Expressing Types as values using GADTS in scala and various other techniques. Using Schemas in this style allows subsequent derivation ot Typeclass instances (and other fun things)
If you want to play around with a minimal example of the involved code here you go: https://scalafiddle.io/sf/YSrSwQy/1
Side by Side - Scala and Java Adaptations of Martin Fowler’s Javascript Refac...Philip Schwarz
Java’s records, sealed interfaces and text blocks are catching up with Scala’s case classes, sealed traits and multiline strings
Judge for yourself in this quick IDE-based visual comparison
of the Scala and Java translations of Martin Fowler’s refactored Javascript code.
Final tagless. The topic strikes fear into the hearts of Scala developers everywhere—and not without reason. Final tagless allows developers to build composable Domain Specific Languages (DSLs) that model interaction with the outside world. Programs written using the final tagless style can be tested deterministically and reasoned about at compile-time. Yet the technique requires confusing, compiler-choking higher-kinded types, like `F[_]`, and pervasive, non-inferable context bounds like `F[_]: Concurrent: Console: Logging`. Many have looked at final tagless and wondered if all the layers of complexity and ceremony are really worth the benefits.
In this presentation, John A. De Goes provides a gentle and accessible introduction to final tagless, explaining what it is and the problem it intends to solve. John shows that while final tagless is easier to use than free monads, the technique suffers from a litany of drawbacks that push developers away from functional programming in Scala. John then introduces a novel approach that shares some of the benefits of final tagless, but which is idiomatic Scala, easy to explain, doesn’t need any complex type machinery, provides flawless type inference, and works beautifully across Scala 2.x and Scala 3.
Come join John for an evening of fun as you learn how to write functional code in Scala that's easy to test and easy to reason about—all without the complexity of free monads or final tagless.
This document introduces functional programming concepts like functors, applicatives, and monads in Scala. It defines type classes like Functor, Applicative, and Monad to capture these concepts. Implementations for Option and List are provided as examples. Laws are discussed to ensure type class instances behave as expected. The Cats library is introduced as providing pre-defined type classes and instances that can be used instead of rolling your own.
This document summarizes a talk on using the Scalaz library. It introduces key Scalaz concepts like typeclasses, monoids, and validation. Typeclasses allow uniform implementation of common patterns across types. Monoids represent structures with an associative binary operation and an identity. Validation provides an applicative way to represent failure without exceptions. The document gives examples of using these concepts to represent positions, filters, and compose validations.
Forget everything you know about Template Haskell. We will enter the beautiful world of Typed Template Haskell where its only possible to construct well-scoped and well-typed terms. Why? By writing our program in multiple stages, we can guarantee to eliminate the interpretative overhead caused by abstraction.
Matthew will give an introduction to Typed Template Haskell and then describe some examples of how to construct abstract and efficient programs using techniques from multi-stage programming. We will construct an "efficient" power function and also give a sketch of how to implement a SQL query compiler as inspired by the functional pearl "A SQL to C Compiler in 500 Lines of Code".
Matthew is a PhD student at the University of Bristol focusing on program generation with applications to optimisation. In the quest to write the perfect program he has become a regular contributor to GHC where he has recently been working on making the compiler easier to extend by using source plugins.
Делаем пользовательское Api на базе ShapelessВадим Челышов
Shapeless is used to create a generic and type-level API for Spark jobs. It allows defining job arguments and context in a typesafe way using HList and type classes. The boilerplate of defining job arguments and context is eliminated by using Shapeless combinators and type level programming.
The document discusses Arrow, a generalization of functions in Scala. Arrows can represent computations with multiple inputs through tuples. The document defines Arrow traits and shows examples of Arrow combinators like first, second, split, and combine. It also compares Arrows to Applicatives and Monads. Arrows provide building blocks for functional reactive programming but have clumsy syntax. The document further discusses Arrow extensions like ArrowChoice and ArrowLoop, which enables feedback loops through lazy evaluation. It concludes with Comonads, which consume context, and MonadFix, which enables stackless recursion.
Scalaz 8 is the latest edition of the popular functional programming library for Scala. In this whirlwind tour, maintainer John A. De Goes discusses some of the hottest features of Scalaz 8, including all of the following:
* A fast, concurrent, and leak-free effect system, which has small, composable, and powerful primitives for building practical, real-world software;
* A non-linear type class hierarchy, which permits a more powerful hierarchy that infers well without devastating ambiguous implicit errors;
* A new encoding for abstractions in category theory that providers higher fidelity and enables new categories of useful software to be developed;
* A Scala 2.12 encoding of opaque types that powers improved performance and better developer UX.
In this tour, you’ll see how the design of Scalaz 8 was inspired by a desire to provide Scala developers with a principled, performant, and pragmatic library that never sacrifices the safety and equational reasoning properties of functional programming. You’ll see live code snippets that show you how solving complex real world problems is simpler, faster, safer, and more reasonable than in previous versions of Scalaz. And hopefully you’ll be inspired at just how far functional programming in Scala has come in the past decade.
