Paradoxes and theorems every developer should know

Transcript

1. 1 Joshua Thijssen jaytaph <?php namespace
2. Disclaimer: I'm not a (mad) scientist nor a mathematician. 2
3. Second disclaimer: I will only tell lies 3
4. German Tank Problem 4
5. 5
6. 5 15
7. 6
8. 6 53 72 8 15
9. 7 k = number of elements m = largest number
10. 72 + (72 / 4) - 1 = 89 8
11. 9 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem
12. 9 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem 122
13. 9 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem 122 271
14. 9 Intelligence Statistics Actual June 1940 1000 169 June 1941 1550 244 August 1942 1550 327 https://en.wikipedia.org/wiki/German_tank_problem 122 271 342
15. 10
16. 10 ➡ Data leakage.
17. 10 ➡ Data leakage. ➡ User-id's, invoice-id's, etc
18. 10 ➡ Data leakage. ➡ User-id's, invoice-id's, etc ➡ Used to approximate the number of iPhones sold in 2008.
19. 10 ➡ Data leakage. ➡ User-id's, invoice-id's, etc ➡ Used to approximate the number of iPhones sold in 2008. ➡ Calculate approximations of datasets with (incomplete) information.
20. ➡ Avoid (semi) sequential data to be leaked. ➡ Adding randomness and offsets will NOT solve the issue. ➡ Use UUIDs (better: timebased short IDs, you don't need UUIDs) 11
21. 12 Collecting (big) data is easy Analyzing big data is the hard part.
22. Confirmation Bias 13
23. 2 4 6 14 Z={…,−2,−1,0,1,2,…}
24. 21% 15
25. 16 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
26. < 10% 17
27. 18 coke beer 35 17 If you drink beer then you must be 18 yrs or older.
28. 18 coke beer 35 17 If you drink beer then you must be 18 yrs or older.
29. 18 coke beer 35 17 If you drink beer then you must be 18 yrs or older.
30. Cognitive Adaption for social exchange 19
31. hint: Try and place your "technical problem" in a more social context. 20
32. BDD 21
33. 22 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
34. 22 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
35. 22 5 8 ? ? If a card shows an even number on one face, then its opposite face is blue.
36. TESTING 23
37. 24 ➡ Step 1: Write code ➡ Step 2: Write tests ➡ Step 3: Profit
38. public function isLeapYeap($year) { return ($year % 4 == 0); } 25 https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing testIs1996ALeapYeap(); testIs2000ALeapYeap(); testIs2004ALeapYeap(); testIs2008ALeapYeap(); testIs2012ALeapYeap(); testIs1997NotALeapYear(); testIs1998NotALeapYear(); testIs2001NotALeapYear(); testIs2013NotALeapYear();
39. public function isLeapYeap($year) { return ($year % 4 == 0); } 25 https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing testIs1996ALeapYeap(); testIs2000ALeapYeap(); testIs2004ALeapYeap(); testIs2008ALeapYeap(); testIs2012ALeapYeap(); testIs1997NotALeapYear(); testIs1998NotALeapYear(); testIs2001NotALeapYear(); testIs2013NotALeapYear();
40. public function isLeapYeap($year) { return ($year % 4 == 0); } 26 https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing
41. 27 ➡ Tests where written based on actual code. ➡ Tests where written to CONFIRM actual code, not to DISPROVE actual code!
42. 28 TDD
43. 29 ➡ Step 1: Write tests ➡ Step 2: Write code ➡ Step 3: Profit, as less prone to confirmation bias (as there is nothing to bias!)
