Python函数式编程入门教程： Ted Herman著

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"A-Functional-Start-To-Computing-With-Python" 是一本由 Ted Herman 所著的专业计算机科学教材，它作为 Chapman & Hall/CRC 计算机科学系列的一部分出版。该系列书籍旨在为学习者提供广泛且深入的计算机基础知识，包括但不限于语义网技术的基础、数字图像处理算法、软件设计模式和敏捷开发方法、计算机科学入门、社会计算以及不同编程语言如 Python 的应用。在《A Functional Start to Computing with Python》这本书中，作者以函数式编程的角度引导读者入门计算机科学，特别强调 Python 这一现代、易学且功能强大的编程语言。Python以其简洁的语法和丰富的库支持，使得初学者能够快速理解编程概念，如递归、函数组合、高阶函数等核心理念。通过本书，读者将掌握如何使用 Python 实现数据结构、算法设计、面向对象编程和模块化编程，这些都是构建高效软件的基础。作者 Ted Herman 是霍夫斯特拉大学的教授 Emeritus，他结合自己的教学经验，确保了本书内容既严谨又易于理解。同时，与 Andrew McGettrick 合作，他们可能关注于提供一种实践导向的教学方法，帮助学生不仅掌握理论知识，还培养解决问题和实际项目中的编程能力。书中的每个章节都可能包含一系列练习和案例研究，鼓励读者动手实践，加深对函数式编程原则的理解。此外，书中可能还会探讨 Python 在Web开发、数据分析、人工智能等领域的应用，让学生看到编程技术在现实世界中的广泛应用。《A-Functional-Start-To-Computing-With-Python》是一本适合初学者的计算机科学入门读物，它不仅介绍了基础的编程概念，还注重培养读者的函数式思维，为他们后续在 IT 行业的发展打下坚实的基础。

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Part I

Motivation and Background

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Chapter 1: Inspirations of Computing

The discovery of music recording was a transformative event in human history. Here was an

invention that almost everyone instantly desire d. Recorded music can be enjoyed by people

who cannot read or write, who cannot themselves play an instrument or sing. It is hard to

look back on the ﬁrst crude recording technologies with their scratchy noises and wobbly

playback and understand how people could be satisﬁed. An amazing accumulation of clever

technology has improved the quality of recording and playback to a remarkable extent.

Even in the early days, engineers worked hard to reduce noise a nd ensure good ﬁdelity in

reproducing original sound.

In the middle o f the last century, magnetic tape be came a preferred medium for reco rd-

ing. This medium gives an engineer more options to overcome defects in what has been

recorded. A good engineer may be able to cut the tape, splice in part of another recording,

and use other tricks of modiﬁcation. If some piece of equipment fell during a recording

session, a recor ding engineer might even be able to remove the sound o f the accident, so

the listener would never know. More challenging are live recording sessions in front of an

audience. The placement of micropho ne s, the sound levels, a nd mitigating audience noise

become problematic. Another proble m during live recording can be that performers (espe-

cially in rock venues) might not be able to hear themselves singing; a singer co uld well miss a

note, sing slightly oﬀ-key, yet be unaware o f the mistake. This leaves the recording engineer

with a nother problem: can the recording be tweaked so that oﬀ-key notes are transfor med

into the proper pitch? It is not so easy. You can speed up the tape to raise the pitch, but

that changes the timing. Eventually, with hard work and long hours of trying many things,

recording engineers found tricks to overcome the problem of sour notes.

Arguably the discovery with the greatest impact in recent history of recordings is dig-

ital technology. By converting the electronic signals from microphone s to digital form, the

recording s can be stored in highly eﬃcient pa ckages, like MP3s and other compressed for-

mats. Further, the playback and rec ording devices get smaller and cheaper. Relating back

to how engineers improve recording quality, the digital formats can be manipulated in many

new ways with far less laborious processes. When this technology ﬁr st became widespread,

recording engineers were somewhat secre tive about how they improved recordings. A new

technique automated the work of co rrecting the pitch of a singer. The magic was due to

a proprietary product called Auto-Tune. For some years the public was kept in the dar k

about the existence of Auto-Tune. Then, in 1998 , a hit song by Cher exploited and even

emphasized the use of Auto-Tune, and it was no secret. What used to be a trick of the trade

became widely known to the public.

