Classification - 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.'

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

NICEHASH PLATFORM TERMS OF USE AND NICEHASH MINING TERMS OF SERVICE PLEASE READ THESE NICEHASH PLATFORM TERMS OF USE AND NICEHASH MINING TERMS OF SERVICE (“Terms”) CAREFULLY BEFORE USING THE THE PLATFORM OR SERVICES DESCRIBED HEREIN. BY SELECTING “I AGREE”, ACCESSING THE PLATFORM, USING NICEHASH MINING SERVICES OR DOWNLOADING OR USING NICEHASH MINING SOFTWARE, YOU ARE ACKNOWLEDGING THAT YOU HAVE READ THESE TERMS, AS AMENDED FROM TIME TO TIME, AND YOU ARE AGREEING TO BE BOUND BY THEM. IF YOU DO NOT AGREE TO THESE TERMS, OR ANY SUBSEQUENT AMENDMENTS, CHANGES OR UPDATES, DO NOT ACCESS THE PLATFORM, USE NICEHASH MINING SERVICES OR USE THE NICEHASH MINING SOFTWARE. GENERAL These Terms apply to users of the NiceHash Platform (“Platform” and NiceHash Mining Services (“Services”) which are provided to you by NICEHASH Ltd, company organized and existing under the laws of the British Virgin Islands, with registered address at Intershore Chambers, Road Town, Tortola, British Virgin Islands, registration number: 2048669, hereinafter referred to as “NiceHash, as well as “we” or “us”. ELIGIBILITY By using the NiceHash platform and NiceHash Mining Services, you represent and warrant that you: are at least Minimum Age and have capacity to form a binding contract; 
 have not previously been suspended or removed from the NiceHash Platform;
 have full power and authority to enter into this agreement and in doing so will not violate any other agreement to which you are a party;
 are not not furthering, performing, undertaking, engaging in, aiding, or abetting any unlawful activity through your relationship with us, through your use of NiceHash Platform or use of NiceHash Mining Services;
 will not use NiceHash Platform or NiceHash Mining Services if any applicable laws in your country prohibit you from doing so in accordance with these Terms.
 We reserve the right to terminate your access to the NiceHash Platform and Mining Services for any reason and in our sole and absolute discretion. Use of NiceHash Platform and Mining Services is void where prohibited by applicable law. Depending on your country of residence or incorporation or registered office, you may not be able to use all the functions of the NiceHash Platform or services provided therein. It is your responsibility to follow the rules and laws in your country of residence and/or country from which you access the NiceHash Platform. DEFINITIONS NiceHash Platform means a website located on the following web address: www.nicehash.com. NiceHash Mining Services mean all services provided by NiceHash, namely the provision of the NiceHash Platform, NiceHash Hashing power marketplace, NiceHash API, NiceHash OS, NiceHash Mining Software including licence for NiceHash Miner, NiceHash Private Endpoint, NiceHash Account, NiceHash mobile apps, and all other software products, applications and services associated with these products, except for the provision of NiceHash Exchange Services. NiceHash Exchange Service means a service which allows trading of digital assets in the form of digital tokens or cryptographic currency for our users by offering them a trading venue, helping them find a trading counterparty and providing the means for transaction execution. NiceHash Exchange Services are provided by NICEX Ltd and accessible at the NiceHash Platform under NiceHash Exchange Terms of Service. Hashing power marketplace means an infrastructure provided by the NiceHash which enables the Hashing power providers to point their rigs towards NiceHash stratum servers where Hashing power provided by different Hashing power providers is gathered and sold as generic Hashing power to the Hashing power buyers. Hashing power buyer means a legal entity or individual who buys the gathered and generic hashing power on the Hashing power marketplace from undefined Hashing power providers. Hashing power provider means a legal entity or individual who sells his hashing power on the Hashing power marketplace to undefined Hashing power buyers. NiceHash Mining Software means NiceHash Miner and any other software available via the NiceHash Platform. NiceHash Miner means a comprehensive software with graphical user interface and web interface, owned by NiceHash. NiceHash Miner is a process manager software which enables the Hashing power providers to point their rigs towards NiceHash stratum servers and sell their hashing power to the Hashing power buyers. NiceHash Miner also means any and all of its code, compilations, updates, upgrades, modifications, error corrections, patches and bug fixes and similar. NiceHash Miner does not mean third party software compatible with NiceHash Miner (Third Party Plugins and Miners). NiceHash QuickMiner means a software accessible at https://www.nicehash.com/quick-miner which enables Hashing power providers to point their PCs or rigs towards NiceHash stratum servers and sell their hashing power to the Hashing power buyers. NiceHash QuickMiner is intended as a tryout tool. Hashing power rig means all hardware which produces hashing power that represents computation power which is required to calculate the hash function of different type of cryptocurrency. Secondary account is an account managed by third party from which the Account holder deposits funds to his NiceHash Wallet or/and to which the Account holder withdraws funds from his NiceHash Wallet. Stratum is a lightweight mining protocol: https://slushpool.com/help/manual/stratum-protocol. NiceHash Account means an online account available on the NiceHash Platform and created by completing the registration procedure on the NiceHash Platform. Account holder means an individual or legal entity who completes the registration procedure and successfully creates the NiceHash Account. Minimum Age means 18 years old or older, if in order for NiceHash to lawfully provide the Services to you without parental consent (including using your personal data). NiceHash Wallet means a wallet created automatically for the Account holder and provided by the NiceHash Wallet provider. NiceHash does not hold funds on behalf of the Account holder but only transfers Account holder’s requests regarding the NiceHash Wallet transaction to the NiceHash Wallet provider who executes the requested transactions. In this respect NiceHash only processes and performs administrative services related to the payments regarding the NiceHash Mining Services and NiceHash Exchange Services, if applicable. NiceHash Wallet provider is a third party which on the behalf of the Account holder provides and manages the NiceHash Wallet, holds, stores and transfers funds and hosts NiceHash Wallet. For more information about the NiceHash Wallet provider, see the following website: https://www.bitgo.com/. Blockchain network is a distributed database that is used to maintain a continuously growing list of records, called blocks. Force Majeure Event means any governmental or relevant regulatory regulations, acts of God, war, riot, civil commotion, fire, flood, or any disaster or an industrial dispute of workers unrelated to you or NiceHash. Any act, event, omission, happening or non-happening will only be considered Force Majeure if it is not attributable to the wilful act, neglect or failure to take reasonable precautions of the affected party, its agents, employees, consultants, contractors and sub-contractors. SALE AND PURCHASE OF HASHING POWER Hashing power providers agree to sell and NiceHash agrees to proceed Hashing power buyers’ payments for the provided hashing power on the Hashing power marketplace, on the Terms set forth herein. According to the applicable principle get-paid-per-valid-share (pay as you go principle) Hashing power providers will be paid only for validated and accepted hashing power to their NiceHash Wallet or other wallet, as indicated in Account holder’s profile settings or in stratum connection username. In some cases, no Hashing power is sent to Hashing power buyers or is accepted by NiceHash Services, even if Hashing power is generated on the Hashing power rigs. These cases include usage of slower hardware as well as software, hardware or network errors. In these cases, Hashing power providers are not paid for such Hashing power. Hashing power buyers agree to purchase and NiceHash agrees to process the order and forward the purchased hashing power on the Hashing power marketplace, on the Terms set forth herein. According to the applicable principle pay-per-valid-share (pay as you go principle) Hashing power buyers will pay from their NiceHash Wallet only for the hashing power that was validated by our engine. When connection to the mining pool which is selected on the Hashing power order is lost or when an order is cancelled during its lifetime, Hashing power buyer pays for additional 10 seconds worth of hashing power. Hashing power order is charged for extra hashing power when mining pool which is selected on the Hashing power order, generates rapid mining work changes and/or rapid mining job switching. All payments including any fees will be processed in crypto currency and NiceHash does not provide an option to sale and purchase of the hashing power in fiat currency. RISK DISCLOSURE If you choose to use NiceHash Platform, Services and NiceHash Wallet, it is important that you remain aware of the risks involved, that you have adequate technical resources and knowledge to bear such risks and that you monitor your transactions carefully. General risk You understand that NiceHash Platform and Services, blockchain technology, Bitcoin, all other cryptocurrencies and cryptotokens, proof of work concept and other associated and related technologies are new and untested and outside of NiceHash’s control. You acknowledge that there are major risks associated with these technologies. In addition to the risks disclosed below, there are risks that NiceHash cannot foresee and it is unreasonable to believe that such risk could have been foreseeable. The performance of NiceHash’s obligation under these Terms will terminate if market or technology circumstances change to such an extent that (i) these Terms clearly no longer comply with NiceHash’s expectations, (ii) it would be unjust to enforce NiceHash’s obligations in the general opinion or (iii) NiceHash’s obligation becomes impossible. NiceHash Account abuse You acknowledge that there is risk associated with the NiceHash Account abuse and that you have been fully informed and warned about it. The funds stored in the NiceHash Wallet may be disposed by third party in case the third party obtains the Account holder’s login credentials. The Account holder shall protect his login credentials and his electronic devices where the login credentials are stored against unauthorized access. Regulatory risks You acknowledge that there is risk associated with future legislation which may restrict, limit or prohibit certain aspects of blockchain technology which may also result in restriction, limitation or prohibition of NiceHash Services and that you have been fully informed and warned about it. Risk of hacking You acknowledge that there is risk associated with hacking NiceHash Services and NiceHash Wallet and that you have been fully informed and warned about it. Hacker or other groups or organizations may attempt to interfere with NiceHash Services or NiceHash Wallet in any way, including without limitation denial of services attacks, Sybil attacks, spoofing, smurfing, malware attacks, mining attacks or consensus-based attacks. Cryptocurrency risk You acknowledge that there is risk associated with the cryptocurrencies which are used as payment method and that you have been fully informed and warned about it. Cryptocurrencies are prone to, but not limited to, value volatility, transaction costs and times uncertainty, lack of liquidity, availability, regulatory restrictions, policy changes and security risks. NiceHash Wallet risk You acknowledge that there is risk associated with funds held on the NiceHash Wallet and that you have been fully informed and warned about it. You acknowledge that NiceHash Wallet is provided by NiceHash Wallet provider and not NiceHash. You acknowledge and agree that NiceHash shall not be responsible for any NiceHash Wallet provider’s services, including their accuracy, completeness, timeliness, validity, copyright compliance, legality, decency, quality or any other aspect thereof. NiceHash does not assume and shall not have any liability or responsibility to you or any other person or entity for any Hash Wallet provider’s services. Hash Wallet provider’s services and links thereto are provided solely as a convenience to you and you access and use them entirely at your own risk and subject to NiceHash Wallet provider’s terms and conditions. Since the NiceHash Wallet is a cryptocurrency wallet all funds held on it are entirely uninsured in contrast to the funds held on the bank account or other financial institutions which are insured. Connection risk You acknowledge that there are risks associated with usage of NiceHash Services which are provided through the internet including, but not limited to, the failure of hardware, software, configuration and internet connections and that you have been fully informed and warned about it. You acknowledge that NiceHash will not be responsible for any configuration, connection or communication failures, disruptions, errors, distortions or delays you may experience when using NiceHash Services, however caused. Hashing power provision risk You acknowledge that there are risks associated with the provisions of the hashing power which is provided by the Hashing power providers through the Hashing power marketplace and that you have been fully informed and warned about it. You acknowledge that NiceHash does not provide the hashing power but only provides the Hashing power marketplace as a service. Hashing power providers’ Hashing power rigs are new and untested and outside of NiceHash’s control. There is a major risk that the Hashing power rigs (i) will stop providing hashing power, (ii) will provide hashing power in an unstable way, (iii) will be wrongly configured or (iv) provide insufficient speed of the hashing power. Hashing power rigs as hardware could be subject of damage, errors, electricity outage, misconfiguration, connection or communication failures and other malfunctions. NiceHash will not be responsible for operation of Hashing power rigs and its provision of hashing power. By submitting a Hashing power order you agree to Hashing power no-refund policy – all shares forwarded to mining pool, selected on the Hashing power order are final and non-refundable. Hashing power profitability risk You acknowledge that there is risk associated with the profitability of the hashing power provision and that you have been fully informed and warned about it. You acknowledge that all Hashing power rig’s earning estimates and profitability calculations on NiceHash Platform are only for informational purposes and were made based on the Hashing power rigs set up in the test environments. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. There is risk that your Hashing power rig would not produce desired hashing power quantity and quality and that your produced hashing power would differentiate from the hashing power produced by our Hashing power rigs set up in the test environments. There is risk that your Hashing power rigs would not be as profitable as our Hashing power rigs set up in the test environments or would not be profitable at all. WARRANTIES NiceHash Platform and Mining Services are provided on the “AS IS” and “AS AVAILABLE” basis, including all faults and defects. To the maximum extent permitted by applicable law, NiceHash makes no representations and warranties and you waive all warranties of any kind. Particularly, without limiting the generality of the foregoing, the NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding NiceHash Platform and Mining Services or other services related to NiceHash Platform and provided by third parties, including any warranty that such services will be uninterrupted, harmless, secure or not corrupt or damaged, meet your requirements, achieve any intended results, be compatible or work with any other software, applications, systems or services, meet any performance or error free or that any errors or defects can or will be corrected. Additionally NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise of merchantability, suitability, reliability, availability, timeliness, accuracy, satisfactory quality, fitness for a particular purpose or quality, title and non-infringement with respect to any of the Mining Services or other services related to NiceHash Platform and provided by third parties, or quiet enjoyment and any warranties arising out of any course of dealing, course of performance, trade practice or usage of NiceHash Platform and Mining Services including information, content and material contained therein. Especially NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding any payment services and systems, NiceHash Wallet which is provided by third party or any other financial services which might be related to the NiceHash Platform and Mining Services. You acknowledge that you do not rely on and have not been induced to accept the NiceHash Platform and Mining Services according to these Terms on the basis of any warranties, representations, covenants, undertakings or any other statement whatsoever, other than expressly set out in these Terms that neither the NiceHash nor any of its respective agents, officers, employees or advisers have given any such warranties, representations, covenants, undertakings or other statements. LIABILITY NiceHash and their respective officers, employees or agents will not be liable to you or anyone else, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages (including but not limited to lost profits, trading losses or damages that result from use or loss of use of NiceHash Services or NiceHash Wallet), even if NiceHash has been advised of the possibility of such damages or losses, including, without limitation, from the use or attempted use of NiceHash Platform and Mining Services, NiceHash Wallet or other related websites or services. NiceHash does not assume any obligations to users in connection with the unlawful alienation of Bitcoins, which occurred on 6. 12. 