Monday, March 13, 2017
Tuesday, February 28, 2017
- February 28th, 2017: Today's #haskell problem is a 'little' side-excursion to ask: why don't finite lists have embedded length? So, the #haskell solution has lists with embedded length in a few lines of code. Sweet!
- February 27th, 2017: Gearing up to take on large sequences and subsequences for today's #haskell problem. Subsequences of size x delivered in today's #haskell solution, but not a silver bullet!
- February 24th, 2017: Today's #haskell problem invites you to RULE THE GALAXY TOGETHER AS FATHER AND SON! with Binomial distributions. Darth Vader was right (then again, when is he ever wrong?) when he said: "Binomial distributions rock!" ... probably
- February 23rd, 2017: Today's #haskell problem is to find protein motifs (specifically Nglycos) in FASTA files. With UniProt files that do not redirect, we have today's #haskell solution to finding Nglycos motifs in FASTA files.
- February 22nd, 2017: Today's #haskell problem looks at improving fibonacci to be more correct and more memory efficient. Today's #haskell solution is where we fib down low on the nacci, yo.
- February 21st, 2017: For today's #haskell problem we look at BUNNEHZ! LOTS OF FIBONACCI BUNNEHZ! Oh, ... some of them die. It's very sad. ERMIGOSH!
SO! MANY! BUNNEHZ!
for today's #haskell solution.
- February 20th, 2017: length list is O(n) ... is there a better way than length? We look at that in today's #haskell problem. Usually you're doing something to a list other than length; today's #haskell solution uses that other information.
- February 17th, 2017: Today's #haskell problem looks at offspring of parents with multiple traits. My answer for today's #haskell problem includes crossing traits, and needs work on probabilities!
- February 16th, 2017: Today's #haskell exercise looks at genotypes and the possible outcomes of a genotype in offspring. Today's #haskell solution has fun with combinatorics and probability distributions to compute genotypes in offspring
- February 15th, 2017: Today's #haskell exercise examines what it is for a probability distribution to be an applicative functor. Distributing functions over values (probabilistically) gets us today's #haskell solution.
- February 14th, 2017: Oh, and from all of us at @1HaskellADay to all our dear fans: KAWWWWAAAAAAIIIIIII!!! Happy St. Valentine's Day!
- February 14th, 2017: Today's #haskell problem looks at probability distributions. Today's #haskell solution shows how to condense probability distributions for Ord keys.
- February 13th, 2017: We look for the majority element in small lists, then in large lists for today's #haskell problem. As of now, only 953 IN THE WORLD have solved this #rosalind problem. Are you going to be one of the elite 1,000 to have solved it? Today's #haskell solution traverses only some of the list to find the majority element.
- February 10th, 2017: Today's #haskell problem is about "binary search," according to rosalind.info; but I think this is easy. Using Data.Map.Map today's #haskell solution finds the indices in binary-search time!
- February 9th, 2017: For today's #haskell problem, we look at fibonacci numbers, ... then we go large. Today's #haskell solution is a little bit of fibo-nacci-ness ... and more than just a little bit!
- February 8th, 2017: Today's #haskell problem looks at a population by genotype to compute expected offspring via rosalind.info. We find out that there are a LOT of bunnies out there with dominant genotypes for today's #haskell solution.
- February 7th, 2017: Today's #haskell exercise looks at sets from sets using set-operators... not that I'm a Set theorist, or anything... update: I've added a bonus to today's #haskell exercise: read in a pair of gzipped sets from rosalind.info and write out the answer to file. Today's #haskell solution uses Data.Set operators and a bit of category theory. Answer is a set of very big sets!
- February 6th, 2017: Today's #haskell problem we return to rosalind.info to look at number of subsets of sets and applications. Sections rule the day for today's #haskell solution to subsets of sets
- February 3rd, 2017: Today's #haskell problem shows us that LOVE is a ROSE, but you better not PINK it ... wait: PICK it. Yeah. #pinkrose Today's #haskell solution taught me the word 'pone' and what that word means. Huh. 'pone.' Whatevs.
