We Keep Coding

How to calculate a cubic function using the Vietea's formula in Java?

I have to write a program in which I write a,b c,d (coefficients of equation 3 degree) and as a result I should get X1, X2, X3 (solutions of equation). I have to use Viete's formulas and BigDecimal for this, because my lecturer requires it from me.
I came to the conclusion that I have to solve the following system of equations:
x1+x2+x3=-b/a
x1*x2+x1*x3+x2*x3=c/a
x1*x2*x3=-d/a
I have no idea how I can do it in Java.
I tried to use the JAMA package, but I don't think I can use it to solve such a system of equations.
How can I do that?

If you want to find the roots of a cubic polynomial in Java you can do it easily using Newton-Raphson's method.
The algorithm -
1. Input: initial x, func(x), derivFunc(x)
Output: Root of Func()
2. Compute values of func(x) and derivFunc(x) for given initial x
3. Compute h: h = func(x) / derivFunc(x)
4. While h is greater than allowed error ε
- h = func(x) / derivFunc(x)
- x = x – h
Here is a demonstration for solving the cubic equation x^3-x^2+2
class XYZ {
static final double EPSILON = 0.001;
// An example function whose solution
// is determined using Bisection Method.
// The function is x^3 - x^2 + 2
static double func(double x)
{
return x * x * x - x * x + 2;
}
// Derivative of the above function
// which is 3*x^x - 2*x
static double derivFunc(double x)
{
return 3 * x * x - 2 * x;
}
// Function to find the root
static void newtonRaphson(double x)
{
double h = func(x) / derivFunc(x);
while (Math.abs(h) >= EPSILON)
{
h = func(x) / derivFunc(x);
// x(i+1) = x(i) - f(x) / f'(x)
x = x - h;
}
System.out.print("The value of the"
+ " root is : "
+ Math.round(x * 100.0) / 100.0);
}
// Driver code
public static void main (String[] args)
{
// Initial values assumed
double x0 = -20;
newtonRaphson(x0);
}
}
Output - The value of root is : -1.00
To do it your way you have to solve a system of non-linear equations which is harder but can be done using the Newton Raphson's Multivariate method. You might want to look it up. Also note that this is an approximate method and guesses the roots after you put an initial 'guess' of your own (in this case its -20)

The Newton (Raphson, Kantorovich) method for the Viete equations gives you the (Weierstrass-)Durand-Kerner method of simultaneous root approximation. However, in the completed method you will no longer see the Viete identities, they kind of cancel out. You will need complex numbers over the demanded real numbers data type.
If you go with the simple Newton method like in the other answer, then after computing the one real root you can split off the linear factor belonging to it via the Horner-Ruffini scheme and then solve the remaining quadratic equation directly. Then you only need to consider the possible complex nature of the roots in constructing the output strings, as the real and imaginary parts have easy direct formulas.

Calculating speed from set of longitude and latitudes values obtained in one minute?

I need to calculate speed after each 10 seconds or less (currently i am using fused location api to get the location after each 10 seconds). The problem is that the equipment is too slow and sometimes it gives the distance covers equal to zero.
I have tried using Location.distanceBetween() but it also produces zeros even when the equipment is moving. I have tried to calculate distance by a formula but sometimes distance is too small that it gives zero.
Now i want to calculate average speed. I want to save the points obtained in 1 minute (6 lat long values). And then after each 10 seconds, i want to calculate average speed between them. Thus after each 10 seconds I will add one points at the end and remove one point from the start. That will remove the possibility of zero.
Now is there any formula that can calculate speed or distance from set of lat long values or any better approach will be highly appreciated.

You can calculate distance between two point, that are close enough, using simple geometry
deltaLngMeters = R * cos(latitude) * deltaLongitudeRadians;
deltaLatMeters = R * deltaLatitudeRadians;
whereas deltas are in radians, deltaLatitudeRadians = deltaLatitudeDegrees * pi / 180
Hence distance = sqrt(deltaLngMeters ^2 + deltaLatMeters ^ 2).
To sum up
function distance(point1, point2) {
var degToRad = Math.PI / 180;
return R * degToRad * Math.sqrt(Math.pow(Math.cos(point1.lat * degToRad ) * (point1.lng - point2.lng) , 2) + Math.pow(point1.lat - point2.lat, 2));
}
If you have array of six points, you can calculate average speed.
points = [{lat: .., lng: ..}, ... ]; // 6 points
distancesSum = 0;
for(i = 0; i < distances.length - 1; i++) {
distancesSum += distance(points[i], points[i + 1]);
}
return (distancesSum / (points.length - 1));
Yes, R is for the Earth radius, R = 6371000;// meters

You can use multi threading(Thread.sleep()) to calculate a formula repeatedly for every 10 seconds. You can verify it here https://beginnersbook.com/2013/03/multithreading-in-java/.

