上节我们讨论了Zipper－串形不可变集合（immutable sequential collection）游标，在串形集合中左右游走及元素维护操作。这篇我们谈谈Tree。在电子商务应用中对于xml,json等格式文件的处理要求非常之普遍，scalaz提供了Tree数据类型及相关的游览及操作函数能更方便高效的处理xml,json文件及系统目录这些树形结构数据的相关编程。scalaz Tree的定义非常简单：scalaz/Tree.scala
* A multi-way tree, also known as a rose tree. Also known as Cofree[Stream, A].
*/
sealed abstract class Tree[A] {
import Tree._
/** The label at the root of this tree. */
def rootLabel: A
/** The child nodes of this tree. */
def subForest: Stream[Tree[A]]
...
Tree是由一个A值rootLabel及一个流中子树Stream[Tree[A]]组成。Tree可以只由一个A类型值rootLabel组成，这时流中子树subForest就是空的Stream.empty。只有rootLabel的Tree俗称叶（leaf），有subForest的称为节（node）。scalaz为任何类型提供了leaf和node的构建注入方法：syntax/TreeOps.scala
final class TreeOps[A](self: A) {
def node(subForest: Tree[A]*): Tree[A] = Tree.node(self, subForest.toStream)
def leaf: Tree[A] = Tree.leaf(self)
}
trait ToTreeOps {
implicit def ToTreeOps[A](a: A) = new TreeOps(a)
}
实际上注入方法调用了Tree里的构建函数：
trait TreeFunctions {
/** Construct a new Tree node. */
def node[A](root: => A, forest: => Stream[Tree[A]]): Tree[A] = new Tree[A] {
lazy val rootLabel = root
lazy val subForest = forest
override def toString = "<tree>"
}
/** Construct a tree node with no children. */
def leaf[A](root: => A): Tree[A] = node(root, Stream.empty)
Tree提供了构建和模式拆分函数：
object Tree extends TreeInstances with TreeFunctions {
/** Construct a tree node with no children. */
def apply[A](root: => A): Tree[A] = leaf(root)
object Node {
def unapply[A](t: Tree[A]): Option[(A, Stream[Tree[A]])] = Some((t.rootLabel, t.subForest))
}
}
我们可以直接构建Tree：
1 Tree("ALeaf") === "ALeaf".leaf //> res5: Boolean = true
2 val tree: Tree[Int] =
3 1.node(
4 11.leaf,
5 12.node(
6 121.leaf),
7 2.node(
8 21.leaf,
9 22.leaf)
10 ) //> tree : scalaz.Tree[Int] = <tree>
11 tree.drawTree //> res6: String = "1
12 //| |
13 //| +- 11
14 //| |
15 //| +- 12
16 //| | |
17 //| | `- 121
18 //| |
19 //| `- 2
20 //| |
21 //| +- 21
22 //| |
23 //| `- 22
24 //| "
Tree实现了下面众多的接口函数：
sealed abstract class TreeInstances {
implicit val treeInstance: Traverse1[Tree] with Monad[Tree] with Comonad[Tree] with Align[Tree] with Zip[Tree] = new Traverse1[Tree] with Monad[Tree] with Comonad[Tree] with Align[Tree] with Zip[Tree] {
def point[A](a: => A): Tree[A] = Tree.leaf(a)
def cobind[A, B](fa: Tree[A])(f: Tree[A] => B): Tree[B] = fa cobind f
def copoint[A](p: Tree[A]): A = p.rootLabel
override def map[A, B](fa: Tree[A])(f: A => B) = fa map f
def bind[A, B](fa: Tree[A])(f: A => Tree[B]): Tree[B] = fa flatMap f
def traverse1Impl[G[_]: Apply, A, B](fa: Tree[A])(f: A => G[B]): G[Tree[B]] = fa traverse1 f
override def foldRight[A, B](fa: Tree[A], z: => B)(f: (A, => B) => B): B = fa.foldRight(z)(f)
override def foldMapRight1[A, B](fa: Tree[A])(z: A => B)(f: (A, => B) => B) = (fa.flatten.reverse: @unchecked) match {