- February 1st, 2017: Today's #haskell problem gets distances between the Earth and Mars. Also, we'll plot orbits as a bonus. Today's #haskell solution computes distances using planetary #ephemeris data.
Tuesday, January 31, 2017
- January 31th, 2017: For today's #haskell problem we look at #ephemeris data (double-precision triples) for EMbary (Earth/Moon) and Mars. We parse some big-Ds (double-precision numbers) to get Earth and Mars orbital #ephemeris data from the JPL.
- January 30th, 2017: This week we'll be looking at JPL Earth and Mars Ephemeris data sets. Today's #haskell problem reads in Julian dates. Today's solution uses Prolog Definite Clause Grammars (DCGs) - in #haskell - to read the date string.
- January 27th, 2017: Today's #haskell problem is a little word-guessing puzzle. Today's #haskell solution is another puzzle: To which band do witches band? Will we ever know?
- January 26th, 2017: Today's Haskell exercise tally the word-frequency of a file, so: tally-ho! Today's #haskell solution shows me 'County' is the most popular word in #haskell?!? but okay.
- January 25th, 2017: For today's #haskell exercise, we see what Haskell files are in a directory, eh? Today's #haskell solution to find the Haskell files in a directory structure got me all Monad-y down low on the IO!
- January 20th, 2017: For Today's #haskell problem we look at Merkle Trees of the #blockchain as Traversable.
- January 19th, 2017: Lastly, today's #haskell problem looks at the Traversable function: lastest via @Jose_A_Alonso @yoeight. Today's #haskell solution shows the First shall be the lastest
- January 18th, 2017: As sum is already defined for Traversibles, today's #haskell problem looks at average via @Jose_A_Alonso @yoeight. For today's #haskell solution we find the average of the elements of any Traversable type
- January 17th, 2017: Today's #haskell problem continues Traversable exploration by @Jose_A_Alonso @yoeight with minAF and maxAF...in O(1). So, we have today's #haskell solution for minAF, minT, maxAF, and maxT. lolneat!
- January 16th, 2017: Today's #haskell problem looks at find, any, and all as functions over Traversable types via @Jose_A_Alonso @yoeight. Today's #haskell solution not only defines anyAF and allAF but also the important gtfo, that is: find (> 5)-function.
- January 13th, 2017: Today's #haskell problem does 2 things: examines Applicative Functors (AF) and lets me say transpose...AF in a tweet. Today's #haskell solution shows a succinct applicative solution to transposeAF for matrices
- January 12th, 2017: Partial functions everywhere via @Jose_A_Alonso @bitemyapp! Today's #haskell exercise takes on maximum and minimum. We 'full-i-tize' the partial functions of minimal and maximal for all Foldable t in today's #haskell solution
- January 11th, 2017: Today's #haskell exercise handles the next pitfall from @Jose_A_Alonso @bitemyapp: Prelude partial functions, particularly for the evil List! Today's #haskell solution TOTALly unpartializes some Prelude list functions ... geddit? TOTALly? GEDDIT?
- January 10th, 2017: Continuing #haskell pitfalls review via @Jose_A_Alonso @bitemyapp we study Data.Text to process large text documents. Today's #haskell solution shows Dickens was ahead of his time, citing URLs in Christmas Carol LIKE A WEB GANGSTA!
- Side note: I had no problem processing 200k size text document using String, but, hey: knock yourselves out with Text for text processing.
- January 9th, 2017: For today's #haskell exercise we take a leaf from @bitemyapp via @Jose_A_Alonso and look into Data.Aeson quasiquotes. Using Data.Aeson.QQ today's #haskell solution basically wrote itself.
- January 6th, 2017: Today we look at combined US State SAIPE/poverty and debt data, rescale those data, and cluster the results #Haskell
- January 5th, 2017: For today's #haskell exercise we combine US State SAIPE/poverty data along with US State total and per capita debt. Today's #haskell solution on SAIPE/poverty data and US State debts has CHARTS! Never. Underestimate. Charts. 👌💯💯💯
- January 4th, 2017: Today's #haskell exercise we re-look SAIPE/poverty data, reorganizing by US State instead of by US County. We collate US State SAIPE/poverty data, then do a bit of analysis with a pensé about results thrown in at the end.
