I'm not sure if this the right place to ask and I'm certainly not sure
if this has been answered elsewhere so don't shoot me if it is. Maybe
I'm not using the right words when searching.
I'm trying to create a generic float method to calculate related values in c++ but limited by their min, max values. Math isn't my
thing so I'd love some help at this stage.
To the point: Let's say that we have a float variable named "health" that could get any value from 0 to 100. We also
have a second float variable named "walkingSpeed" that can get
any value from 100 to 200.
I need a method to calculate and return what "walkingSpeed" would be in relation to "health" while always taking in account the min and
max values of these two variables ( if Health = HealthMax then
walkingSpeed = walking SpeedMax etc ). I tried to work with % but I
couldn't figure it out. Any ideas?
Thanks in advance!
EDIT:
Generic Method based on #Felipe 's suggestion:
/*
* Returns valueY based on a relative value X
*
* #RelXMin - Minimum relative value
* #RelXMax - Maximum relative value
* #relX - Relative value
* #valueYMin - Minimum value
* #valueYMax - Maximum value
* #bInverse - set true if the y is inverse related to x
*/
float calcRelativeFloatValue(float RelXMin, float RelXMax, float relX, float valueYMin, float valueYMax, bool bInverse)
{
float xRange, yRelative;
xRange = RelXMax - RelXMin;
yRelative = (valueYMax - valueYMin) / xRange;
if (bInverse)
{
return valueYMax - (yRelative * relX);
}
return valueYMin + (yRelative * relX) ;
}
It looks like you are asking for linear interpolation.
The two known points are the minimum and the maximum values. So you can use a general interpolation function like this (just the equation from wikipedia written as code):
float interpolate(float x, float x_0, float x_1, float y_0, float y_1) {
return y_0*(y_1-y_0)*(x-x_0)/(x_1/x_0);
}
Your example would then be
walkingSpeed=interpolate(health, minHealth, maxHealth, minWalkingSpeed, maxWalkingSpeed);
In this specific case, the task is pretty trivial:
float estimated_walking_speed = health + 100;
For the more generic task of mapping a value in one range to the corresponding value in some other range, you'd be looking at something like this:
auto input_range = input_upper - input_lower;
auto relative_input_loc = (input_value - input_lower) / input_range;
auto output_range = output_upper - output_lower;
auto output = output_lower + (relative_input_loc * output_range);
[This is open to simplification/optimization--I'm writing it all out to keep it as understandable as possible.]
Try this.
float calculateSpeed(float minHealth, float maxHealth, float currentHealth, float minSpeed, float maxSpeed) {
float healthLength = maxHealth - minHealth;
float speedPerHealth = (maxSpeed - minSpeed) / healthLength;
float partialSpeed = speedPerHealth * currentHealth;
float currentSpeed = partialSpeed + minSpeed;
return currentSpeed;
}
Related
I am attempting to implement Perlin Noise in c++.
Firstly, the problem (I think) is that the output is not what I expect. Currently I simply use the generated Perlin Noise values in a greyscaled image, and this is the results I get:
However, from my understanding, it's supposed to look more along the lines of:
That is, the noise I am producing currently seems to be more along the lines of "standard" irregular noise.
