# 3.5 - Streams

## 1 Streams are Delayed Lists

### 1.1 Notes

(define x (delay 5)) ;; is equivalent to (define x (lambda () 5)) ;; A "thunk" (a function of no arguments, used to delay evaluation of its return value) (force x) ;; => 5

The MIT implementation of `delay`

returns a `promise`

value. Force
will evaluate a `promise`

if it hasn't yet been computed. The result
of the evaluation will be memoized for future calls to force.

Stream processing explanation: MIT OpenCourseWare Lecture 6A

A stream a sequence built as a pair of an initial value, and a procedure to generate the next value and the next procedure to continue the sequence.

### 1.2 Code

(define (cons-stream a b) (cons a (delay b))) (define (stream-car s) (car s)) (define (stream-cdr s) (force (cdr s))) (define the-empty-stream '()) (define (stream-ref s n) (if (= n 0) (stream-car s) (stream-ref (stream-cdr s) (- n 1)))) (define (stream-map proc s) (if (stream-null? s) the-empty-stream (cons-stream (proc (stream-car s)) (stream-map proc (stream-cdr s))))) (define (stream-for-each proc s) (if (stream-null? s) 'done (begin (proc (stream-car s)) (stream-for-each proc (stream-cdr s)))))

;; =================================================================== ;; 3.5.1: Streams are Delayed Lists ;; =================================================================== (define (display-stream s) (stream-for-each display-line s)) (define (display-line x) (newline) (display x)) (define (stream-enumerate-interval low high) (if (> low high) the-empty-stream (cons-stream low (stream-enumerate-interval (+ low 1) high)))) (define (stream-filter pred stream) (cond ((stream-null? stream) the-empty-stream) ((pred (stream-car stream)) (cons-stream (stream-car stream) (stream-filter pred (stream-cdr stream)))) (else (stream-filter pred (stream-cdr stream)))))

### 1.3 Exercises

#### 1.3.1 Exercise 3.50

Complete the following definition, which generalizes `stream-map' to allow procedures that take multiple arguments, analogous to `map' in section *Note 2-2-3::, footnote *Note Footnote 12::.

(define (stream-map proc . argstreams) (if (<??> (car argstreams)) the-empty-stream (<??> (apply proc (map <??> argstreams)) (apply stream-map (cons proc (map <??> argstreams))))))

;; ------------------------------------------------------------------- ;; Exercise 3.50 ;; ------------------------------------------------------------------- (define (stream-map proc . argstreams) (if (stream-null? (car argstreams)) the-empty-stream (cons-stream (apply proc (map stream-car argstreams)) (apply stream-map (cons proc (map stream-cdr argstreams))))))

#### 1.3.2 Exercise 3.51

In order to take a closer look at delayed evaluation, we will use the following procedure, which simply returns its argument after printing it:

(define (show x) (display-line x) x)

What does the interpreter print in response to evaluating each expression in the following sequence?(7)

(define x (stream-map show (stream-enumerate-interval 0 10))) (stream-ref x 5) (stream-ref x 7)

#### 1.3.3 Exercise 3.52

Consider the sequence of expressions

(define sum 0) (define (accum x) (set! sum (+ x sum)) sum) (define seq (stream-map accum (stream-enumerate-interval 1 20))) (define y (stream-filter even? seq)) (define z (stream-filter (lambda (x) (= (remainder x 5) 0)) seq)) (stream-ref y 7) (display-stream z)

What is the value of `sum' after each of the above expressions is evaluated? What is the printed response to evaluating the `stream-ref' and `display-stream' expressions? Would these responses differ if we had implemented `(delay <EXP>)' simply as `(lambda () <EXP>)' without using the optimization provided by `memo-proc'? Explain

1 ]=> sum ;Value: 210 1 ]=> (stream-head y 10) ;Value 18: (210 204 200 182 174 144 132 90 74 20) 1 ]=> (display-stream z) 210 200 195 165 155 105 90 20 ;Value: done

After the definition of `seq`

, `sum`

is equal to 210. It remains at
210 through the remainder of the operations.This would not be the
case if delay were not memoized, as without being so it would be
recalculated each time the items in the node were resolved, adding to
the value of `sum`

each time, and changing the results captured by `y`

and `z`

.

## 2 Infinite Streams

### 2.1 Notes

Streams can continue forever if the promise never returns an empty stream.

Streams can be combined to model complex sequences.

