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Fundamentals - Data Structures Summary. Singly-Linked Lists; Containers; Stacks; Queues; Deques; Ordered Lists; Sorted Lists; Singly-Linked Lists Theory.And since this is a. The following figure shows the empty list representation for each. Note that the sentinel is always present. Items can be added until. Implementation. The implementation that follows uses variation B since it supports. The following figure illustrates the. There are two main classes: Element class which is used to. Element class. has three fields - list. Linked. List class which is the main. A Linked. List instance contains 0. Elements. Linked. List. class has two fields - Head and. Tail. Therefore, the best solution is to. Singly. Linked. List< T> list. Container internal T. T); // get default value of. Tinternal Element< T>. Constructors // Used to initialize member variablespublic Element(Singly. Linked. List< T> container, T value. Element< T> next. Element). Visualize the situation// by drawing a linked list. Element< T> (list, o. Value. this. next); // Check if we are inserting at the. Tail == this)list. Tail =. next. The following// statement, does the above but it. For example, which element. Element< T> e. New = new. Element< T> (list, o. Value, this); // Check for boundary conditions: If. New should become the. Head points to the// current element, then Head should. Now make its next element, the newly created element. Previous. next = e. New. Again// refer to linked list implementation to explain the. Element< T> head = null; // always points to the first element in the list (unless empty) protected Element< T> tail = null; // always points to the last element in the list (unless empty) #endregion #region Constructors // When the list is first created, it is empty. Therefore. fields head and tail// are initially null as per their declaration. The. constructor therefore, does nothing public Singly. Linked. List() . No need to traverse list. GC will take care this. To clear the list. The newly. inserted element becomes// the head of the list public void Prepend(T o. Value). Because the. Element< T> e = new Element< T> (this. Value, head); // Recall the meaning of head and. If list is not empty, the // new element is now the first. The newly. inserted element becomes// the tail of the list public void Append(T o. Value). Because the. Element< T> e = new Element< T> (this. Value, null); // Recall the meaning of head and. Now,if the list is initially empty, the new// element is the only element (i. B). If list is not empty, the new element is now the// last element and tail should point. First, clear all. Clear(); // Then copy. Note the for- loop structure for a. Element< T> e. Current =. source. Current != null; e. Current = e. Current. Note the syntax of the for loop// used to iterate over the linked. Element< T> e. Element. To. Delete = head; Element< T> e. Previous. Element = head; for (Element< T> e. Current = this. head. Current != null; e. Current = e. Current. B now. becomes head str. Value = sll. Head. Value; // . There are two. The sections that followi present a linked- list based implementation as it much cleaner and more. Typically, you would develop a container by developing a set of. The. following uses the . NET model to explain how common interfaces can be used to. The most basic container interface is. ICollection< T> :public interface ICollection< T> : IEnumerable< T>. IEnumerable. The. ICollection< T> interface is usually enough to implement containers that. Queue. and Stack. Note that containers implementing. ICollection< T> do not support access by index, i. To provide this support for containers. IList< T> which derives from. ICollection< T>. IList< T>. represents a collection which can be accessed by index: public interface IList< T> : ICollection< T>. IEnumerable< T> , IEnumerable. This technique (mentioned. IList< T> to re- implement these two base interfaces. Another interface that specializes ICollection. IDictionary< T> which represents a generic. Consider a pile of printed papers on your desk. Suppose you add papers only. At any. point in time, the top paper is the only visible paper. What you have here is a. From an implementation perspective, a stack has only two methods to. Push to add an. item (to the top) and Pop to remove an item (from. Typically, stacks have an additional method called Top that returns the. The following presents an implementation of the Stack container. IStack< T> : ICollection. T : IEquatable< T>. As customers. arrive, they join the end of the line waiting to be served. When a customer is. What you have. here is a queue. A queue is used when activities must be done in a. FIFO - First- In First- Out)From an implementation perspective, a queue has only two methods to. Enqueue to add an. Dequeue to remove an item (from. Typically, stacks have an additional method called. Head that returns the. The following presents an implementation of the queue container. IQueue< T> : ICollection. T: IEquatable< T>. Cards can be added/removed to/from the top. Cards can also be added/removed to/from the bottom of the deck. A. deque can be considered as the general case in which items can be added/deleted. The word deque is derived from doubled- ended queue. Typically, deques have additional methods called. Head and Tail to return the. IDeque< T> : ICollection. T: IEquatable< T>. We have also illustrated so far three containers - . One of the most versatile containers is the. A list is a series of items which can be individually accessed by. In general, there are two kinds of lists - the. In a sorted list, the. For example, a book index is a. In an ordered list, the sequence in which items occur is not important. Furthermore, the operations. IList< T> generic interface: public interface IList< T>. The main approach is start at. Appropriate checks must be added to ensure that index is