this question is in theoretical math perspective.
In the space S = [-1, 1]^d, given a volume V, can I always define object in S with exactly this volume? also - can I surround any x in S with an object of volume V?
I would answer yes on these 2 questions because I only need to find multiplication of d positive real numbers that would be lengths of lines, and I assume I can construct it around any x in S, but I just want to be sure, and get some nice explanation.
thanks
Basically yes, let's say n is ceil(log2(V)), so V is c 2^n with 0.5 < c <= 1,
then one possible solution is the c th part of an n dimensional cube,
so for 7 you take 7/8ths of a cube, for 9 you have 9/16ths of a hypercube, etc...
The only assumption here is that "Volume" should be interpreted as the nth-dimensional measure in n-dimensional space.
If I get it right your space S has coordinates in range <-1,+1> which limits its volumes to:
V = 2^3
HV = 2^d
where V is standard 3D volume and HV is d-dimensional hyper-volume that can fit into your space S. So you can construct objects with volumes and hypervolumes up to this limit.
So if you want to construct object with volumes V or HV then you can create axis aligned cube of size a:
a*a*a = V
a = V^(1/3)
a^d = HV
a = HV^(1/d)
if a<=2 otherwise your S is too small ...
Related
I want to find a line having a number of points which are around that line. Line is in 2D space and is defined by two points, or by one point and an angle. What would be the algorithm for it?
There's a lot about this on SO and in internet and also in Numerical Receipes, but all examples seem to focus on function form of the line (y=ax+b) which is not going to work well for (almost) vertical lines.
I could possibly detect if the line is more horizontal or more vertical, and swap coordinates in the other case, but maybe there exists some more elegant solution?
I'm using C# ATM but can translate from any code probably.
I'm sorry I can't provide a reference, but here's how:
Suppose your N (2d) data points are p[] and you want to find a vector a and a scalar d to minimise
E = Sum{ i | sqr( a'*p[i] - d) }/N
(The line is { q | a'*q = d} E is the sum of the squares of the distances of the data points from the line).
Some tedious algebra shows that
E = a'*C*a + sqr(d - a'*M)
where M is the mean and C the covariance of the data, ie
M = Sum{ i | p[i] } / N
C = Sum{ i | (p[i]-M)*(p[i]-M)' } / N
E will be minimised by choosing d = a'*M, and a to be an eigenvector of C corresponding to the smaller eigenvalue.
So the algorithm is:
Compute M and C
Find the smaller eigenvalue of C and the corresponding eigenvector a
Compute d = a'*M
(Note that the same thing works in higher dimensions too. For example in 3d we would find the 'best' plane).
How can i sort vectors by distance from point?
For example i have three vectors: A, B, C and the point
Example image with point and vectors
And the sorted result must be something like this: (A, C, B)
Okay, this is more of a math question, but let me explain it here anyways. Take a look at this picture:
Let's define a line segment by vector A for the start point and a for the vector running through that line segment which end at the arrows end. Same is valid for the other segments B and C respectively. The point P as coordinates as also a vector.
Now let's make linear algebra our friend, yet be programatically efficient.
:-)
At the example of segment a you can do this and with the other respectively:
With the dot product of a and AP (vector from A to P) you get the projection projA on a where the where P is closest.
If you set A+ (projA)*na (na is the the normalized a vector) you get the closest Point in the vector a of P.
Let's set dA = A+projA*na - P and with its length you get the closest distance to compare.
Instead of saving the distances, try to store and compare squared distance of dA, dB and dC and compare those instead. It will save you to compute the square root which might become very expensive.
Here is some pseudocode:
vector3 AP = P-A;
vector3 projA = a.dot(AP);
vector3 nA = a.normalized();
dA = A + projA*na - P;
dA2 = dA.x*dA.x + dA.y*dA.y + dA.z*dA.z;
-> Compare and sort them by that value
Hope it helps a bit...
I have 4 points in space A(x,y,z), B(x,y,z), C(x,y,z) and D(x,y,z). How can I check if these points are the corner points of a rectangle?