Side by Side - Scala and Java Adaptations of Martin Fowler’s Javascript Refac...Philip Schwarz
Java’s records, sealed interfaces and text blocks are catching up with Scala’s case classes, sealed traits and multiline strings
Judge for yourself in this quick IDE-based visual comparison
of the Scala and Java translations of Martin Fowler’s refactored Javascript code.
Final tagless. The topic strikes fear into the hearts of Scala developers everywhere—and not without reason. Final tagless allows developers to build composable Domain Specific Languages (DSLs) that model interaction with the outside world. Programs written using the final tagless style can be tested deterministically and reasoned about at compile-time. Yet the technique requires confusing, compiler-choking higher-kinded types, like `F[_]`, and pervasive, non-inferable context bounds like `F[_]: Concurrent: Console: Logging`. Many have looked at final tagless and wondered if all the layers of complexity and ceremony are really worth the benefits.
In this presentation, John A. De Goes provides a gentle and accessible introduction to final tagless, explaining what it is and the problem it intends to solve. John shows that while final tagless is easier to use than free monads, the technique suffers from a litany of drawbacks that push developers away from functional programming in Scala. John then introduces a novel approach that shares some of the benefits of final tagless, but which is idiomatic Scala, easy to explain, doesn’t need any complex type machinery, provides flawless type inference, and works beautifully across Scala 2.x and Scala 3.
Come join John for an evening of fun as you learn how to write functional code in Scala that's easy to test and easy to reason about—all without the complexity of free monads or final tagless.
This document introduces functional programming concepts like functors, applicatives, and monads in Scala. It defines type classes like Functor, Applicative, and Monad to capture these concepts. Implementations for Option and List are provided as examples. Laws are discussed to ensure type class instances behave as expected. The Cats library is introduced as providing pre-defined type classes and instances that can be used instead of rolling your own.
This document summarizes a talk on using the Scalaz library. It introduces key Scalaz concepts like typeclasses, monoids, and validation. Typeclasses allow uniform implementation of common patterns across types. Monoids represent structures with an associative binary operation and an identity. Validation provides an applicative way to represent failure without exceptions. The document gives examples of using these concepts to represent positions, filters, and compose validations.
Forget everything you know about Template Haskell. We will enter the beautiful world of Typed Template Haskell where its only possible to construct well-scoped and well-typed terms. Why? By writing our program in multiple stages, we can guarantee to eliminate the interpretative overhead caused by abstraction.
Matthew will give an introduction to Typed Template Haskell and then describe some examples of how to construct abstract and efficient programs using techniques from multi-stage programming. We will construct an "efficient" power function and also give a sketch of how to implement a SQL query compiler as inspired by the functional pearl "A SQL to C Compiler in 500 Lines of Code".
Matthew is a PhD student at the University of Bristol focusing on program generation with applications to optimisation. In the quest to write the perfect program he has become a regular contributor to GHC where he has recently been working on making the compiler easier to extend by using source plugins.
Делаем пользовательское Api на базе ShapelessВадим Челышов
Shapeless is used to create a generic and type-level API for Spark jobs. It allows defining job arguments and context in a typesafe way using HList and type classes. The boilerplate of defining job arguments and context is eliminated by using Shapeless combinators and type level programming.
The document discusses Arrow, a generalization of functions in Scala. Arrows can represent computations with multiple inputs through tuples. The document defines Arrow traits and shows examples of Arrow combinators like first, second, split, and combine. It also compares Arrows to Applicatives and Monads. Arrows provide building blocks for functional reactive programming but have clumsy syntax. The document further discusses Arrow extensions like ArrowChoice and ArrowLoop, which enables feedback loops through lazy evaluation. It concludes with Comonads, which consume context, and MonadFix, which enables stackless recursion.
Scalaz 8 is the latest edition of the popular functional programming library for Scala. In this whirlwind tour, maintainer John A. De Goes discusses some of the hottest features of Scalaz 8, including all of the following:
* A fast, concurrent, and leak-free effect system, which has small, composable, and powerful primitives for building practical, real-world software;
* A non-linear type class hierarchy, which permits a more powerful hierarchy that infers well without devastating ambiguous implicit errors;
* A new encoding for abstractions in category theory that providers higher fidelity and enables new categories of useful software to be developed;
* A Scala 2.12 encoding of opaque types that powers improved performance and better developer UX.
In this tour, you’ll see how the design of Scalaz 8 was inspired by a desire to provide Scala developers with a principled, performant, and pragmatic library that never sacrifices the safety and equational reasoning properties of functional programming. You’ll see live code snippets that show you how solving complex real world problems is simpler, faster, safer, and more reasonable than in previous versions of Scalaz. And hopefully you’ll be inspired at just how far functional programming in Scala has come in the past decade.
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
This document provides an overview of Scalaz and functional programming concepts like Functor, Applicative, and Monad as they relate to the Option type in Scalaz. It discusses Option syntax, constructing Options, working with Options using fold, map, and other methods, and defines Option as an instance of Functor, Applicative, and Monoid. It also briefly touches on concepts like context bounds and alternative monoids for Option.
