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earthly / EarthlySuper simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
timothystewart6 / K3s AnsibleThe easiest way to bootstrap a self-hosted High Availability Kubernetes cluster. A fully automated HA k3s etcd install with kube-vip, MetalLB, and more. Build. Destroy. Repeat.
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rpm-software-management / MockMock ensures your RPM package builds are repeatable and reliable.
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sayantann11 / All Classification Templetes For MLClassification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'
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zelogx / Proxmox Msl Setup BasicZelogx™ Multi-Project Secure Lab Setup (AKA MSL setup) is an open-source toolkit for creating secure, project-isolated development environments on Proxmox, using Proxmox SDN, Firewall (Security Groups), and Pritunl. Build multi-tenant, zero-trust, L2-isolated labs with repeatable architecture and best practices.
Gurupatil0003 / Django Tutorial LearnDjango is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It's designed to help developers build web applications quickly, with a focus on reusability and "pluggability" of components. Django follows the "Don't Repeat Yourself" (DRY) principle, which means avoiding repetition of code, and it promotes
Tinkprocodes / Fca UnofficialThis repo is a fork from main repo and will usually have new features bundled faster than main repo (and maybe bundle some bugs, too). # Unofficial Facebook Chat API <img alt="version" src="https://img.shields.io/github/package-json/v/ProCoderMew/fca-unofficial?label=github&style=flat-square"> Facebook now has an official API for chat bots [here](https://developers.facebook.com/docs/messenger-platform). This API is the only way to automate chat functionalities on a user account. We do this by emulating the browser. This means doing the exact same GET/POST requests and tricking Facebook into thinking we're accessing the website normally. Because we're doing it this way, this API won't work with an auth token but requires the credentials of a Facebook account. _Disclaimer_: We are not responsible if your account gets banned for spammy activities such as sending lots of messages to people you don't know, sending messages very quickly, sending spammy looking URLs, logging in and out very quickly... Be responsible Facebook citizens. See [below](#projects-using-this-api) for projects using this API. ## Install If you just want to use fca-unofficial, you should use this command: ```bash npm install procodermew/fca-unofficial ``` It will download `fca-unofficial` from NPM repositories ## Testing your bots If you want to test your bots without creating another account on Facebook, you can use [Facebook Whitehat Accounts](https://www.facebook.com/whitehat/accounts/). ## Example Usage ```javascript const login = require("fca-unofficial"); // Create simple echo bot login({email: "FB_EMAIL", password: "FB_PASSWORD"}, (err, api) => { if(err) return console.error(err); api.listen((err, message) => { api.sendMessage(message.body, message.threadID); }); }); ``` Result: <img width="517" alt="screen shot 2016-11-04 at 14 36 00" src="https://cloud.githubusercontent.com/assets/4534692/20023545/f8c24130-a29d-11e6-9ef7-47568bdbc1f2.png"> ## Documentation You can see it [here](DOCS.md). ## Main Functionality ### Sending a message #### api.sendMessage(message, threadID[, callback][, messageID]) Various types of message can be sent: * *Regular:* set field `body` to the desired message as a string. * *Sticker:* set a field `sticker` to the desired sticker ID. * *File or image:* Set field `attachment` to a readable stream or an array of readable streams. * *URL:* set a field `url` to the desired URL. * *Emoji:* set field `emoji` to the desired emoji as a string and set field `emojiSize` with size of the emoji (`small`, `medium`, `large`) Note that a message can only be a regular message (which can be empty) and optionally one of the following: a sticker, an attachment or a url. __Tip__: to find your own ID, you can look inside the cookies. The `userID` is under the name `c_user`. __Example (Basic Message)__ ```js const login = require("fca-unofficial"); login({email: "FB_EMAIL", password: "FB_PASSWORD"}, (err, api) => { if(err) return console.error(err); var yourID = "000000000000000"; var msg = "Hey!"; api.sendMessage(msg, yourID); }); ``` __Example (File upload)__ ```js const login = require("fca-unofficial"); login({email: "FB_EMAIL", password: "FB_PASSWORD"}, (err, api) => { if(err) return console.error(err); // Note this example uploads an image called image.jpg var yourID = "000000000000000"; var msg = { body: "Hey!", attachment: fs.createReadStream(__dirname + '/image.jpg') } api.sendMessage(msg, yourID); }); ``` ------------------------------------ ### Saving session. To avoid logging in every time you should save AppState (cookies etc.) to a file, then you can use it without having password in your scripts. __Example__ ```js const fs = require("fs"); const login = require("fca-unofficial"); var credentials = {email: "FB_EMAIL", password: "FB_PASSWORD"}; login(credentials, (err, api) => { if(err) return console.error(err); fs.writeFileSync('appstate.json', JSON.stringify(api.getAppState())); }); ``` Alternative: Use [c3c-fbstate](https://github.com/c3cbot/c3c-fbstate) to get fbstate.json (appstate.json) ------------------------------------ ### Listening to a chat #### api.listen(callback) Listen watches for messages sent in a chat. By default this won't receive events (joining/leaving a chat, title change etc…) but it can be activated with `api.setOptions({listenEvents: true})`. This will by default ignore messages sent by the current account, you can enable listening to your own messages with `api.setOptions({selfListen: true})`. __Example__ ```js const fs = require("fs"); const login = require("fca-unofficial"); // Simple echo bot. It will repeat everything that you say. // Will stop when you say '/stop' login({appState: JSON.parse(fs.readFileSync('appstate.json', 'utf8'))}, (err, api) => { if(err) return console.error(err); api.setOptions({listenEvents: true}); var stopListening = api.listenMqtt((err, event) => { if(err) return console.error(err); api.markAsRead(event.threadID, (err) => { if(err) console.error(err); }); switch(event.type) { case "message": if(event.body === '/stop') { api.sendMessage("Goodbye…", event.threadID); return stopListening(); } api.sendMessage("TEST BOT: " + event.body, event.threadID); break; case "event": console.log(event); break; } }); }); ``` ## FAQS 1. How do I run tests? > For tests, create a `test-config.json` file that resembles `example-config.json` and put it in the `test` directory. From the root >directory, run `npm test`. 2. Why doesn't `sendMessage` always work when I'm logged in as a page? > Pages can't start conversations with users directly; this is to prevent pages from spamming users. 3. What do I do when `login` doesn't work? > First check that you can login to Facebook using the website. If login approvals are enabled, you might be logging in incorrectly. For how to handle login approvals, read our docs on [`login`](DOCS.md#login). 4. How can I avoid logging in every time? Can I log into a previous session? > We support caching everything relevant for you to bypass login. `api.getAppState()` returns an object that you can save and pass into login as `{appState: mySavedAppState}` instead of the credentials object. If this fails, your session has expired. 5. Do you support sending messages as a page? > Yes, set the pageID option on login (this doesn't work if you set it using api.setOptions, it affects the login process). > ```js > login(credentials, {pageID: "000000000000000"}, (err, api) => { … } > ``` 6. I'm getting some crazy weird syntax error like `SyntaxError: Unexpected token [`!!! > Please try to update your version of node.js before submitting an issue of this nature. We like to use new language features. 