Lecture 2: Variables and Primitive Data Types
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Metaobject Protocols: Why We Want Them and What Else They Can Do
Metaobject protocols: Why we want them and what else they can do Gregor Kiczales, J.Michael Ashley, Luis Rodriguez, Amin Vahdat, and Daniel G. Bobrow Published in A. Paepcke, editor, Object-Oriented Programming: The CLOS Perspective, pages 101 ¾ 118. The MIT Press, Cambridge, MA, 1993. © Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Metaob ject Proto cols WhyWeWant Them and What Else They Can Do App ears in Object OrientedProgramming: The CLOS Perspective c Copyright 1993 MIT Press Gregor Kiczales, J. Michael Ashley, Luis Ro driguez, Amin Vahdat and Daniel G. Bobrow Original ly conceivedasaneat idea that could help solve problems in the design and implementation of CLOS, the metaobject protocol framework now appears to have applicability to a wide range of problems that come up in high-level languages. This chapter sketches this wider potential, by drawing an analogy to ordinary language design, by presenting some early design principles, and by presenting an overview of three new metaobject protcols we have designed that, respectively, control the semantics of Scheme, the compilation of Scheme, and the static paral lelization of Scheme programs. Intro duction The CLOS Metaob ject Proto col MOP was motivated by the tension b etween, what at the time, seemed liketwo con icting desires. The rst was to have a relatively small but p owerful language for doing ob ject-oriented programming in Lisp. The second was to satisfy what seemed to b e a large numb er of user demands, including: compatibility with previous languages, p erformance compara- ble to or b etter than previous implementations and extensibility to allow further exp erimentation with ob ject-oriented concepts see Chapter 2 for examples of directions in which ob ject-oriented techniques might b e pushed. -
Package 'Pinsplus'
Package ‘PINSPlus’ August 6, 2020 Encoding UTF-8 Type Package Title Clustering Algorithm for Data Integration and Disease Subtyping Version 2.0.5 Date 2020-08-06 Author Hung Nguyen, Bang Tran, Duc Tran and Tin Nguyen Maintainer Hung Nguyen <[email protected]> Description Provides a robust approach for omics data integration and disease subtyping. PIN- SPlus is fast and supports the analysis of large datasets with hundreds of thousands of sam- ples and features. The software automatically determines the optimal number of clus- ters and then partitions the samples in a way such that the results are ro- bust against noise and data perturbation (Nguyen et.al. (2019) <DOI: 10.1093/bioinformat- ics/bty1049>, Nguyen et.al. (2017)<DOI: 10.1101/gr.215129.116>). License LGPL Depends R (>= 2.10) Imports foreach, entropy , doParallel, matrixStats, Rcpp, RcppParallel, FNN, cluster, irlba, mclust RoxygenNote 7.1.0 Suggests knitr, rmarkdown, survival, markdown LinkingTo Rcpp, RcppArmadillo, RcppParallel VignetteBuilder knitr NeedsCompilation yes Repository CRAN Date/Publication 2020-08-06 21:20:02 UTC R topics documented: PINSPlus-package . .2 AML2004 . .2 KIRC ............................................3 PerturbationClustering . .4 SubtypingOmicsData . .9 1 2 AML2004 Index 13 PINSPlus-package Perturbation Clustering for data INtegration and disease Subtyping Description This package implements clustering algorithms proposed by Nguyen et al. (2017, 2019). Pertur- bation Clustering for data INtegration and disease Subtyping (PINS) is an approach for integraton of data and classification of diseases into various subtypes. PINS+ provides algorithms support- ing both single data type clustering and multi-omics data type. PINSPlus is an improved version of PINS by allowing users to customize the based clustering algorithm and perturbation methods. -
Chapter 2 Basics of Scanning And
Chapter 2 Basics of Scanning and Conventional Programming in Java In this chapter, we will introduce you to an initial set of Java features, the equivalent of which you should have seen in your CS-1 class; the separation of problem, representation, algorithm and program – four concepts you have probably seen in your CS-1 class; style rules with which you are probably familiar, and scanning - a general class of problems we see in both computer science and other fields. Each chapter is associated with an animating recorded PowerPoint presentation and a YouTube video created from the presentation. It is meant to be a transcript of the associated presentation that contains little graphics and thus can be read even on a small device. You should refer to the associated material if you feel the need for a different instruction medium. Also associated with each chapter is hyperlinked code examples presented here. References to previously presented code modules are links that can be traversed to remind you of the details. The resources for this chapter are: PowerPoint Presentation YouTube Video Code Examples Algorithms and Representation Four concepts we explicitly or implicitly encounter while programming are problems, representations, algorithms and programs. Programs, of course, are instructions executed by the computer. Problems are what we try to solve when we write programs. Usually we do not go directly from problems to programs. Two intermediate steps are creating algorithms and identifying representations. Algorithms are sequences of steps to solve problems. So are programs. Thus, all programs are algorithms but the reverse is not true. -