For decades, the Functor, Monoid, and Foldable type class hierarchies have dominated functional programming. Implemented in libraries like Scalaz and Cats, these type classes have an ancient origin in Haskell, and they have repeatedly proven useful for advanced functional programmers, who use them to maximize code reuse and increase code correctness.
Yet, as these type classes have been copied into Scala and aged, there is a growing awareness of their drawbacks, ranging from being difficult to teach to weird operators that don’t make sense in Scala (ap from Applicative), to overlapping and lawless type classes (Semigroupal), to a complete inability to abstract over data types that possess related structure (such as isomorphic applicatives).
In this presentation, John A. De Goes introduces a new Scala library with a completely different factoring of functional type classes—one which throws literally everything away and starts from a clean slate. In this new factoring, type classes leverage Scala’s strengths, including variance and modularity. Pieces fit together cleanly and uniformly, and in a way that satisfies existing use cases, but enables new ones never before possible. Finally, type classes are named, organized, and described in a way that makes teaching them easier, without compromising on algebraic principles.
If you’ve ever thought functional type classes were too impractical or too confusing or too restrictive, now’s your chance to get a fresh perspective on a library that just might make understanding functional programming easier than ever before!
This document discusses principled error handling in Scala programs. It introduces the MonadError type class for representing programs that can fail with errors. While useful, MonadError has limitations in representing error handling in the type system. The document proposes an ErrorControl type class that uses two type constructors to represent fallible and non-fallible program types. ErrorControl allows error handling to be represented more precisely in types. Several instances of ErrorControl are discussed, including for IO, StateT, and EitherT. In conclusion, error handling is challenging and the document explores some alternatives to representing it in programs.
The document discusses functions in C programming. It begins by explaining what functions are and why they are useful. Functions help modularize programs, avoid code repetition, and allow for software reusability. Key benefits of using functions include divide and conquer programming and abstraction. The document then provides examples of function definitions, prototypes, parameters, return values, and scope. It also discusses calling functions, libraries, recursion, and other concepts related to functions in C.
Type classes 101 - classification beyond inheritanceAlexey Raga
Inheritance and interfaces implementation are often used in languages like Java in order to express "Is-a" and "Can-do" capabilities. In Scala we can do better by separating these concerns using the concept of type classes.
This document discusses arrays in C programming. It defines an array as a collection of variables of the same type that are referenced by a common name. It describes single-dimensional and multi-dimensional arrays. Single-dimensional arrays are comprised of finite, homogeneous elements while multi-dimensional arrays have elements that are themselves arrays. The document provides examples of declaring, initializing, accessing, and implementing arrays in memory for both single and double-dimensional arrays. It includes sample programs demonstrating various array operations.
This document discusses writing a domain specific language (DSL) for data transformations using applicative functors in Scala. It introduces the concepts of Picker, Reader, and Result to parse heterogeneous data formats into a common format. Reader is defined as an applicative functor to allow combining multiple readers. Later, Reader is enhanced to take type parameters for both input and output to avoid reparsing data and support XML parsing. Type lambdas are used to make Reader work as an applicative functor.
In this presentation, You will get to know about Function Literal,Higher Order Function,Partial Function,Partial Applied Function,Nested Function,Closures.
Scalaz is a library that provides new datatypes like Validation and NonEmptyList as well as extensions to standard classes like Option and List. It implements general functions using ad-hoc polymorphism through traits, implicit parameters, and implicit conversions. Some key features include typeclasses for monoid, functor, monad, and more. The library uses implicit conversions called "pimps" to extend existing types with these functions and typeclasses in a clean way.
An array is a collection of homogeneous data items stored in successive memory locations. It allows storing multiple elements of the same type using a single name. Elements in an array can be accessed using the array name and index number. Common operations on arrays include storing/retrieving elements, searching for a particular element or largest/smallest element, and calculating sum of elements based on certain criteria like even/odd indexing. Arrays can also be modified by inserting or deleting elements.
Scala is a multi-paradigm programming language that supports functional, object-oriented and imperative programming paradigms. The document discusses Scala's support for types, expressions, objects, functions, and provides examples of using Scala for expressions, functions, control structures, patterns, spaces and actors.
Testing in the World of Functional ProgrammingLuka Jacobowitz
This document summarizes a presentation about testing in functional programming. It discusses:
1. Property-based testing and how it can find bugs early by generating many test cases, including edge cases. However, it can be painful to write tests for complex functions interacting with external systems.
2. Mocking external dependencies to test internal logic, made possible through techniques like tagless final which separate interfaces from implementations. Complex interactions can be modeled with state machines.
3. Other FP testing techniques like newtypes, refined types, and libraries that make testing easier by restricting valid inputs. The presentation provides examples of testing parsing, network calls and database access in isolation.
Moore's Law may be dead, but CPUs acquire more cores every year. If you want to minimize response latency in your application, you need to use every last core - without wasting resources that would destroy performance and throughput. Traditional locks grind threads to a halt and are prone to deadlocks, and although actors provide a saner alternative, they compose poorly and are at best weakly-typed.
