The Elephant and the Dragon: The Rise of India and China and What It Means for All of Us 龙与象──中国和印度的崛起对世界意味着什么 fb2 免费 lrf 下载 kindle azw3 pdf 阿里云

Meredith, a foreign correspondent, describes the global power shift occurring in India and in China as computers continue to change the way business is conducted. The U.S. and Europe have lost both low- and high-paying jobs to these countries, and there are other factors at play, such as the unquenchable global thirst for oil and massive environmental issues. This is a complicated story because as jobs are lost, cheap goods are being imported and sold at low prices to American consumers, and some retailers' stock prices are rising, to the benefit of workers' 401K accounts. The author notes, "In this decade, a clear pattern emerged: China became factory to the world, the United States became buyer to the world, and India began to become back office to the world." In this thought-provoking and well-researched book, the author advises that the U.S. must strengthen its education system, promote innovation, forget about protectionism or unfettered free markets, and focus on creating jobs. Whaley, Mary

Introduction: Tectonic Economics

CHAPTER 1 Where Mao Meets the Middle Class

CHAPTER 2 From the Spinning Wheel to the Fiber-Optic Wire

CHAPTER 3 Made by America in China

CHAPTER 4 The Internet's Spice Route

CHAPTER 5 The Disassembly Line

CHAPTER 6 India's Cultural Revolution

CHAPTER 7 Revolution by Dinner Party

CHAPTER 8 Geopolitics Mixed with Oil and Water

CHAPTER 9 A Catalyst for Competitiveness

Acknowledgments

Notes

Index

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在线购买地址：The Elephant and the Dragon: The Rise of India and China and What It Means for All of Us 龙与象──中国和印度的崛起对世界意味着什么在线购买

NumPy提供了常规数组（至少缺乏dtype限制）的广义形式，但是我们暂时坚持使用常规数组，以观察这种专门化措施对性能的影响。

做个简单的练习，假定我们要生成一个5000 x 5000个元素组成的矩阵/数组，填入标准正态分布（伪）随机数，然后计算所有元素的总和。首先采用纯Python方法，大量使用列表推导和函数式编程方法以及lambda函数：

In [111]: import random

I = 5000

In [112]: %time mat = [[random.gauss(0,1) for j in range(l)] for i in range(l)] » a nested list comprehension

0ut[112]: CPU times: user 36.5 s, sys: 408 ms, total: 36.9 s Wall time: 36.4 s

In [113]: %time reduce(lambda x, y: x + y, reduce(lambda x, y: x + y, row for row in mat])

0ut[113]: CPU times: user 4.3 s, sys: 52ms, total: 4.35s

678.5908519876647

现在转向NumPy,看看同一个问题如何解决。为方便起见，NumPy子库random提供了多种函数，以初始化numpy.ndarray对象，同时填入（伪）随机数：

In [114]: %time mat = np.random.standard_normal((I, I))

0ut[114]: CPU times: user 1.83 s, sys: 40 ms, total: 1.87 s Wall time: 1.87 s in [115]:...

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