case h #:: t => t.foldLeft(z(h))((b, a) => f(a, b))
}
override def foldLeft[A, B](fa: Tree[A], z: B)(f: (B, A) => B): B =
fa.flatten.foldLeft(z)(f)
override def foldMapLeft1[A, B](fa: Tree[A])(z: A => B)(f: (B, A) => B): B = fa.flatten match {
case h #:: t => t.foldLeft(z(h))(f)
}
override def foldMap[A, B](fa: Tree[A])(f: A => B)(implicit F: Monoid[B]): B = fa foldMap f
def alignWith[A, B, C](f: (&/[A, B]) ⇒ C) = {
def align(ta: Tree[A], tb: Tree[B]): Tree[C] =
Tree.node(f(&/(ta.rootLabel, tb.rootLabel)), Align[Stream].alignWith[Tree[A], Tree[B], Tree[C]]({
case &/.This(sta) ⇒ sta map {a ⇒ f(&/.This(a))}
case &/.That(stb) ⇒ stb map {b ⇒ f(&/.That(b))}
case &/.Both(sta, stb) ⇒ align(sta, stb)
})(ta.subForest, tb.subForest))
align _
}
def zip[A, B](aa: => Tree[A], bb: => Tree[B]) = {
val a = aa
val b = bb
Tree.node(
(a.rootLabel, b.rootLabel),
Zip[Stream].zipWith(a.subForest, b.subForest)(zip(_, _))
)
}
}
implicit def treeEqual[A](implicit A0: Equal[A]): Equal[Tree[A]] =
new TreeEqual[A] { def A = A0 }
implicit def treeOrder[A](implicit A0: Order[A]): Order[Tree[A]] =
new Order[Tree[A]] with TreeEqual[A] {
def A = A0
import std.stream._
override def order(x: Tree[A], y: Tree[A]) =
A.order(x.rootLabel, y.rootLabel) match {
case Ordering.EQ =>
Order[Stream[Tree[A]]].order(x.subForest, y.subForest)
case x => x
}
}
那么Tree就是个Monad，也是Functor,Applicative，还是traversable,foldable。Tree也实现了Order,Equal实例，可以进行值的顺序比较。我们就用些例子来说明吧：
1 // 是 Functor...
2 (tree map { v: Int => v + 1 }) ===
3 2.node(
4 12.leaf,
5 13.node(
6 122.leaf),
7 3.node(
8 22.leaf,
9 23.leaf)
10 ) //> res7: Boolean = true
11
12 // ...是 Monad
13 1.point[Tree] === 1.leaf //> res8: Boolean = true
14 val t2 = tree >>= (x => (x == 2) ? x.leaf | x.node((-x).leaf))
15 //> t2 : scalaz.Tree[Int] = <tree>
16 t2 === 1.node((-1).leaf, 2.leaf, 3.node((-3).leaf, 4.node((-4).leaf)))
17 //> res9: Boolean = false
18 t2.drawTree //> res10: String = "1
19 //| |
20 //| +- -1
21 //| |
22 //| +- 11
23 //| | |
24 //| | `- -11
25 //| |
26 //| +- 12
27 //| | |
28 //| | +- -12
29 //| | |
30 //| | `- 121
31 //| | |
32 //| | `- -121
33 //| |
34 //| `- 2
35 //| |
36 //| +- 21
37 //| | |
38 //| | `- -21
39 //| |
40 //| `- 22
41 //| |
42 //| `- -22
43 //| "
44 // ...是 Foldable
45 tree.foldMap(_.toString) === "1111212122122" //> res11: Boolean = true
说到构建Tree，偶然在网上发现了这么一个Tree构建函数：
def pathTree[E](root: E, paths: Seq[Seq[E]]): Tree[E] = {
root.node(paths groupBy (_.head) map {
case (parent, subpaths) =>
pathTree(parent, subpaths collect {
case pp +: rest if rest.nonEmpty => rest
})
} toSeq: _*)
}
据说这个pathTree函数能把List里的目录结构转化成Tree。先看看到底是不是具备如此功能：
1 val paths = List(List("A","a1","a2"),List("B","b1"))
2 //> paths : List[List[String]] = List(List(A, a1, a2), List(B, b1))
3 pathTree("root",paths) drawTree //> res0: String = ""root"
4 //| |
5 //| +- "A"
6 //| | |