In this presentation, created exclusively for Scalar Conf, John reveals a new alternative to the horrors of traditional concurrency, directly comparing it to other leading approaches in Scala. Witness the beauty, simplicity, and power of declarative concurrency, as you discover how functional programming is the most practical solution to solving the tough challenges of concurrency.
This document summarizes a presentation about type classes in Scala. It introduces type classes like Monoid and Functor. It provides examples of Monoid instances for types like Int and Option. It explains how to define type classes for new types. It shows how to generalize functions like sequence and traverse to work for any Applicative functor using type classes. Finally, it discusses related concepts like Semigroup, Semigroupal functors, and NonEmptyList.
The Ring programming language version 1.8 book - Part 29 of 202Mahmoud Samir Fayed
This document provides information on mathematical functions available in the Ring programming language. It lists common trigonometric, logarithmic, exponential and other mathematical functions like sin(), cos(), tan(), log(), sqrt(), random() and provides examples of using some of these functions to calculate values.
The document summarizes Scala, a functional programming language that runs on the Java Virtual Machine (JVM). It discusses Scala's core features like being object-oriented, type inference, and support for functional programming with immutable data structures and passing functions as parameters. It also provides examples of using Scala collections like List and Array, and functions like map, filter, flatMap, and foldLeft/reduceLeft. Finally, it demonstrates using Scala for domain-specific languages and shows examples of defining DSLs for querying and generating JavaScript.
The document provides information about error handling in Python programming. It discusses different types of exceptions that may occur during program execution and how to handle them using try, except, else and finally blocks. It gives examples of programs that handle errors from inputting non-integer values or dividing by zero. The document also covers other Python programming concepts like lists, random numbers, and comparing Python to C/C++.
The document discusses various mathematical functions and operations in MATLAB including:
- Rounding functions such as fix(), ceil(), floor(), and round() that round numbers in different ways.
- Modulus and remainder functions mod() and rem() that return the remainder of a division.
- Matrix indexing that allows accessing and assigning values to specific elements, rows, columns or slices of a matrix.
- Eigenvalues and eigenvectors that are solutions to the characteristic equation of a linear system.
- Logical operators such as <, <=, >, >=, ==, ~= that return logical arrays.
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
This document provides an overview of Scalaz and functional programming concepts like Functor, Applicative, and Monad as they relate to the Option type in Scalaz. It discusses Option syntax, constructing Options, working with Options using fold, map, and other methods, and defines Option as an instance of Functor, Applicative, and Monoid. It also briefly touches on concepts like context bounds and alternative monoids for Option.
For decades, the Functor, Monoid, and Foldable type class hierarchies have dominated functional programming. Implemented in libraries like Scalaz and Cats, these type classes have an ancient origin in Haskell, and they have repeatedly proven useful for advanced functional programmers, who use them to maximize code reuse and increase code correctness.
Yet, as these type classes have been copied into Scala and aged, there is a growing awareness of their drawbacks, ranging from being difficult to teach to weird operators that don’t make sense in Scala (ap from Applicative), to overlapping and lawless type classes (Semigroupal), to a complete inability to abstract over data types that possess related structure (such as isomorphic applicatives).
In this presentation, John A. De Goes introduces a new Scala library with a completely different factoring of functional type classes—one which throws literally everything away and starts from a clean slate. In this new factoring, type classes leverage Scala’s strengths, including variance and modularity. Pieces fit together cleanly and uniformly, and in a way that satisfies existing use cases, but enables new ones never before possible. Finally, type classes are named, organized, and described in a way that makes teaching them easier, without compromising on algebraic principles.
If you’ve ever thought functional type classes were too impractical or too confusing or too restrictive, now’s your chance to get a fresh perspective on a library that just might make understanding functional programming easier than ever before!
This document discusses principled error handling in Scala programs. It introduces the MonadError type class for representing programs that can fail with errors. While useful, MonadError has limitations in representing error handling in the type system. The document proposes an ErrorControl type class that uses two type constructors to represent fallible and non-fallible program types. ErrorControl allows error handling to be represented more precisely in types. Several instances of ErrorControl are discussed, including for IO, StateT, and EitherT. In conclusion, error handling is challenging and the document explores some alternatives to representing it in programs.
The document discusses functions in C programming. It begins by explaining what functions are and why they are useful. Functions help modularize programs, avoid code repetition, and allow for software reusability. Key benefits of using functions include divide and conquer programming and abstraction. The document then provides examples of function definitions, prototypes, parameters, return values, and scope. It also discusses calling functions, libraries, recursion, and other concepts related to functions in C.
Type classes 101 - classification beyond inheritanceAlexey Raga
Inheritance and interfaces implementation are often used in languages like Java in order to express "Is-a" and "Can-do" capabilities. In Scala we can do better by separating these concerns using the concept of type classes.
This document discusses arrays in C programming. It defines an array as a collection of variables of the same type that are referenced by a common name. It describes single-dimensional and multi-dimensional arrays. Single-dimensional arrays are comprised of finite, homogeneous elements while multi-dimensional arrays have elements that are themselves arrays. The document provides examples of declaring, initializing, accessing, and implementing arrays in memory for both single and double-dimensional arrays. It includes sample programs demonstrating various array operations.
