Let's say I have the following:
scoringObject =
a : -1
b : 0
c : 1
d : 2
resultsArray = ['a','c','b','b','c','c','d']
Using Coffescript, how can I calculate aggregateScore (+4 in the example) ?
Since your example doesn't make much sense as is, I'm going to assume that what you have is:
resultsArray = ['a','c','b','b','c','c','d']
with the scoringObject from your post. Then you could calculate like this:
aggregateScore = 0
aggregateScore += scoringObject[k] for k in resultsArray
# => 4
Let me know if I assumed wrongly.
If you don't mind using features from ECMAScript 5, Array::reduce lets you express this kind of thing quite succinctly:
aggregateScore = resultsArray.reduce ((sum, x) -> sum + scoringObject[x]), 0
(I feel that the parameter order of reduce is quite unfortunate; the initial value should be the first one, and the reducing function the last one)
Underscore.js provides a cross-browser reduce implementation :)
Related
I am currently taking an online algorithms course in which the teacher doesn't give code to solve the algorithm, but rather rough pseudo code. So before taking to the internet for the answer, I decided to take a stab at it myself.
In this case, the algorithm that we were looking at is merge sort algorithm. After being given the pseudo code we also dove into analyzing the algorithm for run times against n number of items in an array. After a quick analysis, the teacher arrived at 6nlog(base2)(n) + 6n as an approximate run time for the algorithm.
The pseudo code given was for the merge portion of the algorithm only and was given as follows:
C = output [length = n]
A = 1st sorted array [n/2]
B = 2nd sorted array [n/2]
i = 1
j = 1
for k = 1 to n
if A(i) < B(j)
C(k) = A(i)
i++
else [B(j) < A(i)]
C(k) = B(j)
j++
end
end
He basically did a breakdown of the above taking 4n+2 (2 for the declarations i and j, and 4 for the number of operations performed -- the for, if, array position assignment, and iteration). He simplified this, I believe for the sake of the class, to 6n.
This all makes sense to me, my question arises from the implementation that I am performing and how it effects the algorithms and some of the tradeoffs/inefficiencies it may add.
Below is my code in swift using a playground:
func mergeSort<T:Comparable>(_ array:[T]) -> [T] {
guard array.count > 1 else { return array }
let lowerHalfArray = array[0..<array.count / 2]
let upperHalfArray = array[array.count / 2..<array.count]
let lowerSortedArray = mergeSort(array: Array(lowerHalfArray))
let upperSortedArray = mergeSort(array: Array(upperHalfArray))
return merge(lhs:lowerSortedArray, rhs:upperSortedArray)
}
func merge<T:Comparable>(lhs:[T], rhs:[T]) -> [T] {
guard lhs.count > 0 else { return rhs }
guard rhs.count > 0 else { return lhs }
var i = 0
var j = 0
var mergedArray = [T]()
let loopCount = (lhs.count + rhs.count)
for _ in 0..<loopCount {
if j == rhs.count || (i < lhs.count && lhs[i] < rhs[j]) {
mergedArray.append(lhs[i])
i += 1
} else {
mergedArray.append(rhs[j])
j += 1
}
}
return mergedArray
}
let values = [5,4,8,7,6,3,1,2,9]
let sortedValues = mergeSort(values)
My questions for this are as follows:
Do the guard statements at the start of the merge<T:Comparable> function actually make it more inefficient? Considering we are always halving the array, the only time that it will hold true is for the base case and when there is an odd number of items in the array.
This to me seems like it would actually add more processing and give minimal return since the time that it happens is when we have halved the array to the point where one has no items.
Concerning my if statement in the merge. Since it is checking more than one condition, does this effect the overall efficiency of the algorithm that I have written? If so, the effects to me seems like they vary based on when it would break out of the if statement (e.g at the first condition or the second).
Is this something that is considered heavily when analyzing algorithms, and if so how do you account for the variance when it breaks out from the algorithm?
Any other analysis/tips you can give me on what I have written would be greatly appreciated.