7 //| | `- "a1"
8 //| | |
9 //| | `- "a2"
10 //| |
11 //| `- "B"
12 //| |
13 //| `- "b1"
14 //| "
15 val paths = List(List("A","a1","a2"),List("B","b1"),List("B","b2","b3"))
16 //> paths : List[List[String]] = List(List(A, a1, a2), List(B, b1), List(B, b2,
17 //| b3))
18 pathTree("root",paths) drawTree //> res0: String = ""root"
19 //| |
20 //| +- "A"
21 //| | |
22 //| | `- "a1"
23 //| | |
24 //| | `- "a2"
25 //| |
26 //| `- "B"
27 //| |
28 //| +- "b2"
29 //| | |
30 //| | `- "b3"
31 //| |
32 //| `- "b1"
33 //| "
果然能行，而且还能把"B"节点合并汇集。这个函数的作者简直就是个神人，起码是个算法和FP语法运用大师。我虽然还无法达到大师的程度能写出这样的泛函程序，但好奇心是挡不住的，总想了解这个函数是怎么运作的。可以用一些测试数据来逐步跟踪一下：
1 val paths = List(List("A")) //> paths : List[List[String]] = List(List(A))
2 val gpPaths =paths.groupBy(_.head) //> gpPaths : scala.collection.immutable.Map[String,List[List[String]]] = Map(A-> List(List(A)))
3 List(List("A")) collect { case pp +: rest if rest.nonEmpty => rest }
4 //> res0: List[List[String]] = List()
通过上面的跟踪约化我们看到List(List(A))在pathTree里的执行过程。这里把复杂的groupBy和collect函数的用法和结果了解了。实际上整个过程相当于：
1 "root".node(
2 "A".node(List().toSeq: _*)
3 ) drawTree //> res3: String = ""root"
4 //| |
5 //| `- "A"
6 //| "
如果再增加一个点就相当于：
1 "root".node(
2 "A".node(List().toSeq: _*),
3 "B".node(List().toSeq: _*)
4 ) drawTree //> res4: String = ""root"
5 //| |
6 //| +- "A"
7 //| |
8 //| `- "B"
9 //| "
加多一层：
1 val paths = List(List("A","a1")) //> paths : List[List[String]] = List(List(A, a1))
2 val gpPaths =paths.groupBy(_.head) //> gpPaths : scala.collection.immutable.Map[String,List[List[String]]] = Map(A
3 //| -> List(List(A, a1)))
4 List(List("A","a1")) collect { case pp +: rest if rest.nonEmpty => rest }
5 //> res0: List[List[String]] = List(List(a1))
6
7 //化解成
8 "root".node(
9 "A".node(
10 "a1".node(
11 List().toSeq: _*)
12 )
13 ) drawTree //> res3: String = ""root"
14 //| |
15 //| `- "A"
16 //| |
17 //| `- "a1"
18 //| "
合并目录：
1 val paths = List(List("A","a1"),List("A","a2")) //> paths : List[List[String]] = List(List(A, a1), List(A, a2))
2 val gpPaths =paths.groupBy(_.head) //> gpPaths : scala.collection.immutable.Map[String,List[List[String]]] = Map(A
3 //| -> List(List(A, a1), List(A, a2)))
4 List(List("A","a1"),List("A","a2")) collect { case pp +: rest if rest.nonEmpty => rest }
5 //> res0: List[List[String]] = List(List(a1), List(a2))
6
7 //相当产生结果
8 "root".node(
9 "A".node(
10 "a1".node(
11 List().toSeq: _*)
12 ,
13 "a2".node(
14 List().toSeq: _*)
15 )
16 ) drawTree //> res3: String = ""root"
17 //| |
18 //| `- "A"
19 //| |
20 //| +- "a1"
21 //| |
22 //| `- "a2"
23 //| "
相信这些跟踪过程足够了解整个函数的工作原理了。
有了Tree构建方法后就需要Tree的游动和操作函数了。与串形集合的直线游动不同的是，树形集合游动方式是分岔的。所以Zipper不太适用于树形结构。scalaz特别提供了树形集合的定位游标TreeLoc，我们看看它的定义：scalaz/TreeLoc.scala
final case class TreeLoc[A](tree: Tree[A], lefts: TreeForest[A],
rights: TreeForest[A], parents: Parents[A]) {
...
