private void pickSmart() {
this.buildingFound = false;
synchronized (ContextManager.randomLock) {
Collections.shuffle(GlobalVars.burgleMap); // Shuffle the map
for (Building b : GlobalVars.burgleMap) {
// TODO: Tune this logic for Objective #2 of the project. Consider game theory or some other
// type
// of SoS decision making process to decide if it is best to travel to burgle location
// or
// prevent other burgles by traveling to less-burgled parts of the map (e.g. replace
// the 25%
// value with "game theory" idea where the cost/benefits are adjusted (trade study) to
// analyze behavior on crime
Uniform uniform = RandomHelper.createUniform(0, 1);
if (uniform.nextDouble()
< GlobalVars.probTraveltoRecentCrime) { // 25% chance of traveling near recent crime
if ((b.jurisdiction == this.jurisdiction) && !(this.burgleMapSave.contains(b))) {
this.b_pick = b;
this.route = new Route(this, b.getCoords(), b_pick); // Initialize route to target;
this.buildingFound = true;
this.burgleMapSave.add(b);
break; // stop searching
}
}
}
if (this.buildingFound == false) {
pickRandom(); // no previously burgled building exists in memory map for given jurisdiction
}
}
}
@Override
public void eventOccurred(final AuctionEvent event) {
super.eventOccurred(event);
if (event instanceof SimulationStartedEvent) {
minValueDist =
new Uniform(
generator.getMinValueMin(),
generator.getMinValueMax(),
Galaxy.getInstance().getDefaultTyped(GlobalPRNG.class).getEngine());
rangeDist =
new Uniform(
generator.getRangeMin(),
generator.getRangeMax(),
Galaxy.getInstance().getDefaultTyped(GlobalPRNG.class).getEngine());
} else if (event instanceof GameStartingEvent) {
final double minValue = minValueDist.nextDouble();
final double maxValue = minValue + rangeDist.nextDouble();
setDistribution(
new Uniform(
minValue,
maxValue,
Galaxy.getInstance().getDefaultTyped(GlobalPRNG.class).getEngine()));
drawRandomValue();
}
}
@Override
public Double mutate(Uniform uniform, Object previousValue) {
double y = (Double) previousValue;
double yl = min;
double yu = max;
double delta1 = (y - yl) / (yu - yl);
double delta2 = (yu - y) / (yu - yl);
double rnd = uniform.nextDoubleFromTo(0.0, 1.0);
double mut_pow = 1 / (eta_m + 1);
double deltaq;
if (rnd <= 0.5) {
double xy = 1 - delta1;
double val = 2 * rnd + (1 - 2 * rnd) * (Math.pow(xy, (eta_m + 1)));
deltaq = Math.pow(val, mut_pow) - 1;
} else {
double xy = 1 - delta2;
double val = 2 * (1 - rnd) + 2 * (rnd - 0.5) * (Math.pow(xy, (eta_m + 1)));
deltaq = 1 - (Math.pow(val, mut_pow));
}
y = y + deltaq * (yu - yl);
if (y < yl) y = yl;
if (y > yu) y = yu;
return y;
}
/**
* Generic bootstrap resampling. Quite optimized - Don't be afraid to try it. Executes
* <tt>resamples</tt> resampling steps. In each resampling step does the following:
*
* <ul>
* <li>Uniformly samples (chooses) <tt>size()</tt> random elements <i>with replacement</i> from
* <tt>this</tt> and fills them into an auxiliary bin <tt>b1</tt>.
* <li>Uniformly samples (chooses) <tt>other.size()</tt> random elements <i>with replacement</i>
* from <tt>other</tt> and fills them into another auxiliary bin <tt>b2</tt>.
* <li>Executes the comparison function <tt>function</tt> on both auxiliary bins
* (<tt>function.apply(b1,b2)</tt>) and adds the result of the function to an auxiliary
* bootstrap bin <tt>b3</tt>.
* </ul>
*
* <p>Finally returns the auxiliary bootstrap bin <tt>b3</tt> from which the measure of interest
* can be read off.
