I am trying to write a code for the following problem:
Input
The input begins with the number t of test cases in a single line (t<=10). In each of the next t lines there are two numbers m and n (1 <= m <= n <= 1000000000, n-m<=100000) separated by a space.
Output
For every test case print all prime numbers p such that m <= p <= n, one number per line, test cases separated by an empty line.
Sample Input:
2
1 10
3 5
Sample Output:
2
3
5
7
3
5
My code:
def prime?(number)
return false if number == 1
(2..number-1).each do |n|
return false if number % n == 0
end
true
end
t = gets.strip.to_i
for i in 1..t
mi, ni = gets.strip.split(' ')
mi = mi.to_i
ni = ni.to_i
i = mi
while i <= ni
puts i if prime?(i)
i += 1
end
puts "\n"
end
The code is running fine, only problem I am having is that it is taking a lot of time when run against big input ranges as compared to other programming languages.
Am I doing something wrong here? Can this code be further optimized for faster runtime?
I have tried using a for loop, normal loop, creating an array and then printing it.
Any suggestions.
Ruby is slower than some other languages, depending on what language you compare it to; certainly slower than C/C++. But your problem is not the language (although it influences the run-time behavior), but your way of finding primes. There are many better algorithms for finding primes, such as the Sieve of Eratosthenes or the Sieve of Atkin. You might also read the “Generating Primes” page on Wikipedia and follow the links there.
By the way, for the Sieve of Eratosthenes, there is even a ready-to-use piece of code on Stackoverflow. I'm sure a little bit of googling will turn up implementations for other algorithms, too.
Since your problem is finding primes within a certain range, this is the Sieve of Eratosthenes code found at the above link modified to suit your particular problem:
def better_sieve_upto(first, last)
sieve = [nil, nil] + (2..last).to_a
sieve.each do |i|
next unless i
break if i*i > last
(i*i).step(last, i) {|j| sieve[j] = nil }
end
sieve.reject {|i| !i || i < first}
end
Note the change from "sieve.compact" to a complexer "sieve.reject" with a corresponding condition.
Return true if the number is 2, false if the number is evenly divisible by 2.
Start iterating at 3, instead of 2. Use a step of two.
Iterate up to the square root of the number, instead of the number minus one.
def prime?(number)
return true if number == 2
return false if number <= 1 or number % 2 == 0
(3..Math.sqrt(number)).step(2) do |n|
return false if number % n == 0
end
true
end
This will be much faster, but still not very fast, as #Technation explains.
Here's how to do it using the Sieve of Eratosthenes built into Ruby. You'll need to precompute all the primes up to the maximum maximum, which will be very quick, and then select the primes that fall within each range.
require 'prime'
ranges = Array.new(gets.strip.to_i) do
min, max = gets.strip.split.map(&:to_i)
Range.new(min, max)
end
primes = Prime.each(ranges.map(&:max).max, Prime::EratosthenesGenerator.new)
ranges.each do |range|
primes.each do |prime|
next if prime < range.min
break if prime > range.max
puts prime
end
primes.rewind
puts "\n"
end
Here's how the various solutions perform with the range 50000 200000:
Your original prime? function: 1m49.639s
My modified prime? function: 0m0.687s
Prime::EratosthenesGenerator: 0m0.221s
The more ranges being processed, the faster the Prime::EratosthenesGenerator method should be.
Related
I'm trying to write a method that returns the nth prime number.
I've worked out a solution but the problem is in my method. I create a large array of numbers that seems to process super slow. (1..104729).to_a to be exact. I chose 104729 because the max n can be is 10000 and the 10000th integer is 104729. I'm looking for a way to optimize my method.
Is 104729 is too large a value? Is there a way to write this so that I'm not creating a large array?
Here's the method:
def PrimeMover(num)
def is_prime(x)
i = 0
nums = (2..x).to_a
while nums[i] < nums.max
if x % nums[i] != 0
i += 1
else
return false
end
end
return true
end
primes_arr = (3..104729).to_a.select {|y| is_prime(y)}
primes_arr[num]
end
require "prime"
def find_prime(nth)
Prime.take(nth).last
end
Combine Ruby's built-in prime library, and a lazy enumerator for performance:
require 'prime'
(1...100_000).lazy.select(&:prime?).take(100).to_a
Or simply, as highlighted by Arturo:
Prime.take(100)
You can use Ruby's built in #prime? method, which seems pretty efficient.
The code:
require 'prime'
primes_arr = (3..104729).to_a.select &:prime?
runs in 2-3 seconds on my machine, which I find somewhat acceptable.