### 2.2 Code

;; =================================================================== ;; 3.5.2: Infinite Streams ;; =================================================================== (define (integers-starting-from n) (cons-stream n (integers-starting-from (+ n 1)))) (define integers (integers-starting-from 1)) (define (divisible? x y) (= (remainder x y) 0)) (define no-sevens (stream-filter (lambda (x) (not (divisible? x 7))) integers)) (define (fibgen a b) (cons-stream a (fibgen b (+ a b)))) (define fibs (fibgen 0 1)) (define (sieve stream) (cons-stream (stream-car stream) (sieve (stream-filter (lambda (x) (not (divisible? x (stream-car stream)))) (stream-cdr stream))))) (define primes (sieve (integers-starting-from 2)))

#### 2.2.1 Defining streams implicitly

(define ones (cons-stream 1 ones)) (define (add-streams s1 s2) (stream-map + s1 s2)) (define integers (cons-stream 1 (add-streams ones integers))) (define fibs (cons-stream 0 (cons-stream 1 (add-streams (stream-cdr fibs) fibs)))) (define (scale-stream stream factor) (stream-map (lambda (x) (* x factor)) stream)) (define double (cons-stream 1 (scale-stream double 2))) (define primes (cons-stream 2 (stream-filter prime? (integers-starting-from 3)))) (define (prime? n) (define (iter ps) (cond ((> (square (stream-car ps)) n) true) ((divisible? n (stream-car ps)) false) (else (iter (stream-cdr ps))))) (iter primes))

### 2.3 Exercises

#### 2.3.1 Exercise 3.53

Without running the program, describe the elements of the stream defined by

(define s (cons-stream 1 (add-streams s s)))

\[ \sum_{i=1}^\infty 2^i \]

#### 2.3.2 Exercise 3.54

Define a procedure `mul-streams', analogous to `add-streams', that produces the elementwise product of its two input streams. Use this together with the stream of `integers' to complete the following definition of the stream whose nth element (counting from 0) is n + 1 factorial:

(define factorials (cons-stream 1 (mul-streams <??> <??>)))

(define (mul-streams s1 s2) (stream-map * s1 s2)) (define factorials (cons-stream 1 (mul-streams (add-streams ones integers) factorials)))

#### 2.3.3 Exercise 3.55

Define a procedure `partial-sums' that takes as argument a stream S and returns the stream whose elements are S_0, S_0 + S_1, S_0 + S_1 + S_2, …. For example, `(partial-sums integers)' should be the stream 1, 3, 6, 10, 15, ….

## 3 Exploiting the Stream Paradigm

### 3.1 Notes

Streams and their property of delayed evaluation can be used to build abstractions over the sequences and computations used to generate them. The examples are the square-root stream and the pi streams being accelerated via a generic stream transformation method.

## 4 Streams and Delayed Evaluation

### 4.1 Notes

…stream models of systems with loops may require uses of delay beyond the “hidden” delay supplied by cons-stream.

Unfortunately, including delays in procedure calls wreaks havoc with our ability to design programs that depend on the order of events, such as programs that use assignment, mutate data, or perform input or output.

…

As far as anyone knows, mutability and delayed evaluation do not mix well in programming languages, and devising ways to deal with both of these at once is an active area of research.

## 5 Modularity of Functional Programs and Modularity of Objects

### 5.1 Notes

Random number generation can be implemented as an infinite stream instantiated with some seed.

#### 5.1.1 A functional-programming view of time

We can model a changing quantity, such as the local state of some object, using a stream that represents the time history of successive states. In essence, we represent time explicitly, using streams, so that we decouple time in our simulated world from the sequence of events that take place during evaluation.

Events over time can be merged / serialized (deterministically?) into a stream of events.

This is precisely the same constraint that we had to deal with in 3.4.1, where we found the need to introduce explicit synchronization to ensure a “correct” order of events in concurrent processing of objects with state. Thus, in an attempt to support the functional style, the need to merge inputs from different agents reintroduces the same problems that the functional style was meant to eliminate.

With a working merge solution, a system can be designed in a functional way, operating on a stream of state and inputs.

The Erlang/OTP generic server, generic fsm and other behaviours are implemented in such a way that input streams received concurrently are merged by the vm and combined with the state of the process as a single stream pairing the current state with the next input to process, allowing an Erlang developer to build a functional interface with the complexities of concurrency abstracted away.