GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Statistics can be selected from the "Statistics Selection" bank on the bottom left. They will become "active" and appear on the top left. These are the statistics that will be used to rank the states and color the map according to the key and color palette. You may chose to weight all statistics the same defaultor give them different weights depending on your preferences.

This will likely not change the relative coloring very much, but it will change the coloring slightly. The most saturated states are the "best" states. The least saturated states are the "worst" states. This is according to the statistics that have been selected. There will always be a state completely saturated colored and a state completely unsaturated white in light themes and black in dark themes. The state window displays additional ranking and statistic information. The "rank" in the top left corner is the rank of the state corresponding with the color compared to all the other states overall.

Both comparisons are made based on what the user has selected. In yet another display of similar information, the bar chart at the bottom of the state window shows how the state ranks compared to the other states overall. The state is highlighted, so it's easier to see. Not all the states are labelled on the bottom because there's not enough space. It is a known issue. In case your eyes are bad, we've built in a magnifying glass that works better than simply zooming in.

Designed with touch screens in mind, clicking the magnifying glass in the top right corner brings up a popup with the same functionality as the main 50 states, just bigger. It includes the 11 most Northeastern states. Some of us like to know where and when are data comes from. To find out more metadata on our statistics, click on the i icon on the right side of each of the statistic sliders.

This will trigger a popup with more details on your data, similar to the state window. Different themes can be chosen by hovering over the paintbrush in the bottom right, and selecting a theme from the popup menu. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. HTML Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit c8bc Apr 9, Dynamic Map Coloring: The most saturated states are the "best" states. Additional Features: State Window: The state window displays additional ranking and statistic information.Some people show their creativity skills through art or music, I show mine through coding. Born in the Philippines, raised in Central Jersey.

Other than computers, my other attraction is traveling. Something about experiencing different cultures and people, makes me broaden my perspective on this world. An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity.

Initial Version Revised Version. About Resume Projects Contact English Hey, welcome! I'm Carlo, hope you enjoy my page! Follow me to see some above average content. Principles of Operating Systems CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity.

Database System Design and Management CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity. Intensive Programming in Linux CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity.

Introduction to Computer Networks CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity. Foundations of Computer Science II CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity.

Advanced Datastructures and Algorithms CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity. Guided Design in Software Engineering CS An introduction to the foundations of computer science with emphasis on the development of techniques for the design and proof of correctness of algorithms and the analysis of their computational complexity.

More projects found on my Github. Hello Professor Velez! To visit all my assigments please click here. Assignment 1 Initial Version Revised Version. Chapter 1 Initial Version Revised Version. Chapter 2 Initial Version Revised Version. Chapter 4 Initial Version Revised Version.

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Chapter 5 Initial Version Revised Version. Chapter 6 Initial Version Revised Version. Chapter 7 Initial Version Revised Version. Chapter 8 Initial Version Revised Version. Midterm Initial Version Revised Version.

Proposal Initial Version Revised Version. Chapter 10 Initial Version Revised Version.

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Progress Report Initial Version. PowerPoint Initial Version. Final Proposal Initial Version.Study of the design and the analsis of algorithms - in particular, the correctness proved via formal proofs and efficiency proved using time complexity analysis. Interesting things to know are lower bounds on possible algorithms to solve a particular problems, problems that cannot be solved by any algorithm undecidabilityand problems that cannot be solved efficiently by any algorithm, but can be solved NP-hardness.

Useful design strategies we will go over are divide and conquer, greedy algorithms, dynamic programming, breadth-first and depth-first search, local search, and linear programming. An example of an algorithm design is the maximum problem: given an array of integers Afind the maximum integer in A. A simple solution to this is as follows:. This seems to be obviously correct, but we can also use program verification to prove it correct.

In this case, we will use induction to prove the loop invariant - at the end of each iteration of the loop, current is equal to the largest element encountered so far in the array.

We'd check the base case for arrays of length 1, then prove the inductive hypothesis to formally verify the program. We also know that this algorithm is asymptotically optimal, at least in terms of the number of comparisons done. Since a correct solution to the Maximum problem must look at every element in the array, it must therefore do at least n - 1 comparisons.

Let's formally prove this:. Suppose there was an algorithm that could determine the maximum element of an array using fewer than n - 1 comparisons. Let G be a graph such that each vertex in G corresponds to an element in Aand each comparison done by a run of the algorithm between any two elements results in an edge between those two elements.

Clearly, there are n - 2 edges or less, and n vertices. Therefore, there are at least 2 components in Gsince the graph cannot be connected. Clearly, the solution could be in any of the components, and can only be found by comparing them. Therefore, the algorithm cannot exist. However, it is possible to design another algorithm that does fewer than 2n - 2 comparisons though the time complexity might be worse. One way to do this is to consider elements two at a time - we compare elements one pair at a time, the larger of the pair with the maximum, and the smaller of which with the minimum.