You must first determine whether or not the points are all coplanar, since a rectangle is a 2D geometric object, but your points are in 3-space. You can determine they are coplanar by comparing cross products as in:
V1 = (B-A)×(B-C)
V2 = (C-A)×(C-D)
This will give you two vectors which, if A, B, C, and D are coplanar are linearly dependent. By considering what Wolfram has to say on vector dependence, we can test the vectors for linear dependence by using
C = (V1∙V1)(V2∙V2) - (V1∙V2)(V2∙V1)
If C is 0 then the vectors V1 and V2 are linearly dependent and all the points are coplanar.
Next compute the distances between each pair of points. There should be a total of 6 such distances.
D1 = |A-B|
D2 = |A-C|
D3 = |A-D|
D4 = |B-C|
D5 = |B-D|
D6 = |C-D|
Assuming none of these distances are 0, these points form a rectangle if and only if the vertices are coplanar (already verified) and these lengths can be grouped into three pairs where elements of each pair have the same length. If the figure is a square, two sets of the pairs will have be the same length and will be shorter than the remaining pair.
Update: Reading this again, I realize the the above could define a parallelogram, so an additional check is required to check that the square of the longest distance is equal to the sum of the squares of the two shorter distances. Only then will the parallelogram also be a rectangle.
Keep in mind all of this is assuming infinite precision and within a strictly mathematical construct. If you're planning to code this up, you will need to account for rounding and accept a degree of imprecision that's not really a player when speaking in purely mathematical terms.
Check if V1=B-A and V2=D-A are orthogonal using the dot product. Then check if
C-A == V1+V2
within numerical tolerances. If both are true, the points are coplanar and form a rectangle.
Here a function is defined to check whether the 4 points represents the rectangle or not .
from math import sqrt
def Verify(A, B, C, D, epsilon=0.0001):
# Verify A-B = D-C
zero = sqrt( (A[0]-B[0]+C[0]-D[0])**2 + (A[1]-B[1]+C[1]-D[1])**2 + (A[2]-B[2]+C[2]-D[2])**2 )
if zero > epsilon:
raise ValueError("Points do not form a parallelogram; C is at %g distance from where it should be" % zero)
# Verify (D-A).(B-A) = 0
zero = (D[0]-A[0])*(B[0]-A[0]) + (D[1]-A[1])*(B[1]-A[1]) + (D[2]-A[2])*(B[2]-A[2])
if abs(zero) > epsilon:
raise ValueError("Corner A is not a right angle; edge vector dot product is %g" % zero)
else:
print('rectangle')
A = [x1,y1,z1]
print(A)
B = [x2,y2,z2]
C = [x3,y3,z3]
D = [x4,y4,z4]
Verify(A, B, C, D, epsilon=0.0001)
Ok, I know this sounds really daft to be asking here, but it is programming related.
I'm working on a game, and I'm thinking of implementing a system that allows users to triangulate their 3D coordinates to locate something (eg for a task).
I also want to be able to let the user make the coordinates of the points they are using for triangulation have user-determined coordinates (so the location's coordinate is relative, probably by setting up a beacon or something).
I have a method in place for calculating the distance between the points, so essentially I can calculate the lengths of the sides of the triangle/pyramid as well as all but the coordinate I am after.
It has been a long time since I have done any trigonometry and I am rusty with the sin, cos and tan functions, I have a feeling they are required but have no clue how to implement them.
Can anyone give me a demonstration as to how I would go about doing this in a mathematical/programatical way?
extra info:
My function returns the exact distance between the two points, so say you set two points to 0,0,0 and 4,4,0 respectively, and those points are set to scale(the game world is divided into a very large 3d grid, with each 'block' area being represented by a 3d coordinate) then it would give back a value at around 5.6.
The key point about it varying is that the user can set the points, so say they set a point to read 0,0,0, the actual location could be something like 52, 85, 93. However, providing they then count the blocks and set their other points correctly (eg, set a point 4,4,0 at the real point 56, 89, 93) then the final result will return the relative position (eg the object they are trying to locate is at real point 152, 185, 93, it will return the relative value 100,100,0). I need to be able to calculate it knowing every point but the one it's trying to locate, as well as the distances between all points.
