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newlisp/nl-matrix.c

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/* nl-matrix.c --- matrix functions for newLISP
Copyright (C) 2016 Lutz Mueller
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/* Since version 8.9.9 all matrix operations work on lists and
arrays. Previously only lists where supported.
*/
#include "newlisp.h"
#include "protos.h"
#include "float.h"
double * * multiply(double ** A, double ** B, int m, int n, int k, int l);
double * * invert(double * * A, int n, int * err, double tiny);
int ludcmp(double * * a, int n, int * indx, double * d, double tiny);
void lubksb(double * * a, int n, int * indx, double * b);
double * * getMatrix(CELL * params, int * type, int * n, int * m, int * err);
double * * makeMatrix(CELL * number, int n, int m);
int getDimensions(CELL * mat, int * n, int * m);
double * * data2matrix(CELL * list, int n, int m);
CELL * matrix2data(double * * matrix, int type, int n, int m);
double * * allocateMatrix(int rows, int cols);
void freeMatrix(double * * m, int rows);
/* since version 7.4.8 'transpose' tramspose any matrix not only
matrices containing numbers.
some of the algorithms used come from C LAPACK and index
starting 1 not 0 as usual in C
*/
CELL * p_matTranspose(CELL * params)
{
CELL * A;
CELL * B;
CELL * rowA = NULL;
CELL * cell = NULL;
CELL * conCell;
CELL * new;
int n, m, i, j;
CELL * * Brows;
A = evaluateExpression(params);
if(A->type == CELL_ARRAY)
return(arrayTranspose(A));
if(A->type != CELL_EXPRESSION)
return(errorProcExt(ERR_NOT_MATRIX, params));
if(getDimensions(A, &n, &m) == FALSE)
return(errorProcExt(ERR_NOT_MATRIX, params));
if(n == 0 || m == 0)
return(errorProcExt(ERR_WRONG_DIMENSIONS, params));
Brows = allocMemory(sizeof(CELL *) * m);
for(j = 0; j < m; j++)
{
Brows[j] = getCell(CELL_EXPRESSION);
if(j > 0) Brows[j-1]->next = Brows[j];
}
B = makeCell(CELL_EXPRESSION, (UINT)Brows[0]);
for(i = 0; i < n; i++)
{
if(i == 0)
rowA = (CELL*)A->contents;
else
rowA = rowA->next;
if(rowA->type != CELL_EXPRESSION)
conCell = rowA;
else
conCell = NULL;
for(j = 0; j < m; j++)
{
if(conCell == NULL)
{
if(j == 0)
cell = (CELL*)rowA->contents;
else
cell = cell->next;
new = copyCell(cell);
}
else
new = copyCell(conCell);
if( i == 0)
Brows[j]->contents = (UINT)new;
else
Brows[j]->next = new;
Brows[j] = new;
}
}
free(Brows);
return(B);
}
CELL * p_matMultiply(CELL * params)
{
CELL * C;
double * * X = NULL;
double * * Y = NULL;
int typeX, typeY, typeM;
double * * M = NULL;
int n, m, k, l;
int err = 0;
if((X = getMatrix(params, &typeX, &n, &m, &err)) == NULL)
return(errorProcExt(err, params));
if((Y = getMatrix(params->next, &typeY, &k, &l, &err)) == NULL)
{
freeMatrix(X, n);
return(errorProcExt(err, params->next));
}
typeM = (typeX == CELL_ARRAY || typeY == CELL_ARRAY) ? CELL_ARRAY : CELL_EXPRESSION;
if(m != k)
err = ERR_WRONG_DIMENSIONS;
else if((M = multiply(X, Y, n, m, k, l)) == NULL)
err = ERR_NOT_ENOUGH_MEMORY;
freeMatrix(X, n);
freeMatrix(Y, k);
if(err) return(errorProc(err));
C = matrix2data(M, typeM, n, l);
freeMatrix(M, n);
return(C);
}
CELL * p_matInvert(CELL * params)
{
CELL * dataY;
int n, m, err = 0;
int typeA;
double * * A;
double * * Y;
#ifdef FP_NAN
double tiny = FP_NAN;
#else
double tiny = sqrt(-1);
#endif