2017 with NICEHASH, d. o. o., and has been fully reimbursed with the completion of the NiceHash Repayment Program. NiceHash will not be responsible for any compensation, reimbursement, or damages arising in connection with: (i) your inability to use the NiceHash Platform and Mining Services, including without limitation as a result of any termination or suspension of the NiceHash Platform or these Terms, power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance, (ii) the cost of procurement of substitute goods or services, (iii) any your investments, expenditures, or commitments in connection with these Terms or your use of or access to the NiceHash Platform and Mining Services, (iv) your reliance on any information obtained from NiceHash, (v) Force Majeure Event, communications failure, theft or other interruptions or (vi) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with NiceHash Platform and Mining Services or NiceHash Wallet. Our aggregate liability (including our directors, members, employees and agents), whether in contract, warranty, tort (including negligence, whether active, passive or imputed), product liability, strict liability or other theory, arising out of or relating to the use of NiceHash Platform and Mining Services, or inability to use the Platform and Services under these Terms or under any other document or agreement executed and delivered in connection herewith or contemplated hereby, shall in any event not exceed 100 EUR per user. You will defend, indemnify, and hold NiceHash harmless and all respective employees, officers, directors, and representatives from and against any claims, demand, action, damages, loss, liabilities, costs and expenses (including reasonable attorney fees) arising out of or relating to (i) any third-party claim concerning these Terms, (ii) your use of, or conduct in connection with, NiceHash Platform and Mining Services, (iii) any feedback you provide, (iv) your violation of these Terms, (v) or your violation of any rights of any other person or entity. If you are obligated to indemnify us, we will have the right, in our sole discretion, to control any action or proceeding (at our expense) and determine whether we wish to settle it. If we are obligated to respond to a third-party subpoena or other compulsory legal order or process described above, you will also reimburse us for reasonable attorney fees, as well as our employees’ and contractors’ time and materials spent responding to the third-party subpoena or other compulsory legal order or process at reasonable hourly rates. The Services and the information, products, and services included in or available through the NiceHash Platform may include inaccuracies or typographical errors. Changes are periodically added to the information herein. Improvements or changes on the NiceHash Platform can be made at any time. NICEHASH ACCOUNT The registration of the NiceHash Account is made through the NiceHash Platform, where you are required to enter your email address and password in the registration form. After successful completion of registration, the confirmation email is sent to you. After you confirm your registration by clicking on the link in the confirmation email the NiceHash Account is created. NiceHash will send you proof of completed registration once the process is completed. When you create NiceHash Account, you agree to (i) create a strong password that you change frequently and do not use for any other website, (ii) implement reasonable and appropriate measures designed to secure access to any device which has access to your email address associated with your NiceHash Account and your username and password for your NiceHash Account, (iii) maintain the security of your NiceHash Account by protecting your password and by restricting access to your NiceHash Account; (iv) promptly notify us if you discover or otherwise suspect any security breaches related to your NiceHash Account so we can take all required and possible measures to secure your NiceHash Account and (v) take responsibility for all activities that occur under your NiceHash Account and accept all risks of any authorized or unauthorized access to your NiceHash Account, to the maximum extent permitted by law. Losing access to your email, registered at NiceHash Platform, may also mean losing access to your NiceHash Account. You may not be able to use the NiceHash Platform or Mining Services, execute withdrawals and other security sensitive operations until you regain access to your email address, registered at NiceHash Platform. If you wish to change the email address linked to your NiceHash Account, we may ask you to complete a KYC procedure for security purposes. This step serves solely for the purpose of identification in the process of regaining access to your NiceHash Account. Once the NiceHash Account is created a NiceHash Wallet is automatically created for the NiceHash Account when the request for the first deposit to the NiceHash Wallet is made by the user. Account holder’s NiceHash Wallet is generated by NiceHash Wallet provider. Account holder is strongly suggested to enhance the security of his NiceHash Account by adding an additional security step of Two-factor authentication (hereinafter “2FA”) when logging into his account, withdrawing funds from his NiceHash Wallet or placing a new order. Account holder can enable this security feature in the settings of his NiceHash Account. In the event of losing or changing 2FA code, we may ask the Account holder to complete a KYC procedure for security reasons. This step serves solely for the purpose of identification in the process of reactivating Account holders 2FA and it may be subject to an a In order to use certain functionalities of the NiceHash Platform, such as paying for the acquired hashing power, users must deposit funds to the NiceHash Wallet, as the payments for the hashing power could be made only through NiceHash Wallet. Hashing power providers have two options to get paid for the provided hashing power: (i) by using NiceHash Wallet to receive the payments or (ii) by providing other Bitcoin address where the payments shall be received to. Hashing power providers provide their Bitcoin address to NiceHash by providing such details via Account holder’s profile settings or in a form of a stratum username while connecting to NiceHash stratum servers. Account holder may load funds on his NiceHash Wallet from his Secondary account. Account holder may be charged fees by the Secondary account provider or by the blockchain network for such transaction. NiceHash is not responsible for any fees charged by Secondary account providers or by the blockchain network or for the management and security of the Secondary accounts. Account holder is solely responsible for his use of Secondary accounts and Account holder agrees to comply with all terms and conditions applicable to any Secondary accounts. The timing associated with a load transaction will depend in part upon the performance of Secondary accounts providers, the performance of blockchain network and performance of the NiceHash Wallet provider. NiceHash makes no guarantee regarding the amount of time it may take to load funds on to NiceHash Wallet. NiceHash Wallet shall not be used by Account holders to keep, save and hold funds for longer period and also not for executing other transactions which are not related to the transactions regarding the NiceHash Platform. The NiceHash Wallet shall be used exclusively and only for current and ongoing transactions regarding the NiceHash Platform. Account holders shall promptly withdraw any funds kept on the NiceHash Wallet that will not be used and are not intended for the reasons described earlier. Commission fees may be charged by the NiceHash Wallet provider, by the blockchain network or by NiceHash for any NiceHash Wallet transactions. Please refer to the NiceHash Platform, for more information about the commission fees for NiceHash Wallet transactions which are applicable at the time of the transaction. NiceHash reserves the right to change these commission fees according to the provisions to change these Terms at any time for any reason. You have the right to use the NiceHash Account only in compliance with these Terms and other commercial terms and principles published on the NiceHash Platform. In particular, you must observe all regulations aimed at ensuring the security of funds and financial transactions. Provided that the balance of funds in your NiceHash Wallet is greater than any minimum balance requirements needed to satisfy any of your open orders, you may withdraw from your NiceHash Wallet any amount of funds, up to the total amount of funds in your NiceHash Wallet in excess of such minimum balance requirements, to Secondary Account, less any applicable withdrawal fees charged by NiceHash or by the blockchain network for such transaction. Withdrawals are not processed instantly and may be grouped with other withdrawal requests. Some withdrawals may require additional verification information which you will have to provide in order to process the withdrawal. It may take up to 24 hours before withdrawal is fully processed and distributed to the Blockchain network. Please refer to the NiceHash Platform for more information about the withdrawal fees and withdrawal processing. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. You have the right to close the NiceHash Account. In case you have funds on your NiceHash Wallet you should withdraw funds from your account prior to requesting NiceHash Account closure. After we receive your NiceHash Account closure request we will deactivate your NiceHash Account. You can read more about closing the NiceHash Account in our Privacy Policy. Your NiceHash Account may be deactivated due to your inactivity. Your NiceHash account may be locked and a mandatory KYC procedure is applied for security reasons, if it has been more than 6 month since your last login. NiceHash or any of its partners or affiliates are not responsible for the loss of the funds, stored on or transferred from the NiceHash Wallet, as well as for the erroneous implementation of the transactions made via NiceHash Wallet, where such loss or faulty implementation of the transaction are the result of a malfunction of the NiceHash Wallet and the malfunction was caused by you or the NiceHash Wallet provider. You are obliged to inform NiceHash in case of loss or theft, as well as in the case of any possible misuse of the access data to your NiceHash Account, without any delay, and demand change of access data or closure of your existing NiceHash Account and submit a request for new access data. NiceHash will execute the change of access data or closure of the NiceHash Account and the opening of new NiceHash Account as soon as technically possible and without any undue delay. All information pertaining to registration, including a registration form, generation of NiceHash Wallet and detailed instructions on the use of the NiceHash Account and NiceHash Wallet are available at NiceHash Platform. The registration form as well as the entire system is properly protected from unwanted interference by third parties. KYC PROCEDURE NiceHash is appropriately implementing AML/CTF and security measures to diligently detect and prevent any malicious or unlawful use of NiceHash Services or use, which is strictly prohibited by these Terms, which are deemed as your agreement to provide required personal information for identity verification. Security measures include a KYC procedure, which is aimed at determining the identity of an individual user or an organisation. We may ask you to complete this procedure before enabling some or all functionalities of the NiceHash platform and provide its services. A KYC procedure might be applied as a security measure when: changing the email address linked to your NiceHash Account, 
 losing or changing your 2FA code; 
 logging in to your NiceHash Account for the first time after the launch of the new NiceHash Platform in August 2019, 
 gaining access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof if they were disabled due to and 
 activating your NiceHash Account if it has been deactivated due to its inactivity and/or security or other reasons. 
 HASHING POWER TRANSACTIONS General NiceHash may, at any time and in our sole discretion, (i) refuse any order submitted or provided hashing power, (ii) cancel an order or part of the order before it is executed, (iii) impose limits on the order amount permitted or on provided hashing power or (iv) impose any other conditions or restrictions upon your use of the NiceHash Platform and Mining Services without prior notice. For example, but not limited to, NiceHash may limit the number of open orders that you may establish or limit the type of supported Hashing power rigs and mining algorithms or NiceHash may restrict submitting orders or providing hashing power from certain locations. Please refer to the NiceHash Platform, for more information about terminology, hashing power transactions’ definitions and descriptions, order types, order submission, order procedure, order rules and other restrictions and limitations of the hashing power transactions. NiceHash reserves the right to change any transaction, definitions, description, order types, procedure, rules, restrictions and limitations at any time for any reason. Orders, provision of hashing power, payments, deposits, withdrawals and other transactions are accepted only through the interface of the NiceHash Platform, NiceHash API and NiceHash Account and are fixed by the software and hardware tools of the NiceHash Platform. If you do not understand the meaning of any transaction option, NiceHash strongly encourages you not to utilize any of those options. Hashing Power Order In order to submit an Hashing Power Order via the NiceHash Account, the Hashing power buyer must have available funds in his NiceHash Wallet. Hashing power buyer submits a new order to buy hashing power via the NiceHash Platform or via the NiceHash API by setting the following parameters in the order form: NiceHash service server location, third-party mining pool, algorithm to use, order type, set amount he is willing to spend on this order, set price per hash he is willing to pay, optionally approximate limit maximum hashing power for his order and other parameters as requested and by confirming his order. Hashing power buyer may submit an order in maximum amount of funds available on his NiceHash Wallet at the time of order submission. Order run time is only approximate since order’s lifetime is based on the number of hashes that it delivers. Particularly during periods of high volume, illiquidity, fast movement or volatility in the marketplace for any digital assets or hashing power, the actual price per hash at which some of the orders are executed may be different from the prevailing price indicated on NiceHash Platform at the time of your order. You understand that NiceHash is not liable for any such price fluctuations. In the event of market disruption, NiceHash Services disruption, NiceHash Hashing Power Marketplace disruption or manipulation or Force Majeure Event, NiceHash may do one or more of the following: (i) suspend access to the NiceHash Account or NiceHash Platform, or (ii) prevent you from completing any actions in the NiceHash Account, including closing any open orders. Following any such event, when trading resumes, you acknowledge that prevailing market prices may differ significantly from the prices available prior to such event. When Hashing power buyer submits an order for purchasing of the Hashing power via NiceHash Platform or via the NiceHash API he authorizes NiceHash to execute the order on his behalf and for his account in accordance with such order. Hashing power buyer acknowledges and agrees that NiceHash is not acting as his broker, intermediary, agent or advisor or in any fiduciary capacity. NiceHash executes the order in set order amount minus NiceHash’s processing fee. Once the order is successfully submitted the order amount starts to decrease in real time according to the payments for the provided hashing power. Hashing power buyer agrees to pay applicable processing fee to NiceHash for provided services. The NiceHash’s fees are deducted from Hashing power buyer’s NiceHash Wallet once the whole order is exhausted and completed. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. All orders submitted prior the fee change but not necessary completed prior the fee change will be charged according to the fees applicable at the time of the submission of the order. NiceHash will attempt, on a commercially reasonable basis, to execute the Hashing power buyer’s purchase of the hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power buyer may cancel a submitted order during order’s lifetime. If an order has been partially executed, Hashing power buyer may cancel the unexecuted remainder of the order. In this case the NiceHash’s processing fee will apply only for the partially executed order. NiceHash reserves the right to refuse any order cancellation request once the order has been submitted. Selling Hashing Power and the Provision of Hashing Power In order to submit the hashing power to the NiceHash stratum server the Hashing power provider must first point its Hashing power rig to the NiceHash stratum server. Hashing power provider is solely responsible for configuration of his Hashing power rig. The Hashing power provider gets paid by Hashing power buyers for all validated and accepted work that his Hashing power rig has produced. The provided hashing power is validated by NiceHash’s stratum engine and validator. Once the hashing power is validated the Hashing power provider is entitled to receive the payment for his work. NiceHash logs all validated hashing power which was submitted by the Hashing power provider. The Hashing power provider receives the payments of current globally weighted average price on to his NiceHash Wallet or his selected personal Bitcoin address. The payments are made periodically depending on the height of payments. NiceHash reserves the right to hold the payments any time and for any reason by indicating the reason, especially if the payments represent smaller values. Please refer to the NiceHash Platform, for more information about the height of payments for provided hashing power, how the current globally weighted average price is calculated, payment periods, payment conditions and conditions for detention of payments. NiceHash reserves the right to change this payment policy according to the provisions to change these Terms at any time for any reason. All Hashing power rig’s earnings and profitability calculations on NiceHash Platform are only for informational purposes. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. You hereby acknowledge that it is possible that your Hashing power rigs would not be as profitable as indicated in our informational calculations or would not be profitable at all. Hashing power provider agrees to pay applicable processing fee to NiceHash for provided Services. The NiceHash’s fees are deducted from all the payments made to the Hashing power provider for his provided work. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. Hashing power provider which has not submitted any hashing power to the NiceHash stratum server for a period of 90 days agrees that a processing fee of 0.00001000 BTC or less, depending on the unpaid mining balance, will be deducted from his unpaid mining balance. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. NiceHash will attempt, on a commercially reasonable basis, to execute the provision of Hashing power providers’ hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power provider may disconnect the Hashing power rig from the NiceHash stratum server any time. NiceHash reserves the right to refuse any Hashing power rig once the Hashing power rig has been pointed towards NiceHash stratum server. RESTRICTIONS When accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet, you warrant and agree that you: will not use the Services for any purpose that is unlawful or prohibited by these Terms,
 will not violate any law, contract, intellectual property or other third-party right or commit a tort,
 are solely responsible for your conduct while accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet,
 will not access the NiceHash Platform or use the Mining Services in any manner that could damage, disable, overburden, or impair the provision of the Services or interfere with any other party's use and enjoyment of the Services, 
 will not misuse and/or maliciously use Hashing power rigs, you will particularly refrain from using network botnets or using NiceHash Platform or Mining Services with Hashing power rigs without the knowledge or awareness of Hashing power rig owner(s), 
 will not perform or attempt to perform any kind of malicious attacks on blockchains with the use of the NiceHash Platform or Mining Services, intended to maliciously gain control of more than 50% of the network's mining hash rate, 
 will not use the NiceHash Platform or Mining Services for any kind of market manipulation or disruption, such as but not limited to NiceHash Mining Services disruption and NiceHash Hashing Power Marketplace manipulation.