If you need even better performance or if you really need to write your own method, try implementing the Sieve of Erathostenes. Here are some Ruby samples of that: http://rosettacode.org/wiki/Sieve_of_Eratosthenes#Ruby
Here's an optimal a trial division implementation of is_prime without relying on the Prime class:
A prime number is a whole number divisible only by 1 and itself, and 1 is not prime. So we want to know if x divides into anything less than x and greater than 1. So we start the count at 2, and we end at x - 1.
def prime?(x)
return false if x < 2
2.upto(x - 1) do |n|
return false if (x % n).zero?
end
true
end
As soon as x % n has a remainder, we can break the loop and say this number is not prime. This saves you from looping over the entire range. If all the possible numbers were exhausted, we know the number is prime.
This is still not optimal. For that you would need a sieve, or a different detection algorithm to trial division. But it's a big improvement on your code. Taking the nth up to you.
I am relatively new to Ruby but it seems simple enough as far as a language goes. I am working through the Euler Project with Ruby and I'm having a huge issue with speed on the following:
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
My code:
beginning_time = Time.now
(1..10000).each { |i| i }
def isPrime(num)
factors = 0
primecount = 1
while primecount <= num
if (num%primecount == 0)
factors += 1
end
if (factors > 2)
return false
end
primecount += 1
end
return true
end
def make_sieve(num)
sieve = Array.new(num)
summation = 0
for i in 1..num
if(isPrime(i) == true)
summation += i
puts i
for x in i..num
if x%i == 0
# Go through entire array and make all multiples of i False
sieve[x] = false
else
sieve[i] = true
end
end
else
# If i is NOT prime, move to the next number. in the For Loop
next
end
end
puts summation
end
make_sieve(2000000)
end_time = Time.now
puts "Time elapsed #{(end_time - beginning_time)*1000} milliseconds"
I think I have the right idea with the sieve but I really have no clue what's going on that makes this program run so slow. I run it with 20,000 and it takes about 15 seconds, which seems slow even still, although the output comes out MUCH faster than when I put 2,000,000.
Am I going about this the wrong way logically or syntactically or both?
Your isPrime() test is very slow on primes; but you don't even need it. The key to sieve is, initially all the numbers are marked as prime; then for each prime we mark off all its multiples. So when we get to a certain entry in the sieve, we already know whether it is a prime or not - whether it is marked true for being prime, or it is marked false for being composite (a multiple of some smaller prime).
There is no need to test it being prime, at all.
And to find the multiples, we just count: for 5, it's each 5th entry after it; for 7 - each 7th. No need to test them with % operator, just set to false right away. No need to set any of them to true, because all numbers were set to true at the start.
You seem to be writing JavaScript code in Ruby, and are missing the subtleties that makes Ruby so elegant. You should take a look at something like Ruby Best Practices, which is quite a light read but deals with using Ruby idioms instead of imposing the concepts of another language.
As has been said, the whole point of an Eratosthenes sieve is that you just remove all compound numbers from a list, leaving just the primes. There is no need to check each element for primeness.
This is a Rubyish solution. It runs in about 1.5 seconds. It is a little complicated by the representing number N by array element N-1, so (i+i+1 .. num).step(i+1) is equivalent to (n * 2 .. num).step(n)
def make_sieve(num)
sieve = Array.new(num, true)
sieve.each_with_index do |is_prime, i|
next if i == 0 or not is_prime
(i+i+1 .. num).step(i+1) { |i| sieve[i] = false }
end
puts sieve.each_index.select { |i| sieve[i] }.map { |i| i+1 }.inject(:+)
end
make_sieve(2_000_000)
output
142913828923
I have this code that seems to be working for number 6-8 digits, in a normal time period.
When I enter bigger values the amount get ridiculously bug. It takes more than 4 hours to complete.
Here is my code.
#Fermat's factorization method
def get_largest_prime n
t=(Math.sqrt(n)+1).floor
k=0
prime_numbers=[]
while (t+k)<n
element = (t+k)**2-n
if is_integer? Math.sqrt(element)
#store prime numbers
prime_numbers << t+k+Math.sqrt(element)
prime_numbers << t+k-Math.sqrt(element)
#puts "Prime Factors of #{n} are: #{t+k+Math.sqrt(element)} and #{t+k-Math.sqrt(element)}"
end
k+=1
end
puts "Prime Factors: "+prime_numbers.to_s
end
#making sure 450.0 is 450, for example.
def is_integer? number
number.to_i == number ? true : false
end
get_largest_prime 600851475143
Running this will take more than 4 hours.
But running it for value ' 600851' for example or ' 60085167' does not take a lot of time. Any help ?
First note that Fermat factorisation doesn't give you prime factors in general.
