484 lines
13 KiB
JavaScript
484 lines
13 KiB
JavaScript
// Extend the Array class
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Array.prototype.max = function() {
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return Math.max.apply(null, this);
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};
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Array.prototype.min = function() {
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return Math.min.apply(null, this);
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};
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Array.prototype.mean = function() {
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var i, sum;
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for(i=0,sum=0;i<this.length;i++)
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sum += this[i];
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return sum / this.length;
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};
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Array.prototype.pip = function(x, y) {
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var i, j, c = false;
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for(i=0,j=this.length-1;i<this.length;j=i++) {
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if( ((this[i][1]>y) != (this[j][1]>y)) &&
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(x<(this[j][0]-this[i][0]) * (y-this[i][1]) / (this[j][1]-this[i][1]) + this[i][0]) ) {
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c = !c;
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}
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}
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return c;
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}
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var kriging = function() {
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var kriging = {};
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var createArrayWithValues = function(value, n) {
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var array = [];
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for ( var i = 0; i < n; i++) {
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array.push(value);
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}
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return array;
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},
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// Matrix algebra
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kriging_matrix_diag = function(c, n) {
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var Z = createArrayWithValues(0, n * n);
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for(i=0;i<n;i++) Z[i*n+i] = c;
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return Z;
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},
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kriging_matrix_transpose = function(X, n, m) {
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var i, j, Z = Array(m*n);
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for(i=0;i<n;i++)
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for(j=0;j<m;j++)
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Z[j*n+i] = X[i*m+j];
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return Z;
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},
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kriging_matrix_scale = function(X, c, n, m) {
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var i, j;
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for(i=0;i<n;i++)
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for(j=0;j<m;j++)
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X[i*m+j] *= c;
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},
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kriging_matrix_add = function(X, Y, n, m) {
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var i, j, Z = Array(n*m);
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for(i=0;i<n;i++)
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for(j=0;j<m;j++)
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Z[i*m+j] = X[i*m+j] + Y[i*m+j];
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return Z;
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},
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// Naive matrix multiplication
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kriging_matrix_multiply = function(X, Y, n, m, p) {
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var i, j, k, Z = Array(n*p);
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for(i=0;i<n;i++) {
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for(j=0;j<p;j++) {
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Z[i*p+j] = 0;
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for(k=0;k<m;k++)
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Z[i*p+j] += X[i*m+k]*Y[k*p+j];
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}
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}
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return Z;
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},
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// Cholesky decomposition
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kriging_matrix_chol = function(X, n) {
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var i, j, k, sum, p = Array(n);
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for(i=0;i<n;i++) p[i] = X[i*n+i];
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for(i=0;i<n;i++) {
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for(j=0;j<i;j++)
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p[i] -= X[i*n+j]*X[i*n+j];
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if(p[i]<=0) return false;
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p[i] = Math.sqrt(p[i]);
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for(j=i+1;j<n;j++) {
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for(k=0;k<i;k++)
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X[j*n+i] -= X[j*n+k]*X[i*n+k];
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X[j*n+i] /= p[i];
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}
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}
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for(i=0;i<n;i++) X[i*n+i] = p[i];
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return true;
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},
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// Inversion of cholesky decomposition
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kriging_matrix_chol2inv = function(X, n) {
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var i, j, k, sum;
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for(i=0;i<n;i++) {
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X[i*n+i] = 1/X[i*n+i];
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for(j=i+1;j<n;j++) {
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sum = 0;
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for(k=i;k<j;k++)
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sum -= X[j*n+k]*X[k*n+i];
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X[j*n+i] = sum/X[j*n+j];
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}
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}
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for(i=0;i<n;i++)
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for(j=i+1;j<n;j++)
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X[i*n+j] = 0;
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for(i=0;i<n;i++) {
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X[i*n+i] *= X[i*n+i];
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for(k=i+1;k<n;k++)
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X[i*n+i] += X[k*n+i]*X[k*n+i];
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for(j=i+1;j<n;j++)
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for(k=j;k<n;k++)
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X[i*n+j] += X[k*n+i]*X[k*n+j];
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}
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for(i=0;i<n;i++)
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for(j=0;j<i;j++)
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X[i*n+j] = X[j*n+i];
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},
