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How To Iterate Through Every Latitude/Longitude in Java

I'm accessing this API that gives me global weather:
https://callforcode.weather.com/doc/v3-global-weather-notification-headlines/
However, it takes lat/lng as an input parameter, and I need the data for the entire world.
I figure I can loop through every latitude longitude, every 2 latitudes and 2 longitude degrees, giving me a point on the world, every ~120 miles across and roughly 100 degrees north/south, which should give me all the data in 16,200 API calls ((360/2) * (180/2)).
How can I do this effectively in Java?
I'd conceived something like this; but is there a better way of doing this?
for(int i = 0; i < 360; i+2){
var la = i;
for(int x = 0 x < 180; x+2) {
var ln = x;
//call api with lat = i, lng = x;
}
}

It's somewhat of a paradigm shift, but I would NOT use a nested for-loop for this problem. In many situations where you are looking at iterating over an entire result set, it is often possible to trim the coverage dramatically without losing much or any effectiveness. Caching, trimming, prioritizing... these are the things you need: not a for-loop.
Cut sections entirely - maybe you can ignore ocean, maybe you can ignore Antartica and the North Pole (since people there have better ways of checking weather anyway)
Change your search frequency based on population density. Maybe northern Canada doesn't need to be checked as thoroughly as Los Angeles or Chicago.
Rely on caching in low-usage areas - presumably you can track what areas are actually being used and can then more frequently refresh those sections.
So what you end up with is some sort of weighted caching system that takes into account population density, usage patterns, and other priorities to determine what latitude/longitude coordinates to check and how frequently.
High-level code might look something like this:
void executeUpdateSweep(List<CoordinateCacheItem> cacheItems)
{
for(CoordinateCacheItem item : cacheItems)
{
if(shouldRefreshCache(item))
{
//call api with lat = item.y , lng = item.x
}
}
}
boolean shouldRefreshCache(item)
{
long ageWeight = calculateAgeWeight(item);//how long since last update?
long basePopulationWeight = item.getBasePopulationWeight();//how many people (users and non-users) live here?
long usageWeight = calculateUsageWeight(item);//how much is this item requested?
return ageWeight + basePopulationWeight + usageWeight > someArbitraryThreshold;
}

Java/CGAL verify if a graph is connected (some constraints in description)

it's my first time with CGAL, some of you may argue why do I have to learn CGAL from something like that, but it's a new project that I must do (and... yes, I must use CGAL and Java combined) :/ Long story short... I only have:
Two double arrays, representing x and y coordinates of my vertices. Let's call them double[] x, y;.
Both arrays have S random values.
Two vertices, u and w are connected if distance(x[u], y[u], x[w], y[w]) < CONSTANT (ofc. I do distanceSquared(x[u], y[u], x[w], y[w]) < CONSTANT_SQUARED, so I avoid to call sqrt()).
x and y are filled randomly with values from 0 to UPPER_LIMIT, no other infos are given.
Question, do x and y describes a connected graph?
Right now I have two algoritms:
Algorithm 1:
Build adjacency list (Arraylist<Integer>[] adjLists;) for each vertex (only upper triangular matrix explored). Complexity O(|V|^2) (V = vertices set).
Recursive graph exploration, vertex marking and counting, if visited vertex equals S my graph have only one connected component, my graph is connected. Complexity O(|E|) (E = edges set).
Algorithm 2:
private static boolean algorithmGraph(double[] x, double[] y) {
int unchecked, inside = 0, current = 0;
double switchVar;
while (current <= inside && inside != S - 1) {
unchecked = inside + 1;
while (unchecked < S) {
if ((x[current] - x[unchecked]) * (x[current] - x[unchecked]) + (y[current] - y[unchecked]) * (y[current] - y[unchecked]) <= CONSTANT_SQUARED) {
inside++;
// switch x coordinates | unchecked <-> inside
switchVar = x[unchecked];
x[unchecked] = x[inside];
x[inside] = switchVar;
// switch y coordinates | unchecked <-> inside
switchVar = y[unchecked];
y[unchecked] = y[inside];
y[inside] = switchVar;
}
unchecked++;
}
current++;
}
return inside == S - 1;
}
Funny thing the second one is slower, I do not use data structures, the code is iterative and in-place but the heavy use of switch makes it slow as hell.
The problem spec changed and now I must do it with CGAL and Java, I'll read the whole "https://github.com/CGAL/cgal-swig-bindings" to learn how to use CGAL within Java.... but I'd like some help about this specific instance of CGAL code... Are there faster algorithms already implemented in CGAL?
Thank you for your times guys! Happy coding!