Then, you run it until t+k >= n, that means you run the while loop n - t times, since t is roughly sqrt(n), that is an O(n) algorithm. For a largish n like 600851475143 (about 6*10^11), that is bound to take long.
You need to change the algorithm. When you have found a pair of divisors (both larger than 1), factorise them both recursively. If the smaller of the found factors is 1, that is a prime factor.
Doing that (forgive the bad style, I barely know ruby):
#Fermat's factorization method
def get_largest_prime n
t=(Math.sqrt(n)+1).floor
k=0
prime_numbers=[]
while (t+k)<n
element = (t+k)**2-n
if is_integer? Math.sqrt(element)
#store prime numbers
a = t+k+Math.sqrt(element)
b = t+k-Math.sqrt(element)
if b == 1
prime_numbers << a
break
end
prime_numbers += get_largest_prime a
prime_numbers += get_largest_prime b
break
#puts "Prime Factors of #{n} are: #{t+k+Math.sqrt(element)} and #{t+k-Math.sqrt(element)}"
end
k+=1
end
return prime_numbers
end
#making sure 450.0 is 450, for example.
def is_integer? number
number.to_i == number ? true : false
end
a = get_largest_prime 600851475143
puts "Prime Factors: "+a.to_s
solves the given problem quickly.
However, it will still take a long time for numbers that have no divisors close to the square root.
The standard factorisation by trial division has much better worst-case behaviour (O(sqrt(n) worst case). A mixed approach can be slightly faster than pure trial division, though.
Two effects here:
1) When an integer gets larger than 2**31 in Ruby, it uses a different, and slower, representation
2) There are no known factorisation algorithms that don't eventually perform badly once the number gets large enough - technically they all get slower worse than any polynomial of the (number of digits of) the number you want to factorise.
You could speed things up by using
Math.sqrt(element)
less. Assign result of it to a variable, before all the tests. Note this will not "fix" your problem. Ultimately it won't run fast enough above a certain number - even if you transferred everything to C (although you might squeeze out a couple of extra digits before C got slow)
Possibly, you can try the code below.
def prime_factor limit
(2..Math.sqrt(limit).to_i).inject([]) do |memo, var|
memo << var if limit % var == 0 and !memo.any? {|d| var % d == 0}
memo
end
end
prime_result = prime_factor 600851475143
puts prime_result.max
The less loops you use, the faster your code will run ;-) (less cpu cycles). Try to do everything recursively like here on this program that finds the largest prime factor
This is a (pretty bad) solution to one of the project Euler problems. The problem was to find the 10_001st prime number. The code below does it, but it takes 8 minutes to run. Can you explain why that is the case and how to optimize it?
primes = []
number = 2.0
until primes[10000] != nil
if (2..(number - 1)).any? do |n|
number % n == 0
end == false
primes << number
end
number = number + 1.0
end
puts primes[10000]
Some simple optimizations to prime finding:
Start by pushing 2 onto your primes list, and start by checking if 3 is a prime. (This eliminates needing to write special case code for the numbers 0 to 2)
You only have to check numbers that are odd for prime candidacy. (Or, if you start by adding 2/3/5 and checking 7, you only need to check numbers that are 1 or 5 after doing % 6. Or... You get the idea)
You only have to see if your current candidate x is divisible by factors up to sqrt(x)—because any factor above sqrt(x) divides x into a number below sqrt(x), and you've already checked all of those.
You only have to check numbers in your prime list, instead of all numbers, for divisors of x - since all composite numbers are divisible by primes. For example, 81 is 9*9 - but 9*9 is 3*3*9, 9 being composite, so you'll discover it's a prime when you check it against 3. Therefore you never need to test if 9 is a factor, and so on for every composite factor.
There are very optimized, sped up prime finding functions (see the Sieve of Atkin for a start), but these are the common optimizations that are easy to come up with.
Do you really have to check if the number divides with all previous numbers? Check only with the smaller primes you already discovered. Also, why using floats where integers are perfectly fine?
EDIT:
Some possible changes (not best algorithm, can be improved):
primes = [2, 3, 5]
num = 7
until primes[10000]
is_prime = true
i = 0
sqrtnum = Math.sqrt(num).ceil
while (n=primes[i+=1]) <= sqrtnum
if num % n == 0
is_prime = false
break
end
end
if is_prime
primes << num
end
num += 2
end
puts primes[10000]
On my computer (for 1000 primes):
Yours:
real 0m3.300s
user 0m3.284s
sys 0m0.000s
Mine:
real 0m0.045s
user 0m0.040s
sys 0m0.004s
I'm taking my first steps into recursion and dynamic programming and have a question about forming subproblems to model the recursion.
Problem:
How many different ways are there to
flip a fair coin 5 times and not have
three or more heads in a row?
