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sayantann11 / All Classification Templetes For MLClassification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'
ManojKumarPatnaik / Major Project ListA list of practical projects that anyone can solve in any programming language (See solutions). These projects are divided into multiple categories, and each category has its own folder. To get started, simply fork this repo. CONTRIBUTING See ways of contributing to this repo. You can contribute solutions (will be published in this repo) to existing problems, add new projects, or remove existing ones. Make sure you follow all instructions properly. Solutions You can find implementations of these projects in many other languages by other users in this repo. Credits Problems are motivated by the ones shared at: Martyr2’s Mega Project List Rosetta Code Table of Contents Numbers Classic Algorithms Graph Data Structures Text Networking Classes Threading Web Files Databases Graphics and Multimedia Security Numbers Find PI to the Nth Digit - Enter a number and have the program generate PI up to that many decimal places. Keep a limit to how far the program will go. Find e to the Nth Digit - Just like the previous problem, but with e instead of PI. Enter a number and have the program generate e up to that many decimal places. Keep a limit to how far the program will go. Fibonacci Sequence - Enter a number and have the program generate the Fibonacci sequence to that number or to the Nth number. Prime Factorization - Have the user enter a number and find all Prime Factors (if there are any) and display them. Next Prime Number - Have the program find prime numbers until the user chooses to stop asking for the next one. Find Cost of Tile to Cover W x H Floor - Calculate the total cost of the tile it would take to cover a floor plan of width and height, using a cost entered by the user. Mortgage Calculator - Calculate the monthly payments of a fixed-term mortgage over given Nth terms at a given interest rate. Also, figure out how long it will take the user to pay back the loan. For added complexity, add an option for users to select the compounding interval (Monthly, Weekly, Daily, Continually). Change Return Program - The user enters a cost and then the amount of money given. The program will figure out the change and the number of quarters, dimes, nickels, pennies needed for the change. Binary to Decimal and Back Converter - Develop a converter to convert a decimal number to binary or a binary number to its decimal equivalent. Calculator - A simple calculator to do basic operators. Make it a scientific calculator for added complexity. Unit Converter (temp, currency, volume, mass, and more) - Converts various units between one another. The user enters the type of unit being entered, the type of unit they want to convert to, and then the value. The program will then make the conversion. Alarm Clock - A simple clock where it plays a sound after X number of minutes/seconds or at a particular time. Distance Between Two Cities - Calculates the distance between two cities and allows the user to specify a unit of distance. This program may require finding coordinates for the cities like latitude and longitude. Credit Card Validator - Takes in a credit card number from a common credit card vendor (Visa, MasterCard, American Express, Discoverer) and validates it to make sure that it is a valid number (look into how credit cards use a checksum). Tax Calculator - Asks the user to enter a cost and either a country or state tax. It then returns the tax plus the total cost with tax. Factorial Finder - The Factorial of a positive integer, n, is defined as the product of the sequence n, n-1, n-2, ...1, and the factorial of zero, 0, is defined as being 1. Solve this using both loops and recursion. Complex Number Algebra - Show addition, multiplication, negation, and inversion of complex numbers in separate functions. (Subtraction and division operations can be made with pairs of these operations.) Print the results for each operation tested. Happy Numbers - A happy number is defined by the following process. Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers, while those that do not end in 1 are unhappy numbers. Display an example of your output here. Find the first 8 happy numbers. Number Names - Show how to spell out a number in English. You can use a preexisting implementation or roll your own, but you should support inputs up to at least one million (or the maximum value of your language's default bounded integer type if that's less). Optional: Support for inputs other than positive integers (like zero, negative integers, and floating-point numbers). Coin Flip Simulation - Write some code that simulates flipping a single coin however many times the user decides. The code should record the outcomes and count the number of tails and heads. Limit Calculator - Ask the user to enter f(x) and the limit value, then return the value of the limit statement Optional: Make the calculator capable of supporting infinite limits. Fast Exponentiation - Ask the user to enter 2 integers a and b and output a^b (i.e. pow(a,b)) in O(LG n) time complexity. Classic Algorithms Collatz Conjecture - Start with a number n > 1. Find the number of steps it takes to reach one using the following process: If n is even, divide it by 2. If n is odd, multiply it by 3 and add 1. Sorting - Implement two types of sorting algorithms: Merge sort and bubble sort. Closest pair problem - The closest pair of points problem or closest pair problem is a problem of computational geometry: given n points in metric space, find a pair of points with the smallest distance between them. Sieve of Eratosthenes - The sieve of Eratosthenes is one of the most efficient ways to find all of the smaller primes (below 10 million or so). Graph Graph from links - Create a program that will create a graph or network from a series of links. Eulerian Path - Create a program that will take as an input a graph and output either an Eulerian path or an Eulerian cycle, or state that it is not possible. An Eulerian path starts at one node and traverses every edge of a graph through every node and finishes at another node. An Eulerian cycle is an eulerian Path that starts and finishes at the same node. Connected Graph - Create a program that takes a graph as an input and outputs whether every node is connected or not. Dijkstra’s Algorithm - Create a program that finds the shortest path through a graph using its edges. Minimum Spanning Tree - Create a program that takes a connected, undirected graph with weights and outputs the minimum spanning tree of the graph i.e., a subgraph that is a tree, contains all the vertices, and the sum of its weights is the least possible. Data Structures Inverted index - An Inverted Index is a data structure used to create full-text search. Given a set of text files, implement a program to create an inverted index. Also, create a user interface to do a search using that inverted index which returns a list of files that contain the query term/terms. The search index can be in memory. Text Fizz Buzz - Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Reverse a String - Enter a string and the program will reverse it and print it out. Pig Latin - Pig Latin is a game of alterations played in the English language game. To create the Pig Latin form of an English word the initial consonant sound is transposed to the end of the word and an ay is affixed (Ex.: "banana" would yield anana-bay). Read Wikipedia for more information on rules. Count Vowels - Enter a string and the program counts the number of vowels in the text. For added complexity have it report a sum of each vowel found. Check if Palindrome - Checks if the string entered by the user is a palindrome. That is that it reads the same forwards as backward like “racecar” Count Words in a String - Counts the number of individual words in a string. For added complexity read these strings in from a text file and generate a summary. Text Editor - Notepad-style application that can open, edit, and save text documents. Optional: Add syntax highlighting and other features. RSS Feed Creator - Given a link to RSS/Atom Feed, get all posts and display them. Quote Tracker (market symbols etc) - A program that can go out and check the current value of stocks for a list of symbols entered by the user. The user can set how often the stocks are checked. For CLI, show whether the stock has moved up or down. Optional: If GUI, the program can show green up and red down arrows to show which direction the stock value has moved. Guestbook / Journal - A simple application that allows people to add comments or write journal entries. It can allow comments or not and timestamps for all entries. Could also be made into a shoutbox. Optional: Deploy it on Google App Engine or Heroku or any other PaaS (if possible, of course). Vigenere / Vernam / Ceasar Ciphers - Functions for encrypting and decrypting data messages. Then send them to a friend. Regex Query Tool - A tool that allows the user to enter a text string and then in a separate control enter a regex pattern. It will run the regular expression against the source text and return any matches or flag errors in the regular expression. Networking FTP Program - A file transfer program that can transfer files back and forth from a remote web sever. Bandwidth Monitor - A small utility program that tracks how much data you have uploaded and downloaded from the net during the course of your current online session. See if you can find out what periods of the day you use more and less and generate a report or graph that shows it. Port Scanner - Enter an IP address and a port range where the program will then attempt to find open ports on the given computer by connecting to each of them. On any successful connections mark the port as open. Mail Checker (POP3 / IMAP) - The user enters various account information include web server and IP, protocol type (POP3 or IMAP), and the application will check for email at a given interval. Country from IP Lookup - Enter an IP address and find the country that IP is registered in. Optional: Find the Ip automatically. Whois Search Tool - Enter an IP or host address and have it look it up through whois and return the results to you. Site Checker with Time Scheduling - An application that attempts to connect to a website or server every so many minute or a given time and check if it is up. If it is down, it will notify you by email or by posting a notice on the screen. Classes Product Inventory Project - Create an application that manages an inventory of products. Create a product class that has a price, id, and quantity on hand. Then create an inventory class that keeps track of various products and can sum up the inventory value. Airline / Hotel Reservation System - Create a reservation system that books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. For example, first class is going to cost more than a coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. Company Manager - Create a hierarchy of classes - abstract class Employee and subclasses HourlyEmployee, SalariedEmployee, Manager, and Executive. Everyone's pay is calculated differently, research a bit about it. After you've established an employee hierarchy, create a Company class that allows you to manage the employees. You should be able to hire, fire, and raise employees. Bank Account Manager - Create a class called Account which will be an abstract class for three other classes called CheckingAccount, SavingsAccount, and BusinessAccount. Manage credits and debits from these accounts through an ATM-style program. Patient / Doctor Scheduler - Create a patient class and a doctor class. Have a doctor that can handle multiple patients and set up a scheduling program where a doctor can only handle 16 patients during an 8 hr workday. Recipe Creator and Manager - Create a recipe class with ingredients and put them in a recipe manager program that organizes them into categories like desserts, main courses, or by ingredients like chicken, beef, soups, pies, etc. Image Gallery - Create an image abstract class and then a class that inherits from it for each image type. Put them in a program that displays them in a gallery-style format for viewing. Shape Area and Perimeter Classes - Create an abstract class called Shape and then inherit from it other shapes like diamond, rectangle, circle, triangle, etc. Then have each class override the area and perimeter functionality to handle each shape type. Flower Shop Ordering To Go - Create a flower shop application that deals in flower objects and use those flower objects in a bouquet object which can then be sold. Keep track of the number of objects and when you may need to order more. Family Tree Creator - Create a class called Person which will have a name, when they were born, and when (and if) they died. Allow the user to create these Person classes and put them into a family tree structure. Print out the tree to the screen. Threading Create A Progress Bar for Downloads - Create a progress bar for applications that can keep track of a download in progress. The progress bar will be on a separate thread and will communicate with the main thread using delegates. Bulk Thumbnail Creator - Picture processing can take a bit of time for some transformations. Especially if the image is large. Create an image program that can take hundreds of images and converts them to a specified size in the background thread while you do other things. For added complexity, have one thread handling re-sizing, have another bulk renaming of thumbnails, etc. Web Page Scraper - Create an application that connects to a site and pulls out all links, or images, and saves them to a list. Optional: Organize the indexed content and don’t allow duplicates. Have it put the results into an easily searchable index file. Online White Board - Create an application that allows you to draw pictures, write notes and use various colors to flesh out ideas for projects. Optional: Add a feature to invite friends to collaborate on a whiteboard online. Get Atomic Time from Internet Clock - This program will get the true atomic time from an atomic time clock on the Internet. Use any one of the atomic clocks returned by a simple Google search. Fetch Current Weather - Get the current weather for a given zip/postal code. Optional: Try locating the user automatically. Scheduled Auto Login and Action - Make an application that logs into a given site on a schedule and invokes a certain action and then logs out. This can be useful for checking webmail, posting regular content, or getting info for other applications and saving it to your computer. E-Card Generator - Make a site that allows people to generate their own little e-cards and send them to other people. Do not use Flash. Use a picture library and perhaps insightful mottos or quotes. Content Management System - Create a content management system (CMS) like Joomla, Drupal, PHP Nuke, etc. Start small. Optional: Allow for the addition of modules/addons. Web Board (Forum) - Create a forum for you and your buddies to post, administer and share thoughts and ideas. CAPTCHA Maker - Ever see those images with letters numbers when you signup for a service and then ask you to enter what you see? It keeps web bots from automatically signing up and spamming. Try creating one yourself for online forms. Files Quiz Maker - Make an application that takes various questions from a file, picked randomly, and puts together a quiz for students. Each quiz can be different and then reads a key to grade the quizzes. Sort Excel/CSV File Utility - Reads a file of records, sorts them, and then writes them back to the file. Allow the user to choose various sort style and sorting based on a particular field. Create Zip File Maker - The user enters various files from different directories and the program zips them up into a zip file. Optional: Apply actual compression to the files. Start with Huffman Algorithm. PDF Generator - An application that can read in a text file, HTML file, or some other file and generates a PDF file out of it. Great for a web-based service where the user uploads the file and the program returns a PDF of the file. Optional: Deploy on GAE or Heroku if possible. Mp3 Tagger - Modify and add ID3v1 tags to MP3 files. See if you can also add in the album art into the MP3 file’s header as well as other ID3v2 tags. Code Snippet Manager - Another utility program that allows coders to put in functions, classes, or other tidbits to save for use later. Organized by the type of snippet or language the coder can quickly lookup code. Optional: For extra practice try adding syntax highlighting based on the language. Databases SQL Query Analyzer - A utility application in which a user can enter a query and have it run against a local database and look for ways to make it more efficient. Remote SQL Tool - A utility that can execute queries on remote servers from your local computer across the Internet. It should take in a remote host, user name, and password, run the query and return the results. Report Generator - Create a utility that generates a report based on some tables in a database. Generates sales reports based on the order/order details tables or sums up the day's current database activity. Event Scheduler and Calendar - Make an application that allows the user to enter a date and time of an event, event notes, and then schedule those events on a calendar. The user can then browse the calendar or search the calendar for specific events. Optional: Allow the application to create re-occurrence events that reoccur every day, week, month, year, etc. Budget Tracker - Write an application that keeps track of a household’s budget. The user can add expenses, income, and recurring costs to find out how much they are saving or losing over a period of time. Optional: Allow the user to specify a date range and see the net flow of money in and out of the house budget for that time period. TV Show Tracker - Got a favorite show you don’t want to miss? Don’t have a PVR or want to be able to find the show to then PVR it later? Make an application that can search various online TV Guide sites, locate the shows/times/channels and add them to a database application. The database/website then can send you email reminders that a show is about to start and which channel it will be on. Travel Planner System - Make a system that allows users to put together their own little travel itinerary and keep track of the airline/hotel arrangements, points of interest, budget, and schedule. Graphics and Multimedia Slide Show - Make an application that shows various pictures in a slide show format. Optional: Try adding various effects like fade in/out, star wipe, and window blinds transitions. Stream Video from Online - Try to create your own online streaming video player. Mp3 Player - A simple program for playing your favorite music files. Add features you think are missing from your favorite music player. Watermarking Application - Have some pictures you want copyright protected? Add your own logo or text lightly across the background so that no one can simply steal your graphics off your site. Make a program that will add this watermark to the picture. Optional: Use threading to process multiple images simultaneously. Turtle Graphics - This is a common project where you create a floor of 20 x 20 squares. Using various commands you tell a turtle to draw a line on the floor. You have moved forward, left or right, lift or drop the pen, etc. Do a search online for "Turtle Graphics" for more information. Optional: Allow the program to read in the list of commands from a file. GIF Creator A program that puts together multiple images (PNGs, JPGs, TIFFs) to make a smooth GIF that can be exported. Optional: Make the program convert small video files to GIFs as well. Security Caesar cipher - Implement a Caesar cipher, both encoding, and decoding. The key is an integer from 1 to 25. This cipher rotates the letters of the alphabet (A to Z). The encoding replaces each letter with the 1st to 25th next letter in the alphabet (wrapping Z to A). So key 2 encrypts "HI" to "JK", but key 20 encrypts "HI" to "BC". This simple "monoalphabetic substitution cipher" provides almost no security, because an attacker who has the encoded message can either use frequency analysis to guess the key, or just try all 25 keys.
clohfink / RendezvousHashRendezvous or Highest Random Weight (HRW) hashing algorithm
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
cdanek / KaimiraWeightedListA generic collection for selecting random elements by weight in O(1) in C#
himanshub1007 / Alzhimers Disease Prediction Using Deep Learning# AD-Prediction Convolutional Neural Networks for Alzheimer's Disease Prediction Using Brain MRI Image ## Abstract Alzheimers disease (AD) is characterized by severe memory loss and cognitive impairment. It associates with significant brain structure changes, which can be measured by magnetic resonance imaging (MRI) scan. The observable preclinical structure changes provides an opportunity for AD early detection using image classification tools, like convolutional neural network (CNN). However, currently most AD related studies were limited by sample size. Finding an efficient way to train image classifier on limited data is critical. In our project, we explored different transfer-learning methods based on CNN for AD prediction brain structure MRI image. We find that both pretrained 2D AlexNet with 2D-representation method and simple neural network with pretrained 3D autoencoder improved the prediction performance comparing to a deep CNN trained from scratch. The pretrained 2D AlexNet performed even better (**86%**) than the 3D CNN with autoencoder (**77%**). ## Method #### 1. Data In this project, we used public brain MRI data from **Alzheimers Disease Neuroimaging Initiative (ADNI)** Study. ADNI is an ongoing, multicenter cohort study, started from 2004. It focuses on understanding the diagnostic and predictive value of Alzheimers disease specific biomarkers. The ADNI study has three phases: ADNI1, ADNI-GO, and ADNI2. Both ADNI1 and ADNI2 recruited new AD patients and normal control as research participants. Our data included a total of 686 structure MRI scans from both ADNI1 and ADNI2 phases, with 310 AD cases and 376 normal controls. We randomly derived the total sample into training dataset (n = 519), validation dataset (n = 100), and testing dataset (n = 67). #### 2. Image preprocessing Image preprocessing were conducted using Statistical Parametric Mapping (SPM) software, version 12. The original MRI scans were first skull-stripped and segmented using segmentation algorithm based on 6-tissue probability mapping and then normalized to the International Consortium for Brain Mapping template of European brains using affine registration. Other configuration includes: bias, noise, and global intensity normalization. The standard preprocessing process output 3D image files with an uniform size of 121x145x121. Skull-stripping and normalization ensured the comparability between images by transforming the original brain image into a standard image space, so that same brain substructures can be aligned at same image coordinates for different participants. Diluted or enhanced intensity was used to compensate the structure changes. the In our project, we used both whole brain (including both grey matter and white matter) and grey matter only. #### 3. AlexNet and Transfer Learning Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers are either convolutional, pooling or fully connected. ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. #### 3.1. AlexNet The net contains eight layers with weights; the first five are convolutional and the remaining three are fully connected. The overall architecture is shown in Figure 1. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. AlexNet maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of the correct label under the prediction distribution. The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel maps in the previous layer which reside on the same GPU (as shown in Figure1). The kernels of the third convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully connected layers are connected to all neurons in the previous layer. Response-normalization layers follow the first and second convolutional layers. Max-pooling layers follow both response-normalization layers as well as the fifth convolutional layer. The ReLU non-linearity is applied to the output of every convolutional and fully-connected layer.  The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5x5x48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3x3x256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3x3x192 , and the fifth convolutional layer has 256 kernels of size 3x3x192. The fully-connected layers have 4096 neurons each. #### 3.2. Transfer Learning Training an entire Convolutional Network from scratch (with random initialization) is impractical[14] because it is relatively rare to have a dataset of sufficient size. An alternative is to pretrain a Conv-Net on a very large dataset (e.g. ImageNet), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. Typically, there are three major transfer learning scenarios: **ConvNet as fixed feature extractor:** We can take a ConvNet pretrained on ImageNet, and remove the last fully-connected layer, then treat the rest structure as a fixed feature extractor for the target dataset. In AlexNet, this would be a 4096-D vector. Usually, we call these features as CNN codes. Once we get these features, we can train a linear classifier (e.g. linear SVM or Softmax classifier) for our target dataset. **Fine-tuning the ConvNet:** Another idea is not only replace the last fully-connected layer in the classifier, but to also fine-tune the parameters of the pretrained network. Due to overfitting concerns, we can only fine-tune some higher-level part of the network. This suggestion is motivated by the observation that earlier features in a ConvNet contains more generic features (e.g. edge detectors or color blob detectors) that can be useful for many kind of tasks. But the later layer of the network becomes progressively more specific to the details of the classes contained in the original dataset. **Pretrained models:** The released pretrained model is usually the final ConvNet checkpoint. So it is common to see people use the network for fine-tuning. #### 4. 3D Autoencoder and Convolutional Neural Network We take a two-stage approach where we first train a 3D sparse autoencoder to learn filters for convolution operations, and then build a convolutional neural network whose first layer uses the filters learned with the autoencoder.  #### 4.1. Sparse Autoencoder An autoencoder is a 3-layer neural network that is used to extract features from an input such as an image. Sparse representations can provide a simple interpretation of the input data in terms of a small number of \parts by extracting the structure hidden in the data. The autoencoder has an input layer, a hidden layer and an output layer, and the input and output layers have same number of units, while the hidden layer contains more units for a sparse and overcomplete representation. The encoder function maps input x to representation h, and the decoder function maps the representation h to the output x. In our problem, we extract 3D patches from scans as the input to the network. The decoder function aims to reconstruct the input form the hidden representation h. #### 4.2. 3D Convolutional Neural Network Training the 3D convolutional neural network(CNN) is the second stage. The CNN we use in this project has one convolutional layer, one pooling layer, two linear layers, and finally a log softmax layer. After training the sparse autoencoder, we take the weights and biases of the encoder from trained model, and use them a 3D filter of a 3D convolutional layer of the 1-layer convolutional neural network. Figure 2 shows the architecture of the network. #### 5. Tools In this project, we used Nibabel for MRI image processing and PyTorch Neural Networks implementation.
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
Aelto / Tw3 Random Encounters ReworkedAdd randomly generated monster contracts, bounties, ambushes and hunts to the game. Dynamically change the vanilla spawns for unique and varied playthroughs. Simulate an ecosystem and a food chain. Add dynamic events to give more weight to your actions
McDonnell-Research-Lab / RanPACRanPAC: Random Projections and Pre-trained Models for Continual Learning - Official code repository for NeurIPS 2023 Published Paper
Schoonology / WeightedA dead-simple module for picking a random item with weights.
chrundle / BipropIdentify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.