trait TreeLocFunctions {
type TreeForest[A] =
Stream[Tree[A]]
type Parent[A] =
(TreeForest[A], A, TreeForest[A])
type Parents[A] =
Stream[Parent[A]]
树形集合游标TreeLoc由当前节点tree、左子树lefts、右子树rights及父树parents组成。lefts,rights,parents都是在流中的树形Stream[Tree[A]]。
用Tree.loc可以直接对目标树生成TreeLoc：
1 /** A TreeLoc zipper of this tree, focused on the root node. */
2 def loc: TreeLoc[A] = TreeLoc.loc(this, Stream.Empty, Stream.Empty, Stream.Empty)
3
4 val tree: Tree[Int] =
5 1.node(
6 11.leaf,
7 12.node(
8 121.leaf),
9 2.node(
10 21.leaf,
11 22.leaf)
12 ) //> tree : scalaz.Tree[Int] = <tree>
13
14 tree.loc //> res7: scalaz.TreeLoc[Int] = TreeLoc(<tree>,Stream(),Stream(),Stream())
TreeLoc的游动函数：
def root: TreeLoc[A] =
parent match {
case Some(z) => z.root
case None => this
}
/** Select the left sibling of the current node. */
def left: Option[TreeLoc[A]] = lefts match {
case t #:: ts => Some(loc(t, ts, tree #:: rights, parents))
case Stream.Empty => None
}
/** Select the right sibling of the current node. */
def right: Option[TreeLoc[A]] = rights match {
case t #:: ts => Some(loc(t, tree #:: lefts, ts, parents))
case Stream.Empty => None
}
/** Select the leftmost child of the current node. */
def firstChild: Option[TreeLoc[A]] = tree.subForest match {
case t #:: ts => Some(loc(t, Stream.Empty, ts, downParents))
case Stream.Empty => None
}
/** Select the rightmost child of the current node. */
def lastChild: Option[TreeLoc[A]] = tree.subForest.reverse match {
case t #:: ts => Some(loc(t, ts, Stream.Empty, downParents))
case Stream.Empty => None
}
/** Select the nth child of the current node. */
def getChild(n: Int): Option[TreeLoc[A]] =
for {lr <- splitChildren(Stream.Empty, tree.subForest, n)
ls = lr._1
} yield loc(ls.head, ls.tail, lr._2, downParents)
我们试着用这些函数游动：
1 val tree: Tree[Int] =
2 1.node(
3 11.leaf,
4 12.node(
5 121.leaf),
6 2.node(
7 21.leaf,
8 22.leaf)
9 ) //> tree : scalaz.Tree[Int] = <tree>
10 tree.loc //> res7: scalaz.TreeLoc[Int] = TreeLoc(<tree>,Stream(),Stream(),Stream())
11 val l = for {
12 l1 <- tree.loc.some
13 l2 <- l1.firstChild
14 l3 <- l1.lastChild
15 l4 <- l3.firstChild
16 } yield (l1,l2,l3,l4) //> l : Option[(scalaz.TreeLoc[Int], scalaz.TreeLoc[Int], scalaz.TreeLoc[Int],
17 //| scalaz.TreeLoc[Int])] = Some((TreeLoc(<tree>,Stream(),Stream(),Stream()),T
18 //| reeLoc(<tree>,Stream(),Stream(<tree>, <tree>),Stream((Stream(),1,Stream()),
19 //| ?)),TreeLoc(<tree>,Stream(<tree>, <tree>),Stream(),Stream((Stream(),1,Stre
20 //| am()), ?)),TreeLoc(<tree>,Stream(),Stream(<tree>, ?),Stream((Stream(<tree>,
21 //| <tree>),2,Stream()), ?))))
