I've refactored my code for day 12 of advent of code by using monocle, a lens library in scala.
Is it possible to improve this code :
type Register = String
type Mem = Map[String, Int]
#Lenses
case class State(mem: Mem, pointer: Int)
def processInstruction(instructions: Seq[Instruction]): State => State = { s =>
(instructions(s.pointer) match {
case Inc(r) =>
State.pointer.modify( _ + 1) andThen (State.mem composeLens at(r)).modify(_.map(_ + 1))
case Dec(r) =>
State.pointer.modify( _ + 1) andThen (State.mem composeLens at(r)).modify(_.map(_ - 1))
case CpyInt(v, to) =>
State.pointer.modify( _ + 1) andThen (State.mem composeLens at(to)).set(Some(v))
case CpyReg(from, to) =>
State.pointer.modify( _ + 1) andThen (State.mem composeLens at(to)).set(Some(s.mem(from)))
case Jnz(r, v) => if (r != "1" && s.mem(r) == 0)
State.pointer.modify( _ + 1)
else
State.pointer.modify( _ + v )
}).apply(s)
}
And here is another try, separating the modification of each field
def processInstruction2(instructions: Seq[Instruction]): State => State = { s =>
val ptr = instructions(s.pointer) match {
case Jnz(r, v) if !(r != "1" && s.mem(r) == 0) => State.pointer.modify(_ + v)
case _ => State.pointer.modify(_ + 1)
}
val mem = instructions(s.pointer) match {
case Inc(r) => (State.mem composeLens at(r)).modify(_.map(_ + 1))
case Dec(r) => (State.mem composeLens at(r)).modify(_.map(_ - 1))
case CpyInt(v, to) => (State.mem composeLens at(to)).set(Some(v))
case CpyReg(from, to) => (State.mem composeLens at(to)).set(Some(s.mem(from)))
case _ => identity[State]
}
(ptr andThen mem)(s)
}
One more question : is there a way to use Map.withDefaultValue with monocle ?
The full code is here : https://gist.github.com/YannMoisan/b8ba25afc041d88706545527d9ec1988
You might want to use the second approach, because it separates handling of two fields.
However, the functions shouldn't be interpreted in sequence (andThen), but rather they should be combined as PartialFunctions with orElse.
def processInstruction3(instructions: Seq[Instruction]): State => State = {
val ptr: PartialFunction[Instruction, State => State] = {
case Jnz(r, v) =>
State.pointer.modify(_ + v)
}
val incPointer: State => State = State.pointer.modify( _ + 1)
def reg(r: String): Lens[State, Option[Int]] = State.mem composeLens at(r)
val mem: PartialFunction[Instruction, State => State] = {
case Inc(r) => reg(r).modify(_.orElse(Option(0)).map(_ + 1))
case Dec(r) => reg(r).modify(_.orElse(Option(0)).map(_ - 1))
case CpyInt(v, to) => reg(to).set(Some(v))
case CpyReg(from, to) => s => reg(to).set(reg(from).get(s))(s)
}
val interpreter = ptr orElse (mem andThen (_ andThen incPointer))
s => instructions.foldLeft(s)((s, i) => interpreter(i)(s))
}
UPDATE (after the Yann Moisan comment)
The execution may not terminate in case of infinite loop in user's program. So instead of the foldLeft we need some recursive function that will extract the next instruction by pointer:
#tailrec
def loop(s: State): State = {
if(s.pointer>=instructions.length)
s
else {
val instruction = instructions(s.pointer)
val nextState = interpreter(instruction)(s)
loop(nextState)
}
}
loop _
(The last line of processInstruction3 should be replaced with the above code)
Related
By using Observer, I'm trying to build the code, which:
1.Generate some (random) values;
2.Combine this values;
3.If someone combined value exceed the threshold, the value have to be passed to another handler.
So I expect the resulted value to be returned for further usage
My code:
//generate
val o: Observable[Int] = Observable.repeatEval(Random.nextInt(10))
//handle
val f = o.foldLeft(0) { (acc, el) =>
if (acc < 15) {
el + acc
} else {
println("handled " + acc)
acc
}
}
//use handled
.flatMap{res =>
println("mapped " + res + 1)
Observable(res + 1)
}
But nothing passed to the flatMap-method.