*
* <p><b>Background:</b>
*
* <p>Also see a more <A HREF="http://garnet.acns.fsu.edu/~pkelly/bootstrap.html"> in-depth
* discussion</A> on bootstrapping and related randomization methods. The classical statistical
* test for comparing the means of two samples is the <i>t-test</i>. Unfortunately, this test
* assumes that the two samples each come from a normal distribution and that these distributions
* have the same standard deviation. Quite often, however, data has a distribution that is
* non-normal in many ways. In particular, distributions are often unsymmetric. For such data, the
* t-test may produce misleading results and should thus not be used. Sometimes asymmetric data
* can be transformed into normally distributed data by taking e.g. the logarithm and the t-test
* will then produce valid results, but this still requires postulation of a certain distribution
* underlying the data, which is often not warranted, because too little is known about the data
* composition.
*
* <p><i>Bootstrap resampling of means differences</i> (and other differences) is a robust
* replacement for the t-test and does not require assumptions about the actual distribution of
* the data. The idea of bootstrapping is quite simple: simulation. The only assumption required
* is that the two samples <tt>a</tt> and <tt>b</tt> are representative for the underlying
* distribution with respect to the statistic that is being tested - this assumption is of course
* implicit in all statistical tests. We can now generate lots of further samples that correspond
* to the two given ones, by sampling <i>with replacement</i>. This process is called
* <i>resampling</i>. A resample can (and usually will) have a different mean than the original
* one and by drawing hundreds or thousands of such resamples <tt>a<sub>r</sub></tt> from
* <tt>a</tt> and <tt>b<sub>r</sub></tt> from <tt>b</tt> we can compute the so-called bootstrap
* distribution of all the differences "mean of <tt>a<sub>r</sub></tt> minus mean of
* <tt>b<sub>r</sub></tt>". That is, a bootstrap bin filled with the differences. Now we can
* compute, what fraction of these differences is, say, greater than zero. Let's assume we have
* computed 1000 resamples of both <tt>a</tt> and <tt>b</tt> and found that only <tt>8</tt> of the
* differences were greater than zero. Then <tt>8/1000</tt> or <tt>0.008</tt> is the p-value
* (probability) for the hypothesis that the mean of the distribution underlying <tt>a</tt> is
* actually larger than the mean of the distribution underlying <tt>b</tt>. From this bootstrap
* test, we can clearly reject the hypothesis.
*
* <p>Instead of using means differences, we can also use other differences, for example, the
* median differences.
*
* <p>Instead of p-values we can also read arbitrary confidence intervals from the bootstrap bin.
* For example, <tt>90%</tt> of all bootstrap differences are left of the value <tt>-3.5</tt>,
* hence a left <tt>90%</tt> confidence interval for the difference would be
* <tt>(3.5,infinity)</tt>; in other words: the difference is <tt>3.5</tt> or larger with
* probability <tt>0.9</tt>.
*
* <p>Sometimes we would like to compare not only means and medians, but also the variability
* (spread) of two samples. The conventional method of doing this is the <i>F-test</i>, which
* compares the standard deviations. It is related to the t-test and, like the latter, assumes the
* two samples to come from a normal distribution. The F-test is very sensitive to data with
* deviations from normality. Instead we can again resort to more robust bootstrap resampling and
* compare a measure of spread, for example the inter-quartile range. This way we compute a
* <i>bootstrap resampling of inter-quartile range differences</i> in order to arrive at a test
* for inequality or variability.
