Introduction to java programming lang free pdf download

If order does not matter to you, use CsvToBeanBuilder. The performance benefit is not large, but it is measurable. The ordering or lack thereof applies to data as well as any captured exceptions. If your getter returns an empty Optional, opencsv uses null. By simply defining a bean and annotating the fields, opencsv can do all of the rest.

Actually, if you use annotations, opencsv uses reflection not introspection on reading, so all you need is a POJO plain old Java object that does not have to conform to the Java Bean Specification, but is required to be public and have a public nullary constructor.

If getters and setters are present and accessible, they are used. Otherwise, opencsv bypasses access control restrictions to get to member variables. Besides the basic mapping strategy, there are various mechanisms for processing certain kinds of data.

CSV files should have header names for all fields in the file, and these can be used to great advantage. By annotating a bean field with the name of the header whose data should be written in the field, opencsv can do all of the matching and copying for you.

This also makes you independent of the order in which the headers occur in the file. For data like this:. Here we simply name the fields identically to the header names. After that, reading is a simple job:. This will give you a list of the two beans as defined in the example input file.

Note how type conversions to basic data types wrapped and unwrapped primitives, enumerations, Strings, and java. Currency occur automatically. Input can get more complicated, though, and opencsv gives you the tools to deal with that. In this case, we have spaces in the names and one header with a number as the initial character.

Other problems can be encountered, such as international characters in header names. Additionally, we would like to require that at least the name be mandatory. But we really only want the first name. Do we have to write a custom converter? No, friends, there is an easier way:. The capture option to all of the binding annotations except the custom binding annotations, of course allows you to tell opencsv just what part of the input field should actually be considered significant.

In this example, we take everything up to but not including the first space and discard the rest. Please read the Javadoc for more details and handling of edge cases. Not every scribe of CSV files is kind enough to provide header names.

To that end, we have also accounted for the possibility that there are no headers, and data must be divined from column position. We will return to our previous input file sans header names:. Enumerations work exactly like regular primitive fields.

There is only one more thing to say about them: input is checked against the declared values of the enumeration type without regard to case.

On writing, the enumeration value will always be written exactly as declared. Converting to and from ISO currency codes via java. Currency works exactly like regular primitive fields.

We have also not considered locales other than the default locale or formatting options beyond those provided by a locale. Here we shall do all of this at the same time.

Consider this input file:. The dates are dd. For this input we create the following bean:. The date is handled with the annotation CsvDate in addition to the mapping annotation. CsvDate can take a format string, and incidentally handles all common date-type classes. See the Javadocs for more details. The format of the salary, including thousands separator and currency symbol, are dealt with using a combination of the German locale, one of many countries where the thousands separator is a dot, and CsvNumber.

CSV files are lists, right? Well, some people like lists within lists. For them, we have the ability to annotate bean fields that are declared to be some type implementing java. The input is split along this delimiter and the results are put in a Collection and assigned to the bean field.

What kind of Collection? Any kind you want. If opencsv knows it, it instantiates an implementing class for you. Some examples would doubtless illuminate my meaning. This shows us much of the power of these annotations in a few lines. It is defined to be a Collection of Floats. Note, please, the annotation CsvBindAndSplitByName or the equivalent for position always requires the type of an element of the collection being created.

Nothing else is mandatory. The next field is a List of something derived from Number. Besides that, in this line we are not satisfied with the List implementation opencsv chooses, so we specify LinkedList with the collectionType parameter to the annotation. The third field is a SortedSet of dates when a student was tardy to class. Sorted for convenience, and a set to avoid clerical errors of double entry. For this field we have specified that the string separating elements of this list in the input is one or more semicolons.

This string is always interpreted as a regular expression. Interestingly, in case we write these data out to a CSV file later, the elements of the list should be separated with a single semicolon. Perhaps someone is trying to convert the data from a older format or remove redundancies. The forth field is a list of teachers the student has. This field demonstrates the combination of collection-based fields and custom converters.