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// Inversion via gauss-jordan elimination
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kriging_matrix_solve = function(X, n) {
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var m = n;
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var b = Array(n*n);
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var indxc = Array(n);
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var indxr = Array(n);
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var ipiv = Array(n);
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var i, icol, irow, j, k, l, ll;
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var big, dum, pivinv, temp;
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for(i=0;i<n;i++)
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for(j=0;j<n;j++) {
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if(i==j) b[i*n+j] = 1;
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else b[i*n+j] = 0;
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}
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for(j=0;j<n;j++) ipiv[j] = 0;
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for(i=0;i<n;i++) {
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big = 0;
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for(j=0;j<n;j++) {
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if(ipiv[j]!=1) {
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for(k=0;k<n;k++) {
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if(ipiv[k]==0) {
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if(Math.abs(X[j*n+k])>=big) {
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big = Math.abs(X[j*n+k]);
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irow = j;
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icol = k;
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}
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}
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}
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}
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}
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++(ipiv[icol]);
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if(irow!=icol) {
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for(l=0;l<n;l++) {
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temp = X[irow*n+l];
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X[irow*n+l] = X[icol*n+l];
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X[icol*n+l] = temp;
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}
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for(l=0;l<m;l++) {
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temp = b[irow*n+l];
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b[irow*n+l] = b[icol*n+l];
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b[icol*n+l] = temp;
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}
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}
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indxr[i] = irow;
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indxc[i] = icol;
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if(X[icol*n+icol]==0) return false; // Singular
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pivinv = 1 / X[icol*n+icol];
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X[icol*n+icol] = 1;
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for(l=0;l<n;l++) X[icol*n+l] *= pivinv;
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for(l=0;l<m;l++) b[icol*n+l] *= pivinv;
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for(ll=0;ll<n;ll++) {
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if(ll!=icol) {
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dum = X[ll*n+icol];
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X[ll*n+icol] = 0;
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for(l=0;l<n;l++) X[ll*n+l] -= X[icol*n+l]*dum;
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for(l=0;l<m;l++) b[ll*n+l] -= b[icol*n+l]*dum;
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}
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}
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}
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for(l=(n-1);l>=0;l--)
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if(indxr[l]!=indxc[l]) {
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for(k=0;k<n;k++) {
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temp = X[k*n+indxr[l]];
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X[k*n+indxr[l]] = X[k*n+indxc[l]];
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X[k*n+indxc[l]] = temp;
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}
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}
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return true;
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},
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// Variogram models
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kriging_variogram_gaussian = function(h, nugget, range, sill, A) {
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return nugget + ((sill-nugget)/range)*
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( 1.0 - Math.exp(-(1.0/A)*Math.pow(h/range, 2)) );
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},
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kriging_variogram_exponential = function(h, nugget, range, sill, A) {
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return nugget + ((sill-nugget)/range)*
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( 1.0 - Math.exp(-(1.0/A) * (h/range)) );
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},
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kriging_variogram_spherical = function(h, nugget, range, sill, A) {
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if(h>range) return nugget + (sill-nugget)/range;
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return nugget + ((sill-nugget)/range)*
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( 1.5*(h/range) - 0.5*Math.pow(h/range, 3) );
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};
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// Train using gaussian processes with bayesian priors
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kriging.train = function(t, x, y, model, sigma2, alpha) {
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var variogram = {
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t : t,
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x : x,
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y : y,
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nugget : 0.0,
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range : 0.0,
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sill : 0.0,
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A : 1/3,
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n : 0
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};
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switch(model) {
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case "gaussian":
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variogram.model = kriging_variogram_gaussian;
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break;
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case "exponential":
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variogram.model = kriging_variogram_exponential;
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break;
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case "spherical":
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variogram.model = kriging_variogram_spherical;
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break;
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};
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// Lag distance/semivariance
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var i, j, k, l, n = t.length;
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var distance = Array((n*n-n)/2);
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for(i=0,k=0;i<n;i++)
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for(j=0;j<i;j++,k++) {
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distance[k] = Array(2);
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distance[k][0] = Math.pow(
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Math.pow(x[i]-x[j], 2)+
