libavcodec/lpc.c:208: error: Uninitialized Value
  The value read from weight was never initialized.

libavcodec/lpc.c:208:9: 
206.         LLSModel m[2];
207.         LOCAL_ALIGNED(32, double, var, [FFALIGN(MAX_LPC_ORDER+1,4)]);
208.         double av_uninit(weight);
             ^
209.         memset(var, 0, FFALIGN(MAX_LPC_ORDER+1,4)*sizeof(*var));
210. 

int ff_lpc_calc_coefs(LPCContext *s,
                      const int32_t *samples, int blocksize, int min_order,
                      int max_order, int precision,
                      int32_t coefs[][MAX_LPC_ORDER], int *shift,
                      enum FFLPCType lpc_type, int lpc_passes,
                      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 = 0;
    int opt_order;
    assert(max_order >= MIN_LPC_ORDER && max_order <= MAX_LPC_ORDER &&
           lpc_type > FF_LPC_TYPE_FIXED);
    if (blocksize != s->blocksize || max_order != s->max_order ||
        lpc_type  != s->lpc_type) {
        ff_lpc_end(s);
        ff_lpc_init(s, blocksize, max_order, lpc_type);
    }
    if (lpc_type == FF_LPC_TYPE_LEVINSON || (lpc_type == FF_LPC_TYPE_CHOLESKY && lpc_passes > 1)) {
        s->lpc_apply_welch_window(samples, blocksize, s->windowed_samples);
        s->lpc_compute_autocorr(s->windowed_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]);
        pass++;
    }
    if (lpc_type == FF_LPC_TYPE_CHOLESKY) {
        LLSModel m[2];
        LOCAL_ALIGNED(32, double, var, [FFALIGN(MAX_LPC_ORDER+1,4)]);
        double av_uninit(weight);
        memset(var, 0, FFALIGN(MAX_LPC_ORDER+1,4)*sizeof(*var));
        for(j=0; j<max_order; j++)
            m[0].coeff[max_order-1][j] = -lpc[max_order-1][j];
        for(; pass<lpc_passes; pass++){
            avpriv_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= m[(pass-1)&1].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++;
                m[pass&1].update_lls(&m[pass&1], var);
            }
            avpriv_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(LPCContext *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,\n                      enum FFLPCType lpc_type, int lpc_passes,\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 = 0;\n    int opt_order;\n    assert(max_order >= MIN_LPC_ORDER && max_order <= MAX_LPC_ORDER &&\n           lpc_type > FF_LPC_TYPE_FIXED);\n    if (blocksize != s->blocksize || max_order != s->max_order ||\n        lpc_type  != s->lpc_type) {\n        ff_lpc_end(s);\n        ff_lpc_init(s, blocksize, max_order, lpc_type);\n    }\n    if (lpc_type == FF_LPC_TYPE_LEVINSON || (lpc_type == FF_LPC_TYPE_CHOLESKY && lpc_passes > 1)) {\n        s->lpc_apply_welch_window(samples, blocksize, s->windowed_samples);\n        s->lpc_compute_autocorr(s->windowed_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        pass++;\n    }\n    if (lpc_type == FF_LPC_TYPE_CHOLESKY) {\n        LLSModel m[2];\n        LOCAL_ALIGNED(32, double, var, [FFALIGN(MAX_LPC_ORDER+1,4)]);\n        double av_uninit(weight);\n        memset(var, 0, FFALIGN(MAX_LPC_ORDER+1,4)*sizeof(*var));\n        for(j=0; j<max_order; j++)\n            m[0].coeff[max_order-1][j] = -lpc[max_order-1][j];\n        for(; pass<lpc_passes; pass++){\n            avpriv_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= m[(pass-1)&1].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                m[pass&1].update_lls(&m[pass&1], var);\n            }\n            avpriv_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}']