JavaScript source code visualization, static analysis, and complexity tool

code complexity metrics visualization and exploration tool for ruby and javascript

This project shows how to integrate AWS CodePipeline and AWS Step Functions state machines. The integration enables developers to build much simpler CodePipeline actions that perform a single task and to delegate the complexity of dealing with workflow-driven behavior associated with that task to a proper state machine engine. As such, developers will be able to build more intuitive pipelines and still being able to visualize and troubleshoot their pipeline actions in detail by examining the state machine execution logs.

Data Visualizations is emerging as one of the most essential skills in almost all of the IT and Non IT Background Sectors and Jobs. Using Data Visualizations to make wiser decisions which could land the Business to make bigger profits and understand the root cause and behavioral analysis of people and customers associated to it. In this Repository I have deeply discussed about Line Plots, Bar plots, Scatter Plots, and Pie Charts, Apart from that I have Discussed scientific plots, 3d plots, animated plots, interactive plots to visualize any kind of business problem and that too of any complexity.

Visual is a Javascript library for data visualization developed by the Statistical Institute of Catalonia (Idescat). It is based on popular open source solutions. Visual offers a simple interface that encapsulates the complexity of these solutions for the most common chart types.

A visual complexity dataset across seven different categories, including Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism for computer vision application.

Visualize source code complexity with Sabik. This tool is to find the bad smell code. I think your helpful refactoring.

Ruby gem to store, query, and visualize map data without all the complexity

Convolutional Neural Networks (CNNs) are being widely used for various tasks in Computer Vision. We focus on the task of image classification particularly using CNNs with more focus on the relation or similarity between class labels. The similarity between labels is judged using label word embeddings and incorporated into the loss layer. We propose that shallower networks be learnt with more complex and structured losses, in order to gain from shorter training time and equivalent complexity. We train two variants of CNNs with multiple architectures , all limited to a maximum of ten convolution layers to obtain an accuracy of 93.27% on the Fashion-MNIST dataset and 86.40% on the CIFAR 10 dataset. We further probe the adversarial robustness of the model as well the classspecific behavior by visualizing the class confusion matrix.We also show some preliminary results towards extending a trained variant to zero-shot learning.

attractor (code complexity metrics visualization and exploration tool for ruby and javascript) running in a rails engine

An interactive web app to visualize and explore data structures and algorithms. Users can perform operations like insertion, deletion, and search, with real-time feedback and complexity explanations.

Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

Web-Service for plato - JavaScript source code visualization, static analysis, and complexity tool

Visualize time complexity, interactively

Graph visualization and exploration software. Leverages cytoscape.js and provides rich and customized graph visualizations. Aims ultimate complexity management, customization, and user-friendliness.

Static analysis for Effect-TS code. Analyze Effect code to extract structure, calculate complexity, and generate visualizations.

📈 Visualize the time complexity of algorithms

Enhanced code testing suite that includes checks for linting, programmer tendencies, and time and space complexity, and supplies the user with a comprehensive visualization of this data for self-improvement and hiring standards.

Visualizing data behind the Atlas of Economic Complexity

Interactive sorting visualizer with animated bars and time complexity charts

Visualize the complexity of the HR tech ecosystem with interactive D3.js charts.

A lightweight C static analysis tool that measures function-level cyclomatic complexity and visualizes risk with colored output.

Web site to visualize code quality trends (maintainablity index, cyclcomatic complexity, lines of code, code coverage, code churn). The metrics are collected by two of my other data collector applications.

Convolutional Neural Network has proven its impressive effectiveness in Deep Learning, especially Computer Vision. It remarkably reduces the complexity in many Computer Vision tasks and make complex tasks possible, such as Real-time Object Detection. Inspired by the curiousity why it works so well, many prominent research scientists have conducted research to get a better understanding of what Convolutional Neural Network actually does. This project provides a source code which allow everyone examine and visualize the activations in different convnet layers themselves to develop a better understanding and intuition about Convolutional Neural Network.

Notebooks for running and visualizing results using trained models for linguistic complexity.

🔍 Analyze and visualize code health with metrics like cyclomatic complexity, TODOs, and more — for C#, Java, and beyond.

Interactive web application developed with Streamlit to visualize and analyze EEG (Electroencephalography) signals. It allows uploading and processing EEG data to analyze complexity and entropy.

One of the major projects I have worked on till now outside of curriculum is a Twitter Government Sentiment Analysis. It is not just a regular sentiment analysis from a tweet input but has a lot more functionalities and complexity. To give a brief idea about what it does, the project searches a hashtag and displays real time tweets, the user who tweeted it, total retweet count of that tweet, all the hashtags used in each tweet, and most importantly the sentiment analysis of each tweet (whether it is a positive tweet or negative). The result shows the most recent 200 tweets from the day you want it to be searched from by taking a hashtag and date as input from the user. At the top of the result table, you get the total positive tweets percentage and negative tweets percentage of that hashtag. It is a full-fledged website with attractive frontend and smooth backend developed by me. I have developed the sentiment analysis model using logistic regression algorithm, and sqlite3 for database management. The major libraries I needed in the machine learning part are sklearn for logistic regression, nltk for preprocessing and tweepy for twitter authentication and tweets handling. I used matplotlib and seaborn libraries for result visualization to improve the accuracy of my project. The final accuracy I achieved is 98%. Coming to the website building, I have used Flask as my backend language and HTML, CSS, Javascript for frontend. Using Javascript, I was able to add beautiful scroll-animation effect to my project which gave it a more subtle and pleasing user experience. This project can be very useful for companies wanting to take a quick review on what's being said about their product on social media, especially from a specific period where they have made a significant change in their servicing or any other prospect of their product. They can understand the percentage of people who find their product/service positive or negative within seconds.

Create and Visualize your Personal Networks to Navigate Life's Complexities

A tool for analyzing code complexity using McCabe's Cyclomatic Complexity metric, visualizing control flow graphs, and generating refactoring suggestions using AI.