The converter, which must be derived from AbstractCsvConverter, could look like this:. The first student has never been tardy, so that list will be empty but never null. The school secretary accidentally entered a tardy for the second student twice, but this will be eliminated by the SortedSet. There are two ways of doing this. We already saw one: specify the implementation you want to use in the annotation with the parameter "collectionType". The only stipulations on the implementing class are that it be public and have a nullary constructor.

The other way is to declare the type of the bean field using the implementing class rather than the interface implemented, thus:. That class can be parameterized, naturally e. All of the other features you know, love, and depend on, such as a field being required, or support for locales, is equally well supported for Collection-based members. If Collection-based bean fields were there to split one element into many, MultiValuedMap-based bean fields are there to consolidate many elements into one.

What if you have the following input? The first difficulty you will encounter is that three columns have the same name. The second difficulty is that the number of tracks in the header might increase over time, but you want them all.

Both problems are easily solved, as are all problems in the opencsv-world:. The second field is a MultiValuedMap that collects all of the values under all of the columns with the name "Album". The first parameter is the index, and the second parameter is the value. The value should be of a type to which the elementType from the annotation is assignable. Why would we choose to use such a cumbersome data type as a MultiValuedMap to implement this feature?

Why not a simple List and everyone is happy? Two reasons: First, someone will want to know what the header was actually named on reading, and second, opencsv needs to know what the header is named when it writes beans to a CSV file.

Back to our topic, the second field will be a MultiValuedMap with exactly one key: "Artist". Under this key, there will be a list with up to three entries, in this case "Michael Jackson", "Lionel Richie" and "Stevie Wonder". It only remains to note that the type of the elements being read must always be specified for the same reason it is necessary for Collection-based bean fields. The third field sums up most of the rest of the features this annotation provides.

As you can see, the definition of the column names is a regular expression. In this annotation we have also requested a specific implementation of MultiValuedMap, which opencsv will honor. We have decided that this field is mandatory, which in this case means that at least one matching header must be in the input, and every record must have a non-empty value for at least one of the matching columns.

Given the input from above, this MultiValuedMap will have four entries, one for each column, and each of these entries will have a list of one element as its value. The elements will be the track titles. All of the usual features apply: conversion locale, combination with CsvDate, custom converters as with collection-based fields, and specifying your own implementation of MultiValuedMap either through the annotation or by defining the field with the specific implementation default implementations for the applicable interface are documented in the Javadoc for CsvBindAndJoinByName.

The latter being said, if the MultiValuedMap is already present and possibly contains values , say through the use of a constructor, it will not be overwritten, but rather added to. What about precedence? What does opencsv do with this? It follows the general computing principle of "specific trumps general": It puts any information found under the header "Track21" into the new field, not the MultiValuedMap.

The results are undefined. While minding the last caveat, it is possible to use this feature to collect everything not otherwise mapped:. Perhaps they are in different languages.

In one file, the header is:. But wait! Perhaps the first version of the CSV file only included one artist, and the other two fields for artist were added at two different points in time after that. The tracks grew over time as well. So now our input looks like this:.

In other words, first the album name, then the first artist, followed by two tracks, then the second artist followed by one more track, then the third artist again followed by one track. The bean for these data would look like this:. The first thing to notice in this example is that we have used CsvBindAndJoinByPosition, which takes a list of zero-based column numbers and ranges as its most important argument.

The list is comma-separated, and can include any number of column indices as well as closed e. Values are saved under the index of the column position they were found in. The last thing to notice is that as long as new column positions are added to the end of the file, and these are all new tracks, they will all be placed in the variable "tracks" because the column position definition from the CsvBindAndJoinByPosition annotation defines an open range starting at index 7.