You will very soon learn about Big-O and Big-Theta where you don't care about exact runtimes (believe me when I say very soon, like in a lecture or two). Until then, this is what you need to know:
Yes, the guards take some time, but it is the same amount of time in every iteration. So if each iteration takes X amount of time without the guard and you do n function calls, then it takes X*n amount of time in total. Now add in the guards who take Y amount of time in each call. You now need (X+Y)*n time in total. This is a constant factor, and when n becomes very large the (X+Y) factor becomes negligible compared to the n factor. That is, if you can reduce a function X*n to (X+Y)*(log n) then it is worthwhile to add the Y amount of work because you do fewer iterations in total.
The same reasoning applies to your second question. Yes, checking "if X or Y" takes more time than checking "if X" but it is a constant factor. The extra time does not vary with the size of n.
In some languages you only check the second condition if the first fails. How do we account for that? The simplest solution is to realize that the upper bound of the number of comparisons will be 3, while the number of iterations can be potentially millions with a large n. But 3 is a constant number, so it adds at most a constant amount of work per iteration. You can go into nitty-gritty details and try to reason about the distribution of how often the first, second and third condition will be true or false, but often you don't really want to go down that road. Pretend that you always do all the comparisons.
So yes, adding the guards might be bad for your runtime if you do the same number of iterations as before. But sometimes adding extra work in each iteration can decrease the number of iterations needed.
I am taking coursera course and,for an assignment, I have written a code to count the change of an amount given a list of denominations. A doing a lot of research, I found explanations of various algorithms. In the recursive implementation, one of the base cases is if the amount money is 0 then the count is 1. I don't understand why but this is the only way the code works. I feel that is the amount is 0 then there is no way to make change for it and I should throw an exception. The code look like:
function countChange(amount : Int, denoms :List[Int]) : Int = {
if (amount == 0 ) return 1 ....
Any explanation is much appreciated.
To avoid speaking specifically about the Coursera problem, I'll refer to a simpler but similar problem.
How many outcomes are there for 2 coin flips? 4.
(H,H),(H,T),(T,H),(T,T)
How many outcomes are there for 1 coin flip? 2.
(H),(T)
How many outcomes are there for 0 coin flips? 1.
()
Expressing this recursively, how many outcomes are there for N coin flips? Let's call it f(N) where
f(N) = 2 * f(N - 1), for N > 0
f(0) = 1
The N = 0 trivial (base) case is chosen so that the non-trivial cases, defined recursively, work out correctly. Since we're doing multiplication in this example and the identity element for multiplication is 1, it makes sense to choose that as the base case.
Alternatively, you could argue from combinatorics: n choose 0 = 1, 0! = 1, etc.
So I've spent hours trying to work out exactly how this code produces prime numbers.
lazy val ps: Stream[Int] = 2 #:: Stream.from(3).filter(i =>
ps.takeWhile{j => j * j <= i}.forall{ k => i % k > 0});
I've used a number of printlns etc, but nothings making it clearer.
This is what I think the code does:
/**
* [2,3]
*
* takeWhile 2*2 <= 3
* takeWhile 2*2 <= 4 found match
* (4 % [2,3] > 1) return false.
* takeWhile 2*2 <= 5 found match
* (5 % [2,3] > 1) return true
* Add 5 to the list
* takeWhile 2*2 <= 6 found match
* (6 % [2,3,5] > 1) return false
* takeWhile 2*2 <= 7
* (7 % [2,3,5] > 1) return true
* Add 7 to the list
*/
But If I change j*j in the list to be 2*2 which I assumed would work exactly the same, it causes a stackoverflow error.
I'm obviously missing something fundamental here, and could really use someone explaining this to me like I was a five year old.
Any help would be greatly appreciated.
I'm not sure that seeking a procedural/imperative explanation is the best way to gain understanding here. Streams come from functional programming and they're best understood from that perspective. The key aspects of the definition you've given are:
It's lazy. Other than the first element in the stream, nothing is computed until you ask for it. If you never ask for the 5th prime, it will never be computed.