I believe that, without a method of spatial indexing, the best performance you are going to achieve in the worst-case-scenario (all connected) is going to be O(n*(n-1)/2).
If you can afford to build a spatial index (have enough memory to pay for the boost in speed), you may consider R-tree and variants - insertion is O(n) searching is O(log2(n)): this will get your "outlier detection by examining distances" approach for a cost of of O(n*log2(n)) in the worst-case-scenario.
A notable result

Algorithm to detect and combine overlapping / colliding circles

I'm trying to write a time efficient algorithm that can detect a group of overlapping circles and make a single circle in the "middle" of the group that will represent that group. The practical application of this is representing GPS locations over a map, put the conversion in to Cartesian co-ordinates is already handled so that's not relevant, the desired effect is that at different zoom levels clusters of close together points just appear as a single circle (that will have the number of points printed in the centre in the final version)
In this example the circles just have a radius of 15 so the distance calculation (Pythagoras) is not being square rooted and compared to 225 for the collision detection. I was trying anything to shave off time, but the problem is this really needs to happen very quickly becasue it's a user facing bit of code that needs to be snappy and good looking.
I've given this a go and I it works with small data sets pretty well. 2 big problems, it takes too long and it can run out of memory if all the points are on top of one another.
The route I've taken is to calculate distance between each point in a first pass, and then take the shortest distance first and start to combine from there, anything that's been combined becomes ineligible for combination on that pass, and the whole list is passed back around to the distance calculations again until nothing changes.
To be honest I think it needs a radical shift in approach and I think it's a little beyond me. I've re factored my code in to one class for ease of posting and generated random points to give an example.
package mergepoints;
import java.awt.Point;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
public class Merger {
public static void main(String[] args) {
Merger m = new Merger();
m.subProcess(m.createRandomList());
}
private List<Plottable> createRandomList() {
List<Plottable> points = new ArrayList<>();
for (int i = 0; i < 50000; i++) {
Plottable p = new Plottable();
p.location = new Point((int) Math.floor(Math.random() * 1000),
(int) Math.floor(Math.random() * 1000));
points.add(p);
}
return points;
}
private List<Plottable> subProcess(List<Plottable> visible) {
List<PlottableTuple> tuples = new ArrayList<PlottableTuple>();
// create a tuple to store distance and matching objects together,
for (Plottable p : visible) {
PlottableTuple tuple = new PlottableTuple();
tuple.a = p;
tuples.add(tuple);
}
// work out each Plottable relative distance from
// one another and order them by shortest first.
// We may need to do this multiple times for one set so going in own
// method.
// this is the bit that takes ages
setDistances(tuples);
// Sort so that smallest distances are at the top.
// parse the set and combine any pair less than the smallest distance in
// to a combined pin.
// any plottable thats been combine is no longer eligable for combining
// so ignore on this parse.
List<PlottableTuple> sorted = new ArrayList<>(tuples);
Collections.sort(sorted);
Set<Plottable> done = new HashSet<>();
Set<Plottable> mergedSet = new HashSet<>();
for (PlottableTuple pt : sorted) {
if (!done.contains(pt.a) && pt.distance <= 225) {
Plottable merged = combine(pt, done);
done.add(pt.a);
for (PlottableTuple tup : pt.others) {
done.add(tup.a);
}
mergedSet.add(merged);
}
}
// if we haven't processed anything we are done just return visible
// list.
if (done.size() == 0) {
return visible;
} else {
// change the list to represent the new combined plottables and
// repeat the process.
visible.removeAll(done);
visible.addAll(mergedSet);
return subProcess(visible);
}
}
private Plottable combine(PlottableTuple pt, Set<Plottable> done) {
List<Plottable> plottables = new ArrayList<>();
plottables.addAll(pt.a.containingPlottables);
for (PlottableTuple otherTuple : pt.others) {
if (!done.contains(otherTuple.a)) {
plottables.addAll(otherTuple.a.containingPlottables);
}
}
int x = 0;
int y = 0;
for (Plottable p : plottables) {
Point position = p.location;
x += position.x;
y += position.y;
}
x = x / plottables.size();
y = y / plottables.size();
Plottable merged = new Plottable();
merged.containingPlottables.addAll(plottables);
merged.location = new Point(x, y);
return merged;
}
private void setDistances(List<PlottableTuple> tuples) {
System.out.println("pins: " + tuples.size());
int loops = 0;
// Start from the first item and loop through, then repeat but starting
// with the next item.
for (int startIndex = 0; startIndex < tuples.size() - 1; startIndex++) {
// Get the data for the start Plottable
PlottableTuple startTuple = tuples.get(startIndex);
Point startLocation = startTuple.a.location;
for (int i = startIndex + 1; i < tuples.size(); i++) {
loops++;
PlottableTuple compareTuple = tuples.get(i);
double distance = distance(startLocation, compareTuple.a.location);
setDistance(startTuple, compareTuple, distance);
setDistance(compareTuple, startTuple, distance);
}
}
System.out.println("loops " + loops);
}
private void setDistance(PlottableTuple from, PlottableTuple to,
double distance) {
if (distance < from.distance || from.others == null) {
from.distance = distance;
from.others = new HashSet<>();
from.others.add(to);
} else if (distance == from.distance) {
from.others.add(to);
}
}
private double distance(Point a, Point b) {
if (a.equals(b)) {
return 0.0;
}
double result = (((double) a.x - (double) b.x) * ((double) a.x - (double) b.x))
+ (((double) a.y - (double) b.y) * ((double) a.y - (double) b.y));
return result;
}
class PlottableTuple implements Comparable<PlottableTuple> {
public Plottable a;
public Set<PlottableTuple> others;
public double distance;
#Override
public int compareTo(PlottableTuple other) {
return (new Double(distance)).compareTo(other.distance);
}
}
class Plottable {
public Point location;
private Set<Plottable> containingPlottables;
public Plottable(Set<Plottable> plots) {
this.containingPlottables = plots;
}
public Plottable() {
this.containingPlottables = new HashSet<>();
this.containingPlottables.add(this);
}
public Set<Plottable> getContainingPlottables() {
return containingPlottables;
}
}
}