Abstract Data Types
Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code. -
5. Data Types
IEEE FOR THE FUNCTIONAL VERIFICATION LANGUAGE e Std 1647-2011 5. Data types The e language has a number of predefined data types, including the integer and Boolean scalar types common to most programming languages. In addition, new scalar data types (enumerated types) that are appropriate for programming, modeling hardware, and interfacing with hardware simulators can be created. The e language also provides a powerful mechanism for defining OO hierarchical data structures (structs) and ordered collections of elements of the same type (lists). The following subclauses provide a basic explanation of e data types. 5.1 e data types Most e expressions have an explicit data type, as follows: — Scalar types — Scalar subtypes — Enumerated scalar types — Casting of enumerated types in comparisons — Struct types — Struct subtypes — Referencing fields in when constructs — List types — The set type — The string type — The real type — The external_pointer type — The “untyped” pseudo type Certain expressions, such as HDL objects, have no explicit data type. See 5.2 for information on how these expressions are handled. 5.1.1 Scalar types Scalar types in e are one of the following: numeric, Boolean, or enumerated. Table 17 shows the predefined numeric and Boolean types. Both signed and unsigned integers can be of any size and, thus, of any range. See 5.1.2 for information on how to specify the size and range of a scalar field or variable explicitly. See also Clause 4. 5.1.2 Scalar subtypes A scalar subtype can be named and created by using a scalar modifier to specify the range or bit width of a scalar type. -
Csci 658-01: Software Language Engineering Python 3 Reflexive
CSci 658-01: Software Language Engineering Python 3 Reflexive Metaprogramming Chapter 3 H. Conrad Cunningham 4 May 2018 Contents 3 Decorators and Metaclasses 2 3.1 Basic Function-Level Debugging . .2 3.1.1 Motivating example . .2 3.1.2 Abstraction Principle, staying DRY . .3 3.1.3 Function decorators . .3 3.1.4 Constructing a debug decorator . .4 3.1.5 Using the debug decorator . .6 3.1.6 Case study review . .7 3.1.7 Variations . .7 3.1.7.1 Logging . .7 3.1.7.2 Optional disable . .8 3.2 Extended Function-Level Debugging . .8 3.2.1 Motivating example . .8 3.2.2 Decorators with arguments . .9 3.2.3 Prefix decorator . .9 3.2.4 Reformulated prefix decorator . 10 3.3 Class-Level Debugging . 12 3.3.1 Motivating example . 12 3.3.2 Class-level debugger . 12 3.3.3 Variation: Attribute access debugging . 14 3.4 Class Hierarchy Debugging . 16 3.4.1 Motivating example . 16 3.4.2 Review of objects and types . 17 3.4.3 Class definition process . 18 3.4.4 Changing the metaclass . 20 3.4.5 Debugging using a metaclass . 21 3.4.6 Why metaclasses? . 22 3.5 Chapter Summary . 23 1 3.6 Exercises . 23 3.7 Acknowledgements . 23 3.8 References . 24 3.9 Terms and Concepts . 24 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Note: This chapter adapts David Beazley’s debugly example presentation from his Python 3 Metaprogramming tutorial at PyCon’2013 [Beazley 2013a]. -
Chapter 4 Variables and Data Types
PROG0101 Fundamentals of Programming PROG0101 FUNDAMENTALS OF PROGRAMMING Chapter 4 Variables and Data Types 1 PROG0101 Fundamentals of Programming Variables and Data Types Topics • Variables • Constants • Data types • Declaration 2 PROG0101 Fundamentals of Programming Variables and Data Types Variables • A symbol or name that stands for a value. • A variable is a value that can change. • Variables provide temporary storage for information that will be needed during the lifespan of the computer program (or application). 3 PROG0101 Fundamentals of Programming Variables and Data Types Variables Example: z = x + y • This is an example of programming expression. • x, y and z are variables. • Variables can represent numeric values, characters, character strings, or memory addresses. 