OG-Frogger / Toad 3 Blooketjavascript:(function()%7Bfunction start() %7B%0A loadGUI()%3B%0A addUtils()%3B%0A%7D%0A%0Afunction wait(time) %7B%0A return new Promise(resolve %3D> setTimeout(resolve%2C time))%3B%0A%7D%0A%0Avar getValues %3D () %3D> new Promise((e%2C t) %3D> %7B%0A try %7B%0A let n %3D window.webpackJsonp.map(e %3D> Object.keys(e%5B1%5D).map(t %3D> e%5B1%5D%5Bt%5D)).reduce((e%2C t) %3D> %5B...e%2C ...t%5D%2C %5B%5D).find(e %3D> %2F%5Cw%7B8%7D-%5Cw%7B4%7D-%5Cw%7B4%7D-%5Cw%7B4%7D-%5Cw%7B12%7D%2F.test(e.toString()) %26%26 %2F%5C(new TextEncoder%5C)%5C.encode%5C(%5C"(.%2B%3F)%5C"%5C)%2F.test(e.toString())).toString()%3B%0A e(%7B%0A blooketBuild%3A n.match(%2F%5Cw%7B8%7D-%5Cw%7B4%7D-%5Cw%7B4%7D-%5Cw%7B4%7D-%5Cw%7B12%7D%2F)%5B0%5D%2C%0A secret%3A n.match(%2F%5C(new TextEncoder%5C)%5C.encode%5C(%5C"(.%2B%3F)%5C"%5C)%2F)%5B1%5D%0A %7D)%0A %7D catch %7B%0A t("Could not fetch auth details")%0A %7D%0A%7D)%3B%0Avar encodeValues %3D async (e%2C t) %3D> %7B%0A let d %3D window.crypto.getRandomValues(new Uint8Array(12))%3B%0A return window.btoa(Array.from(d).map(e %3D> String.fromCharCode(e)).join("") %2B Array.from(new Uint8Array(await window.crypto.subtle.encrypt(%7B%0A name%3A "AES-GCM"%2C%0A iv%3A d%0A %7D%2C await window.crypto.subtle.importKey("raw"%2C await window.crypto.subtle.digest("SHA-256"%2C (new TextEncoder).encode(t))%2C %7B%0A name%3A "AES-GCM"%0A %7D%2C !1%2C %5B"encrypt"%5D)%2C (new TextEncoder).encode(JSON.stringify(e))))).map(e %3D> String.fromCharCode(e)).join(""))%0A%7D%3B%0A%0A%0Afunction loadGUI() %7B%0A var frame %3D document.createElement("iframe")%3B%0A frame.id %3D "blooo"%0A frame.style.display %3D "none"%3B%0A frame.style.width %3D "1px"%3B%0A frame.style.height %3D "1px"%0A document.body.appendChild(frame)%3B%0A%0A window.alert %3D frame.contentWindow.alert%3B%0A window.prompt %3D frame.contentWindow.prompt%3B%0A window.confirm %3D frame.contentWindow.confirm%3B%0A%0A%0A let element %3D document.createElement('div')%3B%0A element.innerHTML %3D %60<div id%3D"GUI"> <style>details > summary%7Bcursor%3A pointer%3B transition%3A 1s%3B list-style%3A circle%3B%7D.hack%7Bborder%3A none%3B background%3A hsl(0%2C 0%25%2C 20%25)%3B padding%3A 7px%3B margin%3A 5px%3B width%3A 70%25%3B color%3A white%3B transition%3A 0.1s%3B border-radius%3A 5px%3B cursor%3A pointer%3B%7D.hack%3Ahover%7Bbackground%3A hsl(0%2C 1%25%2C 31%25)%3B%7D<%2Fstyle> <div style%3D"cursor%3A all-scroll%3B padding-top%3A 2px%3B font-size%3A 1.5rem%3B text-align%3A center%3B">Toad_UI<button id%3D"gui-" style%3D"background%3A black%3B height%3A 45px%3B width%3A 45px%3B border%3A none%3B cursor%3A pointer%3B position%3A absolute%3B top%3A -10px%3B right%3A 90%25%3B font-size%3A 2.5rem%3B border-radius%3A 10px%3B font-family%3A Nunito%3B font-weight%3A bolder%3B padding-top%3A -10px%3B padding-right%3A -15px%3B color%3A white%3B">-<%2Fbutton> <button id%3D"guiX" style%3D"background%3A black%3B height%3A 45px%3B width%3A 45px%3B border%3A none%3B cursor%3A pointer%3B position%3A absolute%3B top%3A -10px%3B right%3A -10px%3B font-size%3A 1.5rem%3B border-radius%3A 10px%3B font-family%3A Nunito%3B font-weight%3A bolder%3B padding-top%3A 10px%3B padding-right%3A 15px%3B color%3A white%3B">X<%2Fbutton> <%2Fdiv><div style%3D"display%3A block%3B margin%3A 10px%3B min-height%3A 70px%3B"> <div id%3D"curPage">No Game Found?<%2Fdiv><div id%3D"name">Name%3A None<%2Fdiv><div>(E To Hide UI)<%2Fdiv><details open%3D""> <summary style%3D"padding%3A 10px%3B font-size%3A 1.5em%3B font-weight%3A bolder">Main Mods<%2Fsummary> <button id%3D"token" class%3D"hack">Daily 500 Tokens/XP<%2Fbutton> <button id%3D"spoof" class%3D"hack">Unlock Blooks<%2Fbutton> <button id%3D"open" class%3D"hack">Spam Open Boxes<%2Fbutton> <button id%3D"sell" class%3D"hack"> Sell Duplicates<%2Fbutton> <button id%3D"correct" class%3D"hack">All Answer Correct<%2Fbutton> <%2Fdetails><br><div id%3D"LoadedGame"> <%2Fdiv><div> Open source on <a href%3D"https%3A%2F%2F">Deez<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 350px%3B background%3A rgb(64%2C 64%2C 64)%3B border-radius%3A 8px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A%7D%0Astart()%3B%0Aasync function debuggerHelp(how) %7B%0A const response %3D await fetch(%27https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fverify-token%27%2C %7B%0A method%3A "GET"%2C%0A headers%3A %7B%0A "accept"%3A "application%2Fjson%2C text%2Fplain%2C *%2F*"%2C%0A "accept-language"%3A "en-US%2Cen%3Bq%3D0.9%2Cru%3Bq%3D0.8"%2C%0A %7D%2C%0A credentials%3A "include"%0A %7D)%3B%0A const data %3D await response.json()%3B%0A let name %3D data.name%3B%0A let role %3D data.role%3B%0A window.blooketname %3D name%3B%0A window.blooketrole %3D role%3B%0A startDebugger(name)%3B%0A%7D%0A%0Afunction addtokens(event) %7B%0A try %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers"%2C %7B%0A credentials%3A "include"%0A %7D).then(x %3D> x.json()).then(x %3D> %7B%0A getValues().then(async e %3D> %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fadd-rewards"%2C %7B%0A method%3A "put"%2C%0A credentials%3A "include"%2C%0A headers%3A %7B%0A "content-type"%3A "application%2Fjson"%2C%0A "X-Blooket-Build"%3A e.blooketBuild%0A %7D%2C%0A body%3A await encodeValues(%7B%0A name%3A x.name%2C%0A addedTokens%3A 500%2C%0A addedXp%3A 300%0A %7D%2C e.secret)%0A %7D).then(() %3D> alert(%27Added daily rewawrds!%27)).catch(() %3D> alert(%27There was an error when adding rewards!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error encoding requests!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error getting username!%27))%3B%0A window.console.clear()%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C would you like to open the debugger%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction selldupes(event) %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers"%2C %7B%0A credentials%3A "include"%0A %7D).then(x %3D> x.json()).then(x %3D> %7B%0A let blooks %3D Object.entries(x.unlocks).map(x %3D> %5Bx%5B0%5D%2C x%5B1%5D - 1%5D).filter(x %3D> x%5B1%5D > 0)%3B%0A let wait %3D ms %3D> new Promise(r %3D> setTimeout(r%2C ms))%3B%0A getValues().then(async e %3D> %7B%0A let error %3D false%3B%0A alert(%27Selling duplicate blooks%2C please wait%27)%3B%0A for (let %5Bblook%2C numSold%5D of blooks) %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fsellblook"%2C %7B%0A method%3A "put"%2C%0A credentials%3A "include"%2C%0A headers%3A %7B%0A "content-type"%3A "application%2Fjson"%2C%0A "X-Blooket-Build"%3A e.blooketBuild%0A %7D%2C%0A body%3A await encodeValues(%7B%0A name%3A x.name%2C%0A blook%2C%0A numSold%0A %7D%2C e.secret)%0A %7D).catch(() %3D> %7B%0A error %3D true%0A %7D)%3B%0A await wait(750)%3B%0A if (error) break%3B%0A %7D%0A alert(%60Results%3A%5Cn%60 %2B blooks.map((x) %3D> %60 %24%7Bx%5B1%5D%7D %24%7Bx%5B0%5D%7D%60).join(%60%5Cn%60))%3B%0A %7D).catch(() %3D> alert(%27There was an error encoding requests!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error getting user data!%27))%3B%0A%7D%0A%0Afunction spoofblooks(event) %7B%0A try %7B%0A if (window.location.pathname %3D%3D "%2Fplay%2Flobby") %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%3B%0A hack.stateNode.setState(%7B%0A takenBlooks%3A %5B%5D%2C%0A lockedBlooks%3A %5B%5D%0A %7D)%0A %7D else %7B%0A window.alert("Run this in a lobby (https%3A%2F%2Fblooket.com%2Fplay%2Flobby%2F)")%0A %7D%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C would you like to open the debugger%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction openboxes(event) %7B%0A try %7B%0A (async function() %7B%0A let box %3D prompt(%27Which box do you want to open%3F (e.g. Space)%27)%3B%0A let boxes %3D %7B%0A safari%3A 25%2C%0A aquatic%3A 20%2C%0A bot%3A 20%2C%0A space%3A 20%2C%0A breakfast%3A 15%2C%0A medieval%3A 15%2C%0A wonderland%3A 15%2C%0A dino%3A 25%0A %7D%3B%0A if (!Object.keys(boxes).includes(box.toLowerCase())) %7B%0A return alert(%27I could not find that box!%27)%0A %7D%3B%0A let amount %3D prompt(%27How many boxes do you want to open%3F%27)%3B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers"%2C %7B%0A credentials%3A "include"%0A %7D).then(x %3D> x.json()).then(x %3D> %7B%0A if (x.tokens < boxes%5Bbox.toLowerCase()%5D * amount) amount %3D Math.floor(x.tokens %2F boxes%5Bbox.toLowerCase()%5D)%3B%0A if (!amount) return alert(%27You do not have enough tokens!%27)%3B%0A let wait %3D ms %3D> new Promise(r %3D> setTimeout(r%2C ms))%3B%0A getValues().then(async e %3D> %7B%0A let error %3D false%2C%0A blooks %3D %5B%5D%3B%0A for (let i %3D 0%3B i < amount%3B i%2B%2B) %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Funlockblook"%2C %7B%0A method%3A "put"%2C%0A credentials%3A "include"%2C%0A headers%3A %7B%0A "content-type"%3A "application%2Fjson"%2C%0A "X-Blooket-Build"%3A e.blooketBuild%0A %7D%2C%0A body%3A await encodeValues(%7B%0A name%3A x.name%2C%0A box%3A box.charAt(0).toUpperCase() %2B box.slice(1).toLowerCase()%0A %7D%2C e.secret)%0A %7D).then(async x %3D> %7B%0A let blook %3D await x.json()%3B%0A blooks.push(blook.unlockedBlook)%3B%0A alert(%60%24%7Bblook.unlockedBlook%7D (%24%7Bi %2B 1%7D%2F%24%7Bamount%7D)%60)%3B%0A %7D).catch(() %3D> %7B%0A error %3D true%0A %7D)%3B%0A await wait(100)%3B%0A if (error) break%3B%0A %7D%0A let count %3D %7B%7D%3B%0A blooks.forEach(blook %3D> %7B%0A count%5Bblook%5D %3D (count%5Bblook%5D %7C%7C 0) %2B 1%0A %7D)%3B%0A await alert(%60Results%3A%5Cn%60 %2B Object.entries(count).map((x) %3D> %60 %24%7Bx%5B1%5D%7D %24%7Bx%5B0%5D%7D%60).join(%60%5Cn%60))%3B%0A %7D).catch(() %3D> alert(%27There was an error encoding requests!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error getting username!%27))%3B%0A %7D)()%3B%0A window.console.clear()%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C sorry it didnt work try again on a different mode!%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction allcorrect(event) %7B%0A try %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%3B%0A hack.stateNode.questions %3D %5B%7B%0A "text"%3A "Toad_UI moment"%2C%0A "answers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "correctAnswers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "number"%3A 1%2C%0A "random"%3A false%2C%0A "timeLimit"%3A "999"%2C%0A "image"%3A "https%3A%2F%2Fmedia.blooket.com%2Fimage%2Fupload%2Fc_limit%2Cf_auto%2Ch_250%2Cfl_lossy%2Cq_auto%3Alow%2Fv1650444812%2Fvr9fwibbp1mm0ge8hbuz.jpg"%2C%0A "audio"%3A null%0A %7D%5D%0A hack.stateNode.freeQuestions %3D %5B%7B%0A "text"%3A "Toad_ Hacks"%2C%0A "answers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "correctAnswers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "number"%3A 1%2C%0A "random"%3A false%2C%0A "timeLimit"%3A "999"%2C%0A "image"%3A "https%3A%2F%2Fmedia.blooket.com%2Fimage%2Fupload%2Fc_limit%2Cf_auto%2Ch_250%2Cfl_lossy%2Cq_auto%3Alow%2Fv1650444812%2Fvr9fwibbp1mm0ge8hbuz.jpg"%2C%0A "audio"%3A null%0A %7D%5D%0A var z %3D document.getElementsByTagName("iframe")%0A z%5Bz.length - 1%5D.remove()%0A x.remove()%0A window.console.clear()%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C would you like to open the debugger%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction guiexit(event) %7B%0A const GUI %3D document.getElementById("GUI")%3B%0A const GUIX %3D document.getElementById("guiX")%3B%0A const IFR %3D document.getElementById("blooo")%3B%0A const tokens %3D document.getElementById("token")%3B%0A const spoof %3D document.getElementById("spoof")%3B%0A const open %3D document.getElementById("open")%3B%0A const sell %3D document.getElementById("sell")%3B%0A const correct %3D document.getElementById("correct")%3B%0A GUIX.removeEventListener(%27click%27%2C guiexit)%3B%0A tokens.removeEventListener(%27click%27%2C addtokens)%3B%0A spoof.removeEventListener(%27click%27%2C spoofblooks)%3B%0A open.removeEventListener(%27click%27%2C openboxes)%3B%0A sell.removeEventListener(%27click%27%2C selldupes)%3B%0A correct.removeEventListener(%27click%27%2C allcorrect)%3B%0A window.onkeydown %3D null%3B%0A GUI.remove()%3B%0A GUIX.remove()%3B%0A IFR.remove()%3B%0A%7D%0A%0Afunction toggleVisGUI() %7B%0A var GUI %3D document.getElementById("GUI")%3B%0A if (GUI.style.display %3D%3D "none") %7B%0A GUI.style.display %3D "block"%3B%0A %7D else %7B%0A GUI.style.display %3D "none"%3B%0A %7D%0A%7D%0A%0Awindow.addEventListener(%27keydown%27%2C function(e) %7B%0A if (e.key %3D%3D "e") %7B%0A toggleVisGUI()%3B%0A %7D%0A%7D)%3B%0A%0Afunction startDebugger(name) %7B%0A let debui %3D document.getElementById("deb")%0A if (debui !%3D null) %7B%0A window.alert("The debugger is already open.")%0A %7D else %7B%0A let element %3D document.createElement(%27div%27)%3B%0A element.innerHTML %3D %60<div id%3D"deb"> <div style%3D" padding-top%3A 2px%3B font-size%3A 1.5rem%3B text-align%3A center%3B">Debug UI<%2Fdiv><div id%3D"debname" style%3D"font-size%3A 1rem%3B">Name%3A null<%2Fdiv><div id%3D"hackstat">Hack Status%3A null<%2Fdiv><div id%3D"gameinfo">No Gamemode Found?<%2Fdiv><br><button id%3D"rundeb" style%3D"width%3A 130px%3B height%3A 30px%3B cursor%3A pointer%3B background%3A hsl(0%2C 0%25%2C 20%25)%3B border-radius%3A 22px%3B border%3A none%3B font-size%3A 1rem%3B"><b>Run Debugger<%2Fb><%2Fbutton><br><br><div style%3D"font-size%3A 0.8rem%3B">ui by <a href%3D"https%3A%2F%2F<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 175px%3B background%3A rgb(64%2C 64%2C 64)%3B border-radius%3A 8px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A %7D%0A let mode %3D "No game detected"%3B%0A let site %3D window.location.pathname%3B%0A switch (site) %7B%0A case "%2Fplay%2Frush"%3A%0A mode %3D "Blook Rush"%3B%0A break%3B%0A case "%2Fplay%2Fdino"%3A%0A mode %3D "Deceptive Dino"%3B%0A break%3B%0A case "%2Fplay%2Fracing"%3A%0A mode %3D "Racing"%0A break%3B%0A case "%2Fplay%2Ffishing"%3A%0A mode %3D "Fishing Frenzy"%0A break%3B%0A case "%2Fplay%2Fgold"%3A%0A mode %3D "Gold Quest"%0A break%3B%0A case "%2Fplay%2Ffactory"%3A%0A mode %3D "Factory"%3B%0A break%3B%0A case "%2Fcafe"%3A%0A mode %3D "Cafe"%0A break%3B%0A case "%2Fkingdom"%3A%0A mode %3D "Crazy Kingdom"%0A break%3B%0A case "%2Ftower%2Fmap"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Ftower%2Fbattle"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Fdefense"%3A%0A mode %3D "Tower Defense"%0A break%3B%0A %7D%0A const Rundeb %3D document.getElementById("rundeb")%0A const gameinfo %3D document.getElementById("gameinfo")%0A const hackstat %3D document.getElementById("hackstat")%0A const debname %3D document.getElementById("debname")%0A Rundeb.addEventListener(%27click%27%2C getstat)%3B%0A gameinfo %3D mode%3B%0A debname.innerHTML %3D %60Name%3A %24%7Bname%7D%60%3B%0A hackstat.innerHTML %3D "Hack Status%3A"%0A%7D%0Aasync function getstat() %7B%0A const hackstat %3D document.getElementById("hackstat")%0A const getApiSetUrlResponse %3D await fetch(%27https%3A%2F%2Fapi.blooket.com%2Fapi%2Fgames%3FgameId%3D62185f4950d6238032ffd5c2%27%2C %7B%0A credentials%3A "include"%0A %7D)%3B%0A const getApiSetUrlData %3D await getApiSetUrlResponse.json()%3B%0A if (getApiSetUrlData.title %3D%3D "online") %7B%0A hackstat.innerHTML %3D "Hack Status%3A Online"%0A %7D else %7B%0A hackstat.innerHTML %3D "Hack Status%3A Offline"%0A %7D%0A%7D%0Aasync function handleData(type) %7B%0A if (type %3D "elements") %7B%0A const response %3D await fetch(%27https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fverify-token%27%2C %7B%0A method%3A "GET"%2C%0A headers%3A %7B%0A "accept"%3A "application%2Fjson%2C text%2Fplain%2C *%2F*"%2C%0A "accept-language"%3A "en-US%2Cen%3Bq%3D0.9%2Cru%3Bq%3D0.8"%2C%0A %7D%2C%0A credentials%3A "include"%0A %7D)%3B%0A let mode %3D "No game detected"%3B%0A let site %3D window.location.pathname%0A switch (site) %7B%0A case "%2Fplay%2Frush"%3A%0A mode %3D "Blook Rush"%3B%0A break%3B%0A case "%2Fplay%2Fdino"%3A%0A mode %3D "Deceptive Dino"%3B%0A break%3B%0A case "%2Fplay%2Fracing"%3A%0A mode %3D "Racing"%0A break%3B%0A case "%2Fplay%2Ffishing"%3A%0A mode %3D "Fishing Frenzy"%0A break%3B%0A case "%2Fplay%2Fgold"%3A%0A mode %3D "Gold Quest"%0A break%3B%0A case "%2Fplay%2Ffactory"%3A%0A mode %3D "Factory"%3B%0A break%3B%0A case "%2Fcafe"%3A%0A mode %3D "Cafe"%0A break%3B%0A case "%2Fkingdom"%3A%0A mode %3D "Crazy Kingdom"%0A break%3B%0A case "%2Ftower%2Fmap"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Ftower%2Fbattle"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Fdefense"%3A%0A mode %3D "Tower Defense"%0A break%3B%0A %7D%0A const data %3D await response.json()%3B%0A let Name %3D data.name%3B%0A const nameElement %3D document.getElementById("name")%3B%0A const game %3D document.getElementById("curPage")%0A game.innerHTML %3D mode%3B%0A nameElement.innerHTML %3D %60Name%3A %24%7BName%7D%60%3B%0A %7D else %7B%0A console.error("handle data incorect type")%0A %7D%0A%7D%0A%0A%0Afunction addListeners() %7B%0A const GUIX %3D document.getElementById("guiX")%0A const GUIM %3D document.getElementById("gui-")%0A const tokens %3D document.getElementById("token")%0A const spoof %3D document.getElementById("spoof")%0A const open %3D document.getElementById("open")%0A const sell %3D document.getElementById("sell")%0A const correct %3D document.getElementById("correct")%0A GUIX.addEventListener(%27click%27%2C guiexit)%3B%0A GUIM.addEventListener(%27click%27%2C toggleVisGUI)%3B%0A tokens.addEventListener(%27click%27%2C addtokens)%3B%0A spoof.addEventListener(%27click%27%2C spoofblooks)%3B%0A open.addEventListener(%27click%27%2C openboxes)%3B%0A sell.addEventListener(%27click%27%2C selldupes)%3B%0A correct.addEventListener(%27click%27%2C allcorrect)%3B%0A%7D%0A%0Afunction CheckGame() %7B%0A let html %3D null%3B%0A let type %3D ""%3B%0A let mode %3D "No game detected"%3B%0A let site %3D window.location.pathname%3B%0A switch (site) %7B%0A case "%2Fplay%2Frush"%3A%0A type %3D "rush"%3B%0A mode %3D "Blook Rush"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"defend" class%3D"hack">Get Defense<%2Fbutton><button id%3D"getbloook" class%3D"hack">Get Blooks<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fdino"%3A%0A type %3D "dino"%3B%0A mode %3D "Deceptive Dino"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"multifos" class%3D"hack">Fossil