For small distances(hope the device won't move at speeds above 1 km/s), earth's surface can be treated as a plane. Then the latitude and longitude will be the coordinates of the device on the Cartesian plane attached to earth. Hence you can calculate the distance by this formula:
√(delta(longitude)^2 + delta(latitude)^2)
delta: difference

dot product of two quaternion rotations

I understand that the dot (or inner) product of two quaternions is the angle between the rotations (including the axis-rotation). This makes the dot product equal to the angle between two points on the quaternion hypersphere.
I can not, however, find how to actually compute the dot product.
Any help would be appreciated!
current code:
public static float dot(Quaternion left, Quaternion right){
float angle;
//compute
return angle;
}
Defined are Quaternion.w, Quaternion.x, Quaternion.y, and Quaternion.z.
Note: It can be assumed that the quaternions are normalised.

The dot product for quaternions is simply the standard Euclidean dot product in 4D:
dot = left.x * right.x + left.y * right.y + left.z * right.z + left.w * right.w
Then the angle your are looking for is the arccos of the dot product (note that the dot product is not the angle): acos(dot).
However, if you are looking for the relative rotation between two quaternions, say from q1 to q2, you should compute the relative quaternion q = q1^-1 * q2 and then find the rotation associated withq.

Just NOTE: acos(dot) is very not stable from numerical point of view.
as was said previos, q = q1^-1 * q2 and than angle = 2*atan2(q.vec.length(), q.w)

Should it be 2 x acos(dot) to get the angle between quaternions.