within limits. Start at the head. Check in each iteration that the element is valid (i. Element< T> element = sll. Head; for (int i = 0; i < index & & element. Next. Element; // Return. Value. Insert new element element. Next. Element = new. Element< T> (sll, item, element. Next. Element). However, in a sorted list, the position of an item is not arbitrary - . For example, items may appear ordered from smallest to largest. When inserting an item into a sorted list, we has as a. Once an item is inserted, we. Therefore, all the. The following figure illustrates: The algorithm used to insert an item in a sorted list (based on a. The existing sorted linked list is traversed to. The traversal is done using two variables. Previous and. element. Current. In the event that an item to be inserted is smaller than. Prepend method is. A sorted list implementation is exactly the same as that of the. The only difference is in the Insert method which is given below (. Insert with the method. IList interface/implementation): public void Insert(T item). An Introduction to Data Structures. We'll examine both built- in data structures present in the . NET Framework, as well as essential data structures we'll have to build ourselves. This first installment focuses on defining what data structures are, how the efficiency of data structures is analyzed, and why this analysis is important. In this article, we'll also examine the Array and Array. List, two of the most commonly used data structures present in the . NET Framework. Throughout this article series we will be examining a variety of data structures, some of which are included in the . NET Framework Base Class Library and others that we'll build ourselves. If you're unfamiliar with the term, data structures are abstract structures, or classes, that are used to organize data and provide various operations upon their data. The most common and likely well- known data structure is the array, which contains a contiguous collection of data items that can be accessed by an ordinal index. Before jumping into the content for this article, let's first take a quick peek at the roadmap for this six- part article series, so that you can see what lies ahead. If there are any topics you think are missing from this outline, I invite you to e- mail me at mitchell@4guysfromrolla. Space permitting, I'll be happy to add your suggestions to the appropriate installment or, if needed, add a seventh part to the series. In this first part of the six- part series, we'll look at why data structures are important, and their effect on the performance of an algorithm. To determine a data structure's effect on performance, we'll need to examine how the various operations performed by a data structure can be rigorously analyzed. Finally, we'll turn our attention to two data structures present in the . NET Framework. Chances are you've used both of these data structures in past projects. In this article, we'll examine what operations they provide and the efficiency of these operations. In Part 2, we'll explore the Array. List class in more detail and examine its counterparts, the Queue class and Stack class. Like the Array. List, both the Queue and Stack classes store a contiguous collection of data and are data structures available in the . NET Framework Base Class Library. However, unlike an Array. List from which you can retrieve any data item, Queues and Stacks only allow data to be accessed in a predetermined sequential order. We'll examine some applications of Queues and Stacks, and see how to implement both of these classes by extending the Array. List class. After examining Queues and Stacks, we'll look at Hash. Tables, which allow for direct access like an Array. List, but store data indexed by a string key. While Array. Lists are ideal for directly accessing and storing contents, they are suboptimal candidates when the data needs to be searched. In Part 3, we'll examine the binary search tree data structure, which provides a much more efficient means for searching than the Array. List. The . NET Framework does not include any built- in binary search tree data structures, so we will have to build our own. The efficiency of searching a binary search trees is sensitive to the order with which the data was inserted into the tree. If the data was inserted in sorted or near- sorted order, the binary search tree loses virtually all of its efficiency advantages over the Array. List. To combat this issue, in Part 4 we'll examine an interesting randomized data structure. Skip. Lists provide the efficiency of searching a binary search tree, but without the sensitivity to the order with which data is entered. In Part 5 we'll turn our attention to data structures that can be used to represent graphs. A graph is a collection of nodes, with a set of edges connecting the various nodes. For example, a map can be visualized as a graph, with cities as nodes and the highways between them as edged between the nodes. Many real- world problems can be abstractly defined in terms of graphs, thereby making graphs an often- used data structure. Finally, in Part 6 we'll look at data structures to represent sets and disjoint sets. A set is an unordered collection of items. Disjoint sets are a collection of sets that have no elements in common with one another. Both sets and disjoint sets have many uses in everyday programs, which we'll examine in detail in this final part. Analyzing the Performance of Data Structures. When thinking about a particular application or programming problem, many developers (myself included) find themselves most interested in writing the algorithm to tackle the problem at hand, or adding cool features to the application to enhance the user's experience. Rarely, if ever, will you hear someone excited about what type of data structure they are using. However, the data structures used for a particular algorithm can greatly impact its performance. A