The story of how Auto-Tune came into existence is one of those surprising seq ue nc es of

connections that link up, as happens sometimes in the history of innovation. Exploring for

oil is now an advanced scientiﬁc enterprise, drawing on sa tellite images, geological research,

and measuring seismic r eaction to low-frequency vibration in exploratory prospects. The

study of vibration is essentially based on acoustic principles, which also apply to the tones

of musical instruments. An engineer working at Exxon noticed a connection between the

signal processing of sound in oil exploration and the detection of proper pitch in music.

Engineering principles used for classifying and removing noise from shock waves during

exploratory tests could also be used to reshape sound waves, thus cor recting the pitch of

oﬀ-key notes. This obser vation eventually resulted, with lots o f hard work solving other

problems, in Auto- Tune.

After Auto-Tune be came more w ide ly known, and accepted by a t least some listeners,

performers started using it openly, more often, and some would say, more creatively. Rap

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4 A Functional Start to Computing with Python

artist T-Pain used Auto-Tune c ontinuously in performances. Used heavily, Auto-Tune gives

the voice clipped sound, with instant transition between notes that c an resemble yodeling.

The hip-hop group Black Eyed Peas has cultivated this sound, selectively. Other performers

came under the inﬂuence of the “new sound” of Auto-Tune, to the extent that it beca me

something to emulate even without using Auto-Tune itself. Thus, we have a strange circle

of de velopment intertwining technology and popular culture, which appears to be a theme

in other areas of modern life.

✰ ✰ ✰

Tracking athletic performance and ranking athletes in diﬀerent ways has become an industry.

It is not just the records of times for track athletes and swimmers, nor are sports fans content

with counting strings of victories. Now fantasy sports leagues are million dollar business

communities, which track the numbers, events, and achievements of players in new ways.

Where did all of this originate? Perhaps the roo ts go to the motivations of conversation.

There is an old saying that all conversation is either bragging, complaining, or gossip (who

did what to whom). Whatever the reason, fans of sports follow the tabulations of the teams

and their players with enthusiasm.

In America we can credit base ball for the earliest, large scale publication of numbers

and records. As early as 1859 journalists started counting va rious events on the playing

ﬁeld, r ecording the number of errors, the batting averag e over the course of a season, and

RBI (r uns batted in). The event counts, averages, and other numbers became part of the

sp orts news stream, which fans followed, making it easy for them to identify rising star s in

baseball. Players with the best reco rds draw an audience. Teams compete to get the best

players—team managers also read the papers. The rich teams have suﬃcient budgets to

buy talent from the poo rer teams, leading to fame and fortune for players with the best

individual statistics. Another way that teams obtain talent is by shrewdly selecting talent,

by dr afting the right combination of players and cultivating their skills in mino r leagues.

The colorful history of baseball and the lore of draft picks, trades, rule changes, and

scheduling was for many decades a guild where wisdom was passed down through sto-

rytelling. Somewhere during the 1970s, the question of baseball statistics was revisited,

questioning w he ther the right sort of events were being counted. Trade publications began

tracking more numbers. Out of the numbers people tried to ﬁnd correlations between player

and team statistics and winning games. The thinking is that if a model can be fo und which

accurately pre dic ts winning on the basis of event counts, like number of walks, number of

hits, and so on, then players and managers will k now what counts most toward winning

games. Even if this prediction is only accurate to a reasonable percentage, it might still win

out over the cour se of a long sea son.