As with a header-based mapping, it is possible to create a mop-up field, if no other fields are mapped with CsvBindAndJoinByPosition, by mapping to a MultiValuedMap using the fully open range expression "-". Both include ambiguous information about the source of the data, one in the form of regular expressions, and the other in the form of ranges. Once the data have been read in, there is no way from this information alone to determine which column each header came from.

That, as we have already said, is why we use a MultiValuedMap: the index gives us this vital information. That said, it should be obvious that when writing, the MultiValuedMap must be completely filled out for every bean before sending it off to be written.

That is, every index that is expected in the output must be present in the map and have at least a null value. Now, we know that input data can get very messy, so we have provided our users with the ability to deal with the messiest of data by allowing you to define your own custom converters. The custom converters here are used at the level of the entire field, not like the custom converters previously covered in collection-based and MultiValuedMap-based bean fields.

Every converter must be derived from AbstractBeanField, must be public, and must have a public nullary constructor. For reading, the convert method must be overridden. Graham and Durbin also told allies that they were stunned that the other lawmakers were present — and that Trump's tone seemed so different than it had been days or even hours before, according to people close to them. Graham declined to comment on the president's reported obscenity. He has told others in his circle that commenting would only hurt the chance of a deal and that he wants to keep a relationship with the president.

There had initially been hope for the Thursday meeting. Trump had told lawmakers during a partially televised session two days earlier that he was flexible. He even said he would be willing to lock the door of the Cabinet room if they wanted to negotiate at the White House, according to people who heard his comments. Trump went on to say at the earlier meeting that he wanted a deal and that even those in the conservative House Freedom Caucus should work with Durbin.

In the hours and days afterward, a bipartisan group of senators — Graham, Durbin, Sen. Jeff Flake R-Ariz. Robert Menendez D-N. Michael F. But some White House officials, including conservative adviser Stephen Miller, feared that Graham and Durbin would try to trick Trump into signing a bill that was damaging to him and would hurt him with his political base.

As word trickled out Thursday morning on Capitol Hill that Durbin and Graham were heading over to the White House, legislative affairs director Marc Short began to make calls to lawmakers and shared many of Miller's concerns. Soon, Goodlatte, one of the more conservative House members on immigration, was headed to the White House. David Perdue R-Ga. In the late morning, before Durbin and Graham arrived, Kelly — who had already been briefed on the deal — talked to Trump to tell him that the proposal would probably not be good for his agenda, White House officials said.

Kelly, a former secretary of homeland security, has taken an increasingly aggressive and influential role in the immigration negotiations, calling lawmakers and meeting with White House aides daily — more than he has on other topics.

He has "very strong feelings," in the words of one official. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression.

Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters. Think Python 2e is a Free Book. A previous edition of this book was published by Cambridge University press with the title Python for Software Design. This edition is available from Amazon. This is the second computer oganization course and focuses on computer hardware design.

Basic cache coherence and synchronization. This course surveys methods and algorithms used in modern operating systems. Concurrent distributed operation is emphasized. This introductory course will present basic principles of robotics with an emphasis to computer science aspects. Algorithms for planning and perception will be studied and implemented on actual robots.

While planning is a fundamental problem in artificial intelligence and decision making, robot planning refers to finding a path from A to B in the presence of obstacles and by complying with the kinematic constraints of the robot.

Perception involves the estimation of the robots motion and path as well as the shape of the environment from sensors. In this course, algorithms will be implemented in Python on mobile platforms on ground and in the air. No prior experience with Python is needed but we require knowledge of data structures, linear algebra, and basic probability. The purpose of this course is to introduce undergraduate students in computer computer science and engineering to quantum computers QC and quantum information science QIS.

This course is meant primarly for juniors and seniors in Computer Science. No prior knowledge of quantum mechanics QM is assumed. Enrollment is by permission of the instructor. Design and implementation of a significant piece of work: software, hardware or theory. In addition, emphasis on technical writing and oral communication skills. Students must have an abstract of their Senior Project, which is approved and signed by a Project Adviser, at the end of the second week of Fall classes.

