libavcodec/lpc.c:126: error: Uninitialized Value
  The value read from weight was never initialized.

libavcodec/lpc.c:126:9: 
124.     }else{
125.         LLSModel m[2];
126.         double var[MAX_LPC_ORDER+1], av_uninit(weight);
             ^
127. 
128.         for(pass=0; pass<use_lpc-1; pass++){

int ff_lpc_calc_coefs(DSPContext *s,
                      const int32_t *samples, int blocksize, int min_order,
                      int max_order, int precision,
                      int32_t coefs[][MAX_LPC_ORDER], int *shift, int use_lpc,
                      int omethod, int max_shift, int zero_shift)
{
    double autoc[MAX_LPC_ORDER+1];
    double ref[MAX_LPC_ORDER];
    double lpc[MAX_LPC_ORDER][MAX_LPC_ORDER];
    int i, j, pass;
    int opt_order;
    assert(max_order >= MIN_LPC_ORDER && max_order <= MAX_LPC_ORDER && use_lpc > 0);
    if(use_lpc == 1){
        s->flac_compute_autocorr(samples, blocksize, max_order, autoc);
        compute_lpc_coefs(autoc, max_order, &lpc[0][0], MAX_LPC_ORDER, 0, 1);
        for(i=0; i<max_order; i++)
            ref[i] = fabs(lpc[i][i]);
    }else{
        LLSModel m[2];
        double var[MAX_LPC_ORDER+1], av_uninit(weight);
        for(pass=0; pass<use_lpc-1; pass++){
            av_init_lls(&m[pass&1], max_order);
            weight=0;
            for(i=max_order; i<blocksize; i++){
                for(j=0; j<=max_order; j++)
                    var[j]= samples[i-j];
                if(pass){
                    double eval, inv, rinv;
                    eval= av_evaluate_lls(&m[(pass-1)&1], var+1, max_order-1);
                    eval= (512>>pass) + fabs(eval - var[0]);
                    inv = 1/eval;
                    rinv = sqrt(inv);
                    for(j=0; j<=max_order; j++)
                        var[j] *= rinv;
                    weight += inv;
                }else
                    weight++;
                av_update_lls(&m[pass&1], var, 1.0);
            }
            av_solve_lls(&m[pass&1], 0.001, 0);
        }
        for(i=0; i<max_order; i++){
            for(j=0; j<max_order; j++)
                lpc[i][j]=-m[(pass-1)&1].coeff[i][j];
            ref[i]= sqrt(m[(pass-1)&1].variance[i] / weight) * (blocksize - max_order) / 4000;
        }
        for(i=max_order-1; i>0; i--)
            ref[i] = ref[i-1] - ref[i];
    }
    opt_order = max_order;
    if(omethod == ORDER_METHOD_EST) {
        opt_order = estimate_best_order(ref, min_order, max_order);
        i = opt_order-1;
        quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], max_shift, zero_shift);
    } else {
        for(i=min_order-1; i<max_order; i++) {
            quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], max_shift, zero_shift);
        }
    }
    return opt_order;
}
['int ff_lpc_calc_coefs(DSPContext *s,\n                      const int32_t *samples, int blocksize, int min_order,\n                      int max_order, int precision,\n                      int32_t coefs[][MAX_LPC_ORDER], int *shift, int use_lpc,\n                      int omethod, int max_shift, int zero_shift)\n{\n    double autoc[MAX_LPC_ORDER+1];\n    double ref[MAX_LPC_ORDER];\n    double lpc[MAX_LPC_ORDER][MAX_LPC_ORDER];\n    int i, j, pass;\n    int opt_order;\n    assert(max_order >= MIN_LPC_ORDER && max_order <= MAX_LPC_ORDER && use_lpc > 0);\n    if(use_lpc == 1){\n        s->flac_compute_autocorr(samples, blocksize, max_order, autoc);\n        compute_lpc_coefs(autoc, max_order, &lpc[0][0], MAX_LPC_ORDER, 0, 1);\n        for(i=0; i<max_order; i++)\n            ref[i] = fabs(lpc[i][i]);\n    }else{\n        LLSModel m[2];\n        double var[MAX_LPC_ORDER+1], av_uninit(weight);\n        for(pass=0; pass<use_lpc-1; pass++){\n            av_init_lls(&m[pass&1], max_order);\n            weight=0;\n            for(i=max_order; i<blocksize; i++){\n                for(j=0; j<=max_order; j++)\n                    var[j]= samples[i-j];\n                if(pass){\n                    double eval, inv, rinv;\n                    eval= av_evaluate_lls(&m[(pass-1)&1], var+1, max_order-1);\n                    eval= (512>>pass) + fabs(eval - var[0]);\n                    inv = 1/eval;\n                    rinv = sqrt(inv);\n                    for(j=0; j<=max_order; j++)\n                        var[j] *= rinv;\n                    weight += inv;\n                }else\n                    weight++;\n                av_update_lls(&m[pass&1], var, 1.0);\n            }\n            av_solve_lls(&m[pass&1], 0.001, 0);\n        }\n        for(i=0; i<max_order; i++){\n            for(j=0; j<max_order; j++)\n                lpc[i][j]=-m[(pass-1)&1].coeff[i][j];\n            ref[i]= sqrt(m[(pass-1)&1].variance[i] / weight) * (blocksize - max_order) / 4000;\n        }\n        for(i=max_order-1; i>0; i--)\n            ref[i] = ref[i-1] - ref[i];\n    }\n    opt_order = max_order;\n    if(omethod == ORDER_METHOD_EST) {\n        opt_order = estimate_best_order(ref, min_order, max_order);\n        i = opt_order-1;\n        quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], max_shift, zero_shift);\n    } else {\n        for(i=min_order-1; i<max_order; i++) {\n            quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], max_shift, zero_shift);\n        }\n    }\n    return opt_order;\n}']