- January 2nd, 2017: For the beginning of AD 2017, today's #haskell exercise wishes you a less divisive year than the last. LITERALLY!
Thursday, January 19, 2017
Sunday, January 1, 2017
- December 27th, 2016: For today's #haskell exercise we expand our inquiry of SAIPE/poverty data by analyzing by US State.
- December 26th, 2016: Today's #haskell exercise looks at partitioning data by each unit's 'size'; then we visualize the partitioned data. Today's #haskell solution shows we can cluster and analyze US counties by their relative sizes
- December 23rd, 2016: There are many (3000+) US Counties. Today's #haskell exercise visualizes them clustered by SAIPE/poverty data. Today's #haskell solution shows the US Counties clustered by SAIPE/poverty data visualized in @neo4j.
- December 22nd, 2016: For today's #haskell exercise we shift focus from SAIPE/poverty data to debt by US State, total and per capita. Today's #haskell solution shows US State debt, totals and per capital, with a caution about the IO monad in a REPL.
- December 21st, 2016: For today's #haskell exercise we look at clustering SAIPE/poverty data and looking at patterns in the clusters. Today's #haskell solution shows US counties fall in 4 clusters using SAIPE/poverty data with Los Angeles a stand out.
- December 20th, 2016: Today we look at enumerating US Counties from SAIPE/poverty data then determining their US State, deterministically. We find in today's #haskell solution that there a a lot of Counties of the US, so we index and enumerate them.
- December 19th, 2016: For today's #haskell exercise we make a set of (static) String values enumerable and indexible. On the shoulders of our previous parsing work, today's #haskell solution parses SAIPE data to index US State names.
- December 16th, 2016: Today's #haskell Exercise looks at score-cards for US Census data
- December 15th, 2016: For today's #haskell exercise, we're back looking at US Census data: SAIPE/poverty by State and county #DataAnalytics. Today's #haskell solution shows how to parse by-line whilst carrying-over structure-context from prior lines
- December 14th, 2016: Today's #haskell exercise we begin to look at qubits and pauli rotations on qubits. Today's #haskell solution represents Qubits and rotates |0> and |1> through the Pauli X operator.
- December 13th, 2016: Today's #haskell exercise looks at the US Census data and asks some by-State questions around incomes. Today's #haskell solution uses Network.HTTP and Applicative Functors to examine populations and mean/median incomes.
- December 12th, 2016: For today's #haskell exercise we observe a dinner-table conversation of a math professor and her husband. Today's #haskell solution shows us happiness for children is having a parent as a math professor. I know this.
- December 9th, 2016: Today is #FF on @1HaskellADay. I mean, that's today's #haskell exercise: Examine Twitter JSON for #FF-analytics. Today's #haskell solution shows us who NOT to #FF if you want the follow back. Doesn't it.
- December 8th, 2016: Today's #haskell exercise comes to you all the way from Smyrna! Construct a word-square. And from 2000 common English words we have #haskell solutions for the 3x3 and 4x4 word-squares
- December 6th, 2016: Today's #haskell exercise will look at EMA/Exponential Moving Averages to analyze trends of, e.g. #BitCoin. Today's #haskell solution inlines state into the EMA-recursive function to analyze #BitCoin price history.
- December 5th, 2016: Today's #haskell exercise we look at the SMA/Simple Moving Average as a trend-estimator for, e.g.: #BitCoin price. Today's #haskell solution uses the SMA-function as a Comonad. Below are 1 year and 3 months of #BitCoin SMA-analyses. CORRECTION! SMA 15 and SMA 50 are industry norms in the markets. Corrected (and generalized) #haskell solution here.
- December 1st, 2016: For today's #haskell exercise we look at a larger data set with a year's worth of BitCoin price history. I love the #haskell standard library. I love that you can write today's solution: #BitCoin prices in one line of code.