if((A = getMatrix(params, &typeA, &n, &m, &err)) == NULL)
return(errorProcExt(err, params));
if(params->next != nilCell)
getFloat(params->next, &tiny);
if(n != m)
{
freeMatrix(A, n);
return(errorProcExt(ERR_WRONG_DIMENSIONS, params));
}
if( (Y = invert(A, n, &err, tiny)) == NULL)
{
freeMatrix(A, n);
if(err) return(errorProc(err));
return(nilCell);
}
dataY = matrix2data(Y, typeA, n, n);
freeMatrix(A, n);
freeMatrix(Y, n);
return(dataY);
}
CELL * p_determinant(CELL * params)
{
double * * M;
double d;
int typeM, n, m, i, err;
int * indx;
#ifdef FP_NAN
double tiny = FP_NAN;
#else
double tiny = sqrt(-1);
#endif
if( (M = getMatrix(params, &typeM, &n, &m, &err)) == NULL)
return(errorProcExt(err, params));
if(params->next != nilCell)
getFloat(params->next, &tiny);
if(n != m)
{
freeMatrix(M, n);
return(errorProcExt(ERR_WRONG_DIMENSIONS, params));
}
indx = (int *)calloc((n + 1), sizeof(int));
if(ludcmp(M, n, indx, &d, tiny) == FALSE)
{
free(indx);
freeMatrix(M, n);
return(nilCell);
}
for(i = 1; i <= n; i++)
d *= M[i][i];
free(indx);
freeMatrix(M, n);
return(stuffFloat(d));
}
CELL * p_matScalar(CELL * params)
{
CELL * op;
double * * A = NULL;
double * * B = NULL;
double * * M = NULL;
int typeA, typeB, typeC = 0;
int type;
int n, m, k, l, err = 0;
CELL * result;
op = params;
op = evaluateExpression(op);
if(op->type == CELL_SYMBOL)
type = *((SYMBOL *)op->contents)->name;
else if(op->type == CELL_PRIMITIVE)
type = *(char *)op->aux;
else
return(errorProcExt(ERR_ILLEGAL_TYPE, params));
params = params->next;
if( (A = getMatrix(params, &typeA, &n, &m, &err)) == NULL)
return(errorProcExt(err, params));
getEvalDefault(params->next, &result);
if(isNumber(result->type))
{
k = n, l = m;
if((B = makeMatrix(result, k, l)) == NULL)
{
err = ERR_NOT_ENOUGH_MEMORY;
goto MAT_SCALAR_FIN;
}
typeB = CELL_EXPRESSION;
}
else
{
params = makeCell(CELL_QUOTE, (UINT)result);
if( (B = getMatrix(params, &typeB, &k, &l, &err)) == NULL)
{
freeMatrix(A, n);
return(errorProc(err));
}
else
{
params->contents = (UINT)nilCell;
deleteList(params);
}
}
typeC = (typeA == CELL_ARRAY || typeB == CELL_ARRAY) ? CELL_ARRAY : CELL_EXPRESSION;
if(n != k || m != l)
{
freeMatrix(A, n);
freeMatrix(B, k);
return(errorProc(ERR_WRONG_DIMENSIONS));
}
else if((M = allocateMatrix(n, m)) == NULL)
err = ERR_NOT_ENOUGH_MEMORY;
else
{
for(k = 1; k <= n; k++)
for(l = 1; l <= m; l++)
{
switch(type)
{
case '+': M[k][l] = A[k][l] + B[k][l]; break;
case '-': M[k][l] = A[k][l] - B[k][l]; break;
case '*': M[k][l] = A[k][l] * B[k][l]; break;
case '/': M[k][l] = A[k][l] / B[k][l]; break;
default:
return(errorProcExt(ERR_ILLEGAL_TYPE, op));
}
}
}
MAT_SCALAR_FIN:
freeMatrix(A, n);
freeMatrix(B, n);
if(err) return (errorProc(err));
result = matrix2data(M, typeC, n, m);
freeMatrix(M, n);
return(result);
}
/* ------------------- C = A*B, A and B unchanged --------------------- */
double * * multiply(double ** A, double ** B, int n, int m, int k, int l)
{
double * * C;
int i, j, s;
double sum;
if(m != k)
{
errorProc(ERR_WRONG_DIMENSIONS);
return(NULL);
}
if((C = allocateMatrix(n, l)) == NULL)
return(NULL);
for(i = 1; i <= n; i++)
{
for(j = 1; j <= l; j++)
{
sum = 0.0;
for(s = 1; s <= m; s++)
sum += A[i][s] * B[s][j];
C[i][j] = sum;
}
}
return(C);
}
/* ----- return inverse of A in Y, A will contain LU decomposition --- */