22
23 l.get._1.getLabel //> res8: Int = 1
24 l.get._2.getLabel //> res9: Int = 11
25 l.get._3.getLabel //> res10: Int = 2
26 l.get._4.getLabel //> res11: Int = 21
跳动函数：
/** Select the nth child of the current node. */
def getChild(n: Int): Option[TreeLoc[A]] =
for {lr <- splitChildren(Stream.Empty, tree.subForest, n)
ls = lr._1
} yield loc(ls.head, ls.tail, lr._2, downParents)
/** Select the first immediate child of the current node that satisfies the given predicate. */
def findChild(p: Tree[A] => Boolean): Option[TreeLoc[A]] = {
@tailrec
def split(acc: TreeForest[A], xs: TreeForest[A]): Option[(TreeForest[A], Tree[A], TreeForest[A])] =
(acc, xs) match {
case (acc, Stream.cons(x, xs)) => if (p(x)) Some((acc, x, xs)) else split(Stream.cons(x, acc), xs)
case _ => None
}
for (ltr <- split(Stream.Empty, tree.subForest)) yield loc(ltr._2, ltr._1, ltr._3, downParents)
}
/**Select the first descendant node of the current node that satisfies the given predicate. */
def find(p: TreeLoc[A] => Boolean): Option[TreeLoc[A]] =
Cobind[TreeLoc].cojoin(this).tree.flatten.find(p)
find用法示范：
1 val tree: Tree[Int] =
2 1.node(
3 11.leaf,
4 12.node(
5 121.leaf),
6 2.node(
7 21.leaf,
8 22.leaf)
9 ) //> tree : scalaz.Tree[Int] = <tree>
10 tree.loc //> res7: scalaz.TreeLoc[Int] = TreeLoc(<tree>,Stream(),Stream(),Stream())
11 val l = for {
12 l1 <- tree.loc.some
13 l2 <- l1.find{_.getLabel == 2}
14 l3 <- l1.find{_.getLabel == 121}
15 l4 <- l2.find{_.getLabel == 22}
16 l5 <- l1.findChild{_.rootLabel == 12}
17 l6 <- l1.findChild{_.rootLabel == 2}
18 } yield l6 //> l : Option[scalaz.TreeLoc[Int]] = Some(TreeLoc(<tree>,Stream(<tree>, ?),St
19 //| ream(),Stream((Stream(),1,Stream()), ?)))