7. I don't want all of these logging messages! > You can use `api.setOptions` to silence the logging. You get the `api` object from `login` (see example above). Do > ```js > api.setOptions({ > logLevel: "silent" > }); > ``` <a name="projects-using-this-api"></a> ## Projects using this API: - [c3c](https://github.com/lequanglam/c3c) - A bot that can be customizable using plugins. Support Facebook & Discord. - [Miraiv2](https://github.com/miraiPr0ject/miraiv2) - A simple Facebook Messenger Bot made by CatalizCS and SpermLord. ## Projects using this API (original repository, facebook-chat-api): - [Messer](https://github.com/mjkaufer/Messer) - Command-line messaging for Facebook Messenger - [messen](https://github.com/tomquirk/messen) - Rapidly build Facebook Messenger apps in Node.js - [Concierge](https://github.com/concierge/Concierge) - Concierge is a highly modular, easily extensible general purpose chat bot with a built in package manager - [Marc Zuckerbot](https://github.com/bsansouci/marc-zuckerbot) - Facebook chat bot - [Marc Thuckerbot](https://github.com/bsansouci/lisp-bot) - Programmable lisp bot - [MarkovsInequality](https://github.com/logicx24/MarkovsInequality) - Extensible chat bot adding useful functions to Facebook Messenger - [AllanBot](https://github.com/AllanWang/AllanBot-Public) - Extensive module that combines the facebook api with firebase to create numerous functions; no coding experience is required to implement this. - [Larry Pudding Dog Bot](https://github.com/Larry850806/facebook-chat-bot) - A facebook bot you can easily customize the response - [fbash](https://github.com/avikj/fbash) - Run commands on your computer's terminal over Facebook Messenger - [Klink](https://github.com/KeNt178/klink) - This Chrome extension will 1-click share the link of your active tab over Facebook Messenger - [Botyo](https://github.com/ivkos/botyo) - Modular bot designed for group chat rooms on Facebook - [matrix-puppet-facebook](https://github.com/matrix-hacks/matrix-puppet-facebook) - A facebook bridge for [matrix](https://matrix.org) - [facebot](https://github.com/Weetbix/facebot) - A facebook bridge for Slack. - [Botium](https://github.com/codeforequity-at/botium-core) - The Selenium for Chatbots - [Messenger-CLI](https://github.com/AstroCB/Messenger-CLI) - A command-line interface for sending and receiving messages through Facebook Messenger. - [AssumeZero-Bot](https://github.com/AstroCB/AssumeZero-Bot) – A highly customizable Facebook Messenger bot for group chats. - [Miscord](https://github.com/Bjornskjald/miscord) - An easy-to-use Facebook bridge for Discord. - [chat-bridge](https://github.com/rexx0520/chat-bridge) - A Messenger, Telegram and IRC chat bridge. - [messenger-auto-reply](https://gitlab.com/theSander/messenger-auto-reply) - An auto-reply service for Messenger. - [BotCore](https://github.com/AstroCB/BotCore) – A collection of tools for writing and managing Facebook Messenger bots. - [mnotify](https://github.com/AstroCB/mnotify) – A command-line utility for sending alerts and notifications through Facebook Messenger.
Jille / DockpinA tool for pinning Docker image and apt package versions
nasa / ML Airport Taxi OutThe ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
nasa / ML Airport ConfigurationThe ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configuration as a time series. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Mdshobu / Liberty House Club Whitepaper# Liberty House Club **A Parallel Binance Chain to Enable Smart Contracts** _NOTE: This document is under development. Please check regularly for updates!_ ## Table of Contents - [Motivation](#motivation) - [Design Principles](#design-principles) - [Consensus and Validator Quorum](#consensus-and-validator-quorum) * [Proof of Staked Authority](#proof-of-staked-authority) * [Validator Quorum](#validator-quorum) * [Security and Finality](#security-and-finality) * [Reward](#reward) - [Token Economy](#token-economy) * [Native Token](#native-token) * [Other Tokens](#other-tokens) - [Cross-Chain Transfer and Communication](#cross-chain-transfer-and-communication) * [Cross-Chain Transfer](#cross-chain-transfer) * [BC to BSC Architecture](#bc-to-bsc-architecture) * [BSC to BC Architecture](#bsc-to-bc-architecture) * [Timeout and Error Handling](#timeout-and-error-handling) * [Cross-Chain User Experience](#cross-chain-user-experience) * [Cross-Chain Contract Event](#cross-chain-contract-event) - [Staking and Governance](#staking-and-governance) * [Staking on BC](#staking-on-bc) * [Rewarding](#rewarding) * [Slashing](#slashing) - [Relayers](#relayers) * [BSC Relayers](#bsc-relayers) * [Oracle Relayers](#oracle-relayers) - [Outlook](#outlook) # Motivation After its mainnet community [launch](https://www.binance.com/en/blog/327334696200323072/Binance-DEX-Launches-on-Binance-Chain-Invites-Further-Community-Development) in April 2019, [Binance Chain](https://www.binance.org) has exhibited its high speed and large throughput design. Binance Chain’s primary focus, its native [decentralized application](https://en.wikipedia.org/wiki/Decentralized_application) (“dApp”) [Binance DEX](https://www.binance.org/trade), has demonstrated its low-latency matching with large capacity headroom by handling millions of trading volume in a short time. Flexibility and usability are often in an inverse relationship with performance. The concentration on providing a convenient digital asset issuing and trading venue also brings limitations. Binance Chain's most requested feature is the programmable extendibility, or simply the [Smart Contract](https://en.wikipedia.org/wiki/Smart_contract) and Virtual Machine functions. Digital asset issuers and owners struggle to add new decentralized features for their assets or introduce any sort of community governance and activities. Despite this high demand for adding the Smart Contract feature onto Binance Chain, it is a hard decision to make. The execution of a Smart Contract may slow down the exchange function and add non-deterministic factors to trading. If that compromise could be tolerated, it might be a straightforward idea to introduce a new Virtual Machine specification based on [Tendermint](https://tendermint.com/core/), based on the current underlying consensus protocol and major [RPC](https://docs.binance.org/api-reference/node-rpc.html) implementation of Binance Chain. But all these will increase the learning requirements for all existing dApp communities, and will not be very welcomed. We propose a parallel blockchain of the current Binance Chain to retain the high performance of the native DEX blockchain and to support a friendly Smart Contract function at the same time. # Design Principles After the creation of the parallel blockchain into the Binance Chain ecosystem, two blockchains will run side by side to provide different services. The new parallel chain will be called “**Binance Smart Chain**” (short as “**BSC**” for the below sections), while the existing mainnet remains named “**Binance Chain**” (short as “**BC**” for the below sections). Here are the design principles of **BSC**: 1. **Standalone Blockchain**: technically, BSC is a standalone blockchain, instead of a layer-2 solution. Most BSC fundamental technical and business functions should be self-contained so that it can run well even if the BC stopped for a short period. 2. **Ethereum Compatibility**: The first practical and widely-used Smart Contract platform is Ethereum. To take advantage of the relatively mature applications and community, BSC chooses to be compatible with the existing Ethereum mainnet. This means most of the **dApps**, ecosystem components, and toolings will work with BSC and require zero or minimum changes; BSC node will require similar (or a bit higher) hardware specification and skills to run and operate. The implementation should leave room for BSC to catch up with further Ethereum upgrades. 3. **Staking Involved Consensus and Governance**: Staking-based consensus is more environmentally friendly and leaves more flexible option to the community governance. Expectedly, this consensus should enable better network performance over [proof-of-work](https://en.wikipedia.org/wiki/Proof_of_work) blockchain system, i.e., faster blocking time and higher transaction capacity. 4. **Native Cross-Chain Communication**: both BC and BSC will be implemented with native support for cross-chain communication among the two blockchains. The communication protocol should be bi-directional, decentralized, and trustless. It will concentrate on moving digital assets between BC and BSC, i.e., [BEP2](https://github.com/binance-chain/BEPs/blob/master/BEP2.md) tokens, and eventually, other BEP tokens introduced later. The protocol should care for the minimum of other items stored in the state of the blockchains, with only a few exceptions. # Consensus and Validator Quorum Based on the above design principles, the consensus protocol of BSC is to fulfill the following goals: 1. Blocking time should be shorter than Ethereum network, e.g. 5 seconds or even shorter. 