double * * invert(double * * A, int n, int * err, double tiny)
{
double * * Y = NULL;
double * col;
double d;
int i, j;
int * indx;
col = (double *)calloc(n + 1, sizeof(double));
indx = (int *)calloc((n + 1), sizeof(int));
if(ludcmp(A, n, indx, &d, tiny) == FALSE)
goto INVERT_FIN;
if((Y = allocateMatrix(n, n)) == NULL)
{
*err = ERR_NOT_ENOUGH_MEMORY;
goto INVERT_FIN;
}
for(j = 1; j <= n; j++)
{
for(i = 1; i <= n; i++) col[i] = 0.0;
col[j] = 1.0;
lubksb(A, n, indx, col);
for(i = 1; i <= n; i++) Y[i][j] = col[i];
}
INVERT_FIN:
free(col);
free(indx);
return(Y);
}
/* ------------------------- LU decomposition ---------------------------
// algorithms ludcmp() and lubskb() adapted from:
// Numerical Recipes in 'C', 2nd Edition
// W.H. Press, S.A. Teukolsky
// W.T. Vettering, B.P. Flannery
*/
int ludcmp(double * * a, int n, int * indx, double * d, double tiny)
{
int i, imax = 0, j, k;
double big, dum, sum, temp;
double * vv;
vv = (double *)calloc(n + 4, sizeof(double));
*d = 1.0;
/* find abs biggest number in each row and put 1/big in vector */
for (i = 1; i <= n; i++)
{
big = 0.0;
for(j = 1;j <= n; j++)
if ((temp = fabs(a[i][j])) > big) big = temp;
if(big == 0.0)
{
free(vv);
return(FALSE);
}
vv[i] = 1.0 / big;
}
for (j = 1; j <= n; j++)
{
for (i = 1; i < j; i++)
{
sum = a[i][j];
for(k = 1; k < i; k++) sum -= a[i][k] * a[k][j];
a[i][j] = sum;
}
big = 0.0;
for (i = j; i <= n; i++)
{
sum = a[i][j];
for(k = 1; k < j; k++)
sum -= a[i][k] * a[k][j];
a[i][j] = sum;
if ( (dum = vv[i] * fabs(sum)) >= big)
{
big = dum;
imax = i;
}
}
if (j != imax)
{
for (k = 1; k <= n; k++)
{
dum = a[imax][k];
a[imax][k] = a[j][k];
a[j][k] = dum;
}
*d = -(*d);
vv[imax] = vv[j];
}
indx[j] = imax;
if (a[j][j] == 0.0)
{
#ifdef FP_NAN
if(tiny != FP_NAN)
#else
if(!isnan(tiny))
#endif
a[j][j] = tiny;
else
{
free(vv);
return(FALSE);
}
}
if (j != n)
{
dum = 1.0 / (a[j][j]);
for(i = j+1; i <= n; i++) a[i][j] *= dum;
}
}
free(vv);
return(TRUE);
}
void lubksb(double * * a, int n, int * indx, double * b)
{
int i, ii = 0, ip, j;
double sum;
for (i = 1; i <= n; i++)
{
ip = indx[i];
sum = b[ip];
b[ip] = b[i];
if (ii)
for(j = ii; j <= i-1; j++) sum -= a[i][j] * b[j];
else
if(sum) ii = i;
b[i] = sum;
}
for (i = n; i >= 1; i--)
{
sum = b[i];
for(j = i+1; j <= n; j++) sum -= a[i][j] * b[j];
b[i] = sum / a[i][i];
}
}
/* ------------ KMEANS clustering ------------------------------- */
#ifdef KMEANS
#define DOUBLE_MAX 1.0e308 /* approximate for IEEE 754 */
/* (kmeans-train data k context [centroidsi [criterium]])
data = n * m matrix (list or array)
k = number of clusters
centroids = k * m matrix of start centroids (optional) (list or array)
when centroids then optional convergence criterium, 1e-10 by default
the m of optional centroids may be smaller than m in data to force
reduction of dimension, e.g. when the last column of date is pre-labeled
data and optional centroids as either array or list
if no centroids are given, they are calculated from
inital random membership
returns a list of idecreasing inner SSQs from start to
end of iterations
result context ctx
ctx:centroids (m * k list)
ctx:clusters list with k sublists of indices into data (k sublists)
cts:labels list with n cluster numbers for each data records (n list)
labels go from 1 to k
NOTE, indices in C-source are not 0-based as usually in C but
