Primitive Data Types
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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. -
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. -
Lecture 2: Variables and Primitive Data Types
Lecture 2: Variables and Primitive Data Types MIT-AITI Kenya 2005 1 In this lecture, you will learn… • What a variable is – Types of variables – Naming of variables – Variable assignment • What a primitive data type is • Other data types (ex. String) MIT-Africa Internet Technology Initiative 2 ©2005 What is a Variable? • In basic algebra, variables are symbols that can represent values in formulas. • For example the variable x in the formula f(x)=x2+2 can represent any number value. • Similarly, variables in computer program are symbols for arbitrary data. MIT-Africa Internet Technology Initiative 3 ©2005 A Variable Analogy • Think of variables as an empty box that you can put values in. • We can label the box with a name like “Box X” and re-use it many times. • Can perform tasks on the box without caring about what’s inside: – “Move Box X to Shelf A” – “Put item Z in box” – “Open Box X” – “Remove contents from Box X” MIT-Africa Internet Technology Initiative 4 ©2005 Variables Types in Java • Variables in Java have a type. • The type defines what kinds of values a variable is allowed to store. • Think of a variable’s type as the size or shape of the empty box. • The variable x in f(x)=x2+2 is implicitly a number. • If x is a symbol representing the word “Fish”, the formula doesn’t make sense. MIT-Africa Internet Technology Initiative 5 ©2005 Java Types • Integer Types: – int: Most numbers you’ll deal with. – long: Big integers; science, finance, computing. – short: Small integers. -
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). -
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. -
Subtyping, Declaratively an Exercise in Mixed Induction and Coinduction
Subtyping, Declaratively An Exercise in Mixed Induction and Coinduction Nils Anders Danielsson and Thorsten Altenkirch University of Nottingham Abstract. It is natural to present subtyping for recursive types coin- ductively. However, Gapeyev, Levin and Pierce have noted that there is a problem with coinductive definitions of non-trivial transitive inference systems: they cannot be \declarative"|as opposed to \algorithmic" or syntax-directed|because coinductive inference systems with an explicit rule of transitivity are trivial. We propose a solution to this problem. By using mixed induction and coinduction we define an inference system for subtyping which combines the advantages of coinduction with the convenience of an explicit rule of transitivity. The definition uses coinduction for the structural rules, and induction for the rule of transitivity. We also discuss under what condi- tions this technique can be used when defining other inference systems. The developments presented in the paper have been mechanised using Agda, a dependently typed programming language and proof assistant. 1 Introduction Coinduction and corecursion are useful techniques for defining and reasoning about things which are potentially infinite, including streams and other (poten- tially) infinite data types (Coquand 1994; Gim´enez1996; Turner 2004), process congruences (Milner 1990), congruences for functional programs (Gordon 1999), closures (Milner and Tofte 1991), semantics for divergence of programs (Cousot and Cousot 1992; Hughes and Moran 1995; Leroy and Grall 2009; Nakata and Uustalu 2009), and subtyping relations for recursive types (Brandt and Henglein 1998; Gapeyev et al. 2002). However, the use of coinduction can lead to values which are \too infinite”. For instance, a non-trivial binary relation defined as a coinductive inference sys- tem cannot include the rule of transitivity, because a coinductive reading of transitivity would imply that every element is related to every other (to see this, build an infinite derivation consisting solely of uses of transitivity). -
Generic Programming
Generic Programming July 21, 1998 A Dagstuhl Seminar on the topic of Generic Programming was held April 27– May 1, 1998, with forty seven participants from ten countries. During the meeting there were thirty seven lectures, a panel session, and several problem sessions. The outcomes of the meeting include • A collection of abstracts of the lectures, made publicly available via this booklet and a web site at http://www-ca.informatik.uni-tuebingen.de/dagstuhl/gpdag.html. • Plans for a proceedings volume of papers submitted after the seminar that present (possibly extended) discussions of the topics covered in the lectures, problem sessions, and the panel session. • A list of generic programming projects and open problems, which will be maintained publicly on the World Wide Web at http://www-ca.informatik.uni-tuebingen.de/people/musser/gp/pop/index.html http://www.cs.rpi.edu/˜musser/gp/pop/index.html. 1 Contents 1 Motivation 3 2 Standards Panel 4 3 Lectures 4 3.1 Foundations and Methodology Comparisons ........ 4 Fundamentals of Generic Programming.................. 4 Jim Dehnert and Alex Stepanov Automatic Program Specialization by Partial Evaluation........ 4 Robert Gl¨uck Evaluating Generic Programming in Practice............... 6 Mehdi Jazayeri Polytypic Programming........................... 6 Johan Jeuring Recasting Algorithms As Objects: AnAlternativetoIterators . 7 Murali Sitaraman Using Genericity to Improve OO Designs................. 8 Karsten Weihe Inheritance, Genericity, and Class Hierarchies.............. 8 Wolf Zimmermann 3.2 Programming Methodology ................... 9 Hierarchical Iterators and Algorithms................... 9 Matt Austern Generic Programming in C++: Matrix Case Study........... 9 Krzysztof Czarnecki Generative Programming: Beyond Generic Programming........ 10 Ulrich Eisenecker Generic Programming Using Adaptive and Aspect-Oriented Programming . -
Does Personality Matter? Temperament and Character Dimensions in Panic Subtypes
325 Arch Neuropsychiatry 2018;55:325−329 RESEARCH ARTICLE https://doi.org/10.5152/npa.2017.20576 Does Personality Matter? Temperament and Character Dimensions in Panic Subtypes Antonio BRUNO1 , Maria Rosaria Anna MUSCATELLO1 , Gianluca PANDOLFO1 , Giulia LA CIURA1 , Diego QUATTRONE2 , Giuseppe SCIMECA1 , Carmela MENTO1 , Rocco A. ZOCCALI1 1Department of Psychiatry, University of Messina, Messina, Italy 2MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom ABSTRACT Introduction: Symptomatic heterogeneity in the clinical presentation of and 12.78% of the total variance. Correlations analyses showed that Panic Disorder (PD) has lead to several attempts to identify PD subtypes; only “Somato-dissociative” factor was significantly correlated with however, no studies investigated the association between temperament T.C.I. “Self-directedness” (p<0.0001) and “Cooperativeness” (p=0.009) and character dimensions and PD subtypes. The study was aimed to variables. Results from the regression analysis indicate that the predictor verify whether personality traits were differentially related to distinct models account for 33.3% and 24.7% of the total variance respectively symptom dimensions. in “Somatic-dissociative” (p<0.0001) and “Cardiologic” (p=0.007) factors, while they do not show statistically significant effects on “Respiratory” Methods: Seventy-four patients with PD were assessed by the factor (p=0.222). After performing stepwise regression analysis, “Self- Mini-International Neuropsychiatric Interview (M.I.N.I.), and the directedness” resulted the unique predictor of “Somato-dissociative” Temperament and Character Inventory (T.C.I.). Thirteen panic symptoms factor (R²=0.186; β=-0.432; t=-4.061; p<0.0001). from the M.I.N.I.