This document discusses writing a domain specific language (DSL) for data transformations using applicative functors in Scala. It introduces the concepts of Picker, Reader, and Result to parse heterogeneous data formats into a common format. Reader is defined as an applicative functor to allow combining multiple readers. Later, Reader is enhanced to take type parameters for both input and output to avoid reparsing data and support XML parsing. Type lambdas are used to make Reader work as an applicative functor.
In this presentation, You will get to know about Function Literal,Higher Order Function,Partial Function,Partial Applied Function,Nested Function,Closures.
Scalaz is a library that provides new datatypes like Validation and NonEmptyList as well as extensions to standard classes like Option and List. It implements general functions using ad-hoc polymorphism through traits, implicit parameters, and implicit conversions. Some key features include typeclasses for monoid, functor, monad, and more. The library uses implicit conversions called "pimps" to extend existing types with these functions and typeclasses in a clean way.
An array is a collection of homogeneous data items stored in successive memory locations. It allows storing multiple elements of the same type using a single name. Elements in an array can be accessed using the array name and index number. Common operations on arrays include storing/retrieving elements, searching for a particular element or largest/smallest element, and calculating sum of elements based on certain criteria like even/odd indexing. Arrays can also be modified by inserting or deleting elements.
Scala is a multi-paradigm programming language that supports functional, object-oriented and imperative programming paradigms. The document discusses Scala's support for types, expressions, objects, functions, and provides examples of using Scala for expressions, functions, control structures, patterns, spaces and actors.
Testing in the World of Functional ProgrammingLuka Jacobowitz
This document summarizes a presentation about testing in functional programming. It discusses:
1. Property-based testing and how it can find bugs early by generating many test cases, including edge cases. However, it can be painful to write tests for complex functions interacting with external systems.
2. Mocking external dependencies to test internal logic, made possible through techniques like tagless final which separate interfaces from implementations. Complex interactions can be modeled with state machines.
3. Other FP testing techniques like newtypes, refined types, and libraries that make testing easier by restricting valid inputs. The presentation provides examples of testing parsing, network calls and database access in isolation.
Moore's Law may be dead, but CPUs acquire more cores every year. If you want to minimize response latency in your application, you need to use every last core - without wasting resources that would destroy performance and throughput. Traditional locks grind threads to a halt and are prone to deadlocks, and although actors provide a saner alternative, they compose poorly and are at best weakly-typed.
In this presentation, created exclusively for Scalar Conf, John reveals a new alternative to the horrors of traditional concurrency, directly comparing it to other leading approaches in Scala. Witness the beauty, simplicity, and power of declarative concurrency, as you discover how functional programming is the most practical solution to solving the tough challenges of concurrency.
This document summarizes a presentation about type classes in Scala. It introduces type classes like Monoid and Functor. It provides examples of Monoid instances for types like Int and Option. It explains how to define type classes for new types. It shows how to generalize functions like sequence and traverse to work for any Applicative functor using type classes. Finally, it discusses related concepts like Semigroup, Semigroupal functors, and NonEmptyList.
The Ring programming language version 1.8 book - Part 29 of 202Mahmoud Samir Fayed
This document provides information on mathematical functions available in the Ring programming language. It lists common trigonometric, logarithmic, exponential and other mathematical functions like sin(), cos(), tan(), log(), sqrt(), random() and provides examples of using some of these functions to calculate values.
The document summarizes Scala, a functional programming language that runs on the Java Virtual Machine (JVM). It discusses Scala's core features like being object-oriented, type inference, and support for functional programming with immutable data structures and passing functions as parameters. It also provides examples of using Scala collections like List and Array, and functions like map, filter, flatMap, and foldLeft/reduceLeft. Finally, it demonstrates using Scala for domain-specific languages and shows examples of defining DSLs for querying and generating JavaScript.
The document provides information about error handling in Python programming. It discusses different types of exceptions that may occur during program execution and how to handle them using try, except, else and finally blocks. It gives examples of programs that handle errors from inputting non-integer values or dividing by zero. The document also covers other Python programming concepts like lists, random numbers, and comparing Python to C/C++.
The document discusses various mathematical functions and operations in MATLAB including:
- Rounding functions such as fix(), ceil(), floor(), and round() that round numbers in different ways.
- Modulus and remainder functions mod() and rem() that return the remainder of a division.
- Matrix indexing that allows accessing and assigning values to specific elements, rows, columns or slices of a matrix.
- Eigenvalues and eigenvectors that are solutions to the characteristic equation of a linear system.
- Logical operators such as <, <=, >, >=, ==, ~= that return logical arrays.
This document discusses three functional patterns: continuations, format combinators, and nested data types. It provides examples of each pattern in Scala. Continuations are represented using explicit continuation-passing style and delimited control operators. Format combinators represent formatted output as typed combinators rather than untyped strings. Nested data types generalize recursive data types to allow the type parameter of recursive invocations to differ, as in square matrices represented as a nested data type. The document concludes by drawing an analogy between fast exponentiation and representing square matrices as a nested data type.