go from 1 to N
*/
CELL * p_kmeansTrain(CELL * params)
{
double * * X; /* data */
double * * C; /* centroids `*/
int * labels; /* membership */
int * counts; /* cluster sizes */
double * ssqs; /* inner SSQs of clusters */
double ssqTotal; /* total squared sum inner deviations */
double ssqRecord; /* inner ssq of a record in a cluster */
double ssqMin; /* minimum ssq for one record */
double ssqPrev = DOUBLE_MAX;
double dist;
double crit = 1.0e-10;
CELL * centroids;
CELL * clusters;
CELL * deviations;
CELL * SSQlist = NULL;
CELL * membership;
CELL * * cellArray;
SYMBOL * ctx;
SYMBOL * sPtr;
int n, m, k; /* rows, columns, groups */
int i, j, l; /* loop imdices */
int err = 0, type;
int kc, mc;
UINT p;
if((X = getMatrix(params, &type, &n, &m, &err)) == NULL)
return(errorProcExt(err, params));
params = getInteger(params->next, &p);
k = p;
if((ctx = getCreateContext(params, TRUE)) == NULL)
return(errorProcExt(ERR_SYMBOL_OR_CONTEXT_EXPECTED, params));
membership = getCell(CELL_EXPRESSION);
sPtr = translateCreateSymbol("labels", CELL_EXPRESSION, ctx, TRUE);
assignSymbol(sPtr, membership);
/* make centroid matrix */
params = params->next;
if(params == nilCell)
C = allocateMatrix(k, m);
/* or get predefined centroids from user */
else
{
if((C = getMatrix(params, &type, &kc, &mc, &err)) == NULL)
return(errorProcExt(err, params));
/* allow forcing less columns via optional centroids mc <= m */
if((kc != k) || (mc > m))
return(errorProcExt(ERR_WRONG_DIMENSIONS, params));
m = mc;
/* optionally custom criterium */
if(params->next != nilCell)
getFloat(params->next, &crit);
}
counts = allocMemory((k + 1) * sizeof(int));
labels = allocMemory((n + 1) * sizeof(int));
ssqs = callocMemory((k + 1) * sizeof(double));
if(params != nilCell)
goto ASSIGN_MEMBERSHIP;
/* asssign random membership */
for(i = 1; i <= n; i++)
labels[i] = (i % k) + 1;
ITERATE_KMEANS:
/* calc KMEANS centroids from membership */
for(l = 1; l <= k; l++)
{
counts[l] = 0;
ssqs[l] = 0.0;
for(j = 1; j <= m; j++)
C[l][j] = 0;
}
for(i = 1; i <= n; i++)
{
l = labels[i];
counts[l] += 1;
for(j = 1; j <= m; j++)
C[l][j] += X[i][j];
}
for(l = 1; l <= k; l++)
for(j = 1; j <= m; j++)
C[l][j] = C[l][j] / counts[l];
/* assign each record in X to closest centroid */
ASSIGN_MEMBERSHIP:
ssqTotal = 0.0;
for(i = 1; i <= n; i++)
{
ssqMin = DOUBLE_MAX;
for(l = 1; l <= k; l++)
{
ssqRecord = 0.0;
for(j = 1; j <= m; j++)
{
dist = X[i][j] - C[l][j];
ssqRecord += dist * dist;
}
if(ssqRecord < ssqMin)
{
ssqMin = ssqRecord;
labels[i] = l;
}
}
ssqTotal += ssqMin;
ssqs[labels[i]] += ssqMin;
}
if(fabs(ssqPrev - ssqTotal) > crit)
{
ssqPrev = ssqTotal;
if(SSQlist == NULL) SSQlist = getCell(CELL_EXPRESSION);
addList(SSQlist, stuffFloat(ssqTotal));
goto ITERATE_KMEANS;
}
/* entroid matrix as nested list */
centroids = matrix2data(C, CELL_EXPRESSION, k, m);
sPtr = translateCreateSymbol("centroids", CELL_EXPRESSION, ctx, TRUE);
assignSymbol(sPtr, centroids);
/* average intra cluster deviation */
deviations = getCell(CELL_EXPRESSION);
sPtr = translateCreateSymbol("deviations", CELL_EXPRESSION, ctx, TRUE);
assignSymbol(sPtr, deviations);
/* cluster memberships, for each cluster a list of X data record indices */
clusters = getCell(CELL_EXPRESSION);
cellArray = (CELL * *)allocMemory(k * sizeof(CELL));
