int save_params()

in utils_nlp/models/glove/src/glove.c [178:290]
96 lines of code
43 McCabe index (conditional complexity)

int save_params(int nb_iter) {
    /*
     * nb_iter is the number of iteration (= a full pass through the cooccurrence matrix).
     *   nb_iter > 0 => checkpointing the intermediate parameters, so nb_iter is in the filename of output file.
     *   else        => saving the final paramters, so nb_iter is ignored.
     */

    long long a, b;
    char format[20];
    char output_file[MAX_STRING_LENGTH], output_file_gsq[MAX_STRING_LENGTH];
    char *word = malloc(sizeof(char) * MAX_STRING_LENGTH + 1);
    FILE *fid, *fout, *fgs;
    
    if (use_binary > 0) { // Save parameters in binary file
        if (nb_iter <= 0)
            sprintf(output_file,"%s.bin",save_W_file);
        else
            sprintf(output_file,"%s.%03d.bin",save_W_file,nb_iter);

        fout = fopen(output_file,"wb");
        if (fout == NULL) {fprintf(stderr, "Unable to open file %s.\n",save_W_file); return 1;}
        for (a = 0; a < 2 * (long long)vocab_size * (vector_size + 1); a++) fwrite(&W[a], sizeof(real), 1,fout);
        fclose(fout);
        if (save_gradsq > 0) {
            if (nb_iter <= 0)
                sprintf(output_file_gsq,"%s.bin",save_gradsq_file);
            else
                sprintf(output_file_gsq,"%s.%03d.bin",save_gradsq_file,nb_iter);

            fgs = fopen(output_file_gsq,"wb");
            if (fgs == NULL) {fprintf(stderr, "Unable to open file %s.\n",save_gradsq_file); return 1;}
            for (a = 0; a < 2 * (long long)vocab_size * (vector_size + 1); a++) fwrite(&gradsq[a], sizeof(real), 1,fgs);
            fclose(fgs);
        }
    }
    if (use_binary != 1) { // Save parameters in text file
        if (nb_iter <= 0)
            sprintf(output_file,"%s.txt",save_W_file);
        else
            sprintf(output_file,"%s.%03d.txt",save_W_file,nb_iter);
        if (save_gradsq > 0) {
            if (nb_iter <= 0)
                sprintf(output_file_gsq,"%s.txt",save_gradsq_file);
            else
                sprintf(output_file_gsq,"%s.%03d.txt",save_gradsq_file,nb_iter);

            fgs = fopen(output_file_gsq,"wb");
            if (fgs == NULL) {fprintf(stderr, "Unable to open file %s.\n",save_gradsq_file); return 1;}
        }
        fout = fopen(output_file,"wb");
        if (fout == NULL) {fprintf(stderr, "Unable to open file %s.\n",save_W_file); return 1;}
        fid = fopen(vocab_file, "r");
        sprintf(format,"%%%ds",MAX_STRING_LENGTH);
        if (fid == NULL) {fprintf(stderr, "Unable to open file %s.\n",vocab_file); return 1;}
        if (write_header) fprintf(fout, "%lld %d\n", vocab_size, vector_size);
        for (a = 0; a < vocab_size; a++) {
            if (fscanf(fid,format,word) == 0) return 1;
            // input vocab cannot contain special <unk> keyword
            if (strcmp(word, "<unk>") == 0) return 1;
            fprintf(fout, "%s",word);
            if (model == 0) { // Save all parameters (including bias)
                for (b = 0; b < (vector_size + 1); b++) fprintf(fout," %lf", W[a * (vector_size + 1) + b]);
                for (b = 0; b < (vector_size + 1); b++) fprintf(fout," %lf", W[(vocab_size + a) * (vector_size + 1) + b]);
            }
            if (model == 1) // Save only "word" vectors (without bias)
                for (b = 0; b < vector_size; b++) fprintf(fout," %lf", W[a * (vector_size + 1) + b]);
            if (model == 2) // Save "word + context word" vectors (without bias)
                for (b = 0; b < vector_size; b++) fprintf(fout," %lf", W[a * (vector_size + 1) + b] + W[(vocab_size + a) * (vector_size + 1) + b]);
            fprintf(fout,"\n");
            if (save_gradsq > 0) { // Save gradsq
                fprintf(fgs, "%s",word);
                for (b = 0; b < (vector_size + 1); b++) fprintf(fgs," %lf", gradsq[a * (vector_size + 1) + b]);
                for (b = 0; b < (vector_size + 1); b++) fprintf(fgs," %lf", gradsq[(vocab_size + a) * (vector_size + 1) + b]);
                fprintf(fgs,"\n");
            }
            if (fscanf(fid,format,word) == 0) return 1; // Eat irrelevant frequency entry
        }

        if (use_unk_vec) {
            real* unk_vec = (real*)calloc((vector_size + 1), sizeof(real));
            real* unk_context = (real*)calloc((vector_size + 1), sizeof(real));
            word = "<unk>";

            int num_rare_words = vocab_size < 100 ? vocab_size : 100;

            for (a = vocab_size - num_rare_words; a < vocab_size; a++) {
                for (b = 0; b < (vector_size + 1); b++) {
                    unk_vec[b] += W[a * (vector_size + 1) + b] / num_rare_words;
                    unk_context[b] += W[(vocab_size + a) * (vector_size + 1) + b] / num_rare_words;
                }
            }

            fprintf(fout, "%s",word);
            if (model == 0) { // Save all parameters (including bias)
                for (b = 0; b < (vector_size + 1); b++) fprintf(fout," %lf", unk_vec[b]);
                for (b = 0; b < (vector_size + 1); b++) fprintf(fout," %lf", unk_context[b]);
            }
            if (model == 1) // Save only "word" vectors (without bias)
                for (b = 0; b < vector_size; b++) fprintf(fout," %lf", unk_vec[b]);
            if (model == 2) // Save "word + context word" vectors (without bias)
                for (b = 0; b < vector_size; b++) fprintf(fout," %lf", unk_vec[b] + unk_context[b]);
            fprintf(fout,"\n");

            free(unk_vec);
            free(unk_context);
        }

        fclose(fid);
        fclose(fout);
        if (save_gradsq > 0) fclose(fgs);
    }
    return 0;
}