This is the Perlin Noise Algorithm I have implemented so far:
float perlinNoise2D(float x, float y)
{
// Find grid cell coordinates
int x0 = (x > 0.0f ? static_cast<int>(x) : (static_cast<int>(x) - 1));
int x1 = x0 + 1;
int y0 = (y > 0.0f ? static_cast<int>(y) : (static_cast<int>(y) - 1));
int y1 = y0 + 1;
float s = calculateInfluence(x0, y0, x, y);
float t = calculateInfluence(x1, y0, x, y);
float u = calculateInfluence(x0, y1, x, y);
float v = calculateInfluence(x1, y1, x, y);
// Local position in the grid cell
float localPosX = 3 * ((x - (float)x0) * (x - (float)x0)) - 2 * ((x - (float)x0) * (x - (float)x0) * (x - (float)x0));
float localPosY = 3 * ((y - (float)y0) * (y - (float)y0)) - 2 * ((y - (float)y0) * (y - (float)y0) * (y - (float)y0));
float a = s + localPosX * (t - s);
float b = u + localPosX * (v - u);
return lerp(a, b, localPosY);
}
The function calculateInfluence has the job of generating the random gradient vector and distance vector for one of the corner points of the current grid cell and returning the dot product of these. It is implemented as:
float calculateInfluence(int xGrid, int yGrid, float x, float y)
{
// Calculate gradient vector
float gradientXComponent = dist(rdEngine);
float gradientYComponent = dist(rdEngine);
// Normalize gradient vector
float magnitude = sqrt( pow(gradientXComponent, 2) + pow(gradientYComponent, 2) );
gradientXComponent = gradientXComponent / magnitude;
gradientYComponent = gradientYComponent / magnitude;
magnitude = sqrt(pow(gradientXComponent, 2) + pow(gradientYComponent, 2));
// Calculate distance vectors
float dx = x - (float)xGrid;
float dy = y - (float)yGrid;
// Compute dot product
return (dx * gradientXComponent + dy * gradientYComponent);
}
Here, dist is a random number generator from C++11:
std::mt19937 rdEngine(1);
std::normal_distribution<float> dist(0.0f, 1.0f);
And lerp is simply implemented as:
float lerp(float v0, float v1, float t)
{
return ( 1.0f - t ) * v0 + t * v1;
}
To implement the algorithm, I primarily made use of the following two resources:
Perlin Noise FAQ
Perlin Noise Pseudo Code
It's difficult for me to pinpoint exactly where I seem to be messing up. It could be that I am generating the gradient vectors incorrectly, as I'm not quite sure what type of distribution they should have. I have tried with a uniform distribution, however this seemed to generate repeating patterns in the texture!
Likewise, it could be that I am averaging the influence values incorrectly. It has been a bit difficult to discern exactly how it should be done from from the Perlin Noise FAQ article.
Does anyone have any hints as to what might be wrong with the code? :)
It seems like you are only generating a single octave of Perlin Noise. To get a result like the one shown, you need to generate multiple octaves and add them together. In a series of octaves, each octave should have a grid cell size double that of the last.
To generate multi-octave noise, use something similar to this:
float multiOctavePerlinNoise2D(float x, float y, int octaves)
{
float v = 0.0f;
float scale = 1.0f;
float weight = 1.0f;
float weightTotal = 0.0f;
for(int i = 0; i < octaves; i++)
{
v += perlinNoise2D(x * scale, y * scale) * weight;
weightTotal += weight;
// "ever-increasing frequencies and ever-decreasing amplitudes"
// (or conversely decreasing freqs and increasing amplitudes)
scale *= 0.5f;
weight *= 2.0f;
}
return v / weightTotal;
}
For extra randomness you could use a differently seeded random generator for each octave. Also, the weights given to each octave can be varied to adjust the aesthetic quality of the noise. If the weight variable is not adjusted each iteration, then the example above is "pink noise" (each doubling of frequency carries the same weight).
Also, you need to use a random number generator that returns the same value each time for a given xGrid, yGrid pair.
I want to use KDE with the Gaussian Kernel. If I'm correct, the sum of all f(x) must be 1 ( ~ rounding ) ?
My Implementation looks like this:
float K( float const& val)
{
const float p=1.0 / std::sqrt( 2.0 * M_PI);
float result = 0.5 * (val*val);
result = p * std::exp(- result);
return result;
};
std::vector< std::pair<float, float> kde( float *val, int len float h)
{
std::vector< std::pair<float, float>> density( len );
const float p = 1.0 / (h * len );
for(int r=0;r<len;r++)
{
float sum = 0;
for(int i=0;i<len;i++)
sum += k( (val[r] - val[i]) / h );
density[r] = std::make_pair( val[r], p*sum );
}
return density;
}
And I choosed h > 0. Am i right that p*sum is the probability for the value val[r] ? The sum over all probability is > 1 ( but looks ok for me ).
You misinterpreted the assumptions on the probability density here. The density integrates to one, whereas its values at certain points are definitely not 1.
Let's discuss it using the following formula from the linked Wikipedia article which you seem to use:
This formula provides the density f_h(x) evaluated at point x.
From my review, your code correctly evaluates this quantity. Yet, you misinterpreted the quantity which should be one. As a density, the integral over the complete space should yield one, i.e.
This property is called normalization of the density.
Moreover, being a density itself, each summand of f_h(x) should yield 1/n when integrated over the whole space, when one also includes the normalization constant. Again, there's no guarantee on the values of the summands.
In one dimension, you can easily confirm the normalization by using the trapezoidal rule or another quadrature scheme (--if you provide a working example, I can try to do that.)