for(l = 1; l <= k; l++)
{
cellArray[l] = getCell(CELL_EXPRESSION);
addList(clusters, cellArray[l]);
dist = sqrt(ssqs[l] / counts[l]);
addList(deviations, stuffFloat(dist));
}
for(i = 1; i <= n; i++)
{
l = labels[i];
addList(cellArray[l], stuffInteger(i - 1));
}
sPtr = translateCreateSymbol("clusters", CELL_EXPRESSION, ctx, TRUE);
assignSymbol(sPtr, clusters);
for(i = 1; i <= n; i++)
addList(membership, stuffInteger(labels[i]));
freeMatrix(C, k);
freeMatrix(X, n);
free(cellArray);
free(ssqs);
free(labels);
free(counts);
return(SSQlist);
}
/* (kmeans-query data-record-vector centroids)
calculates eucledian distances to all centroids
from a previous (kmeans-train data k context [centroids])
can also be used on the original data to get
eucledian distance from one data record to all
other records (a KNN fucntion could be built this way)
(kmeans-query data-record-vector data)
*/
CELL * p_kmeansQuery(CELL * params)
{
CELL * data;
CELL * centroids;
CELL * dat;
CELL * result;
CELL * row;
CELL * cent;
double dist;
double ssq;
params = getEvalDefault(params, &data);
if(data->type != CELL_EXPRESSION)
errorProcExt(ERR_LIST_EXPECTED, data);
getEvalDefault(params, &centroids);
if(centroids->type == CELL_ARRAY)
{
centroids = arrayList(centroids, TRUE);
pushResult(centroids);
}
else if(centroids->type != CELL_EXPRESSION)
return(errorProcExt(ERR_NOT_MATRIX, centroids));
row = (CELL *)centroids->contents;
if(row->type != CELL_EXPRESSION)
return(errorProcExt(ERR_LIST_EXPECTED, row));
result = getCell(CELL_EXPRESSION);
while(row != nilCell) /* for each centroid */
{
cent = (CELL *)row->contents;
dat = (CELL *)data->contents;
ssq = 0.0;
while(cent != nilCell && dat != nilCell)
{
dist = fabs(getDirectFloat(cent) - getDirectFloat(dat));
ssq += dist * dist;
cent = cent->next;
dat = dat->next;
}
dist = sqrt(ssq);
addList(result, stuffFloat(dist));
row = row->next;
}
return(result);
}
#endif
/* ------------ make a matrix from list or array expr ----------- */
double * * getMatrix(CELL * params, int * type, int * n, int * m, int *err)
{
CELL * data;
double * * M;
getEvalDefault(params, &data);
if(data->type != CELL_EXPRESSION && data->type != CELL_ARRAY)
{
*err = ERR_NOT_MATRIX;
return(NULL);
}
*type = data->type;
if(getDimensions(data, n, m) == FALSE)
{
*err = ERR_NOT_MATRIX;
return(NULL);
}
if( (M = data2matrix(data, *n, *m)) == NULL)
{
*err = ERR_NOT_ENOUGH_MEMORY;
return(NULL);
}
return(M);
}
double * * makeMatrix(CELL * number, int n, int m)
{
double s;
double * * matrix;
int i, j;
#ifndef NEWLISP64
if(number->type == CELL_FLOAT)
s = *(double *)&number->aux;
else if(number->type == CELL_INT64)
s = *(INT64 *)&number->aux;
#else
if(number->type == CELL_FLOAT)
s = *(double *)&number->contents;
#endif
else
s = number->contents;
if((matrix = allocateMatrix(n, m)) == NULL)
return(NULL);
for(i = 1; i <= n; i++)
for(j = 1; j <= m; j++)
matrix[i][j] = s;
return(matrix);
}
/* --------------------------- get dimensons on a matrix --------------- */
int getDimensions(CELL * mat, int * n, int * m)
{
CELL * row;
CELL * cell;
int rows, cols;
CELL * * addr;
if(mat->type == CELL_ARRAY)
{
addr = (CELL * *)mat->contents;
*n = (mat->aux - 1) / sizeof(CELL *);
cell = *addr;
if(cell->type != CELL_ARRAY)
{
errorProcExt(ERR_WRONG_DIMENSIONS, mat);
return(FALSE);
}
