CSIS-385: Homework 1
See schedule for due date.
Overview
Part 1: Implement an algorithm that randomly generates N points in a unit cube and then finds the two
closest points. Output the distance between the two points and the x,y-values of the two points.
Part 2: Randomly generate N points, calculate the distance between every pair of points and generate
histogram data.
Part 3: Describe in words or pseudocode (you do not have to actually implement it) an algorithm that
takes a circle of radius R and N points in a unit cube as input and finds the optimal location such that the
maximum number of points falls inside the circle.
Details
Part 1: The user should enter the value N, which is the number of random points to generate. The
points should be in unit cube, which basically means that the x,y-values of the points must be floating
point values between 0 and 1. The points should be stored in some data structure. For example, you
could create a Class called Pair, i.e., for storing the x and y values, and the data structure could simply
be an array of Pairs. Once the points have been generated use the nested loop we discussed in class to
compare and compute the distance between all pairs of points. Keep track of the minimum distance and
the two points that achieve that distance. After you’ve compared all pairs, output the minimum distance
(a float) and the two points, i.e., two floating point pairs. Be sure to label your output.
Part 2:You can copy the code from Part 1, but be sure to create a completely new program. Again, the
program should generate N random points in a unit cube. The program should compute the distance
between all pairs of points and generate histogram data. A Histogram basically splits the data (all the
computed distances) into bins and counts how many numbers are in each bin. Here is an example:
Histogram
77
32
65
43
72
26
85
27
9
83
0
33
23
16
8
0
20
40
60
80
100
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1. 4 1.5
Bin Ranges
Counts