If some could put up some heavily commented code (Ruby preferred but not essential) to help me get there. I am not a student if that matters, this is a modification of a Project Euler problem to make it very simple for me to grasp. I just need to get the hang of writing recursion formulas.
If you would like to abstract the problem into how many different ways are there to flip a fair coin Y times and not have Z or more heads in a row, that may be beneficial as well. Thanks again, this website rocks.
You can simply create a formula for that:
The number of ways to flip a coin 5 times without having 3 heads in a row is equal to the number of combinations of 5 coin flips minus the combinations with at least three heads in a row. In this case:
HHH-- (4 combinations)
THHH- (2 combinations)
TTHHH (1 combination)
The total number of combinations = 2^5 = 32. And 32 - 7 = 25.
If we flip a coin N times without Q heads in a row, the total amount is 2^N and the amount with at least Q heads is 2^(N-Q+1)-1. So the general answer is:
Flip(N,Q) = 2^N - 2^(N-Q+1) +1
Of course you can use recursion to simulate the total amount:
flipme: N x N -> N
flipme(flipsleft, maxhead) = flip(flipsleft, maxhead, 0)
flip: N x N x N -> N
flip(flipsleft, maxhead, headcount) ==
if flipsleft <= 0 then 0
else if maxhead<=headcount then 0
else
flip(flipsleft - 1, maxhead, headcount+1) + // head
flip(flipsleft - 1, maxhead, maxhead) // tail
Here's my solution in Ruby
def combination(length=5)
return [[]] if length == 0
combination(length-1).collect {|c| [:h] + c if c[0..1]!= [:h,:h]}.compact +
combination(length-1).collect {|c| [:t] + c }
end
puts "There are #{combination.length} ways"
All recursive methods start with an early out for the end case.
return [[]] if length == 0
This returns an array of combinations, where the only combination of zero length is []
The next bit (simplified) is...
combination(length-1).collect {|c| [:h] + c } +
combination(length-1).collect {|c| [:t] + c }
So.. this says.. I want all combinations that are one shorter than the desired length with a :head added to each of them... plus all the combinations that are one shorter with a tail added to them.
The way to think about recursion is.. "assuming I had a method to do the n-1 case.. what would I have to add to make it cover the n case". To me it feels like proof by induction.
This code would generate all combinations of heads and tails up to the given length.
We don't want ones that have :h :h :h. That can only happen where we have :h :h and we are adding a :h. So... I put an if c[0..1] != [:h,:h] on the adding of the :h so it will return nil instead of an array when it was about to make an invalid combination.
I then had to compact the result to ignore all results that are just nil
Isn't this a matter of taking all possible 5 bit sequences and removing the cases where there are three sequential 1 bits (assuming 1 = heads, 0 = tails)?
Here's one way to do it in Python:
#This will hold all possible combinations of flipping the coins.
flips = [[]]
for i in range(5):
#Loop through the existing permutations, and add either 'h' or 't'
#to the end.
for j in range(len(flips)):
f = flips[j]
tails = list(f)
tails.append('t')
flips.append(tails)
f.append('h')
#Now count how many of the permutations match our criteria.
fewEnoughHeadsCount = 0
for flip in flips:
hCount = 0
hasTooManyHeads = False
for c in flip:
if c == 'h': hCount += 1
else: hCount = 0
if hCount >= 3: hasTooManyHeads = True
if not hasTooManyHeads: fewEnoughHeadsCount += 1
print 'There are %s ways.' % fewEnoughHeadsCount
This breaks down to:
How many ways are there to flip a fair coin four times when the first flip was heads + when the first flip was tails:
So in python:
HEADS = "1"
TAILS = "0"
def threeOrMoreHeadsInARow(bits):
return "111" in bits
def flip(n = 5, flips = ""):
if threeOrMoreHeadsInARow(flips):
return 0
if n == 0:
return 1
return flip(n - 1, flips + HEADS) + flip(n - 1, flips + TAILS)
Here's a recursive combination function using Ruby yield statements:
def combinations(values, n)
if n.zero?
yield []
else
combinations(values, n - 1) do |combo_tail|
values.each do |value|
yield [value] + combo_tail
end
end
end
end
And you could use regular expressions to parse out three heads in a row:
def three_heads_in_a_row(s)
([/hhh../, /.hhh./, /..hhh/].collect {|pat| pat.match(s)}).any?
end
Finally, you would get the answer using something like this:
total_count = 0
filter_count = 0
combinations(["h", "t"], 5) do |combo|
count += 1
unless three_heads_in_a_row(combo.join)
filter_count += 1
end
end
puts "TOTAL: #{ total_count }"
puts "FILTERED: #{ filter_count }"
So that's how I would do it :)