4 PROG0101 Fundamentals of Programming Variables and Data Types Variables • Variables store everything in your program. • The purpose of any useful program is to modify variables. • In a program every, variable has: – Name (Identifier) – Data Type – Size – Value 5 PROG0101 Fundamentals of Programming Variables and Data Types Types of Variable • There are two types of variables: – Local variable – Global variable 6 PROG0101 Fundamentals of Programming Variables and Data Types Types of Variable • Local variables are those that are in scope within a specific part of the program (function, procedure, method, or subroutine, depending on the programming language employed). • Global variables are those that are in scope for the duration of the programs execution. They can be accessed by any part of the program, and are read- write for all statements that access them. 7 PROG0101 Fundamentals of Programming Variables and Data Types Types of Variable MAIN PROGRAM Subroutine Global Variables Local Variable 8 PROG0101 Fundamentals of Programming Variables and Data Types Rules in Naming a Variable • There a certain rules in naming variables (identifier). -
Kind of Quantity’
NIST Technical Note 2034 Defning ‘kind of quantity’ David Flater This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 NIST Technical Note 2034 Defning ‘kind of quantity’ David Flater Software and Systems Division Information Technology Laboratory This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 February 2019 U.S. Department of Commerce Wilbur L. Ross, Jr., Secretary National Institute of Standards and Technology Walter Copan, NIST Director and Undersecretary of Commerce for Standards and Technology Certain commercial entities, equipment, or materials may be identifed in this document in order to describe an experimental procedure or concept adequately. Such identifcation is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. National Institute of Standards and Technology Technical Note 2034 Natl. Inst. Stand. Technol. Tech. Note 2034, 7 pages (February 2019) CODEN: NTNOEF This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 NIST Technical Note 2034 1 Defning ‘kind of quantity’ David Flater 2019-02-06 This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 Abstract The defnition of ‘kind of quantity’ given in the International Vocabulary of Metrology (VIM), 3rd edition, does not cover the historical meaning of the term as it is most commonly used in metrology. Most of its historical meaning has been merged into ‘quantity,’ which is polysemic across two layers of abstraction. -
Source Code Auditing: Day 2
Source Code Auditing: Day 2 Penetration Testing & Vulnerability Analysis Brandon Edwards [email protected] Data Types Continued Data Type Signedness Remember, by default all data types are signed unless specifically declared otherwise But many functions which accept size arguments take unsigned values What is the difference of the types below? char y; unsigned char x; x = 255; y = -1; 3 Data Type Signedness These types are the same size (8-bits) char y; unsigned char x; 4 Data Type Signedness A large value in the unsigned type (highest bit set) is a negative value in the signed type char y; unsigned char x; 5 Data Type Bugs Same concept applies to 16 and 32 bit data types What are the implications of mixing signed & unsigned types ? #define MAXSOCKBUF 4096 int readNetworkData(int sock) { char buf[MAXSOCKBUF]; int length; read(sock, (char *)&length, 4); if (length < MAXSOCKBUF) { read(sock, buf, length); } } 6 Data Type Signedness The check is between two signed values… #define MAXSOCKBUF 4096 if (length < MAXSOCKBUF) So if length is negative (highest bit / signed bit set), it will evaluate as less than MAXSOCKBUF But the read() function takes only unsigned values for it’s size Remember, the highest bit (or signed bit is set), and the compiler implicitly converts the length to unsigned for read() 7 Data Type Signedness So what if length is -1 (or 0xFFFFFFFF in hex)? #define MAXSOCKBUF 4096 if (length < MAXSOCKBUF) { read(sock, buf, length); } When the length check is performed, it is asking if -1 is less than 4096 When the length is passed to read, it is converted to unsigned and becomes the unsigned equivalent of -1, which for 32bits is 4294967295 8 Data Type Bugs Variation in data type sizes can also introduce bugs Remember the primitive data type sizes? (x86): An integer type is 32bits A short type is 16bits A char type is 8 bits Sometimes code is written without considering differences between these. -
Subtyping Recursive Types