2. It requires limited time to confirm the finality of transactions, e.g. around 1-min level or shorter. 3. There is no inflation of native token: BNB, the block reward is collected from transaction fees, and it will be paid in BNB. 4. It is compatible with Ethereum system as much as possible. 5. It allows modern [proof-of-stake](https://en.wikipedia.org/wiki/Proof_of_stake) blockchain network governance. ## Proof of Staked Authority Although Proof-of-Work (PoW) has been recognized as a practical mechanism to implement a decentralized network, it is not friendly to the environment and also requires a large size of participants to maintain the security. Ethereum and some other blockchain networks, such as [MATIC Bor](https://github.com/maticnetwork/bor), [TOMOChain](https://tomochain.com/), [GoChain](https://gochain.io/), [xDAI](https://xdai.io/), do use [Proof-of-Authority(PoA)](https://en.wikipedia.org/wiki/Proof_of_authority) or its variants in different scenarios, including both testnet and mainnet. PoA provides some defense to 51% attack, with improved efficiency and tolerance to certain levels of Byzantine players (malicious or hacked). It serves as an easy choice to pick as the fundamentals. Meanwhile, the PoA protocol is most criticized for being not as decentralized as PoW, as the validators, i.e. the nodes that take turns to produce blocks, have all the authorities and are prone to corruption and security attacks. Other blockchains, such as EOS and Lisk both, introduce different types of [Delegated Proof of Stake (DPoS)](https://en.bitcoinwiki.org/wiki/DPoS) to allow the token holders to vote and elect the validator set. It increases the decentralization and favors community governance. BSC here proposes to combine DPoS and PoA for consensus, so that: 1. Blocks are produced by a limited set of validators 2. Validators take turns to produce blocks in a PoA manner, similar to [Ethereum’s Clique](https://eips.ethereum.org/EIPS/eip-225) consensus design 3. Validator set are elected in and out based on a staking based governance ## Validator Quorum In the genesis stage, a few trusted nodes will run as the initial Validator Set. After the blocking starts, anyone can compete to join as candidates to elect as a validator. The staking status decides the top 21 most staked nodes to be the next validator set, and such an election will repeat every 24 hours. **BNB** is the token used to stake for BSC. In order to remain as compatible as Ethereum and upgradeable to future consensus protocols to be developed, BSC chooses to rely on the **BC** for staking management (Please refer to the below “[Staking and Governance](#staking-and-governance)” section). There is a **dedicated staking module for BSC on BC**. It will accept BSC staking from BNB holders and calculate the highest staked node set. Upon every UTC midnight, BC will issue a verifiable `ValidatorSetUpdate` cross-chain message to notify BSC to update its validator set. While producing further blocks, the existing BSC validators check whether there is a `ValidatorSetUpdate` message relayed onto BSC periodically. If there is, they will update the validator set after an **epoch period**, i.e. a predefined number of blocking time. For example, if BSC produces a block every 5 seconds, and the epoch period is 240 blocks, then the current validator set will check and update the validator set for the next epoch in 1200 seconds (20 minutes). ## Security and Finality Given there are more than ½\*N+1 validators are honest, PoA based networks usually work securely and properly. However, there are still cases where certain amount Byzantine validators may still manage to attack the network, e.g. through the “[Clone Attack](https://arxiv.org/pdf/1902.10244.pdf)”. To secure as much as BC, BSC users are encouraged to wait until receiving blocks sealed by more than ⅔\*N+1 different validators. In that way, the BSC can be trusted at a similar security level to BC and can tolerate less than ⅓\*N Byzantine validators. With 21 validators, if the block time is 5 seconds, the ⅔\*N+1 different validator seals will need a time period of (⅔\*21+1)*5 = 75 seconds. Any critical applications for BSC may have to wait for ⅔\*N+1 to ensure a relatively secure finality. However, besides such arrangement, BSC does introduce **Slashing** logic to penalize Byzantine validators for **double signing** or **inavailability**, which will be covered in the “Staking and Governance” section later. This Slashing logic will expose the malicious validators in a very short time and make the “Clone Attack” very hard or extremely non-beneficial to execute. With this enhancement, ½\*N+1 or even fewer blocks are enough as confirmation for most transactions. ## Reward All the BSC validators in the current validator set will be rewarded with transaction **fees in BNB**. As BNB is not an inflationary token, there will be no mining rewards as what Bitcoin and Ethereum network generate, and the gas fee is the major reward for validators. As BNB is also utility tokens with other use cases, delegators and validators will still enjoy other benefits of holding BNB. The reward for validators is the fees collected from transactions in each block. Validators can decide how much to give back to the delegators who stake their BNB to them, in order to attract more staking. Every validator will take turns to produce the blocks in the same probability (if they stick to 100% liveness), thus, in the long run, all the stable validators may get a similar size of the reward. Meanwhile, the stakes on each validator may be different, so this brings a counter-intuitive situation that more users trust and delegate to one validator, they potentially get less reward. So rational delegators will tend to delegate to the one with fewer stakes as long as the validator is still trustful (insecure validator may bring slashable risk). In the end, the stakes on all the validators will have less variation. This will actually prevent the stake concentration and “winner wins forever” problem seen on some other networks. Some parts of the gas fee will also be rewarded to relayers for Cross-Chain communication. Please refer to the “[Relayers](#relayers)” section below. # Token Economy BC and BSC share the same token universe for BNB and BEP2 tokens. This defines: 1. The same token can circulate on both networks, and flow between them bi-directionally via a cross-chain communication mechanism. 2. The total circulation of the same token should be managed across the two networks, i.e. the total effective supply of a token should be the sum of the token’s total effective supply on both BSC and BC. 3. The tokens can be initially created on BSC in a similar format as ERC20 token standard, or on BC as a BEP2, then created on the other. There are native ways on both networks to link the two and secure the total supply of the token. ## Native Token BNB will run on BSC in the same way as ETH runs on Ethereum so that it remains as “native token” for both BSC and BC. This means, in addition to BNB is used to pay most of the fees on Binance Chain and Binance DEX, BNB will be also used to: 1. pay “fees“ to deploy smart contracts on BSC 2. stake on selected BSC validators, and get corresponding rewards 3. perform cross-chain operations, such as transfer token assets across BC and BSC ### Seed Fund Certain amounts of BNB will be burnt on BC and minted on BSC during its genesis stage. This amount is called “Seed Fund” to circulate on BSC after the first block, which will be dispatched to the initial BC-to-BSC Relayer(described in later sections) and initial validator set introduced at genesis. These BNBs are used to pay transaction fees in the early stage to transfer more BNB from BC onto BSC via the cross-chain mechanism. The BNB cross-chain transfer is discussed in a later section, but for BC to BSC transfer, it is generally to lock BNB on BC from the source address of the transfer to a system-controlled address and unlock the corresponding amount from special contract to the target address of the transfer on BSC, or reversely, when transferring from BSC to BC, it is to lock BNB from the source address on BSC into a special contract and release locked amount on BC from the system address to the target address. The logic is related to native code on BC and a series of smart contracts on BSC. ## Other Tokens BC supports BEP2 tokens and