common example is finding an element in a data structure. With an array, this process takes time proportional to the number of elements in the array. With binary search trees or Skip. Lists, the time required is sub- linear. When searching large amounts of data, the data structure chosen can make a difference in the application's performance that can be visibly measured in seconds or even minutes. Since the data structure used by an algorithm can greatly affect the algorithm's performance, it is important that there exists a rigorous method by which to compare the efficiency of various data structures. What we, as developers utilizing a data structure, are primarily interested in is how the data structures performance changes as the amount of data stored increases. That is, for each new element stored by the data structure, how are the running times of the data structure's operations effected? Consider a scenario in which you have a program that uses the System. IO. Directory. Get. Files(path) method to return the list of the files in a specified directory as a string array. Now, imagine that you wanted to search through the array to determine if an XML file existed in the list of files (namely one whose extension was . One approach to do this would be to scan through the array and set some flag once an XML file was encountered. The code might look like so. System. Collections. System. IO. public class My. Class. . To analyze the array's efficiency at sorting, we must ask ourselves the following, . If I add another element, so the array has n + 1 elements, what is the new running time? When working with arrays, typically the steps considered are how many array accesses one needs to perform.) To search for a value in an array, we need to potentially visit every array value, so if we have n + 1 array elements, we might have to perform n + 1 checks. That is, the time it takes to search an array is linearly proportional to the number of elements in the array. The sort of analysis described here is called asymptotic analysis, as it examines how the efficiency of a data structure changes as the data structure's size approaches infinity. The notation commonly used in asymptotic analysis is called big- Oh notation. The big- Oh notation to describe the performance of searching an array would be denoted as O(n). The large script O is where the terminology big- Oh notation comes from, and the n indicates that the number of steps required to search an array grows linearly as the size of the array grows. A more methodical way of computing the asymptotic running time of a block of code is to do follow these simple steps. Determine the steps that constitute the algorithm's running time. As aforementioned, with arrays, typically the steps considered are the read and write accesses to the array. For other data structures the steps might differ. Typically, you want to concern yourself with steps that involve the data structure itself, and not simple, atomic operations performed by the computer. That is, with the block of code above, I analyzed its running time by only counting how many times the array needs to be accessed, and did not bother worrying about the time for creating and initializing variables or the check to see if the two strings were equal. Find the line(s) of code that perform the steps you are interested in counting. Put a 1 next to each of those lines. For each line with a 1 next to it, see if it is in a loop. If so, change the 1 to 1 times the maximum number of repetitions the loop may perform. If you have two or more nested loops, continue the multiplication for each loop. Find the largest single term you have written down. This is the running time. Let's apply these steps to the block of code above. We've already identified that the steps we're interested in are the number of array accesses. Moving onto step 2 note that there are two lines on which the array, fs, is being accessed. Now, applying step 3 notice that the access to fs in the String. Compare() method occurs within a loop that runs at most n times (where n is the size of the array). So, scratch out the 1 in the loop and replace it with n. Finally, we see that the largest value is n, so the running time is denoted as O(n). O(n), or linear- time, represents just one of a myriad of possible asymptotic running times. Others include O(log. O(n log. 2n), O(n. O(2n), and so on. Without getting into the gory mathematical details of big- Oh, the lower the term inside the parenthesis for large values of n, the better the data structure's operation's performance. For example, an operation that runs in O(log n) is more efficient than one that runs in O(n) since log n < n. Note. So, log. 2 4 = 2, since 2. Similarly, log. 2 8 = 3, since 2. Clearly, log. 2n grows much slower than n alone, because when n = 8, log. In Part 3 we'll examine binary search trees whose search operation provides an O(log. Throughout this article series, we'll be computing each new data structure and its operations asymptotic running time and comparing it to the running time for similar operations on other data structures. Sledujte film MASH online nebo ke sta Robert Altman - MASH (1. Quote: Although he was not the first choice to direct it, the hit black comedy MASH established Robert Altman as one of the leading figures of Hollywood's 1. MASH (1970)BDRip 720p MASH (1970)BluRay Rip 720p Director: Robert Altman. Writers: Leigh Brackett. Scripted by Hollywood veteran Ring Lardner, Jr., this war comedy details the exploits of military doctors and nurses at a Mobile Army Surgical Hospital in the Korean War. 