ACM Transactions on Programming Languages and Systems, 15(4), pp. 575-631, 1993. Subtyping Recursive Types Roberto M. Amadio1 Luca Cardelli CNRS-CRIN, Nancy DEC, Systems Research Center Abstract We investigate the interactions of subtyping and recursive types, in a simply typed λ-calculus. The two fundamental questions here are whether two (recursive) types are in the subtype relation, and whether a term has a type. To address the first question, we relate various definitions of type equivalence and subtyping that are induced by a model, an ordering on infinite trees, an algorithm, and a set of type rules. We show soundness and completeness between the rules, the algorithm, and the tree semantics. We also prove soundness and a restricted form of completeness for the model. To address the second question, we show that to every pair of types in the subtype relation we can associate a term whose denotation is the uniquely determined coercion map between the two types. Moreover, we derive an algorithm that, when given a term with implicit coercions, can infer its least type whenever possible. 1This author's work has been supported in part by Digital Equipment Corporation and in part by the Stanford-CNR Collaboration Project. Page 1 Contents 1. Introduction 1.1 Types 1.2 Subtypes 1.3 Equality of Recursive Types 1.4 Subtyping of Recursive Types 1.5 Algorithm outline 1.6 Formal development 2. A Simply Typed λ-calculus with Recursive Types 2.1 Types 2.2 Terms 2.3 Equations 3. Tree Ordering 3.1 Subtyping Non-recursive Types 3.2 Folding and Unfolding 3.3 Tree Expansion 3.4 Finite Approximations 4. -
The Art of the Javascript Metaobject Protocol
The Art Of The Javascript Metaobject Protocol enough?Humphrey Ephraim never recalculate remains giddying: any precentorship she expostulated exasperated her nuggars west, is brocade Gus consultative too around-the-clock? and unbloody If dog-cheapsycophantical and or secularly, norman Partha how slicked usually is volatilisingPenrod? his nomadism distresses acceptedly or interlacing Card, and send an email to a recipient with. On Auslegung auf are Schallabstrahlung download the Aerodynamik von modernen Flugtriebwerken. This poll i send a naming convention, the art of metaobject protocol for the corresponding to. What might happen, for support, if you should load monkeypatched code in one ruby thread? What Hooks does Ruby have for Metaprogramming? Sass, less, stylus, aura, etc. If it finds one, it calls that method and passes itself as value object. What bin this optimization achieve? JRuby and the psd. Towards a new model of abstraction in software engineering. Buy Online in Aruba at aruba. The current run step approach is: Checkpoint. Python object room to provide usable string representations of hydrogen, one used for debugging and logging, another for presentation to end users. Method handles can we be used to implement polymorphic inline caches. Mop is not the metaobject? Rails is a nicely designed web framework. Get two FREE Books of character Moment sampler! The download the number IS still thought. This proxy therefore behaves equivalently to the card dispatch function, and no methods will be called on the proxy dispatcher before but real dispatcher is available. While desertcart makes reasonable efforts to children show products available in your kid, some items may be cancelled if funny are prohibited for import in Aruba. -
Julia's Efficient Algorithm for Subtyping Unions and Covariant
Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples Benjamin Chung Northeastern University, Boston, MA, USA [email protected] Francesco Zappa Nardelli Inria of Paris, Paris, France [email protected] Jan Vitek Northeastern University, Boston, MA, USA Czech Technical University in Prague, Czech Republic [email protected] Abstract The Julia programming language supports multiple dispatch and provides a rich type annotation language to specify method applicability. When multiple methods are applicable for a given call, Julia relies on subtyping between method signatures to pick the correct method to invoke. Julia’s subtyping algorithm is surprisingly complex, and determining whether it is correct remains an open question. In this paper, we focus on one piece of this problem: the interaction between union types and covariant tuples. Previous work normalized unions inside tuples to disjunctive normal form. However, this strategy has two drawbacks: complex type signatures induce space explosion, and interference between normalization and other features of Julia’s type system. In this paper, we describe the algorithm that Julia uses to compute subtyping between tuples and unions – an algorithm that is immune to space explosion and plays well with other features of the language. We prove this algorithm correct and complete against a semantic-subtyping denotational model in Coq. 2012 ACM Subject Classification Theory of computation → Type theory Keywords and phrases Type systems, Subtyping, Union types Digital Object Identifier 10.4230/LIPIcs.ECOOP.2019.24 Category Pearl Supplement Material ECOOP 2019 Artifact Evaluation approved artifact available at https://dx.doi.org/10.4230/DARTS.5.2.8 Acknowledgements The authors thank Jiahao Chen for starting us down the path of understanding Julia, and Jeff Bezanson for coming up with Julia’s subtyping algorithm.