我正在尝试解决一些 Google Code Jam 问题,其中输入矩阵通常以这种形式给出:
2 3 #matrix dimensions
1 2 3 4 5 6 7 8 9 # all 3 elements in the first row
2 3 4 5 6 7 8 9 0 # each element is composed of three integers
其中矩阵的每个元素都由三个整数组成。所以这个例子应该转换成
#!scala
Array(
Array(A(1,2,3),A(4,5,6),A(7,8,9),
Array(A(2,3,4),A(5,6,7),A(8,9,0),
)
命令式解决方案的形式是
#!python
input = """2 3
1 2 3 4 5 6 7 8 9
2 3 4 5 6 7 8 9 0
"""
lines = input.split('\n')
class Aclass:
def __init__(self,a,b,c):
pass
print lines[0]
m,n = (int(x) for x in lines[0].split())
array = []
row = []
A = []
for line in lines[1:]:
for elt in line.split():
A.append(elt)
if len(A)== 3:
row.append(Aclass(A[0],A[1],A[2]))
A = []
array.append(row)
row = []
from pprint import pprint
pprint(array)
我想到的一个功能性解决方案是
#!scala
def splitList[A](l:List[A],i:Int):List[List[A]] = {
if (l.isEmpty) return List[List[A]]()
val (head,tail) = l.splitAt(i)
return head :: splitList(tail,i)
}
def readMatrix(src:Iterator[String]):Array[Array[TrafficLight]] = {
val Array(x,y) = src.next.split(" +").map(_.trim.toInt)
val mat = src.take(x).toList.map(_.split(" ").
map(_.trim.toInt)).
map(a => splitList(a.toList,3).
map(b => TrafficLight(b(0),b(1),b(2))
).toArray
).toArray
return mat
}
但我真的觉得这是错误的方式,因为:
- 我正在为每一行使用功能性
List
结构,然后将其转换为数组。整个代码似乎效率低得多 - 我发现它比 python 解决方案更不优雅,可读性也更差。哪个映射函数对什么进行操作更难,因为它们都使用相同的语义。
什么是正确的功能方法?
最佳答案
val x = """2 3
1 2 3 4 5 6 7 8 9
2 3 4 5 6 7 8 9 0
"""
val a = x split "\n" map (_.trim.split(" "))
val rows = a(0)(0).toInt
val columns = a(0)(1).toInt
val matrix = (a drop 1) map (_ grouped columns toList) toList
并打印结果:
matrix.map(_.map(_.mkString("(",",",")")).mkString("(",",",")")).mkString("\n")
res1: String =
((1,2,3),(4,5,6),(7,8,9))
((2,3,4),(5,6,7),(8,9,0))
假设:
assert(rows == matrix.length)
assert(matrix.forall(_.forall(_.size == columns)))
生成数组 tabulate
更合适:
val a = x split "\n" map (_.trim.split(" "))
val rows = a(0)(0).toInt
val columns = a(0)(1).toInt
val matrix = Array.tabulate(rows, a(1).size / columns, columns)(
(i,j,k) => a(i + 1)(j * columns + k))
关于arrays - 从文本中获取矩阵的函数式方法,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/2522904/