
## 1     1 1:23.456  83.456
## 2     2 2:34.567 154.567
## 3     3 3:45.678 225.678

You can also do the coercion with lubridate, which has a specialized parser:

library("lubridate")
tt5 <- tt3 %>% mutate(seconds = as.double(as.duration(ms(time))))
identical(tt4, tt5)
## [1] TRUE

Source https://stackoverflow.com/questions/71075108

QUESTION

Memoize multi-dimensional recursive solutions in haskell

Asked 2022-Jan-13 at 14:28

I was solving a recursive problem in haskell, although I could get the solution I would like to cache outputs of sub problems since has over lapping sub-problem property.

The question is, given a grid of dimension n*m, and an integer k, how many ways are there to reach the gird (n, m) from (1, 1) with not more than k change of direction?

Here is the code without of memoization

paths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
paths i j n m k dir
    | i > n || j > m || k < 0 = 0
    | i == n && j == m = 1
    | dir == 0 = paths (i+1) j n m k 1 + paths i (j+1) n m k 2        -- is in grid (1,1)
    | dir == 1 = paths (i+1) j n m k 1 + paths i (j+1) n m (k-1) 2    -- down was the direction took to reach here
    | dir == 2 = paths (i+1) j n m (k-1) 1 + paths i (j+1) n m k 2    -- right was the direction took to reach here 
    | otherwise = -1

Here the dependent variables are i, j, k, dir. In languages like C++/Java a 4-d DP array could have been used (dp[n][m][k][3], in Haskell I can't find a way to implement that.

ANSWER

Answered 2021-Dec-16 at 16:23

In Haskell these kinds of things aren't the most trivial ones, indeed. You would really like to have some in-place mutations going on to save up on memory and time, so I don't see any better way than equipping the frightening ST monad.

This could be done over various data structures, arrays, vectors, repa tensors. I chose HashTable from hashtables because it is the simplest to use and is performant enough to make sense in my example.

First of all, introduction:

{-# LANGUAGE Rank2Types #-}
module Solution where

import Control.Monad.ST
import Control.Monad
import Data.HashTable.ST.Basic as HT

Rank2Types are useful when dealing with ST, because of the phantom types. I picked the Basic variant of the hashtable, because authors claim it has the fastest lookups --- and we are going to lookup a lot.

It is advised to use a type alias for the map, so here we go:

type Mem s = HT.HashTable s (Int, Int, Int, Int) Integer

ST-free entrypoint just to create the map and call our monster:

runpaths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
runpaths i j n m k dir = runST $ do
  mem <- HT.new
  paths mem i j n m k dir

Here is memorized computation of paths. We just try to search for the result in the map, and if it is not there then we save it and return:

mempaths mem i j n m k dir = do
  res <- HT.lookup mem (i, j, k, dir)
  case res of
    Just x -> return x
    Nothing -> do
      x <- paths mem i j n m k dir
      HT.insert mem (i, j, k, dir) x
      return x

And here goes the brain of the algorithm. It is just a monadic action that uses calls with memorization in place of plain recursion:

paths mem i j n m k dir
    | i > n || j > m || k < 0 = return 0
    | i == n && j == m = return 1
    | dir == 0 = do
        x1 <- mempaths mem (i+1) j n m k 1
        x2 <- mempaths mem i (j+1) n m k 2        -- is in grid (1,1)
        return $ x1 + x2
    | dir == 1 = do 
        x1 <- mempaths mem (i+1) j n m k 1
        x2 <- mempaths mem i (j+1) n m (k-1) 2    -- down was the direction took to reach here
        return $ x1 + x2
    | dir == 2 = do
        x1 <- mempaths mem (i+1) j n m (k-1) 1 
        x2 <- mempaths mem i (j+1) n m k 2    -- right was the direction took to reach here 
        return $ x1 + x2
    | otherwise = return (-1)

Source https://stackoverflow.com/questions/70376569

QUESTION

Edit part of a Message Embed (Discord.JS)

Asked 2022-Jan-02 at 01:40

I have a channel that contains 10 embedded messages (1 embed per message). Each embed is a leaderboard for people's best lap times, by Track.

