Thursday, October 1, 2015

September 2015 1HaskellADay Problems and Solutions

  • September 30th, 2015: Now, not only DLists are Functors, for today's #haskell problem we make them Applicative! Come to find that Applied Applicative DLists taste like Apples
  • September 29th, 2015: For today's #haskell problem we look at DLists as Functors! I know! Exciting! and we enfunctorfy DLists ... which are already functions ... ... hmmm ... That sounds like DLists are APPLICATIVE!
  • September 28th, 2015: So we indexed a set of rows last week, let's re(re)cluster them, AGAIN! for today's #haskell problem. And now we re(re)clustered the data, now with colors!
  • September 24th, 2015: Okay, yesterday we indexed rows, so, for today's #haskell problem, let's save and load those rows as CSV The solution to this problem has no title (oh, well!)
  • September 23rd, 2015: Data Row, o, Data Row: wherefore art thoust identity? Today's #haskell problem adds unique ids for rows of data Simply by using Data.Array we get our data in (uniquely-identified) rows
  • September 22nd, 2015: For today's #haskell problem we go To Infinity ... and Beyond. Yes: we're coding Haskell-on-the-web, yo! simpleHTTP makes HTTP GET-requests, ... well: simple!
  • September 21st, 2015: For today's #haskell problem, we'll fade a circle to black
  • September 17th, 2015: For today's #haskell problem, we receive data one way, but want to see it in another way. What to do? Data. EnCSVified. (that's a word, now)
  • September 16th, 2015: Today's #haskell problem asks 'why JSONify when you can represent clusters yourself?' Why, indeed!
  • September 15th, 2015: For today's #haskell problem we 'unJSONify' some, well, JSON
  • September 14th, 2015: For today's #haskell problem, we relook and recenterclusters from the cluster center So, re-en-cluster-i-fied ... uh: clusters! YAY! (with, ooh! pics!) 
  • September 11th, 2015: Yesterday we displayed one cluster. For today's #haskell problem, let's display them all!
  • September 10th, 2015: This past week we've been clustering data, for today's #Haskell problem we look at visualizing one of these clusters Cluster: shone! ('Schön'? sure!) 
  • September 9th, 2015: Okay, yesterday we clustered some data. For today's #haskell problem: let's see some clustered results, then! It don't mean a thing, ... If it ain't got the (spreadsheet/CSV) schwing.
  • September 8th, 2015: Today we get to do what all those other peeps do in other programming languages. Today we get to WRITE A PROGRAM! wow. I'M K-MEANSIN' ON FIRE TODAY!(okay, geophf, calm down now) A program in Haskell
  • September 7th, 2015: Happy Labor Day in the U.S.A. Today's #haskell problem is to look at recentering clusters for the K-Means algorithm SEMIGROUPOID! (not 'monoid') is the key the solution for today's #haskell problem (ScoreCard has no valid 'zero')
  • September 4th, 2015: Today's #haskell problem we store color-coding for score cards we obtain from rows of data And, color-coded score cards ... SAVED! (makes me wanna scream 'SAIL!')
  • September 3rd, 2015: For today's #haskell problem we look at reclustering the rows of data using K-Means clustering K-Means clustering in #haskell (well, for 1 epoch. Something's not right with step 3: recentered) (and it's DOG-slow)
  • September 2nd, 2015: Drawing information from #BigData is magical, or so says today's #haskell problem Ooh! Big Data is o-so-pretty! But what does it mean? Stay tuned! 
  • September 1st, 2015: For today's #haskell problem we look (obliquely) at the problem of 'indices as identity' What is identity, anyway? 100+ clusters for 3,000 rows? Sounds legit.

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