Talk from the 2015 City Code Chicago conference

1. Programming`s
Greatest Hits of the
60s and 70s
Michelle Brush
Engineering Director, Cerner Corporation
Chapter Leader, Kansas City Girl Develop It
Conference Organizer, Midwest.io

2. Where
Are
We?
(41.911582,
-­‐87.634803)

3. Where
Are
We?
(41.911582,
-­‐87.634803)
Latitude:
Y
value

4. Where
Are
We?
(41.911582,
-­‐87.634803)
Latitude:
Y
value Longitude:
X
value

5. Assuming
we
have
a
data
store
of
the
locations
of
all
bars
in
the
world…
https://www.flickr.com/photos/shutterrunner/4334879403/
“Today. The Green Door Tavern.” Shutter Runner CC BY 2.0

6. Let’s find a fun place
nearby
to have a beer.

7. Interview Question:
Write
an
API
to
support
searching
for
the
nearest
bars.
How would you do it?

8. Right
Answer
Google
Places
API

9. Sometimes our job is to find
ways to not write code.

10. Let’s rephrase the question.

11. Interview Question:
Write
an
API
to
support
searching
for
the
nearest
bars
from
scratch.
How would you do it?

14. Binary Search
2 4 7 9 11 13 34 37 92 105
0 1 2 3 4 5 6 7 8 9
?
?
?
Found It!

15. Everyone
used
to
know
binary
search.
They
just
didn’t
know
what
it
was
called.
(cc)
http://www.recyclethis.co.uk
(and picked inefficient pivot points)

16. SOME SORTING
REQUIRED.
INSERT SORT
ALGORITHM NOW.

17. Sort by X, then Y!

18. Well, that breaks.

19. Which is closer?
United
Lily
Growers
in
Smith
River
California
~1800
miles
The
Green
Door
Tavern
not
that
far
in
Chicago
1.26
miles
or

20. X gets preferential treatment over Y.
It doesn’t deserve it.

21. How about brute force?

22. 1. Iterate
over
every
bar.
2. Calculate
the
distance.
3. Sort
by
the
distance.

23. 1630 bars in Chicago alone.

24. Is there a right answer?

25. Was I just a mean
interviewer?

26. Some people got it right.

27. Physics Majors

28. In particular…
Physics
majors
that
care
about
particle
collisions.

29. Divide
&
Conquer
the
Space

30. Divide
&
Conquer
the
Space

31. Divide
&
Conquer
the
Space

32. Divide
&
Conquer
the
Space

33. Rules
Quadrants
are
not
always
equal
in
space.
They
can
be
equal
in
capacity.
Capacity
=
Number
of
Objects

34. Center
Point
(X,
Y)

35. Or
Bounding
Box
(
X1,
Y1
)
(
X2,
Y2
)

36. Create
a
Tree
of
Nested
Quadrants

37. Follow
the
Rectangles
Down

38. Follow
the
Rectangles
Down

39. Follow
the
Rectangles
Down

40. Follow
the
Rectangles
Down

41. 1974
Jon L. Bentley &
Raphael Finkel
Quad-Trees

42. 1974
Jon L. Bentley (includes buggy
implementation of binary
search)

43. Quad-tree is a very good answer.
It can be generalized to more dimensions.

44. Octrees
WhiteTimberwolf 2010, Schematic drawing of an octree, a data structure of computer science.

45. 37

46. 37

47. 37

48. 37

49. 37

50. 37

51. 37

52. 37

53. Binary Search
2 4 7 9 11 13 34 37 92 105
0 1 2 3 4 5 6 7 8 9
?
?
?
Found It!

54. SOME SORTING
REQUIRED.
INSERT SORT
ALGORITHM NOW.

55. I have 2 values. I need 1.

56. Bit Concatenation
(x, y)
(x3x2x1x0, y3y2y1y0)
x3x2x1x0 y3y2y1y0
(9, 3)
(1001, 0011)
10010011

57. Bit Interleaving
(x, y)
(x3x2x1x0, y3y2y1y0)
x3y3x2y2x1y1x0y0
(9, 3)
(1001, 0011)
10000111

58. 1966
G.M. Morton
Z Order

59. 3 (0,3) (1,3) (2,3) (3,3)
2 (0,2) (1,2) (2,2) (3,2)
1 (0,1) (1,1) (2,1) (3,1)
0 (0,0) (1,0) (2,0) (3,0)
0 1 2 3

60. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

61. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3
Newly created number
determines order.

62. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3
Newly created number
determines order.
start

63. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3
Newly created number
determines order.
start
finish

64. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

65. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

66. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

67. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

68. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

69. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

70. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

71. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

72. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

73. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

74. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

75. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

76. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

77. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

78. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

79. 3
1010
10
1011
11
1110
14
1111
15
2
1000
8
1001
9
1100
12
1101
13
1
0010
2
0011
3
0110
6
0111
7
0
0000
0
0001
1
0100
4
0101
5
0 1 2 3

81. (0,0)
(1,1)

91. space-filling
curve

92. Bit-interleaving can
be extended to
multiple dimensions.

93. from space to graphs…

94. lost in
suburbia

95. Blue Spruce Dr.
Arborwood Ct.
Elmwood St.
Blue Fir Ln.

96. Go right.

97. Go right.
19th Century
Charles Pierre Trémaux
depth-first search

113. BREADTH-FIRST SEARCH

114. BREADTH-FIRST SEARCH
1950s

115. BREADTH-FIRST SEARCH
1950s

117. A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph

118. Open List
A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph

119. Open List
A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph
sum of all costs to get here

120. Open List Closed List
A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph
sum of all costs to get here

121. Open List Closed List
A 0
A

122. Open List Closed List
A 0
A
B
2
C
3
D
4

123. Open List Closed List
A 0
A
B
2
C
3
D
4
B 2
C 3
D 4

124. Open List Closed List
B 2
C 3
D 4
A 0
A
B
2
C
3
2
D
4
E
4
My Example Graph

125. Open List Closed List
C 3
D 4
E 6
A 0
B 2
A
B
2
C
3
2
D
4
E
4
My Example Graph

126. Open List Closed List
C 3
D 4
E 6
A 0
B 2
A
B
2
C
3
2
D
4
2
E
1
4
My Example Graph

127. Open List Closed List
D 4
E 4
A 0
B 2
C 3
A
B
2
C
3
2
D
4
2
E
1
4
My Example Graph

128. Open List Closed List
E 4 A 0
B 2
C 3
D 4
A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph

129. Open List Closed List
E 4 A 0
B 2
C 3
D 4
A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph

130. 1956
Edsger W. Djikstra
Djikstra’s
AlgorithmHamilton Richards - manuscripts of Edsger W. Dijkstra, University Texas at Austin

131. 1956
Edsger W. Djikstra
Djikstra’s
AlgorithmHamilton Richards - manuscripts of Edsger W. Dijkstra, University Texas at Austin

132. 1968
Peter Hart, Nils Nilsson,
& Bertram Raphael
A*
A

133. Open List Closed List
A
B
2
C
3
2
D
4
2
E
4
1
4
My Example Graph
sum cost to get here
plus distance to goal
0
2
2
1
3

134. Open List Closed List
A 3
A
B
2
C
3
D
4
My Example Graph
2
2
1
3

135. Open List Closed List
B 4
C 4
D 6
A 3
A
B
2
C
3
D
4
My Example Graph
2
2
1
3

136. Open List Closed List
A 3
A
B
2
C
3
2
D
4
E
4
My Example Graph
0
2
2
1
3
B 4
C 4
D 6

137. Open List Closed List
A 3
B 4
A
B
2
C
3
2
D
4
E
4
My Example Graph
0
2
2
1
3
C 4
D 6
E 6

138. Open List Closed List
A
B
2
C
3
2
D
4
2
E
1
4
My Example Graph
0
2
2
1
3
A 3
B 4
C 4
D 6
E 6

139. Open List Closed List
A
B
2
C
3
2
D
4
2
E
1
4
My Example Graph
0
2
2
1
3
A 3
B 4
C 4
E 4
D 6

140. Open List Closed List
A
B
2
C
3
2
D
4
2
E
1
4
My Example Graph
0
2
2
1
3
A 3
B 4
C 4
E 4
D 6
We didn’t have to close D.

141. Breadth-first Search

142. Breadth-first Search
Djikstra’s Algorithm

143. Breadth-first Search
Djikstra’s Algorithm
A*

144. The power of
heuristics.
Breadth-first Search
Djikstra’s Algorithm
A*

145. Are all edge weights equal?
What’s your most expensive operation?
Do you have insights about your target?
Breadth
-First
Djikstra
A*
yes
no
visits
heuristics
?
?
?

146. breadth-first search f(x) = c
djikstra’s algorithm f(x) = g(x) + c
a* f(x) = g(x) + h(x) + c
The others were specializations.