The layout of each embed is

const trackName = new MessageEmbed
.setTitle(trackName) 
.addField(user1, lapTime)
.addField(user2, lapTime) 
.addField(user3, lapTime)

Assume for example the 3rd embed looks something like this:

| Track 3 Name

| John 37 seconds

| Chris 39 seconds

| Jeff 40 seconds

Beyond simply editing the embed and sending all the information updated manually, how could I update just one particular slot? For example, say Clark comes in with a 38 second lap, how would I move Chris to 3rd, remove Jeff, and add Clark to 2nd so the embed looks as such

| Track 3 Name

| John 37 seconds

| Clark 38 seconds

| Chris 39 seconds

Without changing any other embeds in the channel

ANSWER

Answered 2021-Sep-08 at 13:10

You don't necessarily need to create an entirely new embed and input all of the information into it. You could get the current embed from the message, and edit the specific fields you need to edit. Here is an example that you could adapt to work with your system:

function editTrack(msg, newUser, newTime) {
    //Assume `msg` is the message in the channel containing the embed you want to edit
    //currentEmbed is now the embed you want to edit
    let currentEmbed = msg.embeds[0];

    //Add `newUser` and its `newTime` to the embed as a new field, in the last position
    currentEmbed.addField(newUser, newTime);

    //Sorts the embed's fields by the lap times of each user, from lowest to highest
    //This example numerically sorts the fields by the number in their value
    //This does most of the work for you; the laps are now in the correct order
    currentEmbed.fields.sort((a, b) => Number(a.value.split(" second")[0]) - Number(b.value.split(" second")[0]));

    //If you want to display only the top 3, remove the 4th field of the embed (if any)
    if (currentEmbed.fields.length == 4) currentEmbed.fields.splice(3, 1);

    //Now, we need to edit the message with our updated embed (djs 13.x syntax)
    return msg.edit({embeds: [currentEmbed]});
}

I tested this editTrack method using one of my bots' eval commands:

BEFORE -

AFTER -

The original embed is successfully edited with the new information. And it only required the Message object containing the embed, the new lap's user, and the new lap's time.

Edit based on OP's comment:

For the answer to work when editing the track embed with an existing user, who has a new lap time, a slight modification needs to be made. Before removing the 4th field of the embed, you must do something like this:

const names = new Set();
currentEmbed.fields = currentEmbed.fields.filter(field =>
    !names.has(field.name) && names.add(field.name)
);

Here's what that code is doing. First, we create a Set. Sets are iterable structures similar to arrays, but they can only contain unique values. We can therefore use sets to ensure there are no repeats of a user's name. Next, we filter the fields; only the fields whose user is not already contained in names will remain. If a user's name is not already in names, it gets added to the set via .add(). And because the fields are already sorted with the quickest lap time coming first, only the quickest lap of a given user will remain; any longer lap times by the same user will be filtered out.

Note that I only tested this edit briefly, let me know if there are any logical errors or further issues caused by it (or if it fails to work entirely).

Source https://stackoverflow.com/questions/69004210

QUESTION

Wrong type casting in FOR-IN loop wrecks seemingly unrelated variable

Asked 2021-Dec-20 at 21:20

I am building a simple stopwatch with State widget.

Initialize variables in State:

final laps = <int>[];
int milliseconds = 0;

milliseconds are updated in Timer every 0.1 seconds (after start button pressed):

setState((){
  milliseconds += 100;
});

lap method updates list of laps (after lap button is pressed, when timer is running):

setState((){
  laps.add(milliseconds);
});

And a method for displaying laps (running in build() on every setState):

for(int milliseconds in laps)
  Text(_format(milliseconds))

The question is, if I forget to do type casting in for-in method, like this:

for(milliseconds in laps)

this wrecks the timer, and it just stops incrementing. It keeps running but milliseconds are just stuck at last value when lap was pressed. How so? I thought milliseconds is one variable, and laps list is another and it's values are yet another variables... This is confusing.

***DartPad with full code is https://dartpad.dev/?id=09114fd8f5332610042b1af85afdcae8 and without type casting (laps not working version). To get it work add int milliseconds at line 149. It takes a minute to run.

ANSWER

Answered 2021-Dec-20 at 21:20

when declaring a variable, you must either use Type variableName = value; or var variableName = value (you can also use final variableName = value; or var variableName; and I'm sure some others, but that's not the point).

when you do for (milliseconds in laps) you neither put the type nor add var or final keyword. This would give you an error if you had not declared the milliseconds variable before, because you have, it just assigns the value of each iteration to said variable, effectively breaking your code.

It's akin to this example

int i=183;
for (i=0; i<3; i++) {
  print(i);
}
print(i);

If i run this code I get the following output:

If I add var, I get the following output:

So basically if you add var or int to the loop, you are redefining the variable inside the for-loop scope, if you don't you assign directly to the already existing variable, which in turn breaks the app.

Source https://stackoverflow.com/questions/70427845

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Vulnerabilities

No vulnerabilities reported

Install lap

In addition to above, running the tests requires:.
Python (2.7, 3.7, 3.8, 3.9)
NumPy (>=1.10.1)
SciPy, pytest, pytest-timeout
Install a C compiler (e.g., g)
Python headers (e.g., python-dev package on Debian/Ubuntu)
Install Cython (>=0.21)
Clone git clone https://github.com/gatagat/lap.git
Under the root of the repo python setup.py build python setup.py install

Support

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