注意：上面6个跳动都成功了。如果无法跳转结果会是None
insert,modify,delete这些操作函数：
/** Replace the current node with the given one. */
def setTree(t: Tree[A]): TreeLoc[A] = loc(t, lefts, rights, parents)
/** Modify the current node with the given function. */
def modifyTree(f: Tree[A] => Tree[A]): TreeLoc[A] = setTree(f(tree))
/** Modify the label at the current node with the given function. */
def modifyLabel(f: A => A): TreeLoc[A] = setLabel(f(getLabel))
/** Get the label of the current node. */
def getLabel: A = tree.rootLabel
/** Set the label of the current node. */
def setLabel(a: A): TreeLoc[A] = modifyTree((t: Tree[A]) => node(a, t.subForest))
/** Insert the given node to the left of the current node and give it focus. */
def insertLeft(t: Tree[A]): TreeLoc[A] = loc(t, lefts, Stream.cons(tree, rights), parents)
/** Insert the given node to the right of the current node and give it focus. */
def insertRight(t: Tree[A]): TreeLoc[A] = loc(t, Stream.cons(tree, lefts), rights, parents)
/** Insert the given node as the first child of the current node and give it focus. */
def insertDownFirst(t: Tree[A]): TreeLoc[A] = loc(t, Stream.Empty, tree.subForest, downParents)
/** Insert the given node as the last child of the current node and give it focus. */
def insertDownLast(t: Tree[A]): TreeLoc[A] = loc(t, tree.subForest.reverse, Stream.Empty, downParents)
/** Insert the given node as the nth child of the current node and give it focus. */
def insertDownAt(n: Int, t: Tree[A]): Option[TreeLoc[A]] =
for (lr <- splitChildren(Stream.Empty, tree.subForest, n)) yield loc(t, lr._1, lr._2, downParents)
/** Delete the current node and all its children. */
def delete: Option[TreeLoc[A]] = rights match {
case Stream.cons(t, ts) => Some(loc(t, lefts, ts, parents))
case _ => lefts match {
case Stream.cons(t, ts) => Some(loc(t, ts, rights, parents))
case _ => for (loc1 <- parent) yield loc1.modifyTree((t: Tree[A]) => node(t.rootLabel, Stream.Empty))
}
}
用法示范：
1 val tr = 1.leaf //> tr : scalaz.Tree[Int] = <tree>
2 val tl = for {
3 l1 <- tr.loc.some
4 l3 <- l1.insertDownLast(12.leaf).some
5 l4 <- l3.insertDownLast(121.leaf).some
6 l5 <- l4.root.some
7 l2 <- l5.insertDownFirst(11.leaf).some
8 l6 <- l2.root.some
9 l7 <- l6.find{_.getLabel == 12}
10 l8 <- l7.setLabel(102).some
11 } yield l8 //> tl : Option[scalaz.TreeLoc[Int]] = Some(TreeLoc(<tree>,Stream(<tree>, ?),S
12 //| tream(),Stream((Stream(),1,Stream()), ?)))
13
14 tl.get.toTree.drawTree //> res8: String = "1
15 //| |
16 //| +- 11
17 //| |
18 //| `- 102
19 //| |
20 //| `- 121
21 //| "
22
setTree和delete会替换当前节点下的所有子树：
1 val tree: Tree[Int] =
2 1.node(
3 11.leaf,
4 12.node(
5 121.leaf),
6 2.node(
7 21.leaf,
8 22.leaf)
9 ) //> tree : scalaz.Tree[Int] = <tree>
10 def modTree(t: Tree[Int]): Tree[Int] = {
11 val l = for {
12 l1 <- t.loc.some
13 l2 <- l1.find{_.getLabel == 22}
14 l3 <- l2.setTree { 3.node (31.leaf) }.some
15 } yield l3
16 l.get.toTree
17 } //> modTree: (t: scalaz.Tree[Int])scalaz.Tree[Int]
18 val l = for {
19 l1 <- tree.loc.some
20 l2 <- l1.find{_.getLabel == 2}
21 l3 <- l2.modifyTree{modTree(_)}.some
22 l4 <- l3.root.some
23 l5 <- l4.find{_.getLabel == 12}
24 l6 <- l5.delete
25 } yield l6 //> l : Option[scalaz.TreeLoc[Int]] = Some(TreeLoc(<tree>,Stream(<tree>, ?),St
26 //| ream(),Stream((Stream(),1,Stream()), ?)))
27 l.get.toTree.drawTree //> res7: String = "1
28 //| |
29 //| +- 11
30 //| |
31 //| `- 2
32 //| |
33 //| +- 21
34 //| |
35 //| `- 3
36 //| |
37 //| `- 31
38 //| "
通过scalaz的Tree和TreeLoc数据结构，以及一整套树形结构游览、操作函数，我们可以方便有效地实现FP风格的不可变树形集合编程。