Multiplier<%2Fbutton><button id%3D"foshack" class%3D"hack">Fossil Hack<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fracing"%3A%0A type %3D "race"%3B%0A mode %3D "Racing"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"finish" class%3D"hack">Finish Race<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Ffishing"%3A%0A type %3D "fishing"%3B%0A mode %3D "Fishing Frenzy"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"setweight" class%3D"hack">Set Weight<%2Fbutton><button id%3D"setlure" class%3D"hack">Set Lure<%2Fbutton><button id%3D"frenzy" class%3D"hack">Always Frenzy<%2Fbutton><%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fgold"%3A%0A type %3D "gold"%3B%0A mode %3D "Gold Quest"%0A html %3D %27<div id%3D"LoadedGame"> <button id%3D"setgold" class%3D"hack">Set Gold<%2Fbutton> <button id%3D"choiceesp" class%3D"hack">Choice ESP<%2Fbutton> <%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Ffactory"%3A%0A type %3D "factory"%3B%0A mode %3D "Factory"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"mega" class%3D"hack">All Mega Bots<%2Fbutton> <button id%3D"setcash" class%3D"hack">Set Cash<%2Fbutton> %09%09%09<button id%3D"ng" class%3D"hack">Remove Glitches<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fcafe"%3A%0A type %3D "cafe"%3B%0A mode %3D "Cafe"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"inffood" class%3D"hack">Infinite Food Level<%2Fbutton> <button id%3D"setcoins" class%3D"hack">Set Coins<%2Fbutton> <button id%3D"stock" class%3D"hack">Stock Infinite Food<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fcafe%2Fshop"%3A%0A type %3D "cafe"%3B%0A mode %3D "Cafe"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"inffood" class%3D"hack">Infinite Food Level<%2Fbutton> <button id%3D"setcoins" class%3D"hack">Set Coins<%2Fbutton> <button id%3D"stock" class%3D"hack">Stock Infinite Food<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fhack"%3A%0A type %3D "crypto"%3B%0A mode %3D "Crypto-Hack"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"set" class%3D"hack">Set Crypto<%2Fbutton> <button id%3D"esp" class%3D"hack">Change Name<%2Fbutton> <button id%3D"guesspass" class%3D"hack">Autoguess Password<%2Fbutton><%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fkingdom"%3A%0A type %3D "kingdom"%3B%0A mode %3D "Crazy Kingdom"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"esp" class%3D"hack">ChoiceESP<%2Fbutton><button id%3D"max" class%3D"hack">Max Stats<%2Fbutton> <button id%3D"taxes" class%3D"hack">No Taxes<%2Fbutton> <button id%3D"setgold" class%3D"hack">Set Gold<%2Fbutton> <button id%3D"sethappy" class%3D"hack">Set Happiness<%2Fbutton> <button id%3D"setmaterials" class%3D"hack">Set Materials<%2Fbutton> <button id%3D"setpeople" class%3D"hack">Set People<%2Fbutton><%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Ftower%2Fmap"%3A%0A type %3D "doom"%0A mode %3D "Tower of Doom"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"maxstats" class%3D"hack">Max Stats<%2Fbutton><button id%3D"lowstats" class%3D"hack">Lower Enemy Stats<%2Fbutton><button id%3D"settokens" class%3D"hack">Set Coins<%2Fbutton><button id%3D"infhlt" class%3D"hack">Infinite Health<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Ftower%2Fbattle"%3A%0A type %3D "doom"%0A mode %3D "Tower of Doom"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"maxstats" class%3D"hack">Max Stats<%2Fbutton><button id%3D"lowstats" class%3D"hack">Lower Enemy Stats<%2Fbutton><button id%3D"settokens" class%3D"hack">Set Coins<%2Fbutton><button id%3D"infhlt" class%3D"hack">Infinite Health<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fdefense"%3A%0A type %3D "defense"%3B%0A mode %3D "Tower Defense"%0A html %3D %27<div id%3D"LoadedGame"> <button id%3D"settokens" class%3D"hack">Set Tokens<%2Fbutton> <button id%3D"sethealth" class%3D"hack">Set Health<%2Fbutton> <button id%3D"setround" class%3D"hack">Set Round<%2Fbutton> <button id%3D"maxtowers" class%3D"hack">Max All Towers<%2Fbutton> <button id%3D"towersany" class%3D"hack">Place Towers Anywhere<%2Fbutton> <%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A default%3A%0A let element %3D document.getElementById("LoadedGame")%0A element.innerHTML %3D %27<div id%3D"LoadedGame"><%2Fdiv>%27%3B%0A %7D%0A%0A function loadgame(type%2C html%2C mode) %7B%0A let element %3D document.getElementById("LoadedGame")%0A let curPage %3D document.getElementById("curPage")%0A element.innerHTML %3D html%3B%0A curPage.innerHTML %3D mode%3B%0A addEvents(type)%3B%0A %7D%0A%0A function addEvents(type) %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%0A switch (type) %7B%0A case "crypto"%3A%0A const set %3D document.getElementById("set")%0A const autoguess %3D document.getElementById("guesspass")%0A const esp2 %3D document.getElementById("esp")%0A set.addEventListener(%27click%27%2C () %3D> %7B%0A var cf %3D window.prompt("How much Crypto would you like%3F")%0A let num %3D Number(cf)%0A if (num !%3D null %7C%7C num !%3D undefined) %7B%0A hack.stateNode.state.crypto %3D num%3B%0A %7D%0A %7D)%0A autoguess.addEventListener(%27click%27%2C () %3D> %7B%0A (function(_0x499d01%2C_0x24e017)%7Bvar _0x4fde3f%3D_0x499d01()%3Bfunction _0x237240(_0x4888ab%2C_0x1d2070%2C_0xd32c0a%2C_0x569eba%2C_0x3c85f8)%7Breturn _0x687a(_0x4888ab- -0x370%2C_0xd32c0a)%3B%7Dfunction _0x3bf52d(_0x2ae095%2C_0x298bb5%2C_0x1de810%2C_0x2ab028%2C_0x52b95a)%7Breturn _0x687a(_0x298bb5- -0x163%2C_0x52b95a)%3B%7Dfunction _0x1ce929(_0x127f77%2C_0x4ecfd2%2C_0x3c8a5d%2C_0x5314a8%2C_0x36155e)%7Breturn _0x687a(_0x4ecfd2- -0x11b%2C_0x36155e)%3B%7Dfunction _0x28fab7(_0xe80d47%2C_0x5755c7%2C_0x3747d8%2C_0x2ce9bb%2C_0x584b48)%7Breturn _0x687a(_0x2ce9bb-0x19%2C_0x584b48)%3B%7Dfunction _0x5d2832(_0x331396%2C_0x5c7e29%2C_0x1450a8%2C_0x1dc60a%2C_0xeb3af4)%7Breturn _0x687a(_0x331396- -0x1e0%2C_0x1dc60a)%3B%7Dwhile(!!%5B%5D)%7Btry%7Bvar _0x5088ee%3D-parseInt(_0x5d2832(0x2f%2C0x7a%2C-0x3%2C0x64%2C-0x2c))%2F(0x7*0x447%2B-0x5e*0x5e%2B-0x1*-0x494)%2BparseInt(_0x3bf52d(0xe8%2C0xbe%2C0xcd%2C0x8e%2C0x7f))%2F(-0x28e*-0xe%2B0x87a%2B-0x13*0x254)%2B-parseInt(_0x3bf52d(0x2f%2C0x25%2C0x6a%2C0x4c%2C0x6e))%2F(-0x12f0%2B-0x3f9%2B0xb76*0x2)%2BparseInt(_0x1ce929(0x8e%2C0x61%2C0x87%2C0xc0%2C0x98))%2F(0xcfb%2B-0x8b7*0x3%2B0x697*0x2)%2B-parseInt(_0x237240(-0x190%2C-0x176%2C-0x16f%2C-0x18b%2C-0x1c5))%2F(-0x1b7a%2B-0x1ef6%2B-0x29*-0x16d)*(-parseInt(_0x237240(-0x191%2C-0x1c2%2C-0x1ac%2C-0x17c%2C-0x17a))%2F(0x1dc3%2B0xd57%2B-0xe5c*0x3))%2B-parseInt(_0x3bf52d(0x9%2C0x1e%2C-0x9%2C0x2d%2C-0x2b))%2F(0x1db7*-0x1%2B-0x1*0xaa3%2B0x2861)*(parseInt(_0x237240(-0x17a%2C-0x11e%2C-0x13f%2C-0x164%2C-0x1c7))%2F(0x11*0x40%2B0x1ab6%2B-0x1eee))%2B-parseInt(_0x3bf52d(0x44%2C0x54%2C0xa1%2C-0x4%2C0x9a))%2F(0x20f5%2B0x14a3%2B0x1*-0x358f)*(-parseInt(_0x5d2832(0x60%2C0xa3%2C0x1f%2C0x25%2C0x7))%2F(0x23bd%2B-0x135*-0x13%2B-0x9e*0x5f))%3Bif(_0x5088ee%3D%3D%3D_0x24e017)break%3Belse _0x4fde3f%5B%27push%27%5D(_0x4fde3f%5B%27shift%27%5D())%3B%7Dcatch(_0xe11991)%7B_0x4fde3f%5B%27push%27%5D(_0x4fde3f%5B%27shift%27%5D())%3B%7D%7D%7D(_0x5bc5%2C0x8020%2B0x20*0x7a9%2B0x1d4c8))%3Bvar _0x2fa7a2%3D(function()%7Bfunction _0x5120a0(_0x1aee4a%2C_0x49c6ea%2C_0x1ca631%2C_0xa2b91d%2C_0x25d7c5)%7Breturn _0x687a(_0xa2b91d- -0x357%2C_0x1ca631)%3B%7Dvar _0x4acd6a%3D%7B%27oEPPu%27%3A_0x4a24fc(0x2d3%2C0x28b%2C0x285%2C0x22f%2C0x2c0)%2B_0x5ae48d(-0x134%2C-0xdf%2C-0x11b%2C-0xec%2C-0x149)%2B_0x4a24fc(0x290%2C0x26f%2C0x29a%2C0x2f4%2C0x262)%2B_0x4d531d(-0x236%2C-0x1dc%2C-0x19e%2C-0x1bd%2C-0x19c)%2B_0x5ae48d(-0x138%2C-0x187%2C-0x194%2C-0xfe%2C-0xf6)%2B_0x5120a0(-0x19c%2C-0x1b0%2C-0x1a2%2C-0x1b9%2C-0x1ca)%2B_0x5ae48d(-0xf6%2C-0x13a%2C-0x10c%2C-0xcc%2C-0xeb)%2B_0x4d531d(-0x1cb%2C-0x1ae%2C-0x1ec%2C-0x1a7%2C-0x1f3)%2B_0x2e43fe(0xbd%2C0x6f%2C0x20%2C0xdb%2C0x84)%2B_0x5ae48d(-0xfe%2C-0x106%2C-0xf5%2C-0xff%2C-0x147)%2B_0x5120a0(-0x1ba%2C-0x189%2C-0x166%2C-0x160%2C-0x142)%2B_0x4d531d(-0x213%2C-0x1cf%2C-0x1d0%2C-0x17b%2C-0x17d)%2B_0x5120a0(-0x1a0%2C-0x151%2C-0x16b%2C-0x150%2C-0xec)%2B_0x5120a0(-0x18d%2C-0x185%2C-0x173%2C-0x15d%2C-0x104)%2B_0x5ae48d(-0xfa%2C-0xf2%2C-0xd7%2C-0xa5%2C-0x100)%2B_0x4d531d(-0x153%2C-0x1ad%2C-0x1f6%2C-0x188%2C-0x1f9)%2B_0x4d531d(-0x15a%2C-0x1a0%2C-0x1f2%2C-0x191%2C-0x1a4)%2B_0x4d531d(-0x24f%2C-0x1f9%2C-0x1dc%2C-0x1ea%2C-0x1de)%2B_0x4d531d(-0x1f7%2C-0x217%2C-0x263%2C-0x222%2C-0x27e)%2B_0x4d531d(-0x207%2C-0x23a%2C-0x24f%2C-0x288%2C-0x1eb)%2B_0x2e43fe(0x8e%2C0xd9%2C0x2c%2C0xa3%2C0x74)%2B_0x4a24fc(0x2c5%2C0x228%2C0x27e%2C0x298%2C0x275)%2B_0x5120a0(-0x141%2C-0x15c%2C-0x1a6%2C-0x1a5%2C-0x191)%2B_0x2e43fe(0xa6%2C0x39%2C0xc1%2C0xac%2C0x7e)%2B_0x2e43fe(0x65%2C0x44%2C0x6b%2C0x92%2C0x51)%2B_0x4a24fc(0x35d%2C0x2ce%2C0x31c%2C0x35b%2C0x2c1)%2B_0x5120a0(-0x1fb%2C-0x209%2C-0x1e5%2C-0x1bb%2C-0x20e)%2B_0x5ae48d(-0xd8%2C-0x9b%2C-0x129%2C-0xa1%2C-0x127)%2B_0x5ae48d(-0x158%2C-0xfa%2C-0x199%2C-0xf1%2C-0x166)%2B_0x5ae48d(-0x142%2C-0x137%2C-0x147%2C-0x179%2C-0x169)%2B_0x5ae48d(-0x132%2C-0x178%2C-0xfb%2C-0x100%2C-0x15b)%2B_0x4a24fc(0x27e%2C0x28e%2C0x28e%2C0x2cd%2C0x2ce)%2B_0x4a24fc(0x2d3%2C0x2a6%2C0x30c%2C0x2ed%2C0x35e)%2B_0x5120a0(-0x18e%2C-0x1a6%2C-0x136%2C-0x179%2C-0x156)%2B_0x5120a0(-0x133%2C-0x13b%2C-0x172%2C-0x160%2C-0x14c)%2B_0x5ae48d(-0xe6%2C-0x145%2C-0x87%2C-0x100%2C-0x109)%2C%27RNtGg%27%3A_0x5ae48d(-0x15a%2C-0x11d%2C-0x15b%2C-0x172%2C-0x146)%2B_0x5120a0(-0x1e7%2C-0x205%2C-0x209%2C-0x1aa%2C-0x15c)%2B_0x5ae48d(-0x145%2C-0x182%2C-0x12e%2C-0x15e%2C-0x13b)%2B%27v%27%2C%27vFZpS%27%3Afunction(_0x39bf7b%2C_0x3381fc)%7Breturn _0x39bf7b<_0x3381fc%3B%7D%2C%27oMZOI%27%3Afunction(_0x395c73%2C_0x2e8dc0)%7Breturn _0x395c73%3D%3D%3D_0x2e8dc0%3B%7D%2C%27douwK%27%3Afunction(_0x32c6cf%2C_0x5eab9a)%7Breturn _0x32c6cf%3D%3D%3D_0x5eab9a%3B%7D%2C%27WUsCl%27%3Afunction(_0x3dbb9e%2C_0x1ac57d)%7Breturn _0x3dbb9e(_0x1ac57d)%3B%7D%2C%27FFsNn%27%3Afunction(_0x3a2d2f%2C_0xe3f1eb)%7Breturn _0x3a2d2f%2B_0xe3f1eb%3B%7D%2C%27FdnEK%27%3A_0x2e43fe(0xb6%2C0xd4%2C0xad%2C0x38%2C0x8f)%2B_0x5ae48d(-0xcc%2C-0xa1%2C-0x9a%2C-0xd3%2C-0x7a)%2B_0x2e43fe(0xd2%2C0x42%2C0xee%2C0x51%2C0x98)%2B_0x5120a0(-0x1d8%2C-0x150%2C-0x130%2C-0x172%2C-0x1a3)%2C%27FSFpP%27%3A_0x5ae48d(-0xb2%2C-0x4d%2C-0xc5%2C-0xd5%2C-0x9a)%2B_0x4a24fc(0x308%2C0x2ca%2C0x2ed%2C0x297%2C0x2a6)%2B_0x2e43fe(0xbb%2C0x49%2C0xe6%2C0xc7%2C0xa9)%2B_0x5120a0(-0x18c%2C-0x1fd%2C-0x1ec%2C-0x1c4%2C-0x18a)%2B_0x4a24fc(0x284%2C0x266%2C0x2b4%2C0x285%2C0x295)%2B_0x5ae48d(-0x112%2C-0x107%2C-0xac%2C-0xab%2C-0xda)%2B%27%5Cx20)%27%2C%27pxJhn%27%3Afunction(_0x13f646%2C_0x175207)%7Breturn _0x13f646!%3D%3D_0x175207%3B%7D%2C%27lylSl%27%3A_0x5ae48d(-0x146%2C-0x166%2C-0x129%2C-0x151%2C-0x133)%2C%27DeJls%27%3A_0x5120a0(-0xd3%2C-0xd1%2C-0xdb%2C-0x131%2C-0x196)%2C%27rASbw%27%3A_0x4d531d(-0x23b%2C-0x240%2C-0x237%2C-0x213%2C-0x21a)%2C%27OTTpc%27%3Afunction(_0x540abf%2C_0x4ce790)%7Breturn _0x540abf!%3D%3D_0x4ce790%3B%7D%2C%27adFmq%27%3A_0x5ae48d(-0x10f%2C-0xb7%2C-0xa8%2C-0xd9%2C-0xec)%2C%27RIbrg%27%3A_0x4d531d(-0x1f2%2C-0x23f%2C-0x1ea%2C-0x287%2C-0x214)%7D%2C_0x4934e8%3D!!%5B%5D%3Bfunction _0x2e43fe(_0x2a09f3%2C_0x3f2f80%2C_0x24dc23%2C_0x49942c%2C_0x53337e)%7Breturn _0x687a(_0x53337e- -0x170%2C_0x2a09f3)%3B%7Dfunction _0x4d531d(_0x5e7443%2C_0x5d7ba8%2C_0x5bc42b%2C_0x14c10e%2C_0x273d30)%7Breturn _0x687a(_0x5d7ba8- -0x3c4%2C_0x14c10e)%3B%7Dfunction _0x5ae48d(_0x45dd4d%2C_0x2694fa%2C_0x2ced50%2C_0x4836fa%2C_0x2f1a3a)%7Breturn _0x687a(_0x45dd4d- -0x2e1%2C_0x2f1a3a)%3B%7Dfunction _0x4a24fc(_0x407cca%2C_0xabce7a%2C_0x48b8a9%2C_0x50f511%2C_0x12940e)%7Breturn _0x687a(_0x48b8a9-0xfe%2C_0x407cca)%3B%7Dreturn function(_0x222e1d%2C_0x1b1865)%7Bfunction _0x3b5248(_0x33fc58%2C_0x19d4df%2C_0x59814e%2C_0x13ad26%2C_0x2b8240)%7Breturn _0x2e43fe(_0x59814e%2C_0x19d4df-0x1c4%2C_0x59814e-0x3b%2C_0x13ad26-0xfc%2C_0x2b8240-0x5b)%3B%7Dfunction _0x1d7f17(_0x3eee8c%2C_0x198db3%2C_0x413d1a%2C_0x96db9b%2C_0x46f11a)%7Breturn _0x2e43fe(_0x413d1a%2C_0x198db3-0x3f%2C_0x413d1a-0x7a%2C_0x96db9b-0x158%2C_0x198db3-0x516)%3B%7Dfunction _0xd1f1ce(_0x1d8217%2C_0x4dbe06%2C_0x4a5bae%2C_0x6a87c7%2C_0x2acf1a)%7Breturn _0x5120a0(_0x1d8217-0x1e4%2C_0x4dbe06-0x76%2C_0x4dbe06%2C_0x2acf1a-0x6aa%2C_0x2acf1a-0x15)%3B%7Dfunction _0x288927(_0x349347%2C_0x4d0270%2C_0x6b162b%2C_0x501720%2C_0x2e1fc8)%7Breturn _0x5120a0(_0x349347-0x156%2C_0x4d0270-0x12b%2C_0x6b162b%2C_0x2e1fc8-0x107%2C_0x2e1fc8-0xb1)%3B%7Dvar _0x5c4e46%3D%7B%27bvIqh%27%3Afunction(_0x4f1944%2C_0x5206b1)%7Bfunction _0x59c2d0(_0xae4e4%2C_0x45a38a%2C_0x5ba605%2C_0x45c436%2C_0x237576)%7Breturn _0x687a(_0xae4e4-0x342%2C_0x237576)%3B%7Dreturn _0x4acd6a%5B_0x59c2d0(0x54c%2C0x4f1%2C0x5b2%2C0x53e%2C0x5aa)%5D(_0x4f1944%2C_0x5206b1)%3B%7D%2C%27zAYFs%27%3Afunction(_0x5e4a13%2C_0x5eb3a7)%7Bfunction _0x4e026d(_0x388bdf%2C_0x2d3b80%2C_0x571578%2C_0x4435cd%2C_0x19a721)%7Breturn _0x687a(_0x388bdf-0x91%2C_0x2d3b80)%3B%7Dreturn _0x4acd6a%5B_0x4e026d(0x2bf%2C0x2fb%2C0x2fa%2C0x2d7%2C0x31c)%5D(_0x5e4a13%2C_0x5eb3a7)%3B%7D%2C%27TnCyP%27%3Afunction(_0x5e6a83%2C_0x54d275)%7Bfunction _0xfe0bbf(_0x120b31%2C_0x1dbb31%2C_0x1289a9%2C_0x1d43dc%2C_0x3e8346)%7Breturn _0x687a(_0x1dbb31- -0x2d2%2C_0x1d43dc)%3B%7Dreturn _0x4acd6a%5B_0xfe0bbf(-0x127%2C-0xc1%2C-0xfb%2C-0xc9%2C-0xd0)%5D(_0x5e6a83%2C_0x54d275)%3B%7D%2C%27eKJFv%27%3A_0x4acd6a%5B_0x288927(-0x7a%2C-0x15%2C-0x85%2C-0x61%2C-0x74)%5D%2C%27saCdn%27%3A_0x4acd6a%5B_0x288927(-0xdb%2C-0xf9%2C-0x110%2C-0x116%2C-0xbc)%5D%2C%27kuQLk%27%3Afunction(_0xe0bb4f%2C_0x391275)%7Bfunction _0x3d5f84(_0x56667d%2C_0x342b64%2C_0x6240d2%2C_0x80384%2C_0x36bbff)%7Breturn _0x288927(_0x56667d-0x10e%2C_0x342b64-0xb%2C_0x6240d2%2C_0x80384-0x127%2C_0x80384-0x26c)%3B%7Dreturn _0x4acd6a%5B_0x3d5f84(0x1f7%2C0x1f8%2C0x1df%2C0x20f%2C0x242)%5D(_0xe0bb4f%2C_0x391275)%3B%7D%2C%27ZGwxh%27%3A_0x4acd6a%5B_0xfe816c(-0x43%2C-0x8c%2C-0x4%2C-0x1e%2C-0x4f)%5D%2C%27WTpcU%27%3A_0x4acd6a%5B_0x288927(-0xe0%2C-0xcc%2C-0xd0%2C-0xdb%2C-0x91)%5D%2C%27YMMAA%27%3A_0x4acd6a%5B_0xfe816c(-0x5c%2C-0x20%2C-0x53%2C-0xad%2C-0xb1)%5D%7D%3Bfunction _0xfe816c(_0x30e133%2C_0x925bf7%2C_0x3ed8c3%2C_0x1ebdd1%2C_0x37237b)%7Breturn _0x5ae48d(_0x30e133-0x65%2C_0x925bf7-0x182%2C_0x3ed8c3-0x156%2C_0x1ebdd1-0x8d%2C_0x37237b)%3B%7Dif(_0x4acd6a%5B_0xd1f1ce(0x52f%2C0x53f%2C0x582%2C0x52d%2C0x553)%5D(_0x4acd6a%5B_0x288927(-0xf%2C-0xc2%2C-0x85%2C-0x1c%2C-0x6e)%5D%2C_0x4acd6a%5B_0x288927(-0x19%2C0x26%2C-0x16%2C0x3c%2C-0xd)%5D))%7Bvar _0x484b9d%3D_0x4934e8%3Ffunction()%7Bfunction _0x50990c(_0x3ca8be%2C_0x3f0636%2C_0x386b19%2C_0x1601f8%2C_0x19bd59)%7Breturn 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_0x372d4a<_0x5d99a2%3B%7D%2C%27mFqWk%27%3Afunction(_0x1c5c7f%2C_0x4acc1b)%7Breturn _0x1c5c7f%3D%3D%3D_0x4acc1b%3B%7D%2C%27bkyhQ%27%3A_0x4a0252(0x20d%2C0x1d3%2C0x203%2C0x21f%2C0x220)%2B_0x2da3b0(0x2f2%2C0x383%2C0x34d%2C0x32b%2C0x331)%2B%270%27%2C%27ipifN%27%3Afunction(_0x2b066b%2C_0x3a369d)%7Breturn _0x2b066b!%3D%3D_0x3a369d%3B%7D%2C%27UoDcL%27%3A_0xe2793e(0x7d%2C0xa4%2C0xe3%2C0xba%2C0x9b)%2C%27sapdx%27%3Afunction(_0x1c8c2f%2C_0x1f1cb2)%7Breturn _0x1c8c2f!%3D%3D_0x1f1cb2%3B%7D%2C%27yjdRf%27%3A_0x1abc1b(0xd1%2C0xf3%2C0x86%2C0xec%2C0xc2)%2C%27hXkzc%27%3A_0x4a0252(0x25f%2C0x255%2C0x24f%2C0x218%2C0x1b1)%2C%27HDkCV%27%3Afunction(_0x1069d5%2C_0x3dabd0)%7Breturn _0x1069d5(_0x3dabd0)%3B%7D%2C%27AlEdf%27%3Afunction(_0x1c8833%2C_0x2b46e0)%7Breturn _0x1c8833%2B_0x2b46e0%3B%7D%2C%27TxNwp%27%3A_0x4e3d0c(0x1bd%2C0x223%2C0x1f2%2C0x1ca%2C0x216)%2B_0x2da3b0(0x353%2C0x3c1%2C0x31f%2C0x385%2C0x342)%2B_0x2da3b0(0x35f%2C0x335%2C0x331%2C0x378%2C0x328)%2B_0xe2793e(0x121%2C0xe2%2C0x113%2C0x96%2C0xee)%2C%27NXzBk%27%3A_0x1abc1b(0x157%2C0xda%2C0x15a%2C0x10b%2C0x14d)%2B_0x4e3d0c(0x225%2C0x213%2C0x22a%2C0x26e%2C0x251)%2B_0x1abc1b(0xfd%2C0x155%2C0x121%2C0xf5%2C0x13b)%2B_0x2da3b0(0x338%2C0x34f%2C0x33f%2C0x303%2C0x2a1)%2B_0x1abc1b(0x3d%2C0xba%2C0x45%2C0x92%2C0xdb)%2B_0x1abc1b(0xca%2C0x62%2C0x4c%2C0xab%2C0xbb)%2B%27%5Cx20)%27%2C%27xIoNv%27%3A_0x2da3b0(0x351%2C0x374%2C0x337%2C0x333%2C0x2e4)%2C%27jVtep%27%3Afunction(_0x2188ca)%7Breturn _0x2188ca()%3B%7D%2C%27jPogU%27%3A_0x1abc1b(0xc5%2C0xf5%2C0x160%2C0x108%2C0x120)%2C%27vmAVg%27%3A_0x2da3b0(0x2dc%2C0x34b%2C0x351%2C0x315%2C0x348)%2C%27WkRIP%27%3A_0xe2793e(0x14d%2C0xe6%2C0x113%2C0x11e%2C0xd1)%2C%27icbTT%27%3A_0x4a0252(0x1c0%2C0x250%2C0x277%2C0x21c%2C0x1c7)%2C%27FIeZD%27%3A_0x4e3d0c(0x243%2C0x1e8%2C0x218%2C0x19f%2C0x235)%2B_0x4e3d0c(0x17d%2C0x1b6%2C0x19c%2C0x1e3%2C0x15b)%2C%27HuWJR%27%3A_0x1abc1b(0x10a%2C0x13b%2C0x154%2C0xee%2C0x121)%2C%27owvqL%27%3A_0x2da3b0(0x36c%2C0x3bc%2C0x381%2C0x3b5%2C0x412)%2C%27vgbxu%27%3Afunction(_0xa62d5c%2C_0x151639)%7Breturn _0xa62d5c<_0x151639%3B%7D%2C%27tGgaT%27%3Afunction(_0x43768b%2C_0x2057bf)%7Breturn _0x43768b%3D%3D%3D_0x2057bf%3B%7D%2C%27nrVWE%27%3A_0x4a0252(0x272%2C0x2a5%2C0x2c8%2C0x28e%2C0x2c7)%2C%27lCBnw%27%3A_0x2da3b0(0x353%2C0x359%2C0x381%2C0x347%2C0x341)%2C%27KNqqk%27%3A_0x1abc1b(0x154%2C0xc9%2C0x116%2C0x118%2C0xfd)%2B_0x4a0252(0x1cf%2C0x226%2C0x230%2C0x1f4%2C0x214)%2B%272%27%7D%3Bfunction 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_0x2da3b0(_0x2fc3e6%2C_0x110d30-0x37%2C_0x197558-0xe4%2C_0x197558- -0x1fb%2C_0x1070ce-0x62)%3B%7Dfunction _0x3054e8(_0x28c704%2C_0x3c1212%2C_0x2e2115%2C_0x3d2198%2C_0x186b36)%7Breturn _0x2da3b0(_0x28c704%2C_0x3c1212-0xa4%2C_0x2e2115-0x1dc%2C_0x3c1212- -0x498%2C_0x186b36-0x173)%3B%7Dvar _0x29d9ab%3D_0x360f99%3Bif(_0x34ed88%5B_0x511fa6(0x28b%2C0x29b%2C0x291%2C0x2c0%2C0x267)%5D(_0x34ed88%5B_0x596f3d(0x152%2C0x163%2C0x129%2C0x18e%2C0xd9)%5D%2C_0x34ed88%5B_0x511fa6(0x2a3%2C0x2c3%2C0x345%2C0x2de%2C0x313)%5D))%7Bvar _0x124739%3D_0x29d9ab%5B_0x2c45a5(0xf1%2C0xba%2C0xfa%2C0x124%2C0x122)%5D%5B_0x596f3d(0x16f%2C0x162%2C0x1ab%2C0x1b4%2C0x203)%5D(%27%7C%27)%2C_0x9b7ca1%3D0x1*0x5a1%2B0x0%2B0x5a1*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x124739%5B_0x9b7ca1%2B%2B%5D)%7Bcase%270%27%3A_0x99e455%5B_0x433f5f%5D%3D_0x15cadb%3Bcontinue%3Bcase%271%27%3Avar _0x433f5f%3D_0x30c8af%5B_0x2a88d2%5D%3Bcontinue%3Bcase%272%27%3Avar _0x15cadb%3D_0x343f32%5B_0x511fa6(0x2a9%2C0x2a7%2C0x279%2C0x2c3%2C0x274)%2B_0x2c45a5(0x179%2C0x162%2C0x1d0%2C0x197%2C0x1ca)%2B%27r%27%5D%5B_0x2c45a5(0x101%2C0x118%2C0x156%2C0x149%2C0x164)%2B_0x1561b8(-0xb1%2C-0xfa%2C-0x55%2C-0x8d%2C-0xe4)%5D%5B_0x1561b8(-0x103%2C-0xfc%2C-0x135%2C-0x10d%2C-0xe5)%5D(_0x1c6e54)%3Bcontinue%3Bcase%273%27%3A_0x15cadb%5B_0x3054e8(-0xe6%2C-0x11b%2C-0x15a%2C-0xd0%2C-0x10e)%2B_0x2c45a5(0x13f%2C0x139%2C0x143%2C0x18d%2C0xdc)%5D%3D_0x13dc4f%5B_0x596f3d(0x1b0%2C0x147%2C0x182%2C0x17f%2C0x122)%2B_0x2c45a5(0x13f%2C0x106%2C0xf6%2C0xf8%2C0x138)%5D%5B_0x511fa6(0x299%2C0x257%2C0x297%2C0x2b5%2C0x2fd)%5D(_0x13dc4f)%3Bcontinue%3Bcase%274%27%3Avar _0x13dc4f%3D_0x3831ab%5B_0x433f5f%5D%7C%7C_0x15cadb%3Bcontinue%3Bcase%275%27%3A_0x15cadb%5B_0x1561b8(-0x54%2C-0x18%2C0x12%2C-0x95%2C-0x2c)%2B_0x1561b8(-0xbd%2C-0xe6%2C-0x75%2C-0xd1%2C-0x77)%5D%3D_0x29d2f9%5B_0x1561b8(-0x103%2C-0x168%2C-0xd6%2C-0xbd%2C-0xc9)%5D(_0x5974b1)%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x1b2755%3Btry%7Bif(_0x34ed88%5B_0x2c45a5(0x16f%2C0x1cc%2C0x142%2C0x1b8%2C0x1a4)%5D(_0x34ed88%5B_0x511fa6(0x2e4%2C0x2f0%2C0x2e1%2C0x303%2C0x307)%5D%2C_0x34ed88%5B_0x1561b8(-0x65%2C-0x8c%2C-0x68%2C-0x61%2C-0x34)%5D))_0x1b2755%3D_0x34ed88%5B_0x511fa6(0x395%2C0x375%2C0x34a%2C0x36e%2C0x3c8)%5D(Function%2C_0x34ed88%5B_0x2c45a5(0x11d%2C0x14d%2C0xfa%2C0x111%2C0xe7)%5D(_0x34ed88%5B_0x3054e8(-0x1a9%2C-0x17e%2C-0x154%2C-0x140%2C-0x145)%5D(_0x34ed88%5B_0x2c45a5(0x1b9%2C0x212%2C0x206%2C0x1e1%2C0x174)%5D%2C_0x34ed88%5B_0x3054e8(-0x134%2C-0x110%2C-0xfe%2C-0x171%2C-0xf0)%5D)%2C%27)%3B%27))()%3Belse%7Bif(_0x368b44%5B_0x1561b8(-0x7b%2C-0xde%2C-0x7d%2C-0x56%2C-0x75)%2B_0x1561b8(-0xf6%2C-0x9a%2C-0xcc%2C-0xa1%2C-0x115)%2B_0x1561b8(-0xee%2C-0xf7%2C-0xc4%2C-0xe5%2C-0x8e)%5D(_0x34ed88%5B_0x511fa6(0x2fb%2C0x313%2C0x2ce%2C0x30b%2C0x354)%5D))%7Bvar _0x21374c%3D_0x1c3d83%5B_0x596f3d(0x145%2C0x145%2C0x188%2C0x18b%2C0x1e2)%2B_0x2c45a5(0x10b%2C0xe4%2C0xb5%2C0xb1%2C0x15f)%2B_0x596f3d(0xd8%2C0x14f%2C0x115%2C0xbf%2C0x123)%5D(_0x34ed88%5B_0x2c45a5(0x1a8%2C0x1d1%2C0x192%2C0x171%2C0x189)%5D)%5B_0x511fa6(0x30c%2C0x311%2C0x332%2C0x2da%2C0x323)%2B_0x596f3d(0x149%2C0x15a%2C0x12d%2C0xd6%2C0x182)%5D%2C_0x1c80a0%3D_0x3289c6%5B_0x1561b8(-0x115%2C-0x151%2C-0xf8%2C-0x17b%2C-0x116)%2B%27s%27%5D(_0x76dfb0%5B_0x2c45a5(0x186%2C0x14d%2C0x1be%2C0x1ba%2C0x1d1)%2B_0x2c45a5(0x10b%2C0xce%2C0xea%2C0xae%2C0x126)%2B_0x596f3d(0x14e%2C0x129%2C0x115%2C0x110%2C0xb7)%5D(_0x34ed88%5B_0x511fa6(0x315%2C0x2db%2C0x30f%2C0x32e%2C0x2f7)%5D))%5B-0x393*-0x9%2B-0x475%2B0x1bb5*-0x1%5D%5B_0x511fa6(0x2da%2C0x300%2C0x2b5%2C0x2da%2C0x340)%2B_0x596f3d(0x10c%2C0x18a%2C0x12d%2C0xd5%2C0x105)%5D%5B-0x1*-0x1d8b%2B-0x1fdb%2B0x251%5D%5B_0x511fa6(0x2b6%2C0x2a0%2C0x2fc%2C0x2ff%2C0x358)%2B%27r%27%5D%5B_0x3054e8(-0x14e%2C-0xf1%2C-0xec%2C-0x91%2C-0x157)%2B_0x3054e8(-0xe3%2C-0xf5%2C-0x103%2C-0xf8%2C-0xa2)%5D%5B_0x1561b8(-0x57%2C-0x6d%2C0xd%2C-0x61%2C-0xb7)%5D%5B_0x2c45a5(0x160%2C0x187%2C0x14a%2C0x175%2C0x13d)%2B_0x511fa6(0x31d%2C0x31b%2C0x31d%2C0x355%2C0x3b8)%2B_0x511fa6(0x2c7%2C0x2df%2C0x383%2C0x32d%2C0x33e)%5D%3Bfor(var _0x45a6a8%3D0xc*0x1b%2B0x1049*0x2%2B-0x2*0x10eb%3B_0x34ed88%5B_0x596f3d(0x15e%2C0x14d%2C0x1b0%2C0x1e1%2C0x20c)%5D(_0x45a6a8%2C_0x21374c%5B_0x511fa6(0x395%2C0x301%2C0x381%2C0x345%2C0x2e6)%2B%27h%27%5D)%3B_0x45a6a8%2B%2B)%7B_0x34ed88%5B_0x511fa6(0x329%2C0x342%2C0x314%2C0x2fd%2C0x2e6)%5D(_0x21374c%5B_0x45a6a8%5D%5B_0x511fa6(0x287%2C0x297%2C0x312%2C0x2c7%2C0x286)%2B_0x1561b8(-0x111%2C-0xc1%2C-0x124%2C-0x12a%2C-0x14d)%2B%27t%27%5D%2C_0x1c80a0)%26%26_0x21374c%5B_0x45a6a8%5D%5B_0x3054e8(-0x143%2C-0x15b%2C-0x126%2C-0x13e%2C-0x1b9)%5D()%3B%7D%7D%7D%7Dcatch(_0x3ec10c)%7Bif(_0x34ed88%5B_0x596f3d(0x1cb%2C0x13c%2C0x171%2C0x18d%2C0x1b9)%5D(_0x34ed88%5B_0x3054e8(-0xc5%2C-0x109%2C-0xc7%2C-0xed%2C-0x167)%5D%2C_0x34ed88%5B_0x3054e8(-0x104%2C-0x109%2C-0xaa%2C-0xdf%2C-0x118)%5D))%7Bvar _0x4efc8f%3D_0x11a188%3Ffunction()%7Bfunction _0x5ef706(_0x214aee%2C_0x2c4b6d%2C_0x6aaa4e%2C_0x2afa36%2C_0x1e2bb4)%7Breturn _0x596f3d(_0x2c4b6d%2C_0x2c4b6d-0x1c%2C_0x6aaa4e- -0x18e%2C_0x2afa36-0x159%2C_0x1e2bb4-0x107)%3B%7Dif(_0x2507c2)%7Bvar _0x5a8c1c%3D_0x2f6274%5B_0x5ef706(-0x101%2C-0x40%2C-0x9f%2C-0x8a%2C-0xba)%5D(_0x264a41%2Carguments)%3Breturn _0x47b71d%3Dnull%2C_0x5a8c1c%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x3b5e69%3D!%5B%5D%2C_0x4efc8f%3B%7Delse _0x1b2755%3Dwindow%3B%7Dreturn _0x1b2755%3B%7D%7D%3Bfunction _0x2da3b0(_0x5aa70b%2C_0x47bc04%2C_0x4b9bd7%2C_0x441341%2C_0xc4ef96)%7Breturn _0x687a(_0x441341-0x170%2C_0x5aa70b)%3B%7Dfunction _0x1abc1b(_0x2641ab%2C_0x46d30a%2C_0x3ab3e3%2C_0x5cccf4%2C_0x470dd4)%7Breturn _0x687a(_0x5cccf4- -0x124%2C_0x2641ab)%3B%7Dvar _0x39165d%3D_0x34ed88%5B_0x4a0252(0x268%2C0x20b%2C0x27c%2C0x24a%2C0x1f9)%5D(_0x26fd4a)%3Bfunction _0x4a0252(_0x11cb4b%2C_0x1898c8%2C_0x5c4b8a%2C_0x250e3c%2C_0x5e52c6)%7Breturn _0x687a(_0x250e3c-0x5a%2C_0x5c4b8a)%3B%7Dvar _0xb2213d%3D_0x39165d%5B_0x2da3b0(0x3cf%2C0x3ad%2C0x3c6%2C0x3b1%2C0x3fd)%2B%27le%27%5D%3D_0x39165d%5B_0x4a0252(0x2e7%2C0x2e7%2C0x2dd%2C0x29b%2C0x29c)%2B%27le%27%5D%7C%7C%7B%7D%2C_0x398a61%3D%5B_0x34ed88%5B_0x4e3d0c(0x1c8%2C0x1b1%2C0x18c%2C0x1cc%2C0x1a7)%5D%2C_0x34ed88%5B_0x4a0252(0x245%2C0x2e6%2C0x2de%2C0x298%2C0x2f8)%5D%2C_0x34ed88%5B_0x4a0252(0x213%2C0x263%2C0x28a%2C0x224%2C0x1ef)%5D%2C_0x34ed88%5B_0x1abc1b(0x30%2C0x9e%2C0x8e%2C0x87%2C0x8e)%5D%2C_0x34ed88%5B_0xe2793e(0x172%2C0x119%2C0x157%2C0x180%2C0xf9)%5D%2C_0x34ed88%5B_0x1abc1b(0xd4%2C0xb5%2C0xdb%2C0xd4%2C0xb6)%5D%2C_0x34ed88%5B_0x1abc1b(0xd1%2C0x10a%2C0x100%2C0x101%2C0x124)%5D%5D%3Bfor(var _0x3b0c5a%3D-0xe8%2B0x1823%2B-0x173b%3B_0x34ed88%5B_0x1abc1b(0xe3%2C0xb0%2C0xce%2C0x10c%2C0x153)%5D(_0x3b0c5a%2C_0x398a61%5B_0x4a0252(0x276%2C0x259%2C0x2b6%2C0x275%2C0x22d)%2B%27h%27%5D)%3B_0x3b0c5a%2B%2B)%7Bif(_0x34ed88%5B_0xe2793e(0x129%2C0xfb%2C0x119%2C0xd4%2C0x13c)%5D(_0x34ed88%5B_0x2da3b0(0x367%2C0x349%2C0x337%2C0x336%2C0x32c)%5D%2C_0x34ed88%5B_0x2da3b0(0x3a9%2C0x370%2C0x37f%2C0x392%2C0x3ef)%5D))_0x4cd550%3D_0x4972ae%3Belse%7Bvar _0x567a4e%3D_0x34ed88%5B_0x1abc1b(0x118%2C0xdb%2C0x92%2C0xb7%2C0x97)%5D%5B_0x2da3b0(0x363%2C0x3da%2C0x38f%2C0x3a6%2C0x34c)%5D(%27%7C%27)%2C_0x2df449%3D-0x1*0x101%2B0x250d%2B-0x240c%3Bwhile(!!%5B%5D)%7Bswitch(_0x567a4e%5B_0x2df449%2B%2B%5D)%7Bcase%270%27%3A_0x542916%5B_0x2da3b0(0x371%2C0x401%2C0x345%2C0x3aa%2C0x3e2)%2B_0x2da3b0(0x340%2C0x345%2C0x30b%2C0x341%2C0x311)%5D%3D_0x14ebc%5B_0x1abc1b(0xb4%2C0x71%2C0x9a%2C0x67%2C0xb0)%5D(_0x14ebc)%3Bcontinue%3Bcase%271%27%3Avar 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_0x435aa1%3D_0x3a0916%3Ffunction()%7Bfunction _0x228bd2(_0x445a30%2C_0x57f38c%2C_0xf3bba5%2C_0x303084%2C_0x36472b)%7Breturn _0x2faf2d(_0x445a30-0x1d2%2C_0xf3bba5%2C_0xf3bba5-0xbe%2C_0x36472b- -0x58e%2C_0x36472b-0x1bc)%3B%7Dif(_0x5777ec)%7Bvar _0x646968%3D_0x37cf8d%5B_0x228bd2(-0x5b%2C-0x44%2C0x7%2C-0x86%2C-0x48)%5D(_0x12e767%2Carguments)%3Breturn _0x320301%3Dnull%2C_0x646968%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x5823cd%3D!%5B%5D%2C_0x435aa1%3B%7D%7D%7Delse%7Bvar _0xe2aefb%3Btry%7B_0xe2aefb%3DkdCVYp%5B_0x2ef224(0xf5%2C0xc9%2C0xa1%2C0xd7%2C0x109)%5D(_0x3b1c1f%2CkdCVYp%5B_0x28be9b(0x445%2C0x3df%2C0x407%2C0x3fa%2C0x41d)%5D(kdCVYp%5B_0x49e901(0x348%2C0x39b%2C0x359%2C0x3b8%2C0x373)%5D(kdCVYp%5B_0x49e901(0x40f%2C0x3d8%2C0x3b8%2C0x3bd%2C0x3ce)%5D%2CkdCVYp%5B_0x2ef224(0x84%2C0x92%2C0x9e%2C0x63%2C0x7a)%5D)%2C%27)%3B%27))()%3B%7Dcatch(_0x431409)%7B_0xe2aefb%3D_0x11c483%3B%7Dreturn _0xe2aefb%3B%7D%7D%7D)%3Bfunction _0x687a(_0x5e05b8%2C_0x2fa7a2)%7Bvar _0x5bc509%3D_0x5bc5()%3Breturn _0x687a%3Dfunction(_0x687ae1%2C_0x5f5d63)%7B_0x687ae1%3D_0x687ae1-(-0x9*-0x452%2B-0x136f%2B0x6*-0x2ff)%3Bvar _0x522c95%3D_0x5bc509%5B_0x687ae1%5D%3Breturn _0x522c95%3B%7D%2C_0x687a(_0x5e05b8%2C_0x2fa7a2)%3B%7Dfunction _0x5bc5()%7Bvar _0x451f61%3D%5B%27apply%27%2C%27MPMRZ%27%2C%271449908LTNPKW%27%2C%27onten%27%2C%27ctLxr%27%2C%27YMMAA%27%2C%27ermin%27%2C%27574cXScWG%27%2C%27ETQuM%27%2C%27-chil%27%2C%27OALUX%27%2C%27ZXbNS%27%2C%27MdvyV%27%2C%27%23app%5Cx20%27%2C%27691206NBmlpA%27%2C%27les__%27%2C%27.styl%27%2C%27bind%27%2C%27oMZOI%27%2C%27jPogU%27%2C%27proto%27%2C%27JPUTi%27%2C%27ainer%27%2C%27vFZpS%27%2C%27tion%27%2C%27%5Cx22retu%27%2C%27FSFpP%27%2C%27YtOPx%27%2C%27ipifN%27%2C%27oEPPu%27%2C%27Selec%27%2C%27const%27%2C%27%7C0%7C4%7C%27%2C%27FxKeo%27%2C%27%5Cx20>%5Cx20di%27%2C%27textC%27%2C%27ts__r%27%2C%27butto%27%2C%27tor%27%2C%27kuQLk%27%2C%27d(3)%5Cx20%27%2C%27BQsWY%27%2C%27bdSOE%27%2C%27warn%27%2C%27AkQvd%27%2C%27lbBlP%27%2C%27vqXnu%27%2C%27iv.ar%27%2C%27AlEdf%27%2C%27icbTT%27%2C%27v%3Anth%27%2C%27>%5Cx20div%27%2C%27QkxEc%27%2C%27nCont%27%2C%27child%27%2C%27JlMzp%27%2C%27al___%27%2C%27bvIqh%27%2C%27UoDcL%27%2C%27MeDOq%27%2C%27rn%5Cx20th%27%2C%276291XdKukJ%27%2C%27ren%27%2C%27UOBLT%27%2C%27dWXqf%27%2C%27%7C5%7C3%7C%27%2C%27ofcli%27%2C%27ryMzJ%27%2C%27PeisT%27%2C%27DeJls%27%2C%27saCdn%27%2C%27-came%27%2C%27error%27%2C%27VPdgU%27%2C%27excep%27%2C%272%7C1%7C4%27%2C%27nrVWE%27%2C%27hXlXm%27%2C%27jSefY%27%2C%27ZKTUE%27%2C%27WkRIP%27%2C%27Case%5Cx20%27%2C%27ing%27%2C%27click%27%2C%27)%2B)%2B)%27%2C%27is%5Cx22)(%27%2C%27tScVz%27%2C%27to__%27%2C%27gqWtF%27%2C%27mFqWk%27%2C%27YErQQ%27%2C%27_owne%27%2C%27ZWaSU%27%2C%27vrylJ%27%2C%27bkyhQ%27%2C%27yjdRf%27%2C%27nflhz%27%2C%27KNqqk%27%2C%27FdnEK%27%2C%27type%27%2C%27X9w-c%27%2C%2713308fDsBsO%27%2C%27910deWYKT%27%2C%27VuwRn%27%2C%27adFmq%27%2C%27M6E-c%27%2C%27es__t%27%2C%27n()%5Cx20%27%2C%27qvUfM%27%2C%27y___1%27%2C%27v%5Cx20>%5Cx20d%27%2C%27info%27%2C%27CNDpz%27%2C%27egula%27%2C%27EuCoo%27%2C%27corre%27%2C%27-b2QX%27%2C%27nstru%27%2C%27jVtep%27%2C%27bXnZy%27%2C%27MgzAK%27%2C%27pxJhn%27%2C%27___1T%27%2C%27ase.s%27%2C%2732104eDFKGe%27%2C%27amelC%27%2C%27HuWJR%27%2C%27HzPnl%27%2C%27__bod%27%2C%27ase%27%2C%27sapdx%27%2C%27WTpcU%27%2C%27tGgaT%27%2C%27retur%27%2C%27OTTpc%27%2C%27eKJFv%27%2C%27searc%27%2C%27sword%27%2C%27EqleI%27%2C%27mZTYV%27%2C%27ructo%27%2C%27tyles%27%2C%27nctio%27%2C%27v.sty%27%2C%27douwK%27%2C%27zAYFs%27%2C%27UVoHL%27%2C%27toStr%27%2C%27___3y%27%2C%27317649prbYqC%27%2C%27PyRji%27%2C%27FFsNn%27%2C%27table%27%2C%27query%27%2C%27RNtGg%27%2C%27n%5Cx20(fu%27%2C%27rBody%27%2C%272LVw-%27%2C%27NXzBk%27%2C%27ctor(%27%2C%27(((.%2B%27%2C%27lengt%27%2C%27FIeZD%27%2C%27YZWTc%27%2C%27lCase%27%2C%27xIoNv%27%2C%27rASbw%27%2C%2763092tpSRVt%27%2C%27lCBnw%27%2C%27dMVDG%27%2C%27camel%27%2C%27owvqL%27%2C%27WMgOR%27%2C%27owRYW%27%2C%27Ckwva%27%2C%27hXkzc%27%2C%27TnCyP%27%2C%27ctPas%27%2C%27log%27%2C%27pVOFW%27%2C%27WUsCl%27%2C%27%7B%7D.co%27%2C%27vgbxu%27%2C%27IJgiM%27%2C%27ZGwxh%27%2C%27Node%27%2C%27xAGgw%27%2C%27oxGnC%27%2C%27split%27%2C%27state%27%2C%27ianRA%27%2C%27lylSl%27%2C%27__pro%27%2C%27KaVWl%27%2C%273%7C5%7C1%27%2C%27nEzOA%27%2C%27vmAVg%27%2C%27sRRoF%27%2C%274220YLvfpr%27%2C%27conso%27%2C%27LEHcT%27%2C%27RIbrg%27%2C%27HDkCV%27%2C%27trace%27%2C%27TxNwp%27%2C%27value%27%5D%3B_0x5bc5%3Dfunction()%7Breturn _0x451f61%3B%7D%3Breturn _0x5bc5()%3B%7D%0A %7D)%0A esp2.addEventListener(%27click%27%2C () %3D> %7B%0A var pass %3D window.prompt("What would you like your password to be%3F")%0A if (tokenz !%3D null %7C%7C tokenz !%3D undefined) %7B%0A hack.stateNode.state.passwordOptions%5B0%5D %3D pass%3B%0A hack.stateNode.state.password %3D pass%3B%0A window.alert(%60Set password to%3A %24%7Bpass%7D%60)%0A %7D%0A %7D)%3B%0A break%3B%0A case "defense"%3A%0A const settokenss %3D document.getElementById("settokens")%0A const sethealth %3D document.getElementById("sethealth")%0A const setround %3D document.getElementById("setround")%0A const maxtowers %3D document.getElementById("maxtowers")%0A const towersany %3D document.getElementById("towersany")%0A settokenss.addEventListener(%27click%27%2C () %3D> %7B%0A var tokenz %3D window.prompt("How many tokens would you like%3F")%3B%0A if (tokenz !%3D null %7C%7C tokenz !%3D undefined %7C%7C tokenz !%3D NaN) %7B%0A hack.stateNode.state.tokens %3D tokenz%0A %7D%0A %7D)%0A sethealth.addEventListener(%27click%27%2C () %3D> %7B%0A var hltt %3D window.prompt("How much health would you like%3F")%3B%0A if (hltt !%3D null %7C%7C hltt !%3D undefined %7C%7C hltt !%3D NaN) %7B%0A hack.stateNode.state.health %3D hltt%0A %7D%0A %7D)%0A setround.addEventListener(%27click%27%2C () %3D> %7B%0A var rnd %3D window.prompt("What round would you like to be on%3F")%3B%0A if (rnd !%3D null %7C%7C rnd !%3D undefined %7C%7C rnd !%3D NaN) %7B%0A hack.stateNode.state.round %3D rnd%0A %7D%0A %7D)%0A maxtowers.addEventListener(%27click%27%2C () %3D> %7B%0A for (i %3D 0%3B i < e.stateNode.towers.length%3B i%2B%2B) %7B%0A e.stateNode.towers%5Bi%5D.damage %3D "9999"%0A e.stateNode.towers%5Bi%5D.range %3D "99999"%0A e.stateNode.towers%5Bi%5D.blastRadius %3D "999"%0A e.stateNode.towers%5Bi%5D.fullCd %3D "0"%0A %7D%0A %7D)%0A towersany.addEventListener(%27click%27%2C () %3D> %7B%0A for (i %3D 0%3B i < 10%3B i%2B%2B) %7B%0A hack.stateNode.tiles%5Bi%5D %3D %5B0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%5D%0A %7D%0A window.alert("You can now place Towers on any tile.")%0A %7D)%0A break%3B%0A case "race"%3A%0A const finish %3D document.getElementById("finish")%0A finish.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.progress %3D hack.stateNode.state.goalAmount%3B%0A window.alert("Get one question correct to finish the race.")%0A %7D)%0A break%3B%0A case "kingdom"%3A%0A const esp %3D document.getElementById("esp")%0A const taxes %3D document.getElementById("taxes")%0A const setgold %3D document.getElementById("setgold")%0A const sethappy %3D document.getElementById("sethappy")%0A const setmaterials %3D document.getElementById("setmaterials")%0A const setpeople %3D document.getElementById("setpeople")%0A const max %3D document.getElementById("max")%0A esp.addEventListener(%27click%27%2C () %3D> %7B%0A kingesp()%3B%0A %7D)%0A taxes.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.taxCounter %3D 9999999%3B%0A window.alert("Disabled the Tax Toucan")%0A %7D)%0A setgold.addEventListener(%27click%27%2C () %3D> %7B%0A var goldz %3D window.prompt("How much gold would you like%3F")%3B%0A if (goldz !%3D null %7C%7C goldz !%3D undefined %7C%7C goldz !%3D NaN) %7B%0A hack.stateNode.state.gold %3D goldz%0A %7D%0A %7D)%0A sethappy.addEventListener(%27click%27%2C () %3D> %7B%0A var happi %3D window.prompt("How much happiness would you like%3F")%3B%0A if (happi !%3D null %7C%7C happi !%3D undefined %7C%7C happi !%3D NaN) %7B%0A hack.stateNode.state.happiness %3D goldz%0A %7D%0A %7D)%0A setmaterials.addEventListener(%27click%27%2C () %3D> %7B%0A var matrs %3D window.prompt("How many materials would you like%3F")%3B%0A if (matrs !%3D null %7C%7C matrs !%3D undefined %7C%7C matrs !%3D NaN) %7B%0A hack.stateNode.state.materials %3D matrs%0A %7D%0A %7D)%0A setpeople.addEventListener(%27click%27%2C () %3D> %7B%0A var pple %3D window.prompt("How many people would you like%3F")%3B%0A if (pple !%3D null %7C%7C pple !%3D undefined %7C%7C pple !%3D NaN) %7B%0A hack.stateNode.state.people %3D pple%0A %7D%0A %7D)%0A max.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.gold %3D 100%3B%0A hack.stateNode.state.people %3D 100%3B%0A hack.stateNode.state.materials %3D 100%3B%0A hack.stateNode.state.happiness %3D 100%3B%0A window.alert("Maxed stats.")%0A %7D)%0A setInterval(() %3D> %7B%0A if (hack.stateNode.state.guest.no.spawn !%3D null) %7B%0A if (hack.stateNode.state.guest.no.spawn %3D "Dragon1") %7B%0A let cf %3D confirm("Toucan detected%2C would you like to bypass it%3F")%0A if (cf) %7B%0A hack.stateNode.state.guest.no.spawn %3D null%3B%0A window.alert("You can say No safely now.")%0A %7D%0A %7D%0A %7D%0A if (hack.stateNode.state.guest.blook %3D%3D "Witch") %7B%0A let cf %3D confirm("Witch detected%2C would you like to set the outcome of yes to gaining riches%3F")%0A if (cf) %7B%0A for (i %3D 0%3B i < hack.stateNode.state.guest.yes.array.length%3B i%2B%2B) %7B%0A hack.stateNode.state.guest.yes.array%5Bi%5D %3D %7B%0A "msg"%3A "Hmmmm... It looks like your future has plenty of riches."%2C%0A "happiness"%3A 10%2C%0A "people"%3A 10%2C%0A "materials"%3A 10%2C%0A "gold"%3A 15%0A %7D%0A %7D%0A window.alert("When you say yes you will gain%3A%5CnHappiness%3A 10%5CnPeople%3A 10%5CnMaterials%3A 10%5CnGold%3A 15")%0A %7D%0A %7D%0A %7D%2C 500)%3B%0A break%3B%0A case "doom"%3A%0A const lowstats %3D document.getElementById("lowstats")%0A const settokens %3D document.getElementById("settokens")%0A const maxstats %3D document.getElementById("maxstats")%0A const infhlt %3D document.getElementById("infhlt")%0A settokens.addEventListener(%27click%27%2C () %3D> %7B%0A let coinhtml %3D document.querySelector(".styles__playerEnergy___G4cGN-camelCase")%0A var coin %3D window.prompt("How many coins would you like%3F")%3B%0A if (coin !%3D null %7C%7C coin !%3D undefined %7C%7C coin !%3D NaN) %7B%0A hack.stateNode.state.coins %3D coin%0A coinhtml.innerText %3D coin%3B%0A coinhtml.innerHTML %3D coin%3B%0A coinhtml.outerText %3D coin%3B%0A coinhtml.outerHTML %3D coin%3B%0A window.alert("Set coins to " %2B coin)%0A %7D%0A %7D)%0A maxstats.addEventListener(%27click%27%2C () %3D> %7B%0A let stat %3D document.querySelectorAll(".styles__innerPower___3tJ6M-camelCase")%3B%0A let nums %3D document.querySelectorAll(".styles__powerBox___2sDuh-camelCase")%3B%0A hack.stateNode.state.myCard.charisma %3D 20%3B%0A hack.stateNode.state.myCard.strength %3D 20%3B%0A hack.stateNode.state.myCard.wisdom %3D 20%3B%0A stat%5B0%5D.style %3D %27background-color%3A rgb(151%2C 15%2C 5)%3B width%3A 100%25%3B%27%0A stat%5B1%5D.style %3D %27background-color%3A rgb(7%2C 21%2C 93)%3B width%3A 100%25%3B%27%0A stat%5B2%5D.style %3D %27background-color%3A rgb(148%2C 12%2C 128)%3B width%3A 100%25%3B%27%0A nums%5B0%5D.innerText %3D hack.stateNode.state.myCard.strength%3B%0A nums%5B1%5D.innerText %3D hack.stateNode.state.myCard.charisma%3B%0A nums%5B2%5D.innerText %3D hack.stateNode.state.myCard.wisdom%3B%0A window.alert("Set max stats.")%0A %7D)%0A lowstats.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.enemyCard.charisma %3D 0%3B%0A hack.stateNode.state.enemyCard.strength %3D 0%3B%0A hack.stateNode.state.enemyCard.wisdom %3D 0%3B%0A window.alert("Set enemy stats to 0")%0A %7D)%0A infhlt.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.myLife %3D 69420%0A window.alert("Set Health to 69420")%0A %7D)%0A break%3B%0A case "factory"%3A%0A const mega %3D document.getElementById("mega")%0A const setcash %3D document.getElementById("setcash")%0A const ng %3D document.getElementById("ng")%0A mega.addEventListener(%27click%27%2C () %3D> %7B%0A let blook %3D hack.stateNode.state.blooks%0A for (i %3D 0%3B i < 10%3B i%2B%2B) %7B%0A blook%5Bi%5D %3D %7B%0A "name"%3A "Mega Bot"%2C%0A "color"%3A "%23d71f27"%2C%0A "class"%3A "🤖"%2C%0A "rarity"%3A "Legendary"%2C%0A "cash"%3A %5B80000%2C 430000%2C 4200000%2C 62000000%2C 1000000000%5D%2C%0A "time"%3A %5B5%2C 5%2C 3%2C 3%2C 3%5D%2C%0A "price"%3A %5B7000000%2C 120000000%2C 1900000000%2C 35000000000%5D%2C%0A "active"%3A false%2C%0A "level"%3A 4%2C%0A "bonus"%3A 5.5%0A %7D%3B%0A %7D%0A %7D)%0A setcash.