[GRASS-dev] Re: [GRASS-user] Compiling addons from svn repository

[gianluca massei]

thanks Glynn, I'll try it soon!
gianluca
Glynn Clements ha scritto:
> gianluca massei wrote:
>
>   
>>>> thank you for interesting about mcda. I need help and suggest for
>>>> better coding!
>>>>
>>>> - Yes, r.mcda.electre, r.mcda.regime use
>>>> G_alloc_matrix((nrows*ncols),(nrows*ncols)) with related memory
>>>> problem. I'm looking for a better algorithm, especially for pairwise
>>>> comparison that need large matrix ((nrows*ncols),(nrows*ncols)) for
>>>> derive concordance and discordance matrix. Now I don't find it but I'
>>>> still looking for. Could you give me a suggest ?.
>>>>     
>>>>         
>>> Is this a sparse matrix? Or do you actually store a value for every
>>> possible combination?
>>>
>>> How is the resulting matrix used? If you don't need repeated, random
>>> access, it would be better to calculate the values as they are
>>> required instead of storing them.
>>>       
>> I need  matrix for storage a pairwise comparison pixel to pixel for 
>> generate an index for each row/colum an re-generate the output grid. For 
>> avoid the problem I'm tinking  storage the value in a file... but It 
>> dosen't a smart solution.
>>     
>
> Looking at the code, it would appear that you don't need to store the
> entire matrix, only the row/column sums. Something like the following
> (untested):
>
> void build_dominance_matrix(int nrows, int ncols, int ncriteria,
> 			    double *weight_vect, double ***decision_vol)
> {
>     int row1, col1, row2, col2;
>     int i, j, k, cont;
>     double *row_sum_conc = G_alloc_vector(nrows * ncols);
>     double *col_sum_conc = G_alloc_vector(nrows * ncols);
>     double *row_sum_disc = G_alloc_vector(nrows * ncols);
>     double *col_sum_disc = G_alloc_vector(nrows * ncols);
>
>     k = 0;			/* make pairwise comparation and build concordance/discordance matrix */
>     for (row1 = 0; row1 < nrows; row1++) {
> 	G_percent(row1, nrows, 2);
> 	for (col1 = 0; col1 < ncols; col1++) {
> 	    j = 0;
> 	    for (row2 = 0; row2 < nrows; row2++) {
> 		for (col2 = 0; col2 < ncols; col2++) {
> 		    double conc = 0, disc = 0;
> 		    for (i = 0; i < ncriteria; i++) {
> 			double d = decision_vol[row1][col1][i] - decision_vol[row2][col2][i];
> 			if (d >= 0)
> 			    conc += weight_vect[i];
> 			if (d > conc)
> 			    disc = -d;
> 		    }
> 		    row_sum_conc[k] += conc;
> 		    col_sum_conc[j] += conc;
> 		    row_sum_disc[k] += disc;
> 		    col_sum_disc[j] += disc;
>
> 		    j++;	/* increase rows index up to nrows*ncols */
> 		}
> 	    }
> 	    k++;		/* increase columns index up to nrows*ncols */
> 	}
>     }
>
>     /*calculate concordance and discordance index and storage in decision_vol */
>     cont = 0;			/*variabile progressiva per riga/colonna della concordance_map */
>     for (row1 = 0; row1 < nrows; row1++) {
> 	G_percent(row1, nrows, 2);
> 	for (col1 = 0; col1 < ncols; col1++) {
> 	    /* da un valore incrementale ad ogni ciclo per progredire nella concordance_mat */
> 	    decision_vol[row1][col1][ncriteria] = row_sum_conc[cont] - col_sum_conc[cont];	/*fill matrix with concordance index for each DCELL */
> 	    decision_vol[row1][col1][ncriteria + 1] = row_sum_disc[cont] - col_sum_disc[cont];	/*fill matrix with discordance index for each DCELL */
> 	    cont++;
> 	}
>     }
> }
>
>   
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>
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