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Math.pow(y[i]-y[j], 2), 0.5);
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distance[k][1] = Math.abs(t[i]-t[j]);
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}
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distance.sort(function(a, b) { return a[0] - b[0]; });
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variogram.range = distance[(n*n-n)/2-1][0];
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// Bin lag distance
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var lags = ((n*n-n)/2)>30?30:(n*n-n)/2;
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var tolerance = variogram.range/lags;
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var lag = createArrayWithValues(0,lags);
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var semi = createArrayWithValues(0,lags);
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if(lags<30) {
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for(l=0;l<lags;l++) {
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lag[l] = distance[l][0];
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semi[l] = distance[l][1];
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}
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}
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else {
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for(i=0,j=0,k=0,l=0;i<lags&&j<((n*n-n)/2);i++,k=0) {
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while( distance[j][0]<=((i+1)*tolerance) ) {
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lag[l] += distance[j][0];
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semi[l] += distance[j][1];
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j++;k++;
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if(j>=((n*n-n)/2)) break;
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}
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if(k>0) {
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lag[l] /= k;
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semi[l] /= k;
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l++;
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}
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}
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if(l<2) return variogram; // Error: Not enough points
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}
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// Feature transformation
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n = l;
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variogram.range = lag[n-1]-lag[0];
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var X = createArrayWithValues(1,2 * n);
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var Y = Array(n);
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var A = variogram.A;
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for(i=0;i<n;i++) {
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switch(model) {
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case "gaussian":
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X[i*2+1] = 1.0-Math.exp(-(1.0/A)*Math.pow(lag[i]/variogram.range, 2));
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break;
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case "exponential":
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X[i*2+1] = 1.0-Math.exp(-(1.0/A)*lag[i]/variogram.range);
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break;
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case "spherical":
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X[i*2+1] = 1.5*(lag[i]/variogram.range)-
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0.5*Math.pow(lag[i]/variogram.range, 3);
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break;
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};
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Y[i] = semi[i];
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}
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// Least squares
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var Xt = kriging_matrix_transpose(X, n, 2);
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var Z = kriging_matrix_multiply(Xt, X, 2, n, 2);
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Z = kriging_matrix_add(Z, kriging_matrix_diag(1/alpha, 2), 2, 2);
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var cloneZ = Z.slice(0);
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if(kriging_matrix_chol(Z, 2))
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kriging_matrix_chol2inv(Z, 2);
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else {
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kriging_matrix_solve(cloneZ, 2);
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Z = cloneZ;
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}
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var W = kriging_matrix_multiply(kriging_matrix_multiply(Z, Xt, 2, 2, n), Y, 2, n, 1);
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// Variogram parameters
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variogram.nugget = W[0];
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variogram.sill = W[1]*variogram.range+variogram.nugget;
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variogram.n = x.length;
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// Gram matrix with prior
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n = x.length;
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var K = Array(n*n);
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for(i=0;i<n;i++) {
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for(j=0;j<i;j++) {
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K[i*n+j] = variogram.model(Math.pow(Math.pow(x[i]-x[j], 2)+
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Math.pow(y[i]-y[j], 2), 0.5),
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variogram.nugget,
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variogram.range,
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variogram.sill,
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variogram.A);
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K[j*n+i] = K[i*n+j];
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}
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K[i*n+i] = variogram.model(0, variogram.nugget,
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variogram.range,
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variogram.sill,
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variogram.A);
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}
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// Inverse penalized Gram matrix projected to target vector
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var C = kriging_matrix_add(K, kriging_matrix_diag(sigma2, n), n, n);
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var cloneC = C.slice(0);
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if(kriging_matrix_chol(C, n))
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kriging_matrix_chol2inv(C, n);
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else {
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kriging_matrix_solve(cloneC, n);
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C = cloneC;
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}
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// Copy unprojected inverted matrix as K
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var K = C.slice(0);
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var M = kriging_matrix_multiply(C, t, n, n, 1);
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variogram.K = K;
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variogram.M = M;
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return variogram;
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};
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// Model prediction
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kriging.predict = function(x, y, variogram) {
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var i, k = Array(variogram.n);
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for(i=0;i<variogram.n;i++)
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k[i] = variogram.model(Math.pow(Math.pow(x-variogram.x[i], 2)+