 In case of any of the above mentioned events, NiceHash reserves the right to immediately suspend your NiceHash Account, freeze or block the funds in the NiceHash Wallet, and suspend your access to NiceHash Platform, particularly if NiceHash believes that such NiceHash Account are in violation of these Terms or Privacy Policy, or any applicable laws and regulation. RIGHTS AND OBLIGATIONS In the event of disputes with you, NiceHash is obliged to prove that the NiceHash service which is the subject of the dispute was not influenced by technical or other failure. You will have possibility to check at any time, subject to technical availability, the transactions details, statistics and available balance of the funds held on the NiceHash Wallet, through access to the NiceHash Account. You may not obtain or attempt to obtain any materials or information through any means not intentionally made available or provided to you or public through the NiceHash Platform or Mining Services. We may, in our sole discretion, at any time, for any or no reason and without liability to you, with prior notice (i) terminate all rights and obligations between you and NiceHash derived from these Terms, (ii) suspend your access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof and delete or deactivate your NiceHash Account and all related information and files in such account (iii) modify, suspend or discontinue, temporarily or permanently, any portion of NiceHash Platform or (iv) provide enhancements or improvements to the features and functionality of the NiceHash Platform, which may include patches, bug fixes, updates, upgrades and other modifications. Any such change may modify or delete certain portion, features or functionalities of the NiceHash Services. You agree that NiceHash has no obligation to (i) provide any updates, or (ii) continue to provide or enable any particular portion, features or functionalities of the NiceHash Services to you. You further agree that all changes will be (i) deemed to constitute an integral part of the NiceHash Platform, and (ii) subject to these Terms. In the event of your breach of these Terms, including but not limited to, for instance, in the event that you breach any term of these Terms, due to legal grounds originating in anti-money laundering and know your client regulation and procedures, or any other relevant applicable regulation, all right and obligations between you and NiceHash derived from these Terms terminate automatically if you fail to comply with these Terms within the notice period of 8 days after you have been warned by NiceHash about the breach and given 8 days period to cure the breaches. NiceHash reserves the right to keep these rights and obligations in force despite your breach of these Terms. In the event of termination, NiceHash will attempt to return you any funds stored on your NiceHash Wallet not otherwise owed to NiceHash, unless NiceHash believes you have committed fraud, negligence or other misconduct. You acknowledge that the NiceHash Services and NiceHash Wallet may be suspended for maintenance. Technical information about the hashing power transactions, including information about chosen server locations, algorithms used, selected mining pools, your business or activities, including all financial and technical information, specifications, technology together with all details of prices, current transaction performance and future business strategy represent confidential information and trade secrets. NiceHash shall, preserve the confidentiality of all before mentioned information and shall not disclose or cause or permit to be disclosed without your permission any of these information to any person save to the extent that such disclosure is strictly to enable you to perform or comply with any of your obligations under these Terms, or to the extent that there is an irresistible legal requirement on you or NiceHash to do so; or where the information has come into the public domain otherwise than through a breach of any of the terms of these Terms. NiceHash shall not be entitled to make use of any of these confidential information and trade secrets other than during the continuance of and pursuant to these Terms and then only for the purpose of carrying out its obligations pursuant to these Terms. NICEHASH MINER LICENSE (NICEHASH MINING SOFTWARE LICENSE) NiceHash Mining Software whether on disk, in read only memory, or any other media or in any other form is licensed, not sold, to you by NiceHash for use only under these Terms. NiceHash retains ownership of the NiceHash Mining Software itself and reserves all rights not expressly granted to you. Subject to these Terms, you are granted a limited, non-transferable, non-exclusive and a revocable license to download, install and use the NiceHash Mining Software. You may not distribute or make the NiceHash Mining Software available over a network where it could be used by multiple devices at the same time. You may not rent, lease, lend, sell, redistribute, assign, sublicense host, outsource, disclose or otherwise commercially exploit the NiceHash Mining Software or make it available to any third party. There is no license fee for the NiceHash Mining Software. NiceHash reserves the right to change the license fee policy according to the provisions to change these Terms any time and for any reason, including to decide to start charging the license fee for the NiceHash Mining Software. You are responsible for any and all applicable taxes. You may not, and you agree not to or enable others to, copy, decompile, reverse engineer, reverse compile, disassemble, attempt to derive the source code of, decrypt, modify, or create derivative works of the NiceHash Mining Software or any services provided by the NiceHash Mining Software, or any part thereof (except as and only to the extent any foregoing restriction is prohibited by applicable law or to the extent as may be permitted by the licensing terms governing use of open-sourced components included with the NiceHash Mining Software). If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the NiceHash Mining Software and, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. You agree that NiceHash may collect and use technical and related information, including but not limited to technical information about your computer, system and application software, and peripherals, that is gathered periodically to facilitate the provision of software updates, product support and other services to you (if any) related to the NiceHash Mining Software and to verify compliance with these Terms. NiceHash may use this information, as long as it is in a form that does not personally identify you, to improve our NiceHash Services. NiceHash Mining Software contains features that rely upon information about your selected mining pools. You agree to our transmission, collection, maintenance, processing, and use of all information obtained from you about your selected mining pools. You can opt out at any time by going to settings in the NiceHash Mining Software. NiceHash may provide interest-based advertising to you. If you do not want to receive relevant ads in the NiceHash Mining Software, you can opt out at any time by going to settings in the NiceHash Mining Software. If you opt out, you will continue to receive the same number of ads, but they may be less relevant because they will not be based on your interest. NiceHash Mining Software license is effective until terminated. All provisions of these Terms regarding the termination apply also for the NiceHash Mining Software license. Upon the termination of NiceHash Mining Software license, you shall cease all use of the NiceHash Mining Software and destroy or delete all copies, full or partial, of the NiceHash Mining Software. THIRD PARTY MINERS AND PLUGINS Third Party Miners and Plugins are a third party software which enables the best and most efficient mining operations. NiceHash Miner integrates third party mining software using a third party miner plugin system. Third Party Mining Software is a closed source software which supports mining algorithms for cryptocurrencies and can be integrated into NiceHash Mining Software. Third Party Miner Plugin enables the connection between NiceHash Mining Software and Third Party Mining Software and it can be closed, as well as open sourced. NiceHash Mining Software user interface enables the user to manually select which available Third Party Miners and Plugins will be downloaded and integrated. Users can select or deselect Third Party Miners and Plugins found in the Plugin Manager window. Some of the available Third Party Miners and Plugins which are most common are preselected by NiceHash, but can be deselected, depending on users' needs. The details of the Third Party Miners and Plugins available for NiceHash Mining Software are accessible within the NiceHash Mining Software user interface. The details include, but not limited to, the author of the software and applicable license information, if applicable information about developer fee for Third Party Miners, software version etc. Developer fees may apply to the use of Third Party Miners and Plugins. NiceHash will not be liable, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages, arising out of using Third Party Miners and Plugins. The latter includes, but is not limited to: i) any power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance; ii) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with usage of Third Party Miners and Plugins and ii) Force Majeure Event, communications failure, theft or other interruptions. If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the installed Third Party Miners and Plugins, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. NICEHASH QUICKMINER NiceHash QuickMiner is a software application that allows the visitors of the NiceHash Quick Miner web page, accessible athttps://www.nicehash.com/quick-miner, to connect their PC or a mining rig to the NiceHash Hashing Power Marketplace. Visitors of the NiceHash Quick Miner web page can try out and experience crypto currency mining without having to register on the NiceHash Platform and create a NiceHash Account. Users are encouraged to do so as soon as possible in order to collect the funds earned using NiceHash Quick Miner. Users can download NiceHash QuickMiner free of charge. In order to operate NiceHash QuickMiner software needs to automatically detect technical information about users' computer hardware. You agree that NiceHash may collect and use technical and related information. For more information please refer to NiceHash Privacy Policy. Funds arising from the usage of NiceHash QuickMiner are transferred to a dedicated cryptocurrency wallet owned and managed by NiceHash. NiceHash QuickMiner Users expressly agree and acknowledge that completing the registration process and creating a NiceHash Account is necessary in order to collect the funds arising from the usage of NiceHash QuickMiner. Users of NiceHash QuickMiner who do not successfully register a NiceHash Account will lose their right to claim funds arising from their usage of NiceHash QuickMiner. Those funds, in addition to the condition that the user has not been active on the NiceHash QuickMiner web page for consecutive 7 days, will be donated to the charity of choice. NICEHASH PRIVATE ENDPOINT NiceHash Private Endpoint is a network interface that connects users privately and securely to NiceHash Stratum servers. Private Endpoint uses a private IP address and avoids additional latency caused by DDOS protection. All NiceHash Private Mining Proxy servers are managed by NiceHash and kept up-to-date. Users can request a dedicated private access endpoint by filling in the form for NiceHash Private Endpoint Solution available at the NiceHash Platform. In the form the user specifies the email address, country, number of connections and locations and algorithms used. Based on the request NiceHash prepares an individualized offer based on the pricing stipulated on the NiceHash Platform, available at https://www.nicehash.com/private-endpoint-solution. NiceHash may request additional information from the users of the Private Endpoint Solution in order to determine whether we are obligated to collect VAT from you, including your VAT identification number. INTELLECTUAL PROPERTY NiceHash retains all copyright and other intellectual property rights, including inventions, discoveries, knowhow, processes, marks, methods, compositions, formulae, techniques, information and data, whether or not patentable, copyrightable or protectable in trademark, and any trademarks, copyrights or patents based thereon over all content and other materials contained on NiceHash Platform or provided in connection with the Services, including, without limitation, the NiceHash logo and all designs, text, graphics, pictures, information, data, software, source code, as well as the compilation thereof, sound files, other files and the selection and arrangement thereof. This material is protected by international copyright laws and other intellectual property right laws, namely trademark. These Terms shall not be understood and interpreted in a way that they would mean assignment of copyright or other intellectual property rights, unless it is explicitly defined so in these Terms. NiceHash hereby grants you a limited, nonexclusive and non-sublicensable license to access and use NiceHash’s copyrighted work and other intellectual property for your personal or internal business use. Such license is subject to these Terms and does not permit any resale, the distribution, public performance or public display, modifying or otherwise making any derivative uses, use, publishing, transmission, reverse engineering, participation in the transfer or sale, or any way exploit any of the copyrighted work and other intellectual property other than for their intended purposes. This granted license will automatically terminate if NiceHash suspends or terminates your access to the Services, NiceHash Wallet or closes your NiceHash Account. NiceHash will own exclusive rights, including all intellectual property rights, to any feedback including, but not limited to, suggestions, ideas or other information or materials regarding NiceHash Services or related products that you provide, whether by email, posting through our NiceHash Platform, NiceHash Account or otherwise and you irrevocably assign any and all intellectual property rights on such feedback unlimited in time, scope and territory. Any Feedback you submit is non-confidential and shall become the sole property of NiceHash. NiceHash will be entitled to the unrestricted use, modification or dissemination of such feedback for any purpose, commercial or otherwise, without acknowledgment or compensation to you. You waive any rights you may have to the feedback. We have the right to remove any posting you make on NiceHash Platform if, in our opinion, your post does not comply with the content standards defined by these Terms. PRIVACY POLICY Please refer to our NiceHash Platform and Mining Services Privacy Policy published on the NiceHash Platform for information about how we collect, use and share your information, as well as what options do you have with regards to your personal information. COMMUNICATION AND SUPPORT You agree and consent to receive electronically all communications, agreements, documents, receipts, notices and disclosures that NiceHash provides in connection with your NiceHash Account or use of the NiceHash Platform and Services. You agree that NiceHash may provide these communications to you by posting them via the NiceHash Account or by emailing them to you at the email address you provide. You should maintain copies of electronic communications by printing a paper copy or saving an electronic copy. It is your responsibility to keep your email address updated in the NiceHash Account so that NiceHash can communicate with you electronically. You understand and agree that if NiceHash sends you an electronic communication but you do not receive it because your email address is incorrect, out of date, blocked by your service provider, or you are otherwise unable to receive electronic communications, it will be deemed that you have been provided with the communication. You can update your NiceHash Account preferences at any time by logging into your NiceHash Account. If your email address becomes invalid such that electronic communications sent to you by NiceHash are returned, NiceHash may deem your account to be inactive and close it. You may give NiceHash a notice under these Terms by sending an email to support@nicehash.com or contact NiceHash through support located on the NiceHash Platform. All communication and notices pursuant to these Terms must be given in English language. FEES Please refer to the NiceHash Platform for more information about the fees or administrative costs which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the Services provided after the change of the fees. You authorize us, or our designated payment processor, to charge or deduct your NiceHash Account for any applicable fees in connection with the transactions completed via the Services. TAX It is your responsibility to determine what, if any, taxes apply to the transactions you complete or services you provide via the NiceHash Platform, Mining Services and NiceHash Wallet, it is your responsibility to report and remit the correct tax to the appropriate tax authority and all your factual and potential tax obligations are your concern. You agree that NiceHash is not in any case and under no conditions responsible for determining whether taxes apply to your transactions or services or for collecting, reporting, withholding or remitting any taxes arising from any transactions or services. You also agree that NiceHash is not in any case and under no conditions bound to compensate for your tax obligation or give you any advice related to tax issues. All fees and charges payable by you to NiceHash are exclusive of any taxes, and shall certain taxes be applicable, they shall be added on top of the payable amounts. Upon our request, you will provide to us any information that we reasonably request to determine whether we are obligated to collect VAT from you, including your VAT identification number. If any deduction or withholding is required by law, you will notify NiceHash and will pay NiceHash any additional amounts necessary to ensure that the net amount received by NiceHash, after any deduction and withholding, equals the amount NiceHash would have received if no deduction or withholding had been required. Additionally, you will provide NiceHash with documentation showing that the withheld and deducted amounts have been paid to the relevant taxing authority. FINAL PROVISIONS Natural persons and legal entities that are not capable of holding legal rights and obligations are not allowed to create NiceHash Account and use NiceHash Platform or other related services. If NiceHash becomes aware that such natural person or legal entity has created the NiceHash Account or has used NiceHash Services, NiceHash will delete such NiceHash Account and disable any Services and block access to NiceHash Account and NiceHash Services to such natural person or legal entity. If you register to use the NiceHash Services on behalf of a legal entity, you represent and warrant that (i) such legal entity is duly organized and validly existing under the applicable laws of the jurisdiction of its organization; and (ii) you are duly authorized by such legal entity to act on its behalf. These Terms do not create any third-party beneficiary rights in any individual or entity. These Terms forms the entire agreement and understanding relating to the subject matter hereof and supersede any previous and contemporaneous agreements, arrangements or understandings relating to the subject matter hereof to the exclusion of any terms implied by law that may be excluded by contract. If at any time any provision of these Terms is or becomes illegal, invalid or unenforceable, the legality, validity and enforceability of every other provisions will not in any way be impaired. Such illegal, invalid or unenforceable provision of these Terms shall be deemed to be modified and replaced by such legal, valid and enforceable provision or arrangement, which corresponds as closely as possible to our and your will and business purpose pursued and reflected in these Terms. Headings of sections are for convenience only and shall not be used to limit or construe such sections. No failure to enforce nor delay in enforcing, on our side to the Terms, any right or legal remedy shall function as a waiver thereof, nor shall any individual or partial exercise of any right or legal remedy prevent any further or other enforcement of these rights or legal remedies or the enforcement of any other rights or legal remedies. NiceHash reserves the right to make changes, amendments, supplementations or modifications from time to time to these Terms including but not limited to changes of licence agreement for NiceHash Mining Software and of any fees and compensations policies, in its sole discretion and for any reason. We suggest that you review these Terms periodically for changes. If we make changes to these Terms, we will provide you with notice of such changes, such as by sending an email, providing notice on the NiceHash Platform, placing a popup window after login to the NiceHash Account or by posting the amended Terms on the NiceHash Platform and updating the date at the top of these Terms. The amended Terms will be deemed effective immediately upon posting for any new users of the NiceHash Services. In all other cases, the amended Terms will become effective for preexisting users upon the earlier of either: (i) the date users click or press a button to accept such changes in their NiceHash Account, or (ii) continued use of NiceHash Services 30 days after NiceHash provides notice of such changes. Any amended Terms will apply prospectively to use of the NiceHash Services after such changes become effective. The notice of change of these Terms is considered as notice of termination of all rights and obligations between you and NiceHash derived from these Terms with notice period of 30 days, if you do not accept the amended Terms. If you do not agree to any amended Terms, (i) the agreement between you and NiceHash is terminated by expiry of 30 days period which starts after NiceHash provides you a notice of change of these Terms, (ii) you must discontinue using NiceHash Services and (iii) you must inform us regarding your disagreement with the changes and request closure of your NiceHash Account. If you do not inform us regarding your disagreement and do not request closure of you NiceHash Account, we will deem that you agree with the changed Terms. You may not assign or transfer your rights or obligations under these Terms without the prior written consent of NiceHash. NiceHash may assign or transfer any or all of its rights under these Terms, in whole or in part, without obtaining your consent or approval. These Terms shall be governed by and construed and enforced in accordance with the Laws of the British Virgin Islands, and shall be interpreted in all respects as a British Virgin Islands contract. Any transaction, dispute, controversy, claim or action arising from or related to your access or use of the NiceHash Platform or these Terms of Service likewise shall be governed by the Laws of the British Virgin Islands, exclusive of choice-of-law principles. The rights and remedies conferred on NiceHash by, or pursuant to, these Terms are cumulative and are in addition, and without prejudice, to all other rights and remedies otherwise available to NiceHash at law. NiceHash may transfer its rights and obligations under these Terms to other entities which include, but are not limited to H-BIT, d.o.o. and NICEX Ltd, or any other firm or business entity that directly or indirectly acquires all or substantially all of the assets or business of NICEHASH Ltd. If you do not consent to any transfer, you may terminate this agreement and close your NiceHash Account. These Terms are not boilerplate. If you disagree with any of them, believe that any should not apply to you, or wish to negotiate these Terms, please contact NiceHash and immediately navigate away from the NiceHash Platform. Do not use the NiceHash Mining Services, NiceHash Wallet or other related services until you and NiceHash have agreed upon new terms of service. Last updated: March 1, 2021