*
* <p><b>Example:</b>
*
* <table>
* <td class="PRE">
* <pre>
* // v1,v2 - the two samples to compare against each other
* double[] v1 = { 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 21, 22,23,24,25,26,27,28,29,30,31};
* double[] v2 = {10,11,12,13,14,15,16,17,18,19, 20, 30,31,32,33,34,35,36,37,38,39};
* hep.aida.bin.DynamicBin1D X = new hep.aida.bin.DynamicBin1D();
* hep.aida.bin.DynamicBin1D Y = new hep.aida.bin.DynamicBin1D();
* X.addAllOf(new cern.colt.list.DoubleArrayList(v1));
* Y.addAllOf(new cern.colt.list.DoubleArrayList(v2));
* cern.jet.random.engine.RandomEngine random = new cern.jet.random.engine.MersenneTwister();
*
* // bootstrap resampling of differences of means:
* BinBinFunction1D diff = new BinBinFunction1D() {
* public double apply(DynamicBin1D x, DynamicBin1D y) {return x.mean() - y.mean();}
* };
*
* // bootstrap resampling of differences of medians:
* BinBinFunction1D diff = new BinBinFunction1D() {
* public double apply(DynamicBin1D x, DynamicBin1D y) {return x.median() - y.median();}
* };
*
* // bootstrap resampling of differences of inter-quartile ranges:
* BinBinFunction1D diff = new BinBinFunction1D() {
* public double apply(DynamicBin1D x, DynamicBin1D y) {return (x.quantile(0.75)-x.quantile(0.25)) - (y.quantile(0.75)-y.quantile(0.25)); }
* };
*
* DynamicBin1D boot = X.sampleBootstrap(Y,1000,random,diff);
*
* cern.jet.math.Functions F = cern.jet.math.Functions.functions;
* System.out.println("p-value="+ (boot.aggregate(F.plus, F.greater(0)) / boot.size()));
* System.out.println("left 90% confidence interval = ("+boot.quantile(0.9) + ",infinity)");
*
* -->
* // bootstrap resampling of differences of means:
* p-value=0.0080
* left 90% confidence interval = (-3.571428571428573,infinity)
*
* // bootstrap resampling of differences of medians:
* p-value=0.36
* left 90% confidence interval = (5.0,infinity)
*
* // bootstrap resampling of differences of inter-quartile ranges:
* p-value=0.5699
* left 90% confidence interval = (5.0,infinity)
* </pre>
* </td>
* </table>
*
* @param other the other bin to compare the receiver against.
* @param resamples the number of times resampling shall be done.
* @param randomGenerator a random number generator. Set this parameter to <tt>null</tt> to use a
* default random number generator seeded with the current time.
* @param function a difference function comparing two samples; takes as first argument a sample
* of <tt>this</tt> and as second argument a sample of <tt>other</tt>.
* @return a bootstrap bin holding the results of <tt>function</tt> of each resampling step.
* @see cern.colt.GenericPermuting#permutation(long,int)
*/
public synchronized DynamicBin1D sampleBootstrap(
DynamicBin1D other,
int resamples,
cern.jet.random.engine.RandomEngine randomGenerator,
BinBinFunction1D function) {
if (randomGenerator == null) randomGenerator = cern.jet.random.Uniform.makeDefaultGenerator();
// since "resamples" can be quite large, we care about performance and memory
int maxCapacity = 1000;
int s1 = size();
int s2 = other.size();
// prepare auxiliary bins and buffers
DynamicBin1D sample1 = new DynamicBin1D();
cern.colt.buffer.DoubleBuffer buffer1 = sample1.buffered(Math.min(maxCapacity, s1));
DynamicBin1D sample2 = new DynamicBin1D();
cern.colt.buffer.DoubleBuffer buffer2 = sample2.buffered(Math.min(maxCapacity, s2));
DynamicBin1D bootstrap = new DynamicBin1D();
cern.colt.buffer.DoubleBuffer bootBuffer = bootstrap.buffered(Math.min(maxCapacity, resamples));
// resampling steps
for (int i = resamples; --i >= 0; ) {
sample1.clear();
sample2.clear();
this.sample(s1, true, randomGenerator, buffer1);
other.sample(s2, true, randomGenerator, buffer2);
bootBuffer.add(function.apply(sample1, sample2));
}
bootBuffer.flush();
return bootstrap;
}
/**
* Randomly permutes the part of the receiver between <code>from</code> (inclusive) and <code>to
* </code> (inclusive).
*
* @param from the index of the first element (inclusive) to be permuted.
* @param to the index of the last element (inclusive) to be permuted.
