Assignment 5

Simple multiple choice questions, 1 point each:

1. Look at the code for the solution distributed for MP2. Suppose that class Scanner was not final. What changes would this allow to class MyScanner and its uses?
   a) There would be no need to include a constructor.
   b) There would be no need to declare methods like hasNext().
   c) The getNext() methods would not need to use self.
   d) the formal parameter to readCommunity() could be any kind of Scanner.
   e) the actual parameter passed to readCommuity() could be a plain ordinary Scanner.

2. Suppose you wanted to add a method to class Person that returned true if the person was infected. This would be a parameterless method with a one line body. Which of the following would work assuming that infectionState was defined in terms of the enumeration States:
   a) return infectionState > uninfected;
   b) return this.infectionState > States.uninfected;
   c) return infectionState.compareTo( uninfected ) > 0;
   d) return this.infectionState.compareTo( States.uninfected ) > 0;
   e) return (this.infectionState - States.uninfected) > 0;

3. The code for buildCommunity in the distrubuted solution creates people and then decides who to infect based on the number of people remaining who need to be infected infected and the number of people who have not yet been considered for infection pop-i. At each step, one person is considered. The probability that that person will be infected is:
   a) infected / (pop-i)
   b) (pop-i) / infected
   c) pop / (infected-i)
   d) (infected-i) / pop
   e) none of the above

4. A quick scan through census data and school data reveals that the actual distribution of both household sizes and school sizes follows a log-normal distribution. Java's class Math offers exp() and log() for exponentiation and logarithms base e, and. pow(10,) and log10() for exponentiation and logarithms base 10. Assuming that rand is an instance of Random, which of the following returns a random number with a log-normal distribution?
   a) log10( rand.nextGaussian() )
   b) log( rand.nextGaussian() )
   c) pow( 10, rand.nextDouble() )
   d) exp( rand.nextGaussian() )
   e) exp( rand.nextDouble() )

5. In the Bash script distributed with the notes for Lecture 9, the code does not test the exit success or failure reported by the program under test. We could record the exit status in the output file by replacing this line

   java RoadNetwork test 2> tests/out2
   

   with this:

   if java RoadNetwork test 2> tests/out2 ; then
       echo "SUCCESS" >> tests/out2
   else
       echo "FAILURE" >> tests/out2
   fi
   

   Where does this put the SUCCESS or FAILURE message?
   a) on standard output
   b) on standard error
   c) at the start of tests/out2
   d) at the end of tests/out2
   e) somewhere else

Machine Problem 3 -- due Monday, Sept 28 due Tuesday, Sept 29

Write version 3 of the epidemic model. This should accept input from a text file named by the first command line parameter, where the file format is as in machine-problem 2, with additional fields:

pop 25 ;
house 3.2 , 1 ;         // median and scatter of houshold sizes
infected 12 ;
employed 0.5 ;        // probability of employment
workplace 10.5, 12 ;  // median and scatter of workplace size (syntax error?)

The employed line gives the probability that an individual is employed. 1.0 would indicate a 100% probability of employment, 0.0 would indicate that nobody is employed.

The house and workplace lines give the median number of residents or employees at each such place, and the scatter of the number, where a log-normal distribution is used for the populations of the places. The definitions of median and scatter are on the Wikipedia page for log-normal distributions.

Some math: If r is a random value drawn from a normal distribution with mean 0.0 and standard deviation 1.0 (that is the distribution that nextGaussian() in class Random delivers), then σr+μ comes from a distribution with mean μ and standard deviation σ, and e^σr+μ (which is the same as e^σre^μ) comes from a distribution with median e^μ and scatter e^σ.

Directly selecting a scatter is not at all intuitive. It would be nice if we could describe a distribution with a pair [m, s] where m is the median and s is something like the scatter, but s=0 creates a distribution with a sharp peak, and larger values of s widen the distribution so that when s=m the distribution spreads wide enough that 1 is just included, and as s grows greater than m the distribution spreads wider and wider. A bit of experminting shows that we get this when:

In the simulation input file, house 3.2 , 1 ; sets [m, s] to [3.2, 1.0].

Of course, you will need to convert the log-normally distributed random values you get to integer values for the number of employees and household size, since no real workplace or household can have fractional members. Values from 0.0 to 1.0 should be converted to 1, values from 1.0 to 2.0 should be converted to 2, etc., noting that all values returned by the log-normal distribution are strictly positive. That is, the probability of the value 0.0 is exactly 0.0, while nonzero positive values are possible.

To do this assignment, you will need to add a subclass of Person called Employee, and subclasses of place called Home and Workplace.

A student asked: What is the output?

PEOPLE
Employee@15116 Home@14321 Workplace@05100
Person@31700 Home@14321
PLACES
Home14321 Person@31700 Employee@15116
Workplace@05100 Employee@15116

A student asked: What are we to do with the solution to MP2 you distributed?

A student asked: Do we have to use some sort of random mechanism to assign employed/unemployed status of each person?

A student asked: How do we use the mean and scatter values to decide how many people are in a household.

A student asked: What about spaces before commas and semicolons, are these still required?