The "right way" to compute the angle between two quaternions
There is really no such thing as the angle between two quaternions, there is only the quaternion that takes one quaternion to another via multiplication. However, you can measure the total angle of rotation of that mapping transformation, by computing the difference between the two quaternions (e.g. qDiff = q1.mul(q2.inverse()), or your library might be able to compute this directly using a call like qDiff = q1.difference(q2)), and then measuring the angle about the axis of the quaternion (your quaternion library probably has a routine for this, e.g. ang = qDiff.angle()).
Note that you will probably need to fix the value, since measuring the angle about an axis doesn't necessarily give the rotation "the short way around", e.g.:
if (ang > Math.PI) {
ang -= 2.0 * Math.PI;
} else if (ang < -Math.PI) {
ang += 2.0 * Math.PI;
}
Measuring the similarity of two quaternions using the dot product
Update: See this answer instead.
I assume that in the original question, the intent of treating the quaternions as 4d vectors is to enable a simple method for measuring the similarity of two quaternions, while still keeping in mind that the quaternions represent rotations. (The actual rotation mapping from one quaternion to another is itself a quaternion, not a scalar.)
Several answers suggest using the acos of the dot product. (First thing to note: the quaternions must be unit quaternions for this to work.) However, the other answers don't take into account the "double cover issue": both q and -q represent the exact same rotation.
Both acos(q1 . q2) and acos(q1 . (-q2)) should return the same value, since q2 and -q2 represent the same rotation. However (with the exception of x == 0), acos(x) and acos(-x) do not return the same value. Therefore, on average (given random quaternions), acos(q1 . q2) will not give you what you expect half of the time, meaning that it will not give you a measure of the angle between q1 and q2, assuming that you care at all that q1 and q2 represent rotations. So even if you only plan to use the dot product or acos of the dot product as a similarity metric, to test how similar q1 and q2 are in terms of the effect they have as a rotation, the answer you get will be wrong half the time.
More specifically, if you are trying to simply treat quaternions as 4d vectors, and you compute ang = acos(q1 . q2), you will sometimes get the value of ang that you expect, and the rest of the time the value you actually wanted (taking into account the double cover issue) will be PI - acos(-q1 . q2). Which of these two values you get will randomly fluctuate between these values depending on exactly how q1 and q2 were computed!.
To solve this problem, you have to normalize the quaternions so that they are in the same "hemisphere" of the double cover space. There are several ways to do this, and to be honest I'm not even sure which of these is the "right" or optimal way. They do all produce different results from other methods in some cases. Any feedback on which of the three normalization forms above is the correct or optimal one would be greatly appreciated.
import java.util.Random;
import org.joml.Quaterniond;
import org.joml.Vector3d;
public class TestQuatNorm {
private static Random random = new Random(1);
private static Quaterniond randomQuaternion() {
return new Quaterniond(
random.nextDouble() * 2 - 1, random.nextDouble() * 2 - 1,
random.nextDouble() * 2 - 1, random.nextDouble() * 2 - 1)
.normalize();
}
public static double normalizedDot0(Quaterniond q1, Quaterniond q2) {
return Math.abs(q1.dot(q2));
}
public static double normalizedDot1(Quaterniond q1, Quaterniond q2) {
return
(q1.w >= 0.0 ? q1 : new Quaterniond(-q1.x, -q1.y, -q1.z, -q1.w))
.dot(
q2.w >= 0.0 ? q2 : new Quaterniond(-q2.x, -q2.y, -q2.z, -q2.w));
}
public static double normalizedDot2(Quaterniond q1, Quaterniond q2) {
Vector3d v1 = new Vector3d(q1.x, q1.y, q1.z);
Vector3d v2 = new Vector3d(q2.x, q2.y, q2.z);
double dot = v1.dot(v2);
Quaterniond q2n = dot >= 0.0 ? q2
: new Quaterniond(-q2.x, -q2.y, -q2.z, -q2.w);
return q1.dot(q2n);
}
public static double acos(double val) {
return Math.toDegrees(Math.acos(Math.max(-1.0, Math.min(1.0, val))));
}
public static void main(String[] args) {
for (int i = 0; i < 1000; i++) {
var q1 = randomQuaternion();
var q2 = randomQuaternion();
double dot = q1.dot(q2);
double dot0 = normalizedDot0(q1, q2);
double dot1 = normalizedDot1(q1, q2);
double dot2 = normalizedDot2(q1, q2);
System.out.println(acos(dot) + "\t" + acos(dot0) + "\t" + acos(dot1)
+ "\t" + acos(dot2));
}
}
}
Also note that:
acos is known to not be very numerically accurate (given some worst-case inputs, up to half of the least significant digits can be wrong);
the implementation of acos is exceptionally slow in the JDK standard libraries;
acos returns NaN if its parameter is even slightly outside [-1,1], which is a common occurrence for dot products of even unit quaternions -- so you need to bound the value of the dot product to that range before calling acos. See this line in the code above:
return Math.toDegrees(Math.acos(Math.max(-1.0, Math.min(1.0, val))));
According to this cheatsheet Eq. (42), there is a more robust and accurate way of computing the angle between two vectors that replaces acos with atan2 (although note that this does not solve the double cover problem either, so you will need to use one of the above normalization forms before applying the following):
ang(q1, q2) = 2 * atan2(|q1 - q2|, |q1 + q2|)
I admit though that I don't understand this formulation, since quaternion subtraction and addition has no geometrical meaning.

Logical error in Java programming

case R.id.bTanx:
temp=(float) (number/0.0174532925);
num=Math.tan(temp);
display.setText("Your Result is " + num);
Guys I'm not able to get "Your Result is 1" when number = 45 ,by this code.Please help.
As tan(45)=1 in degrees.i have converted it.but no desired result.

To convert degrees to radian you first need to convert the degrees to a factor (of the circles circumference) by dividing by 360 degrees. Next you multiply by 2PI rad (which is the circumference of a 'unit circle').
When looking at the units you do this: degrees / degrees * radians = radians
So where you divide by 0.017 (2*PI / 360), you need to multiply instead:
temp = (float) (number * 0.0174532925);
Furthermore it is nicer (more clear) if you do not use 'magic numbers' and add comments (so people know what you are doing):
// Convert to rad
temp = (float) (number * 2 * Math.PI / 360);
And/or even use the available Java functionality:
// Convert to rad
temp = Math.toRadians(number);

SQlite Getting nearest locations (with latitude and longitude)