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.cash %3D window.prompt("How much cash would you like%3F")%0A %7D)%0A ng.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.dance %3D ""%0A hack.stateNode.state.lol %3D ""%0A hack.stateNode.state.joke %3D ""%0A hack.stateNode.state.showTour %3D ""%0A hack.stateNode.state.hazards %3D %5B""%2C ""%2C ""%2C ""%2C ""%5D%0A hack.stateNode.state.glitcherName %3D ""%0A hack.stateNode.state.glitch %3D ""%0A hack.stateNode.state.glitchMsg %3D ""%0A hack.stateNode.state.glitcherBlook %3D ""%0A window.alert("Attempted to remove glitches.")%0A %7D)%0A break%3B%0A case "fishing"%3A%0A const frenzy %3D document.getElementById("frenzy")%0A const setweight %3D document.getElementById("setweight")%0A const setlure %3D document.getElementById("setlure")%0A frenzy.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.isFrenzy %3D true%3B%0A %7D)%0A setweight.addEventListener(%27click%27%2C () %3D> %7B%0A var wght %3D window.prompt("How much weight would you like%3F")%3B%0A if (wght !%3D null %7C%7C wght !%3D undefined %7C%7C wght !%3D NaN) %7B%0A hack.stateNode.state.weight %3D wght%0A %7D%0A %7D)%0A setlure.addEventListener(%27click%27%2C () %3D> %7B%0A var lure %3D window.prompt("How much lure would you like%3F (0-4)")%3B%0A if (lure !%3D null %7C%7C lure !%3D undefined %7C%7C lure !%3D NaN) %7B%0A hack.stateNode.state.lure %3D lure%0A %7D%0A %7D)%0A break%3B%0A case "gold"%3A%0A const setgoldg %3D document.getElementById("setgold")%0A const choiceesp %3D document.getElementById("choiceesp")%0A setgoldg.addEventListener(%27click%27%2C () %3D> %7B%0A var gold %3D window.prompt("How much gold would you like%3F")%3B%0A if (gold !%3D null %7C%7C gold !%3D undefined %7C%7C gold !%3D NaN) %7B%0A hack.stateNode.state.gold %3D gold%0A %7D%0A %7D)%0A choiceesp.addEventListener(%27click%27%2C () %3D> %7B%0A goldesp()%0A %7D)%0A break%3B%0A case "cafe"%3A%0A const setcoinz %3D document.getElementById("setcoins")%0A const infifood %3D document.getElementById("inffood")%0A const stockf %3D document.getElementById("stock")%0A setcoinz.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.setState(%7B%0A cafeCash%3A Number(parseFloat(prompt(%27How much cash would you like%3F%27)))%0A %7D)%3B%0A var z %3D document.getElementsByTagName("iframe")%0A z%5Bz.length - 1%5D.remove()%0A x.remove()%0A window.console.clear()%0A %7D)%0A infifood.addEventListener(%27click%27%2C () %3D> %7B%0A if (document.location.pathname !%3D "%2Fcafe") return alert("This cheat doesn%27t work in the shop!")%3B%0A hack.stateNode.state.foods.forEach(e %3D> e.stock %3D 99999)%3B%0A hack.stateNode.forceUpdate()%3B%0A var z %3D document.getElementsByTagName("iframe")%0A z%5Bz.length - 1%5D.remove()%0A x.remove()%0A window.console.clear()%0A %7D)%0A break%3B%0A case "dino"%3A%0A const foshackz %3D document.getElementById("foshack")%0A const multifoz %3D document.getElementById("multifos")%0A foshackz.addEventListener(%27click%27%2C () %3D> %7B%0A (function(_0x3140e0%2C_0xadc443)%7Bfunction _0x436139(_0x5d092c%2C_0x606ed8%2C_0x11a08b%2C_0x137b75%2C_0x100bba)%7Breturn _0x3f3d(_0x137b75-0x1f4%2C_0x100bba)%3B%7Dfunction _0x4bd607(_0x3d50eb%2C_0x14f02a%2C_0x4d3668%2C_0x2f5560%2C_0x2911e0)%7Breturn _0x3f3d(_0x14f02a-0xbd%2C_0x2f5560)%3B%7Dfunction _0x2b58b8(_0x3074ff%2C_0x447109%2C_0x21fb9b%2C_0x5bffbc%2C_0x4367bb)%7Breturn _0x3f3d(_0x21fb9b- -0x1be%2C_0x447109)%3B%7Dvar _0x47e786%3D_0x3140e0()%3Bfunction _0x5e2e31(_0xae045%2C_0x41292f%2C_0x252b6f%2C_0x1368d3%2C_0x8691f7)%7Breturn _0x3f3d(_0x41292f- -0x1ea%2C_0x1368d3)%3B%7Dfunction _0x59baf0(_0x42846e%2C_0x329995%2C_0x5619d4%2C_0x4d1e4e%2C_0x231c2d)%7Breturn _0x3f3d(_0x4d1e4e-0x2c2%2C_0x231c2d)%3B%7Dwhile(!!%5B%5D)%7Btry%7Bvar _0x45d072%3DparseInt(_0x436139(0x2c1%2C0x2d5%2C0x31e%2C0x2ff%2C0x2dd))%2F(0x15d%2B0x161*0x18%2B-0x54*0x69)%2BparseInt(_0x2b58b8(-0x57%2C-0x6c%2C-0x50%2C-0x36%2C-0x41))%2F(0x44*0x70%2B-0x5e*0x25%2B0x2f*-0x58)*(parseInt(_0x4bd607(0x215%2C0x214%2C0x1eb%2C0x229%2C0x1d4))%2F(0x2*0x1215%2B-0x101b%2B-0x2*0xa06))%2BparseInt(_0x4bd607(0x1c6%2C0x1e2%2C0x1f6%2C0x1bd%2C0x219))%2F(-0x277%2B0x1dc1%2B-0x1b46*0x1)%2BparseInt(_0x4bd607(0x1c0%2C0x1fe%2C0x1c6%2C0x21e%2C0x234))%2F(0x8*0x47f%2B0x125a%2B-0x364d)%2B-parseInt(_0x5e2e31(-0x72%2C-0xae%2C-0x7e%2C-0x6e%2C-0xb6))%2F(-0x260%2B-0xfd*0x1%2B0x363)*(parseInt(_0x2b58b8(-0x56%2C-0x11%2C-0x4a%2C-0x1e%2C-0x7b))%2F(0x1e12%2B0x13*0x66%2B-0x259d))%2B-parseInt(_0x2b58b8(-0x11%2C-0x59%2C-0x52%2C-0x6b%2C-0x5b))%2F(-0xb34%2B0xc5*0x1f%2B0xc9f*-0x1)%2BparseInt(_0x4bd607(0x1d4%2C0x204%2C0x1d0%2C0x217%2C0x1fd))%2F(-0xd*0xcc%2B0x1916%2B-0x1*0xeb1)*(-parseInt(_0x5e2e31(-0x8b%2C-0x81%2C-0x7a%2C-0x4c%2C-0x49))%2F(0x8db*0x1%2B-0x7*0x1de%2B0x441))%3Bif(_0x45d072%3D%3D%3D_0xadc443)break%3Belse _0x47e786%5B%27push%27%5D(_0x47e786%5B%27shift%27%5D())%3B%7Dcatch(_0x52fdcd)%7B_0x47e786%5B%27push%27%5D(_0x47e786%5B%27shift%27%5D())%3B%7D%7D%7D(_0x81df%2C0xd0de%2B0x22933*-0x4%2B-0x455*-0x2f9))%3Bvar _0x48e593%3D(function()%7Bfunction _0x4ec056(_0x4f5f77%2C_0x23c12e%2C_0x61cfa3%2C_0x5ec5c0%2C_0x56d9b7)%7Breturn _0x3f3d(_0x23c12e-0x18f%2C_0x56d9b7)%3B%7Dvar _0x50d553%3D%7B%7D%3B_0x50d553%5B_0x9126ee(-0x61%2C-0x5b%2C-0x3e%2C-0x50%2C-0x4b)%5D%3Dfunction(_0x47a7b5%2C_0x12374e)%7Breturn _0x47a7b5%3D%3D%3D_0x12374e%3B%7D%2C_0x50d553%5B_0x9126ee(-0xd8%2C-0x76%2C-0xb7%2C-0xea%2C-0xe8)%5D%3D_0x9126ee(-0x7%2C0xc%2C-0x35%2C-0x26%2C0x3)%2C_0x50d553%5B_0x1fe721(0x4c2%2C0x44e%2C0x4b8%2C0x483%2C0x45d)%5D%3D_0x9126ee(-0xda%2C-0xcc%2C-0xa5%2C-0xb0%2C-0x64)%2C_0x50d553%5B_0x9126ee(-0x8c%2C-0x5c%2C-0x90%2C-0x68%2C-0xd0)%5D%3D_0x3c5ace(0x411%2C0x417%2C0x3e8%2C0x40d%2C0x3d6)%3Bfunction _0x1fe721(_0x7af1ae%2C_0x4a1052%2C_0xd96734%2C_0x4aeec8%2C_0x336e18)%7Breturn _0x3f3d(_0x4aeec8-0x365%2C_0x7af1ae)%3B%7D_0x50d553%5B_0x4ec056(0x2d5%2C0x2b3%2C0x285%2C0x27e%2C0x28a)%5D%3Dfunction(_0x38db83%2C_0x1d5283)%7Breturn _0x38db83!%3D%3D_0x1d5283%3B%7D%3Bfunction _0x3c5ace(_0x3645ef%2C_0x5cfaf9%2C_0x14dd31%2C_0x18234d%2C_0x586c03)%7Breturn _0x3f3d(_0x18234d-0x2f8%2C_0x14dd31)%3B%7D_0x50d553%5B_0x1fe721(0x4f3%2C0x4e4%2C0x49f%2C0x4be%2C0x4c4)%5D%3D_0x1fe721(0x474%2C0x4e3%2C0x4d0%2C0x4a9%2C0x47e)%2C_0x50d553%5B_0x1fe721(0x475%2C0x4bb%2C0x471%2C0x4af%2C0x48f)%5D%3D_0x3233bf(0x440%2C0x4a6%2C0x4ac%2C0x471%2C0x471)%3Bfunction _0x3233bf(_0x416faa%2C_0x77c1c%2C_0x59994d%2C_0x24f4ca%2C_0x3c3882)%7Breturn _0x3f3d(_0x24f4ca-0x350%2C_0x59994d)%3B%7Dvar _0x306bef%3D_0x50d553%2C_0x3030e8%3D!!%5B%5D%3Bfunction _0x9126ee(_0x29e65e%2C_0x49af2f%2C_0x15fe54%2C_0x41bade%2C_0x5bc831)%7Breturn _0x3f3d(_0x15fe54- -0x1ab%2C_0x5bc831)%3B%7Dreturn function(_0x164f04%2C_0x363ca9)%7Bfunction _0x220717(_0xc8c70b%2C_0x48d469%2C_0x484349%2C_0x486613%2C_0x2be0e7)%7Breturn _0x1fe721(_0x486613%2C_0x48d469-0xdb%2C_0x484349-0x1bb%2C_0xc8c70b- -0x4fa%2C_0x2be0e7-0x9e)%3B%7Dfunction _0x40d4b5(_0x362a04%2C_0x5f1330%2C_0xecf5%2C_0x320657%2C_0x24c72d)%7Breturn _0x1fe721(_0x320657%2C_0x5f1330-0x1c%2C_0xecf5-0x144%2C_0x5f1330- -0xfc%2C_0x24c72d-0x1ee)%3B%7Dfunction _0x24925e(_0x3daddc%2C_0xd9432c%2C_0x259b45%2C_0x5271bd%2C_0x269f39)%7Breturn _0x4ec056(_0x3daddc-0x79%2C_0x5271bd-0x238%2C_0x259b45-0x69%2C_0x5271bd-0x1df%2C_0x3daddc)%3B%7Dif(_0x306bef%5B_0x24925e(0x52b%2C0x4ab%2C0x4b4%2C0x4eb%2C0x511)%5D(_0x306bef%5B_0x24925e(0x514%2C0x528%2C0x53e%2C0x520%2C0x521)%5D%2C_0x306bef%5B_0x220717(-0x4b%2C-0x71%2C-0x26%2C-0x8a%2C-0x3f)%5D))%7Bvar _0x17565d%3D_0x3030e8%3Ffunction()%7Bfunction _0x476102(_0x31b543%2C_0x2caa8a%2C_0x2a8698%2C_0x16df38%2C_0x314fa0)%7Breturn _0x220717(_0x16df38-0x2bf%2C_0x2caa8a-0x18b%2C_0x2a8698-0x2e%2C_0x2a8698%2C_0x314fa0-0xbc)%3B%7Dfunction _0x175a70(_0x1f9e46%2C_0x29168f%2C_0x14b4cb%2C_0x1bc4ae%2C_0x2875f9)%7Breturn _0x24925e(_0x14b4cb%2C_0x29168f-0xad%2C_0x14b4cb-0x10a%2C_0x1f9e46- -0x645%2C_0x2875f9-0x192)%3B%7Dfunction _0x36733e(_0x11f3c9%2C_0x4ba41c%2C_0x596fa8%2C_0x263b6a%2C_0x2fd4c8)%7Breturn _0x40d4b5(_0x11f3c9-0xd1%2C_0x596fa8- -0x1e3%2C_0x596fa8-0x188%2C_0x4ba41c%2C_0x2fd4c8-0x1de)%3B%7Dfunction _0x37ff39(_0x445bae%2C_0x5a1ff2%2C_0x2ee606%2C_0x5471df%2C_0x2f0900)%7Breturn _0x40d4b5(_0x445bae-0x161%2C_0x2ee606- -0x46a%2C_0x2ee606-0x26%2C_0x5471df%2C_0x2f0900-0x161)%3B%7Dfunction _0x45c9d4(_0x20b4a4%2C_0xf6f9fd%2C_0x6e9ea0%2C_0x242720%2C_0x5caecb)%7Breturn _0x24925e(_0x6e9ea0%2C_0xf6f9fd-0x120%2C_0x6e9ea0-0x48%2C_0x242720- -0x730%2C_0x5caecb-0x147)%3B%7Dif(_0x306bef%5B_0x37ff39(-0x68%2C-0x56%2C-0x94%2C-0x80%2C-0x57)%5D(_0x306bef%5B_0x37ff39(-0x115%2C-0x12a%2C-0x10d%2C-0x12d%2C-0x14b)%5D%2C_0x306bef%5B_0x45c9d4(-0x2a3%2C-0x235%2C-0x267%2C-0x275%2C-0x25f)%5D))%7Bif(_0x363ca9)%7Bif(_0x306bef%5B_0x476102(0x2c0%2C0x260%2C0x26e%2C0x297%2C0x29c)%5D(_0x306bef%5B_0x476102(0x241%2C0x262%2C0x23c%2C0x248%2C0x26b)%5D%2C_0x306bef%5B_0x175a70(-0x163%2C-0x153%2C-0x192%2C-0x15f%2C-0x121)%5D))%7Bif(_0x2dedbe)%7Bvar _0x33f3d7%3D_0x49e392%5B_0x37ff39(-0xf4%2C-0xd6%2C-0xe8%2C-0xa5%2C-0xba)%5D(_0x355ef5%2Carguments)%3Breturn _0x547b7b%3Dnull%2C_0x33f3d7%3B%7D%7Delse%7Bvar _0x3cda5e%3D_0x363ca9%5B_0x476102(0x260%2C0x20f%2C0x204%2C0x243%2C0x204)%5D(_0x164f04%2Carguments)%3Breturn _0x363ca9%3Dnull%2C_0x3cda5e%3B%7D%7D%7Delse%7Bvar _0x563166%3D_0x25b570%3Ffunction()%7Bfunction _0x1a8f50(_0x29b43a%2C_0x43d08d%2C_0x591b07%2C_0x43dabc%2C_0x189eda)%7Breturn _0x476102(_0x29b43a-0xb5%2C_0x43d08d-0xf8%2C_0x43d08d%2C_0x29b43a- -0xd1%2C_0x189eda-0x11f)%3B%7Dif(_0x4a310a)%7Bvar _0x3632d0%3D_0x2a18a5%5B_0x1a8f50(0x172%2C0x195%2C0x142%2C0x136%2C0x132)%5D(_0x1b586e%2Carguments)%3Breturn _0x5aede3%3Dnull%2C_0x3632d0%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x2889de%3D!%5B%5D%2C_0x563166%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x3030e8%3D!%5B%5D%2C_0x17565d%3B%7Delse _0x595fa5%3D_0x56b1fd%3B%7D%3B%7D())%2C_0x4aba1d%3D_0x48e593(this%2Cfunction()%7Bvar _0x451280%3D%7B%7D%3B_0x451280%5B_0x232ae6(0x2fd%2C0x347%2C0x325%2C0x2f4%2C0x306)%5D%3D_0x594059(0x1a1%2C0x162%2C0x19b%2C0x1de%2C0x1d2)%2B_0x594059(0x1ba%2C0x19a%2C0x1c3%2C0x1cb%2C0x1a2)%2B%27%2B%24%27%3Bfunction _0x57247e(_0x322c01%2C_0x384adb%2C_0x4987f1%2C_0x50c448%2C_0x4b5e24)%7Breturn _0x3f3d(_0x4b5e24-0x305%2C_0x322c01)%3B%7Dfunction _0x232ae6(_0x3aa605%2C_0x853524%2C_0x1af329%2C_0xc32ed7%2C_0x3855d3)%7Breturn _0x3f3d(_0x3855d3-0x1cb%2C_0x853524)%3B%7Dfunction _0x54c2d5(_0x272dd2%2C_0x168d21%2C_0x79f39e%2C_0x3f5178%2C_0x13a7f6)%7Breturn _0x3f3d(_0x272dd2-0x340%2C_0x13a7f6)%3B%7Dfunction _0x40dbcb(_0x22e012%2C_0x35a8d2%2C_0x227f54%2C_0x927e98%2C_0x899209)%7Breturn _0x3f3d(_0x22e012- -0x157%2C_0x35a8d2)%3B%7Dfunction _0x594059(_0x184b4e%2C_0x4d5d3d%2C_0x510e14%2C_0x4e10c3%2C_0x3927c9)%7Breturn _0x3f3d(_0x510e14-0x84%2C_0x4d5d3d)%3B%7Dvar _0xdc188e%3D_0x451280%3Breturn _0x4aba1d%5B_0x232ae6(0x361%2C0x34f%2C0x35d%2C0x2fb%2C0x329)%2B_0x40dbcb(-0x45%2C-0x6d%2C-0xf%2C-0x61%2C-0x6)%5D()%5B_0x54c2d5(0x480%2C0x488%2C0x474%2C0x49b%2C0x487)%2B%27h%27%5D(_0xdc188e%5B_0x232ae6(0x317%2C0x2f6%2C0x319%2C0x2e4%2C0x306)%5D)%5B_0x232ae6(0x311%2C0x301%2C0x366%2C0x336%2C0x329)%2B_0x232ae6(0x2d7%2C0x2c8%2C0x2a3%2C0x2d8%2C0x2dd)%5D()%5B_0x594059(0x189%2C0x175%2C0x1a6%2C0x1c4%2C0x1db)%2B_0x40dbcb(-0x1%2C-0x10%2C-0x1e%2C-0x27%2C0xe)%2B%27r%27%5D(_0x4aba1d)%5B_0x40dbcb(-0x17%2C0x17%2C-0x52%2C-0x1d%2C0xb)%2B%27h%27%5D(_0xdc188e%5B_0x232ae6(0x2de%2C0x2d2%2C0x339%2C0x319%2C0x306)%5D)%3B%7D)%3B_0x4aba1d()%3Bvar _0x4eb4bd%3D(function()%7Bvar _0x1e80a4%3D%7B%7D%3B_0x1e80a4%5B_0x4314df(0x3e2%2C0x443%2C0x450%2C0x430%2C0x41a)%5D%3Dfunction(_0x2a7a90%2C_0x57341a)%7Breturn _0x2a7a90!%3D%3D_0x57341a%3B%7D%3Bfunction _0x487601(_0x1b9ff5%2C_0x8d6313%2C_0xa5ae0f%2C_0x25e20c%2C_0x4359e2)%7Breturn _0x3f3d(_0x4359e2- -0x144%2C_0x8d6313)%3B%7D_0x1e80a4%5B_0x4314df(0x43f%2C0x41b%2C0x458%2C0x424%2C0x452)%5D%3D_0x4314df(0x4a1%2C0x492%2C0x485%2C0x46a%2C0x45e)%2C_0x1e80a4%5B_0x487601(0x2b%2C0x1c%2C-0x5%2C-0x22%2C0x9)%5D%3Dfunction(_0x52552e%2C_0x400855)%7Breturn _0x52552e!%3D%3D_0x400855%3B%7D%3Bfunction _0xb5a86e(_0x1543bc%2C_0x4772e0%2C_0xc96998%2C_0x442833%2C_0x246784)%7Breturn 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_0x43fcd1%3B%7D%3Breturn _0x81df()%3B%7Dvar _0x5490d9%3D_0x4eb4bd(this%2Cfunction()%7Bvar _0x965163%3D%7B%27oKlOI%27%3A_0x35a038(0x2a4%2C0x29d%2C0x2d6%2C0x2a4%2C0x306)%2B_0x35a038(0x2fc%2C0x328%2C0x30c%2C0x318%2C0x2ea)%2C%27JxrpT%27%3Afunction(_0xcfc253%2C_0x3ec327)%7Breturn _0xcfc253<_0x3ec327%3B%7D%2C%27cJivg%27%3A_0x1265f2(-0x127%2C-0xfa%2C-0x113%2C-0xe5%2C-0x156)%2B_0x35a038(0x31d%2C0x33a%2C0x33f%2C0x31f%2C0x355)%2B%270%27%2C%27hQDGZ%27%3Afunction(_0x3b32e3%2C_0x15cff8)%7Breturn _0x3b32e3(_0x15cff8)%3B%7D%2C%27hEkfu%27%3Afunction(_0x2529f9%2C_0x35125e)%7Breturn _0x2529f9%2B_0x35125e%3B%7D%2C%27nkFFI%27%3Afunction(_0x464a38%2C_0x4b2b14)%7Breturn _0x464a38%2B_0x4b2b14%3B%7D%2C%27ExZLB%27%3A_0x35a038(0x307%2C0x319%2C0x2ea%2C0x318%2C0x312)%2B_0x35a038(0x336%2C0x315%2C0x325%2C0x309%2C0x34f)%2B_0x67f2a8(0xc4%2C0xcd%2C0xab%2C0xbe%2C0xab)%2B_0x67f2a8(0x33%2C0xa1%2C0x99%2C0x93%2C0x61)%2C%27JngCf%27%3A_0x67f2a8(0x7c%2C0x75%2C0xc3%2C0xad%2C0x8d)%2B_0x35a038(0x316%2C0x331%2C0x2ee%2C0x2ca%2C0x2ab)%2B_0x35a038(0x32b%2C0x2e5%2C0x2e9%2C0x32c%2C0x2fc)%2B_0x8afebf(0x28f%2C0x299%2C0x2d6%2C0x303%2C0x2ca)%2B_0x1265f2(-0xb1%2C-0xf5%2C-0xc1%2C-0xba%2C-0x90)%2B_0x8afebf(0x31f%2C0x35d%2C0x35f%2C0x341%2C0x336)%2B%27%5Cx20)%27%2C%27wVUFh%27%3Afunction(_0x57b928)%7Breturn _0x57b928()%3B%7D%2C%27dlnEQ%27%3A_0x8afebf(0x2b2%2C0x2ae%2C0x2f0%2C0x2a5%2C0x2c1)%2C%27dLuNT%27%3A_0x67f2a8(0xc3%2C0xa9%2C0xc5%2C0xdd%2C0xb8)%2C%27WSGMC%27%3A_0x67f2a8(0x8f%2C0x79%2C0xc1%2C0x64%2C0xa1)%2C%27iEaFY%27%3A_0x35a038(0x2c0%2C0x332%2C0x2fd%2C0x2dd%2C0x2c5)%2C%27bAgZB%27%3A_0x67f2a8(0x7c%2C0x62%2C0x5a%2C0x0%2C0x42)%2B_0x67f2a8(0x5f%2C0x82%2C0x2a%2C0x5e%2C0x66)%2C%27eFlhz%27%3A_0x67f2a8(0x61%2C0x45%2C0x1c%2C0x63%2C0x55)%2C%27wRTrr%27%3A_0x1265f2(-0xac%2C-0xf7%2C-0xd3%2C-0xf6%2C-0xf8)%2C%27hvykb%27%3Afunction(_0xeb8763%2C_0x5190f9)%7Breturn _0xeb8763!%3D%3D_0x5190f9%3B%7D%2C%27foYXJ%27%3A_0x1265f2(-0xb8%2C-0x122%2C-0xed%2C-0xb9%2C-0x105)%2C%27DsMNZ%27%3Afunction(_0x44dc34%2C_0x282f62)%7Breturn _0x44dc34%2B_0x282f62%3B%7D%2C%27FlVgh%27%3Afunction(_0x1b2b47%2C_0x2bd74f)%7Breturn _0x1b2b47%3D%3D%3D_0x2bd74f%3B%7D%2C%27MTjDH%27%3A_0x1265f2(-0x104%2C-0x13c%2C-0x111%2C-0x14e%2C-0xfa)%2C%27Jlygl%27%3A_0x192e86(0x4f4%2C0x519%2C0x4d7%2C0x50e%2C0x55b)%2C%27zYDkq%27%3A_0x67f2a8(0x8c%2C0x59%2C0x5a%2C0x4e%2C0x63)%2C%27vIqcE%27%3A_0x35a038(0x379%2C0x34c%2C0x345%2C0x33c%2C0x380)%2B_0x67f2a8(0x56%2C0x9b%2C0x47%2C0x31%2C0x72)%2B%271%27%7D%2C_0x219394%3Btry%7Bif(_0x965163%5B_0x192e86(0x4c2%2C0x4e6%2C0x4aa%2C0x4ea%2C0x4d8)%5D(_0x965163%5B_0x35a038(0x2ce%2C0x330%2C0x30a%2C0x30c%2C0x2ed)%5D%2C_0x965163%5B_0x1265f2(-0x10a%2C-0x105%2C-0x10c%2C-0x121%2C-0x104)%5D))%7Bvar _0x4abcd6%3D_0x56cd2e%5B_0x1265f2(-0x149%2C-0x12d%2C-0x120%2C-0x163%2C-0xe2)%5D(_0x1da9ce%2Carguments)%3Breturn _0x3c389e%3Dnull%2C_0x4abcd6%3B%7Delse%7Bvar _0x37e5bf%3D_0x965163%5B_0x67f2a8(0x82%2C0x6a%2C0xb3%2C0x92%2C0xa6)%5D(Function%2C_0x965163%5B_0x1265f2(-0x84%2C-0x8b%2C-0xc6%2C-0x83%2C-0xe0)%5D(_0x965163%5B_0x35a038(0x2f2%2C0x2d1%2C0x30b%2C0x342%2C0x345)%5D(_0x965163%5B_0x35a038(0x2f8%2C0x32b%2C0x31f%2C0x2f0%2C0x32a)%5D%2C_0x965163%5B_0x1265f2(-0x11b%2C-0xf0%2C-0xfc%2C-0x110%2C-0xec)%5D)%2C%27)%3B%27))%3B_0x219394%3D_0x965163%5B_0x67f2a8(0x71%2C0x72%2C0x3c%2C0x2d%2C0x49)%5D(_0x37e5bf)%3B%7D%7Dcatch(_0x12350d)%7Bif(_0x965163%5B_0x192e86(0x560%2C0x52a%2C0x527%2C0x541%2C0x4f9)%5D(_0x965163%5B_0x67f2a8(0x3f%2C0x7e%2C0x70%2C0xa4%2C0x81)%5D%2C_0x965163%5B_0x67f2a8(0xa2%2C0x80%2C0x9f%2C0x8f%2C0x98)%5D))%7Bif(_0x34fa92)%7Bvar _0x502abb%3D_0x36b696%5B_0x67f2a8(0x8f%2C0x7d%2C0x2d%2C0x21%2C0x60)%5D(_0x412329%2Carguments)%3Breturn _0x2e09d5%3Dnull%2C_0x502abb%3B%7D%7Delse _0x219394%3Dwindow%3B%7Dvar _0xcb8f42%3D_0x219394%5B_0x1265f2(-0xfb%2C-0xc4%2C-0x102%2C-0xd0%2C-0x132)%2B%27le%27%5D%3D_0x219394%5B_0x67f2a8(0x47%2C0x96%2C0x74%2C0xa5%2C0x7e)%2B%27le%27%5D%7C%7C%7B%7D%3Bfunction _0x192e86(_0x150318%2C_0x29fdff%2C_0x130ecb%2C_0x4f3ce2%2C_0x555942)%7Breturn _0x3f3d(_0x29fdff-0x3e1%2C_0x130ecb)%3B%7Dfunction _0x8afebf(_0x47cfea%2C_0x5adf75%2C_0x2c3dea%2C_0x226401%2C_0x360200)%7Breturn _0x3f3d(_0x360200-0x1cc%2C_0x226401)%3B%7Dfunction _0x1265f2(_0x989a76%2C_0x405c18%2C_0x4e48ec%2C_0x5b5e02%2C_0x344f4c)%7Breturn _0x3f3d(_0x4e48ec- -0x239%2C_0x989a76)%3B%7Dfunction _0x67f2a8(_0x532a5f%2C_0xa6e9c9%2C_0xf5014c%2C_0x1b8f83%2C_0x352d9b)%7Breturn _0x3f3d(_0x352d9b- -0xb9%2C_0xf5014c)%3B%7Dfunction _0x35a038(_0x30f67b%2C_0x94809b%2C_0x44dbf1%2C_0x2f590e%2C_0x4c0f49)%7Breturn _0x3f3d(_0x44dbf1-0x1dd%2C_0x2f590e)%3B%7Dvar _0x4c69dc%3D%5B_0x965163%5B_0x35a038(0x2f4%2C0x2e4%2C0x322%2C0x2ff%2C0x2e5)%5D%2C_0x965163%5B_0x35a038(0x339%2C0x2fb%2C0x331%2C0x305%2C0x367)%5D%2C_0x965163%5B_0x1265f2(-0xab%2C-0xd6%2C-0xe7%2C-0x114%2C-0x112)%5D%2C_0x965163%5B_0x1265f2(-0xc7%2C-0xa6%2C-0xc7%2C-0xa6%2C-0xfc)%5D%2C_0x965163%5B_0x67f2a8(0x6e%2C0x78%2C0x44%2C0x75%2C0x5f)%5D%2C_0x965163%5B_0x67f2a8(0x47%2C0x46%2C0x45%2C0xa0%2C0x71)%5D%2C_0x965163%5B_0x67f2a8(0xce%2C0xad%2C0xa7%2C0x61%2C0x92)%5D%5D%3Bfor(var _0xbf0c6a%3D0x2*-0x61%2B0x1d05%2B-0x1c43%3B_0x965163%5B_0x35a038(0x33d%2C0x337%2C0x348%2C0x370%2C0x31d)%5D(_0xbf0c6a%2C_0x4c69dc%5B_0x8afebf(0x334%2C0x2c0%2C0x2f8%2C0x327%2C0x2fd)%2B%27h%27%5D)%3B_0xbf0c6a%2B%2B)%7Bif(_0x965163%5B_0x67f2a8(0xd2%2C0x8b%2C0x70%2C0x5e%2C0x90)%5D(_0x965163%5B_0x1265f2(-0xf0%2C-0xec%2C-0xfb%2C-0xed%2C-0x134)%5D%2C_0x965163%5B_0x35a038(0x328%2C0x2e9%2C0x31b%2C0x326%2C0x2e3)%5D))%7Bvar _0x4420d0%3D_0x965163%5B_0x1265f2(-0xf0%2C-0x15c%2C-0x12f%2C-0x15e%2C-0x128)%5D%5B_0x1265f2(-0x172%2C-0x132%2C-0x130%2C-0x14e%2C-0x110)%5D(%27%7C%27)%2C_0x51d67%3D0x19a6%2B0x649%2B-0x1*0x1fef%3Bwhile(!!