diff --git a/math.scad b/math.scad index 4765983..3a9a818 100644 --- a/math.scad +++ b/math.scad @@ -498,83 +498,73 @@ function rand_int(minval, maxval, N, seed=undef) = // Function: random_points() // Usage: -// points = random_points(n, dim, scale, [seed]); +// points = random_points([N], [dim], [scale], [seed]); // See Also: random_polygon(), gaussian_random_points(), spherical_random_points() // Topics: Random, Points // Description: -// Generate `n` uniform random points of dimension `dim` with data ranging from -scale to +scale. +// Generate `N` uniform random points of dimension `dim` with data ranging from -scale to +scale. // The `scale` may be a number, in which case the random data lies in a cube, // or a vector with dimension `dim`, in which case each dimension has its own scale. // Arguments: -// n = number of points to generate. +// N = number of points to generate. Default: 1 // dim = dimension of the points. Default: 2 // scale = the scale of the point coordinates. Default: 1 // seed = an optional seed for the random generation. -function random_points(n, dim=2, scale=1, seed) = - assert( is_int(n) && n>=0, "The number of points should be a non-negative integer.") +function random_points(N, dim=2, scale=1, seed) = + assert( is_int(N) && N>=0, "The number of points should be a non-negative integer.") assert( is_int(dim) && dim>=1, "The point dimensions should be an integer greater than 1.") assert( is_finite(scale) || is_vector(scale,dim), "The scale should be a number or a vector with length equal to d.") let( rnds = is_undef(seed) - ? rands(-1,1,n*dim) - : rands(-1,1,n*dim, seed) ) + ? rands(-1,1,N*dim) + : rands(-1,1,N*dim, seed) ) is_num(scale) - ? scale*[for(i=[0:1:n-1]) [for(j=[0:dim-1]) rnds[i*dim+j] ] ] - : [for(i=[0:1:n-1]) [for(j=[0:dim-1]) scale[j]*rnds[i*dim+j] ] ]; + ? scale*[for(i=[0:1:N-1]) [for(j=[0:dim-1]) rnds[i*dim+j] ] ] + : [for(i=[0:1:N-1]) [for(j=[0:dim-1]) scale[j]*rnds[i*dim+j] ] ]; // Function: gaussian_rands() // Usage: -// arr = gaussian_rands(mean, stddev, [N], [seed]); +// arr = gaussian_rands([N],[mean], [cov], [seed]); // Description: -// Returns a random number with a gaussian/normal distribution. +// Returns a random number or vector with a Gaussian/normal distribution. // Arguments: -// mean = The average random number returned. -// stddev = The standard deviation of the numbers to be returned. -// N = Number of random numbers to return. Default: 1 +// N = the number of points to return. Default: 1 +// mean = The average of the random value (a number or vector). Default: 0 +// cov = covariance matrix of the random numbers, or variance in the 1D case. Default: 1 // seed = If given, sets the random number seed. -function gaussian_rands(mean, stddev, N=1, seed=undef) = - assert( is_finite(mean+stddev+N) && (is_undef(seed) || is_finite(seed) ), "Input must be finite numbers.") - let(nums = is_undef(seed)? rands(0,1,N*2) : rands(0,1,N*2,seed)) - [for (i = count(N,0,2)) mean + stddev*sqrt(-2*ln(nums[i]))*cos(360*nums[i+1])]; - - -// Function: gaussian_random_points() -// Usage: -// points = gaussian_random_points(n, dim, mean, stddev, [seed]); -// See Also: random_polygon(), random_points(), spherical_random_points() -// Topics: Random, Points -// Description: -// Generate `n` random points of dimension `dim` with coordinates absolute value less than `scale`. -// The gaussian distribution of all the coordinates of the points will have a mean `mean` and -// standard deviation `stddev` -// Arguments: -// n = number of points to generate. -// dim = dimension of the points. Default: 2 -// mean = the gaussian mean of the point coordinates. Default: 0 -// stddev = the gaussian standard deviation of the point coordinates. Default: 0 -// seed = an optional seed for the random generation. -function gaussian_random_points(n, dim=2, mean=0, stddev=1, seed) = - assert( is_int(n) && n>=0, "The number of points should be a non-negative integer.") - assert( is_int(dim) && dim>=1, "The point dimensions should be an integer greater than 1.") - let( rnds = gaussian_rands(mean, stddev, n*dim, seed=seed) ) - [for(i=[0:1:n-1]) [for(j=[0:dim-1]) rnds[i*dim+j] ] ]; +function gaussian_rands(N=1, mean=0, cov=1, seed=undef) = + assert(is_num(mean) || is_vector(mean)) + let( + dim = is_num(mean) ? 1 : len(mean) + ) + assert((dim==1 && is_num(cov)) || is_matrix(cov,dim,dim),"mean and covariance matrix not compatible") + assert(is_undef(seed) || is_finite(seed)) + let( + nums = is_undef(seed)? rands(0,1,dim*N*2) : rands(0,1,dim*N*2,seed), + rdata = [for (i = count(dim*N,0,2)) sqrt(-2*ln(nums[i]))*cos(360*nums[i+1])] + ) + dim==1 ? add_scalar(sqrt(cov)*rdata,mean) : + let( + L = cholesky(cov) + ) + array_group(rdata,dim)*transpose(L); // Function: spherical_random_points() // Usage: -// points = spherical_random_points(n, radius, [seed]); +// points = spherical_random_points([N], [radius], [seed]); // See Also: random_polygon(), random_points(), gaussian_random_points() // Topics: Random, Points // Description: // Generate `n` 3D uniformly distributed random points lying on a sphere centered at the origin with radius equal to `radius`. // Arguments: -// n = number of points to generate. +// n = number of points to generate. Default: 1 // radius = the sphere radius. Default: 1 // seed = an optional seed for the random generation. // See https://mathworld.wolfram.com/SpherePointPicking.html -function spherical_random_points(n, radius=1, seed) = +function spherical_random_points(N=1, radius=1, seed) = assert( is_int(n) && n>=1, "The number of points should be an integer greater than zero.") assert( is_num(radius) && radius>0, "The radius should be a non-negative number.") let( theta = is_undef(seed) @@ -1090,6 +1080,41 @@ function _back_substitute(R, b, x=[]) = _back_substitute(R, b, concat([newvalue],x)); + +// Function: cholesky() +// Usage: +// L = cholesky(A); +// Description: +// Compute the cholesky factor, L, of the symmetric positive definite matrix A. +// The matrix L is lower triangular and L * transpose(L) = A. If the A is +// not symmetric then an error is displayed. If the matrix is symmetric but +// not positive definite then undef is returned. +function cholesky(A) = + assert(is_matrix(A,square=true),"A must be a square matrix") + assert(is_matrix_symmetric(A),"Cholesky factorization requires a symmetric matrix") + echo(A=A,len=len(A)) + _cholesky(A,ident(len(A)), len(A)); + +function _cholesky(A,L,n) = let(ffee=echo(insideA=A,L,n)) + A[0][0]<0 ? undef : // Matrix not positive definite + len(A) == 1 ? submatrix_set(L,[[sqrt(A[0][0])]], n-1,n-1): + let( + i = n+1-len(A),ff=echo(i=i,lenA=len(A)) + ) + let( + sqrtAii = sqrt(A[0][0]), + Lnext = [for(j=[0:n-1]) + [for(k=[0:n-1]) + j