SICP--1.6---高阶函数

SICP--1.6---高阶函数
高阶函数

将函数作为参数
例如

1 def sum_naturals(n):
2         total, k = 0, 1
3         while k <= n:
4             total, k = total + k, k + 1
5         return total

def sum_cubes(n):
        total, k = 0, 1
        while k <= n:
            total, k = total + k*k*k, k + 1
        return total

def pi_sum(n):
        total, k = 0, 1
        while k <= n:
            total, k = total + 8 / ((4*k-3) * (4*k-1)), k + 1
        return total

以上三个例子中有许多共同的部分
def <name>(n):
total, k = 0, 1
while k <= n:
total, k = total + <term>(k), k + 1
return total
高阶函数形式

def summation(n, term):
    total, k = 0, 1
    while k <= n:
        total, k = total + term(k), k + 1
    return total

def cube(x):
    return x*x*x

def sum_cubes(x):
    return summation(x,cube)

函数环境
黄金比例

def improve(update, close, guess=1):
        while not close(guess):
            guess = update(guess)
        return guess

def golden_update(guess):
        return 1/guess + 1

def square_close_to_successor(guess):
        return approx_eq(guess * guess, guess + 1)

def approx_eq(x, y, tolerance=1e-15):
        return abs(x - y) < tolerance

improve(golden_update, square_close_to_successor)

高阶函数的不足
在全局环境中名称复杂
每一个函数的形参个数是由限制的
函数的嵌套定义

解决高阶函数存在的不足

def sqrt(a):
        def sqrt_update(x):
            return average(x, a/x)
        def sqrt_close(x):
            return approx_eq(x * x, a)
        return improve(sqrt_update, sqrt_close)

嵌套定义中的函数作用域
每一个嵌套的函数在定义函数内环境

函数作用域的实现方法
每一个函数都有他的父环境
当函数被调用时，在其父环境中评估
函数作用域的好处
局部环境中的绑定不会影响全局环境

def square(x):
    return x * x

def successor(x):
    return x + 1

def compose1(f,g):
    def h(x):
        return f(g(x))
    return h

square_successor = compose1(square,successor)
result = square_successor(12)

牛顿法

def newton_update(f, df):
        def update(x):
            return x - f(x) / df(x)
        return update

def find_zero(f, df):
        def near_zero(x):
            return approx_eq(f(x), 0)
        return improve(newton_update(f, df), near_zero)

def square_root_newton(a):
        def f(x):
            return x * x - a
        def df(x):
            return 2 * x
        return find_zero(f, df)
Currying

>>> def curried_pow(x):
        def h(y):
            return pow(x, y)
        return h
>>> curried_pow(2)(3)
8

def curry2(f):
        """Return a curried version of the given two-argument function."""
        def g(x):
            def h(y):
                return f(x, y)
            return h
        return g

def uncurry2(g):
        """Return a two-argument version of the given curried function."""
        def f(x, y):
            return g(x)(y)
        return f

环境图
匿名函数

可以看做

　　     lambda            x            :          f(g(x))
"A function that    takes x    and returns     f(g(x))"

函数装饰器

>>> def trace(fn):
        def wrapped(x):
            print('-> ', fn, '(', x, ')')
            return fn(x)
        return wrapped

@trace
def triple(x):
return 3 * x
triple(12)
-> <function triple at 0x102a39848> ( 12 )
36
@trace等同于

>>> def triple(x):
        return 3 * x
>>> triple = trace(triple)