The output is following for example:
0
3
7
11
12
handled 20
What am I doing wrong?
You want to use mapAccumulate + collect instead.
def groupWhile[A, B](o: Observable[A], s: B)(op: (B, A) => B)(predicate: B => Boolean): Observable[B] =
o.mapAccumulate(seed = s) {
case (b, a) =>
val bb = op(b, a)
if (predicate(bb)) (bb, None) else (s, Some(bb))
} collect {
case Some(b) => b
}
Use it like:
// Generate.
val o: Observable[Int] = Observable.repeatEval(Random.nextInt(10))
// Handle.
val f = groupWhile(o, 0)((acc, i) => acc + i)(r => r <= 15)
// Use handled.
f.mapEval { x =>
val res = x + 1
Task(println("Mapped: ${res}")).as(res)
}
As I always say, the Scaladoc is your friend.
I'm trying to figure out how to create an Akka Streams source that generates many Seq[Int].
Basically, given an int n I want to generate all of the Seq[Int] of 1 to n
Here's some code that does this:
def combinations(n: Int): Seq[Seq[Int]] = {
def loop(acc: (Seq[Int], Seq[Seq[Int]]),
remaining: Seq[Int]): Seq[Seq[Int]] = {
remaining match {
case s if s.size == 1 => {
val total: Seq[Seq[Int]] = acc._2
val current: Seq[Int] = acc._1
total :+ (current :+ s.head)
}
case _ => {
for {
x <- remaining
comb <- loop((acc._1 :+ x, acc._2), remaining.filter(_ != x))
} yield comb
}
}
}
loop((Seq(), Seq()), (1 to n))
}
This works fine up to 10... then it blows up because it runs out of memory. Since I just want to process each of them and don't need to keep them all in memory, I thought... Akka Streams. But I'm at a loss for how to turn this into a Source that produces each combination so I can process them. Basically there where it's appending to total I would produce another item onto the stream.
Here is a solution that uses the Johnson-Trotter algorithm for permutations. tcopermutations creates a LazyList that can be evaluated as needed. For more permutations, just pass a different value to printNIterations.
The reason for using the Johnson-Trotter algorithm is that it breaks the recursive structure of the permutation finding algorithm. That's important for being able to evaluate successive instances of the permutation and storing them in some kind of lazy list or stream.
object PermutationsTest {
def main(args: Array[String]) = {
printNIterations(50, tcopermutations(5).iterator)
}
def printNIterations(n: Int, it: Iterator[Seq[Int]]): Unit = {
if (n<=0) ()
else {
if (it.hasNext) {
println(it.next())
printNIterations(n - 1, it)
} else ()
}
}
def naivepermutations(n: Int): Seq[Seq[Int]] = {
def loop(acc: Seq[Int], remaining: Seq[Int]): Seq[Seq[Int]] = {
remaining match {
case s if s.size == 1 => {
val current: Seq[Int] = acc
Seq((current :+ s.head))
}
case _ => {
for {
x <- remaining
comb <- loop(acc :+ x, remaining.filter(_ != x))
} yield comb
}
}
}
loop(Seq(), (1 to n))
}
def tcopermutations(n: Int): LazyList[Seq[Int]] = {
val start = (1 to n).map(Element(_, Left))
def loop(v: Seq[Element]): LazyList[Seq[Element]] = {