44. Birthday paradox 30
45. Question: 31 > 50% chance 4 march 18 september 5 december 25 juli 2 februari 9 october
46. 23 people 32
47. 366 persons = 100% 33
48. Collisions occur more often than you realize 34
49. Hash collisions 35
50. 16 bits means 300 values before >50% collision probability 36
51. Watch out for: 37 ➡ Too small hashes. ➡ Unique data. ➡ Your data might be less "protected" as you might think.
52. Heisenberg uncertainty principle 38
53. It's not about star trek (heisenberg compensators) 39
54. nor crystal meth 40
55. 41 x position p momentum (mass x velocity) ħ 0.0000000000000000000000000000000001054571800 (1.054571800E-34)
56. The more precise you know one property, the less you know the other. 42
57. This is NOT about observing! 43
58. Observer effect 44 heisenbug
59. It's about trade-offs 45
60. Benford's law 46
61. Numbers beginning with 1 are more common than numbers beginning with 9. 47
62. Default behavior for natural numbers. 48
63. 49
64. find . -name \*.php -exec wc -l {} \; | sort | cut -b 1 | uniq -c 50
65. find . -name \*.php -exec wc -l {} \; | sort | cut -b 1 | uniq -c 50 1073 1 886 2 636 3 372 4 352 5 350 6 307 7 247 8 222 9
66. 51
67. Bayesian filtering 52
68. What's the probability of an event, based on conditions that might be related to the event. 53
69. What is the chance that a message is spam when it contains certain words? 54
70. 55 P(A|B) P(A) P(B) P(B|A) Probability event A, if event B (conditional) Probability event A Probability event B Probability event B, if event A
71. 56 ➡ Figure out the probability a {mail, tweet, comment, review} is {spam, negative} etc.
72. ➡ 10 out of 50 comments are "negative". ➡ 25 out of 50 comments uses the word "horrible". ➡ 8 comments with the word "horrible" are marked as "negative". 57
73. 58 negative "horrible" 10 comments 25 comments 8 comments
74. 59
75. 60 ➡ More words? ➡ Complex algorithm, ➡ but, we can assume that words are not independent from eachother ➡ Naive Bayes approach
76. 61
77. 62 We must know beforehand which comments are negative?
78. TRAINING SET 63
79. 64 "Your product is horrible and does not work properly. Also, you suck." "I had a horrible experience with another product. But yours really worked well. Thank you!" Negative: Positive:
80. $trainingset = [ 'negative' => [ 'count' => 1, 'words' => [ 'product' => 1, 'horrible' => 1, 'properly' => 1, 'suck' => 1, ], ], 'positive' => [ 'count' => 1, 'words' => [ 'horrible' => 1, 'experience' => 1, 'product' => 1, 'thank' => 1, ], ], ]; 65
81. 66 $trainingset = [ 'negative' => [ 'count' => 631, 'words' => [ 'product' => 521, 'horrible' => 52, 'properly' => 36, 'suck' => 272, ], ], 'positive' => [ 'count' => 1263, 'words' => [ 'horrible' => 62, 'experience' => 16, 'product' => 311, 'great' => 363 'thank' => 63, ], ], ];
82. 67 ➡ You might want to filter stop-words first. ➡ You might want to make sure negatives are handled property "not great" => negative. ➡ Bonus points if you can spot sarcasm.
83. ➡ Collaborative filtering (mahout): ➡ If user likes product A, B and C, what is the chance that they like product D? 68
84. 69 Mess up your (training) data, and nothing can save you (except a training set reboot)
85. ➡ Binomial probability 70
86. 71 ➡ 30% change of acceptance for CFP ➡ 5 CFP's
87. 71 ➡ 30% change of acceptance for CFP ➡ 5 CFP's 1 - (0.7 * 0.7 * 0.7 * 0.7 * 0.7) = 1 - 0.168 = 0.832 83% on getting selected at least once!
88. Ockham's Razor 72
89. 73 Among competing hypotheses, the one with the fewest assumptions should be selected.
90. 74 82 Everything should be made as simple as possible, but no simpler.
91. YAGNI 75
92. 76 Actually, ➡ The principle of plurality Plurality should not be posited with necessity. ➡ The principle of parsimony It is pointless to do more with what is done with less.
93. ➡ Every element you add needs: design, development, maintenance, connectivity, support, etc etc. ➡ When "adding" elements, you are not adding, you are multiplying! 77
94. 78 Food for thought: Would Ockham accept a Service Oriented Architecture?
95. http://farm1.static.flickr.com/73/163450213_18478d3aa6_d.jpg 79
96. 80 Find me on twitter: @jaytaph Find me for development and training: www.noxlogic.nl / www.techademy.nl Find me on email: jthijssen@noxlogic.nl Find me for blogs: www.adayinthelifeof.nl