The project continues during two semesters; students must enroll in CIS during the second semester. At the end of the first semester, students are required to submit an intermediate report and give a class presentation describing their project and progress. Grades are based on technical writing skills as per submitted report , oral presentation skills as per class presentation and progress on the project. These are evaluated by the Project Adviser and the Course Instructor. Senior standing or permission of instructor.

Continuation of CIS Students are required to submit a final written report and give a final presentation and demonstration of their project. Grades are based on the report, the presentation and the satisfactory completion of the project. The goal of a Senior Thesis project is to complete a major research project under the supervision of a faculty member. The duration of the project is two semesters. To enroll in CIS , students must develop an abstract of the proposed work, and a member of the CIS graduate group must certify that the work is suitable and agree to supervise the project; a second member must agree to serve as a reader.

At the end of the first semester, students must submit an intermediate report; if the supervisor and reader accept it, they can enroll in CIS At the end of the second semester, students must describe their results in a written thesis and must present them publicly, either in a talk at Penn or in a presentation at a conference or workshop. Grades are based on the quality of the research itself which should ideally be published or at least of publishable quality , as well as on the quality of the thesis and the oral presentation.

The latter are evaluated jointly by the supervisor and the reader. Senior Theses are expected to integrate the knowledge and skills from earlier coursework; because of this, students are not allowed to enroll in CIS before their sixth semester. Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real problems.

This course will introduce supervised learning decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning , unsupervised learning clustering, dimensionality reduction , and reinforcement learning.

Additionally, the course will discuss evaluation methodology and recent applications of machine learning, including large scale learning for big data and network analysis. This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include, search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic.

After a brief introduction to the language, programming assignments wil l be in Python. Hardly a week passes by without a major news article detailing the proliferation of data-driven technology, and the ethical failings of these technologies. In this active learning class, we will introduce aspiring data science technologists to the spectrum of ethical concerns, focusing on social norms like fairness, transparency and privacy. We will then introduce technical approaches to a number of these problems, including by hands-on examination of the tradeoffs in fairness and accuracy in predictive technology, introduction to differential privacy and overview of evaluation conventions for predictive technology.

Further, we will provide guidelines for examining system training data for bias, representation of race, gender and other characteristics and ecological validity. Equipped with this knowledge, students will learn how to conduct informed analysis of the usefulness of predictive systems.

They will audit for ethical concerns papers from the contemporary top artificial intelligence venues and the ongoing senior design projects. There will be weekly reading assignments and associated group activities, four technical assignments and a final. Students will implement and experiment with bias mitigation algorithms for machine learning, as well as with algorithms for differentially private computations. At the beginning of class, students will select to read one of the three class books, and after the first month of class, each group will lead a class discussion on the main takeaways of the book they picked.

The goal of this course is to develop a deeper understanding of techniques and concepts used in Computational Biology. The course will strive to focus on a small set of approaches to gain both theoretical and practical understanding of the methods.

We will aim to cover practical issues such as programming and the use of programs, as well as theoretical issues such as algorithm design, statistical data analysis, theory of algorithms and statistics. This course WILL NOT provide a broad survey of the field nor teach specific tools but focus on a deep understanding of a small set of topics. We will discuss string algorithms, hidden markov models, dimension reduction, and machine learning or phylogeny estimation for biomedical problems.

Prerequisite: Probability theory and linear algebra are highly recommended. The goal of this course is to give students greater design and implementation experience in embedded software development and to teach them how to model, design, verify, and validate safety critical systems in a principled manner.

Students will learn the principles, methods, and techniques for building life-critical embedded systems, ranging from requirements and models to design, analysis, optimization, implementation, and validation. Topics will include modeling and analysis methods and tools, real-time programming paradigms and languages, distributed real-time systems, global time, time-triggered communications, assurance case, software architecture, evidence-based certification, testing, verification, and validation.