I have data with latitude and longitude stored in my SQLite database, and I want to get the nearest locations to the parameters I put in (ex. My current location - lat/lng, etc.).
I know that this is possible in MySQL, and I've done quite some research that SQLite needs a custom external function for the Haversine formula (calculating distance on a sphere), but I haven't found anything that is written in Java and works.
Also, if I want to add custom functions, I need the org.sqlite .jar (for org.sqlite.Function), and that adds unnecessary size to the app.
The other side of this is, I need the Order by function from SQL, because displaying the distance alone isn't that much of a problem - I already did it in my custom SimpleCursorAdapter, but I can't sort the data, because I don't have the distance column in my database. That would mean updating the database every time the location changes and that's a waste of battery and performance. So if someone has any idea on sorting the cursor with a column that's not in the database, I'd be grateful too!
I know there are tons of Android apps out there that use this function, but can someone please explain the magic.
By the way, I found this alternative: Query to get records based on Radius in SQLite?
It's suggesting to make 4 new columns for cos and sin values of lat and lng, but is there any other, not so redundant way?

1) At first filter your SQLite data with a good approximation and decrease amount of data that you need to evaluate in your java code. Use the following procedure for this purpose:
To have a deterministic threshold and more accurate filter on data, It is better to calculate 4 locations that are in radius meter of the north, west, east and south of your central point in your java code and then check easily by less than and more than SQL operators (>, <) to determine if your points in database are in that rectangle or not.
The method calculateDerivedPosition(...) calculates those points for you (p1, p2, p3, p4 in picture).
/**
* Calculates the end-point from a given source at a given range (meters)
* and bearing (degrees). This methods uses simple geometry equations to
* calculate the end-point.
*
* #param point
* Point of origin
* #param range
* Range in meters
* #param bearing
* Bearing in degrees
* #return End-point from the source given the desired range and bearing.
*/
public static PointF calculateDerivedPosition(PointF point,
double range, double bearing)
{
double EarthRadius = 6371000; // m
double latA = Math.toRadians(point.x);
double lonA = Math.toRadians(point.y);
double angularDistance = range / EarthRadius;
double trueCourse = Math.toRadians(bearing);
double lat = Math.asin(
Math.sin(latA) * Math.cos(angularDistance) +
Math.cos(latA) * Math.sin(angularDistance)
* Math.cos(trueCourse));
double dlon = Math.atan2(
Math.sin(trueCourse) * Math.sin(angularDistance)
* Math.cos(latA),
Math.cos(angularDistance) - Math.sin(latA) * Math.sin(lat));
double lon = ((lonA + dlon + Math.PI) % (Math.PI * 2)) - Math.PI;
lat = Math.toDegrees(lat);
lon = Math.toDegrees(lon);
PointF newPoint = new PointF((float) lat, (float) lon);
return newPoint;
}
And now create your query:
PointF center = new PointF(x, y);
final double mult = 1; // mult = 1.1; is more reliable
PointF p1 = calculateDerivedPosition(center, mult * radius, 0);
PointF p2 = calculateDerivedPosition(center, mult * radius, 90);
PointF p3 = calculateDerivedPosition(center, mult * radius, 180);
PointF p4 = calculateDerivedPosition(center, mult * radius, 270);
strWhere = " WHERE "
+ COL_X + " > " + String.valueOf(p3.x) + " AND "
+ COL_X + " < " + String.valueOf(p1.x) + " AND "
+ COL_Y + " < " + String.valueOf(p2.y) + " AND "
+ COL_Y + " > " + String.valueOf(p4.y);
COL_X is the name of the column in the database that stores latitude values and COL_Y is for longitude.
So you have some data that are near your central point with a good approximation.
2) Now you can loop on these filtered data and determine if they are really near your point (in the circle) or not using the following methods:
public static boolean pointIsInCircle(PointF pointForCheck, PointF center,
double radius) {
if (getDistanceBetweenTwoPoints(pointForCheck, center) <= radius)
return true;
else
return false;
}
public static double getDistanceBetweenTwoPoints(PointF p1, PointF p2) {
double R = 6371000; // m
double dLat = Math.toRadians(p2.x - p1.x);
double dLon = Math.toRadians(p2.y - p1.y);
double lat1 = Math.toRadians(p1.x);
double lat2 = Math.toRadians(p2.x);
double a = Math.sin(dLat / 2) * Math.sin(dLat / 2) + Math.sin(dLon / 2)
* Math.sin(dLon / 2) * Math.cos(lat1) * Math.cos(lat2);
double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
double d = R * c;
return d;
}
Enjoy!
I used and customized this reference and completed it.