SEIS 665 Assignment 2: Linux & Git Overview This week we will focus on becoming familiar with launching a Linux server and working with some basic Linux and Git commands. We will use AWS to launch and host the Linux server. AWS might seem a little confusing at this point. Don’t worry, we will gain much more hands-on experience with AWS throughout the course. The goal is to get you comfortable working with the technology and not overwhelm you with all the details. Requirements You need to have a personal AWS account and GitHub account for this assignment. You should also read the Git Hands-on Guide and Linux Hands-on Guide before beginning this exercise. A word about grading One of the key DevOps practices we learn about in this class is the use of automation to increase the speed and repeatability of processes. Automation is utilized during the assignment grading process to review and assess your work. It’s important that you follow the instructions in each assignment and type in required files and resources with the proper names. All names are case sensitive, so a name like "Web1" is not the same as "web1". If you misspell a name, use the wrong case, or put a file in the wrong directory location you will lose points on your assignment. This is the easiest way to lose points, and also the most preventable. You should always double-check your work to make sure it accurately reflects the requirements specified in the assignment. You should always carefully review the content of your files before submitting your assignment. The assignment Let’s get started! Create GitHub repository The first step in the assignment is to setup a Git repository on GitHub. We will use a special solution called GitHub Classroom for this course which automates the process of setting up student assignment repositories. Here are the basic steps: Click on the following link to open Assignment 2 on the GitHub Classroom site: https://classroom.github.com/a/K4zcVmX- (Links to an external site.)Links to an external site. Click on the Accept this assignment button. GitHub Classroom will provide you with a URL (https) to access the assignment repository. Either copy this address to your clipboard or write it down somewhere. You will need to use this address to set up the repository on a Linux server. Example: https://github.com/UST-SEIS665/hw2-seis665-02-spring2019-<your github id>.git At this point your new repository to ready to use. The repository is currently empty. We will put some content in there soon! Launch Linux server The second step in the assignment is to launch a Linux server using AWS EC2. The server should have the following characteristics: Amazon Linux 2 AMI 64-bit (usually the first option listed) Located in a U.S. region (us-east-1) t2.micro instance type All default instance settings (storage, vpm, security group, etc.) I’ve shown you how to launch EC2 instances in class. You can review it on Canvas. Once you launch the new server, it may take a few minutes to provision. Log into server The next step is to log into the Linux server using a terminal program with a secure shell (SSH) support. You can use iTerm2 (Links to an external site.)Links to an external site. on a Mac and GitBash/PuTTY (Links to an external site.)Links to an external site. on a PC. You will need to have the private server key and the public IP address before attempting to log into the server. The server key is basically your password. If you lose it, you will need to terminate the existing instance and launch a new server. I recommend reusing the same key when launching new servers throughout the class. Note, I make this recommendation to make the learning process easier and not because it is a common security practice. I’ve shown you how to use a terminal application to log into the instance using a Windows desktop. Your personal computer or lab computer may be running a different OS version, but the process is still very similar. You can review the videos on the Canvas. Working with Linux If you’ve made it this far, congratulations! You’ve made it over the toughest hurdle. By the end of this course, I promise you will be able to launch and log into servers in your sleep. You should be looking at a login screen that looks something like this: Last login: Mon Mar 21 21:17:54 2016 from 174-20-199-194.mpls.qwest.net __| __|_ ) _| ( / Amazon Linux AMI ___|\___|___| https://aws.amazon.com/amazon-linux-ami/2015.09-release-notes/ 8 package(s) needed for security, out of 17 available Run "sudo yum update" to apply all updates. ec2-user@ip-172-31-15-26 ~]$ Your terminal cursor is sitting at the shell prompt, waiting for you to type in your first command. Remember the shell? It is a really cool program that lets you start other programs and manage services on the Linux system. The rest of this assignment will be spent working with the shell. Note, when you are asked to type in a command in the steps below, don’t type in the dollar-sign ($) character. This is just meant to represent the command prompt. The actual commands are represented by the characters to the right of the command prompt. Let’s start by asking the shell for some help. Type in: $ help The shell provides you with a list of commands you can run along with possible command options. Next, check out one of the pages in the built-in manual: $ man ls A man page will appear with information on how to use the ls command. This command is used to list the contents of file directories. Either space through the contents of the man page or hit q to exit. Most of the core Linux commands have man pages available. But honestly, some of these man pages are a bit hard to understand. Sometimes your best bet is to search on Google if you are trying to figure out how to use a specific command. When you initially log into Linux, the system places you in your home directory. Each user on the system has a separate home directory. Let’s see where your home directory is located: $ pwd The response should be /home/ec2-user. The pwd command is handy to remember if you ever forget what file directory you are currently located in. If you recall from the Linux Hands-on Guide, this directory is also your current working directory. Type in: $ cd / The cd command let’s you change to a new working directory on the server. In this case, we changed to the root (/) directory. This is the parent of all the other directories on the file system. Type in: $ ls The ls command lists the contents of the current directory. As you can see, root directory contains many other directories. You will become familiar with these directories over time. The ls command provides a very basic directory listing. You need to supply the command with some options if you want to see more detailed information. Type in: $ ls -la See how this command provides you with much more detailed information about the files and directories? You can use this detailed listing to see the owner, group, and access control list settings for each file or directory. Do you see any files listed? Remember, the first character in the access control list column denotes whether a listed item is a file or a directory. You probably see a couple files with names like .autofsck. How come you didn’t see this file when you typed in the lscommand without any options? (Try to run this command again to convince yourself.) Files names that start with a period are called hidden files. These files won’t appear on normal directory listings. Type in: $ cd /var Then, type in: $ ls You will see a directory listing for the /var directory. Next, type in: $ ls .. Huh. This directory listing looks the same as the earlier root directory listing. When you use two periods (..) in a directory path that means you are referring to the parent directory of the current directory. Just think of the two dots as meaning the directory above the current directory. Now, type in: $ cd ~ $ pwd Whoa. We’re back at our home directory again. The tilde character (~) is another one of those handy little directory path shortcuts. It always refers to our personal home directory. Keep in mind that since every user has their own home directory, the tilde shortcut will refer to a unique directory for each logged-in user. Most students are used to navigating a file system by clicking a mouse in nested graphical folders. When they start using a command-line to navigate a file system, they sometimes get confused and lose track of their current position in the file system. Remember, you can always use the pwd command to quickly figure out what directory you are currently working in. Let’s make some changes to the file system. We can easily make our own directories on the file system. Type: mkdir test Now type: ls Cool, there’s our new test directory. Let’s pretend we don’t like that directory name and delete it. Type: rmdir test Now it’s gone. How can you be sure? You should know how to check to see if the directory still exists at this point. Go ahead and check. Let’s create another directory. Type in: $ mkdir documents Next, change to the new directory: $ cd documents Did you notice that your command prompt displays the name of the current directory? Something like: [ec2-user@ip-172-31-15-26 documents]$. Pretty handy, huh? Okay, let’s create our first file in the documents directory. This is just an empty file for training purposes. Type in: $ touch paper.txt Check to see that the new file is in the directory. Now, go back to the previous directory. Remember the double dot shortcut? $ cd .. Okay, we don’t like our documents directory any more. Let’s blow it away. Type in: $ rmdir documents Uh oh. The shell didn’t like that command because the directory isn’t empty. Let’s change back into the documents directory. But this time don’t type in the full name of the directory. You can let shell auto-completion do the typing for you. Type in the first couple characters of the directory name and then hit the tab key: $ cd doc<tab> You should use the tab auto-completion feature often. It saves typing and makes working with the Linux file system much much easier. Tab is your friend. Now, remove the file by typing: $ rm paper.txt Did you try to use the tab key instead of typing in the whole file name? Check to make sure the file was deleted from the directory. Next, create a new file: $ touch file1 We like file1 so much that we want to make a backup copy. Type: $ cp file1 file1-backup Check to make sure the new backup copy was created. We don’t really like the name of that new file, so let’s rename it. Type: $ mv file1-backup backup Moving a file to the same directory and giving it a new name is basically the same thing as renaming it. We could have moved it to a different directory if we wanted. Let’s list all of the files in the current directory that start with the letter f: $ ls f* Using wildcard pattern matching in file commands is really useful if you want the command to impact or filter a group of files. Now, go up one directory to the parent directory (remember the double dot shortcut?) We tried to remove the documents directory earlier when it had files in it. Obviously that won’t work again. However, we can use a more powerful command to destroy the directory and vanquish its contents. Behold, the all powerful remove command: $ rm -fr documents Did you remember to use auto-completion when typing in documents? This command and set of options forcibly removes the directory and its contents. It’s a dangerous command wielded by the mightiest Linux wizards. Okay, maybe that’s a bit of an exaggeration. Just be careful with it. Check to make sure the documents directory is gone before proceeding. Let’s continue. Change to the directory /var and make a directory called test. Ugh. Permission denied. We created this darn Linux server and we paid for it. Shouldn’t we be able to do anything we want on it? You logged into the system as a user called ec2-user. While this user can create and manage files in its home directory, it cannot change files all across the system. At least it can’t as a normal user. The ec2-user is a member of the root group, so it can escalate its privileges to super-user status when necessary. Let’s try it: $ sudo mkdir test Check to make sure the directory exists now. Using sudo we can execute commands as a super-user. We can do anything we want now that we know this powerful new command. Go ahead and delete the test directory. Did you remember to use sudo before the rmdir command? Check to make sure the directory is gone. You might be asking yourself the question: why can we list the contents of the /var directory but not make changes? That’s because all users have read access to the /var directory and the ls command is a read function. Only the root users or those acting as a super-user can write changes to the directory. Let’s go back to our home directory: $ cd ~ Editing text files is a really common task on Linux systems because many of the application configuration files are text files. We can create a text file by using a text editor. Type in: $ nano myfile.conf The shell starts up the nano text editor and places your terminal cursor in the editing screen. Nano is a simple text-based word processor. Type in a few lines of text. When you’re done writing your novel, hit ctrl-x and answer y to the prompt to save your work. Finally, hit enter to save the text to the filename you specified. Check to see that your file was saved in the directory. You can take a look at the contents of your file by typing: $ cat myfile.conf The cat command displays your text file content on the terminal screen. This command works fine for displaying small text files. But if your file is hundreds of lines long, the content will scroll down your terminal screen so fast that you won’t be able to easily read it. There’s a better way to view larger text files. Type in: $ less myfile.conf The less command will page the display of a text file, allowing you to page through the contents of the file using the space bar. Your text file is probably too short to see the paging in action though. Hit q to quit out of the less text viewer. Hit the up-arrow key on your keyboard a few times until the commmand nano myfile.conf appears next to your command prompt. Cool, huh? The up-arrow key allows you to replay a previously run command. Linux maintains a list of all the commands you have run since you logged into the server. This is called the command history. It’s a really useful feature if you have to re-run a complex command again. Now, hit ctrl-c. This cancels whatever command is displayed on the command line. Type in the following command to create a couple empty files in the directory: $ touch file1 file2 file3 Confirm that the files were created. Some commands, like touch. allow you to specify multiple files as arguments. You will find that Linux commands have all kinds of ways to make tasks more efficient like this. Throughout this assignment, we have been running commands and viewing results on the terminal screen. The screen is the standard place for commands to output results. It’s known as the standard out (stdout). However, it’s really useful to output results to the file system sometimes. Type in: $ ls > listing.txt Take a look at the directory listing now. You just created a new file. View the contents of the listing.txt file. What do you see? Instead of sending the output from the ls command to the screen we sent it to a text file. Let’s try another one. Type: $ cat myfile.conf > listing.txt Take a look at the contents of the listing.txt file again. It looks like your myfile.conf file now. It’s like you made a copy of it. But what happened to the previous content in the listing.txt file? When you redirect the output of a command using the right angle-bracket character (>), the output overwrites the existing file. Type this command in: $ cat myfile.conf >> listing.txt Now look at the contents of the listing.txt file. You should see your original content displayed twice. When you use two angle-bracket characters in the commmand the output appends (or adds to) the file instead of overwriting it. We redirected the output from a command to a text file. It’s also possible to redirect the input to a command. Typically we use a keyboard to provide input, but sometimes it makes more sense to input a file to a command. For example, how many words are in your new listing.txt file? Let’s find out. Type in: $ wc -w < listing.txt Did you get a number? This command inputs the listing.txt file into a word count program called wc. Type in the command: $ ls /usr/bin The terminal screen probably scrolled quickly as filenames flashed by. The /usr/bin directory holds quite a few files. It would be nice if we could page through the contents of this directory. Well, we can. We can use a special shell feature called pipes. In previous steps, we redirected I/O using the file system. Pipes allow us to redirect I/O between programs. We can redirect the output from one program into another. Type in: $ ls /usr/bin | less Now the directory listing is paged. Hit the spacebar to page through the listing. The pipe, represented by a vertical bar character (|), takes the output from the ls command and redirects it to the less command where the resulting output is paged. Pipes are super powerful and used all the time by savvy Linux operators. Hit the q key to quit the paginated directory listing command. Working with shell scripts Now things are going to get interesting. We’ve been manually typing in commands throughout this exercise. If we were running a set of repetitive tasks, we would want to automate the process as much as possible. The shell makes it really easy to automate tasks using shell scripts. The shell provides many of the same features as a basic procedural programming language. Let’s write some code. Type in this command: $ j=123 $ echo $j We just created a variable named j referencing the string 123. The echo command printed out the value of the variable. We had to use a dollar sign ($) when referencing the variable in another command. Next, type in: $ j=1+1 $ echo $j Is that what you expected? The shell just interprets the variable value as a string. It’s not going to do any sort of computation. Typing in shell script commands on the command line is sort of pointless. We want to be able to create scripts that we can run over-and-over. Let’s create our first shell script. Use the nano editor to create a file named myscript. When the file is open in the editor, type in the following lines of code: #!/bin/bash echo Hello $1 Now quit the editor and save your file. We can run our script by typing: $ ./myscript World Er, what happened? Permission denied. Didn’t we create this file? Why can’t we run it? We can’t run the script file because we haven’t set the execute permission on the file. Type in: $ chmod u+x myscript This modifies the file access control list to allow the owner of the file to execute it. Let’s try to run the command again. Hit the up-arrow key a couple times until the ./myscript World command is displayed and hit enter. Hooray! Our first shell script. It’s probably a bit underwhelming. No problem, we’ll make it a little more complex. The script took a single argument called World. Any arguments provided to a shell script are represented as consecutively numbered variables inside the script ($1, $2, etc). Pretty simple. You might be wondering why we had to type the ./ characters before the name of our script file. Try to type in the command without them: $ myscript World Command not found. That seems a little weird. Aren’t we currently in the directory where the shell script is located? Well, that’s just not how the shell works. When you enter a command into the shell, it looks for the command in a predefined set of directories on the server called your PATH. Since your script file isn’t in your special path, the shell reports it as not found. By typing in the ./ characters before the command name you are basically forcing the shell to look for your script in the current directory instead of the default path. Create another file called cleanup using nano. In the file editor window type: #!/bin/bash # My cleanup script mkdir archive mv file* archive Exit the editor window and save the file. Change the permissions on the script file so that you can execute it. Now run the command: $ ./cleanup Take a look at the file directory listing. Notice the archive directory? List the contents of that directory. The script automatically created a new directory and moved three files into it. Anything you can do manually at a command prompt can be automated using a shell script. Let’s create one more shell script. Use nano to create a script called namelist. Here is the content of the script: #!/bin/bash # for-loop test script names='Jason John Jane' for i in $names do echo Hello $i done Change the permissions on the script file so that you can execute it. Run the command: $ ./namelist The script will loop through a set of names stored in a variable displaying each one. Scripts support several programming constructs like for-loops, do-while loops, and if-then-else. These building blocks allow you to create fairly complex scripts for automating tasks. Installing packages and services We’re nearing the end of this assignment. But before we finish, let’s install some new software packages on our server. The first thing we should do is make sure all the current packages installed on our Linux server are up-to-date. Type in: $ sudo yum update -y This is one of those really powerful commands that requires sudo access. The system will review the currently installed packages and go out to the Internet and download appropriate updates. Next, let’s install an Apache web server on our system. Type in: $ sudo yum install httpd -y Bam! You probably never knew that installing a web server was so easy. We’re not going to actually use the web server in this exercise, but we will in future assignments. We installed the web server, but is it actually running? Let’s check. Type in: $ sudo service httpd status Nope. Let’s start it. Type: $ sudo service httpd start We can use the service command to control the services running on the system. Let’s setup the service so that it automatically starts when the system boots up. Type in: $ sudo chkconfig httpd on Cool. We installed the Apache web server on our system, but what other programs are currently running? We can use the pscommand to find out. Type in: $ ps -ax Lots of processes are running on our system. We can even look at the overall performance of our system using the topcommand. Let’s try that now. Type in: $ top The display might seem a little overwhelming at first. You should see lots of performance information displayed including the cpu usage, free memory, and a list of running tasks. We’re almost across the finish line. Let’s make sure all of our valuable work is stored in a git repository. First, we need to install git. Type in the command: $ sudo yum install git -y Check your work It’s very important to check your work before submitting it for grading. A misspelled, misplaced or missing file will cost you points. This may seem harsh, but the reality is that these sorts of mistakes have consequences in the real world. For example, a server instance could fail to launch properly and impact customers because a single required file is missing. Here is what the contents of your git repository should look like before final submission: ┣archive ┃ ┣ file1 ┃ ┣ file2 ┃ ┗ file3 ┣ namelist ┗ myfile.conf Saving our work in the git repository Next, make sure you are still in your home directory (/home/ec2-user). We will install the git repository you created at the beginning of this exercise. You will need to modify this command by typing in the GitHub repository URL you copied earlier. $ git clone <your GitHub URL here>.git Example: git clone https://github.com/UST-SEIS665/hw2-seis665-02-spring2019-<your github id>.git The git application will ask you for your GitHub username and password. Note, if you have multi-factor authentication enabled on your GitHub account you will need to provide a personal token instead of your password. Git will clone (copy) the repository from GitHub to your Linux server. Since the repository is empty the clone happens almost instantly. Check to make sure that a sub-directory called "hw2-seis665-02-spring2019-<username>" exists in the current directory (where <username> is your GitHub account name). Git automatically created this directory as part of the cloning process. Change to the hw2-seis665-02-spring2019-<username> directory and type: $ ls -la Notice the .git hidden directory? This is where git actually stores all of the file changes in your repository. Nothing is actually in your repository yet. Change back to the parent directory (cd ..). Next, let’s move some of our files into the repository. Type: $ mv archive hw2-seis665-02-spring2019-<username> $ mv namelist hw2-seis665-02-spring2019-<username> $ mv myfile.conf hw2-seis665-02-spring2019-<username> Hopefully, you remembered to use the auto-complete function to reduce some of that typing. Change to the hw2-seis665-02-spring2019-<username> directory and list the directory contents. Your files are in the working directory, but are not actually stored in the repository because they haven’t been committed yet. Type in: $ git status You should see a list of untracked files. Let’s tell git that we want these files tracked. Type in: $ git add * Now type in the git status command again. Notice how all the files are now being tracked and are ready to be committed. These files are in the git staging area. We’ll commit them to the repository next. Type: $ git commit -m 'assignment 2 files' Next, take a look at the commit log. Type: $ git log You should see your commit listed along with an assigned hash (long string of random-looking characters). Finally, let’s save the repository to our GitHub account. Type in: $ git push origin master The git client will ask you for your GitHub username and password before pushing the repository. Go back to the GitHub.com website and login if you have been logged out. Click on the repository link for the assignment. Do you see your files listed there? Congratulations, you completed the exercise! Terminate server The last step is to terminate your Linux instance. AWS will bill you for every hour the instance is running. The cost is nominal, but there’s no need to rack up unnecessary charges. Here are the steps to terminate your instance: Log into your AWS account and click on the EC2 dashboard. Click the Instances menu item. Select your server in the instances table. Click on the Actions drop down menu above the instances table. Select the Instance State menu option Click on the Terminate action. Your Linux instance will shutdown and disappear in a few minutes. The EC2 dashboard will continue to display the instance on your instance listing for another day or so. However, the state of the instance will be terminated. Submitting your assignment — IMPORTANT! If you haven’t already, please e-mail me your GitHub username in order to receive credit for this assignment. There is no need to email me to tell me that you have committed your work to GitHub or to ask me if your GitHub submission worked. If you can see your work in your GitHub repository, I can see your work.