It's recursive. The list of prime numbers is defined in terms of itself.
It's infinite. Streams have the interesting property (because they're lazy) that they can represent a sequence with an infinite number of elements. Stream.from(3) is an example of this: it represents the list [3, 4, 5, ...].
Let's see if we can understand why your definition computes the sequence of prime numbers.
The definition starts out with 2 #:: .... This just says that the first number in the sequence is 2 - simple enough so far.
The next part defines the rest of the prime numbers. We can start with all the counting numbers starting at 3 (Stream.from(3)), but we obviously need to filter a bunch of these numbers out (i.e., all the composites). So let's consider each number i. If i is not a multiple of a lesser prime number, then i is prime. That is, i is prime if, for all primes k less than i, i % k > 0. In Scala, we could express this as
nums.filter(i => ps.takeWhile(k => k < i).forall(k => i % k > 0))
However, it isn't actually necessary to check all lesser prime numbers -- we really only need to check the prime numbers whose square is less than or equal to i (this is a fact from number theory*). So we could instead write
nums.filter(i => ps.takeWhile(k => k * k <= i).forall(k => i % k > 0))
So we've derived your definition.
Now, if you happened to try the first definition (with k < i), you would have found that it didn't work. Why not? It has to do with the fact that this is a recursive definition.
Suppose we're trying to decide what comes after 2 in the sequence. The definition tells us to first determine whether 3 belongs. To do so, we consider the list of primes up to the first one greater than or equal to 3 (takeWhile(k => k < i)). The first prime is 2, which is less than 3 -- so far so good. But we don't yet know the second prime, so we need to compute it. Fine, so we need to first see whether 3 belongs ... BOOM!
* It's pretty easy to see that if a number n is composite then the square of one of its factors must be less than or equal to n. If n is composite, then by definition n == a * b, where 1 < a <= b < n (we can guarantee a <= b just by labeling the two factors appropriately). From a <= b it follows that a^2 <= a * b, so it follows that a^2 <= n.
Your explanations are mostly correct, you made only two mistakes:
takeWhile doesn't include the last checked element:
scala> List(1,2,3).takeWhile(_<2)
res1: List[Int] = List(1)
You assume that ps always contains only a two and a three but because Stream is lazy it is possible to add new elements to it. In fact each time a new prime is found it is added to ps and in the next step takeWhile will consider this new added element. Here, it is important to remember that the tail of a Stream is computed only when it is needed, thus takeWhile can't see it before forall is evaluated to true.
Keep these two things in mind and you should came up with this:
ps = [2]
i = 3
takeWhile
2*2 <= 3 -> false
forall on []
-> true
ps = [2,3]
i = 4
takeWhile
2*2 <= 4 -> true
3*3 <= 4 -> false
forall on [2]
4%2 > 0 -> false
ps = [2,3]
i = 5
takeWhile
2*2 <= 5 -> true
3*3 <= 5 -> false
forall on [2]
5%2 > 0 -> true
ps = [2,3,5]
i = 6
...
While these steps describe the behavior of the code, it is not fully correct because not only adding elements to the Stream is lazy but every operation on it. This means that when you call xs.takeWhile(f) not all values until the point when f is false are computed at once - they are computed when forall wants to see them (because it is the only function here that needs to look at all elements before it definitely can result to true, for false it can abort earlier). Here the computation order when laziness is considered everywhere (example only looking at 9):
ps = [2,3,5,7]
i = 9
takeWhile on 2
2*2 <= 9 -> true
forall on 2
9%2 > 0 -> true
takeWhile on 3
3*3 <= 9 -> true
forall on 3
9%3 > 0 -> false
ps = [2,3,5,7]
i = 10
...
Because forall is aborted when it evaluates to false, takeWhile doesn't calculate the remaining possible elements.