upcoming [BEP8 tokens](https://github.com/binance-chain/BEPs/pull/69), which are native assets transferrable and tradable (if listed) via fast transactions and sub-second finality. Meanwhile, as BSC is Ethereum compatible, it is natural to support ERC20 tokens on BSC, which here is called “**BEP2E**” (with the real name to be introduced by the future BEPs,it potentially covers BEP8 as well). BEP2E may be “Enhanced” by adding a few more methods to expose more information, such as token denomination, decimal precision definition and the owner address who can decide the Token Binding across the chains. BSC and BC work together to ensure that one token can circulate in both formats with confirmed total supply and be used in different use cases. ### Token Binding BEP2 tokens will be extended to host a new attribute to associate the token with a BSC BEP2E token contract, called “**Binder**”, and this process of association is called “**Token Binding**”. Token Binding can happen at any time after BEP2 and BEP2E are ready. The token owners of either BEP2 or BEP2E don’t need to bother about the Binding, until before they really want to use the tokens on different scenarios. Issuers can either create BEP2 first or BEP2E first, and they can be bound at a later time. Of course, it is encouraged for all the issuers of BEP2 and BEP2E to set the Binding up early after the issuance. A typical procedure to bind the BEP2 and BEP2E will be like the below: 1. Ensure both the BEP2 token and the BEP2E token both exist on each blockchain, with the same total supply. BEP2E should have 3 more methods than typical ERC20 token standard: * symbol(): get token symbol * decimals(): get the number of the token decimal digits * owner(): get **BEP2E contract owner’s address.** This value should be initialized in the BEP2E contract constructor so that the further binding action can verify whether the action is from the BEP2E owner. 2. Decide the initial circulation on both blockchains. Suppose the total supply is *S*, and the expected initial circulating supply on BC is *K*, then the owner should lock S-K tokens to a system controlled address on BC. 3. Equivalently, *K* tokens is locked in the special contract on BSC, which handles major binding functions and is named as **TokenHub**. The issuer of the BEP2E token should lock the *K* amount of that token into TokenHub, resulting in *S-K* tokens to circulate on BSC. Thus the total circulation across 2 blockchains remains as *S*. 4. The issuer of BEP2 token sends the bind transaction on BC. Once the transaction is executed successfully after proper verification: * It transfers *S-K* tokens to a system-controlled address on BC. * A cross-chain bind request package will be created, waiting for Relayers to relay. 5. BSC Relayers will relay the cross-chain bind request package into **TokenHub** on BSC, and the corresponding request and information will be stored into the contract. 6. The contract owner and only the owner can run a special method of TokenHub contract, `ApproveBind`, to verify the binding request to mark it as a success. It will confirm: * the token has not been bound; * the binding is for the proper symbol, with proper total supply and decimal information; * the proper lock are done on both networks; 10. Once the `ApproveBind` method has succeeded, TokenHub will mark the two tokens are bounded and share the same circulation on BSC, and the status will be propagated back to BC. After this final confirmation, the BEP2E contract address and decimals will be written onto the BEP2 token as a new attribute on BC, and the tokens can be transferred across the two blockchains bidirectionally. If the ApproveBind fails, the failure event will also be propagated back to BC to release the locked tokens, and the above steps can be re-tried later. # Cross-Chain Transfer and Communication Cross-chain communication is the key foundation to allow the community to take advantage of the dual chain structure: * users are free to create any tokenization, financial products, and digital assets on BSC or BC as they wish * the items on BSC can be manually and programmingly traded and circulated in a stable, high throughput, lighting fast and friendly environment of BC * users can operate these in one UI and tooling ecosystem. ## Cross-Chain Transfer The cross-chain transfer is the key communication between the two blockchains. Essentially the logic is: 1. the `transfer-out` blockchain will lock the amount from source owner addresses into a system controlled address/contracts; 2. the `transfer-in` blockchain will unlock the amount from the system controlled address/contracts and send it to target addresses. The cross-chain transfer package message should allow the BSC Relayers and BC **Oracle Relayers** to verify: 1. Enough amount of token assets are removed from the source address and locked into a system controlled addresses/contracts on the source blockchain. And this can be confirmed on the target blockchain. 2. Proper amounts of token assets are released from a system controlled addresses/contracts and allocated into target addresses on the target blockchain. If this fails, it can be confirmed on source blockchain, so that the locked token can be released back (may deduct fees). 3. The sum of the total circulation of the token assets across the 2 blockchains are not changed after this transfer action completes, no matter if the transfer succeeds or not.  The architecture of cross-chain communication is as in the above diagram. To accommodate the 2 heteroid systems, communication handling is different in each direction. ## BC to BSC Architecture BC is a Tendermint-based, instant finality blockchain. Validators with at least ⅔\*N+1 of the total voting power will co-sign each block on the chain. So that it is practical to verify the block transactions and even the state value via **Block Header** and **Merkle Proof** verification. This has been researched and implemented as “**Light-Client Protocol**”, which are intensively discussed in [the Ethereum](https://github.com/ethereum/wiki/wiki/Light-client-protocol) community, studied and implemented for [Cosmos inter-chain communication](https://github.com/cosmos/ics/blob/a4173c91560567bdb7cc9abee8e61256fc3725e9/spec/ics-007-tendermint-client/README.md). BC-to-BSC communication will be verified in an “**on-chain light client**” implemented via BSC **Smart Contracts** (some of them may be **“pre-compiled”**). After some transactions and state change happen on BC, if a transaction is defined to trigger cross-chain communication,the Cross-chain “**package**” message will be created and **BSC Relayers** will pass and submit them onto BSC as data into the "build-in system contracts". The build-in system contracts will verify the package and execute the transactions if it passes the verification. The verification will be guaranteed with the below design: 1. BC blocking status will be synced to the light client contracts on BSC from time to time, via block header and pre-commits, for the below information: * block and app hash of BC that are signed by validators * current validatorset, and validator set update 2. the key-value from the blockchain state will be verified based on the Merkle Proof and information from above #1. After confirming the key-value is accurate and trustful, the build-in system contracts will execute the actions corresponding to the cross-chain packages. Some examples of such packages that can be created for BC-to-BSC are: 1. Bind: bind the BEP2 tokens and BEP2E 2. Transfer: transfer tokens after binding, this means the circulation will decrease (be locked) from BC and appear in the target address balance on BSC 3. Error Handling: to handle any timeout/failure event for BSC-to-BC communication 4. Validatorset update of BSC To ensure no duplication, proper message sequence and timely timeout, there is a “Channel” concept introduced on BC to manage any types of the communication. For relayers, please also refer to the below “Relayers” section. ## BSC to BC Architecture BSC uses Proof of Staked Authority consensus protocol, which has a chance to fork and requires confirmation of more blocks. One block only has the signature of one validator, so that it is not easy to rely on one block to verify data from BSC. To take full advantage of validator quorum of BC, an idea similar to many [Bridge ](https://github.com/poanetwork/poa-bridge)or Oracle blockchains is adopted: 1. The cross-chain communication requests from BSC will be submitted and executed onto BSC as transactions. The execution of the transanction wil emit `Events`, and such events can be observed and packaged in certain “**Oracle**” onto BC. Instead of Block Headers, Hash and Merkle Proof, this type of “Oracle” package directly contains the cross-chain information for actions, such as sender, receiver and amount for transfer. 