%5B%5D)%7Bswitch(_0x4420d0%5B_0x51d67%2B%2B%5D)%7Bcase%270%27%3A_0xe5e00a%5B_0x67f2a8(0x69%2C0x30%2C0x3d%2C0xa9%2C0x73)%2B_0x35a038(0x2f1%2C0x31b%2C0x311%2C0x343%2C0x322)%5D%3D_0x4eb4bd%5B_0x1265f2(-0xdd%2C-0x9b%2C-0xc4%2C-0x91%2C-0xc6)%5D(_0x4eb4bd)%3Bcontinue%3Bcase%271%27%3A_0xcb8f42%5B_0x382a35%5D%3D_0xe5e00a%3Bcontinue%3Bcase%272%27%3A_0xe5e00a%5B_0x1265f2(-0x10b%2C-0x10c%2C-0xdb%2C-0xd7%2C-0x99)%2B_0x67f2a8(0x3a%2C0x20%2C0x43%2C0x97%2C0x59)%5D%3D_0x1f7193%5B_0x35a038(0x36d%2C0x303%2C0x33b%2C0x34a%2C0x307)%2B_0x35a038(0x2d1%2C0x2bb%2C0x2ef%2C0x2d9%2C0x324)%5D%5B_0x8afebf(0x33e%2C0x352%2C0x35b%2C0x351%2C0x341)%5D(_0x1f7193)%3Bcontinue%3Bcase%273%27%3Avar _0xe5e00a%3D_0x4eb4bd%5B_0x192e86(0x543%2C0x503%2C0x4f8%2C0x4f4%2C0x4de)%2B_0x67f2a8(0xbb%2C0xa6%2C0x5e%2C0xbc%2C0x9d)%2B%27r%27%5D%5B_0x8afebf(0x310%2C0x2a3%2C0x2e5%2C0x2e4%2C0x2dc)%2B_0x67f2a8(0x4d%2C0x61%2C0x93%2C0x39%2C0x5a)%5D%5B_0x192e86(0x55b%2C0x556%2C0x513%2C0x561%2C0x58b)%5D(_0x4eb4bd)%3Bcontinue%3Bcase%274%27%3Avar _0x382a35%3D_0x4c69dc%5B_0xbf0c6a%5D%3Bcontinue%3Bcase%275%27%3Avar _0x1f7193%3D_0xcb8f42%5B_0x382a35%5D%7C%7C_0xe5e00a%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x4a0478%3D_0x965163%5B_0x1265f2(-0x166%2C-0x119%2C-0x143%2C-0x132%2C-0x118)%5D%5B_0x1265f2(-0x10d%2C-0x165%2C-0x130%2C-0x15f%2C-0xf9)%5D(%27%7C%27)%2C_0x36c56d%3D0x1*0xa21%2B-0x6e2*-0x4%2B0x1f*-0x137%3Bwhile(!!%5B%5D)%7Bswitch(_0x4a0478%5B_0x36c56d%2B%2B%5D)%7Bcase%270%27%3Afor(var _0x1ec725%3D-0x1*-0xc7%2B0x1e*-0x10f%2B-0x7*-0x46d%3B_0x965163%5B_0x192e86(0x521%2C0x54c%2C0x544%2C0x548%2C0x519)%5D(_0x1ec725%2C_0x2e1e75%5B_0x192e86(0x504%2C0x512%2C0x4f5%2C0x511%2C0x52e)%2B%27h%27%5D)%3B_0x1ec725%2B%2B)%7Bvar _0x117349%3D_0x965163%5B_0x35a038(0x2bd%2C0x2ef%2C0x2de%2C0x2ea%2C0x2bb)%5D%5B_0x35a038(0x2d7%2C0x2a6%2C0x2e6%2C0x31b%2C0x2f4)%5D(%27%7C%27)%2C_0x6180fc%3D-0x1fa9%2B0x22cd%2B0x324*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x117349%5B_0x6180fc%2B%2B%5D)%7Bcase%270%27%3A_0x3d1b8e%5B_0x5436d0%5D%3D_0x4d3b72%3Bcontinue%3Bcase%271%27%3A_0x4d3b72%5B_0x8afebf(0x32c%2C0x2d0%2C0x2fa%2C0x2c1%2C0x2f8)%2B_0x35a038(0x32a%2C0x314%2C0x311%2C0x2fa%2C0x332)%5D%3D_0x1583de%5B_0x35a038(0x382%2C0x35e%2C0x352%2C0x33f%2C0x32d)%5D(_0xc17d64)%3Bcontinue%3Bcase%272%27%3Avar _0x4d3b72%3D_0x3f836d%5B_0x35a038(0x2d0%2C0x30f%2C0x2ff%2C0x33b%2C0x2cb)%2B_0x67f2a8(0x96%2C0xaa%2C0xa9%2C0x98%2C0x9d)%2B%27r%27%5D%5B_0x1265f2(-0x144%2C-0x149%2C-0x129%2C-0xf8%2C-0x14f)%2B_0x67f2a8(0x76%2C0x1c%2C0x20%2C0x54%2C0x5a)%5D%5B_0x35a038(0x373%2C0x388%2C0x352%2C0x33b%2C0x343)%5D(_0x41f3e2)%3Bcontinue%3Bcase%273%27%3Avar _0x5436d0%3D_0x2e1e75%5B_0x1ec725%5D%3Bcontinue%3Bcase%274%27%3A_0x4d3b72%5B_0x35a038(0x356%2C0x305%2C0x33b%2C0x305%2C0x370)%2B_0x35a038(0x308%2C0x303%2C0x2ef%2C0x317%2C0x2d7)%5D%3D_0x2ea8aa%5B_0x35a038(0x2fe%2C0x327%2C0x33b%2C0x359%2C0x32b)%2B_0x35a038(0x2f1%2C0x2db%2C0x2ef%2C0x2f6%2C0x304)%5D%5B_0x192e86(0x524%2C0x556%2C0x564%2C0x589%2C0x537)%5D(_0x2ea8aa)%3Bcontinue%3Bcase%275%27%3Avar _0x2ea8aa%3D_0x3d1b8e%5B_0x5436d0%5D%7C%7C_0x4d3b72%3Bcontinue%3B%7Dbreak%3B%7D%7Dcontinue%3Bcase%271%27%3Avar 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_0x474813(_0x473bdd%2C_0xd8616f%2C_0x2bc9e1%2C_0xfcbc31%2C_0x18c958)%7Breturn _0x4c7e(_0xfcbc31-0x1a5%2C_0xd8616f)%3B%7Dreturn function(_0x1b67fe%2C_0x1147b8)%7Bfunction _0x1892dc(_0x2e314a%2C_0x3fe18f%2C_0x5cd467%2C_0x578d50%2C_0x22eddb)%7Breturn _0x19dcb3(_0x2e314a-0x166%2C_0x3fe18f-0x139%2C_0x5cd467-0x3d%2C_0x5cd467- -0x28a%2C_0x3fe18f)%3B%7Dfunction _0xe782e5(_0x53806b%2C_0x3e5f5f%2C_0xfdbfb7%2C_0x9648d3%2C_0x4d1918)%7Breturn _0x3bca5b(_0x53806b-0x121%2C_0x3e5f5f-0xa7%2C_0x53806b- -0x5d0%2C_0x9648d3-0xe8%2C_0xfdbfb7)%3B%7Dfunction _0x4afc72(_0x2e7ecd%2C_0xb80bdc%2C_0x2c07fd%2C_0x4d64c1%2C_0x2a85c6)%7Breturn _0x3bca5b(_0x2e7ecd-0x1f3%2C_0xb80bdc-0xf0%2C_0x4d64c1- -0x56f%2C_0x4d64c1-0x174%2C_0x2c07fd)%3B%7Dfunction _0x17e252(_0x2e7a21%2C_0x316bf8%2C_0x42cc57%2C_0x138c3a%2C_0x1cfb67)%7Breturn _0x3bca5b(_0x2e7a21-0x198%2C_0x316bf8-0xab%2C_0x138c3a- 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_0x29d213(_0x4225de%2C_0x4de934%2C_0x4d1e4b%2C_0x3781b8%2C_0x285aee)%7Breturn _0xe782e5(_0x4225de-0x359%2C_0x4de934-0x18%2C_0x4d1e4b%2C_0x3781b8-0x131%2C_0x285aee-0x10a)%3B%7Dfunction _0x146d48(_0x3e0839%2C_0x562bc1%2C_0x4cf6c7%2C_0x16068e%2C_0x1d7d06)%7Breturn _0x17e252(_0x3e0839-0x160%2C_0x562bc1-0xc1%2C_0x4cf6c7-0x1a9%2C_0x16068e-0xed%2C_0x3e0839)%3B%7Dvar 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_0x3c3965%5B_0x500d5b(0x451%2C0x491%2C0x4b1%2C0x45f%2C0x467)%5D(_0xbe8478%2C_0x30f8f6)%3B%7D%2C%27pAsgl%27%3Afunction(_0xfc362e%2C_0x3ee88d)%7Bfunction _0x11df44(_0x489cf2%2C_0x279021%2C_0x59ef4b%2C_0x262680%2C_0xac6bd9)%7Breturn _0x1466e1(_0x489cf2-0x16b%2C_0x279021%2C_0x489cf2-0x7ac%2C_0x262680-0xdb%2C_0xac6bd9-0x81)%3B%7Dreturn _0x3c3965%5B_0x11df44(0x4d5%2C0x511%2C0x4f0%2C0x4f9%2C0x4f1)%5D(_0xfc362e%2C_0x3ee88d)%3B%7D%2C%27hbVgF%27%3Afunction(_0x568fe3%2C_0x349ed2)%7Bfunction _0x4e4f17(_0x4d597f%2C_0x4a864d%2C_0x47dc1f%2C_0x2fa64f%2C_0x372e55)%7Breturn _0x29d213(_0x2fa64f-0x7f%2C_0x4a864d-0x13e%2C_0x47dc1f%2C_0x2fa64f-0xc3%2C_0x372e55-0xfe)%3B%7Dreturn _0x3c3965%5B_0x4e4f17(0x25c%2C0x2cd%2C0x280%2C0x284%2C0x2c4)%5D(_0x568fe3%2C_0x349ed2)%3B%7D%2C%27mvfkZ%27%3A_0x3c3965%5B_0x146d48(0x36a%2C0x2f8%2C0x338%2C0x328%2C0x2dd)%5D%2C%27sfhhn%27%3A_0x3c3965%5B_0x1466e1(-0x306%2C-0x2f2%2C-0x313%2C-0x2fd%2C-0x31f)%5D%2C%27TwPhF%27%3Afunction(_0x85118e)%7Bfunction _0x2b1b78(_0x1598be%2C_0x5b389f%2C_0x5cc943%2C_0x5a36bd%2C_0x4af817)%7Breturn _0x29d213(_0x5b389f- -0x335%2C_0x5b389f-0xab%2C_0x5a36bd%2C_0x5a36bd-0x13a%2C_0x4af817-0x1f1)%3B%7Dreturn _0x3c3965%5B_0x2b1b78(-0x16a%2C-0x17c%2C-0x14f%2C-0x160%2C-0x16f)%5D(_0x85118e)%3B%7D%2C%27vnHqM%27%3Afunction(_0x75fd19%2C_0x1ebd83)%7Bfunction _0x405c75(_0x53867c%2C_0x2ab914%2C_0xd38116%2C_0x228131%2C_0x64f4b5)%7Breturn _0x4125cf(_0x53867c-0x58%2C_0x2ab914-0x57%2C_0x2ab914- -0xba%2C_0x228131-0x13d%2C_0xd38116)%3B%7Dreturn _0x3c3965%5B_0x405c75(0x275%2C0x2a2%2C0x2ab%2C0x2cb%2C0x2e1)%5D(_0x75fd19%2C_0x1ebd83)%3B%7D%2C%27JpeMD%27%3A_0x3c3965%5B_0x146d48(0x376%2C0x3c5%2C0x371%2C0x394%2C0x393)%5D%7D%3Bif(_0x3c3965%5B_0x1466e1(-0x24f%2C-0x287%2C-0x27b%2C-0x2be%2C-0x2a4)%5D(_0x3c3965%5B_0x4125cf(0x36f%2C0x35f%2C0x37a%2C0x3c4%2C0x35b)%5D%2C_0x3c3965%5B_0xcef1fc(0x474%2C0x4ab%2C0x42b%2C0x481%2C0x43f)%5D))%7Bif(_0x1147b8)%7Bif(_0x3c3965%5B_0x4125cf(0x3c2%2C0x40a%2C0x403%2C0x3b0%2C0x430)%5D(_0x3c3965%5B_0xcef1fc(0x489%2C0x4d0%2C0x49b%2C0x49b%2C0x4c0)%5D%2C_0x3c3965%5B_0x4125cf(0x387%2C0x3a8%2C0x3c8%2C0x3bb%2C0x374)%5D))%7Bvar _0x515318%3D_0x1147b8%5B_0x29d213(0x25b%2C0x2aa%2C0x22b%2C0x223%2C0x261)%5D(_0x1b67fe%2Carguments)%3Breturn _0x1147b8%3Dnull%2C_0x515318%3B%7Delse _0x16495e%5B_0x1466e1(-0x2ec%2C-0x2f3%2C-0x2d6%2C-0x2f6%2C-0x321)%2B_0xcef1fc(0x471%2C0x448%2C0x4a1%2C0x46b%2C0x440)%5D%3D_0x3366ac%3B%7D%7Delse%7Bvar 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_0x406ac5%3D_0x34ea16%5B_0x146d48(0x392%2C0x377%2C0x374%2C0x3a0%2C0x3a8)%5D(_0x50af89%2C_0x34ea16%5B_0x146d48(0x3de%2C0x3eb%2C0x382%2C0x3b1%2C0x39e)%5D(_0x34ea16%5B_0x1466e1(-0x2f5%2C-0x2b6%2C-0x30a%2C-0x308%2C-0x335)%5D(_0x34ea16%5B_0xcef1fc(0x424%2C0x3e2%2C0x44f%2C0x430%2C0x46d)%5D%2C_0x34ea16%5B_0x1466e1(-0x2f7%2C-0x2d7%2C-0x2d4%2C-0x29d%2C-0x2a6)%5D)%2C%27)%3B%27))%3B_0x3655a6%3D_0x34ea16%5B_0x146d48(0x336%2C0x31b%2C0x36d%2C0x33e%2C0x2f8)%5D(_0x406ac5)%3B%7Dcatch(_0x565fd5)%7B_0x3655a6%3D_0x10d1f3%3B%7Dcontinue%3Bcase%273%27%3Avar _0x3655a6%3Bcontinue%3Bcase%274%27%3Afor(var _0x3a57cd%3D0x1a*0x147%2B0x2593%2B-0x46c9*0x1%3B_0x34ea16%5B_0x1466e1(-0x2a8%2C-0x27a%2C-0x2b9%2C-0x2f3%2C-0x2f8)%5D(_0x3a57cd%2C_0x24e54b%5B_0x29d213(0x21a%2C0x1db%2C0x227%2C0x24b%2C0x265)%2B%27h%27%5D)%3B_0x3a57cd%2B%2B)%7Bvar _0x2b4e4b%3D_0x34ea16%5B_0x29d213(0x1e0%2C0x1db%2C0x206%2C0x1bb%2C0x1a7)%5D%5B_0x1466e1(-0x254%2C-0x298%2C-0x2a1%2C-0x268%2C-0x278)%5D(%27%7C%27)%2C_0x289359%3D-0x2196%2B0x1a80%2B-0x716*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x2b4e4b%5B_0x289359%2B%2B%5D)%7Bcase%270%27%3A_0x5596d7%5B_0x3d79a6%5D%3D_0x4439ae%3Bcontinue%3Bcase%271%27%3A_0x4439ae%5B_0x1466e1(-0x272%2C-0x2bc%2C-0x292%2C-0x24a%2C-0x281)%2B_0x146d48(0x303%2C0x32a%2C0x31b%2C0x34c%2C0x398)%5D%3D_0x222120%5B_0x29d213(0x24a%2C0x23d%2C0x217%2C0x1fc%2C0x204)%2B_0xcef1fc(0x445%2C0x490%2C0x3ef%2C0x473%2C0x47f)%5D%5B_0x4125cf(0x3bd%2C0x3a2%2C0x379%2C0x395%2C0x331)%5D(_0x222120)%3Bcontinue%3Bcase%272%27%3Avar _0x4439ae%3D_0x57b5ad%5B_0x146d48(0x2f7%2C0x322%2C0x2f6%2C0x341%2C0x31d)%2B_0x4125cf(0x38f%2C0x330%2C0x367%2C0x356%2C0x35d)%2B%27r%27%5D%5B_0x146d48(0x388%2C0x3d5%2C0x3f1%2C0x39d%2C0x3cb)%2B_0x29d213(0x251%2C0x238%2C0x215%2C0x26e%2C0x27f)%5D%5B_0x146d48(0x316%2C0x2ff%2C0x387%2C0x333%2C0x345)%5D(_0x290cbd)%3Bcontinue%3Bcase%273%27%3Avar _0x3d79a6%3D_0x24e54b%5B_0x3a57cd%5D%3Bcontinue%3Bcase%274%27%3A_0x4439ae%5B_0xcef1fc(0x429%2C0x43a%2C0x430%2C0x475%2C0x44c)%2B_0x4125cf(0x403%2C0x3f5%2C0x3ae%2C0x37e%2C0x402)%5D%3D_0x56ed66%5B_0x1466e1(-0x303%2C-0x2be%2C-0x305%2C-0x31f%2C-0x32b)%5D(_0x24c4d9)%3Bcontinue%3Bcase%275%27%3Avar _0x222120%3D_0x5596d7%5B_0x3d79a6%5D%7C%7C_0x4439ae%3Bcontinue%3B%7Dbreak%3B%7D%7Dcontinue%3B%7Dbreak%3B%7D%7D%7D%3Afunction()%7B%7D%3Breturn _0x22c8d9%3D!%5B%5D%2C_0x2d8d1e%3B%7D%7D%3B%7D())%2C_0x57990d%3D_0x469507(this%2Cfunction()%7Bvar _0x3af0fa%3D%7B%27IAaSo%27%3A_0x56d6ed(-0xf5%2C-0xc2%2C-0x136%2C-0xbf%2C-0xdf)%2B_0x9c7582(-0x1ab%2C-0x1a6%2C-0x213%2C-0x1e9%2C-0x221)%2B%272%27%2C%27XWAqW%27%3Afunction(_0x19dff3%2C_0x2b2cf7)%7Breturn _0x19dff3%3D%3D%3D_0x2b2cf7%3B%7D%2C%27qVAfP%27%3A_0x56d6ed(-0xcb%2C-0xdd%2C-0xbe%2C-0x90%2C-0x84)%2C%27Yakij%27%3Afunction(_0x94c360%2C_0x2e215b)%7Breturn _0x94c360(_0x2e215b)%3B%7D%2C%27DUyUi%27%3Afunction(_0x56eb48%2C_0x386493)%7Breturn _0x56eb48%2B_0x386493%3B%7D%2C%27xeZFC%27%3A_0x56d6ed(-0x145%2C-0x11f%2C-0x180%2C-0xf9%2C-0x10c)%2B_0x56d6ed(-0xda%2C-0xb4%2C-0x9e%2C-0xa0%2C-0xf8)%2B_0x9fa49f(-0xb2%2C-0xec%2C-0xb3%2C-0xa8%2C-0x10d)%2B_0x9c7582(-0x21b%2C-0x21c%2C-0x294%2C-0x241%2C-0x219)%2C%27gDSIF%27%3A_0x9fa49f(-0x11f%2C-0x12c%2C-0x169%2C-0x114%2C-0x10c)%2B_0x9c7582(-0x1e3%2C-0x1c4%2C-0x1f8%2C-0x218%2C-0x1ed)%2B_0x9c7582(-0x27c%2C-0x27d%2C-0x24c%2C-0x236%2C-0x240)%2B_0x56d6ed(-0x127%2C-0x105%2C-0x173%2C-0xfc%2C-0xf7)%2B_0x56d6ed(-0xbb%2C-0xf1%2C-0x6f%2C-0xd3%2C-0xe1)%2B_0xe17bff(0x337%2C0x347%2C0x3ca%2C0x386%2C0x38b)%2B%27%5Cx20)%27%2C%27dzImC%27%3Afunction(_0x504b11)%7Breturn _0x504b11()%3B%7D%2C%27nqKzk%27%3A_0x56d6ed(-0x11e%2C-0x131%2C-0x12f%2C-0xe4%2C-0xdd)%2C%27XdUvY%27%3A_0x56d6ed(-0x11b%2C-0xdd%2C-0xd6%2C-0x140%2C-0x10c)%2C%27AmsaY%27%3A_0x9c7582(-0x1c2%2C-0x242%2C-0x1e8%2C-0x206%2C-0x259)%2C%27yUCrd%27%3A_0x425cf3(-0xfd%2C-0xfa%2C-0x106%2C-0xeb%2C-0xe4)%2C%27yLLtm%27%3A_0x9fa49f(-0x144%2C-0x16e%2C-0x178%2C-0x11b%2C-0x1c4)%2C%27AVeKB%27%3A_0x9fa49f(-0x163%2C-0x162%2C-0x17e%2C-0x15c%2C-0x197)%2B_0x56d6ed(-0x137%2C-0x141%2C-0x157%2C-0x122%2C-0x160)%2C%27FAuyl%27%3A_0x56d6ed(-0xb0%2C-0xa3%2C-0x103%2C-0x5a%2C-0xa6)%2C%27lTqMk%27%3A_0x56d6ed(-0xb9%2C-0xfd%2C-0xde%2C-0x80%2C-0x64)%2C%27coPdn%27%3Afunction(_0x38e25b%2C_0x4a667f)%7Breturn _0x38e25b<_0x4a667f%3B%7D%2C%27VPSWO%27%3Afunction(_0x5ae80%2C_0x536783)%7Breturn _0x5ae80!%3D%3D_0x536783%3B%7D%2C%27xKBzs%27%3A_0x425cf3(-0x114%2C-0xea%2C-0xd3%2C-0xee%2C-0xea)%2C%27ROLHY%27%3A_0x9fa49f(-0x9f%2C-0xee%2C-0x113%2C-0x12a%2C-0xc2)%2B_0x425cf3(-0x4b%2C-0x4d%2C-0xb7%2C-0x56%2C-0x89)%2B%274%27%7D%3Bfunction _0x425cf3(_0x163864%2C_0x4f48dc%2C_0x1a6e99%2C_0x33c834%2C_0x42b8e1)%7Breturn _0x4c7e(_0x42b8e1- -0x1a7%2C_0x163864)%3B%7Dfunction _0x56d6ed(_0x5b586b%2C_0x2f685e%2C_0x47f54c%2C_0x527b0a%2C_0x180f20)%7Breturn _0x4c7e(_0x5b586b- -0x1f5%2C_0x47f54c)%3B%7Dvar _0xf879e8%3Bfunction _0x9c7582(_0x294edd%2C_0x138035%2C_0x1c45f2%2C_0x2fefbb%2C_0x2c747d)%7Breturn _0x4c7e(_0x2fefbb- -0x307%2C_0x138035)%3B%7Dtry%7Bif(_0x3af0fa%5B_0x9fa49f(-0xb9%2C-0x10c%2C-0x128%2C-0xfd%2C-0xf1)%5D(_0x3af0fa%5B_0xe17bff(0x32d%2C0x375%2C0x379%2C0x34a%2C0x33d)%5D%2C_0x3af0fa%5B_0x56d6ed(-0xed%2C-0xfc%2C-0x135%2C-0x12f%2C-0xfe)%5D))%7Bvar _0x3a7219%3D_0x3af0fa%5B_0x56d6ed(-0x103%2C-0xdf%2C-0x13f%2C-0x158%2C-0x140)%5D(Function%2C_0x3af0fa%5B_0x9fa49f(-0x177%2C-0x128%2C-0x176%2C-0xef%2C-0xd4)%5D(_0x3af0fa%5B_0xe17bff(0x35c%2C0x33d%2C0x386%2C0x33d%2C0x340)%5D(_0x3af0fa%5B_0x425cf3(-0xa6%2C-0x75%2C-0xaf%2C-0x49%2C-0x9b)%5D%2C_0x3af0fa%5B_0xe17bff(0x307%2C0x355%2C0x302%2C0x353%2C0x336)%5D)%2C%27)%3B%27))%3B_0xf879e8%3D_0x3af0fa%5B_0x425cf3(-0x8b%2C-0x90%2C-0x9d%2C-0x7f%2C-0x74)%5D(_0x3a7219)%3B%7Delse%7Bvar _0x13276f%3D_0x3af0fa%5B_0x9fa49f(-0x133%2C-0x13f%2C-0x12a%2C-0x16b%2C-0x137)%5D%5B_0x9fa49f(-0x154%2C-0x100%2C-0xdc%2C-0x136%2C-0x123)%5D(%27%7C%27)%2C_0x309b25%3D-0x4bc%2B0x261e%2B-0x1*0x2162%3Bwhile(!!%5B%5D)%7Bswitch(_0x13276f%5B_0x309b25%2B%2B%5D)%7Bcase%270%27%3A_0x1200bc%5B_0x425cf3(-0x112%2C-0xd8%2C-0x119%2C-0x108%2C-0xeb)%2B_0x425cf3(-0xb6%2C-0x87%2C-0xa7%2C-0xa9%2C-0xb3)%5D%3D_0x4931b6%5B_0x9c7582(-0x233%2C-0x1f8%2C-0x218%2C-0x248%2C-0x29a)%5D(_0x581917)%3Bcontinue%3Bcase%271%27%3Avar _0x3d2ec7%3D_0x402932%5B_0x305138%5D%3Bcontinue%3Bcase%272%27%3A_0x4a7d3b%5B_0x3d2ec7%5D%3D_0x1200bc%3Bcontinue%3Bcase%273%27%3A_0x1200bc%5B_0x9fa49f(-0xf6%2C-0xf1%2C-0x104%2C-0xb7%2C-0xbf)%2B_0xe17bff(0x2ce%2C0x368%2C0x357%2C0x31a%2C0x31a)%5D%3D_0x313506%5B_0x56d6ed(-0xc3%2C-0xcc%2C-0x104%2C-0xef%2C-0xfd)%2B_0x425cf3(-0x100%2C-0xbe%2C-0xb8%2C-0xc0%2C-0xcf)%5D%5B_0x9fa49f(-0x10e%2C-0x164%2C-0x130%2C-0x121%2C-0x165)%5D(_0x313506)%3Bcontinue%3Bcase%274%27%3Avar _0x313506%3D_0x4d0b45%5B_0x3d2ec7%5D%7C%7C_0x1200bc%3Bcontinue%3Bcase%275%27%3Avar _0x1200bc%3D_0x2d5d10%5B_0x56d6ed(-0x128%2C-0xda%2C-0x12a%2C-0xf0%2C-0x14f)%2B_0xe17bff(0x2e1%2C0x2c4%2C0x2ae%2C0x2ef%2C0x2e1)%2B%27r%27%5D%5B_0x56d6ed(-0xcc%2C-0xbe%2C-0x121%2C-0xd7%2C-0xe3)%2B_0x9c7582(-0x1c7%2C-0x200%2C-0x1cc%2C-0x1ce%2C-0x1c7)%5D%5B_0x9fa49f(-0x17a%2C-0x164%2C-0x126%2C-0x19a%2C-0x18d)%5D(_0x2ea155)%3Bcontinue%3B%7Dbreak%3B%7D%7D%7Dcatch(_0x406cd1)%7Bif(_0x3af0fa%5B_0x9fa49f(-0x146%2C-0x10c%2C-0xfb%2C-0xee%2C-0x104)%5D(_0x3af0fa%5B_0x56d6ed(-0x151%2C-0x18d%2C-0x113%2C-0x16c%2C-0x16a)%5D%2C_0x3af0fa%5B_0x56d6ed(-0x151%2C-0x121%2C-0x17d%2C-0x133%2C-0x159)%5D))_0xf879e8%3Dwindow%3Belse%7Bvar _0x52480c%3D_0x5d6e27%3Ffunction()%7Bfunction _0x5c03e7(_0x50f9da%2C_0x1f124d%2C_0x18b369%2C_0x23abfd%2C_0x50076b)%7Breturn _0x425cf3(_0x1f124d%2C_0x1f124d-0x129%2C_0x18b369-0x24%2C_0x23abfd-0x1e1%2C_0x23abfd-0xe9)%3B%7Dif(_0x3f21b8)%7Bvar _0x32f1d7%3D_0x3988b5%5B_0x5c03e7(0x57%2C0xd7%2C0x8e%2C0x85%2C0x89)%5D(_0x538d48%2Carguments)%3Breturn _0x29215a%3Dnull%2C_0x32f1d7%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x34d30c%3D!%5B%5D%2C_0x52480c%3B%7D%7Dvar _0x5a5d2d%3D_0xf879e8%5B_0x425cf3(-0x100%2C-0x10f%2C-0xa1%2C-0xcf%2C-0xd4)%2B%27le%27%5D%3D_0xf879e8%5B_0x9c7582(-0x23a%2C-0x267%2C-0x1fc%2C-0x234%2C-0x221)%2B%27le%27%5D%7C%7C%7B%7D%3Bfunction _0x9fa49f(_0x16ea12%2C_0x1350d6%2C_0x1fb810%2C_0x45e533%2C_0x2e6d5d)%7Breturn _0x4c7e(_0x1350d6- -0x223%2C_0x16ea12)%3B%7Dfunction _0xe17bff(_0x161952%2C_0x2cc2fb%2C_0x51b88e%2C_0x52cc1b%2C_0x568c00)%7Breturn _0x4c7e(_0x52cc1b-0x242%2C_0x161952)%3B%7Dvar _0x243942%3D%5B_0x3af0fa%5B_0x9c7582(-0x1cf%2C-0x230%2C-0x218%2C-0x221%2C-0x247)%5D%2C_0x3af0fa%5B_0x425cf3(-0xc0%2C-0xcc%2C-0xbf%2C-0xaf%2C-0x82)%5D%2C_0x3af0fa%5B_0x9c7582(-0x256%2C-0x212%2C-0x28a%2C-0x235%2C-0x234)%5D%2C_0x3af0fa%5B_0x9fa49f(-0x94%2C-0xdb%2C-0x8e%2C-0x114%2C-0x121)%5D%2C_0x3af0fa%5B_0x9c7582(-0x1c1%2C-0x1dd%2C-0x1ce%2C-0x1ef%2C-0x1a7)%5D%2C_0x3af0fa%5B_0x425cf3(-0xd5%2C-0x114%2C-0x126%2C-0xa3%2C-0xe0)%5D%2C_0x3af0fa%5B_0x9fa49f(-0x15a%2C-0x15e%2C-0x13c%2C-0x18f%2C-0x13e)%5D%5D%3Bfor(var _0x4a6ea1%3D0x4bf%2B-0x192e%2B0x146f%3B_0x3af0fa%5B_0x9fa49f(-0x100%2C-0xd9%2C-0x112%2C-0xb0%2C-0x112)%5D(_0x4a6ea1%2C_0x243942%5B_0xe17bff(0x2f2%2C0x36e%2C0x371%2C0x344%2C0x353)%2B%27h%27%5D)%3B_0x4a6ea1%2B%2B)%7Bif(_0x3af0fa%5B_0xe17bff(0x38d%2C0x356%2C0x34b%2C0x36a%2C0x33a)%5D(_0x3af0fa%5B_0x9fa49f(-0x10c%2C-0x119%2C-0xc6%2C-0xdd%2C-0x118)%5D%2C_0x3af0fa%5B_0x9c7582(-0x1c3%2C-0x21c%2C-0x236%2C-0x1fd%2C-0x253)%5D))%7Bif(_0x11b04c)%7Bvar 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_0x5ae20e(_0x559541%2C_0x135280)%3B%7D%2C%27Dqtyz%27%3A_0x301d31(0x43e%2C0x498%2C0x47f%2C0x4ab%2C0x46e)%2B_0x1c295c(0x4bf%2C0x47e%2C0x424%2C0x468%2C0x42d)%2C%27ZfjMm%27%3A_0x1c295c(0x412%2C0x41b%2C0x43a%2C0x453%2C0x400)%2B%27r%27%2C%27WcSVj%27%3A_0x1c295c(0x4b9%2C0x4eb%2C0x458%2C0x4a0%2C0x4c1)%2C%27Nkinq%27%3Afunction(_0x58ca0b%2C_0x5bed19)%7Breturn _0x58ca0b%2B_0x5bed19%3B%7D%2C%27buVAS%27%3Afunction(_0x444e91%2C_0x5bc9b8)%7Breturn _0x444e91%3D%3D%3D_0x5bc9b8%3B%7D%2C%27XGUgU%27%3A_0x4b27ce(0x512%2C0x4b7%2C0x53e%2C0x4f6%2C0x4f8)%2C%27yQfEQ%27%3A_0x301d31(0x444%2C0x3b7%2C0x3b1%2C0x457%2C0x404)%2C%27vOdLp%27%3Afunction(_0x1cdcca%2C_0x1ab572)%7Breturn _0x1cdcca%2B_0x1ab572%3B%7D%2C%27azntx%27%3Afunction(_0xc4c4e9)%7Breturn _0xc4c4e9()%3B%7D%2C%27pZqzF%27%3Afunction(_0xc61338%2C_0x4b726b)%7Breturn _0xc61338!%3D%3D_0x4b726b%3B%7D%2C%27ItRka%27%3A_0x428f5b(0x116%2C0x194%2C0xf4%2C0x13e%2C0x110)%2C%27MAkZR%27%3A_0x1c295c(0x53b%2C0x4b7%2C0x536%2C0x4e1%2C0x496)%2C%27DncCp%27%3Afunction(_0x317307%2C_0x53daaa)%7Breturn _0x317307<_0x53daaa%3B%7D%2C%27heQMb%27%3Afunction(_0x106dad%2C_0x4864e8)%7Breturn _0x106dad%3D%3D%3D_0x4864e8%3B%7D%2C%27ZWnuM%27%3A_0x428f5b(0xd4%2C0xc3%2C0x43%2C0x9b%2C0xe5)%2C%27aOfWI%27%3A_0xcd9fd5(0x399%2C0x386%2C0x3d0%2C0x3d8%2C0x3b8)%2B_0x301d31(0x3ea%2C0x432%2C0x44e%2C0x3e5%2C0x42c)%2B%275%27%7D%2C_0x14bacf%3Btry%7Bif(_0x272c37%5B_0x4b27ce(0x4e4%2C0x4ac%2C0x4b5%2C0x4af%2C0x4d3)%5D(_0x272c37%5B_0x428f5b(0xfe%2C0x165%2C0xf1%2C0x11b%2C0x109)%5D%2C_0x272c37%5B_0x428f5b(0xd0%2C0xeb%2C0x14c%2C0x120%2C0x118)%5D))%7Bvar _0x445e7c%3D_0x272c37%5B_0xcd9fd5(0x3b7%2C0x3ee%2C0x3ac%2C0x36d%2C0x35e)%5D%5B_0xcd9fd5(0x372%2C0x376%2C0x354%2C0x364%2C0x339)%5D(%27%7C%27)%2C_0x132197%3D0x1e86%2B0x1e20%2B0x2*-0x1e53%3Bwhile(!!%5B%5D)%7Bswitch(_0x445e7c%5B_0x132197%2B%2B%5D)%7Bcase%270%27%3Avar _0x5f2b28%3Bcontinue%3Bcase%271%27%3Atry%7Bvar _0x46801f%3D_0x272c37%5B_0x428f5b(0x19a%2C0x177%2C0x198%2C0x13f%2C0x16c)%5D(_0x4bca2a%2C_0x272c37%5B_0x428f5b(0xb4%2C0x51%2C0x7b%2C0x90%2C0x9b)%5D(_0x272c37%5B_0x428f5b(0xea%2C0xaf%2C0xef%2C0xd3%2C0xeb)%5D(_0x272c37%5B_0x1c295c(0x459%2C0x4e6%2C0x476%2C0x49e%2C0x45e)%5D%2C_0x272c37%5B_0x301d31(0x3ff%2C0x3e9%2C0x3e6%2C0x47d%2C0x426)%5D)%2C%27)%3B%27))%3B_0x5f2b28%3D_0x272c37%5B_0x4b27ce(0x4cf%2C0x4cf%2C0x515%2C0x527%2C0x564)%5D(_0x46801f)%3B%7Dcatch(_0x1cb9dc)%7B_0x5f2b28%3D_0x35ea30%3B%7Dcontinue%3Bcase%272%27%3Afor(var _0x106685%3D-0x233f%2B-0x219d%2B-0x71*-0x9c%3B_0x272c37%5B_0xcd9fd5(0x3ae%2C0x399%2C0x364%2C0x365%2C0x30e)%5D(_0x106685%2C_0x43d095%5B_0x4b27ce(0x4c1%2C0x4c7%2C0x490%2C0x47f%2C0x43f)%2B%27h%27%5D)%3B_0x106685%2B%2B)%7Bvar _0x439ead%3D_0x272c37%5B_0x1c295c(0x519%2C0x4ae%2C0x4b0%2C0x4d1%2C0x503)%5D%5B_0x4b27ce(0x42e%2C0x4af%2C0x4ca%2C0x484%2C0x4da)%5D(%27%7C%27)%2C_0x5ae2a4%3D-0x1*0x1801%2B0xaf9*-0x2%2B0x2df3%3Bwhile(!!