Also, please don't ask why I can't just calculate it by using the real coordinates, I'm hoping to show the equation up on screen as it calculates the result.7
Example:
Here is a diagram
Imagine these are points in my game on a flat plain.
I want to know the point f.
I know the values of points d and e, and the sides A,B and C.
Using only the data I know, I need to find out how to do this.
Answered Edit:
After many days of working on this, Sean Kenny has provided me with his time, patience and intellect, and thus I have now got a working implementation of a triangulation method.
I hope to place the different language equivalents of the code as I test them so that future coders may use this code and not have the same problem I have had.
I spent a bit of time working on a solution but I think the implementer, i.e you, should know what it's doing, so any errors encountered can be tackled later on. As such, I'll give my answer in the form of strong hints.
First off, we have a vector from d to e which we can work out: if we consider the coordinates as position vectors rather than absolute coordinates, how can we determine what the vector pointing from d to e is? Think about how you would determine the displacement you had moved if you only knew where you started and where you ended up? Displacement is a straight line, point A to B, no deviation, not: I had to walk around that house so I walked further. A straight line. If you started at the point (0,0) it would be easy.
Secondly, the cosine rule. Do you know what it is? If not, read up on it. How can we rearrange the form given in the link to find the angle d between vectors DE and DF? Remember you need the angle, not a function of the angle (cos is a function remember).
Next we can use a vector 'trick' called the scalar product. Notice there is a cos function in there. Now, you may be thinking, we've just found the angle, why are we doing it again?
Define DQ = [1,0]. DQ is a vector of length 1, a unit vector, along the x-axis. Which other vector do we know? Do we know of two position vectors?
Once we have two vectors (I hope you worked out the other one) we can use the scalar product to find the angle; again, just the angle, not a function of it.
Now, hopefully, we have 2 angles. Could we take one from the other to get yet another angle to our desired coordinate DF? The choice of using a unit vector earlier was not arbitrary.
The scalar product, after some cancelling, gives us this : cos(theta) = x / r
Where x is the x ordinate for F and r is the length of side A.
The end result being:
theta = arccos( xe / B ) - arccos( ( (A^2) + (B^2) - (C^2) ) / ( 2*A*B ) )
Where theta is the angle formed between a unit vector along the line y = 0 where the origin is at point d.
With this information we can find the x and y coordinates of point f relative to d. How?
Again, with the scalar product. The rest is fairly easy, so I'll give it to you.
x = r.cos(theta)
y = r.sin(theta)
From basic trigonometry.
I wouldn't advise trying to code this into one value.
Instead, try this:
//pseudo code
dx = 0
dy = 0 //initialise coordinates somehow
ex = ex
ey = ey
A = A
B = B
C = C
cosd = ex / B
cosfi = ((A^2) + (B^2) - (C^2)) / ( 2*A*B)
d = acos(cosd) //acos is a method in java.math
fi = acos(cosfi) //you will have to find an equivalent in your chosen language
//look for a method of inverse cos
theta = fi - d
x = A cos(theta)
y = A sin(theta)
Initialise all variables as those which can take decimals. e.g float or double in Java.
The green along the x-axis represents the x ordinate of f, and the purple the y ordinate.
The blue angle is the one we are trying to find because, hopefully you can see, we can then use simple trig to work out x and y, given that we know the length of the hypotenuse.
This yellow line up to 1 is the unit vector for which scalar products are taken, this runs along the x-axis.
We need to find the black and red angles so we can deduce the blue angle by simple subtraction.
Hope this helps. Extensions can be made to 3D, all the vector functions work basically the same for 3D.
If you have the displacements from an origin, regardless of whether this is another user defined coordinate or not, the coordinate for that 3D point are simply (x, y, z).
If you are defining these lengths from a point, which also has a coordinate to take into account, you can simply write (x, y, z) + (x1, y1, z1) = (x2, y2, z2) where x2, y2 and z2 are the displacements from the (0, 0, 0) origin.