Two decades later, in the 1990s, a few general managers with some training in sci-

entiﬁc methodology proposed taking the sta tistics further. The conjecture was that the

stories passed down among coaches, scouts, and players about talent and strategy were not

grounded in statistical knowledge. To test this hypothesis, the manager of the Oak land A’s

based the selectio n of players more rigorously and with ﬁner levels of statistics than any

team had done before. They were up against a major disadvantage: the budget they had

for players was less than a third of the New York Yankees. In spite of being a poo r team,

the team followed decisions based on a s cientiﬁc understanding of statistics. The result was

surprising. During the 2000–2002 seasons one of the poorest teams in baseball, the Oak-

land A’s, had one of the most successful winning records.

A few sports fans are still not

satisﬁed this new approach is valid, pointing out that the A’s did not win the World Series

Read Moneyball: The Art of Winning an Unfair Game by Michael Lewis (W. W. Norton, 2003), for

the full story.

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Inspirations of Computing 5

(actually, the loss of the Series might be attributed to human error in coaching, which de vi-

ated from the strict policy of basing decisions on the statistics). In any case, the success of

Oakland caught the attention of other teams, who began emulating statistics-based decision

techniques of evaluating players, and copied the new strategies for managing a nd selecting

talent.

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If you walk toward the city of Chartres you might see across a ﬂat landscape covered in

grain ﬁelds a small upright structure in the distance. As you continue walking toward that

structure, it begins to rise above the plain and take form. The geo metric form becomes clear

as spires of the great cathedral are seen in more detail. The view of the cathedral growing

up from the plain seems calculated to inspir e. The cathedral of Chartres was a pioneering

architectural achievement of its time, the construction spanning several centuries. An un-

told number of anonymous laborers made lasting contributions in building this re nowned

masterpiece.

What you do not see looking at the cathedral of Chartres or other impressive buildings

dating to the time (13th to 16th centuries) is the number of failed attempts at similar

construction. Cities competed to have the grandest, tallest, and most impressive churches,

however, construction technology coming out of the dark ag es was primitive. Many attempts

to build tall structures, especially ones that had enough windows to light up the interiors,

came to disastrous ends. To be sure, the invention of the ﬂying buttress was a breakthrough

in structural engineering, but the knowledge of how to create successful tall cathedrals

remained more an informal art than a predictable science.

The human desire to push the envelope of daring architecture continues today. Catalogs

of famous buildings of the mode rn era feature skyscrapers competing for height and for

imaginative design. O ther dimensions of competition include innovative materials, unusual

shapes, hidden perspectives, novel use of lighting, and arranging spaces to evoke certain

feelings in a building’s occupants. A prominent example is the Guggenheim Museum in

Bilbao, often cited for the seemingly random use of curves throug hout the ediﬁce. More

recent, and on a larger scale, is the Beijing National Stadium, known worldwide from the

2008 Summer Olympics. Sometimes c alled the “bird’s nest,” the stadium’s construc tion

exposes structural beams that have the appearance, from a distance, of random sticks. In

the night, when the stadium’s inside is illuminated, a glowing interior can be seen through

the lattice of stee l beams.

Construction technology, design methods, and mechanical and civil engineering have

advanced to the point that creating modern buildings is far more reliable (and safer for

construction workers) tha n were the endeavors of the middle ages. The tension between

what an architect can imag ine and what can be built has decreased, though building cost

remains a concern as it was many centuries ago. Modern civilization can realize architecture

that would have been undreamt just a century ago.

✰ ✰ ✰

What is common to the foregoing stories should come as no surprise, given the title of this

book. Ea ch story culminates in technology tha t depends on software. Andy Hildebrand,

working at Exxon with auto correlation software, created the bas ic idea for Auto-Tune.

Billy Beane, in pa rt exploiting the computer analysis of earlier pio ne ers of Sabremetr ics,

led the Oakland A’s to winning seasons. Modern, cutting-edge architecture would not be

possible without packages like Autodesk’s architectural suite of software.

Quietly, computing is fa ding into the background and fabric of modern socie ty. We take

for granted many technologies based on mater ial scie nc e, electromagnetic principles, and

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