This document provides an overview of new features in Java 8 including lambda expressions, interface improvements, and streams. Lambda expressions allow for anonymous functions and method references in Java. Functional interfaces define a single abstract method that lambda expressions can implement. Interface changes allow interfaces to define static and default methods. Streams provide a functional-style way to process collections of objects through intermediate and terminal operations like filter, map, reduce, and forEach.
The document provides an overview of functional programming in Java. It discusses functional programming concepts like higher-order functions and avoiding side effects. It gives examples of functional-style code in Java for filtering lists and transforming objects. It also discusses how functional programming can be used to implement customizable business logic by passing functions as parameters and looking up functions through a dependency injection container.
Machine-level Composition of Modularized Crosscutting Concernssaintiss
The document discusses different approaches to modularizing crosscutting concerns in software, including object-oriented programming (j), inter-type declarations (ij), aspect-oriented programming using pointcuts and advice (aj), and context-oriented programming (cj). It proposes a dedicated machine model based on objects and delegation as a core mechanism to support modularization at the machine level.
This document provides an introduction to the Go programming language. It discusses why Go was created, its key features like performance, concurrency and productivity. It provides examples of basic Go programs and explains basic language concepts like types, functions, interfaces and methods. The document also discusses the history and creators of the Go language.
The document describes ray tracing and how it can be implemented in a modular way using ZIO. It discusses:
1. The key components of ray tracing including a world (spheres, light source, camera), incident rays, reflected rays, discarded rays, and colored pixels on a canvas.
2. Options for computing rays, including computing all rays or only those from the camera through the canvas.
3. How transformations can be handled in a module with services to apply transformations and compose them.
4. Other modules including for camera, world, and rastering to generate colored pixels from the world and camera. The overall architecture is designed to be composable using ZIO.
- Templates in C++ allow functions and classes to be reused for different data types through generic programming. This avoids defining multiple functions to handle the same task on different types.
- A template can be viewed as a variable that can be instantiated to any data type. Two functions min() are shown, one for ints and one for doubles, demonstrating the need for templates.
- A template solution defines a single min() function that accepts any type through a template parameter <Type>. This provides a more flexible solution than separate overloaded functions.
- Templates in C++ allow functions and classes to be reused for different data types through generic programming. This avoids defining multiple functions to handle the same task on different types.
- A template can be viewed as a variable that can be instantiated for any data type. Two min() functions showed how templates provide a cleaner solution than separate functions.
- A stack is an abstract data type that follows LIFO (last-in, first-out) behavior. Elements are added and removed only from one end, called the top. Common stack operations are push, pop, empty, and top.
Introduction to how to test our programs using imperative programming and functional programming with Tagless Final technique and how to make it simpler using ZIO. ZIO provides many features for concurrency and asynchronous programs. This presentation is about how to use ZIO environment to test your application.
This document provides a MATLAB cheat sheet for data science with concise summaries of MATLAB commands and functions organized by category. It includes commands for importing and exporting data, basic math functions, linear algebra, accessing array elements, character/string manipulation, programming constructs, and more. The cheat sheet was created by Ali Habibnia and Eghbal Rahimikia from the London School of Economics as a technical report for MATLAB users in data science.
The document discusses different types of arrays in C including one-dimensional, two-dimensional, and multi-dimensional arrays. It explains how to declare, initialize, and access array elements. Examples are provided to demonstrate array operations like addition, multiplication, and passing arrays to functions. The use of arrays to store strings and various string handling functions are also covered.
This document discusses different types of computations in functional programming such as Option, List, Future, State, and IO. It explains how to create computations of these types by wrapping values, and how to use computations by mapping and applying functions to the wrapped values. It also covers applicative functors and how they allow applying functions to multiple arguments wrapped in computations. Some examples of applicative operations on Option, List, Future, State and IO are given. Finally, it briefly discusses traversals, which allow transforming data structures of type F[A] to F[B] by applying a function A => F[B] to each element.
The document discusses reinventing the free monad to model effects in different ways, including with pure effects, effects with returns, and simpler effects using bind. It also covers composing effects, type-safe mocking, runtime optimization, aspect-oriented programming, and hacking the free monad to add capabilities like parallelism, failure handling, and nondeterminism. However, it notes that excessively hacking the free monad can lead to poor composability and performance due to deeply nested types, and can blur the distinction between effects and machinery.
The document discusses templates and polymorphism in C++. It introduces function templates as a way to write generic functions that can work on different data types. An example Min() function template is provided. Class templates are also discussed as a way to define generic container classes like vectors. The document explains how templates avoid code duplication. Polymorphic functions and virtual functions are compared, with virtual functions allowing runtime polymorphism through derived classes overriding base class functions. The use of templates and polymorphism in the Standard Template Library is covered.
This document summarizes machine learning frameworks and libraries, neural network structures, and the process of building and training a neural network model for image classification. It discusses TensorFlow and PyTorch frameworks, describes the structure of a convolutional neural network, and provides code to import datasets, define a model, train the model on GPUs, and test the model's accuracy.
The document introduces Scala and provides an overview of Scala basics including variables, functions, objects, classes, traits, pattern matching, for-comprehensions and more. It also discusses Scala's capabilities for generic programming, lazy evaluation, and integration with Java. Examples are provided throughout to demonstrate Scala concepts.