Project name : College Management Portal Description : Developed CMS for college which manages the college smartly. Technology used : MySQL, PHP, HTML5, CSS3, Bootstrap College Management System deals with all kind of student details, academic related reports, college details, course details, curriculum, batch details and other resource related details too. It tracks all the details of a student from the day one to the end of his course which can be used for all reporting purpose, tracking of attendance, progress in the course, completed semesters years, coming semester year curriculum details, exam details, project or any other assignment details, final exam result; and all these will be available for future references too.Our program will have the databases of Courses offered by the college under all levels of graduation or main streams, teacher or faculty's details, batch execution details, students' details in all aspects.This program can facilitate us explore all the activities happening in the college, even we can get to know which teacher / faculty is assigned to which batch, the current status of a batch, attendance percentage of a batch and upcoming requirements of a batch. Different reports and Queries can be generated based of vast options related to students, batch, course, teacher / faculty, exams, semesters, certification and even for the entire college. The College management system is an automated version of manual Student Management System. It can handle all details about a student. The details include college details, subject details, student personnel details, academic details, exam details etc... In case of manual system they need a lot of time, manpower etc.Here almost all work is computerized. So the accuracy is maintained. Maintaining backup is very easy. It can do with in a few minutes. Our system has two type of accessing modes, administrator and user. Student management system is managed by an administrator. It is the job of the administrator to insert update and monitor the whole process. When a user log in to the system. He would only view details of the student. He can't perform any changes .

