1,078 skills found · Page 22 of 36
nicoletanyt / Obsidian Plugin GraphsObsidian Plugin to generate graphs given the function.
tomcl7 / X360 Fidb GeneratorGenerates Ghidra Function ID databases from Xbox 360 SDK setup executables
jauhararifin / GomockerGomocker generates Golang code to easily mock a function or interface
pedroborges / Kirby Asset Cachebuster⬢ Provides a simple function that generates versioned URLs from asset paths.
BerndNK / TweenSharpProvides an easy to use fluent API, to quickly generate tweening functions.
jcorrado76 / Lambda Layer Easy SetupThis repo will serve to simplify the steps to generate a compatible zip archive for creating AWS Lambda layers. With some additional changes, it can also be used to simplify the creation of a full lambda function from the generated zip archive.
bytesizedalex / Get SupportInfoThis function will generate and output various log files and support information to assist in troubleshooting computer issues. The exported data is added to an archive file which can be attached to support tickets or forwarded from service desk to senior support.
AISoltani / KNN And Bayes Error In attachment, “data_gen” file is a function for data generation. To generate data according to condition defined in part 1, we should run a Python file entitled “Q1_1”, In attachment, a file entitled “knn” is a function for calculation of knn error.
EAVWing / ZipRadiusThis package has functions which will generate a data frame of US zip codes when given a starting zip code and a radius in miles as well as outputs for integration with choroplethrZip
AbdelhamidLarachi / React Translator I18nextopen-source project made to auto translate react apps using i18next, this project should extract components text, translate, and replace it with i18n function, also it generates the json file for your target languages.
hou2zi0 / Xslt Simple Text Processing ToolboxCollection of description of concepts, procedures, and simple XSLT files for text processing, e.g. simplify InDesign documents (.idml) to simplified xml, or Office formats (.fodt, .odt, .docx) to simplified XML. Subsequently the simplified XML may function as a foundation from where nested TEI-XML may be generated.
amarjitdhillon / Find Shortest Path Using Generic Algorithm In MATLABObjective of this project was to select minimum cost path for sending packets from router A to router B such that all routers are traversed, hence this problem is different to Travelling Salesmen Problem (TSP), where Intermediate nodes can be left off. Initial location for all routers was randomly generated in 3-D space. Hinged upon initially generated locations, distance amidst them is computed using Euclidian formula which serves as Fitness function. Initial Population was selected using Roulette wheel selection using aforementioned Fitness function. Then Crossover was computed if, Probability of crossover. Pc > (Randomly generated probability) using two-point crossover. After this initial population was updated and mutation was done if Pm > (Randomly generated probability). Best chromosome was computed using max fitness function and Inversion / Swapping / Sliding was done on 2nd,3rd,4th chromosome, while 1st chromosome was passed as such using Elite Selection method to preserve best chromosome (Solution in this case). User have laxity to enter number of initial routers, size of initial population and number of iterations for Genetic algorithm to simulate. This method was named as MGA (Modified Genetic Algorithm) and it’s performance was juxtaposed with SGA (Simple Genetic Algorithm) where Initial Selection / Fitness function / Crossover / Mutation method deployed were computed differently using same set of routers co-ordinates used for SGA. Results were shown using six simulation Graphs, three for each case.
LucasHartman / MayaScript HouseGenerator‘Generative House Algorithm’ was constructed for one simple reason, being one click away from creating a range of uniquely designed model houses. At the beginning of 2020, the start of the covid-19 pandemic, I started learning programming. My background is in developing 3D motion graphics, but my work goes into different directions. I feel inspired by trying out new things, but often feel constrained by the software I use. I never found the right software that could satisfy my every need. A few years back, I visited a motion graphics event in Prague. Here I saw a presentation by Simon Homedal from Man vs. Machine and he introduced me to procedural programming for digital art. And so my journey into learning to code started. Being stuck at home because of covid-19, I was presented with a change to really jump in and start developing a few coding projects. I started out with a simple board game in Java, where I was introduced to ‘object oriented programming’ and UI development and many other general concepts. At the end of this project I came to the conclusion that simple programming is not enough, I needed to combine with something I already have experience of. So I started using Python inside Maya, focusing on asset development of simple programs I could execute whenever I’m working on a 3D project. At the time I was wondering if I could deconstruct houses to an algorithm. The inspiration for this project came from wandering around the residential areas where I lived. Zandberg has very diverse styles of architecture; Terrace houses with high ceilings, classical villas with roofs made of straw and modern villas built after WWII. I was captivated by the diversity in design. Breakdown A simple UI inside Maya, where the uses can specify the value for generating a number of houses. Simple things like level and roof height, number of doors, max number of levels, etc. Lastly a button that would take in the value and run the algorithm. The back-end consists of a number of Python modules, textures and .obj files. One Python file called the “Main”, is where the files are assembled and executed. Process Developing a generative algorithm is a process of trial and error. At the start of the project I treated the project like any other modeling project, only every design decision was programmed in with a number of possible solutions. Over time this would become very complex and unstructured. It became impossible to go back and modify what I already wrote down. Another problem was that the algorithm was creating the model for running the code. This meant that selecting, adding and subtracting mesh to the model cost a lot of processing power, to the point my computer would freeze up. I needed to rethink my process and develop a framework which is easy to modify and light on the processor. My new plan of attack was to do as little as possible in Maya. All design instructions needed to be solved before