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15. sealed trait Employee
final case class Subordinate(
name:String,
age:Int,
boss: Employee
) extends Employee
final case class Boss(
name:String,
isActive:Boolean
) extends Employee
19. sealed trait MyPrim[A]
final case object IntP extends MyPrim[Int]
final case object StringP extends MyPrim[String]
final case object BooleanP extends MyPrim[Boolean]
25. val intSchema:SchemaF[MyPrim, Int] = PrimS(IntP)
val strSchema:SchemaF[MyPrim, String] = PrimS(StringP)
val booleanSchema:SchemaF[MyPrim, Boolean] = PrimS(BooleanP)
26. val nt: MyPrim ~> Encoder = new NaturalTransformation[MyPrim, Encoder]{
override def apply[A](prim:MyPrim[A]):Encoder[A] = toPrimEnc(prim)
}
val encoder: Encoder[Int] = toEnc(intSchema, nt)
27.
28. sealed trait SchemaF[Prim[_], A]
final case class PrimS[Prim[_],A](prim:Prim[A]) extends SchemaF[Prim, A]
final case class ProdS[Prim[_],A,B](l:SchemaF[Prim, A], r:SchemaF[Prim, B]) extends SchemaF[Prim, (A,B)]
final case class SumS[Prim[_],A,B](l:SchemaF[Prim, A], r:SchemaF[Prim, B]) extends SchemaF[Prim, A/B]
31. def toEnc[Prim[_], A](schema:SchemaF[Prim, A], toPrimEnc: Prim ~> Encoder):Encoder[A] = schema match {
case PrimS(p) => toPrimEnc(p)
case prod:ProdS[Prim, x, y] => (tpl:(x,y)) =>
toEnc(prod.l, toPrimEnc)(tpl._1) + "," + toEnc(prod.r, toPrimEnc)(tpl._2)
}
32. def toEnc[Prim[_], A](schema:SchemaF[Prim, A], toPrimEnc: Prim ~> Encoder):Encoder[A] = schema match {
case PrimS(p) => toPrimEnc(p)
case prod:ProdS[Prim, x, y] => (tpl:(x,y)) =>
toEnc(prod.l, toPrimEnc)(tpl._1) + "," + toEnc(prod.r, toPrimEnc)(tpl._2)
case sum:SumS[Prim, x, y] => (e:x / y) => e.fold(
lx => toEnc(sum.l, toPrimEnc)(lx),
ry => toEnc(sum.r, toPrimEnc)(ry)
)
}
33.
34.
35. sealed trait SchemaF[Prim[_], A]
final case class PrimS[Prim[_],A](prim:Prim[A]) extends SchemaF[Prim, A]
final case class ProdS[Prim[_],A,B](l:SchemaF[Prim, A], r:SchemaF[Prim, B]) extends SchemaF[Prim, (A,B)]
final case class SumS[Prim[_],A,B](l:SchemaF[Prim, A], r:SchemaF[Prim, B]) extends SchemaF[Prim, A/B]
final case class UnionS[Prim[_],B,A](base:SchemaF[Prim, B], iso:Iso[B,A]) extends SchemaF[Prim, A]
final case class RecordS[Prim[_],B,A](base:SchemaF[Prim, B], iso:Iso[B,A]) extends SchemaF[Prim, A]
final case class IsoS[Prim[_],B,A](base:SchemaF[Prim, B], iso:Iso[B,A]) extends SchemaF[Prim, A]
36.
37. def toEnc[Prim[_], A](schema:SchemaF[Prim, A], toPrimEnc: Prim ~> Encoder):Encoder[A] = schema match {
case PrimS(p) => toPrimEnc(p)
case prod:ProdS[Prim, x, y] => (tpl:(x,y)) =>
toEnc(prod.l, toPrimEnc)(tpl._1) + "," + toEnc(prod.r, toPrimEnc)(tpl._2)
case sum:SumS[Prim, x, y] => (e:x / y) => e.fold(
lx => toEnc(sum.l, toPrimEnc)(lx),
ry => toEnc(sum.r, toPrimEnc)(ry)
)
case isoS:IsoS[Prim, b, A] => (a:A) =>
toEnc(isoS.base, toPrimEnc)(isoS.iso.reverseGet(a))
case union:UnionS[Prim, b, A] => (a:A) =>
toEnc(union.base, toPrimEnc)(union.iso.reverseGet(a))
case record:RecordS[Prim, b, A] => (a:A) =>
toEnc(record.base, toPrimEnc)(record.iso.reverseGet(a))
}
38.
39.