The course will include a series of projects that implements life-critical embedded systems e. This course provides an introduction to the broad field of database and information systems, covering a variety of topics relating to structured data, ranging from data modeling to logical foundations and popular languages, to system implementations. We will study the theory of relational and XML data design; the basics of query languages; efficient storage of data, execution of queries and query optimization; transactions and updates; web-database development; and "big data" and NoSQL systems.

This course focuses on the challenges encountered in building Internet and web systems: scalability, interoperability of data and code , security and fault tolerance, consistency models, and location of resources, services, and data. We will examine how XML standards enable information exchange; how web services support cross-platform interoperability and what their limitations are ; how to build high-performance application servers; how "cloud computing" services work; how to perform Akamai-like content distribution; and how to provide transaction support in distributed environments.

We will study techniques for locating machines, resources, and data including directory systems, information retrieval indexing, ranking, and web search ; and we will investigate how different architectures support scalability and the issues they face. We will also examine ideas that have been proposed for tomorrow's Web, and we will see some of the challenges, research directions, and potential pitfalls. An important goal of the course is not simply to discuss issues and solutions, but to provide hands-on experience with a substantial implementation project.

This semester's project will be a peer-to-peer implementation of a Googe-style search engine, including distributed, scalable crawling; indexing with ranking; and even PageRank. As a side-effect of the material of this course you will learn about some aspects of large-scale software development assimilating large APIs.

Prerequisite: Familiarity with threads and concurrency, strong Java programming skills. This course focuses on programming the essential mathematical and geometric concepts underlying modern computer graphics. Using 3D interactive implementations, it covers fundamental topics such as mesh data structures, transformation sequences, rendering algorithms, and curve interpolation for animation.

The curriculum is heavily project-based, and culminates in a group project focused on building an interactive first-person world exploration application using the various real-time interaction and rendering algorithms learned throughout the semester. Prerequisites: CIS , , This course is designed to provide a comprehensive overview to computer graphics techniques in 3D modeling, image synthesis, and rendering.

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Knowledge of vector geometry is useful. This course covers core subject matter common to the fields of robotics, character animation and embodied intelligent agents. The intent of the course is to provide the student with a solid technical foundation for developing, animating and controlling articulated systems used in interactive computer game virtual reality simulations and high-end animation applications.

The course balances theory with practice by "looking under the hood" of current animation systems and authoring tools and exams the technologies and techniques used from both a computer science and engineering perspective. Topics covered include: geometric coordinate systems and transformations; quaternions; parametric curves and surfaces; forward and inverse kinematics; dynamic systems and control; computer simulation; keyframe, motion capture and procedural animation; behavior-based animation and control; facial animation; smart characters and intelligent agents.

Prerequisite: Previous exposure to major concepts inn linear algebra i. This course will focus on numerical algorithms and scientific computing techniques that are practical and efficient for a number of canonical science and engineering applications. Built on top of classical theories in multi-variable calculus and linear algebra as a prerequisite , the lectures in this course will strongly focus on explaining numerical methods for applying these mathematical theories to practical engineering problems.

Prerequisite: MATH This is the second computer organization course and focuses on computer hardware design.

Prerequisite: Knowledge of at least one programming language. The goal of this course is to provide an opportunity for seniors to define, desand execute a project of your own choosing that demonstrates the technical skiland abilities that you have acquired during your 4 years as undergraduates.

Evaluation is based on selecting an interesting topic, completing appropriate research on the state of the art in that area, communicating your objectives i writing and in presentations, accurately estimating what resources will be reqto complete your chosen task, coding necessary functionality, and executing your plan.

Senior Standing or Permission of the instructor. The Capstone Project provides an opportunity for the student to apply the theoretical ideas and tools learned from other courses. The project is usually applied, rather than theoretical, exercise, and should focus on a real-world problem related to the career goals of the student. The one-semester project may be completed in either the fall or spring term of the senior year, and must be done under the supervision of a sponsoring faculty member. To register for this course, the student must submit a detailed proposal, signed by the supervising professor and the student's faculty advisor, two weeks prior to the start of the term.