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Frank Burns (Robert Duvall) and Army true- believer Maj. Mash 1970 Rapidshare LibraryAbetted by such other hedonists as Duke Forrest (Tom Skerritt) and Painless Pole (John Schuck), as well as such (relative) innocents as Radar O'Reilly (Gary Burghoff), Hawkeye and Trapper John drive Burns and Houlihan crazy while engaging in such additional blasphemies as taking a medical trip to Japan to play golf, staging a mock Last Supper to cure Painless's momentary erectile dysfunction, and using any means necessary to win an inter- MASH football game. MASH creates a casual, chaotic atmosphere emphasizing the constant noise and activity of a surgical unit near battle lines; it marked the beginning of Altman's sustained formal experiments with widescreen photography, zoom lenses, and overlapping sound and dialogue, further enhancing the atmosphere with the improvisational ensemble acting for which Altman's films quickly became known. Although the on- screen war was not Vietnam, MASH's satiric target was obvious in 1. Vietnam War- weary and counter- culturally hip audiences responded to Altman's nose- thumbing attitude towards all kinds of authority and embraced the film's frankly tasteless yet evocative humor and its anti- war, anti- Establishment, anti- religion stance. MASH became the third most popular film of 1. Love Story and Airport, and it was nominated for five Academy Awards, including Best Picture and Best Director. As further evidence of the changes in Hollywood's politics, blacklist survivor Lardner won the Oscar for his screenplay. Miller (1. 97. 1), California Split (1. Nashville (1. 97. It also inspired the long- running TV series starring Alan Alda as Hawkeye and Burghoff as Radar. With its formal and attitudinal impudence, and its great popularity, MASH was one more confirmation in 1. New Employee Training - Is It Worth The Investment. Getting off on the right foot. Many companies provide some sort of introductory training or orientation for most of their new employees. It may take the form of an older employee assigned to show the new employee . They require several weeks or even months of training to familiarize every new employee with the company, its products, its culture and policies, even its competition. There is a measurable cost to that training, but is it worth it? The Training Myth The most valuable employee training. Learn how one company developed a successful in-house leadership training program. Christian Kar slashed his marketing budget to beef up training for the. Training is teaching, or developing in oneself or others.It involves employee training at the place of work while he or she is doing the actual job. Usually a professional trainer (or sometimes an experienced employee). Job Corps is an intensive education and vocational training program that helps. The Employee Benefits. The FY 2013 Budget requests a program increase of $2,800,000 and 9 FTE for the transformation of. Prepare and monitor a training budget 3 Introduction This module is part of a series on planning an income oriented short course.It has been written for individuals or groups interested in organising and delivering training. The goals of the employee training or development program are clear; The employees are involved in determining the knowledge, skills and abilities to be learned. Untrained employees can end up costing you and the bottom line. Learn 6 reasons for the importance of employee training. Brought to you by Line Shape Space. Let's look at some of the issues. Some Background Facts. The technology in the workplace is changing very rapidly and companies that can't keep up will drop out of competition. A survey by the Ontario (Canada) Skills Development Office found 6. The largest part of that (4. Only two percent was spent for New Employee Orientation and three percent on quality, competition and business practices training. Reasons To Not Do New Employee Training. Even at the less than $1. If your profit per employee is less than $1. Besides, we all know it is the responsibility of the school system to train people to be workers. And it is the worker's responsibility to learn how to do a job so they can get hired. Why Do New Employee Training. Not surprisingly, all the reasons not to train new employees (except cost itself) are actually reasons to do that training. If you have high turnover, training new employees will make them more productive. They will feel better about themselves and the job. They will stick around longer. If your profit per employee is less than $1. You need to start training all your employees, not just your new employees, right away. Show your stakeholders the potential ROI of the training as we will discuss below. And if you still believe that our schools provide adequate training to make students labor- ready you are living in a dream world. Yes, some job seekers make the effort to learn on their own the skills needed for a new job, but most get that training on the job. Required Training. Government regulation, insurance coverages, and common sense dictate some training that MUST be given to every new employee. Other Reasons for New Employee Training. American International Assurance is an ISO 9. AIA makes a commitment to training for their staff because AIA . Edward Gordon recommends companies make training a stand- alone function, separate from HR. He points out a twenty percent increase in training expenditure since 1. He suggests Training Managers use Return on Investment (ROI) to demonstrate that the training function is a profit center, not just a cost center. Summary. In Dr. Gordon's article cited above, he points out that companies such as Sprint, Xerox, General Electric and General Motors have opted to establish Corporate Universities, reflecting the importance they place on employee training. The value for smaller companies is arguably even greater. And there is no better time to start employee training than New Employee Orientation.- -- -- -- If you have any questions or comments about this article, or if there is an issue you would like us to address, please post them on our Management Forum to share with the entire group. |
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