147. and we can do more than
search with graphs…

148. 1974
Charles L. Forgy
Rete

149. Premise 1
Premise 2
Premise 3
Conclusion
Rules

150. Premise 1
Premise 2
Premise 3
Take Action
Rules

151. (Premise 1, Premise 2, Premise 3)
(Action)
=>

152. (Premise 1, Premise 2, Premise 3)
(Action)
=>Left Hand Side

153. (Premise 1, Premise 2, Premise 3)
(Action)
=>Left Hand Side
Right Hand Side

154. A
B
C
D
E
Rules are a
directed acyclic
graph.

155. like a trie
C
I OH
I DT
C EY
A K
G E
O N

156. Root Node

157. Root Node
Type Nodes

158. Root Node
Selection Nodes
Type Nodes

159. Root Node
Negation Nodes
Selection Nodes
Type Nodes

160. Root Node
Join Nodes
Negation Nodes
Selection Nodes
Type Nodes

161. Root Node
Terminal Nodes
Join Nodes
Negation Nodes
Selection Nodes
Type Nodes

162. Root Node
Alpha Memory
Terminal Nodes
Join Nodes
Negation Nodes
Selection Nodes
Type Nodes

163. Root Node
Beta Memory
Alpha Memory
Terminal Nodes
Join Nodes
Negation Nodes
Selection Nodes
Type Nodes

164. recommend-diet-changes
Hypertension (severity == moderate)
Demographics (age > 60)
prescribe-bp-meds
Hypertension
Hypertension
Demographics
Hypertension (severity == moderate)
Ryan Brush “Retaking Rules for Developers”

165. Hypertension
DemographicsHypertension (severity == moderate)
Demographics (age > 60)
recommend-diet-changes
prescribe-bp-meds
Ryan Brush “Retaking Rules for Developers”

166. Sometimes our job is to find
ways to not write code.

167. Let’s not build an
expert system!

168. Let’s not build an
expert system!

169. Let’s not build an
expert system!

170. (-­‐>clara)
Let’s not build an
expert system!

171. (-­‐>clara)
+
=

172. (-­‐>clara)
+
=

173. Triskel99 - Own work
AMC Pacer, photographed during August 2009.

174. Efficacy
Cost
Convenience

175. import
java.util.Comparator;
public
class
ChoiceComparator
implements
Comparator<Choice>
{
@Override
public
int
compare(Choice
o1,
Choice
o2)
{
if(
o2.getEfficacy()
==
o1.getEfficacy)
{
if(
o2.getCost()
==
o1.getCost)
{
return
o2.getConvenience()
-­‐
o1.getConvenience();
}
return
o2.getCost()
-­‐
o1.getCost();
}
return
o2.getEfficacy()
-­‐
o2.getEfficacy();
}
}

176. import
java.util.Comparator;
public
class
ChoiceComparator
implements
Comparator<Choice>
{
@Override
public
int
compare(Choice
o1,
Choice
o2)
{
if(
o2.getEfficacy()
==
o1.getEfficacy)
{
if(
o2.getCost()
==
o1.getCost)
{
return
o2.getConvenience()
-­‐
o1.getConvenience();
}
return
o2.getCost()
-­‐
o1.getCost();
}
return
o2.getEfficacy()
-­‐
o2.getEfficacy();
}
}

177. public
class
EfficacyComparator
implements
Comparator<Choice>
public
class
CostComparator
implements
Comparator<Choice>
public
class
ConvenienceComparator
implements
Comparator<Choice>
1.SORT
2.TAKE
10
3.GOTO
1

178. public
class
EfficacyComparator
implements
Comparator<Choice>
public
class
CostComparator
implements
Comparator<Choice>
public
class
ConvenienceComparator
implements
Comparator<Choice>
1.SORT
2.TAKE
10
3.GOTO
1

179. 1974
Jon L. Bentley &
Raphael Finkel
Quad-Trees

180. 1966
G.M. Morton
Z Order

181. for(int i = 0; i < count1; ++i) {
for(int j = 0; j < count2; ++j) {
for(int k = 0; k < count3; ++k) {
for(int l = 0; l < count4; ++l) {
for(int m = 0; m < count5; ++m) {
for(int n = 0; n < count6; ++n) {
for(int o = 0; o < count7; ++o)
for(int p = 0; p < count8; ++
for(int q = 0; q < count9;
for(int r = 0; r < coun
for(int s = 0; s < c
for(int t = 0; t
for(int u = 0;
for(int v =
for(int
for(i
fo
for-nado?

182. You are using the
wrong algorithm.

183. Crack open an algorithms book.

184. Do a Depth-First Search of Wikipedia

185. Do a Depth-First Search of Wikipedia

186. Your solution is out there.
BLOOM FILTER
trie
Gale Shapley
A*
R-Tree
TREAP
Dijkstra’s
Floyd Warshall
D*
Huffman
ID3
CART
Rete

187. And
to
be
honest…
It
is
probably
from
the
60s
or
70s.

188. 1979Engineering Director, Cerner Corporation
Chapter Leader, Kansas City Girl Develop It
Conference Organizer, Midwest.io
@michellebrush
Michelle Brush