コード例 #1
0
ファイル: math_lngamma_test.c プロジェクト: Clivia/liquid-dsp
int main() {
    unsigned int num_iterations = 1000; // number of iterations to run
    float v[2] = {0.0405f, 0.5f};       // 

    gradsearch gs = gradsearch_create(NULL,v,2,gserror,LIQUID_OPTIM_MINIMIZE);

    // execute search one iteration at a time
    unsigned int i;
    float rmse;
    for (i=0; i<num_iterations; i++) {
        rmse = gserror(NULL,v,2);

        gradsearch_step(gs);

        if (((i+1)%100)==0)
            gradsearch_print(gs);
    }

    gradsearch_destroy(gs);

    // print results
    for (i=0; i<41; i++)
        printf(" z = %12.8f, g = %12.8f (%12.8f)\n", z[i], lngamma_test[i], sandbox_lngammaf(z[i], v));
    printf("rmse = %12.4e;\n", rmse);

    printf("v0 = %12.8f\n", v[0]);
    printf("v1 = %12.8f\n", v[1]);

    printf("done.\n");
    return 0;
}
コード例 #2
0
int main() {
    // options
    unsigned int num_samples = 400;     // number of samples
    float sig = 0.1f;                   // noise variance
    unsigned int num_iterations = 1000; // number of iterations to run

    float v[3] = {1, 1, 1};
    unsigned int i;

    // range
    float xmin = 0.0f;
    float xmax = 6.0f;
    float dx = (xmax - xmin) / (num_samples-1);

    // generate data set
    float x[num_samples];
    float y[num_samples];
    for (i=0; i<num_samples; i++) {
        x[i] = xmin + i*dx;
        y[i] = sincf(x[i]) + randnf()*sig;
    }
    struct gsdataset q = {x, y, num_samples};

    // create gradsearch object
    gradsearchprops_s gsprops;
    gradsearchprops_init_default(&gsprops);
    gsprops.delta = 1e-6f;  // gradient approximation step size
    gsprops.gamma = 0.002f; // vector step size
    gsprops.alpha = 0.1f;   // momentum parameter
    gsprops.mu    = 0.999f; // decremental gamma paramter (best if not exactly 1.0)

    gradsearch gs = gradsearch_create((void*)&q, v, 3, gserror, LIQUID_OPTIM_MINIMIZE, &gsprops);

    float rmse;

    // execute search
    //rmse = gradsearch_run(gs, num_iterations, -1e-6f);

     // open output file
    FILE*fid = fopen(OUTPUT_FILENAME,"w");
    fprintf(fid,"%% %s : auto-generated file\n", OUTPUT_FILENAME);
    fprintf(fid,"clear all;\n");
    fprintf(fid,"close all;\n");

   // execute search one iteration at a time
    fprintf(fid,"u = zeros(1,%u);\n", num_iterations);
    for (i=0; i<num_iterations; i++) {
        rmse = gserror((void*)&q,v,3);
        fprintf(fid,"u(%3u) = %12.4e;\n", i+1, rmse);

        gradsearch_step(gs);

        if (((i+1)%100)==0)
            gradsearch_print(gs);
    }

    // print results
    printf("\n");
    gradsearch_print(gs);
    printf("  c0 = %12.8f, opt = 1\n", v[0]);
    printf("  c1 = %12.8f, opt = 0\n", v[1]);
    printf("  c2 = %12.8f, opt = 1\n", v[2]);
    printf("  rmse = %12.4e\n", rmse);

    fprintf(fid,"figure;\n");
    fprintf(fid,"semilogy(u);\n");
    fprintf(fid,"xlabel('iteration');\n");
    fprintf(fid,"ylabel('error');\n");
    fprintf(fid,"title('gradient search results');\n");
    fprintf(fid,"grid on;\n");

    // save sampled data set
    for (i=0; i<num_samples; i++) {
        fprintf(fid,"  x(%4u) = %12.8f;\n", i+1, x[i]);
        fprintf(fid,"  y(%4u) = %12.8f;\n", i+1, y[i]);
        fprintf(fid,"  y_hat(%4u) = %12.8f;\n", i+1, gsfunc(x[i],v));
    }
    fprintf(fid,"figure;\n");
    fprintf(fid,"plot(x,y,'x', x,y_hat,'-');\n");
    fprintf(fid,"xlabel('x');\n");
    fprintf(fid,"ylabel('f(x)');\n");
    fprintf(fid,"grid on;\n");
    fprintf(fid,"legend('data','fit',1);\n");


    fclose(fid);
    printf("results written to %s.\n", OUTPUT_FILENAME);

    gradsearch_destroy(gs);

    return 0;
}