I have such a function that calculates weights according to Gaussian distribution:
const float dx = 1.0f / static_cast<float>(points - 1);
const float sigma = 1.0f / 3.0f;
const float norm = 1.0f / (sqrtf(2.0f * static_cast<float>(M_PI)) * sigma);
const float divsigma2 = 0.5f / (sigma * sigma);
m_weights[0] = 1.0f;
for (int i = 1; i < points; i++)
{
float x = static_cast<float>(i)* dx;
m_weights[i] = norm * expf(-x * x * divsigma2) * dx;
m_weights[0] -= 2.0f * m_weights[i];
}
In all the calc above the number does not matter. The only thing matters is that m_weights[0] = 1.0f; and each time I calculate m_weights[i] I subtract it twice from m_weights[0] like this:
m_weights[0] -= 2.0f * m_weights[i];
to ensure that w[0] + 2 * w[i] (1..N) will sum to exactly 1.0f. But it does not. This assert fails:
float wSum = 0.0f;
for (size_t i = 0; i < m_weights.size(); ++i)
{
float w = m_weights[i];
if (i == 0) {
wSum += w;
} else {
wSum += (w + w);
}
}
assert(wSum == 1.0 && "Weights sum is not 1.");
How can I ensure the sum to be 1.0f on all platforms?
You can't. Floating point isn't like that. Even adding the same values can produce different results according to the cpu used.
All you can do is define some accuracy value and ensure that you end up with 1.0 +/- that value.
See: http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html
Because the precision of float is only 23 bits (see e.g. https://en.wikipedia.org/wiki/Single-precision_floating-point_format ), rounding error quickly accumulates therefore even if the rest of code is correct, your sum becomes something like 1.0000001 or 0.9999999 (have you watched it in the debugger or tried to print it to console, by the way?). To improve precision you can replace float with double, but still the sum will not be exactly 1.0: the error will just be smaller, something like 1e-16 instead of 1e-7.
The second thing to do is to replace strict comparison to 1.0 with a range comparison, like:
assert(fabs(wSum - 1.0) <= 1e-13 && "Weights sum is not 1.");
Here 1e-13 is the epsilon within which you consider two floating-point numbers equal. If you choose to go with float (not double), you may need epsilon like 1e-6 .
Depending on how large your weights are and how many points there are, accumulated error can become larger than that epsilon. In that case you would need special algorithms for keeping the precision higher, such as sorting the numbers by their absolute values prior to summing them up starting with the smallest numbers.
How can I ensure the sum to be 1.0f on all platforms?
As the other answers (and comments) have stated, you can't achieve this, due to the inexactness of floating point calculations.
One solution is that, instead of using double, use a fixed point or multi-precision library such as GMP, Boost Multiprecision Library, or one of the many others out there.
From this question: Random number generator which gravitates numbers to any given number in range? I did some research since I've come across such a random number generator before. All I remember was the name "Mueller", so I guess I found it, here:
Box-Mueller transform
I can find numerous implementations of it in other languages, but I can't seem to implement it correctly in C#.
This page, for instance, The Box-Muller Method for Generating Gaussian Random Numbers says that the code should look like this (this is not C#):
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
double gaussian(void)
{
static double v, fac;
static int phase = 0;
double S, Z, U1, U2, u;
if (phase)
Z = v * fac;
else
{
do
{
U1 = (double)rand() / RAND_MAX;
U2 = (double)rand() / RAND_MAX;
u = 2. * U1 - 1.;
v = 2. * U2 - 1.;
S = u * u + v * v;
} while (S >= 1);
fac = sqrt (-2. * log(S) / S);
Z = u * fac;
}
phase = 1 - phase;
return Z;
}
Now, here's my implementation of the above in C#. Note that the transform produces 2 numbers, hence the trick with the "phase" above. I simply discard the second value and return the first.
public static double NextGaussianDouble(this Random r)
{
double u, v, S;
do
{
u = 2.0 * r.NextDouble() - 1.0;
v = 2.0 * r.NextDouble() - 1.0;
S = u * u + v * v;
}
while (S >= 1.0);
double fac = Math.Sqrt(-2.0 * Math.Log(S) / S);
return u * fac;
}
My question is with the following specific scenario, where my code doesn't return a value in the range of 0-1, and I can't understand how the original code can either.
u = 0.5, v = 0.1
S becomes 0.5*0.5 + 0.1*0.1 = 0.26
fac becomes ~3.22
the return value is thus ~0.5 * 3.22 or ~1.6
That's not within 0 .. 1.