The aim of this assignment is to have you do TCP socket client / server programming using I/O multiplexing, child processes and threads. It also aims at getting you to familiarize yourselves with the inetd superserver daemon, the ‘exec’ family of functions, various socket error scenarios, some socket options, and some basic domain name / IP address conversion functions. Apart from the material in Chapters 1 to 6 covered in class, you will also need to refer to the following : the exec family of functions (Section 4.7 of Chapter 4) using pipes for interprocess communication (IPC) in Unix error scenarios induced by process terminations & host crashes (Sections 5.11 to 5.16, Chapter 5) setsockopt function & SO_REUSEADDR socket option (Section 7.2 & pp.210-213, Chapter 7) gethostbyname & gethostbyaddr functions (Sections 11.3 & 11.4, Chapter 11) the basic structure of inetd (Section 13.5, Chapter 13) programming with threads (Sections 26.1 to 26.5, Chapter 26) Overview I shall present an overview of this assignment and discuss some of the specification details given below in class on Wednesday, September 17 & Monday, September 22. Client The client is evoked with a command line argument giving either the server IP address in dotted decimal notation, or the server domain name. The client has to be able to handle either mode and figure out which of the two is being passed to it. If it is given the IP address, it calls the gethostbyaddr function to get the domain name, which it then prints out to the user in the form of an appropriate message (e.g., ‘The server host is compserv1.cs.stonybrook.edu’). The function gethostbyname, on the other hand, returns the IP address that corresponds to a given domain name. The client then enters an infinite loop in which it queries the user which service is being requested. There are two options : echo and time (note that time is a slightly modified version of the daytime service – see below). The client then forks off a child. After the child is forked off, the parent process enters a second loop in which it continually reads and prints out status messages received from the child via a half-duplex pipe (see below). The parent exits the second loop when the child closes the pipe (how does the parent detect this?), and/or the SIGCHLD signal is generated when the child terminates. The parent then repeats the outer loop, querying the user again for the (next) service s/he desires. This cycle continues till the user responds to a query with quit rather than echo or time. The child process is the one which handles the actual service for the user. It execs (see Section 4.7, Chapter 4) an xterm to generate a separate window through which all interactions with server and user take place. For example, the following exec function call evokes an xterm, and gets the xterm to execute echocli, located in the current directory, passing the string 127.0.0.1 (assumed to be the IP address of the server) as the command line argument argv[1] to echocli (click on the url for further details) : execlp("xterm", "xterm", "-e", "./echocli", "127.0.0.1", (char *) 0) xterm executes one of two client programs (echocli or timecli, say) depending on the service requested. A client program establishes a TCP connection to the server at the ‘well-known port’ for the service (in reality, this port will, of course, be some ephemeral port of your choosing, the value of which is known to both server and client code). All interaction with the user, on the one hand, and with the server, on the other, takes place through the child’s xterm window, not the parent’s window. On the other hand, the child will use a half-duplex pipe to relay status information to the parent which the parent prints out in its window (see below).To terminate the echo client, the user can type in ^D (CTRL D, the EOF character). To terminate the time client, the only option is for the user to type in ^C (CTRL C). (This can also be used as an alternative means of terminating the echo client.) Note that using ^C in the context of the time service will give the server process the impression that the client process has ‘crashed’. It is your responsibility to ensure that the server process handles this correctly and closes cleanly. I shall address this further when discussing the server process. It is also part of your responsibility in this assignment to ensure that the client code is robust with respect to the server process crashing (see Sections 5.12 & 5.13, Chapter 5). Amongst other implications, this means that it would probably be a good idea for you to implement your echo client code along the lines of either : Figure 6.9, p.168 (or even Figure 6.13, p.174) which uses I/O multiplexing with the select function; or of Figure 26.2, p.680, which uses threads; rather than along the lines of Figure 5.5, p.125. When the child terminates, either normally or abnormally, its xterm window disappears instantaneously. Consequently, any status information that the child might want to communicate to the user should not be printed out on the child’s xterm window, since the user will not have time to see the final such message before the window disappears. Instead, as the parent forks off the child at the beginning, a half-duplex pipe should be established from child to parent. The child uses the pipe to send status reports to the parent, which the parent prints out in its window. I leave it up to you to decide what status information exactly should be relayed to the parent but, at a minimum, the parent should certainly be notified, in as precise terms as possible, of any abnormal termination conditions of the service provided by the child. In general, you should try to make your code as robust as possible with respect to handling errors, including confused behaviour by the user (e.g., passing an invalid command line argument; responding to a query incorrectly; trying to interact with the service through the parent process window, not the child process xterm; etc.). Amongst other things, you have to worry about EINTR errors occurring during slow system calls (such as the parent reading from the pipe, or, possibly, printing to stdout, for example) due to a SIGCHLD signal. What about other kinds of errors? Which ones can occur? How should you handle them? Server The server has to be able to handle multiple clients using threads (specifically, detached threads), not child processes (see Sections 26.1 to 26.4, Chapter 26). Furthermore, it has to be able to handle multiple types of service; in our case, two : echo and time. echo is just the standard echo service we have seen in class. time is a slightly modified version of the daytime service (see Figure 1.9, p.14) : instead of sending the client the ‘daytime’ just once and closing, the service sits in an infinite loop, sending the ‘daytime’, sleeping for 5 seconds, and repeating, ad infinitum. The server is loosely based on the way the inetd daemon works : see Figure 13.7, p.374. However, note that the differences between inetd and our server are probably more significant than the similarities: inetd forks off children, whereas our server uses threads; inetd child processes issue exec commands, which our server threads do not; etc. So you should treat Figure 13.7 (and Section 13.5, Chapter 13, generally) as a source of ideas, not as a set of specifications which you must slavishly adhere to and copy. Note, by the way, that there are some similarities between our client and inetd (primarily, forking off children which issue execs), which could be a useful source of ideas. The server creates a listening socket for each type of service that it handles, bound to the ‘well-known port’ for that service. It then uses select to await clients (Chapter 6; or, if you prefer, poll; note that pselect is not supported in Solaris 2.10). The socket on which a client connects identifies the service the client is seeking. The server accepts the connection and creates a thread which provides the service. The thread detaches itself. Meanwhile, the main thread goes back to the select to await further clients. A major concern when using threads is to make sure that operations are thread safe (see p.685 and on into Section 26.5). In this respect, Stevens’ readline function (in Stevens’ file unpv13e/lib/readline.c, see Figure 3.18, pp.91-92) poses a particular problem. On p.686, the authors give three options for dealing with this. The third option is too inefficient and should be discarded. You can implement the second option if you wish. Easiest of all would be the first option, since it involves using a thread-safe version of readline (see Figures 26.11 & 26.12) provided in file unpv13e/threads/readline.c. Whatever you do, remember that Stevens’ library, libunp.a, contains the non-thread-safe version of Figure 3.18, and that is the version that will be link-loaded to your code unless you undertake explicit steps to ensure this does not happen (libunp.a also contains the ‘wrapper’ function Readline, whose code is also in file unpv13e/lib/readline.c). Remaking your copy of libunp.a with the ‘correct’ version of readline is not a viable option because when you hand in your code, it will be compiled and link-loaded with respect to the version of libunp.a in the course account, ~cse533/Stevens/unpv13e_solaris2.10 (I do not intend to change that version since it risks creating confusion later on in the course). Also, you will probably want to use the original version of readline in the client code anyway. I am providing you with a sample Makefile which picks up the thread-safe version of readline from directory ~cse533/Stevens/unpv13e_solaris2.10/threads and uses it when making the executable for the server, but leaves the other executables it makes to link-load the non-thread-safe version from libunp.a. Again, it is part of your responsibility to make sure that your server code is as robust as possible with respect to errors, and that the server threads terminate cleanly under all circumstances. Recall, first of all, that the client user will often use ^C (CTRL C) in the xterm to terminate the service. This will appear to the server thread as if the client process has crashed. You need to think about the error conditions that will be induced (see Sections 5.11 to 5.13, Chapter 5), and how the echo and time server code is to detect and handle these conditions. For example, the time server will almost certainly experience an EPIPE error (see Section 5.13). How should the associated SIGPIPE signal be handled? Be aware that when we return out of the Stevens’ writen function with -1 (indicating an error) and check errno, errno is sometimes equal to 0, not EPIPE (value 32). This can happen under Solaris 2.10, but I am not sure under precisely what conditions nor why. Nor am I sure if it also happens under other Unix versions, or if it also happens when using write rather than writen. The point is, you cannot depend on errno to find out what has happened to the write or writen functions. My suggestion, therefore, is that the time server should use the select function. On the one hand, select’s timeout mechanism can be used to make the server sleep for the 5 seconds. On the other hand, select should also monitor the connection socket read event because, when the client xterm is ^C’ed, a FIN will be sent to the server TCP, which will prime the socket for reading; a read on the socket will then return with value 0 (see Figure 14.3, p. 385 as an example). But what about errors other than EPIPE? Which ones can occur? How should you handle them? Recall, as well, that if a thread terminates without explicitly closing the connection socket it has been using, the connection socket will remain existent until the server process itself dies (why?). Since the server process is supposed, in principle, to run for ever, you risk ending up with an ever increasing number of unused, ‘orphaned’ sockets unless you are careful. Whenever a server thread detects the termination of its client, it should print out a message giving appropriate details: e.g., “Client termination: EPIPE error detected”, “Client termination: socket read returned with value 0”, “Client termination: socket read returned with value -1, errno = . . .”, and so on. When debugging your server code, you will probably find that restarting the server very shortly after it was last running will give you trouble when it comes to bind to its ‘well-known ports’. This is because, when the server side initiates connection termination (which is what will happen if the server process crashes; or if you kill it first, before killing the client) TCP keeps the connections open in the TIME_WAIT state for 2MSLs (Sections 2.6 & 2.7, Chapter 2). This could very quickly become a major irritant. I suggest you explore the possibility of using the SO_REUSEADDR socket option (pp.210-213, Chapter 7; note that the SO_REUSEPORT socket option is not supported in Solaris 2.10), which should help keep the stress level down. You will need to use the setsockopt function (Section 7.2) to enable this option. Figure 8.24, p.263, shows an instance of server code that sets the SO_REUSEADDR socket option. Finally, you should be aware of the sort of problem, described in Section 16.6, pp.461-463, that might occur when (blocking) listening sockets are monitored using select. Such sockets should be made nonblocking, which requires use of the fcntl function after socket creates the socket, but before listen turns the socket into a listening socket.