That code is easier (for me, at least) to read with some variables renamed suggestively, as
lazy val ps: Stream[Int] = 2 #:: Stream.from(3).filter(i =>
ps.takeWhile{p => p * p <= i}.forall{ p => i % p > 0});
This reads left-to-right quite naturally, as
primes are 2, and those numbers i from 3 up, that all of the primes p whose square does not exceed the i, do not divide i evenly (i.e. without some non-zero remainder).
In a true recursive fashion, to understand this definition as defining the ever increasing stream of primes, we assume that it is so, and from that assumption we see that no contradiction arises, i.e. the truth of the definition holds.
The only potential problem after that, is the timing of accessing the stream ps as it is being defined. As the first step, imagine we just have another stream of primes provided to us from somewhere, magically. Then, after seeing the truth of the definition, check that the timing of the access is okay, i.e. we never try to access the areas of ps before they are defined; that would make the definition stuck, unproductive.
I remember reading somewhere (don't recall where) something like the following -- a conversation between a student and a wizard,
student: which numbers are prime?
wizard: well, do you know what number is the first prime?
s: yes, it's 2.
w: okay (quickly writes down 2 on a piece of paper). And what about the next one?
s: well, next candidate is 3. we need to check whether it is divided by any prime whose square does not exceed it, but I don't yet know what the primes are!
w: don't worry, I'l give them to you. It's a magic I know; I'm a wizard after all.
s: okay, so what is the first prime number?
w: (glances over the piece of paper) 2.
s: great, so its square is already greater than 3... HEY, you've cheated! .....
Here's a pseudocode1 translation of your code, read partially right-to-left, with some variables again renamed for clarity (using p for "prime"):
ps = 2 : filter (\i-> all (\p->rem i p > 0) (takeWhile (\p->p^2 <= i) ps)) [3..]
which is also
ps = 2 : [i | i <- [3..], and [rem i p > 0 | p <- takeWhile (\p->p^2 <= i) ps]]
which is a bit more visually apparent, using list comprehensions. and checks that all entries in a list of Booleans are True (read | as "for", <- as "drawn from", , as "such that" and (\p-> ...) as "lambda of p").
So you see, ps is a lazy list of 2, and then of numbers i drawn from a stream [3,4,5,...] such that for all p drawn from ps such that p^2 <= i, it is true that i % p > 0. Which is actually an optimal trial division algorithm. :)
There's a subtlety here of course: the list ps is open-ended. We use it as it is being "fleshed-out" (that of course, because it is lazy). When ps are taken from ps, it could potentially be a case that we run past its end, in which case we'd have a non-terminating calculation on our hands (a "black hole"). It just so happens :) (and needs to ⁄ can be proved mathematically) that this is impossible with the above definition. So 2 is put into ps unconditionally, so there's something in it to begin with.
But if we try to "simplify",
bad = 2 : [i | i <- [3..], and [rem i p > 0 | p <- takeWhile (\p->p < i) bad]]
it stops working after producing just one number, 2: when considering 3 as the candidate, takeWhile (\p->p < 3) bad demands the next number in bad after 2, but there aren't yet any more numbers there. It "jumps ahead of itself".
This is "fixed" with
bad = 2 : [i | i <- [3..], and [rem i p > 0 | p <- [2..(i-1)] ]]
but that is a much much slower trial division algorithm, very far from the optimal one.
--
1 (Haskell actually, it's just easier for me that way :) )
I'm new to CoffeeScript and have been reading the book, The Little Book on CoffeeScript. Here are a few lines from the book's Chapter 2 which confused me while reading :
The only low-level loop that CoffeeScript exposes is the while loop. This has similar behavior to the while loop in pure JavaScript, but has the added advantage that it returns an array of results, i.e. like the Array.prototype.map() function.
num = 6
minstrel = while num -= 1
num + " Brave Sir Robin ran away"
Though it may look good for a CoffeeScript programmer, being a newbie, I'm unable to understand what the code does. Moreover, the words returns an array of results doesn't seem to go together with the fact that while is a loop construct, not a function. So the notion of it returning something seems confusing. Furthermore, the variable num with the string "Brave Sir Robin ran away" in every iteration of the loop seems to be awkward, as the value num is being used as the loop counter.