What am I doing wrong/not understanding?
If I modify my code so that instead of multiplying fac with u, I multiply by S, I get a value that ranges from 0 to 1, but it has the wrong distribution (seems to have a maximum distribution around 0.7-0.8 and then tapers off in both directions.)
Your code is fine. Your mistake is thinking that it should return values exclusively within [0, 1]. The (standard) normal distribution is a distribution with nonzero weight on the entire real line. That is, values outside of [0, 1] are possible. In fact, values within [-1, 0] are just as likely as values within [0, 1], and moreover, the complement of [0, 1] has about 66% of the weight of the normal distribution. Therefore, 66% of the time we expect a value outside of [0, 1].
Also, I think this is not the Box-Mueller transform, but is actually the Marsaglia polar method.
I am no mathematician, or statistician, but if I think about this I would not expect a Gaussian distribution to return numbers in an exact range. Given your implementation the mean is 0 and the standard deviation is 1 so I would expect values distributed on the bell curve with 0 at the center and then reducing as the numbers deviate from 0 on either side. So the sequence would definitely cover both +/- numbers.
Then since it is statistical, why would it be hard limited to -1..1 just because the std.dev is 1? There can statistically be some play on either side and still fulfill the statistical requirement.
The uniform random variate is indeed within 0..1, but the gaussian random variate (which is what Box-Muller algorithm generates) can be anywhere on the real line. See wiki/NormalDistribution for details.
I think the function returns polar coordinates. So you need both values to get correct results.
Also, Gaussian distribution is not between 0 .. 1. It can easily end up as 1000, but probability of such occurrence is extremely low.
This is a monte carlo method so you can't clamp the result, but what you can do is ignore samples.
// return random value in the range [0,1].
double gaussian_random()
{
double sigma = 1.0/8.0; // or whatever works.
while ( 1 ) {
double z = gaussian() * sigma + 0.5;
if (z >= 0.0 && z <= 1.0)
return z;
}
}
I am trying to figure out the formula to get the distance between two objects in 3d space. So far, the answers are wrong when I run the program:
float Distance3D(const float & object1X ,
const float & object1Y ,
const float & object1Z ,
const float & object2X ,
const float & object2Y ,
const float & object2Z )
{
float x = pow ((object2X - object1X),2);// for x
float y = pow ((object2Y - object1Y),2);// for y
float z = pow ((object2Z - object1Z),2);// for z
float objectDistance = 0.0f;
objectDistance = sqrt(object2X*object1X + object2Y*object1Y + object2Z*object1Z);
cout << objectDistance << endl;
return objectDistance;
}
If you want the Euclidean distance between 2 points in 3D space your code should look more like
objectDistance = sqrt((object2X-object1X)*(object2X-object1X) +
(object2Y-object1Y)*(object2Y-object1Y) +
(object2Z-object1Z)*(object2Z-object1Z));
or more simply using your squared distance variables x,y,z :
objectDistance = sqrt(x + y + z);
Unless you are interested strictly in distance of two points in a 3-D space, Gilbert-Johnson-Keerthi distance algorithm is what you might be interested in.
Distance in 3D space is usually found using the Pythagorean Theorem.
The formula for this is
d^2=(x0-x1)^2+(y0-y1)^2+(z0-z1)^2
where d is the distance between the points.
Using this formula, your code should look like
float Distance3D(const float & object1X ,
const float & object1Y ,
const float & object1Z ,
const float & object2X ,
const float & object2Y ,
const float & object2Z )
{
float delta_x = pow (object2X - object1X,2);// for x
float delta_y = pow (object2Y - object1Y,2);// for y
float delta_z = pow (object2Z - object1Z,2);// for z
float objectDistance = 0.0f;
objectDistance = sqrt(delta_x*delta_x + delta_y*delta_y + delta_z*delta_z);
cout << objectDistance << endl;
return objectDistance;
}
Interestingly, for high-dimensional data the usefulness of this metric declines and the Manhatten distance can become a preferable metric. A paper entitled "On the Surprising Behavior of Distance Metrics in High Dimensional Space" by Aggarwal (2001) has been wrote about this.
Your formula is incorrect; take a look at the 2D distance formula, then extend it to 3D.
Also note the similarities to the formula for getting 2D/3D vector length.
You should try the square root of the square of the difference of each component. See formula