johnsonTrotter(v) match {
case Some(s) => v #:: loop(s)
case None => LazyList(v)
}
}
loop(start).map(_.map(_.i))
}
def checkIfMobile(seq: Seq[Element], i: Int): Boolean = {
val e = seq(i)
def getAdjacent(s: Seq[Element], d: Direction, j: Int): Int = {
val adjacentIndex = d match {
case Left => j - 1
case Right => j + 1
}
s(adjacentIndex).i
}
if (e.direction == Left && i == 0) false
else if (e.direction == Right && i == seq.size - 1) false
else if (getAdjacent(seq, e.direction, i) < e.i) true
else false
}
def findLargestMobile(seq: Seq[Element]): Option[Int] = {
val mobiles = (0 until seq.size).filter{j => checkIfMobile(seq, j)}
if (mobiles.isEmpty) None
else {
val folded = mobiles.map(x=>(x,seq(x).i)).foldLeft(None: Option[(Int, Int)]){ case (acc, elem) =>
acc match {
case None => Some(elem)
case Some((i, value)) => if (value > elem._2) Some((i, value)) else Some(elem)
}
}
folded.map(_._1)
}
}
def swapLargestMobile(seq: Seq[Element], index: Int): (Seq[Element], Int) = {
val dir = seq(index).direction
val value = seq(index).i
dir match {
case Right =>
val folded = seq.foldLeft((None, Seq()): (Option[Element], Seq[Element])){(acc, elem) =>
val matched = elem.i == value
val newAccOpt = if (matched) Some(elem) else None
val newAccSeq = acc._1 match {
case Some(swapMe) => acc._2 :+ elem :+ swapMe
case None => if (matched) acc._2 else acc._2 :+ elem
}
(newAccOpt, newAccSeq)
}
(folded._2, index + 1)
case Left =>
val folded = seq.foldRight((None, Seq()): (Option[Element], Seq[Element])){(elem, acc) =>
val matched = elem.i == value
val newAccOpt = if (matched) Some(elem) else None
val newAccSeq = acc._1 match {
case Some(swapMe) => swapMe +: elem +: acc._2
case None => if (matched) acc._2 else elem +: acc._2
}
(newAccOpt, newAccSeq)
}
(folded._2, index - 1)
}
}
def revDirLargerThanMobile(seq: Seq[Element], mobile: Int) = {
def reverse(e: Element) = {
e.direction match {
case Left => Element(e.i, Right)
case Right => Element(e.i, Left)
}
}
seq.map{ elem =>
if (elem.i > seq(mobile).i) reverse(elem)
else elem
}
}
def johnsonTrotter(curr: Seq[Element]): Option[Seq[Element]] = {
findLargestMobile(curr).map { m =>
val (swapped, newMobile) = swapLargestMobile(curr, m)
revDirLargerThanMobile(swapped, newMobile)
}
}
trait Direction
case object Left extends Direction
case object Right extends Direction
case class Element(i: Int, direction: Direction)
}
I wrote an expression simplifier that takes a string as input, converts it into a syntax tree, simplifies that tree, and then finally walks through the tree to convert the output back to a string. I am having a hard time figuring out how to get my parenthesis back into my expression when I am printing a nested solution such as: (a || !b) && c Right now my code gives me a solution of a || !b && c. It seems as though my solution is correct because the simplified tree outputs correctly, I just don't know how to add the parentheses when walking through it.
