Write a program to estimate the value of the percolation threshold via Monte Carlo simulation.
Install a Java programming environment. Install a Java programming environment on your computer by following these stepbystep instructions for your operating system [ Mac OS X · Windows · Linux ]. After following these instructions, the commands javacalgs4 and javaalgs4 will classpath in algs4.jar, which contains Java classes for I/O and all of the algorithms in the textbook.
Note that, as of August 2015, you must use the named package version of algs.jar to access the textbook libraries. To access a class, you need an import statement, such as the ones below:
import edu.princeton.cs.algs4.StdRandom; import edu.princeton.cs.algs4.StdStats; import edu.princeton.cs.algs4.WeightedQuickUnionUF;
Note that your code should be in the default package; if you use a package statement, the autograder will not be able to assess your work.
Percolation. Given a composite systems comprised of randomly distributed insulating and metallic materials: what fraction of the materials need to be metallic so that the composite system is an electrical conductor? Given a porous landscape with water on the surface (or oil below), under what conditions will the water be able to drain through to the bottom (or the oil to gush through to the surface)? Scientists have defined an abstract process known as percolation to model such situations.
The model. We model a percolation system using an nbyn grid of sites. Each site is either open or blocked. A full site is an open site that can be connected to an open site in the top row via a chain of neighboring (left, right, up, down) open sites. We say the system percolates if there is a full site in the bottom row. In other words, a system percolates if we fill all open sites connected to the top row and that process fills some open site on the bottom row. (For the insulating/metallic materials example, the open sites correspond to metallic materials, so that a system that percolates has a metallic path from top to bottom, with full sites conducting. For the porous substance example, the open sites correspond to empty space through which water might flow, so that a system that percolates lets water fill open sites, flowing from top to bottom.)
The problem. In a famous scientific problem, researchers are interested in the following question: if sites are independently set to be open with probability p (and therefore blocked with probability 1 − p), what is the probability that the system percolates? When p equals 0, the system does not percolate; when p equals 1, the system percolates. The plots below show the site vacancy probability p versus the percolation probability for 20by20 random grid (left) and 100by100 random grid (right).
When n is sufficiently large, there is a threshold value p* such that when p < p* a random nbyn grid almost never percolates, and when p > p*, a random nbyn grid almost always percolates. No mathematical solution for determining the percolation threshold p* has yet been derived. Your task is to write a computer program to estimate p*.
Percolation data type. To model a percolation system, create a data type Percolation with the following API:
public class Percolation { public Percolation(int n) // create nbyn grid, with all sites blocked public void open(int row, int col) // open site (row, col) if it is not open already public boolean isOpen(int row, int col) // is site (row, col) open? public boolean isFull(int row, int col) // is site (row, col) full? public int numberOfOpenSites() // number of open sites public boolean percolates() // does the system percolate? public static void main(String[] args) // test client (optional) }
Corner cases. By convention, the row and column indices are integers between 1 and n, where (1, 1) is the upperleft site: Throw a java.lang.IllegalArgumentException if any argument to open(), isOpen(), or isFull() is outside its prescribed range. The constructor should throw a java.lang.IllegalArgumentException if n ≤ 0.
Performance requirements. The constructor should take time proportional to n^{2}; all methods should take constant time plus a constant number of calls to the union–find methods union(), find(), connected(), and count().
Monte Carlo simulation. To estimate the percolation threshold, consider the following computational experiment:
For example, if sites are opened in a 20by20 lattice according to the snapshots below, then our estimate of the percolation threshold is 204/400 = 0.51 because the system percolates when the 204th site is opened.




By repeating this computation experiment T times and averaging the results, we obtain a more accurate estimate of the percolation threshold. Let x_{t} be the fraction of open sites in computational experiment t. The sample mean \(\overline x\) provides an estimate of the percolation threshold; the sample standard deviation s; measures the sharpness of the threshold.
\[ \overline x = \frac{x_1 \, + \, x_2 \, + \, \cdots \, + \, x_{T}}{T}, \quad s^2 = \frac{(x_1  \overline x )^2 \, + \, (x_2  \overline x )^2 \,+\, \cdots \,+\, (x_{T}  \overline x )^2}{T1} \]Assuming T is sufficiently large (say, at least 30), the following provides a 95% confidence interval for the percolation threshold:
\[ \left [ \; \overline x  \frac {1.96 s}{\sqrt{T}}, \;\; \overline x + \frac {1.96 s}{\sqrt{T}} \; \right] \]
To perform a series of computational experiments, create a data type PercolationStats with the following API.
The constructor should throw a java.lang.IllegalArgumentException if either n ≤ 0 or trials ≤ 0.public class PercolationStats { public PercolationStats(int n, int trials) // perform trials independent experiments on an nbyn grid public double mean() // sample mean of percolation threshold public double stddev() // sample standard deviation of percolation threshold public double confidenceLo() // low endpoint of 95% confidence interval public double confidenceHi() // high endpoint of 95% confidence interval public static void main(String[] args) // test client (described below) }
Also, include a main() method that takes two commandline arguments n and T, performs T independent computational experiments (discussed above) on an nbyn grid, and prints the sample mean, sample standard deviation, and the 95% confidence interval for the percolation threshold. Use StdRandom to generate random numbers; use StdStats to compute the sample mean and sample standard deviation.
% java PercolationStats 200 100 mean = 0.5929934999999997 stddev = 0.00876990421552567 95% confidence interval = [0.5912745987737567, 0.5947124012262428] % java PercolationStats 200 100 mean = 0.592877 stddev = 0.009990523717073799 95% confidence interval = [0.5909188573514536, 0.5948351426485464] % java PercolationStats 2 10000 mean = 0.666925 stddev = 0.11776536521033558 95% confidence interval = [0.6646167988418774, 0.6692332011581226] % java PercolationStats 2 100000 mean = 0.6669475 stddev = 0.11775205263262094 95% confidence interval = [0.666217665216461, 0.6676773347835391]
Analysis of running time and memory usage (optional and not graded). Implement the Percolation data type using the quick find algorithm in QuickFindUF.
Now, implement the Percolation data type using the weighted quick union algorithm in WeightedQuickUnionUF. Answer the questions in the previous paragraph.
Deliverables. Submit only Percolation.java (using the weighted quickunion algorithm from WeightedQuickUnionUF) and PercolationStats.java. We will supply algs4.jar. Your submission may not call library functions except those in StdIn, StdOut, StdRandom, StdStats, WeightedQuickUnionUF, and java.lang.
For fun. Create your own percolation input file and share it in the discussion forums. For some inspiration, do an image search for "nonogram puzzles solved."