* @exception IndexOutOfBoundsException index is out of range ( <tt>size()>0 && (from<0 ||
* from>to || to>=size())</tt> ).
*/
public void shuffleFromTo(int from, int to) {
// overridden for performance only.
if (size == 0) {
return;
}
checkRangeFromTo(from, to, size);
cern.jet.random.Uniform gen =
new cern.jet.random.Uniform(new cern.jet.random.engine.DRand(new java.util.Date()));
short tmpElement;
short[] theElements = elements;
int random;
for (int i = from; i < to; i++) {
random = gen.nextIntFromTo(i, to);
// swap(i, random)
tmpElement = theElements[random];
theElements[random] = theElements[i];
theElements[i] = tmpElement;
}
}
/**
* Uniformly samples (chooses) <tt>n</tt> random elements <i>with or without replacement</i> from
* the contained elements and adds them to the given buffer. If the buffer is connected to a bin,
* the effect is that the chosen elements are added to the bin connected to the buffer. Also see
* {@link #buffered(int) buffered}.
*
* @param n the number of elements to choose.
* @param withReplacement <tt>true</tt> samples with replacement, otherwise samples without
* replacement.
* @param randomGenerator a random number generator. Set this parameter to <tt>null</tt> to use a
* default random number generator seeded with the current time.
* @param buffer the buffer to which chosen elements will be added.
* @throws IllegalArgumentException if <tt>!withReplacement && n > size()</tt>.
* @see cern.jet.random.sampling
*/
public synchronized void sample(
int n,
boolean withReplacement,
RandomEngine randomGenerator,
cern.colt.buffer.DoubleBuffer buffer) {
if (randomGenerator == null) randomGenerator = cern.jet.random.Uniform.makeDefaultGenerator();
buffer.clear();
if (!withReplacement) { // without
if (n > size()) throw new IllegalArgumentException("n must be less than or equal to size()");
cern.jet.random.sampling.RandomSamplingAssistant sampler =
new cern.jet.random.sampling.RandomSamplingAssistant(n, size(), randomGenerator);
for (int i = n; --i >= 0; ) {
if (sampler.sampleNextElement()) buffer.add(this.elements.getQuick(i));
}
} else { // with
cern.jet.random.Uniform uniform = new cern.jet.random.Uniform(randomGenerator);
int s = size();
for (int i = n; --i >= 0; ) {
buffer.add(this.elements.getQuick(uniform.nextIntFromTo(0, s - 1)));
}
buffer.flush();
}
}
public static void main(String[] arguments) throws Exception {
int rng_seed = cern.jet.random.Uniform.staticNextIntFromTo(0, Integer.MAX_VALUE); // 3;
// analyze_accuracy_NHBS(rng.nextInt());
List<DrugUser> synthPop = make_from_NEP(40000, rng_seed);
System.out.println("\nLeaving APK-SynthPop");
}
@Override
public double nextDoubleUniform(double min, double max) {
return uniform.nextDoubleFromTo(min, max);
}
@Override
public int nextIntBetween(int min, int max) {
return uniform.nextIntFromTo(min, max);
}
@Override
public double nextDoubleUniform() {
return uniform.nextDouble();
}
/**
* Rain clouds appear with a certain chance, influenced by the weather For every rain cloud in the
* grid the velocity of every rain object is updated Rain clouds are removed if they have passed a
* certain time
*/
@ScheduledMethod(start = 1, interval = 1, priority = 0)
public void rain() {
// Let new raingroups appear with a certain chance
double chance = SimulationParameters.rainProb;