Here is my code:
import scala.util.parsing.combinator._
import scala.io.StdIn.readLine
abstract class Expression
case class And(l: Expression, r: Expression) extends Expression
case class Or(l: Expression, r: Expression) extends Expression
case class Not(arg: Expression) extends Expression
case class Var(n: Char) extends Expression
case object True extends Expression
case object False extends Expression
object Simplifier extends Combinators {
def eval(e: Expression): Expression = e match {
case Or(True, _) => True
case Or(_, True) => True
case Or(l, False) => l
case Or(False, r) => r
case Or(l, r) => {
val leval = eval(l)
val reval = eval(r)
(leval, reval) match {
case (True, _) => True
case (_, True) => True
case (l, False) => l
case (False, r) => r
case (leval, reval) => Or(eval(l),eval(r))
}
}
case And(True, r) => if(r == True) {return True} else {return r}
case And(l, True) => if(l == True) {return True} else {return l}
case And(False, _) => False
case And(_, False) => False
case And(l, r) => {
val leval = eval(l)
val reval = eval(r)
(leval, reval) match {
case (True, r) => if(r == True) {return True} else {return r}
case (l, True) => if(l == True) {return True} else {return l}
case (False, _) => False
case (_, False) => False
case (leval, reval) => And(eval(l), eval(r))
}
}
case Not(True) => False
case Not(False) => True
case Not(arg) => Not(eval(arg))
case Var(n) => Var(n)
}
def stringify(e: Expression): String = e match{
case Or(l, r) => {
stringify(l) + " || " + stringify(r)
}
case And(l, r) => {
stringify(l) + " && " + stringify(r)
}
case Not(arg) => {
"!" + stringify(arg)
}
case Var(n) => n.toString()
case True => "true"
case False => "false"
}
def main(args:Array[String]): Unit = {
var response: String = ""
do {
println("expression? ")
val input = readLine()
val expr: Expression = parseAll(e, input).get
println(eval(expr))
val simplifiedExpression: String = stringify(eval(expr))
println(simplifiedExpression)
println("Would you like to enter another expression? (y/n): ")
response = readLine()
} while (response != "n")
}
}
class Combinators extends JavaTokenParsers {
def e: Parser[Expression] = t ~ or ~ e ^^ {case l ~ _ ~ r => Or(l, r)} | t
def t: Parser[Expression] = f ~ and ~ t ^^ {case l ~ _ ~ r => And(l, r)} | f
def f: Parser[Expression] = not ~ a ^^ {case _ ~ arg => Not(arg)} | a
def a: Parser[Expression] = openparen ~ e ~ closeparen ^^ {case _ ~ e ~ _ => e} | c
def c: Parser[Expression] = "true" ^^ {case "true" => True} | "false" ^^ {case "false" => False} | z
def z: Parser[Var] = "[a-c]".r ^^ {str => Var(str.head)}
def and[Expression] = "&&"
def or[Expression] = "||"
def not[Expression] = "!"
def openparen[Expression] = "("
def closeparen[Expression] = ")"
}
Your stringify method never puts any parentheses, so that cannot be correct, as you figured out. The easiest improvement would be to always put parentheses instead. For example:
case Or(l, r) => {
"(" + stringify(l) + " || " + stringify(r) + ")"
}
And similarly for the other cases.
I have multiple Option's. I want to check if they hold a value. If an Option is None, I want to reply to user about this. Else proceed.
This is what I have done:
val name:Option[String]
val email:Option[String]
val pass:Option[String]
val i = List(name,email,pass).find(x => x match{
case None => true
case _ => false
})
i match{
case Some(x) => Ok("Bad Request")
case None => {
//move forward
}
}
Above I can replace find with contains, but this is a very dirty way. How can I make it elegant and monadic?
Edit: I would also like to know what element was None.
Another way is as a for-comprehension:
val outcome = for {
nm <- name
em <- email
pwd <- pass
result = doSomething(nm, em, pwd) // where def doSomething(name: String, email: String, password: String): ResultType = ???
} yield (result)
This will generate outcome as a Some(result), which you can interrogate in various ways (all the methods available to the collections classes: map, filter, foreach, etc.). Eg:
outcome.map(Ok(result)).orElse(Ok("Bad Request"))
val ok = Seq(name, email, pass).forall(_.isDefined)
If you want to reuse the code, you can do
def allFieldValueProvided(fields: Option[_]*): Boolean = fields.forall(_.isDefined)
If you want to know all the missing values then you can find all missing values and if there is none, then you are good to go.
def findMissingValues(v: (String, Option[_])*) = v.collect {
case (name, None) => name
}
val missingValues = findMissingValues(("name1", option1), ("name2", option2), ...)
if(missingValues.isEmpty) {
Ok(...)