If you wish to find the length of this vector, i.e if you defined the line from A to B to be the x axis, what would the x displacement be, you can use Pythagoras for 3D vectors, it works just the same as with 2D:
Length l = sqrt((x^2) + (y^2) + (z^2))
EDIT:
Say you have a user defined point A (x1, y1, z1) and you want to define this as the origin (0,0,0). You have another user chosen point B (x2, y2, z2) and you know the distance from A to B in the x, y and z plane. If you want to work out what this point is, in relation to the new origin, you can simply do
B relative to A = (x2, y2, z2) - (x1, y1, z1) = (x2-x1, y2-y1, z2-z1) = C
C is the vector A>B, a vector is a quantity which has a magnitude (the length of the lines) and a direction (the angle from A which points to B).
If you want to work out the position of B relative to the origin O, you can do the opposite:
B relative to O = (x2, y2, z2) + (x1, y1, z1) = (x1+x2, y1+y2, z1+z2) = D
D is the vector O>B.
Edit 2:
//pseudo code
userx = x;
usery = y;
userz = z;
//move origin
for (every block i){
xi = xi-x;
yi = yi - y;
zi = zi -z;
}
I have been given some work to do with the fractal visualisation of the Mandelbrot set.
I'm not looking for a complete solution (naturally), I'm asking for help with regard to the orbits of complex numbers.
Say I have a given Complex number derived from a point on the complex plane. I now need to iterate over its orbit sequence and plot points according to whether the orbits increase by orders of magnitude or not.
How do I gather the orbits of a complex number? Any guidance is much appreciated (links etc). Any pointers on Math functions needed to test the orbit sequence e.g. Math.pow()
I'm using Java but that's not particularly relevant here.
Thanks again,
Alex
When you display the Mandelbrot set, you simply translate the real and imaginaty planes into x and y coordinates, respectively.
So, for example the complex number 4.5 + 0.27i translates into x = 4.5, y = 0.27.
The Mandelbrot set is all points where the equation Z = Z² + C never reaches a value where |Z| >= 2, but in practice you include all points where the value doesn't exceed 2 within a specific number of iterations, for example 1000. To get the colorful renderings that you usually see of the set, you assign different colors to points outside the set depending on how fast they reach the limit.
As it's complex numbers, the equation is actually Zr + Zi = (Zr + Zi)² + Cr + Ci. You would divide that into two equations, one for the real plane and one for the imaginary plane, and then it's just plain algebra. C is the coordinate of the point that you want to test, and the initial value of Z is zero.
Here's an image from my multi-threaded Mandelbrot generator :)
Actually the Mandelbrot set is the set of complex numbers for which the iteration converges.
So the only points in the Mandelbrot set are that big boring colour in the middle. and all of the pretty colours you see are doing nothing more than representing the rate at which points near the boundary (but the wrong side) spin off to infinity.
In mathspeak,
M = {c in C : lim (k -> inf) z_k = 0 } where z_0 = c, z_(k+1) = z_k^2 + c
ie pick any complex number c. Now to determine whether it is in the set, repeatedly iterate it z_0 = c, z_(k+1) = z_k^2 + c, and z_k will approach either zero or infinity. If its limit (as k tends to infinity) is zero, then it is in. Otherwise not.
It is possible to prove that once |z_k| > 2, it is not going to converge. This is a good exercise in optimisation: IIRC |Z_k|^2 > 2 is sufficient... either way, squaring up will save you the expensive sqrt() function.
Wolfram Mathworld has a nice site talking about the Mandelbrot set.
A Complex class will be most helpful.
Maybe an example like this will stimulate some thought. I wouldn't recommend using an Applet.
You have to know how to do add, subtract, multiply, divide, and power operations with complex numbers, in addition to functions like sine, cosine, exponential, etc. If you don't know those, I'd start there.
The book that I was taught from was Ruel V. Churchill "Complex Variables".
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m}d/r{rlineto}d/X -2 q 1{d/Y -2 q 2{d/A 0 d/B 0 d 64 -1 1{/f exch d/B
A/A z sub X add d B 2 m m Y add d z add 4 gt{exit}if/f 64 d}for f 64 div
setgray X Y moveto 0 q neg u 0 0 q u 0 r r r r fill/Y}for/X}for showpage