Chris's answer is really useful (thanks!), but will only work if you are using rectilinear coordinates (eg UTM or OS grid references). If using degrees for lat/lng (eg WGS84) then the above only works at the equator. At other latitudes, you need to decrease the impact of longitude on the sort order. (Imagine you're close to the north pole... a degree of latitude is still the same as it is anywhere, but a degree of longitude may only be a few feet. This will mean that the sort order is incorrect).
If you are not at the equator, pre-calculate the fudge-factor, based on your current latitude:
<fudge> = Math.pow(Math.cos(Math.toRadians(<lat>)),2);
Then order by:
((<lat> - LAT_COLUMN) * (<lat> - LAT_COLUMN) +
(<lng> - LNG_COLUMN) * (<lng> - LNG_COLUMN) * <fudge>)
It's still only an approximation, but much better than the first one, so sort order inaccuracies will be much rarer.

I know this has been answered and accepted but thought I'd add my experiences and solution.
Whilst I was happy to do a haversine function on the device to calculate the accurate distance between the user's current position and any particular target location there was a need to sort and limit the query results in order of distance.
The less than satisfactory solution is to return the lot and sort and filter after the fact but this would result in a second cursor and many unnecessary results being returned and discarded.
My preferred solution was to pass in a sort order of the squared delta values of the long and lats:
((<lat> - LAT_COLUMN) * (<lat> - LAT_COLUMN) +
(<lng> - LNG_COLUMN) * (<lng> - LNG_COLUMN))
There's no need to do the full haversine just for a sort order and there's no need to square root the results therefore SQLite can handle the calculation.
EDIT:
This answer is still receiving love. It works fine in most cases but if you need a little more accuracy, please check out the answer by #Teasel below which adds a "fudge" factor that fixes inaccuracies that increase as the latitude approaches 90.

In order to increase performance as much as possible I suggest improve #Chris Simpson's idea with the following ORDER BY clause:
ORDER BY (<L> - <A> * LAT_COL - <B> * LON_COL + LAT_LON_SQ_SUM)
In this case you should pass the following values from code:
<L> = center_lat^2 + center_lon^2
<A> = 2 * center_lat
<B> = 2 * center_lon
And you should also store LAT_LON_SQ_SUM = LAT_COL^2 + LON_COL^2 as additional column in database. Populate it inserting your entities into database. This slightly improves performance while extracting large amount of data.

Try something like this:
//locations to calculate difference with
Location me = new Location("");
Location dest = new Location("");
//set lat and long of comparison obj
me.setLatitude(_mLat);
me.setLongitude(_mLong);
//init to circumference of the Earth
float smallest = 40008000.0f; //m
//var to hold id of db element we want
Integer id = 0;
//step through results
while(_myCursor.moveToNext()){
//set lat and long of destination obj
dest.setLatitude(_myCursor.getFloat(_myCursor.getColumnIndexOrThrow(DataBaseHelper._FIELD_LATITUDE)));
dest.setLongitude(_myCursor.getFloat(_myCursor.getColumnIndexOrThrow(DataBaseHelper._FIELD_LONGITUDE)));
//grab distance between me and the destination
float dist = me.distanceTo(dest);
//if this is the smallest dist so far
if(dist < smallest){
//store it
smallest = dist;
//grab it's id
id = _myCursor.getInt(_myCursor.getColumnIndexOrThrow(DataBaseHelper._FIELD_ID));
}
}
After this, id contains the item you want from the database so you can fetch it:
//now we have traversed all the data, fetch the id of the closest event to us
_myCursor = _myDBHelper.fetchID(id);
_myCursor.moveToFirst();
//get lat and long of nearest location to user, used to push out to map view
_mLatNearest = _myCursor.getFloat(_myCursor.getColumnIndexOrThrow(DataBaseHelper._FIELD_LATITUDE));
_mLongNearest = _myCursor.getFloat(_myCursor.getColumnIndexOrThrow(DataBaseHelper._FIELD_LONGITUDE));
Hope that helps!