} else {
BadRequest("Missing values for " + missingValues.mkString(", ")))
}
val response = for {
n <- name
e <- email
p <- pass
} yield {
/* do something with n, e, p */
}
response getOrElse { /* bad request /* }
Or, with Scalaz:
val response = (name |#| email |#| pass) { (n, e, p) =>
/* do something with n, e, p */
}
response getOrElse { /* bad request /* }
if ((name :: email :: pass :: Nil) forall(!_.isEmpty)) {
} else {
// bad request
}
I think the most straightforward way would be this:
(name,email,pass) match {
case ((Some(name), Some(email), Some(pass)) => // proceed
case _ => // Bad request
}
A version with stone knives and bear skins:
import util._
object Test extends App {
val zero: Either[List[Int], Tuple3[String,String,String]] = Right((null,null,null))
def verify(fields: List[Option[String]]) = {
(zero /: fields.zipWithIndex) { (acc, v) => v match {
case (Some(s), i) => acc match {
case Left(_) => acc
case Right(t) =>
val u = i match {
case 0 => t copy (_1 = s)
case 1 => t copy (_2 = s)
case 2 => t copy (_3 = s)
}
Right(u)
}
case (None, i) =>
val fails = acc match {
case Left(f) => f
case Right(_) => Nil
}
Left(i :: fails)
}
}
}
def consume(name: String, email: String, pass: String) = Console println s"$name/$email/$pass"
def fail(is: List[Int]) = is map List("name","email","pass") foreach (Console println "Missing: " + _)
val name:Option[String] = Some("Bob")
val email:Option[String]= None
val pass:Option[String] = Some("boB")
val res = verify(List(name,email,pass))
res.fold(fail, (consume _).tupled)
val res2 = verify(List(name, Some("bob#bob.org"),pass))
res2.fold(fail, (consume _).tupled)
}
The same thing, using reflection to generalize the tuple copy.
The downside is that you must tell it what tuple to expect back. In this form, reflection is like one of those Stone Age advances that were so magical they trended on twitter for ten thousand years.
def verify[A <: Product](fields: List[Option[String]]) = {
import scala.reflect.runtime._
import universe._
val MaxTupleArity = 22
def tuple = {
require (fields.length <= MaxTupleArity)
val n = fields.length
val tupleN = typeOf[Tuple2[_,_]].typeSymbol.owner.typeSignature member TypeName(s"Tuple$n")
val init = tupleN.typeSignature member nme.CONSTRUCTOR
val ctor = currentMirror reflectClass tupleN.asClass reflectConstructor init.asMethod
val vs = Seq.fill(n)(null.asInstanceOf[String])
ctor(vs: _*).asInstanceOf[Product]
}
def zero: Either[List[Int], Product] = Right(tuple)
def nextProduct(p: Product, i: Int, s: String) = {
val im = currentMirror reflect p
val ts = im.symbol.typeSignature
val copy = (ts member TermName("copy")).asMethod
val args = copy.paramss.flatten map { x =>
val name = TermName(s"_$i")
if (x.name == name) s
else (im reflectMethod (ts member x.name).asMethod)()
}
(im reflectMethod copy)(args: _*).asInstanceOf[Product]
}
(zero /: fields.zipWithIndex) { (acc, v) => v match {
case (Some(s), i) => acc match {
case Left(_) => acc
case Right(t) => Right(nextProduct(t, i + 1, s))
}
case (None, i) =>
val fails = acc match {
case Left(f) => f
case Right(_) => Nil
}
Left(i :: fails)
}
}.asInstanceOf[Either[List[Int], A]]
}
def consume(name: String, email: String, pass: String) = Console println s"$name/$email/$pass"
def fail(is: List[Int]) = is map List("name","email","pass") foreach (Console println "Missing: " + _)
val name:Option[String] = Some("Bob")
val email:Option[String]= None
val pass:Option[String] = Some("boB")
type T3 = Tuple3[String,String,String]
val res = verify[T3](List(name,email,pass))
res.fold(fail, (consume _).tupled)
val res2 = verify[T3](List(name, Some("bob#bob.org"),pass))
res2.fold(fail, (consume _).tupled)
I know this doesn't scale well, but would this suffice?