This course introduces basic concepts and techniques in the foundational study of programming languages. The central theme is the view of programs and programming languages as mathematical objects for which precise claims may be made and proved. Particular topics include operational techniques for formal definition of language features, type systems and type safety properties, polymorphism, constructive logic, and the Coq proof assistant.

This course is appropriate as an upper-level undergraduate CIS elective. Undergraduates who have satisfied the prerequisites are welcome to enroll. No permission from the instructor is needed. Prerequisite: In addition to course prerequisites, at least two additional undergraduate courses in math or theoretical CS. Knowledge of computer organization and basic programming skills. An investigation of paradigms for design and analysis of algorithms.

The course will include dynamic programming, flows and combinatorial optimization algorithms, linear programming, randomization and a brief introduction to intractability and approximation algorithms. The course will include other advanced topics, time permitting. Prerequisite: Data Structures and Algorithms at the undergraduate level. This course provides an introduction to fundamental concepts of distributed systems, and the design principles for building large scale computational systems.

Topics covered include communication, concurrency, programming paradigms, naming, managing shared state, caching, synchronization, reaching agreement, fault tolerance, security, middleware, and distributed applications. Prerequisite: Undergraduate-level knowledge of Operating Systems and Networking, programming experience.

Prerequisite: Undergraduate-level knowledge of Operating Systems and Networking. Prerequisite: CIT Review of regular and context-free languages and machine models. Advanced topics as time permits: Circuit complexity and parallel computation, randomized complexity, approximability, interaction and cryptography. Discrete Mathematics, Automata theory or Algorithms at the undergraduate level.

This course provides firm foundations in linear algebra and basic optimization techniques. Emphasis is placed on teaching methods and tools that are widely used in various areas of computer science. Both theoretical and algorithmic aspects will be discussed.

This course will examine the expressive power of various logical languages over the class of finite structures. The course begins with an exposition of some of the fundamental theorems about the behavior of first-order logic in the context of finite structures, in particular, the Ehrenfeucht-Fraisse Theorem and the Trahktenbrot Theorem.

The first of these results is used to show limitations on the expressive power of first-order logic over finite structures while the second result demonstrates that the problem of reasoning about finite structures using first-order logic is surprisingly complex.

The course then proceeds to consider various extensions of first-order logic including fixed-point operators, generalized quantifiers, infinitary languages, and higher-order languages. The expressive power of these extensions will be studied in detail and will be connected to various problems in the theory of computational complexity.

This last motif, namely the relation between descriptive and computational complexity, will be one of the main themes of the course. This course covers the foundations of statistical machine learning. The focus is on probabilistic and statistical methods for prediction and clustering in high dimensions. Elementary probability, calculus, and linear algebra. Basic programming experience.

Deep learning techniques now touch on data systems of all varieties. Sometimes, deep learning is a product; sometimes, deep learning optimizes a pipeline; sometimes, deep learning provides critical insights; sometimes, deep learning sheds light on neuroscience or vice versa.

The purpose of this course is to deconstruct the hype by teaching deep learning theories, models, skills, and applications that are useful for applications. This class introduces aspiring data science technologists to the spectrum of ethical concerns, focusing on social norms like fairness, transparency and privacy.

It introduces technical approaches to a number of these problems, including by hands-on examination of the tradeoffs in fairness and accuracy in predictive technology, introduction to differential privacy, and overview of evaluation conventions for predictive technology.

It also provides guidelines for examining system training data for bias, representation of race, gender and other characteristics and ecological validity. Equipped with this knowledge, students will learn how to conduct informed analysis of the usefulness of predictive systems; they will audit for ethical concerns papers from the contemporary top artificial intelligence venues and the ongoing senior design projects.

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