2. To ensure the security of the Oracle, the validators of BC will form anothe quorum of “**Oracle Relayers**”. Each validator of the BC should run a **dedicated process** as the Oracle Relayer. These Oracle Relayers will submit and vote for the cross-chain communication package, like Oracle, onto BC, using the same validator keys. Any package signed by more than ⅔\*N+1 Oracle Relayers’ voting power is as secure as any block signed by ⅔\*N+1 of the same quorum of validators’ voting power. By using the same validator quorum, it saves the light client code on BC and continuous block updates onto BC. Such Oracles also have Oracle IDs and types, to ensure sequencing and proper error handling. ## Timeout and Error Handling There are scenarios that the cross-chain communication fails. For example, the relayed package cannot be executed on BSC due to some coding bug in the contracts. **Timeout and error handling logics are** used in such scenarios. For the recognizable user and system errors or any expected exceptions, the two networks should heal themselves. For example, when BC to BSC transfer fails, BSC will issue a failure event and Oracle Relayers will execute a refund on BC; when BSC to BC transfer fails, BC will issue a refund package for Relayer to relay in order to unlock the fund. However, unexpected error or exception may still happen on any step of the cross-chain communication. In such a case, the Relayers and Oracle Relayers will discover that the corresponding cross-chain channel is stuck in a particular sequence. After a Timeout period, the Relayers and Oracle Relayers can request a “SkipSequence” transaction, the stuck sequence will be marked as “Unexecutable”. A corresponding alerts will be raised, and the community has to discuss how to handle this scenario, e.g. payback via the sponsor of the validators, or event clear the fund during next network upgrade. ## Cross-Chain User Experience Ideally, users expect to use two parallel chains in the same way as they use one single chain. It requires more aggregated transaction types to be added onto the cross-chain communication to enable this, which will add great complexity, tight coupling, and maintenance burden. Here BC and BSC only implement the basic operations to enable the value flow in the initial launch and leave most of the user experience work to client side UI, such as wallets. E.g. a great wallet may allow users to sell a token directly from BSC onto BC’s DEX order book, in a secure way. ## Cross-Chain Contract Event Cross-Chain Contract Event (CCCE) is designed to allow a smart contract to trigger cross-chain transactions, directly through the contract code. This becomes possible based on: 1. Standard system contracts can be provided to serve operations callable by general smart contracts; 2. Standard events can be emitted by the standard contracts; 3. Oracle Relayers can capture the standard events, and trigger the corresponding cross-chain operations; 4. Dedicated, code-managed address (account) can be created on BC and accessed by the contracts on the BSC, here it is named as **“Contract Address on BC” (CAoB)**. Several standard operations are implemented: 1. BSC to BC transfer: this is implemented in the same way as normal BSC to BC transfer, by only triggered via standard contract. The fund can be transferred to any addresses on BC, including the corresponding CAoB of the transfer originating contract. 2. Transfer on BC: this is implemented as a special cross-chain transfer, while the real transfer is from **CAoB** to any other address (even another CAoB). 3. BC to BSC transfer: this is implemented as two-pass cross-chain communication. The first is triggered by the BSC contract and propagated onto BC, and then in the second pass, BC will start a normal BC to BSC cross-chain transfer, from **CAoB** to contract address on BSC. A special note should be paid on that the BSC contract only increases balance upon any transfer coming in on the second pass, and the error handling in the second pass is the same as the normal BC to BSC transfer. 4. IOC (Immediate-Or-Cancel) Trade Out: the primary goal of transferring assets to BC is to trade. This event will instruct to trade a certain amount of an asset in CAoB into another asset as much as possible and transfer out all the results, i.e. the left the source and the traded target tokens of the trade, back to BSC. BC will handle such relayed events by sending an “Immediate-Or-Cancel”, i.e. IOC order onto the trading pairs, once the next matching finishes, the result will be relayed back to BSC, which can be in either one or two assets. 5. Auction Trade Out: Such event will instruct BC to send an auction order to trade a certain amount of an asset in **CAoB** into another asset as much as possible and transfer out all the results back to BSC at the end of the auction. Auction function is upcoming on BC. There are some details for the Trade Out: 1. both can have a limit price (absolute or relative) for the trade; 2. the end result will be written as cross-chain packages to relay back to BSC; 3. cross-chain communication fees may be charged from the asset transferred back to BSC; 4. BSC contract maintains a mirror of the balance and outstanding orders on CAoB. No matter what error happens during the Trade Out, the final status will be propagated back to the originating contract and clear its internal state. With the above features, it simply adds the cross-chain transfer and exchange functions with high liquidity onto all the smart contracts on BSC. It will greatly add the application scenarios on Smart Contract and dApps, and make 1 chain +1 chain > 2 chains. # Staking and Governance Proof of Staked Authority brings in decentralization and community involvement. Its core logic can be summarized as the below. You may see similar ideas from other networks, especially Cosmos and EOS. 1. Token holders, including the validators, can put their tokens “**bonded**” into the stake. Token holders can **delegate** their tokens onto any validator or validator candidate, to expect it can become an actual validator, and later they can choose a different validator or candidate to **re-delegate** their tokens<sup>1</sup>. 2. All validator candidates will be ranked by the number of bonded tokens on them, and the top ones will become the real validators. 3. Validators can share (part of) their blocking reward with their delegators. 4. Validators can suffer from “**Slashing**”, a punishment for their bad behaviors, such as double sign and/or instability. 5. There is an “**unbonding period**” for validators and delegators so that the system makes sure the tokens remain bonded when bad behaviors are caught, the responsible will get slashed during this period. ## Staking on BC Ideally, such staking and reward logic should be built into the blockchain, and automatically executed as the blocking happens. Cosmos Hub, who shares the same Tendermint consensus and libraries with Binance Chain, works in this way. BC has been preparing to enable staking logic since the design days. On the other side, as BSC wants to remain compatible with Ethereum as much as possible, it is a great challenge and efforts to implement such logic on it. This is especially true when Ethereum itself may move into a different Proof of Stake consensus protocol in a short (or longer) time. In order to keep the compatibility and reuse the good foundation of BC, the staking logic of BSC is implemented on BC: 1. The staking token is BNB, as it is a native token on both blockchains anyway 2. The staking, i.e. token bond and delegation actions and records for BSC, happens on BC. 3. The BSC validator set is determined by its staking and delegation logic, via a staking module built on BC for BSC, and propagated every day UTC 00:00 from BC to BSC via Cross-Chain communication. 