(name, email, pass) match {
case (None, _, _) => "name"
case (_, None, _) => "email"
case (_, _, None) => "pass"
case _ => "Nothing to see here"
}
I have such case classes:
abstract class Tree
case class Sum(l: Tree, r: Tree) extends Tree
case class Var(n: String) extends Tree
case class Const(v: Int) extends Tree
Now i write such object :
object Main {
type Environment = String => Int
def derive(t: Tree, v: String): Tree = t match {
case Sum(l, r) => Sum(derive(l, v), derive(r, v))
case Var(n) if (v == n) => Const(1)
case _ => Const(0)
}
def eval(t: Tree, env: Environment): Int = t match {
case Sum(l, r) => eval(l, env) + eval(r, env)
case Var(n) => env(n)
case Const(v) => v
}
def simple(t: Tree): Const = t match {
case Sum(l, r) if (l.isInstanceOf[Const] && r.isInstanceOf[Const]) => Const(l.asInstanceOf[Const].v + r.asInstanceOf[Const].v)
case Sum(l, r) if (l.isInstanceOf[Sum] && r.isInstanceOf[Sum]) => Const(simple(l).v+ simple(r).v)
case Sum(l, r) if (l.isInstanceOf[Sum]) => Const(simple(l).v + r.asInstanceOf[Const].v)
case Sum(l, r) if (r.isInstanceOf[Sum]) => Const(simple(r).v + l.asInstanceOf[Const].v)
}
def main(args: Array[String]) {
val exp: Tree = Sum(Sum(Var("x"), Var("x")), Sum(Const(7), Var("y")))
val env: Environment = {
case "x" => 5
case "y" => 7
}
println("Expression: " + exp)
println("Evaluation with x=5, y=7: " + eval(exp, env))
println("Derivative relative to x:\n " + derive(exp, "x"))
println("Derivative relative to y:\n " + derive(exp, "y"))
println("Simplified expression:\n" + simple(derive(exp, "x")))
}
}
I am new in scala. Is it possible write method simple with small count of code and maybe in scala way?
Thanks for advice.
You're almost there. In Scala, extractors can be nested:
def simple(t: Tree): Const = t match {
case Sum(Const(v1), Const(v2)) => Const(v1 + v2)
case Sum(s1 # Sum(_,_), s2 # Sum(_, _)) => Const(simple(s1).v+ simple(s2).v)
case Sum(s # Sum(_, _), Const(v)) => Const(simple(s).v + v)
case Sum(Const(v), s # Sum(_, _)) => Const(simple(s).v + v)
}
Of course, this will give you some warnings about incomplete matches, and the sx # Sum(_, _) repeatedly suggests that there may be a better approach that includes matching on Const and Var at the root level and making more recursive calls to simple.
Although this question has been closed, but I think this version should be a better one,
def simplify(t: Tree): Tree = t match {
case Sum(Const(v1), Const(v2)) => Const(v1 + v2)
case Sum(Const(v1), Sum(Const(v2), rr)) => simplify(Sum(Const(v1 + v2), simplify(rr)))
case Sum(l, Const(v)) => simplify(Sum(Const(v), simplify(l)))
case Sum(l, Sum(Const(v), rr)) => simplify(Sum(Const(v), simplify(Sum(l, rr))))
case Sum(Sum(ll, lr), r) => simplify(Sum(ll, simplify(Sum(lr, r))))
case Sum(Var(n), r) => Sum(simplify(r), Var(n))
case _ => t
}
it seems works with "complex" expressions with variables.
Just a small improvement:
def derive(t: Tree, v: String): Tree = t match {
case Sum(l, r) => Sum(derive(l, v), derive(r, v))
case Var(`v`) => Const(1)
case _ => Const(0)
}
How about this:
def simplify(t: Tree): Tree = t match {
case Sum(Const(v1),Const(v2)) => Const(v1+v2)
case Sum(left,right) => simplify(Sum(simplify(left),simplify(right)))
case _ => t //Not necessary, but for completeness
}
Note that it returns a Tree, not a Const, so it should be able to simplify trees with variables too.
I'm learning Scala so any suggestions as to why this wouldn't work etc. are more than welcome :-)
EDIT: Just discovered that the second case causes an infinite loop when using variables. Substitute it with:
case Sum(left,right) => Sum(simplify(left),simplify(right))
Unfortunately this breaks when left and right return Const, which could be simplified even further (e.g. Sum(Const(2),Const(3))).