Please Read Me First. This is a set of java file of my final version of electronic artifacts. This is a game to map my experience in Disney World, in Orlando during this spring break. However, because of my limited skills in computer science, I really have no idea how to simplify the process to run the game. Sorry for the inconvenience. In order to run the game, you may need to install JAVA. I hope the following links will help you.     http://www.oracle.com/technetwork/java/javase/downloads/index-jsp-138363.html#javasejdk
    http://www.cc.gatech.edu/~simpkins/teaching/gatech/cs1331/guides/install-java.html My main file is called Disney. You can call Disney in console to start the game. However, I failed to putting all the things inside Disney file. Therefore, you may also need to call AdventureLand, MainStreet, and FrontierLand to start other three games. I hope this will help you. Sorry again for the inconvenience. 1. the structure of my project My project only focused on my trip in Magic Kingdom, one part of Disney world in Orlando. It is a game which guides players to choose from six sub-games, which match six sections of the park, Main Street U.S.A, Tomorrowland, Adventureland, Frontierland, Fantasyland and Liberty Square. I chose one of the rides I took in each section which, from my perspective, shows what I found interesting in Disney world. I changed what I experienced in the park into a small computer game. I want to share my experience with others while they play my games. In the following part of self reflection, I explain the background, rules and other things about each game. For convenience of matching them, I use different color to mark different parts. I hope it will help readers a little bit when they are lost in my disordered reflections. 1. the hall of presidents - Liberty Square 2. Festivall parade - Fantasyland (I explain this one in the part of technology skill limitations) 3. Big Thunder Mountain Railroad - Frontierland 4.talking with Woody- Adventureland 5. Stitch Store - Tomorrowland 6. lunch time - Main Street USA 3. my reflection of the trip in Disney World from dream to reality When I exited Disney resort, I found a sign along the street welcomed people back to real world. Actually, when I was in Orlando, I couldn't believe as an adult, people can mess up fantasy world in the theme parks and the real world. Nevertheless, I felt I was still in fantasy world, when I dreamed twice that I fought for the key to open the door of future. As is known to all, while sleeping, people always dream about what people thinks in the daytime. Therefore, my dream shows that my mind still stayed in the world with Mickey and Donald. I believe that it is experiencing fantasy world which is the source of the greatest happiness people get from theme park. On the one hand, everybody has pressure in real life especially for adults. They can get out of pressure for a day trip in theme park. They can experience different lives here with cartoon characters. On the other hand, sometimes, it is a really hard task to fulfill some dreams, such as being a princess. However, in Disney world, you can dress up the same as Snow White, waiting for your prince; you can go to space by rocket; you can also travel all over the world in one day and enjoy the food of each country. These are all the magic of theme parks. Therefore, in my game, I learnt the way which Disney design their rides to focus on the background story of the game instead of the game itself. For example, there is a ride called Big Thunder Mountain Railroad, which streaks through a haunted gold-mining town aboard a rollicking runaway mine train. The views around the ride were like a gold mining town. There were tools for gold-mining around the railroad and the railroad looked like very old. In order to show riders that it was a haunted gold-mining town, the train always took a sudden turn or speed up quickly to scare people. I decided to name one of my game, which was inspired by this ride, the same name, Big Thunder Mountain Railroad. Instead of sitting inside the mine train to travel around the haunted town, mine was for users to use keyboard to control the train to travel around the gridding railroad. I place traps inside several parts of gridding to "scare" players, who cannot know where traps are until they get into them. If I know how to use animation, I will show scary pictures when players drive their train to the traps. Unlike the ride in Disney, my players can no longer travel once they encounter a trap because their train may have some problems to keep moving. Also, the main goal in the game is to find the gold. However, as we know, finding gold is really hard. Therefore, players must go to find Aladdin's Wonderful lamp where also places inside the gridding while players cannot see its exact place until they happen to drive inside the part where lamp is. Aladdin's Wonderful lamp will show players the map of the gold and when people get to the gold mine, they win. However, there is another limitation of the game. Haunted town is so dangerous during the night. Therefore, players only have 12 hours to finish the task. Train can drive one square in 20 min. Therefore, train can only move 36 times or they will also be caught by traps. In this game, I want to show audiences I have a background story like rides in Disney World. Players need to find the gold in a haunted gold-mining town. Also, in order to show the relationship with Disney, I use Aladdin's Wonderful Lamp as the guide for the players, which is a well known characters in Disney cartoon. I created another game, called talking with Woody to show the magic power of Disney characters. There are a lot of chances to meet Disney characters in Disney world. On the one hand, travelers, especially small kids, are really excited to meet the characters they watched on TV. I think some kids may believe they take pictures with real Mickey Mouse. On the hand, staffs in Disney who wear the costumes are really tired. It was hot in Orlando last week, but all costumes were very heavy. I was moved by the staffs inside Mickey. They also need to mimic the actions of characters and also need to show kindness and warmness to children. It seems like a really hard job. Therefore, I decide to show this part of Disney in my project as well. I decided to use Woody, a toy all the toys look up to. He is smart, kind and brave like a cowboy should be. He is more than a top, he is friend to everyone enjoying the movie Toy. In order to create an interactive game, I planned to ask players to guide Woody. Players need to call Woody before their instructions. For instance, if players say (actually players are typing) "Woody, please sit down", Woody will sit down (actually, there will be another line on the screen showing the same as players import). However, if players are rude and just say "sit down" without calling Woody, Woody won't act (actually there is just nothing showing up on the screen). great facilities to provide convenience to everyone The facilities to satisfy needs for special groups of people, like small kids or disabled people, are well developed. In the past in China, it seemed impossible for parents to take infants and small kids to travel. The road is not flat or wide enough for strollers or wheelchairs. However, in Disney world, everything seemed like well prepared for everyone to use. There are strollers rentals, and electric conveyance vehicles rentals, which are available to rent throughout Disney world. There are baby care center for mothers to feed, change and nurse little ones. There are locker rentals for storing personal items. There are also hearing disability services which have sign language interpretation to help disabled people to enjoy fantasy world. There are still a lot other convenient services in Disney world. I think the purpose of these services show the pursue of equality among everyone in the world. On the one hand, I am really touched by the availability of these services here. It seems Disney try its best to service everyone who have desire to experience fantasy land. On the other hand, in this way, Disney can attract more travelers in order to make more money in some ways. Also, in Disney, it seems like a tradition that there are stores at the exit of the famous rides. Somebody may think it is just a strategy to make people shopping a lot. However, I think it also provides some convenience that travelers can buy souvenirs where is memorable. For example, when I finished my trip in Escape Stitch, I entered a store with a lot of kinds of Stitch, like Stitch pillow, Stitch key chain and so on. I really want to buy something in order to remind me the wonderful feelings. Therefore, I showed my opinion inside my game as well. I wrote one part is for shopping. The items are different kinds of Stitch. My codes can act as a robot to help customers to shop in the store. There are a lot of restaurants in Disney. Maps of Disney are full of restaurants' name. The greatest things about the food are in Epcot, I experienced different counties in one day. I felt like I was in fast travel in different parts of the world and tasted their special food and snacks while I was on the way. I remembered I was still eating Japanese food when I was in "Mexico". It was a great experience. However, there were always a long waiting lines for the all restaurants. People needed to reserve a table a day before their trip and even they had the reservation, they still needed to wait for a long time. I think Disney may need some good ways to fix the problems of waiting for a long time. I have no idea of changing the situation of restaurants, but I think if there are robots to customers to order in fast food restaurant, it may help a lot. Thus, I have another code to customers to order in Plaza Restaurant. If this kind of robots can work in the real life, people can order by themselves and there will be more staffs available to prepare food. theme park uses interesting ways to teach knowledge of boring topics Theme part is also a great source of learning knowledge, especially for kids. They use Disney characters, interesting shows, or even games to teach useful things. The ways change the boring knowledge to interesting things, which always attract children's attention. The most amazing one was an interactive game in Epcot's Innoventions, called "where's the fire?", which teaches adults and children basic fire safety in a fun and entertaining way. About every five minutes, the players waiting in line are divided into two groups and move into the home's entry. Here, a host will explain the object of the game and lay out the rules. The scenario is this: you are on a mission to discover a number of fire hazards commonly found around the house. To do this, you move from room to room, looking for potential risks. To help in the task, each player is given a special "safely light" to help uncover lurking dangers. The rooms are large projection screens. When a hazard is discovered, all persons in the room must shine their safety light on the same spot. when they do, the hazard is rendered harmless and points are assigned. After playing in the game to find the hazardous things in the house, I learned a lot of safety tips. It is much easier to remember the tips I learned during the game than those I learned on textbook or internet. I believe kids will enjoy the games and learn from them as well. I also tried to show this reflection in my project. Thus, I planned to make a game, called the hall of presidents, which test people's knowledge of presidents in USA. However, I failed to achieve the goal of making it an entertaining game instead of a quiz. My game was still like a quiz. However, because it is the only code which can work well inside my big game. I decide to still hold the game for my projects in order to what my original ideas are. 4. technology skill limitations I feel terribly sorry for my limited skills in CS. It is my first time to learn JAVA this semester. I just begin to learn the core concepts of JAVA this month. When I choose to use java code for this project, I know I will face plentiful limitations and problems. Here I want to express my gratitude to Dr. Johnson, who encouraged me not to give up my ideas. To be honest, I have no idea of how to change a java code into a real game with animations. I know the background story of the game is more important for English course and pictures are the best way to show the background, but I have no idea to show all these things by JAVA coding. Therefore, I choose to use videos for my presentation. In this way, I can show my animation inside the videos while the code clue of my game is still composed by JAVA coding. Also, video gives me a lot of freedom when choose my contents for presentation. I can explain a lot details of my project clearly through videos. For example, I found the festival parade in the magic kingdom was great and I wanted to share the experience in my project by showing the pictures or videos. However, because of the technology limitations, I can only show the videos in my presentations. Also, I mistakenly deleted my videos which I shot on my trip Orlando, I can only share others' parade show...... Also, I want to apologize for the incompleteness of my game. I only dedicated to writing codes for Magic Kingdom, a part of my trip during spring break. Writing codes is a really time consuming task for me. In general, I need to spend more than eight hours to finish one project for my CS assignment this semester. While for this project, the final artifacts are composed of several parts of codes and in the end I need to write the father code in order to take care of my code family for spring break. Due to my limitation in writing codes, I can only finish one part of Disney world. However, I think my code shows all my reflections and perspectives during my trip, even though it looks like it only shows one part of my trip. The terrible mistake I made is that I found out the most of my codes I wrote had significant errors on Tuesday. I went to CS TA office for help, while the errors were still impossible to fix in order to achieve the goal I planned to get. Consequently, my game have to be separated into several parts. Instead of a big game having others as sub-games inside the big one, my final artifacts are composed by several small games. I need to start them one by one. It may cause some inconvenience for players to map their trip in Disney world.