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Math.pow(y-variogram.y[i], 2), 0.5),
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variogram.nugget, variogram.range,
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variogram.sill, variogram.A);
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return kriging_matrix_multiply(k, variogram.M, 1, variogram.n, 1)[0];
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};
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kriging.variance = function(x, y, variogram) {
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var i, k = Array(variogram.n);
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for(i=0;i<variogram.n;i++)
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k[i] = variogram.model(Math.pow(Math.pow(x-variogram.x[i], 2)+
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Math.pow(y-variogram.y[i], 2), 0.5),
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variogram.nugget, variogram.range,
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variogram.sill, variogram.A);
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return variogram.model(0, variogram.nugget, variogram.range,
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variogram.sill, variogram.A)+
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kriging_matrix_multiply(kriging_matrix_multiply(k, variogram.K,
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1, variogram.n, variogram.n),
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k, 1, variogram.n, 1)[0];
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};
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// Gridded matrices or contour paths
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kriging.grid = function(polygons, variogram, width) {
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var i, j, k, n = polygons.length;
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if(n==0) return;
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// Boundaries of polygons space
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var xlim = [polygons[0][0][0], polygons[0][0][0]];
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var ylim = [polygons[0][0][1], polygons[0][0][1]];
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for(i=0;i<n;i++) // Polygons
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for(j=0;j<polygons[i].length;j++) { // Vertices
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if(polygons[i][j][0]<xlim[0])
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xlim[0] = polygons[i][j][0];
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if(polygons[i][j][0]>xlim[1])
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xlim[1] = polygons[i][j][0];
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if(polygons[i][j][1]<ylim[0])
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ylim[0] = polygons[i][j][1];
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if(polygons[i][j][1]>ylim[1])
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ylim[1] = polygons[i][j][1];
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}
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// Alloc for O(n^2) space
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var xtarget, ytarget;
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var a = Array(2), b = Array(2);
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var lxlim = Array(2); // Local dimensions
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var lylim = Array(2); // Local dimensions
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var x = Math.ceil((xlim[1]-xlim[0])/width);
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var y = Math.ceil((ylim[1]-ylim[0])/width);
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var A = Array(x+1);
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for(i=0;i<=x;i++) A[i] = Array(y+1);
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for(i=0;i<n;i++) {
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// Range for polygons[i]
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lxlim[0] = polygons[i][0][0];
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lxlim[1] = lxlim[0];
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lylim[0] = polygons[i][0][1];
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lylim[1] = lylim[0];
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for(j=1;j<polygons[i].length;j++) { // Vertices
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if(polygons[i][j][0]<lxlim[0])
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lxlim[0] = polygons[i][j][0];
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if(polygons[i][j][0]>lxlim[1])
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lxlim[1] = polygons[i][j][0];
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if(polygons[i][j][1]<lylim[0])
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lylim[0] = polygons[i][j][1];
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if(polygons[i][j][1]>lylim[1])
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lylim[1] = polygons[i][j][1];
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}
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// Loop through polygon subspace
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a[0] = Math.floor(((lxlim[0]-((lxlim[0]-xlim[0])%width)) - xlim[0])/width);
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a[1] = Math.ceil(((lxlim[1]-((lxlim[1]-xlim[1])%width)) - xlim[0])/width);
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b[0] = Math.floor(((lylim[0]-((lylim[0]-ylim[0])%width)) - ylim[0])/width);
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b[1] = Math.ceil(((lylim[1]-((lylim[1]-ylim[1])%width)) - ylim[0])/width);
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for(j=a[0];j<=a[1];j++)
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for(k=b[0];k<=b[1];k++) {
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xtarget = xlim[0] + j*width;
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ytarget = ylim[0] + k*width;
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if(polygons[i].pip(xtarget, ytarget))
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A[j][k] = kriging.predict(xtarget,
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ytarget,
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variogram);
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}
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}
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A.xlim = xlim;
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A.ylim = ylim;
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A.zlim = [variogram.t.min(), variogram.t.max()];
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A.width = width;
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return A;
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};
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kriging.contour = function(value, polygons, variogram) {
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|
|
|
};
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|
|
|
// Plotting on the DOM
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kriging.plot = function(canvas, grid, xlim, ylim, colors) {
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// Clear screen
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var ctx = canvas.getContext("2d");
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ctx.clearRect(0, 0, canvas.width, canvas.height);
|
|
|
|
// Starting boundaries
|
|
var range = [xlim[1]-xlim[0], ylim[1]-ylim[0], grid.zlim[1]-grid.zlim[0]];
|
|
var i, j, x, y, z;
|
|
var n = grid.length;
|
|
var m = grid[0].length;
|
|
var wx = Math.ceil(grid.width*canvas.width/(xlim[1]-xlim[0]));
|
|
var wy = Math.ceil(grid.width*canvas.height/(ylim[1]-ylim[0]));
|
|
for(i=0;i<n;i++)
|
|
for(j=0;j<m;j++) {
|
|
if(grid[i][j]==undefined) continue;
|
|
x = canvas.width*(i*grid.width+grid.xlim[0]-xlim[0])/range[0];
|
|
y = canvas.height*(1-(j*grid.width+grid.ylim[0]-ylim[0])/range[1]);
|
|
z = (grid[i][j]-grid.zlim[0])/range[2];
|
|
if(z<0.0) z = 0.0;
|
|
if(z>1.0) z = 1.0;
|
|
|
|
ctx.fillStyle = colors[Math.floor((colors.length-1)*z)];
|
|
ctx.fillRect(Math.round(x-wx/2), Math.round(y-wy/2), wx, wy);
|
|
}
|
|
|
|
};
|
|
|
|
|
|
return kriging;
|
|
}();
|
|
// if (module && module.exports){
|
|
// module.exports = kriging;
|
|
// }
|