Review of “Algorithms for Functional Programming” by John David Stone, Springer-Verlag, 2018
Bibliographic record
Abstract
Many textbooks start out as informal notes for university courses, refined through repeated offerings.The book under review came out of the author's experiences as a lecturer at an American liberal-arts college, Grinnell, where the first course in computation used Scheme for many years.The first chapter, "Essential Notations", rapidly introduces many features of R7RS Scheme and its libraries, augmented by some primitives written by the author.The next chapter, "The Tool Box", presents or builds a host of abstractions, such as couplers, adapters, and recursion managers (e.g., folds), which are used extensively in the rest of the text.The ambition of this book can be seen in the topic lists for the next two chapters."Data Structures" covers lists, sources (streams without caching, which would necessitate mutation), trees, bushes (rose trees), bags, sets, tables, and buffers (queues), all implemented mostly using pairs and lists (occasionally records)."Sorting" covers insertion sort, selection sort, quicksort, mergesort, binary search trees, treesort, red-black trees (both insertion and deletion), pairing heaps, and order statistics.Considering that the word "Algorithms" is in the title, the book is surprisingly shy about discussing efficiency.Linear time appears briefly; the phrase "proportional to" is used a few times, and "logarithm" only once, in reference to running times.I understand the reluctance; order notation and the proper use of recurrences for analysis of running time are nettles that one hesitates to grasp.They can be avoided in a first course, but only barely, and without them, the motivation for many of these more advanced topics becomes much more difficult.The next chapter, "Combinatorial Constructions", is possibly the most successful, perhaps because it is rich with helper functions and examples, and topics not usually covered: Cartesian products, subsequences and selections (subsets and combinations, but of an ordered list), and finally permutations and partitions, all with ranking and unranking (going from a numerical index to an element of the full answer and vice versa).In the last two chapters, the author's principled insistence on purity and layers of abstraction is at odds with the material."Graphs" presents depth-first and breadth-first traversal, spanning trees, shortest paths (Bellman-Ford, Dijkstra, and Floyd-Warshall), and maximum flow (Ford-Fulkerson)."Sublist Search" is chiefly concerned with Knuth-Morris-Pratt and Boyer-Moore string search.These are topics for which pure functional programming does not have a good story.One should be willing to sacrifice efficiency for
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".