Map all your circles on a 2D grid first. You then only need to compare the circles in a cell with the other circles in that cell and in it's 9 neighbors (you can reduce that to five by using a brick pattern instead of a regular grid).
If you only need to be really approximate, then you can just group all the circles that fall into a cell together. You will probably also want to merge cells that only have a small number of circles together with there neighbors, but this will be fast.

This problem is going to take a reasonable amount of computation no matter how you do it, the question then is: can you do all the computation up-front so that at run-time it's just doing a look-up? I would build a tree-like structure where each layer is all the points that need to be drawn for a given zoom level. It takes more computation up-front, but at run-time you are simply drawing a list of point, fast.
My idea is to decide what the resolution of each zoom level is (ie at zoom level 1 points closer than 15 get merged; at zoom level 2 points closer than 30 get merged), then go through your points making groups of points that are within the 15 of each other and pick a point to represent group that group at the higher zoom. Now you have a 2 layer tree. Then you pass over the second layer grouping all points that are within 30 of each other, and so on all the way up to your highest zoom level. Now save this tree structure to file, and at run-time you can very quickly change zoom levels by simply drawing all points at the appropriate tree level. If you need to add or remove points, that can be done dynamically by figuring out where to attach them to the tree.
There are two downsides to this method that come to mind: 1) it will take a long time to compute the tree, but you only have to do this once, and 2) you'll have to think really carefully about how you build the tree, based on how you want the groupings to be done at higher levels. For example, in the image below the top level may not be the right grouping that you want. Maybe instead building the tree based off the previous layer, you always want to go back to the original points. That said, some loss of precision always happens when you're trying to trade-off for faster run-time.
EDIT
So you have a problem which requires O(n^2) comparisons, you say it has to be done in real-time, can not be pre-computed, and has to be fast. Good luck with that.
Let's analyze the problem a bit; if you do no pre-computation then in order to decide which points can be merged you have to compare every pair of points, that's O(n^2) comparisons. I suggested building a tree before-hand, O(n^2 log n) once, but then runtime is just a lookup, O(1). You could also do something in between where you do some work before and some at run-time, but that's how these problems always go, you have to do a certain amount of computation, you can play games by doing some of it earlier, but at the end of the day you still have to do the computation.
For example, if you're willing to do some pre-computation, you could try keeping two copies of the list of points, one sorted by x-value and one sorted by y-value, then instead of comparing every pair of points, you can do 4 binary searches to find all the points within, say, a 30 unit box of the current point. More complicated so would be slower for a small number of points (say <100), but would reduce the overall complexity to O(n log n), making it faster for large amounts of data.
EDIT 2
If you're worried about multiple points at the same location, then why don't you do a first pass removing the redundant points, then you'll have a smaller "search list"
list searchList = new list()
for pt1 in points :
boolean clean = true
for pt2 in searchList :
if distance(pt1, pt2) < epsilon :
clean = false
break
if clean :
searchList.add(pt1)
// Now you have a smaller list to act on with only 1 point per cluster
// ... I guess this is actually the same as my first suggestion if you make one of these search lists per zoom level. huh.
EDIT 3: Graph Traversal
A totally new approach would be to build a graph out of the points and do some sort of longest-edge-first graph traversal on them. So pick a point, draw it, and traverse its longest edge, draw that point, etc. Repeat this until you come to a point which doesn't have any untraversed edges longer than your zoom resolution. The number of edges per point gives you an easy way to tradeoff speed for correctness. If the number of edges per point was small and constant, say 4, then with a bit of cleverness you could build the graph in O(n) time and also traverse it to draw points in O(n) time. Fast enough to do it on the fly with no pre-computation.