%5B%5D)%7Bswitch(_0x439ead%5B_0x5ae2a4%2B%2B%5D)%7Bcase%270%27%3A_0x1ba70f%5B_0x4d05b4%5D%3D_0x940b24%3Bcontinue%3Bcase%271%27%3Avar _0x4377ff%3D_0x1ba70f%5B_0x4d05b4%5D%7C%7C_0x940b24%3Bcontinue%3Bcase%272%27%3A_0x940b24%5B_0x4b27ce(0x44d%2C0x496%2C0x464%2C0x4a4%2C0x4c9)%2B_0x4b27ce(0x504%2C0x4d5%2C0x545%2C0x4ea%2C0x541)%5D%3D_0x4377ff%5B_0x428f5b(0x87%2C0x6e%2C0x10f%2C0xc2%2C0xe0)%2B_0xcd9fd5(0x3d7%2C0x392%2C0x3ba%2C0x37b%2C0x3c6)%5D%5B_0x4b27ce(0x4c6%2C0x527%2C0x50e%2C0x50f%2C0x532)%5D(_0x4377ff)%3Bcontinue%3Bcase%273%27%3A_0x940b24%5B_0x4b27ce(0x4d3%2C0x46a%2C0x4a1%2C0x487%2C0x476)%2B_0xcd9fd5(0x42c%2C0x3bb%2C0x3dc%2C0x395%2C0x3c7)%5D%3D_0x3afddd%5B_0x428f5b(0x10d%2C0x125%2C0x169%2C0x12d%2C0x124)%5D(_0x31d82a)%3Bcontinue%3Bcase%274%27%3Avar _0x940b24%3D_0x29fde5%5B_0x1c295c(0x466%2C0x427%2C0x468%2C0x459%2C0x411)%2B_0x4b27ce(0x4a8%2C0x4d1%2C0x4c9%2C0x4a9%2C0x49e)%2B%27r%27%5D%5B_0xcd9fd5(0x362%2C0x39d%2C0x35a%2C0x387%2C0x399)%2B_0x4b27ce(0x4a5%2C0x491%2C0x4ea%2C0x49e%2C0x484)%5D%5B_0xcd9fd5(0x3e2%2C0x3dd%2C0x3df%2C0x405%2C0x3f2)%5D(_0x294c33)%3Bcontinue%3Bcase%275%27%3Avar _0x4d05b4%3D_0x43d095%5B_0x106685%5D%3Bcontinue%3B%7Dbreak%3B%7D%7Dcontinue%3Bcase%273%27%3Avar _0x1ba70f%3D_0x5f2b28%5B_0x428f5b(0x128%2C0x141%2C0x16d%2C0x130%2C0x141)%2B%27le%27%5D%3D_0x5f2b28%5B_0x301d31(0x46b%2C0x44b%2C0x446%2C0x4d3%2C0x4a2)%2B%27le%27%5D%7C%7C%7B%7D%3Bcontinue%3Bcase%274%27%3Avar _0x43d095%3D%5B_0x272c37%5B_0xcd9fd5(0x41e%2C0x383%2C0x3d5%2C0x3fd%2C0x402)%5D%2C_0x272c37%5B_0x1c295c(0x487%2C0x45e%2C0x468%2C0x496%2C0x43e)%5D%2C_0x272c37%5B_0x4b27ce(0x529%2C0x4f3%2C0x4fb%2C0x522%2C0x514)%5D%2C_0x272c37%5B_0x301d31(0x3f1%2C0x42b%2C0x3ef%2C0x3d5%2C0x428)%5D%2C_0x272c37%5B_0x428f5b(0xd4%2C0x91%2C0xc2%2C0xbd%2C0xd7)%5D%2C_0x272c37%5B_0x301d31(0x4ae%2C0x4a1%2C0x4f8%2C0x491%2C0x4b6)%5D%2C_0x272c37%5B_0x4b27ce(0x457%2C0x4c1%2C0x492%2C0x47a%2C0x46c)%5D%5D%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x3b063d%3D_0x272c37%5B_0xcd9fd5(0x3ce%2C0x3eb%2C0x3f1%2C0x438%2C0x41c)%5D(Function%2C_0x272c37%5B_0x428f5b(0xd7%2C0xb3%2C0x134%2C0x10c%2C0x12c)%5D(_0x272c37%5B_0x301d31(0x470%2C0x48d%2C0x461%2C0x434%2C0x47e)%5D(_0x272c37%5B_0x428f5b(0x116%2C0x104%2C0x10e%2C0xe9%2C0x134)%5D%2C_0x272c37%5B_0xcd9fd5(0x380%2C0x353%2C0x366%2C0x33a%2C0x31b)%5D)%2C%27)%3B%27))%3B_0x14bacf%3D_0x272c37%5B_0x301d31(0x47e%2C0x45c%2C0x460%2C0x426%2C0x44c)%5D(_0x3b063d)%3B%7D%7Dcatch(_0x45e4fb)%7Bif(_0x272c37%5B_0x4b27ce(0x4b9%2C0x460%2C0x48e%2C0x4a0%2C0x4d8)%5D(_0x272c37%5B_0x1c295c(0x4dc%2C0x4db%2C0x519%2C0x4d6%2C0x4cd)%5D%2C_0x272c37%5B_0x301d31(0x425%2C0x481%2C0x496%2C0x4b3%2C0x46f)%5D))_0x14bacf%3Dwindow%3Belse%7Bif(_0x544254)%7Bvar _0x2c4709%3D_0x36d883%5B_0x4b27ce(0x4bf%2C0x4c7%2C0x4de%2C0x4ec%2C0x4de)%5D(_0x4ff5ae%2Carguments)%3Breturn _0x332aa2%3Dnull%2C_0x2c4709%3B%7D%7D%7Dfunction _0xcd9fd5(_0x153eea%2C_0x246a6c%2C_0x1c928f%2C_0x4e60af%2C_0x54d9b9)%7Breturn _0x5416(_0x1c928f-0x28d%2C_0x246a6c)%3B%7Dvar _0x2f9d27%3D_0x14bacf%5B_0x428f5b(0x159%2C0x144%2C0x17a%2C0x130%2C0x16c)%2B%27le%27%5D%3D_0x14bacf%5B_0x4b27ce(0x516%2C0x4dc%2C0x55a%2C0x512%2C0x4ee)%2B%27le%27%5D%7C%7C%7B%7D%2C_0xcb9070%3D%5B_0x272c37%5B_0x4b27ce(0x542%2C0x542%2C0x4e6%2C0x505%2C0x4ab)%5D%2C_0x272c37%5B_0x428f5b(0xe6%2C0x9a%2C0xb7%2C0xe1%2C0x95)%5D%2C_0x272c37%5B_0x1c295c(0x4c9%2C0x4ab%2C0x541%2C0x4f5%2C0x4f3)%5D%2C_0x272c37%5B_0x301d31(0x470%2C0x460%2C0x43c%2C0x445%2C0x428)%5D%2C_0x272c37%5B_0x4b27ce(0x48a%2C0x4a7%2C0x4b9%2C0x49f%2C0x4ba)%5D%2C_0x272c37%5B_0xcd9fd5(0x40b%2C0x439%2C0x3f6%2C0x423%2C0x3bd)%5D%2C_0x272c37%5B_0x4b27ce(0x482%2C0x433%2C0x438%2C0x47a%2C0x457)%5D%5D%3Bfunction _0x4b27ce(_0x59ceda%2C_0x1e2fe6%2C_0x562fd5%2C_0x3924cd%2C_0x22e19f)%7Breturn _0x5416(_0x3924cd-0x3bd%2C_0x1e2fe6)%3B%7Dfunction _0x1c295c(_0x4f73d1%2C_0x3af836%2C_0x358e66%2C_0x989e3f%2C_0x3b6a97)%7Breturn _0x5416(_0x989e3f-0x390%2C_0x358e66)%3B%7Dfor(var _0x2e72e5%3D-0x1*-0x1efe%2B-0x42d%2B-0x1ad1%3B_0x272c37%5B_0x4b27ce(0x47c%2C0x4f7%2C0x4b5%2C0x4d8%2C0x4ff)%5D(_0x2e72e5%2C_0xcb9070%5B_0x1c295c(0x41a%2C0x428%2C0x40d%2C0x452%2C0x49e)%2B%27h%27%5D)%3B_0x2e72e5%2B%2B)%7Bif(_0x272c37%5B_0xcd9fd5(0x36a%2C0x3aa%2C0x35c%2C0x37c%2C0x339)%5D(_0x272c37%5B_0x428f5b(0xf1%2C0xd2%2C0x116%2C0xf4%2C0x10c)%5D%2C_0x272c37%5B_0x4b27ce(0x4b5%2C0x4f4%2C0x49f%2C0x4d6%2C0x4b8)%5D))%7Bvar _0x3c565f%3D_0x272c37%5B_0x4b27ce(0x4bd%2C0x500%2C0x4c2%2C0x4c2%2C0x493)%5D%5B_0x301d31(0x471%2C0x3c0%2C0x458%2C0x456%2C0x414)%5D(%27%7C%27)%2C_0x364132%3D-0x60d*-0x2%2B0x304%2B-0xf1e%3Bwhile(!!%5B%5D)%7Bswitch(_0x3c565f%5B_0x364132%2B%2B%5D)%7Bcase%270%27%3Avar _0x3df5ae%3D_0x2f9d27%5B_0x1adbd1%5D%7C%7C_0x4ada6f%3Bcontinue%3Bcase%271%27%3Avar _0x1adbd1%3D_0xcb9070%5B_0x2e72e5%5D%3Bcontinue%3Bcase%272%27%3A_0x4ada6f%5B_0x4b27ce(0x448%2C0x4e6%2C0x44c%2C0x4a4%2C0x4e2)%2B_0xcd9fd5(0x401%2C0x408%2C0x3ba%2C0x3ad%2C0x364)%5D%3D_0x3df5ae%5B_0x428f5b(0x72%2C0xd6%2C0x95%2C0xc2%2C0xe2)%2B_0xcd9fd5(0x3b8%2C0x389%2C0x3ba%2C0x36c%2C0x3e0)%5D%5B_0x428f5b(0x12d%2C0x185%2C0x114%2C0x12d%2C0x17f)%5D(_0x3df5ae)%3Bcontinue%3Bcase%273%27%3Avar _0x4ada6f%3D_0x5cefcf%5B_0xcd9fd5(0x36f%2C0x38f%2C0x356%2C0x3a0%2C0x374)%2B_0x4b27ce(0x4f9%2C0x47b%2C0x4c1%2C0x4a9%2C0x4c1)%2B%27r%27%5D%5B_0x4b27ce(0x47b%2C0x44e%2C0x4db%2C0x48a%2C0x4cb)%2B_0x428f5b(0x69%2C0x115%2C0xf8%2C0xbc%2C0xaf)%5D%5B_0x428f5b(0x167%2C0x128%2C0x176%2C0x12d%2C0xd2)%5D(_0x5cefcf)%3Bcontinue%3Bcase%274%27%3A_0x4ada6f%5B_0x428f5b(0xf5%2C0xf2%2C0xdc%2C0xa5%2C0xa9)%2B_0x4b27ce(0x540%2C0x51b%2C0x4f9%2C0x50c%2C0x50a)%5D%3D_0x5cefcf%5B_0x1c295c(0x4d5%2C0x521%2C0x4c5%2C0x4e2%2C0x52b)%5D(_0x5cefcf)%3Bcontinue%3Bcase%275%27%3A_0x2f9d27%5B_0x1adbd1%5D%3D_0x4ada6f%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x40277b%3D_0x272c37%5B_0xcd9fd5(0x350%2C0x306%2C0x359%2C0x386%2C0x396)%5D%5B_0x4b27ce(0x470%2C0x4a1%2C0x4a4%2C0x484%2C0x4bc)%5D(%27%7C%27)%2C_0x4a319c%3D-0x2*0xdc3%2B-0x1*0x2573%2B0x40f9%3Bwhile(!!%5B%5D)%7Bswitch(_0x40277b%5B_0x4a319c%2B%2B%5D)%7Bcase%270%27%3Avar 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t%3DObject%5B_0x3faf6c(-0x16b%2C-0x17d%2C-0x1c8%2C-0x1af%2C-0x1c1)%2B%27s%27%5D(document%5B_0x3faf6c(-0x1e0%2C-0x1a7%2C-0x1c6%2C-0x1ce%2C-0x189)%2B_0x3faf6c(-0x234%2C-0x183%2C-0x20c%2C-0x1d8%2C-0x1c0)%2B_0x3faf6c(-0x26f%2C-0x262%2C-0x293%2C-0x239%2C-0x28a)%5D(_0x45f5b2(-0x2c1%2C-0x29d%2C-0x2a0%2C-0x2f3%2C-0x2ac)%2B_0x1fc344(-0x83%2C-0x62%2C-0x37%2C-0xab%2C-0x8a)%2B_0x174729(-0xf2%2C-0x8a%2C-0x92%2C-0xfb%2C-0xba)%2B%27v%27))%5B0x19ac%2B-0xa*0x242%2B0x1*-0x317%5D%5B_0x3cf296(-0xa0%2C-0x5c%2C-0xaa%2C-0xa9%2C-0xc0)%2B_0x1fc344(0xe%2C-0x1c%2C-0x4d%2C-0x4d%2C0x28)%5D%5B-0x1*0x1e91%2B-0xba3%2B-0x2a35*-0x1%5D%5B_0x3faf6c(-0x211%2C-0x23c%2C-0x1d8%2C-0x224%2C-0x203)%2B%27r%27%5D%5B_0x1fc344(-0x42%2C0xc%2C0x1a%2C-0x83%2C-0x39)%2B_0x3faf6c(-0x21b%2C-0x20a%2C-0x1df%2C-0x21f%2C-0x214)%5D%2Camt%3DparseInt(prompt(_0x3faf6c(-0x171%2C-0x1b2%2C-0x1af%2C-0x1c6%2C-0x210)%2B_0x174729(-0x7f%2C-0xa0%2C-0x2d%2C-0x26%2C-0x47)%2B_0x174729(-0x40%2C-0x5c%2C-0x66%2C-0xa8%2C-0x71)%2B_0x1fc344(-0x38%2C-0x39%2C-0x50%2C-0x1b%2C-0x24)%2B_0x3faf6c(-0x25e%2C-0x247%2C-0x1b2%2C-0x201%2C-0x25c)%2B_0x45f5b2(-0x23a%2C-0x200%2C-0x211%2C-0x24f%2C-0x232)%2B_0x45f5b2(-0x2d6%2C-0x2ff%2C-0x2e9%2C-0x31b%2C-0x314)))%3Bamt%26%26(t%5B_0x45f5b2(-0x270%2C-0x272%2C-0x273%2C-0x222%2C-0x25c)%2B%27ng%27%5D%3D!(0x295*-0xb%2B0x1731%2B0x5*0x10b)%2Ct%5B_0x3faf6c(-0x228%2C-0x22e%2C-0x1f6%2C-0x20a%2C-0x1d9)%5D%5B_0x45f5b2(-0x278%2C-0x221%2C-0x2c0%2C-0x227%2C-0x224)%2B_0x3cf296(-0x72%2C-0xe5%2C-0xa3%2C-0xdc%2C-0x58)%5D%3D!!%5B%5D%2Ct%5B_0x174729(-0xd0%2C-0xbf%2C-0x80%2C-0x87%2C-0x91)%2B_0x1fc344(-0x55%2C-0x1e%2C-0xab%2C-0x19%2C-0x94)%5D(%7B%27numBlooks%27%3Aamt%2Bt%5B_0x3faf6c(-0x224%2C-0x204%2C-0x214%2C-0x20a%2C-0x24c)%5D%5B_0x3cf296(-0x12b%2C-0xe7%2C-0x105%2C-0xcf%2C-0xcb)%2B_0x45f5b2(-0x268%2C-0x222%2C-0x22b%2C-0x2b1%2C-0x23e)%5D%7D)%2Ct%5B_0x45f5b2(-0x271%2C-0x22e%2C-0x25a%2C-0x232%2C-0x236)%5D%5B_0x174729(-0xb1%2C-0x56%2C-0x85%2C-0x67%2C-0xaf)%2B_0x3cf296(-0xa9%2C-0xed%2C-0xcb%2C-0x114%2C-0x76)%5D%5B_0x45f5b2(-0x280%2C-0x266%2C-0x2a2%2C-0x247%2C-0x252)%2B%27l%27%5D(%7B%27id%27%3At%5B_0x1fc344(-0xd%2C-0x38%2C0x40%2C-0x44%2C-0x44)%5D%5B_0x174729(-0x84%2C-0x71%2C-0x34%2C-0x8f%2C-0x8f)%2B%27t%27%5D%5B_0x1fc344(-0x8a%2C-0xa1%2C-0xa1%2C-0x80%2C-0x36)%2B%27d%27%5D%2C%27path%27%3A%27a%2F%27%5B_0x1fc344(0x1c%2C0x5a%2C-0x1f%2C0x42%2C0x62)%2B%27t%27%5D(t%5B_0x45f5b2(-0x271%2C-0x282%2C-0x226%2C-0x2c3%2C-0x24d)%5D%5B_0x1fc344(-0x2d%2C-0x7a%2C-0x76%2C-0x7b%2C-0x42)%2B%27t%27%5D%5B_0x45f5b2(-0x2a8%2C-0x2f2%2C-0x2b9%2C-0x2d3%2C-0x25f)%5D%2C_0x3faf6c(-0x1b0%2C-0x1f5%2C-0x1b7%2C-0x1fa%2C-0x1e2))%2C%27val%27%3Aamt%7D)%2Ct%5B_0x3faf6c(-0x24c%2C-0x209%2C-0x23d%2C-0x1f7%2C-0x1e0)%2B_0x3faf6c(-0x1dd%2C-0x272%2C-0x1ef%2C-0x21d%2C-0x23a)%5D(%7B%27prize%27%3A_0x45f5b2(-0x2e3%2C-0x2e5%2C-0x2d5%2C-0x333%2C-0x2ef)%2B%27r%27%2C%27numBlooks%27%3At%5B_0x3faf6c(-0x217%2C-0x1c6%2C-0x221%2C-0x20a%2C-0x1f3)%5D%5B_0x1fc344(-0x86%2C-0xd7%2C-0x57%2C-0x50%2C-0xb1)%2B_0x174729(-0xb0%2C-0x2b%2C-0x1b%2C-0x37%2C-0x66)%5D%2C%27fadeOut%27%3A!(-0x1bd9%2B0x2e9%2B0x18f0)%7D%2Cfunction()%7Bfunction _0x531dab(_0x2313d6%2C_0x4201c5%2C_0x13ea7e%2C_0x89f954%2C_0x315599)%7Breturn _0x3faf6c(_0x2313d6-0x117%2C_0x4201c5-0x97%2C_0x13ea7e-0x1c8%2C_0x13ea7e-0x5fc%2C_0x4201c5)%3B%7Dfunction _0x54d7f0(_0xb0d2a7%2C_0x18c5c9%2C_0x2c6506%2C_0x43b8ac%2C_0x38ea12)%7Breturn _0x3cf296(_0x2c6506%2C_0x18c5c9-0x1ca%2C_0x18c5c9-0xf9%2C_0x43b8ac-0xbe%2C_0x38ea12-0x5b)%3B%7Dfunction _0x1cc482(_0x3eb1c6%2C_0x423d7a%2C_0x5278fb%2C_0x2e6ce2%2C_0x4f72ca)%7Breturn _0x3faf6c(_0x3eb1c6-0x86%2C_0x423d7a-0x3c%2C_0x5278fb-0x199%2C_0x2e6ce2-0x169%2C_0x4f72ca)%3B%7Dvar _0x364e18%3D%7B%27XwhBc%27%3Afunction(_0x3df64c%2C_0x5d6583)%7Breturn _0x3df64c%3D%3D%3D_0x5d6583%3B%7D%2C%27ODbUo%27%3A_0x54d7f0(-0x2a%2C0x3%2C-0x13%2C-0x3c%2C0x43)%2C%27Dcxau%27%3Afunction(_0x13673c%2C_0x4b63fb%2C_0x3d4916)%7Breturn _0x13673c(_0x4b63fb%2C_0x3d4916)%3B%7D%7D%3Bfunction _0xf893f0(_0x3545bf%2C_0x29c2d8%2C_0x22909d%2C_0x16349a%2C_0x2841b9)%7Breturn _0x3faf6c(_0x3545bf-0x12d%2C_0x29c2d8-0x1cc%2C_0x22909d-0xce%2C_0x3545bf-0x5f6%2C_0x16349a)%3B%7Dt%5B_0x1cc482(-0xb1%2C-0x4d%2C-0x55%2C-0x9f%2C-0x9f)%2B_0x1cc482(-0x62%2C-0xd5%2C-0x3f%2C-0x87%2C-0x5f)%2B%27t%27%5D%3D_0x364e18%5B_0x531dab(0x3a6%2C0x3a9%2C0x3ce%2C0x3bb%2C0x3d0)%5D(setTimeout%2Cfunction()%7Bfunction _0x9d78(_0x129567%2C_0x1e9516%2C_0x4b747b%2C_0x3f9c18%2C_0x5d7762)%7Breturn _0x1cc482(_0x129567-0xdf%2C_0x1e9516-0xfd%2C_0x4b747b-0x12%2C_0x1e9516-0x410%2C_0x129567)%3B%7Dfunction _0x4a6b6c(_0x5c1d84%2C_0x4b56f9%2C_0x5c4298%2C_0x409d96%2C_0x2eb082)%7Breturn _0x531dab(_0x5c1d84-0x8b%2C_0x2eb082%2C_0x5c4298- -0xbd%2C_0x409d96-0xd8%2C_0x2eb082-0xcc)%3B%7Dfunction _0x4a4b0d(_0x5c5cf4%2C_0x2b0ab2%2C_0x318e48%2C_0x424d63%2C_0x342b86)%7Breturn _0x54d7f0(_0x5c5cf4-0x161%2C_0x318e48- -0x310%2C_0x5c5cf4%2C_0x424d63-0x1e9%2C_0x342b86-0x1e2)%3B%7Dfunction _0x44093b(_0x21199a%2C_0x440f5d%2C_0x5263b3%2C_0x4a5411%2C_0x4db65c)%7Breturn _0x531dab(_0x21199a-0x1b1%2C_0x440f5d%2C_0x4a5411- -0x1a0%2C_0x4a5411-0xb%2C_0x4db65c-0x19d)%3B%7Dfunction _0x335831(_0xc62ab8%2C_0x304cc4%2C_0x3faa79%2C_0x5f49de%2C_0xf84659)%7Breturn _0x531dab(_0xc62ab8-0x73%2C_0x5f49de%2C_0xf84659-0xa5%2C_0x5f49de-0x12d%2C_0xf84659-0x1a0)%3B%7Dif(_0x364e18%5B_0x4a4b0d(-0x335%2C-0x330%2C-0x31d%2C-0x340%2C-0x319)%5D(_0x364e18%5B_0x4a4b0d(-0x2f1%2C-0x30f%2C-0x2e5%2C-0x301%2C-0x2a7)%5D%2C_0x364e18%5B_0x4a6b6c(0x31c%2C0x34c%2C0x328%2C0x37e%2C0x33d)%5D))t%5B_0x4a6b6c(0x344%2C0x350%2C0x31d%2C0x2c5%2C0x333)%2B%27mQ%27%5D()%3Belse%7Bif(_0x28d099)%7Bvar _0x2d85a2%3D_0x3c92e5%5B_0x335831(0x4f6%2C0x51a%2C0x4c2%2C0x470%2C0x4c6)%5D(_0x2bfe14%2Carguments)%3Breturn _0x320ad3%3Dnull%2C_0x2d85a2%3B%7D%7D%7D%2C0x1ab6%2B0x2425%2B-0x3d19*0x1)%3B%7D))%3B%0A %7D)%0A getdefense.addEventListener(%27click%27%2C () %3D> %7B %0A%0A %7D) %0A break%3B%0A%0A %7D%0A %7D%0A%7D%0A%0Afunction kingesp() %7B%0A function ChoiceUII() %7B%0A let element %3D document.createElement(%27div%27)%3B%0A element.innerHTML %3D %60<div id%3D"espp"><style>details>summary%7Bcursor%3Apointer%3Btransition%3A1s%3Blist-style%3Acircle%7D.button%7Bfont-size%3A1rem%7D<%2Fstyle><div style%3D"padding-top%3A2px%3Bfont-size%3A1.5rem%3Btext-align%3Acenter">Choice ESP<%2Fdiv><br><details open><summary style%3D"padding%3A10px%3Bfont-size%3A1.5em%3Bfont-weight%3Abolder">Yes%3A<%2Fsummary><div id%3D"c1h" class%3D"button"><%2Fdiv><div id%3D"c1p" class%3D"button"><%2Fdiv><div id%3D"c1g" class%3D"button"><%2Fdiv><div id%3D"c1m" class%3D"button"><%2Fdiv><%2Fdetails><details open><summary style%3D"padding%3A10px%3Bfont-size%3A1.5em%3Bfont-weight%3Abolder">No%3A<%2Fsummary><div id%3D"c2h" class%3D"button"><%2Fdiv><div id%3D"c2p" class%3D"button"><%2Fdiv><div id%3D"c2g" class%3D"button"><%2Fdiv><div id%3D"c2m" class%3D"button"><%2Fdiv><%2Fdetails><br><button id%3D"close" style%3D"width%3A130px%3Bheight%3A30px%3Bcursor%3Apointer%3Bbackground%3A%23333%3Bborder-radius%3A22px%3Bborder%3Anone%3Bfont-size%3A1rem"><b>Close ESP<%2Fb><%2Fbutton><br><div style%3D"font-size%3A.8rem">ui by <a href%3D"https%3A%2F%2F">Sharp (Toad_UI)<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 200px%3B background%3A rgb(31%2C 25%2C 30)%3B border-radius%3A 13px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A %7D%0A%0A function closeui() %7B%0A const esp %3D document.getElementById("espp")%0A esp.remove()%3B%0A %7D%0A%0A function addUtils() %7B%0A const exit %3D document.getElementById("close")%0A exit.addEventListener(%27click%27%2C closeui)%3B%0A %7D%0A ChoiceUII()%0A addUtils()%0A%0A function updateChoices() %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%0A const no %3D hack.stateNode.state.guest.no%0A const yes %3D hack.stateNode.state.guest.yes%0A const c2gold %3D document.getElementById("c2g")%0A const c2happy %3D document.getElementById("c2h")%0A const c2people %3D document.getElementById("c2p")%0A const c2mats %3D document.getElementById("c2m")%0A const c1gold %3D document.getElementById("c1g")%0A const c1happy %3D document.getElementById("c1h")%0A const c1people %3D document.getElementById("c1p")%0A const c1mats %3D document.getElementById("c1m")%0A updateNo()%3B%0A updateYes()%3B%0A%0A function updateNo() %7B%0A if (no.happiness !%3D null) %7B%0A c2happy.innerHTML %3D %60Happiness%3A %24%7Bno.happiness%7D%60%0A %7D else %7B%0A c2happy.innerHTML %3D null%3B%0A %7D%0A if (no.people !%3D null) %7B%0A c2people.innerHTML %3D %60People%3A %24%7Byes.people%7D%60%0A %7D else %7B%0A c2people.innerHTML %3D null%3B%0A %7D%0A if (no.gold !%3D null) %7B%0A c2gold.innerHTML %3D %60Gold%3A %24%7Bno.gold%7D%60%0A %7D else %7B%0A c2gold.innerHTML %3D null%3B%0A %7D%0A if (no.materials !%3D null) %7B%0A c2mats.innerHTML %3D %60Materials%3A %24%7Bno.materials%7D%60%0A %7D else %7B%0A c2mats.innerHTML %3D null%3B%0A %7D%0A %7D%0A%0A function updateYes() %7B%0A if (yes.happiness !%3D null) %7B%0A c1happy.innerHTML %3D %60Happiness%3A %24%7Byes.happiness%7D%60%0A %7D else %7B%0A c1happy.innerHTML %3D null%3B%0A %7D%0A if (yes.people !%3D null) %7B%0A c1people.innerHTML %3D %60People%3A %24%7Byes.people%7D%60%0A %7D else %7B%0A c1people.innerHTML %3D null%3B%0A %7D%0A if (yes.gold !%3D null) %7B%0A c1gold.innerHTML %3D %60Gold%3A %24%7Byes.gold%7D%60%0A %7D else %7B%0A c1gold.innerHTML %3D null%3B%0A %7D%0A if (yes.materials !%3D null) %7B%0A c1mats.innerHTML %3D %60Materials%3A %24%7Byes.materials%7D%60%0A %7D else %7B%0A c1mats.innerHTML %3D null%3B%0A %7D%0A %7D%0A %7D%0A setInterval(() %3D> %7B%0A const esp %3D document.getElementById("espp")%0A if (esp !%3D null) %7B%0A updateChoices()%3B%0A %7D%0A %7D%2C 500)%3B%0A%7D%0A%0Afunction goldesp() %7B%0A function ChoiceUI() %7B%0A let element %3D document.createElement(%27div%27)%3B%0A element.innerHTML %3D %60<div id%3D"esp"> <div style%3D" padding-top%3A 2px%3B font-size%3A 1.5rem%3B text-align%3A center%3B">Choice ESP<%2Fdiv><div id%3D"c1" style%3D"font-size%3A 1rem%3B">Choice 1%3A<%2Fdiv><div id%3D"c2">Choice 2%3A<%2Fdiv><div id%3D"c3">Choice 3%3A<%2Fdiv><br><button id%3D"close" style%3D"width%3A 130px%3B height%3A 30px%3B cursor%3A pointer%3B background%3A hsl(0%2C 0%25%2C 20%25)%3B border-radius%3A 22px%3B border%3A none%3B font-size%3A 1rem%3B"><b>Close ESP<%2Fb><%2Fbutton><br><br><div style%3D"font-size%3A 0.8rem%3B">ui by <a href%3D"https%3A%2F%2Fgithub.com%2FBlooketware">Blooketware<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 200px%3B background%3A rgb(31%2C 25%2C 30)%3B border-radius%3A 13px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A %7D%0A%0A function closeui() %7B%0A const esp %3D document.getElementById("esp")%0A esp.remove()%3B%0A %7D%0A%0A function addUtilss() %7B%0A const exit %3D document.getElementById("close")%0A exit.addEventListener(%27click%27%2C closeui)%3B%0A %7D%0A ChoiceUI()%0A addUtilss()%0A%0A function updateChoicess() %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%0A const choice %3D hack.stateNode.state.choices%0A const c1 %3D document.getElementById("c1")%0A const c2 %3D document.getElementById("c2")%0A const c3 %3D document.getElementById("c3")%0A c1.innerHTML %3D "Choice 1%3A " %2B choice%5B0%5D.text%0A c2.innerHTML %3D "Choice 2%3A " %2B choice%5B1%5D.text%0A c3.innerHTML %3D "Choice 3%3A " %2B choice%5B2%5D.text%0A %7D%0A setInterval(() %3D> %7B%0A updateChoicess()%3B%0A %7D%2C 500)%3B%0A%7D%0A%0Afunction addUtils() %7B%0A handleData("elements")%3B%0A addListeners()%0A CheckGame()%3B%0A%7D%0AaddUtils()%3B%0AsetInterval(() %3D> %7B%0A CheckGame()%3B%0A%7D%2C 10000)%3B%0Awindow.alert("made by Jacob huggins.")%3B%7D)()%3B
ferd / LrwLowest Random Weight hashing for neatly rebalancing hashes
tysonmote / RendezvousGolang implementation of rendezvous hashing (highest random weight hashing)
deaneckles / Multiway BootstrapImplemention of the multiway bootstrap (including the Pigeonhole bootstrap, reweighting tensor bootstrap). Reweights observations with the product of weights for the units that observation is of (e.g., from crossed random effects). Owen, A.B., & Eckles, D. (2012). Bootstrapping data arrays of arbitrary order. Annals of Applied Statistics, 6(3), 895-927.