*m = (cell->aux - 1) / sizeof(CELL *);
return(TRUE);
}
/* mat->type is CELL_EXPRESSSION */
row = (CELL *)mat->contents;
if(row->type != CELL_EXPRESSION)
{
errorProcExt(ERR_NOT_MATRIX, mat);
return(FALSE);
}
cell = (CELL *)row->contents;
rows = cols = 0;
while(row != nilCell)
{
++rows;
row = row->next;
}
while(cell != nilCell)
{
++cols;
cell = cell->next;
}
if(rows == 0 || cols == 0)
{
errorProcExt(ERR_WRONG_DIMENSIONS, mat);
return(FALSE);
}
*n = rows;
*m = cols;
return(TRUE);
}
/* ------------ copy array/list into a matrix of doubles -------- */
/* xlate lists or arrays into C arrays with 1-offest for 1st element
wrong row type result and missing row elelements result on 0.0 */
double * * data2matrix(CELL * list, int n, int m)
{
double ** matrix;
CELL * row;
CELL * cell;
int i, j, size;
CELL * * addr;
CELL * * rowAddr;
if((matrix = allocateMatrix(n, m)) == NULL)
return(NULL);
if(list->type == CELL_ARRAY)
{
addr = (CELL * *)list->contents;
for(i = 1; i <= n; i++)
{
row = *(addr + i - 1);
if(row->type != CELL_ARRAY)
{
for(j = 1; j <= m; j++) matrix[i][j] = 0.0;
continue;
}
rowAddr = (CELL * *)row->contents;
size = (row->aux - 1)/sizeof(CELL *);
for(j = 1; j <= m; j++)
{
if(j <= size)
matrix[i][j] = getDirectFloat((CELL *)*(rowAddr + j - 1));
else matrix[i][j] = 0.0;
}
}
return(matrix);
}
/* list->type == CELL_EXPRESSION */
row = (CELL *)list->contents;
for(i = 1; i <= n; i++)
{
if(row->type != CELL_EXPRESSION)
for(j = 1; j <= m; j++) matrix[i][j] = 0.0;
else
{
cell = (CELL *)row->contents;
for(j = 1; j <= m; j++)
{
matrix[i][j] = getDirectFloat(cell);
cell = cell->next;
}
}
row = row->next;
}
return(matrix);
}
/* ------- allocate an array/list and copy matrix into it ---- */
CELL * matrix2data(double ** matrix, int type, int n, int m)
{
CELL * list;
CELL * row;
CELL * cell;
int i, j;
double fnum;
CELL * * addr;
CELL * * rowAddr;
list = getCell(type);
if(type == CELL_EXPRESSION)
{
row = getCell(CELL_EXPRESSION);
list->contents = (UINT)row;
for(i = 1; i <= n; i++)
{
cell = getCell(CELL_FLOAT);
row->contents = (UINT)cell;
for(j = 1; j <= m; j++)
{
fnum = matrix[i][j];
#ifndef NEWLISP64
*(double *)&cell->aux = fnum;
#else
*(double *)&cell->contents = fnum;
#endif
if(j == m) break;
cell->next = getCell(CELL_FLOAT);
cell = cell->next;
}
if(i == n) break;
row->next = getCell(CELL_EXPRESSION);
row = row->next;
}
}
else /* type is CELL_ARRAY */
{
list->aux = n * sizeof(CELL *) + 1;
addr = (CELL * *)allocMemory(list->aux);
list->contents = (UINT)addr;
for(i = 1; i <= n; i++)
{
cell = getCell(CELL_ARRAY);
cell->aux = m * sizeof(CELL *) + 1;
rowAddr = (CELL * *)allocMemory(cell->aux);
cell->contents = (UINT)rowAddr;
*(addr + i -1) = cell;
for(j = 1; j <= m; j++)
{
fnum = matrix[i][j];
*(rowAddr + j - 1) = stuffFloat(fnum);
}
}
}
return(list);
}
/* ------------------------ allocate/free a matrix ----------------- */
double * * allocateMatrix(int rows, int cols)
{
int i;
double * * m;
m = (double **) calloc(rows + 1, sizeof(double *));
if (!m)
return(NULL);
for(i = 1; i <= rows; i++)
{
m[i] = (double *)calloc(cols + 1, sizeof(double));
if (!m[i])
return(NULL);
}
return(m);
}
void freeMatrix(double * * m, int rows)
{
int i;
if(m == NULL) return;
for(i = 1; i <= rows; i++)
free(m[i]);
free(m);
}
/* end of file */
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