I would be thankful if you could explain the behavior of the code and perhaps illustrate what the author is trying to convey with simpler examples.
Wow! I didn't know that but it absolutely makes sense if you remember that Coffeescript always returns the last expression of a "block".
So in your case it returns (not via the "return" statement if that is what confuses you) the expression
num + " Brave Sir Robin ran away"
from the block associated with the while condition and as you will return multiple such expressions it pushes them on an array.
Have a look on the generated JavaScript and it might be clearer as the generated code is pretty much procedural
var minstrel, num;
num = 6;
minstrel = (function() {
var _results;
_results = [];
while (num -= 1) {
_results.push(num + " Brave Sir Robin ran away");
}
return _results;
})();
I hope that makes sense to you.
Beware, that function call can be very inefficient!
Below is a prime factors generator
'use strict'
exports.generate = (number) ->
return [] if number < 2
primes = []
candidate = 1
while number > 1
candidate++
while number % candidate is 0
primes.push candidate
number /= candidate
candidate = number - 1 if Math.sqrt(number) < candidate
primes
This is the version using while as expression
'use strict'
exports.generate = (number) ->
return [] if number < 2
candidate = 1
while number > 1
candidate++
primes = while number % candidate is 0
number /= candidate
candidate
candidate = number - 1 if Math.sqrt(number) < candidate
primes
First version ran my tests in 4 milliseconds, the last one takes 18 milliseconds. I believe the reason is the generated closure which returns the primes.
I am trying to work more with scalas immutable collection since this is easy to parallelize, but i struggle with some newbie problems. I am looking for a way to create (efficiently) a new Vector from an operation. To be precise I want something like
val v : Vector[Double] = RandomVector(10000)
val w : Vector[Double] = RandomVector(10000)
val r = v + w
I tested the following:
// 1)
val r : Vector[Double] = (v.zip(w)).map{ t:(Double,Double) => t._1 + t._2 }
// 2)
val vb = new VectorBuilder[Double]()
var i=0
while(i<v.length){
vb += v(i) + w(i)
i = i + 1
}
val r = vb.result
}
Both take really long compared to the work with Array:
[Vector Zip/Map ] Elapsed time 0.409 msecs
[Vector While Loop] Elapsed time 0.374 msecs
[Array While Loop ] Elapsed time 0.056 msecs
// with warm-up (10000) and avg. over 10000 runs
Is there a better way to do it? I think the work with zip/map/reduce has the advantage that it can run in parallel as soon as the collections have support for this.
Thanks
Vector is not specialized for Double, so you're going to pay a sizable performance penalty for using it. If you are doing a simple operation, you're probably better off using an array on a single core than a Vector or other generic collection on the entire machine (unless you have 12+ cores). If you still need parallelization, there are other mechanisms you can use, such as using scala.actors.Futures.future to create instances that each do the work on part of the range:
val a = Array(1,2,3,4,5,6,7,8)
(0 to 4).map(_ * (a.length/4)).sliding(2).map(i => scala.actors.Futures.future {
var s = 0
var j = i(0)
while (j < i(1)) {
s += a(j)
j += 1
}
s
}).map(_()).sum // _() applies the future--blocks until it's done
Of course, you'd need to use this on a much longer array (and on a machine with four cores) for the parallelization to improve things.
You should use lazily built collections when you use more than one higher-order methods:
v1.view zip v2 map { case (a,b) => a+b }
If you don't use a view or an iterator each method will create a new immutable collection even when they are not needed.
Probably immutable code won't be as fast as mutable but the lazy collection will improve execution time of your code a lot.
Arrays are not type-erased, Vectors are. Basically, JVM gives Array an advantage over other collections when handling primitives that cannot be overcome. Scala's specialization might decrease that advantage, but, given their cost in code size, they can't be used everywhere.