## CARLO ACLAO

It can actually be proven, using graph theory, that the number of comparisons for this algorithm is optimal. However, it's too lengthy and complicated to cover here.

Suppose we want to determine whether and which three elements of an array of integers sum to 0. Basically, this goes through each value of iand does an O n search for two values that would make it possible to have all three sum to 0.

Proving this correct is left as an exercise to the reader - prove that k - j is monotonically decreasing, and the pairs cover all possible pairs that can possibly sum up to A[i]. It's somewhat reminiscent of the array merge in merge sort.

A problem is a computational task. A problem instance is the input for the computational task.We are not following the text exactly. Lecture notes are included below, along with references to the corresponding sections in the course textbook. Note that problem 3 has a linear time solution. CrowdMark Instructions for Assignments: Your written solutions will be judged not only for correctness but also for the quality of your presentation and explanations.

In questions that involve designing an algorithm, i describe the main idea first, ii present clearly written pseudocode e. The work you hand in must be your own.

Acknowledge any sources you have used. Unless specified otherwise, you can always use any result from the textbook, notes, previous assignment, or previous course, just by citing it. Since solutions will be posted almost immediately online, late assignments will not be accepted under any circumstances. No extensions!

In all assignments and exams, unless otherwise directed, you are expected to justify any claims that you make. The level of explanation we generally expect is "enough to convince a skeptical TA". Usually this means that a complete formal proof from first principles is not needed unless we say so. Furthermore, since this course is essentially all about efficient use of time and space, strive to make your solutions as efficient as possible.

Solutions that are technically correct, but extremely wasteful in terms of time and space, will not receive full credit.

This is not a software engineering course. We will basically never worry about trivial edge cases such as the case of empty inputor inputs that do not match our specifications. We will not test such trivial edge cases for the programming assignments, and therefore will not take off marks for code that doesn't handle these trivial edge cases correctly. In your solutions, there is no need to spend any time dealing with these unless you want to.

However, your program should take care of the edge cases that are crucial to the algorithm's correctness or analysis. For example, unless the problem states so, you should not usually assume that the input size is a power of 2. The default in this course is that all numbers we deal with are integers. If there are exceptions, we'll let you know. Some of the algorithms we will discuss in this class have multiple versions and variations.

All assignment and exam questions deal with the versions we present. If you choose to learn the material from other sources, such as Wikipedia, rather than from the course notes and textbook, be aware that sometimes these minor differences may affect the answers.

C This book is available electronically through the UW library catalog. G35 There are three other resources that you might find useful: Jeff Erickson's notes on algorithms at the University of Illinois. Now also available in print. Available here. Nice small collection of problems and solutions Steven S.Learn more about blocking users. Learn more about reporting abuse.

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Learn more about reporting abuse Report abuse. Sort: Recently created Sort options. Recently created Least recently created Recently updated Least recently updated. View ShowCharacter. View C using System. Collections; using System.

View LoginFirebaseAuth. View file0. View Quetion. View MainActivity. Image ; import android. MediaPlayer ; import android.Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications.

Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers.

The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance.

This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains.

This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Quite explicitly, this book focuses on MapReduce algorithm design, not Hadoop programming.

If you're at a university, your institution may already subscribe to the series, in which case you can access the electronic version directly without cost see this page for a list of institutional subscribers. Otherwise, to purchase:. We are pleased to provide the final pre-production manuscript April 11, as a preview.

If you find this resource helpful, please consider purchasing an actual copy to support our work! Cloud 9 is a MapReduce library for Hadoop designed to serve as both a teaching tool and to support research in data-intensive text processing. It also serves as a repository of many examples discussed in the book. Reference implementations of design patterns and other algorithms discussed in the book are being added gradually, so please come back periodically. Thus far, the repository contains:.

Home 1st Edition 1. N Edition 2nd Edition. Book Information Back to top. Abstract Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Reference Implementations Back to top. Reviews and Adoption Back to top.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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Open the project the. Learn more. How do I run and compile this github project on my pc? Ask Question. Asked 6 years, 1 month ago. Active 1 year, 8 months ago. Viewed 15k times. George Chalhoub George Chalhoub 5, 3 3 gold badges 23 23 silver badges 54 54 bronze badges. Active Oldest Votes. Daniel Daniel 1, 1 1 gold badge 13 13 silver badges 16 16 bronze badges. Visual Studio does not run.

**Урок 3 (часть 1): Распределённый репозиторий, Github**

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## CSE341: Programming Languages

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