4. The reward distribution happens on BC around every day UTC 00:00. ## Rewarding Both the validator update and reward distribution happen every day around UTC 00:00. This is to save the cost of frequent staking updates and block reward distribution. This cost can be significant, as the blocking reward is collected on BSC and distributed on BC to BSC validators and delegators. (Please note BC blocking fees will remain rewarding to BC validators only.) A deliberate delay is introduced here to make sure the distribution is fair: 1. The blocking reward will not be sent to validator right away, instead, they will be distributed and accumulated on a contract; 2. Upon receiving the validator set update into BSC, it will trigger a few cross-chain transfers to transfer the reward to custody addresses on the corresponding validators. The custody addresses are owned by the system so that the reward cannot be spent until the promised distribution to delegators happens. 3. In order to make the synchronization simpler and allocate time to accommodate slashing, the reward for N day will be only distributed in N+2 days. After the delegators get the reward, the left will be transferred to validators’ own reward addresses. ## Slashing Slashing is part of the on-chain governance, to ensure the malicious or negative behaviors are punished. BSC slash can be submitted by anyone. The transaction submission requires **slash evidence** and cost fees but also brings a larger reward when it is successful. So far there are two slashable cases. ### Double Sign It is quite a serious error and very likely deliberate offense when a validator signs more than one block with the same height and parent block. The reference protocol implementation should already have logic to prevent this, so only the malicious code can trigger this. When Double Sign happens, the validator should be removed from the Validator **Set** right away. Anyone can submit a slash request on BC with the evidence of Double Sign of BSC, which should contain the 2 block headers with the same height and parent block, sealed by the offending validator. Upon receiving the evidence, if the BC verifies it to be valid: 1. The validator will be removed from validator set by an instance BSC validator set update Cross-Chain update; 2. A predefined amount of BNB would be slashed from the **self-delegated** BNB of the validator; Both validator and its delegators will not receive the staking rewards. 3. Part of the slashed BNB will allocate to the submitter’s address, which is a reward and larger than the cost of submitting slash request transaction 4. The rest of the slashed BNB will allocate to the other validators’ custody addresses, and distributed to all delegators in the same way as blocking reward. ### Inavailability The liveness of BSC relies on everyone in the Proof of Staked Authority validator set can produce blocks timely when it is their turn. Validators can miss their turn due to any reason, especially problems in their hardware, software, configuration or network. This instability of the operation will hurt the performance and introduce more indeterministic into the system. There can be an internal smart contract responsible for recording the missed blocking metrics of each validator. Once the metrics are above the predefined threshold, the blocking reward for validator will not be relayed to BC for distribution but shared with other better validators. In such a way, the poorly-operating validator should be gradually voted out of the validator set as their delegators will receive less or none reward. If the metrics remain above another higher level of threshold, the validator will be dropped from the rotation, and this will be propagated back to BC, then a predefined amount of BNB would be slashed from the **self-delegated** BNB of the validator. Both validators and delegators will not receive their staking rewards. ### Governance Parameters There are many system parameters to control the behavior of the BSC, e.g. slash amount, cross-chain transfer fees. All these parameters will be determined by BSC Validator Set together through a proposal-vote process based on their staking. Such the process will be carried on BC, and the new parameter values will be picked up by corresponding system contracts via a cross-chain communication. # Relayers Relayers are responsible to submit Cross-Chain Communication Packages between the two blockchains. Due to the heterogeneous parallel chain structure, two different types of Relayers are created. ## BSC Relayers Relayers for BC to BSC communication referred to as “**BSC Relayers**”, or just simply “Relayers”. Relayer is a standalone process that can be run by anyone, and anywhere, except that Relayers must register themselves onto BSC and deposit a certain refundable amount of BNB. Only relaying requests from the registered Relayers will be accepted by BSC. The package they relay will be verified by the on-chain light client on BSC. The successful relay needs to pass enough verification and costs gas fees on BSC, and thus there should be incentive reward to encourage the community to run Relayers. ### Incentives There are two major communication types: 1. Users triggered Operations, such as `token bind` or `cross chain transfer`. Users must pay additional fee to as relayer reward. The reward will be shared with the relayers who sync the referenced blockchain headers. Besides, the reward won't be paid the relayers' accounts directly. A reward distribution mechanism will be brought in to avoid monopolization. 2. System Synchronization, such as delivering `refund package`(caused by failures of most oracle relayers), special blockchain header synchronization(header contains BC validatorset update), BSC staking package. System reward contract will pay reward to relayers' accounts directly. If some Relayers have faster networks and better hardware, they can monopolize all the package relaying and leave no reward to others. Thus fewer participants will join for relaying, which encourages centralization and harms the efficiency and security of the network. Ideally, due to the decentralization and dynamic re-election of BSC validators, one Relayer can hardly be always the first to relay every message. But in order to avoid the monopolization further, the rewarding economy is also specially designed to minimize such chance: 1. The reward for Relayers will be only distributed in batches, and one batch will cover a number of successful relayed packages. 2. The reward a Relayer can get from a batch distribution is not linearly in proportion to their number of successful relayed packages. Instead, except the first a few relays, the more a Relayer relays during a batch period, the less reward it will collect. ## Oracle Relayers Relayers for BSC to BC communication are using the “Oracle” model, and so-called “**Oracle Relayers**”. Each of the validators must, and only the ones of the validator set, run Oracle Relayers. Each Oracle Relayer watches the blockchain state change. Once it catches Cross-Chain Communication Packages, it will submit to vote for the requests. After Oracle Relayers from ⅔ of the voting power of BC validators vote for the changes, the cross-chain actions will be performed. Oracle Replayers should wait for enough blocks to confirm the finality on BSC before submitting and voting for the cross-chain communication packages onto BC. The cross-chain fees will be distributed to BC validators together with the normal BC blocking rewards. Such oracle type relaying depends on all the validators to support. As all the votes for the cross-chain communication packages are recorded on the blockchain, it is not hard to have a metric system to assess the performance of the Oracle Relayers. The poorest performer may have their rewards clawed back via another Slashing logic introduced in the future. # Outlook It is hard to conclude for Binance Chain, as it has never stopped evolving. The dual-chain strategy is to open the gate for users to take advantage of the fast transferring and trading on one side, and flexible and extendable programming on the other side, but it will be one stop along the development of Binance Chain. Here below are the topics to look into so as to facilitate the community better for more usability and extensibility: 1. Add different digital asset model for different business use cases 2. Enable more data feed, especially DEX market data, to be communicated from Binance DEX to BSC 3. Provide interface and compatibility to integrate with Ethereum, including its further upgrade, and other blockchain 4. Improve client side experience to manage wallets and use blockchain more conveniently ------ [1]: BNB business practitioners may provide other benefits for BNB delegators, as they do now for long term BNB holders.