Just a wild guess and something that occurred to me while reading responses from others.
Do a multi-step comparison. Assume your combining distance at the current zoom level is 20 meters. First, subtract (X1 - X2). If This is bigger than 20 meters then you are done, the points are too far. Next, subtract (Y1 - Y2) and do the same thing to reject combining the points.
You could stop here and be happy if you are good with using only horizontal/vertical distances as your metric for combining. Much less math (no squaring or square roots). Pythagoras wouldn't be happy but your users might.
If you really insist on exact answers, do the two subtraction/comparison steps above. If the points are within horizontal and vertical limits, THEN you do the full Pythagoras check with square roots.
Assuming all your points are not highly clustered very close to the combining limit, this should save some CPU cycles.
This is still approximately an O(n^2) technique, but the math should be simpler. If you have the memory, you could store distances between each set of points and then you never have to compute it again. This could take up more memory than you have and also grows at a rate of approximately O(n^2), so be careful.
Also, you could make a linked list or sorted array of all your points, sorted in order of increasing X or increasing Y. (I don't think you need both, just one). Then walk through the list in sorted order. For each point, check the neighbors out until (X1 - X2) is bigger than your combining distance. and then stop. You don't have to compare each set of points for O(N^2), you only have to compare neighbors that are close in one dimension to quickly prune your large list to a small one. As you move through the list, you only have to compare points that have a bigger X than your current candidate, because you already compared and combined with all previous X values. This gets you closer to the O(n) complexity you want. Of course, you would need to check the Y dimension and fully qualify the points to be combined before you actually do it. Don't just use the X distance to make your combining decision.

Light/Ray casting in java?

I've been trying to make a dynamic light system in java, without using libraries. For some reason, though, it seems I can't get light to run efficiently. It flickers and lags a ton. I'm doing this with no previous understanding of lighting engines in games, so I'm open to suggestions. Here is my current update method:
public void updateLight( ArrayList<Block> blocks )
{
//reset light
light.reset();
//add the x and y of this light
light.addPoint( x, y );
//precision for loops
int ires = 1;
int jres = 2;
for( int i = 0; i < width; i += ires )
{
//get radians of current angle
float rdir = (float)Math.toRadians( dir + i - width/2 );
//set up pixel vars
int px, py;
for( int j = 0; j < length; j += jres )
{
//get position of pixel
px = (int)ZZmath.getVectorX( x, rdir, j );
py = (int)ZZmath.getVectorY( y, rdir, j );
//if point gets found
boolean foundpoint = false;
for( int n = 0; n < blocks.size(); n ++ )
{
//check if block is solid
//also check that collision is possible really quickly for efficiency
if( blocks.get( n ).solid )
{
//get info on block
int bx = blocks.get( n ).x;
int by = blocks.get( n ).y;
//quick trim
if( Math.abs( bx - px ) <= 32 && Math.abs( by - py ) <= 32 )
{
int bw = blocks.get( n ).w;
int bh = blocks.get( n ).h;
if( ZZmath.pointInBounds( px, py, bx, by, bw, bh ) )
{
//add point to polygon
light.addPoint( px, py );
//found point
foundpoint = true;
}
}
}
}
//if a point is found, break
if( foundpoint )
{
break;
}
//if at end of loop, add point
//loose definition of "end" to prevent flickers
if( j >= length - jres*2 )
{
light.addPoint( px, py );
}
}
}
}
This modifies a polygon that displays for light. I'll change that later. Any idea of ways I can make this run better? Also, no, no libraries. I don't have anything against them, just don't want to use one now.

You implementation doesn't appear to use much of the stuff I see here:
http://www.cs.utah.edu/~shirley/books/fcg2/rt.pdf
I'd recommend digesting this completely. If your objective is to understand ray tracing deeply, that's how it should be done.