40. sealed trait SchemaF[Prim[_], SumTermId, ProductTermId, A]
...
final case class FieldS[Prim[_], SumTermId, ProductTermId, A](
id:ProductTermId,
base:SchemaF[Prim,SumTermId, ProductTermId, A]
) extends SchemaF[Prim, SumTermId, ProductTermId, A]
final case class BranchS[Prim[_], SumTermId, ProductTermId, A](
id:SumTermId,
base:SchemaF[Prim,SumTermId, ProductTermId, A]
) extends SchemaF[Prim, SumTermId, ProductTermId, A]
59. trait SchemaModule[R <: Realisation] {
val R: R
type Schema[A] = SchemaF[R.Prim, R.SumTermId, R.ProductTermId, A]
type Sum[A, B] = SumS[R.Prim, R.SumTermId, R.ProductTermId, A, B]
// and all the other needed aliases
}
60. trait SchemaModule[R <: Realisation] {
val R: R
final def unit: Schema[Unit]
final def prim[A](prim: R.Prim[A]): Schema[A]
final def union[A, AE](choices: Schema[AE], iso: Iso[AE, A]): Schema[A]
final def optional[A](aSchema: Schema[A]): Schema[Option[A]]
final def record[A, An](terms: Schema[An], isoA: Iso[An, A]): Schema[A]
final def seq[A](element: Schema[A]): Schema[List[A]]
final def iso[A0, A](base: Schema[A0],iso: Iso[A0, A]): Schema[A]
final def self[A](root: => Schema[A]): Schema[A]
}
62. object JsonSchema extends Realisation {
type Prim[A] = JsonPrim[A]
type ProductTermId = String
type SumTermId = String
sealed trait JsonPrim[A]
final case object JsonString extends JsonPrim[String]
final case object JsonNumber extends JsonPrim[BigDecimal]
final case object JsonBool extends JsonPrim[Boolean]
final case object JsonNull extends JsonPrim[Unit]
}
65. sealed trait Employee
final case class Subordinate(
name:String,
age:Int,
boss: Employee
) extends Employee
final case class Boss(
name:String,
isActive:Boolean
) extends Employee
70. object Representation {
type RSum[RA, A, RB, B]
type RProd[RA, A, RB, B]
type RIso[RA, A, B]
type RSelf[A]
type RSeq[R, A]
type -*>[K, V]
type -+>[K, V]
type RRecord[RA, An, A]
type RUnion[RA, An, A]
}
71. sealed trait SchemaF[Prim[_], SumTermId, ProductTermId, R, A]
final case class One[...]() extends SchemaF[... Unit, Unit]
final case class SumF[...](...) extends SchemaF[... RSum[RA, A, RB, B], A / B]
final case class ProdF[...](...) extends SchemaF[... RProd[RA, A, RB, B], (A, B)]
final case class BranchF[...](...) extends SchemaF[... -+>[SumTermId, RA], A]
final case class UnionF[... RA: IsUnion ...](...) extends SchemaF[... RUnion[RA, AE, A], A]
final case class FieldF[...](...) extends SchemaF[... -*>[ProductTermId, RA], A]
final case class RecordF[...RA: IsRecord...](...) extends SchemaF[... RRecord[RA, AP, A], A]
final case class SeqF[...](...) extends SchemaF[... RSeq[RA, A], List[A]]
final case class IsoSchemaF[...](...) extends SchemaF[... RIso[RA, A0, A], A]
final case class SelfReference[...](...) extends SchemaF[... RSelf[A], A]
72. final case class Boss(name:String, isActive:Boolean) extends Employee
def bossSchema = record(
"name" -*>: prim(JsonSchema.JsonString) :*:
"isActive" -*>: prim(JsonSchema.JsonBool),
Iso[(String, Boolean), Boss]((Boss.apply _).tupled)(b => (b.name, b.isActive))
)
74. sealed trait SchemaF[Prim[_], SumTermId, ProductTermId, R, A]
final case class One[...]() extends SchemaF[... Unit, Unit]
final case class SumF[...](...) extends SchemaF[... RSum[RA, A, RB, B], A / B]
final case class ProdF[...](...) extends SchemaF[... RProd[RA, A, RB, B], (A, B)]
final case class BranchF[...](...) extends SchemaF[... -+>[SumTermId, RA], A]
final case class UnionF[... RA: IsUnion ...](...) extends SchemaF[... RUnion[RA, AE, A], A]
final case class FieldF[...](...) extends SchemaF[... -*>[ProductTermId, RA], A]
final case class RecordF[...RA: IsRecord...](...) extends SchemaF[... RRecord[RA, AP, A], A]
final case class SeqF[...](...) extends SchemaF[... RSeq[RA, A], List[A]]
final case class IsoSchemaF[...](...) extends SchemaF[... RIso[RA, A0, A], A]
final case class SelfReference[...](...) extends SchemaF[... RSelf[A], A]
75. @implicitNotFound(
msg = "It seems like the following representation type isn't isomorphic to a product of named
fields: ${A}"
)
trait IsRecord[A]
object IsRecord {
implicit def singleFieldIsRecord[K, V]: IsRecord[K -*> V] = new IsRecord[K -*> V] {}
implicit def productIsRecord[L: IsRecord, R: IsRecord, X, Y]: IsRecord[RProd[L, X, R, Y]] =
new IsRecord[RProd[L, X, R, Y]] {}
implicit def isoIsRecord[R: IsRecord, A0, A]: IsRecord[RIso[R, A0, A]] =
new IsRecord[RIso[R, A0, A]] {}
}