anything can be created in Maya. Going into this direction was a hard choice. First off, it’s not a guarantee for success. The moment I would go too deep, things can get messy very easily. Besides I consider myself more of a visual thinker. Working outside of Maya meant every hurdle would be some sort of math problem. I already knew I had no choice, and understood this is the type of problem solving a programmer has to deal with. So I started out doing a little bit of RnD. My first test was to create a number of lists. Generally every list would hold some type of value. Like positional data, labels, dimensions, objects etc. and the rest would be a range of functions iterating, generating, gathering, and sorting data into these lists. These seemed flexible enough, if I needed to add new details to the model, I would make a new list and apply this into the framework. This type of framework was not very structured as I hoped. Luckily I discarded this ideal before it really began. I was already attracted by the idea of using a matrix instead of lists at the top of lists. The matrix would provide data in three dimensions, like a volume or a box made out of separate units. I would add an extra dimension to each unit, which is a list of six values. Each value would represent each side of a unit. The general ideal of a matrix is like a fluid simulation, which is made out of a matrix of voxels, or like Minecraft where each unit can be some type of block. This would create a data structure that is easy to modify. The next step would be to feed the matrix with values. A value can represent walls, doors, windows, levels, rooftops, position and direction. It starts with an empty matrix, and secondly fill it with values of 1 (later on inside Maya, value 1 would generate a wall, the location within the matrix would be translated to 3D space). If you’d stop here and translate the matrix to mesh in Maya, you would get a cluster of boxes stacked next or on top of each other. Adding more data to the matrix meant it needed to structure itself, so it would generate a cohesive design. If not the final result would be a house with holes in the wall or floating rooms. Therefore a number of functions are needed for searching for patterns, and modifying the data. A standard function would iterate over each unit in the matrix and check the neighboring values. If some sort of condition is met, the proper value will be modified. Going back to our cluster of boxes example. If a has a neighbouring box in front and to the left, but nothing on top, this would be a condition where a corner roof would be generated. And so different functions would solve design problems. In the end you would be left with a matrix of values that would serve as a blueprint for generating in house inside Maya. Finally the model needs to be made in Maya. A number of parts like a wall, door or window are generated or imported in Maya. When iterating over the finished matrix, a certain value in a certain place in the matrix will decide which objects (example wall or roof) needs to be instanced and placed in the right position and direction. When the matrix is fully realised in Maya the model gets a final cleanup, by merging the model, deleting unused parts and empty groups. What is left is the house model. If a range of houses needs to be generated, the process is simply looped over a number of times. Final word This project took way longer than I had anticipated and is far from finished. I learned a lot and at the same time it feels like I have only just begun. I hope to pick up this project again in the near future. I would love to add more elements to the house, like roof-windows or balconies and create procedural shaders. And possibly try out machine learning or some type of genetic algorithm. If you have any questions or are intrigued please contact me at ljh.hartman@gmail.com. Cheers!
leonardovvla / Deep Box PackingThe Packing problem has gained much relevance with the recent upheaval of the delivery and retail industry. Companies all over the world are now subject to massive logistics & operations schemes, and their warehouses‘ e ectiveness is irrevocably bound to how well their products are packed into trucks for distribution. Optimizing this process may lead to huge improvements in performance, time use, resource management and to ultimately increasing profits. Seeking to perform and deliver this optimization, this work proposes a new method called “Deep Box Packing” (DBP), an online system which is able to provide an optimized packing strategy for an arbitrary set of three-dimensional boxes arriving in real-time. DBP was trained using Deep Reinforcement Learning and leverages the power of attention mechanisms in a modified version of the Transformer Network called here the Mapping Transformer. It was conceived to work under partial information, in real-time, and to respond to all of the three inherent questions of packing: which box to take (selection), where to place it in the container (position) and how to place it (orientation) at every given moment in time. Its reward function was tailored not only in terms of optimizing the final Volume utilization of the container but also in terms of the feasibility of the packing sequence, withholding constraints such as box stability and accessibility to the packing positions from the entrance of the container. Under this scenario, DBP was capable of achieving outstanding results in the tested instances up to 100% volume utilization in fully feasible packings. Under comparative tests, DBP considerably improved results obtained from a wall-building LB-Greedy heuristic and showed high generalization capacity to different sizes of the Information window (number of boxes from the whole sequence it can see and choose from at any moment in time). After a set of visual step-by-step analyses of DBP’s behavior in generated packing sequences, it was also shown that it was able to achieve high geometric understanding and great potential for being expanded into a real warehouse scenario.
r-dbi / SQLHelper functions for generating SQL code
minoue-xx / BarChartRaceAnimationThis repository provides a function that generates a bar chart race plot.
hyperthunk / DelegateGenerate Delegate/Wrapper Functions at build time
csurfer / EscaperoomCommand line utility to generate/host a fully functioning virtual escape room from a JSON config.
Open-Attestation / Ethers Contract HooksAutomagically generate hooks from Ethers.js contract functions.
ank1traj / Testcase Generatorautomates the process of generating test cases for cp. It takes input data, specified constraints, and test case conditions, and produces a set of test cases that exercise different paths and functions of the software application under test.