Maybe your objective was to learn by writing your own raytracer. In my experience I would end up rewriting this code several times and still not get it completely right. It's good to get your hands dirty but it's not necessarily the most effective way to go about things.
Overall it looks like you need to study (object oriented) programming concepts, and take a data structures and algorithms course.
The biggest thing is readability. Document your code, for your future self if no one else. This means Clear comments before and during updateLight(). The existing comments are alright (though they paraphrase the code more than justify it), but "really quickly for efficiency" is a lie.
For a small issue of readability that could be a tiny drag on performance, make a local variable for blocks.get(n). Name it something short but descriptive, save typing and only make one method call to retrieve it.
"if at end of loop": I have no idea which loop you mean, and the for loops have definite ends. A comment }//end for or }//end for width is often helpful.
Checking if the block is solid is unnecessary! Just store your blocks in two lists, and only go through the solid blocks. Even if you have some desire to have flickering blocks, one remove and add is cheaper than O(width*length*numbernotsolid) extra work.
There are many ways you could structure how the blocks are stored to facilitate quick testing. You only want or need to test blocks whose coordinates are near to a particular light. The basic strategy is divide the space into a grid, and sort the blocks based on what section of the grid they fall into. Then when you have light in a particular section of the grid, you know you only need to test blocks in that section (and possibly a neighboring section - there are details depending on their width and the light's).
I have no idea whether that is along the lines of the right approach or not. I don't know much about raytracing, although it is or used to be rather slow. It looks like you have a decent naive implementation. There might be a slightly different naive approach that is faster and some more difficult (to code to completion) algorithms that are moderately yet more fast.
Also, I see no need to do this breadth first. Why not solve for one line (you call them pixels?) at a time. Count the number of times this code calls Math.toRadians. It looks like it's just an extraneous line because you could work along the same angle until ready for the next.

Steps to perform document clustering using k-means algorithm in java

I need steps to perform document clustering using k-means algorithm in java.
It will be very useful for me to provide the steps easily.
Thanks in advance.

You need to count the words in each document and make a feature generally called bag of words. Before that you need to remove stop words(very common but not giving much information like the, a etc). You can generally take top n common words from your document. Count the frequency of these words and store them in n dimensional vector.
For distance measure you can use cosine vector.
Here is a simple algorithm for 2 mean for 1 dimensional data points. you can extend it to k mean and n dimensional data point easily. Let me know if you want n dim implementation.
double[] x = {1,2,2.5,3,3.5,4,4.5,5,7,8,8.5,9,9.5,10};
double[] center = new int[2];
double[] precenter = new int[2];
ArrayList[] cluster = new ArrayList[2];
//generate 2 random number from 0 to x.length without replacement
int rand = new int[2];
Random rand = new Random();
rand[0] = rand.nextInt(x.length + 1);
rand[1] = rand.nextInt(x.length + 1);
while(rand[0] == rand[1] ){
rand[1] = rand.nextInt(x.length + 1);
}
center[0] = x[rand[0]];
center[1] = x[rand[1]];
//there is a better way to generate k random number (w/o replacement) just search.
do{
cluster[0].clear();
cluster[1].clear();
for(int i = 0; i < x.length; ++i){
if(abs(x[i]-center1[0]) <= abs(x[i]-center1[1])){
cluster[0].add(x[i]);
}
else{
cluster[0].add(x[i]);
}
precenter[0] = center[0];
precenter[1] = center[1];
center[0] = mean(cluster[0]);
center[1] = mean(cluster[1]);
}
} while(precenter[0] != center[0] && precenter[1] != center[1]);
double mean(ArrayList list){
double mean = 0;
double sum = 0;
for(int index=0;index
}
The cluster[0] and cluster [1] contain points in the clusters and center[0], center[1] are the 2 means.
you need to do some debugging because I have written the code in R and just converted it into java for you :)

Does this help you? Also the wiki article has some links to implementations in other languages ready to be ported to java.
Steps of the algorithm:
Define the number of clusters you want to have
Distribute the points radomly in your problem space.
Link every observation to the nearest point.
calculate the center of mass for each cluster and place the point into the middle.
Link the points again to the centerpoints and repeat until the points dont move any more.

What do you want to cluster the documents based on? If it's by similarity you'll need to do some natural language processing first, and then you'll need a metric (some kind of assignment algorithm) to place the documents into clusters (crp works and is relatively straight forward).
The hardest part will be the NLP (language processing) if you're not clustering them based on something like "length". I can provide more info on all of these, but I won't dive down the rabbit hole if you don't need it.