// The probability of rain appearing decreases if there is already rain in the grid
if (noRainGroups == 1) chance = (chance / (noRainGroups)) * 0.5;
if (noRainGroups == 2) chance = (chance / (noRainGroups)) * 0.1;
if (noRainGroups > 2) chance = (chance / (noRainGroups)) * 0.01;
double f = urng.nextDouble();
if (f < chance) {
// Let rain appear
int x = rand.nextInt((SimulationParameters.gridSize - 0) + 1);
int y = rand.nextInt((SimulationParameters.gridSize - 0) + 1);
int[] newLoc = {x, y};
// Let new raingroup appear in random location
RainGroup rg = new RainGroup(ContextUtils.getContext(this), grid, newLoc);
noRainGroups++;
rainGroups.add(rg);
}
ArrayList<RainGroup> toRemove = new ArrayList<RainGroup>();
for (RainGroup rg : rainGroups) {
// Get velocity vector of the rain
float x = Wind.getWindVelocity().x;
float y = Wind.getWindVelocity().y;
Vector2 velRain = new Vector2(x, y);
velRain.setLength(
Wind.getWindVelocity().len() * 0.9f); // Rain speed is a bit lower than that of the wind
List<Rain> toRemove1 = new ArrayList<Rain>();
// Let rain be carried by the wind
if (urng.nextDouble() < velRain.len()) {
for (Rain rain : rg.getRainObjects()) {
Directions dir = Directions.fromVectorToDir(velRain);
GridPoint pt = grid.getLocation(rain);
int cX = pt.getX() + dir.xDiff;
int cY = pt.getY() + dir.yDiff;
// If new rain-location is out of borders, delete this rain object
// In this way the cloud "travels" out of the grid
if (cX < 0
|| cX >= SimulationParameters.gridSize
|| cY < 0
|| cY >= SimulationParameters.gridSize) {
toRemove1.add(rain);
} else grid.moveTo(rain, cX, cY);
}
}
for (Rain r : toRemove1) {
rg.removeRain(r);
TreeBuilder.performance.decreaseRainCount();
}
}
// Remove the raingroups from our list which were removed from the context
for (RainGroup rg : toRemove) {
rainGroups.remove(rg);
noRainGroups--;
}
}
@Override
public Double[] crossoverSBX(Uniform U, Object genesA, Object genesB) {
Double gA = (Double) genesA;
Double gB = (Double) genesB;
if (U.nextDoubleFromTo(0, 1) < 0.5) {
double rand;
double y1, y2, yl, yu;
double c1, c2;
double alpha, beta, betaq;
if (StrictMath.abs(gA - gB) > EPS) {
if (gA < gB) {
y1 = gA;
y2 = gB;
} else {
y1 = gB;
y2 = gA;
}
yl = min;
yu = max;
rand = U.nextDoubleFromTo(0, 1);
beta = 1.0 + (2.0 * (y1 - yl) / (y2 - y1));
alpha = 2.0 - StrictMath.pow(beta, -(eta_c + 1.0));
if (rand <= (1.0 / alpha)) {
betaq = StrictMath.pow((rand * alpha), (1.0 / (eta_c + 1.0)));
} else {
betaq = StrictMath.pow((1.0 / (2.0 - rand * alpha)), (1.0 / (eta_c + 1.0)));
}
c1 = 0.5 * ((y1 + y2) - betaq * (y2 - y1));
beta = 1.0 + (2.0 * (yu - y2) / (y2 - y1));
alpha = 2.0 - StrictMath.pow(beta, -(eta_c + 1.0));
if (rand <= (1.0 / alpha)) {
betaq = StrictMath.pow((rand * alpha), (1.0 / (eta_c + 1.0)));
} else {
betaq = StrictMath.pow((1.0 / (2.0 - rand * alpha)), (1.0 / (eta_c + 1.0)));
}
c2 = 0.5 * ((y1 + y2) + betaq * (y2 - y1));
if (c1 < yl) c1 = yl;
if (c2 < yl) c2 = yl;
if (c1 > yu) c1 = yu;
if (c2 > yu) c2 = yu;
if (U.nextDoubleFromTo(0, 1) <= 0.5) {
return new Double[] {c2, c1};
} else {
return new Double[] {c1, c2};
}
} else {
if (U.nextDoubleFromTo(0, 1) <= 0.5) {
return new Double[] {gA, gB};
} else {
return new Double[] {gB, gA};
}
}
} else {
return new Double[] {gA, gB};
}
}
@Override
public Double generateRandomnly(Uniform uniform) {
return uniform.nextDoubleFromTo(min, max);
}
