python – 将一个“增长”表的命令式算法转换为纯函数

>从frozenset中派生您的表类.
>每一行都应该是元组的一个次级.

现在你无法修改表格 – >不变,太棒了！下一步
可能是将每个函数都考虑为你应用的变异
表生成一个新表：

f T -> T'

这应该被解读为应用表T上的函数f来产生
新表T’.您也可以尝试对实际处理进行客体化
表数据并将其视为您应用或添加到的操作
表.

add(T,A) -> T'

这里最棒的是添加可以减去而不是给你
一种简单的撤消模型.当你进入这种心态时,你的代码
变得非常容易推理,因为你没有可以的状态
搞砸了.

下面是一个如何实现和处理表的示例
在Python中以纯粹的功能方式构造. Imho,Python不是
学习FP的最佳语言,因为它使它变得容易
程序势在必行.我认为Haskell,F#或Erlang是更好的选择.

class Table(frozenset):
    def __new__(cls,names,rows):
        return frozenset.__new__(cls,rows)

    def __init__(self,rows):
        frozenset.__init__(self,rows)
        self.names = names

def add_column(rows,func):
    return [row + (func(row,idx),) for (idx,row) in enumerate(rows)]

def table_process(t,(name,func)):
    return Table(
        t.names + (name,),add_column(t,lambda row,idx: func(row))
        )

def table_filter(t,func)):
    names = t.names
    idx = names.index(name)
    return Table(
        names,[row for row in t if func(row[idx])]
        )

def table_rank(t,name):
    names = t.names
    idx = names.index(name)
    rows = sorted(t,key = lambda row: row[idx])
    return Table(
        names + ('rank',add_column(rows,idx: idx)
        )

def table_print(t):
    format_row = lambda r: ' '.join('%15s' % c for c in r)
    print format_row(t.names)
    print 'n'.join(format_row(row) for row in t)

if __name__ == '__main__':
    from random import randint
    cols = ('c1','c2','c3')
    T = Table(
        cols,[tuple(randint(0,9) for x in cols) for x in range(10)]
        )
    table_print(T)

    # Columns to add to the table,this is a perfect fit for a
    # reduce. I'd honestly use a boring for loop instead,but reduce
    # is a perfect example for how in FP data and code "becomes one."
    # In fact,this whole program could have been written as just one
    # big reduce.
    actions = [
        ('max',max),('min',min),('sum',sum),('avg',lambda r: sum(r) / float(len(r)))
        ]
    T = reduce(table_process,actions,T)
    table_print(T)

    # Ranking is different because it requires an ordering,which a
    # table does not have.
    T2 = table_rank(T,'sum')
    table_print(T2)

    # Simple where filter: select * from T2 where c2 < 5.
    T3 = table_filter(T2,('c2',lambda c: c < 5))
    table_print(T3)