zulily / BoilerplateScripts and config for fast, repeatable, painless Go builds
RandomGamingDev / EzCopenheimerA basic and easier to use version of the Copenheimer bot created by the 5th Column from 2B2T that you can easily and simply execute using python made for educational purposes. Especially since you don't have to deal with anything like build systems, compilation, and the exact same thing on repeat for dependencies.
wearelighthouse / StemCSSBuild the stem - don't repeat yourself, don't unset yourself.
etherceo1x1 / CodesBUILD YOUR OWN BLOCKCHAIN: A PYTHON TUTORIAL Download the full Jupyter/iPython notebook from Github here Build Your Own Blockchain – The Basics¶ This tutorial will walk you through the basics of how to build a blockchain from scratch. Focusing on the details of a concrete example will provide a deeper understanding of the strengths and limitations of blockchains. For a higher-level overview, I’d recommend this excellent article from BitsOnBlocks. Transactions, Validation, and updating system state¶ At its core, a blockchain is a distributed database with a set of rules for verifying new additions to the database. We’ll start off by tracking the accounts of two imaginary people: Alice and Bob, who will trade virtual money with each other. We’ll need to create a transaction pool of incoming transactions, validate those transactions, and make them into a block. We’ll be using a hash function to create a ‘fingerprint’ for each of our transactions- this hash function links each of our blocks to each other. To make this easier to use, we’ll define a helper function to wrap the python hash function that we’re using. In [1]: import hashlib, json, sys def hashMe(msg=""): # For convenience, this is a helper function that wraps our hashing algorithm if type(msg)!=str: msg = json.dumps(msg,sort_keys=True) # If we don't sort keys, we can't guarantee repeatability! if sys.version_info.major == 2: return unicode(hashlib.sha256(msg).hexdigest(),'utf-8') else: return hashlib.sha256(str(msg).encode('utf-8')).hexdigest() Next, we want to create a function to generate exchanges between Alice and Bob. We’ll indicate withdrawals with negative numbers, and deposits with positive numbers. We’ll construct our transactions to always be between the two users of our system, and make sure that the deposit is the same magnitude as the withdrawal- i.e. that we’re neither creating nor destroying money. In [2]: import random random.seed(0) def makeTransaction(maxValue=3): # This will create valid transactions in the range of (1,maxValue) sign = int(random.getrandbits(1))*2 - 1 # This will randomly choose -1 or 1 amount = random.randint(1,maxValue) alicePays = sign * amount bobPays = -1 * alicePays # By construction, this will always return transactions that respect the conservation of tokens. # However, note that we have not done anything to check whether these overdraft an account return {u'Alice':alicePays,u'Bob':bobPays} Now let’s create a large set of transactions, then chunk them into blocks. In [3]: txnBuffer = [makeTransaction() for i in range(30)] Next step: making our very own blocks! We’ll take the first k transactions from the transaction buffer, and turn them into a block. Before we do that, we need to define a method for checking the valididty of the transactions we’ve pulled into the block. For bitcoin, the validation function checks that the input values are valid unspent transaction outputs (UTXOs), that the outputs of the transaction are no greater than the input, and that the keys used for the signatures are valid. In Ethereum, the validation function checks that the smart contracts were faithfully executed and respect gas limits. No worries, though- we don’t have to build a system that complicated. We’ll define our own, very simple set of rules which make sense for a basic token system: The sum of deposits and withdrawals must be 0 (tokens are neither created nor destroyed) A user’s account must have sufficient funds to cover any withdrawals If either of these conditions are violated, we’ll reject the transaction. In [4]: def updateState(txn, state): # Inputs: txn, state: dictionaries keyed with account names, holding numeric values for transfer amount (txn) or account balance (state) # Returns: Updated state, with additional users added to state if necessary # NOTE: This does not not validate the transaction- just updates the state! # If the transaction is valid, then update the state state = state.copy() # As dictionaries are mutable, let's avoid any confusion by creating a working copy of the data. for key in txn: if key in state.keys(): state[key] += txn[key] else: state[key] = txn[key] return state In [5]: def isValidTxn(txn,state): # Assume that the transaction is a dictionary keyed by account names # Check that the sum of the deposits and withdrawals is 0 if sum(txn.values()) is not 0: return False # Check that the transaction does not cause an overdraft for key in txn.keys(): if key in state.keys(): acctBalance = state[key] else: acctBalance = 0 if (acctBalance + txn[key]) < 0: return False return True Here are a set of sample transactions, some of which are fraudulent- but we can now check their validity! In [6]: state = {u'Alice':5,u'Bob':5} print(isValidTxn({u'Alice': -3, u'Bob': 3},state)) # Basic transaction- this works great! print(isValidTxn({u'Alice': -4, u'Bob': 3},state)) # But we can't create or destroy tokens! print(isValidTxn({u'Alice': -6, u'Bob': 6},state)) # We also can't overdraft our account. print(isValidTxn({u'Alice': -4, u'Bob': 2,'Lisa':2},state)) # Creating new users is valid print(isValidTxn({u'Alice': -4, u'Bob': 3,'Lisa':2},state)) # But the same rules still apply! True False False True False Each block contains a batch of transactions, a reference to the hash of the previous block (if block number is greater than 1), and a hash of its contents and the header Building the Blockchain: From Transactions to Blocks¶ We’re ready to start making our blockchain! Right now, there’s nothing on the blockchain, but we can get things started by defining the ‘genesis block’ (the first block in the system). Because the genesis block isn’t linked to any prior block, it gets treated a bit differently, and we can arbitrarily set the system state. In our case, we’ll create accounts for our two users (Alice and Bob) and give them 50 coins each. In [7]: state = {u'Alice':50, u'Bob':50} # Define the initial state genesisBlockTxns = [state] genesisBlockContents = {u'blockNumber':0,u'parentHash':None,u'txnCount':1,u'txns':genesisBlockTxns} genesisHash = hashMe( genesisBlockContents ) genesisBlock = {u'hash':genesisHash,u'contents':genesisBlockContents} genesisBlockStr = json.dumps(genesisBlock, sort_keys=True) Great! This becomes the first element from which everything else will be linked. In [8]: chain = [genesisBlock] For each block, we want to collect a set of transactions, create a header, hash it, and add it to the chain In [9]: def makeBlock(txns,chain): parentBlock = chain[-1] parentHash = parentBlock[u'hash'] blockNumber = parentBlock[u'contents'][u'blockNumber'] + 1 txnCount = len(txns) blockContents = {u'blockNumber':blockNumber,u'parentHash':parentHash, u'txnCount':len(txns),'txns':txns} blockHash = hashMe( blockContents ) block = {u'hash':blockHash,u'contents':blockContents} return block Let’s use this to process our transaction buffer