# Implement RandomizedMotifSearch solved by 627

July 29, 2015, 1:08 a.m. by Rosalind Team

We will now turn to randomized algorithms that flip coins and roll dice in order to search for motifs. Making random algorithmic decisions may sound like a disastrous idea; just imagine a chess game in which every move would be decided by rolling a die. However, an 18th Century French mathematician and naturalist, Comte de Buffon, first proved that randomized algorithms are useful by randomly dropping needles onto parallel strips of wood and using the results of this experiment to accurately approximate the constant π.

Randomized algorithms may be nonintuitive because they lack the control of traditional algorithms. Some randomized algorithms are Las Vegas algorithms, which deliver solutions that are guaranteed to be exact, despite the fact that they rely on making random decisions. Yet most randomized algorithms are Monte Carlo algorithms. These algorithms are not guaranteed to return exact solutions, but they do quickly find approximate solutions. Because of their speed, they can be run many times, allowing us to choose the best approximation from thousands of runs.

A randomized algorithm for motif finding is given below.

    RANDOMIZEDMOTIFSEARCH(Dna, k, t)        randomly select k-mers Motifs = (Motif1, …, Motift) in each string            from Dna        BestMotifs ← Motifs        while forever            Profile ← Profile(Motifs)            Motifs ← Motifs(Profile, Dna)            if Score(Motifs) < Score(BestMotifs)                BestMotifs ← Motifs            else                return BestMotifs

## Implement RandomizedMotifSearch

Given: Positive integers k and t, followed by a collection of strings Dna.

Return: A collection BestMotifs resulting from running RandomizedMotifSearch(Dna, k, t) 1000 times. Remember to use pseudocounts!

## Sample Dataset

8 5
CGCCCCTCTCGGGGGTGTTCAGTAAACGGCCA
GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG
TAGTACCGAGACCGAAAGAAGTATACAGGCGT
TAGATCAAGTTTCAGGTGCACGTCGGTGAACC
AATCCACCAGCTCCACGTGCAATGTTGGCCTA


## Sample Output

TCTCGGGG
CCAAGGTG
TACAGGCG
TTCAGGTG
TCCACGTG


## Extra Dataset

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