Fina Assignment

🎓 A collection of Course Files, Programming Assignments and Final Project for "Mastering Programming with MATLAB", Coursera, August-September, 2021

Programming Assignments of Savitribai Phule Pune University (SPPU) Second Year (SE), Third Year (TE) & Final Year (BE) Information Technology.

This repository represents my final assignment of "Module 3 - Android App Development" at Syntax Institut.

Course assignments and the final project will be a complete Super Mario 3

This repository contains the Final Papers, mids, quizzes, assignments, labs, projects, books, reports, and my self-made notes semester-wise along with the course teacher name of Batch 2020 CSIT. It also includes the material for the preparation of Huawei Certification. Keep me in your prayers ✨

A final assignment of the course- Machine Learning for Python on Coursera. This notebook gives a good example of using ML framework to realize classification tasks.

The Mini Library System is a text-based Python application that simulates a basic library management tool. Users can add new books, view the list of available or borrowed books, borrow and return books, and save all records locally using file storage. This project was built as part of the final assignment for Code in Place 2025 (Stanford CS106A).

The LittleLemon API is the final assignment for the APIs Course part of the Meta BackEnd Developer Professional Certificate on Coursera.

Overview For this assignment you will be developing and implementing : An On-Demand shortest-hop Routing (ODR) protocol for networks of fixed but arbitrary and unknown connectivity, using PF_PACKET sockets. The implementation is based on (a simplified version of) the AODV algorithm. Time client and server applications that send requests and replies to each other across the network using ODR. An API you will implement using Unix domain datagram sockets enables applications to communicate with the ODR mechanism running locally at their nodes. I shall be discussing the assignment in class on Wednesday, October 29, and Monday, November 3. The following should prove useful reference material for the assignment : Sections 15.1, 15.2, 15.4 & 15.6, Chapter 15, on Unix domain datagram sockets. PF_PACKET(7) from the Linux manual pages. You might find these notes made by a past CSE 533 student useful. Also, the following link http://www.pdbuchan.com/rawsock/rawsock.html contains useful code samples that use PF_PACKET sockets (as well as other code samples that use raw IP sockets which you do not need for this assignment, though you will be using these types of sockets for Assignment 4). Charles E. Perkins & Elizabeth M. Royer. “Ad-hoc On-Demand Distance Vector Routing.” Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, Louisiana, February 1999, pp. 90 - 100. The VMware environment minix.cs.stonybrook.edu is a Linux box running VMware. A cluster of ten Linux virtual machines, called vm1 through vm10, on which you can gain access as root and run your code have been created on minix. See VMware Environment Hosts for further details. VMware instructions takes you to a page that explains how to use the system. The ten virtual machines have been configured into a small virtual intranet of Ethernet LANs whose topology is (in principle) unknown to you. There is a course account cse533 on node minix, with home directory /users/cse533. In there, you will find a subdirectory Stevens/unpv13e , exactly as you are used to having on the cs system. You should develop your source code and makefiles for handing in accordingly. You will be handing in your source code on the minix node. Note that you do not need to link against the socket library (-lsocket) in Linux. The same is true for -lnsl and -lresolv. For example, take a look at how the LIBS variable is defined for Solaris, in /home/courses/cse533/Stevens/unpv13e_solaris2.10/Make.defines (on compserv1, say) : LIBS = ../libunp.a -lresolv -lsocket -lnsl -lpthread But if you take a look at Make.defines on minix (/users/cse533/Stevens/unpv13e/Make.defines) you will find only: LIBS = ../libunp.a -lpthread The nodes vm1 , . . . . . , vm10 are all multihomed : each has two (or more) interfaces. The interface ‘eth0 ’ should be completely ignored and is not to be used for this assignment (because it shows all ten nodes as if belonging to the same single Ethernet 192.168.1.0/24, rather than to an intranet composed of several Ethernets). Note that vm1 , . . . . . , vm10 are virtual machines, not real ones. One implication of this is that you will not be able to find out what their (virtual) IP addresses are by using nslookup and such. To find out these IP addresses, you need to look at the file /etc/hosts on minix. More to the point, invoking gethostbyname for a given vm will return to you only the (primary) IP address associated with the interface eth0 of that vm (which is the interface you will not be using). It will not return to you any other IP address for the node. Similarly, gethostbyaddr will return the vm node name only if you give it the (primary) IP address associated with the interface eth0 for the node. It will return nothing if you give it any other IP address for the node, even though the address is perfectly valid. Because of this, and because it will ease your task to be able to use gethostbyname and gethostbyaddr in a straightforward way, we shall adopt the (primary) IP addresses associated with interfaces eth0 as the ‘canonical’ IP addresses for the nodes (more on this below). Time client and server A time server runs on each of the ten vm machines. The client code should also be available on each vm so that it can be evoked at any of them. Normally, time clients/servers exchange request/reply messages using the TCP/UDP socket API that, effectively, enables them to receive service (indirectly, via the transport layer) from the local IP mechanism running at their nodes. You are to implement an API using Unix domain sockets to access the local ODR service directly (somewhat similar, in effect, to the way that raw sockets permit an application to access IP directly). Use Unix domain SOCK_DGRAM, rather than SOCK_STREAM, sockets (see Figures 15.5 & 15.6, pp. 418 - 419). API You need to implement a msg_send function that will be called by clients/servers to send requests/replies. The parameters of the function consist of : int giving the socket descriptor for write char* giving the ‘canonical’ IP address for the destination node, in presentation format int giving the destination ‘port’ number char* giving message to be sent int flag if set, force a route rediscovery to the destination node even if a non-‘stale’ route already exists (see below) msg_send will format these parameters into a single char sequence which is written to the Unix domain socket that a client/server process creates. The sequence will be read by the local ODR from a Unix domain socket that the ODR process creates for itself. Recall that the ‘canonical’ IP address for a vm node is the (primary) IP address associated with the eth0 interface for the node. It is what will be returned to you by a call to gethostbyname. Similarly, we need a msg_recv function which will do a (blocking) read on the application domain socket and return with : int giving socket descriptor for read char* giving message received char* giving ‘canonical’ IP address for the source node of message, in presentation format int* giving source ‘port’ number This information is written as a single char sequence by the ODR process to the domain socket that it creates for itself. It is read by msg_recv from the domain socket the client/server process creates, decomposed into the three components above, and returned to the caller of msg_recv. Also see the section below entitled ODR and the API. Client When a client is evoked at a node, it creates a domain datagram socket. The client should bind its socket to a ‘temporary’ (i.e., not ‘well-known’) sun_path name obtained from a call to tmpnam() (cf. line 10, Figure 15.6, p. 419) so that multiple clients may run at the same node. Note that tmpnam() is actually highly deprecated. You should use the mkstemp() function instead - look up the online man pages on minix (‘man mkstemp’) for details. As you run client code again and again during the development stage, the temporary files created by the calls to tmpnam / mkstemp start to proliferate since these files are not automatically removed when the client code terminates. You need to explicitly remove the file created by the client evocation by issuing a call to unlink() or to remove() in your client code just before the client code exits. See the online man pages on minix (‘man unlink’, ‘man remove’) for details. The client then enters an infinite loop repeating the steps below. The client prompts the user to choose one of vm1 , . . . . . , vm10 as a server node. Client msg_sends a 1 or 2 byte message to server and prints out on stdout the message client at node vm i1 sending request to server at vm i2 (In general, throughout this assignment, “trace” messages such as the one above should give the vm names and not IP addresses of the nodes.) Client then blocks in msg_recv awaiting response. This attempt to read from the domain socket should be backed up by a timeout in case no response ever comes. I leave it up to you whether you ‘wrap’ the call to msg_recv in a timeout, or you implement the timeout inside msg_recv itself. When the client receives a response it prints out on stdout the message client at node vm i1 : received from vm i2 <timestamp> If, on the other hand, the client times out, it should print out the message client at node vm i1 : timeout on response from vm i2 The client then retransmits the message out, setting the flag parameter in msg_send to force a route rediscovery, and prints out an appropriate message on stdout. This is done only once, when a timeout for a given message to the server occurs for the first time. Client repeats steps 1. - 3. Server The server creates a domain datagram socket. The server socket is assumed to have a (node-local) ‘well-known’ sun_path name which it binds to. This ‘well-known’ sun_path name is designated by a (network-wide) ‘well-known’ ‘port’ value. The time client uses this ‘port’ value to communicate with the server. The server enters an infinite sequence of calls to msg_recv followed by msg_send, awaiting client requests and responding to them. When it responds to a client request, it prints out on stdout the message server at node vm i1 responding to request from vm i2 ODR The ODR process runs on each of the ten vm machines. It is evoked with a single command line argument which gives a “staleness” time parameter, in seconds. It uses get_hw_addrs (available to you on minix in ~cse533/Asgn3_code) to obtain the index, and associated (unicast) IP and Ethernet addresses for each of the node’s interfaces, except for the eth0 and lo (loopback) interfaces, which should be ignored. In the subdirectory ~cse533/Asgn3_code (/users/cse533/Asgn3_code) on minix I am providing you with two functions, get_hw_addrs and prhwaddrs. These are analogous to the get_ifi_info_plus and prifinfo_plus of Assignment 2. Like get_ifi_info_plus, get_hw_addrs uses ioctl. get_hw_addrs gets the (primary) IP address, alias IP addresses (if any), HW address, and interface name and index value for each of the node's interfaces (including the loopback interface lo). prhwaddrs prints that information out. You should modify and use these functions as needed. Note that if an interface has no HW address associated with it (this is, typically, the case for the loopback interface lo for example), then ioctl returns get_hw_addrs a HW address which is the equivalent of 00:00:00:00:00:00 . get_hw_addrs stores this in the appropriate field of its data structures as it would with any HW address returned by ioctl, but when prhwaddrs comes across such an address, it prints a blank line instead of its usual ‘HWaddr = xx:xx:xx:xx:xx:xx’. The ODR process creates one or more PF_PACKET sockets. You will need to try out PF_PACKET sockets for yourselves and familiarize yourselves with how they behave. If, when you read from the socket and provide a sockaddr_ll structure, the kernel returns to you the index of the interface on which the incoming frame was received, then one socket will be enough. Otherwise, somewhat in the manner of Assignment 2, you shall have to create a PF_PACKET socket for every interface of interest (which are all the interfaces of the node, excluding interfaces lo and eth0 ), and bind a socket to each interface. Furthermore, if the kernel also returns to you the source Ethernet address of the frame in the sockaddr_ll structure, then you can make do with SOCK_DGRAM type PF_PACKET sockets; otherwise you shall have to use SOCK_RAW type sockets (although I would prefer you to use SOCK_RAW type sockets anyway, even if it turns out you can make do with SOCK_DGRAM type). The socket(s) should have a protocol value (no larger than 0xffff so that it fits in two bytes; this value is given as a network-byte-order parameter in the call(s) to function socket) that identifies your ODR protocol. The <linux/if_ether.h> include file (i.e., the file /usr/include/linux/if_ether.h) contains protocol values defined for the standard protocols typically found on an Ethernet LAN, as well as other values such as ETH_P_ALL. You should set protocol to a value of your choice which is not a <linux/if_ether.h> value, but which is, hopefully, unique to yourself. Remember that you will all be running your code using the same root account on the vm1 , . . . . . , vm10 nodes. So if two of you happen to choose the same protocol value and happen to be running on the same vm node at the same time, your applications will receive each other’s frames. For that reason, try to choose a protocol value for the socket(s) that is likely to be unique to yourself (something based on your Stony Brook student ID number, for example). This value effectively becomes the protocol value for your implementation of ODR, as opposed to some other cse 533 student's implementation. Because your value of protocol is to be carried in the frame type field of the Ethernet frame header, the value chosen should be not less than 1536 (0x600) so that it is not misinterpreted as the length of an Ethernet 802.3 frame. Note from the man pages for packet(7) that frames are passed to and from the socket without any processing in the frame content by the device driver on the other side of the socket, except for calculating and tagging on the 4-byte CRC trailer for outgoing frames, and stripping that trailer before delivering incoming frames to the socket. Nevertheless, if you write a frame that is less than 60 bytes, the necessary padding is automatically added by the device driver so that the frame that is actually transmitted out is the minimum Ethernet size of 64 bytes. When reading from the socket, however, any such padding that was introduced into a short frame at the sending node to bring it up to the minimum frame size is not stripped off - it is included in what you receive from the socket (thus, the minimum number of bytes you receive should never be less than 60). Also, you will have to build the frame header for outgoing frames yourselves (assuming you use SOCK_RAW type sockets). Bear in mind that the field values in that header have to be in network order. The ODR process also creates a domain datagram socket for communication with application processes at the node, and binds the socket to a ‘well known’ sun_path name for the ODR service. Because it is dealing with fixed topologies, ODR is, by and large, considerably simpler than AODV. In particular, discovered routes are relatively stable and there is no need for all the paraphernalia that goes with the possibility of routes changing (such as maintenance of active nodes in the routing tables and timeout mechanisms; timeouts on reverse links; lifetime field in the RREP messages; etc.) Nor will we be implementing source_sequence_#s (in the RREQ messages), and dest_sequence_# (in RREQ and RREP messages). In reality, we should (though we will not, for the sake of simplicity, be doing so) implement some sort of sequence number mechanism, or some alternative mechanism such as split-horizon for example, if we are to avoid possible scenarios of routing loops in a “count to infinity” context (I shall explain this point in class). However, we want ODR to discover shortest-hop paths, and we want it to do so in a reasonably efficient manner. This necessitates having one or two aspects of its operations work in a different, possibly slightly more complicated, way than AODV does. ODR has several basic responsibilities : Build and maintain a routing table. For each destination in the table, the routing table structure should include, at a minimum, the next-hop node (in the form of the Ethernet address for that node) and outgoing interface index, the number of hops to the destination, and a timestamp of when the the routing table entry was made or last “reconfirmed” / updated. Note that a destination node in the table is to be identified only by its ‘canonical’ IP address, and not by any other IP addresses the node has. Generate a RREQ in response to a time client calling msg_send for a destination for which ODR has no route (or for which a route exists, but msg_send has the flag parameter set or the route has gone ‘stale’ – see below), and ‘flood’ the RREQ out on all the node’s interfaces (except for the interface it came in on and, of course, the interfaces eth0 and lo). Flooding is done using an Ethernet broadcast destination address (0xff:ff:ff:ff:ff:ff) in the outgoing frame header. Note that a copy of the broadcast packet is supposed to / might be looped back to the node that sends it (see p. 535 in the Stevens textbook). ODR will have to take care not to treat these copies as new incoming RREQs. Also note that ODR at the client node increments the broadcast_id every time it issues a new RREQ for any destination node. When a RREQ is received, ODR has to generate a RREP if it is at the destination node, or if it is at an intermediate node that happens to have a route (which is not ‘stale’ – see below) to the destination. Otherwise, it must propagate the RREQ by flooding it out on all the node’s interfaces (except the interface the RREQ arrived on). Note that as it processes received RREQs, ODR should enter the ‘reverse’ route back to the source node into its routing table, or update an existing entry back to the source node if the RREQ received shows a shorter-hop route, or a route with the same number of hops but going through a different neighbour. The timestamp associated with the table entry should be updated whenever an existing route is either “reconfirmed” or updated. Obviously, if the node is going to generate a RREP, updating an existing entry back to the source node with a more efficient route, or a same-hops route using a different neighbour, should be done before the RREP is generated. Unlike AODV, when an intermediate node receives a RREQ for which it generates a RREP, it should nevertheless continue to flood the RREQ it received if the RREQ pertains to a source node whose existence it has heretofore been unaware of, or the RREQ gives it a more efficient route than it knew of back to the source node (the reason for continuing to flood the RREQ is so that other nodes in the intranet also become aware of the existence of the source node or of the potentially more optimal reverse route to it, and update their tables accordingly). However, since an RREP for this RREQ is being sent by our node, we do not want other nodes who receive the RREQ propagated by our node, and who might be in a position to do so, to also send RREPs. So we need to introduce a field in the RREQ message, not present in the AODV specifications, which acts like a “RREP already sent” field. Our node sets this field before further propagating the RREQ and nodes receiving an RREQ with this field set do not send RREPs in response, even if they are in a position to do so. ODR may, of course, receive multiple, distinct instances of the same RREQ (the combination of source_addr and broadcast_id uniquely identifies the RREQ). Such RREQs should not be flooded out unless they have a lower hop count than instances of that RREQ that had previously been received. By the same token, if ODR is in a position to send out a RREP, and has already done so for this, now repeating, RREQ , it should not send out another RREP unless the RREQ shows a more efficient, previously unknown, reverse route back to the source node. In other words, ODR should not generate essentially duplicative RREPs, nor generate RREPs to instances of RREQs that reflect reverse routes to the source that are not more efficient than what we already have. Relay RREPs received back to the source node (this is done using the ‘reverse’ route entered into the routing table when the corresponding RREQ was processed). At the same time, a ‘forward’ path to the destination is entered into the routing table. ODR could receive multiple, distinct RREPs for the same RREQ. The ‘forward’ route entered in the routing table should be updated to reflect the shortest-hop route to the destination, and RREPs reflecting suboptimal routes should not be relayed back to the source. In general, maintaining a route and its associated timestamp in the table in response to RREPs received is done in the same manner described above for RREQs. Forward time client/server messages along the next hop. (The following is important – you will lose points if you do not implement it.) Note that such application payload messages (especially if they are the initial request from the client to the server, rather than the server response back to the client) can be like “free” RREPs, enabling nodes along the path from source (client) to destination (server) node to build a reverse path back to the client node whose existence they were heretofore unaware of (or, possibly, to update an existing route with a more optimal one). Before it forwards an application payload message along the next hop, ODR at an intermediate node (and also at the final destination node) should use the message to update its routing table in this way. Thus, calls to msg_send by time servers should never cause ODR at the server node to initiate RREQs, since the receipt of a time client request implies that a route back to the client node should now exist in the routing table. The only exception to this is if the server node has a staleness parameter of zero (see below). A routing table entry has associated with it a timestamp that gives the time the entry was made into the table. When a client at a node calls msg_send, and if an entry for the destination node already exists in the routing table, ODR first checks that the routing information is not ‘stale’. A stale routing table entry is one that is older than the value defined by the staleness parameter given as a command line argument to the ODR process when it is executed. ODR deletes stale entries (as well as non-stale entries when the flag parameter in msg_send is set) and initiates a route rediscovery by issuing a RREQ for the destination node. This will force periodic updating of the routing tables to take care of failed nodes along the current path, Ethernet addresses that might have changed, and so on. Similarly, as RREQs propagate through the intranet, existing stale table entries at intermediate nodes are deleted and new route discoveries propagated. As noted above when discussing the processing of RREQs and RREPs, the associated timestamp for an existing table entry is updated in response to having the route either “reconfirmed” or updated (this applies to both reverse routes, by virtue of RREQs received, and to forward routes, by virtue of RREPs). Finally, note that a staleness parameter of 0 essentially indicates that the discovered route will be used only once, when first discovered, and then discarded. Effectively, an ODR with staleness parameter 0 maintains no real routing table at all ; instead, it forces route discoveries at every step of its operation. As a practical matter, ODR should be run with staleness parameter values that are considerably larger than the longest RTT on the intranet, otherwise performance will degrade considerably (and collapse entirely as the parameter values approach 0). Nevertheless, for robustness, we need to implement a mechanism by which an intermediate node that receives a RREP or application payload message for forwarding and finds that its relevant routing table entry has since gone stale, can intiate a RREQ to rediscover the route it needs. RREQ, RREP, and time client/server request/response messages will all have to be carried as encapsulated ODR protocol messages that form the data payload of Ethernet frames. So we need to design the structure of ODR protocol messages. The format should contain a type field (0 for RREQ, 1 for RREP, 2 for application payload ). The remaining fields in an ODR message will depend on what type it is. The fields needed for (our simplified versions of AODV’s) RREQ and RREP should be fairly clear to you, but keep in mind that you need to introduce two extra fields: The “RREP already sent” bit or field in RREQ messages, as mentioned above. A “forced discovery” bit or field in both RREQ and RREP messages: When a client application forces route rediscovery, this bit should be set in the RREQ issued by the client node ODR. Intermediate nodes that are not the destination node but which do have a route to the destination node should not respond with RREPs to an RREQ which has the forced discovery field set. Instead, they should continue to flood the RREQ so that it eventually reaches the destination node which will then respond with an RREP. The intermediate nodes relaying such an RREQ must update their ‘reverse’ route back to the source node accordingly, even if the new route is less efficient (i.e., has more hops) than the one they currently have in their routing table. The destination node responds to the RREQ with an RREP in which this field is also set. Intermediate nodes that receive such a forced discovery RREP must update their ‘forward’ route to the destination node accordingly, even if the new route is less efficient (i.e., has more hops) than the one they currently have in their routing table. This behaviour will cause a forced discovery RREQ to be responded to only by the destination node itself and not any other node, and will cause intermediate nodes to update their routing tables to both source and destination nodes in accordance with the latest routing information received, to cover the possibility that older routes are no longer valid because nodes and/or links along their paths have gone down. A type 2, application payload, message needs to contain the following type of information : type = 2 ‘canonical’ IP address of source node ‘port’ number of source application process (This, of course, is not a real port number in the TCP/UDP sense, but simply a value that ODR at the source node uses to designate the sun_path name for the source application’s domain socket.) ‘canonical’ IP address of destination node ‘port’ number of destination application process (This is passed to ODR by the application process at the source node when it calls msg_send. Its designates the sun_path name for an application’s domain socket at the destination node.) hop count (This starts at 0 and is incremented by 1 at each hop so that ODR can make use of the message to update its routing table, as discussed above.) number of bytes in application message The fields above essentially constitute a ‘header’ for the ODR message. Note that fields which you choose to have carry numeric values (rather than ascii characters, for example) must be in network byte order. ODR-defined numeric-valued fields in type 0, RREQ, and type 1, RREP, messages must, of course, also be in network byte order. Also note that only the ‘canonical’ IP addresses are used for the source and destination nodes in the ODR header. The same has to be true in the headers for type 0, RREQ, and type 1, RREP, messages. The general rule is that ODR messages only carry ‘canonical’ IP node addresses. The last field in the type 2 ODR message is essentially the data payload of the message. application message given in the call to msg_send An ODR protocol message is encapsulated as the data payload of an Ethernet frame whose header it fills in as follows : source address = Ethernet address of outgoing interface of the current node where ODR is processing the message. destination address = Ethernet broadcast address for type 0 messages; Ethernet address of next hop node for type 1 & 2 messages. protocol field = protocol value for the ODR PF_PACKET socket(s). Last but not least, whenever ODR writes an Ethernet frame out through its socket, it prints out on stdout the message ODR at node vm i1 : sending frame hdr src vm i1 dest addr ODR msg type n src vm i2 dest vm i3 where addr is in presentation format (i.e., hexadecimal xx:xx:xx:xx:xx:xx) and gives the destination Ethernet address in the outgoing frame header. Other nodes in the message should be identified by their vm name. A message should be printed out for each packet sent out on a distinct interface. ODR and the API When the ODR process first starts, it must construct a table in which it enters all well-known ‘port’ numbers and their corresponding sun_path names. These will constitute permanent entries in the table. Thereafter, whenever it reads a message off its domain socket, it must obtain the sun_path name for the peer process socket and check whether that name is entered in the table. If not, it must select an ‘ephemeral’ ‘port’ value by which to designate the peer sun_path name and enter the pair < port value , sun_path name > into the table. Such entries cannot be permanent otherwise the table will grow unboundedly in time, with entries surviving for ever, beyond the peer processes’ demise. We must associate a time_to_live field with a non-permanent table entry, and purge the entry if nothing is heard from the peer for that amount of time. Every time a peer process for which a non-permanent table entry exists communicates with ODR, its time_to_live value should be reinitialized. Note that when ODR writes to a peer, it is possible for the write to fail because the peer does not exist : it could be a ‘well-known’ service that is not running, or we could be in the interval between a process with a non-permanent table entry terminating and the expiration of its time_to_live value. Notes A proper implementation of ODR would probably require that RREQ and RREP messages be backed up by some kind of timeout and retransmission mechanism since the network transmission environment is not reliable. This would considerably complicate the implementation (because at any given moment, a node could have multiple RREQs that it has flooded out, but for which it has still not received RREPs; the situation is further complicated by the fact that not all intermediate nodes receiving and relaying RREQs necessarily lie on a path to the destination, and therefore should expect to receive RREPs), and, learning-wise, would not add much to the experience you should have gained from Assignment 2.

Quickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.

Final assignment | Android Programming with Kotlin Academy - Sisterslab

🎓 A collection of Code Example Files, Programming Assignments and Final Project for "Introduction to Data, Signal, and Image Analysis with MATLAB", Coursera, September-October, 2021

Description of Final Assignments of Information Retrieval Course 2021-2022

Final Assignment solutions for Coursera course (Databases and SQL for Data Science)

Consensys bootcamp final assignment

Trabalho final da disciplina de LABES (Laboratório de Engenharia de Software) no ano de 2019

Final assignment for the course Data Visualization with Python, part of IBM Data Science Professional Certification on Coursera

A Simple Compiler - Final Assignment of "Compilation Principles Practice"(East China Normal University, 2017-2018)

Network Security Fundamentals final class assignment used to show proficiency and understanding of network security. Built with Python and tested in an isolated GNS3 network environment.

大三计算机图形学课程设计。利用OpenGL做的一个关于田忌赛马动画，包含项目环境。

This web API is a bakery management system using ASP.NET Core, for Campinas Tech Talent's final assignment | Projeto Padaria, TCC Campinas Tech Talents - Grupo 2.

This Problem Data set of San Francisco Contains information about the crime in San Francisco, We are going to analyze the data, Visualize the data using folium maps for geographical understanding. In other words It is called Geo spatial Mapping. This Problem is the final assignment for Coursera and IBM's Data Visualization Course.