into a set of blocks: In [10]: blockSizeLimit = 5 # Arbitrary number of transactions per block- # this is chosen by the block miner, and can vary between blocks! while len(txnBuffer) > 0: bufferStartSize = len(txnBuffer) ## Gather a set of valid transactions for inclusion txnList = [] while (len(txnBuffer) > 0) & (len(txnList) < blockSizeLimit): newTxn = txnBuffer.pop() validTxn = isValidTxn(newTxn,state) # This will return False if txn is invalid if validTxn: # If we got a valid state, not 'False' txnList.append(newTxn) state = updateState(newTxn,state) else: print("ignored transaction") sys.stdout.flush() continue # This was an invalid transaction; ignore it and move on ## Make a block myBlock = makeBlock(txnList,chain) chain.append(myBlock) In [11]: chain[0] Out[11]: {'contents': {'blockNumber': 0, 'parentHash': None, 'txnCount': 1, 'txns': [{'Alice': 50, 'Bob': 50}]}, 'hash': '7c88a4312054f89a2b73b04989cd9b9e1ae437e1048f89fbb4e18a08479de507'} In [12]: chain[1] Out[12]: {'contents': {'blockNumber': 1, 'parentHash': '7c88a4312054f89a2b73b04989cd9b9e1ae437e1048f89fbb4e18a08479de507', 'txnCount': 5, 'txns': [{'Alice': 3, 'Bob': -3}, {'Alice': -1, 'Bob': 1}, {'Alice': 3, 'Bob': -3}, {'Alice': -2, 'Bob': 2}, {'Alice': 3, 'Bob': -3}]}, 'hash': '7a91fc8206c5351293fd11200b33b7192e87fad6545504068a51aba868bc6f72'} As expected, the genesis block includes an invalid transaction which initiates account balances (creating tokens out of thin air). The hash of the parent block is referenced in the child block, which contains a set of new transactions which affect system state. We can now see the state of the system, updated to include the transactions: In [13]: state Out[13]: {'Alice': 72, 'Bob': 28} Checking Chain Validity¶ Now that we know how to create new blocks and link them together into a chain, let’s define functions to check that new blocks are valid- and that the whole chain is valid. On a blockchain network, this becomes important in two ways: When we initially set up our node, we will download the full blockchain history. After downloading the chain, we would need to run through the blockchain to compute the state of the system. To protect against somebody inserting invalid transactions in the initial chain, we need to check the validity of the entire chain in this initial download. Once our node is synced with the network (has an up-to-date copy of the blockchain and a representation of system state) it will need to check the validity of new blocks that are broadcast to the network. We will need three functions to facilitate in this: checkBlockHash: A simple helper function that makes sure that the block contents match the hash checkBlockValidity: Checks the validity of a block, given its parent and the current system state. We want this to return the updated state if the block is valid, and raise an error otherwise. checkChain: Check the validity of the entire chain, and compute the system state beginning at the genesis block. This will return the system state if the chain is valid, and raise an error otherwise. In [14]: def checkBlockHash(block): # Raise an exception if the hash does not match the block contents expectedHash = hashMe( block['contents'] ) if block['hash']!=expectedHash: raise Exception('Hash does not match contents of block %s'% block['contents']['blockNumber']) return In [15]: def checkBlockValidity(block,parent,state): # We want to check the following conditions: # - Each of the transactions are valid updates to the system state # - Block hash is valid for the block contents # - Block number increments the parent block number by 1 # - Accurately references the parent block's hash parentNumber = parent['contents']['blockNumber'] parentHash = parent['hash'] blockNumber = block['contents']['blockNumber'] # Check transaction validity; throw an error if an invalid transaction was found. for txn in block['contents']['txns']: if isValidTxn(txn,state): state = updateState(txn,state) else: raise Exception('Invalid transaction in block %s: %s'%(blockNumber,txn)) checkBlockHash(block) # Check hash integrity; raises error if inaccurate if blockNumber!=(parentNumber+1): raise Exception('Hash does not match contents of block %s'%blockNumber) if block['contents']['parentHash'] != parentHash: raise Exception('Parent hash not accurate at block %s'%blockNumber) return state In [16]: def checkChain(chain): # Work through the chain from the genesis block (which gets special treatment), # checking that all transactions are internally valid, # that the transactions do not cause an overdraft, # and that the blocks are linked by their hashes. # This returns the state as a dictionary of accounts and balances, # or returns False if an error was detected ## Data input processing: Make sure that our chain is a list of dicts if type(chain)==str: try: chain = json.loads(chain) assert( type(chain)==list) except: # This is a catch-all, admittedly crude return False elif type(chain)!=list: return False state = {} ## Prime the pump by checking the genesis block # We want to check the following conditions: # - Each of the transactions are valid updates to the system state # - Block hash is valid for the block contents for txn in chain[0]['contents']['txns']: state = updateState(txn,state) checkBlockHash(chain[0]) parent = chain[0] ## Checking subsequent blocks: These additionally need to check # - the reference to the parent block's hash # - the validity of the block number for block in chain[1:]: state = checkBlockValidity(block,parent,state) parent = block return state We can now check the validity of the state: In [17]: checkChain(chain) Out[17]: {'Alice': 72, 'Bob': 28} And even if we are loading the chain from a text file, e.g. from backup or loading it for the first time, we can check the integrity of the chain and create the current state: In [18]: chainAsText = json.dumps(chain,sort_keys=True) checkChain(chainAsText) Out[18]: {'Alice': 72, 'Bob': 28} Putting it together: The final Blockchain Architecture¶ In an actual blockchain network, new nodes would download a copy of the blockchain and verify it (as we just did above), then announce their presence on the peer-to-peer network and start listening for transactions. Bundling transactions into a block, they then pass their proposed block on to other nodes. We’ve seen how to verify a copy of the blockchain, and how to bundle transactions into a block. If we recieve a block from somewhere else, verifying it and adding it to our blockchain is easy. Let’s say that the following code runs on Node A, which mines the block: In [19]: import copy nodeBchain = copy.copy(chain) nodeBtxns = [makeTransaction() for i in range(5)] newBlock = makeBlock(nodeBtxns,nodeBchain) Now assume that the newBlock is transmitted to our node, and we want to check it and update our state if it is a valid block: In [20]: print("Blockchain on Node A is currently %s blocks long"%len(chain)) try: print("New Block Received; checking validity...") state = checkBlockValidity(newBlock,chain[-1],state) # Update the state- this will throw an error if the block is invalid! chain.append(newBlock) except: print("Invalid block; ignoring and waiting for the next block...") print("Blockchain on Node A is now %s blocks long"%len(chain)) Blockchain on Node A is currently 7 blocks long New Block Received; checking validity... Blockchain on Node A is now 8 blocks long