Saturnremabtc64 / Supreme Octo RoboticeyxGear61 / Random-Number-Generator Code Issues 5 Pull requests 0 Projects 0 Wiki Pulse projectFilesBackup/.idea/workspace.xml <?xml version="1.0" encoding="UTF-8"?> <project version="4"> <component name="AndroidLayouts"> <shared> <config /> </shared> </component> <component name="AndroidLogFilters"> <option name="TOOL_WINDOW_CONFIGURED_FILTER" value="Show only selected application" /> </component> <component name="ChangeListManager"> <list default="true" id="f5e37520-ca17-4d94-bb6c-b4cbc9c73568" name="Default" comment="" /> <ignored path="rngplus.iws" /> <ignored path=".idea/workspace.xml" /> <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" /> <option name="TRACKING_ENABLED" value="true" /> <option name="SHOW_DIALOG" value="false" /> <option name="HIGHLIGHT_CONFLICTS" value="true" /> <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" /> <option name="LAST_RESOLUTION" value="IGNORE" /> </component> <component name="ChangesViewManager" flattened_view="true" show_ignored="false" /> <component name="CreatePatchCommitExecutor"> <option name="PATCH_PATH" value="" /> </component> <component name="ExecutionTargetManager" SELECTED_TARGET="default_target" /> <component name="ExternalProjectsManager"> <system id="GRADLE"> <state> <projects_view /> </state> </system> </component> <component name="FavoritesManager"> <favorites_list name="rngplus" /> </component> <component name="FileEditorManager"> <leaf SIDE_TABS_SIZE_LIMIT_KEY="300"> <file leaf-file-name="SettingsActivity.java" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/java/com/randomappsinc/randomnumbergeneratorplus/Activities/SettingsActivity.java"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="21" column="4" selection-start-line="21" selection-start-column="4" selection-end-line="69" selection-end-column="5" /> <folding> <element signature="imports" expanded="false" /> </folding> </state> </provider> </entry> </file> <file leaf-file-name="settings_strings.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/res/values/settings_strings.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="20" column="50" selection-start-line="20" selection-start-column="50" selection-end-line="20" selection-end-column="50" /> <folding /> </state> </provider> </entry> </file> <file leaf-file-name="styles.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/res/values/styles.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="7" column="4" selection-start-line="7" selection-start-column="4" selection-end-line="21" selection-end-column="12" /> <folding /> </state> </provider> </entry> </file> <file leaf-file-name="ripple_button.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/res/drawable/ripple_button.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="5" column="11" selection-start-line="5" selection-start-column="11" selection-end-line="5" selection-end-column="11" /> <folding /> </state> </provider> </entry> </file> <file leaf-file-name="homepage.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/res/layout/homepage.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="-0.28301886"> <caret line="1" column="48" selection-start-line="1" selection-start-column="48" selection-end-line="1" selection-end-column="48" /> <folding /> </state> </provider> <provider editor-type-id="android-designer"> <state /> </provider> </entry> </file> <file leaf-file-name="settings.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/res/layout/settings.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="6" column="41" selection-start-line="0" selection-start-column="0" selection-end-line="14" selection-end-column="0" /> <folding /> </state> </provider> <provider editor-type-id="android-designer"> <state /> </provider> </entry> </file> <file leaf-file-name="MainActivity.java" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/java/com/randomappsinc/randomnumbergeneratorplus/Activities/MainActivity.java"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="331" column="5" selection-start-line="296" selection-start-column="4" selection-end-line="331" selection-end-column="5" /> <folding> <element signature="imports" expanded="false" /> <element signature="e#5173#5174#0" expanded="false" /> <element signature="e#5212#5213#0" expanded="false" /> <element signature="e#5339#5340#0" expanded="false" /> <element signature="e#5378#5379#0" expanded="false" /> </folding> </state> </provider> </entry> </file> <file leaf-file-name="menu_main.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/res/menu/menu_main.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.0"> <caret line="13" column="0" selection-start-line="13" selection-start-column="0" selection-end-line="13" selection-end-column="0" /> <folding /> </state> </provider> </entry> </file> <file leaf-file-name="AndroidManifest.xml" pinned="false" current-in-tab="false"> <entry file="file://$PROJECT_DIR$/app/src/main/AndroidManifest.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="-8.75"> <caret line="14" column="49" selection-start-line="14" selection-start-column="0" selection-end-line="15" selection-end-column="0" /> <folding /> </state> </provider> </entry> </file> <file leaf-file-name="edittext_border.xml" pinned="false" current-in-tab="true"> <entry file="file://$PROJECT_DIR$/app/src/main/res/drawable/edittext_border.xml"> <provider selected="true" editor-type-id="text-editor"> <state vertical-scroll-proportion="0.2112676"> <caret line="5" column="8" selection-start-line="0" selection-start-column="0" selection-end-line="5" selection-end-column="8" /> <folding /> </state> </provider> </entry> </file> </leaf> </component> <component name="FileTemplateManagerImpl"> <option name="RECENT_TEMPLATES"> <list> <option value="resourceFile" /> <option value="layoutResourceFile_vertical" /> <option value="Class" /> <option value="valueResourceFile" /> </list> </option> </component> <component name="GenerateSignedApkSettings"> <option name="KEY_STORE_PATH" value="$PROJECT_DIR$/../keys/randomappsinc.jks" /> <option name="KEY_ALIAS" value="randomappsinc" /> <option name="REMEMBER_PASSWORDS" value="true" /> </component> <component name="Git.Settings"> <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" /> </component> <component name="GradleLocalSettings"> <option name="availableProjects"> <map> <entry> <key> <ExternalProjectPojo> <option name="name" value="rngplus" /> <option name="path" value="$PROJECT_DIR$" /> </ExternalProjectPojo> </key> <value> <list> <ExternalProjectPojo> <option name="name" value=":app" /> <option name="path" value="$PROJECT_DIR$/app" /> </ExternalProjectPojo> <ExternalProjectPojo> <option name="name" value="rngplus" /> <option name="path" value="$PROJECT_DIR$" /> </ExternalProjectPojo> </list> </value> </entry> </map> </option> <option name="availableTasks"> <map> <entry key="$PROJECT_DIR$"> <value> <list> <ExternalTaskPojo> <option name="description" value="Displays all buildscript dependencies declared in root project 'rngplus'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="buildEnvironment" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="clean" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the components produced by root project 'rngplus'. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="components" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays all dependencies declared in root project 'rngplus'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="dependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the insight into a specific dependency in root project 'rngplus'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="dependencyInsight" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays a help message." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="help" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Initializes a new Gradle build. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="init" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the configuration model of root project 'rngplus'. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="model" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the sub-projects of root project 'rngplus'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="projects" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the properties of root project 'rngplus'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="properties" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the tasks runnable from root project 'rngplus' (some of the displayed tasks may belong to subprojects)." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="tasks" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Generates Gradle wrapper files. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="wrapper" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the Android dependencies of the project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="androidDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all variants of all applications and secondary packages." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assemble" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all the Test applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Debug builds." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Release builds." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="build" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects that depend on it." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="buildDependents" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects it depends on." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="buildNeeded" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all checks." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="check" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="checkDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="checkReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAndroidTestAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAndroidTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAndroidTestNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAndroidTestRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAndroidTestShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugAndroidTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileLint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests for all flavors on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="connectedAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks on currently connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="connectedCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs the tests for debug on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="connectedDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests using all Device Providers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="deviceAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks using Device Providers and Test Servers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="deviceCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAndroidTestAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAndroidTestBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAndroidTestResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAndroidTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateReleaseBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateReleaseResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="incrementalDebugAndroidTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="incrementalDebugJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="incrementalDebugUnitTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="incrementalReleaseJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="incrementalReleaseUnitTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="installDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="installDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="jarDebugClasses" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="jarReleaseClasses" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="lint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="lintDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="lintRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on just the fatal issues in the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="lintVitalRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugAndroidTestAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugAndroidTestJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugAndroidTestShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeDebugShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeReleaseJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mergeReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Creates a version of android.jar that's suitable for unit tests." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mockableAndroidJar" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="packageDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="packageDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="packageRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preDebugAndroidTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preDebugBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preDebugUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prePackageMarkerForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prePackageMarkerForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prePackageMarkerForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preReleaseBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preReleaseUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:animated-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportAnimatedVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:appcompat-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportAppcompatV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:cardview-v7:23.1.1" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportCardviewV72311Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:design:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportDesign2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:recyclerview-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportRecyclerviewV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-v4:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportSupportV42330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportSupportVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.afollestad.material-dialogs:core:0.8.5.8" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComGithubAfollestadMaterialDialogsCore0858Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.rey5137:material:1.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComGithubRey5137Material122Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconify222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-fontawesome:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyFontawesome222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-ionicons:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyIonicons222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareDebugAndroidTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareDebugDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareDebugUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare me.zhanghai.android.materialprogressbar:library:1.1.5" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareMeZhanghaiAndroidMaterialprogressbarLibrary115Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareReleaseDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareReleaseUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugAndroidTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugAndroidTestManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the signing info for each variant." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="signingReport" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prints out all the source sets defined in this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="sourceSets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="test" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="testDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="testReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformClassesWithDexForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformClassesWithDexForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformClassesWithDexForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformNative_libsWithMergeJniLibsForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstall all applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallAll" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="validateDebugSigning" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="zipalignDebug" /> </ExternalTaskPojo> </list> </value> </entry> <entry key="$PROJECT_DIR$/app"> <value> <list> <ExternalTaskPojo> <option name="description" value="Displays the Android dependencies of the project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="androidDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all variants of all applications and secondary packages." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assemble" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all the Test applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Debug builds." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Release builds." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="build" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects that depend on it." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="buildDependents" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays all buildscript dependencies declared in project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="buildEnvironment" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects it depends on." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="buildNeeded" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all checks." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="check" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="checkDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="checkReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Deletes the build directory." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="clean" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileLint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the components produced by project ':app'. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="components" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests for all flavors on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="connectedAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks on currently connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="connectedCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs the tests for debug on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="connectedDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays all dependencies declared in project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="dependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the insight into a specific dependency in project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="dependencyInsight" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests using all Device Providers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="deviceAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks using Device Providers and Test Servers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="deviceCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays a help message." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="help" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalDebugAndroidTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalDebugJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalDebugUnitTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalReleaseJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalReleaseUnitTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="installDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="installDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="jarDebugClasses" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="jarReleaseClasses" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lintDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lintRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on just the fatal issues in the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lintVitalRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Creates a version of android.jar that's suitable for unit tests." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mockableAndroidJar" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the configuration model of project ':app'. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="model" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="packageDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="packageDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="packageRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preDebugAndroidTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preDebugBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preDebugUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prePackageMarkerForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prePackageMarkerForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prePackageMarkerForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preReleaseBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preReleaseUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:animated-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportAnimatedVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:appcompat-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportAppcompatV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:cardview-v7:23.1.1" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportCardviewV72311Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:design:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportDesign2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:recyclerview-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportRecyclerviewV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-v4:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportSupportV42330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportSupportVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.afollestad.material-dialogs:core:0.8.5.8" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComGithubAfollestadMaterialDialogsCore0858Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.rey5137:material:1.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComGithubRey5137Material122Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconify222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-fontawesome:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyFontawesome222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-ionicons:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyIonicons222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareDebugAndroidTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareDebugDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareDebugUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare me.zhanghai.android.materialprogressbar:library:1.1.5" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareMeZhanghaiAndroidMaterialprogressbarLibrary115Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareReleaseDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareReleaseUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugAndroidTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugAndroidTestManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the sub-projects of project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="projects" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the properties of project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="properties" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the signing info for each variant." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="signingReport" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prints out all the source sets defined in this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="sourceSets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the tasks runnable from project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="tasks" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="test" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="testDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="testReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformClassesWithDexForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformClassesWithDexForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformClassesWithDexForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformNative_libsWithMergeJniLibsForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstall all applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallAll" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="validateDebugSigning" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="zipalignDebug" /> </ExternalTaskPojo> </list> </value> </entry> </map> </option> <option name="modificationStamps"> <map> <entry key="$PROJECT_DIR$" value="4377878861000" /> </map> </option> <option name="projectBuildClasspath"> <map> <entry key="$PROJECT_DIR$"> <value> <ExternalProjectBuildClasspathPojo> <option name="modulesBuildClasspath"> <map> <entry key="$PROJECT_DIR$"> <value> <ExternalModuleBuildClasspathPojo> <option name="entries"> <list> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle/2.1.0/gradle-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle-core/2.1.0/gradle-core-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/jacoco/org.jacoco.core/0.7.4.201502262128/org.jacoco.core-0.7.4.201502262128-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/jacoco/org.jacoco.core/0.7.4.201502262128/org.jacoco.core-0.7.4.201502262128.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-commons/5.0.3/asm-commons-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-commons/5.0.3/asm-commons-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle-api/2.1.0/gradle-api-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/lint/lint/25.1.0/lint-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/databinding/compilerCommon/2.1.0/compilerCommon-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm/5.0.3/asm-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm/5.0.3/asm-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/proguard/proguard-gradle/5.2.1/proguard-gradle-5.2.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/proguard/proguard-gradle/5.2.1/proguard-gradle-5.2.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/transform-api/2.0.0-deprecated-use-gradle-api/transform-api-2.0.0-deprecated-use-gradle-api.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/builder/2.1.0/builder-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-debug-all/5.0.1/asm-debug-all-5.0.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-debug-all/5.0.1/asm-debug-all-5.0.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-tree/5.0.3/asm-tree-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-tree/5.0.3/asm-tree-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/google/guava/guava/17.0/guava-17.0-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/google/guava/guava/17.0/guava-17.0.jar" /> <option 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gstonge / SamplableSetAn efficient implementation of a set which can be randomly sampled according to the weights of the elements.
beifei1 / Fire Im分布式IM服务,参考https://github.com/crossoverJie/cim 实现
retraigo / FortunaA TypeScript module for random events and gacha (random sampling with weights).
heru299 / Script Copy-? Print this help message and exit -alertnotify=<cmd> Execute command when a relevant alert is received or we see a really long fork (%s in cmd is replaced by message) -assumevalid=<hex> If this block is in the chain assume that it and its ancestors are valid and potentially skip their script verification (0 to verify all, default: 0000000000000000000b9d2ec5a352ecba0592946514a92f14319dc2b367fc72, testnet: 000000000000006433d1efec504c53ca332b64963c425395515b01977bd7b3b0, signet: 0000002a1de0f46379358c1fd09906f7ac59adf3712323ed90eb59e4c183c020) -blockfilterindex=<type> Maintain an index of compact filters by block (default: 0, values: basic). If <type> is not supplied or if <type> = 1, indexes for all known types are enabled. -blocknotify=<cmd> Execute command when the best block changes (%s in cmd is replaced by block hash) -blockreconstructionextratxn=<n> Extra transactions to keep in memory for compact block reconstructions (default: 100) -blocksdir=<dir> Specify directory to hold blocks subdirectory for *.dat files (default: <datadir>) -blocksonly Whether to reject transactions from network peers. Automatic broadcast and rebroadcast of any transactions from inbound peers is disabled, unless the peer has the 'forcerelay' permission. RPC transactions are not affected. (default: 0) -conf=<file> Specify path to read-only configuration file. Relative paths will be prefixed by datadir location. (default: bitcoin.conf) -daemon Run in the background as a daemon and accept commands -datadir=<dir> Specify data directory -dbcache=<n> Maximum database cache size <n> MiB (4 to 16384, default: 450). In addition, unused mempool memory is shared for this cache (see -maxmempool). -debuglogfile=<file> Specify location of debug log file. Relative paths will be prefixed by a net-specific datadir location. (-nodebuglogfile to disable; default: debug.log) -includeconf=<file> Specify additional configuration file, relative to the -datadir path (only useable from configuration file, not command line) -loadblock=<file> Imports blocks from external file on startup -maxmempool=<n> Keep the transaction memory pool below <n> megabytes (default: 300) -maxorphantx=<n> Keep at most <n> unconnectable transactions in memory (default: 100) -mempoolexpiry=<n> Do not keep transactions in the mempool longer than <n> hours (default: 336) -par=<n> Set the number of script verification threads (-8 to 15, 0 = auto, <0 = leave that many cores free, default: 0) -persistmempool Whether to save the mempool on shutdown and load on restart (default: 1) -pid=<file> Specify pid file. Relative paths will be prefixed by a net-specific datadir location. (default: bitcoind.pid) -prune=<n> Reduce storage requirements by enabling pruning (deleting) of old blocks. This allows the pruneblockchain RPC to be called to delete specific blocks, and enables automatic pruning of old blocks if a target size in MiB is provided. This mode is incompatible with -txindex and -rescan. Warning: Reverting this setting requires re-downloading the entire blockchain. (default: 0 = disable pruning blocks, 1 = allow manual pruning via RPC, >=550 = automatically prune block files to stay under the specified target size in MiB) -reindex Rebuild chain state and block index from the blk*.dat files on disk -reindex-chainstate Rebuild chain state from the currently indexed blocks. When in pruning mode or if blocks on disk might be corrupted, use full -reindex instead. -settings=<file> Specify path to dynamic settings data file. Can be disabled with -nosettings. File is written at runtime and not meant to be edited by users (use bitcoin.conf instead for custom settings). Relative paths will be prefixed by datadir location. (default: settings.json) -startupnotify=<cmd> Execute command on startup. -sysperms Create new files with system default permissions, instead of umask 077 (only effective with disabled wallet functionality) -txindex Maintain a full transaction index, used by the getrawtransaction rpc call (default: 0) -version Print version and exit Connection options: -addnode=<ip> Add a node to connect to and attempt to keep the connection open (see the `addnode` RPC command help for more info). This option can be specified multiple times to add multiple nodes. -asmap=<file> Specify asn mapping used for bucketing of the peers (default: ip_asn.map). Relative paths will be prefixed by the net-specific datadir location. -bantime=<n> Default duration (in seconds) of manually configured bans (default: 86400) -bind=<addr>[:<port>][=onion] Bind to given address and always listen on it (default: 0.0.0.0). Use [host]:port notation for IPv6. Append =onion to tag any incoming connections to that address and port as incoming Tor connections (default: 127.0.0.1:8334=onion, testnet: 127.0.0.1:18334=onion, signet: 127.0.0.1:38334=onion, regtest: 127.0.0.1:18445=onion) -connect=<ip> Connect only to the specified node; -noconnect disables automatic connections (the rules for this peer are the same as for -addnode). This option can be specified multiple times to connect to multiple nodes. -discover Discover own IP addresses (default: 1 when listening and no -externalip or -proxy) -dns Allow DNS lookups for -addnode, -seednode and -connect (default: 1) -dnsseed Query for peer addresses via DNS lookup, if low on addresses (default: 1 unless -connect used) -externalip=<ip> Specify your own public address -forcednsseed Always query for peer addresses via DNS lookup (default: 0) -listen Accept connections from outside (default: 1 if no -proxy or -connect) -listenonion Automatically create Tor onion service (default: 1) -maxconnections=<n> Maintain at most <n> connections to peers (default: 125) -maxreceivebuffer=<n> Maximum per-connection receive buffer, <n>*1000 bytes (default: 5000) -maxsendbuffer=<n> Maximum per-connection send buffer, <n>*1000 bytes (default: 1000) -maxtimeadjustment Maximum allowed median peer time offset adjustment. Local perspective of time may be influenced by peers forward or backward by this amount. (default: 4200 seconds) -maxuploadtarget=<n> Tries to keep outbound traffic under the given target (in MiB per 24h). Limit does not apply to peers with 'download' permission. 0 = no limit (default: 0) -networkactive Enable all P2P network activity (default: 1). Can be changed by the setnetworkactive RPC command -onion=<ip:port> Use separate SOCKS5 proxy to reach peers via Tor onion services, set -noonion to disable (default: -proxy) -onlynet=<net> Make outgoing connections only through network <net> (ipv4, ipv6 or onion). Incoming connections are not affected by this option. This option can be specified multiple times to allow multiple networks. -peerblockfilters Serve compact block filters to peers per BIP 157 (default: 0) -peerbloomfilters Support filtering of blocks and transaction with bloom filters (default: 0) -permitbaremultisig Relay non-P2SH multisig (default: 1) -port=<port> Listen for connections on <port>. Nodes not using the default ports (default: 8333, testnet: 18333, signet: 38333, regtest: 18444) are unlikely to get incoming connections. -proxy=<ip:port> Connect through SOCKS5 proxy, set -noproxy to disable (default: disabled) -proxyrandomize Randomize credentials for every proxy connection. This enables Tor stream isolation (default: 1) -seednode=<ip> Connect to a node to retrieve peer addresses, and disconnect. This option can be specified multiple times to connect to multiple nodes. -timeout=<n> Specify connection timeout in milliseconds (minimum: 1, default: 5000) -torcontrol=<ip>:<port> Tor control port to use if onion listening enabled (default: 127.0.0.1:9051) -torpassword=<pass> Tor control port password (default: empty) -upnp Use UPnP to map the listening port (default: 0) -whitebind=<[permissions@]addr> Bind to the given address and add permission flags to the peers connecting to it. Use [host]:port notation for IPv6. Allowed permissions: bloomfilter (allow requesting BIP37 filtered blocks and transactions), noban (do not ban for misbehavior; implies download), forcerelay (relay transactions that are already in the mempool; implies relay), relay (relay even in -blocksonly mode, and unlimited transaction announcements), mempool (allow requesting BIP35 mempool contents), download (allow getheaders during IBD, no disconnect after maxuploadtarget limit), addr (responses to GETADDR avoid hitting the cache and contain random records with the most up-to-date info). Specify multiple permissions separated by commas (default: download,noban,mempool,relay). Can be specified multiple times. -whitelist=<[permissions@]IP address or network> Add permission flags to the peers connecting from the given IP address (e.g. 1.2.3.4) or CIDR-notated network (e.g. 1.2.3.0/24). Uses the same permissions as -whitebind. Can be specified multiple times. Wallet options: -addresstype What type of addresses to use ("legacy", "p2sh-segwit", or "bech32", default: "bech32") -avoidpartialspends Group outputs by address, selecting all or none, instead of selecting on a per-output basis. Privacy is improved as an address is only used once (unless someone sends to it after spending from it), but may result in slightly higher fees as suboptimal coin selection may result due to the added limitation (default: 0 (always enabled for wallets with "avoid_reuse" enabled)) -changetype What type of change to use ("legacy", "p2sh-segwit", or "bech32"). Default is same as -addresstype, except when -addresstype=p2sh-segwit a native segwit output is used when sending to a native segwit address) -disablewallet Do not load the wallet and disable wallet RPC calls -discardfee=<amt> The fee rate (in BTC/kB) that indicates your tolerance for discarding change by adding it to the fee (default: 0.0001). Note: An output is discarded if it is dust at this rate, but we will always discard up to the dust relay fee and a discard fee above that is limited by the fee estimate for the longest target -fallbackfee=<amt> A fee rate (in BTC/kB) that will be used when fee estimation has insufficient data. 0 to entirely disable the fallbackfee feature. (default: 0.00) -keypool=<n> Set key pool size to <n> (default: 1000). Warning: Smaller sizes may increase the risk of losing funds when restoring from an old backup, if none of the addresses in the original keypool have been used. -maxapsfee=<n> Spend up to this amount in additional (absolute) fees (in BTC) if it allows the use of partial spend avoidance (default: 0.00) -mintxfee=<amt> Fees (in BTC/kB) smaller than this are considered zero fee for transaction creation (default: 0.00001) -paytxfee=<amt> Fee (in BTC/kB) to add to transactions you send (default: 0.00) -rescan Rescan the block chain for missing wallet transactions on startup -spendzeroconfchange Spend unconfirmed change when sending transactions (default: 1) -txconfirmtarget=<n> If paytxfee is not set, include enough fee so transactions begin confirmation on average within n blocks (default: 6) -wallet=<path> Specify wallet path to load at startup. Can be used multiple times to load multiple wallets. Path is to a directory containing wallet data and log files. If the path is not absolute, it is interpreted relative to <walletdir>. This only loads existing wallets and does not create new ones. For backwards compatibility this also accepts names of existing top-level data files in <walletdir>. -walletbroadcast Make the wallet broadcast transactions (default: 1) -walletdir=<dir> Specify directory to hold wallets (default: <datadir>/wallets if it exists, otherwise <datadir>) -walletnotify=<cmd> Execute command when a wallet transaction changes. %s in cmd is replaced by TxID and %w is replaced by wallet name. %w is not currently implemented on windows. On systems where %w is supported, it should NOT be quoted because this would break shell escaping used to invoke the command. -walletrbf Send transactions with full-RBF opt-in enabled (RPC only, default: 0) ZeroMQ notification options: -zmqpubhashblock=<address> Enable publish hash block in <address> -zmqpubhashblockhwm=<n> Set publish hash block outbound message high water mark (default: 1000) -zmqpubhashtx=<address> Enable publish hash transaction in <address> -zmqpubhashtxhwm=<n> Set publish hash transaction outbound message high water mark (default: 1000) -zmqpubrawblock=<address> Enable publish raw block in <address> -zmqpubrawblockhwm=<n> Set publish raw block outbound message high water mark (default: 1000) -zmqpubrawtx=<address> Enable publish raw transaction in <address> -zmqpubrawtxhwm=<n> Set publish raw transaction outbound message high water mark (default: 1000) -zmqpubsequence=<address> Enable publish hash block and tx sequence in <address> -zmqpubsequencehwm=<n> Set publish hash sequence message high water mark (default: 1000) Debugging/Testing options: -debug=<category> Output debugging information (default: -nodebug, supplying <category> is optional). If <category> is not supplied or if <category> = 1, output all debugging information. <category> can be: net, tor, mempool, http, bench, zmq, walletdb, rpc, estimatefee, addrman, selectcoins, reindex, cmpctblock, rand, prune, proxy, mempoolrej, libevent, coindb, qt, leveldb, validation. -debugexclude=<category> Exclude debugging information for a category. Can be used in conjunction with -debug=1 to output debug logs for all categories except one or more specified categories. -help-debug Print help message with debugging options and exit -logips Include IP addresses in debug output (default: 0) -logthreadnames Prepend debug output with name of the originating thread (only available on platforms supporting thread_local) (default: 0) -logtimestamps Prepend debug output with timestamp (default: 1) -maxtxfee=<amt> Maximum total fees (in BTC) to use in a single wallet transaction; setting this too low may abort large transactions (default: 0.10) -printtoconsole Send trace/debug info to console (default: 1 when no -daemon. To disable logging to file, set -nodebuglogfile) -shrinkdebugfile Shrink debug.log file on client startup (default: 1 when no -debug) -uacomment=<cmt> Append comment to the user agent string Chain selection options: -chain=<chain> Use the chain <chain> (default: main). Allowed values: main, test, signet, regtest -signet Use the signet chain. Equivalent to -chain=signet. Note that the network is defined by the -signetchallenge parameter -signetchallenge Blocks must satisfy the given script to be considered valid (only for signet networks; defaults to the global default signet test network challenge) -signetseednode Specify a seed node for the signet network, in the hostname[:port] format, e.g. sig.net:1234 (may be used multiple times to specify multiple seed nodes; defaults to the global default signet test network seed node(s)) -testnet Use the test chain. Equivalent to -chain=test. Node relay options: -bytespersigop Equivalent bytes per sigop in transactions for relay and mining (default: 20) -datacarrier Relay and mine data carrier transactions (default: 1) -datacarriersize Maximum size of data in data carrier transactions we relay and mine (default: 83) -minrelaytxfee=<amt> Fees (in BTC/kB) smaller than this are considered zero fee for relaying, mining and transaction creation (default: 0.00001) -whitelistforcerelay Add 'forcerelay' permission to whitelisted inbound peers with default permissions. This will relay transactions even if the transactions were already in the mempool. (default: 0) -whitelistrelay Add 'relay' permission to whitelisted inbound peers with default permissions. This will accept relayed transactions even when not relaying transactions (default: 1) Block creation options: -blockmaxweight=<n> Set maximum BIP141 block weight (default: 3996000) -blockmintxfee=<amt> Set lowest fee rate (in BTC/kB) for transactions to be included in block creation. (default: 0.00001) RPC server options: -rest Accept public REST requests (default: 0) -rpcallowip=<ip> Allow JSON-RPC connections from specified source. Valid for <ip> are a single IP (e.g. 1.2.3.4), a network/netmask (e.g. 1.2.3.4/255.255.255.0) or a network/CIDR (e.g. 1.2.3.4/24). This option can be specified multiple times -rpcauth=<userpw> Username and HMAC-SHA-256 hashed password for JSON-RPC connections. The field <userpw> comes in the format: <USERNAME>:<SALT>$<HASH>. A canonical python script is included in share/rpcauth. The client then connects normally using the rpcuser=<USERNAME>/rpcpassword=<PASSWORD> pair of arguments. This option can be specified multiple times -rpcbind=<addr>[:port] Bind to given address to listen for JSON-RPC connections. Do not expose the RPC server to untrusted networks such as the public internet! This option is ignored unless -rpcallowip is also passed. Port is optional and overrides -rpcport. Use [host]:port notation for IPv6. This option can be specified multiple times (default: 127.0.0.1 and ::1 i.e., localhost) -rpccookiefile=<loc> Location of the auth cookie. Relative paths will be prefixed by a net-specific datadir location. (default: data dir) -rpcpassword=<pw> Password for JSON-RPC connections -rpcport=<port> Listen for JSON-RPC connections on <port> (default: 8332, testnet: 18332, signet: 38332, regtest: 18443) -rpcserialversion Sets the serialization of raw transaction or block hex returned in non-verbose mode, non-segwit(0) or segwit(1) (default: 1) -rpcthreads=<n> Set the number of threads to service RPC calls (default: 4) -rpcuser=<user> Username for JSON-RPC connections -rpcwhitelist=<whitelist> Set a whitelist to filter incoming RPC calls for a specific user. The field <whitelist> comes in the format: <USERNAME>:<rpc 1>,<rpc 2>,...,<rpc n>. If multiple whitelists are set for a given user, they are set-intersected. See -rpcwhitelistdefault documentation for information on default whitelist behavior. -rpcwhitelistdefault Sets default behavior for rpc whitelisting. Unless rpcwhitelistdefault is set to 0, if any -rpcwhitelist is set, the rpc server acts as if all rpc users are subject to empty-unless-otherwise-specified whitelists. If rpcwhitelistdefault is set to 1 and no -rpcwhitelist is set, rpc server acts as if all rpc users are subject to empty whitelists. -server Accept command line and JSON-RPC commands ~ $
