Exemplo n.º 1
0
int main(void){
    WDTCTL = WDTPW | WDTHOLD; //Disable WDT
    P1DIR = 0x41; //P1.6 (green led) and P1.0 (red LED) outputs

    //oscillator control
    DCOCTL |= DCO1 | DCO0;
    BCSCTL1 |= RSEL3 | RSEL2 | RSEL0; 
    
    //UART io setup (RX: P1.1, TX: P1.2)
    P1SEL  |= 0x03;
    P1SEL2 |= 0x03;

    init_auto();
    
    //set global interrupt enable bit
    _BIS_SR(GIE);

    for(;;){
		comm_loop();
        loop_auto();
        unsigned int i = 0xffff;
        while(--i);
    }
}
Exemplo n.º 2
0
Arquivo: mlem.cpp Projeto: glo/ee384b
void CvEM::init_em( const CvVectors& train_data )
{
    CvMat *w = 0, *u = 0, *tcov = 0;

    CV_FUNCNAME( "CvEM::init_em" );

    __BEGIN__;

    double maxval = 0;
    int i, force_symm_plus = 0;
    int nclusters = params.nclusters, nsamples = train_data.count, dims = train_data.dims;

    if( params.start_step == START_AUTO_STEP || nclusters == 1 || nclusters == nsamples )
        init_auto( train_data );
    else if( params.start_step == START_M_STEP )
    {
        for( i = 0; i < nsamples; i++ )
        {
            CvMat prob;
            cvGetRow( params.probs, &prob, i );
            cvMaxS( &prob, 0., &prob );
            cvMinMaxLoc( &prob, 0, &maxval );
            if( maxval < FLT_EPSILON )
                cvSet( &prob, cvScalar(1./nclusters) );
            else
                cvNormalize( &prob, &prob, 1., 0, CV_L1 );
        }
        EXIT; // do not preprocess covariation matrices,
              // as in this case they are initialized at the first iteration of EM
    }
    else
    {
        CV_ASSERT( params.start_step == START_E_STEP && params.means );
        if( params.weights && params.covs )
        {
            cvConvert( params.means, means );
            cvReshape( weights, weights, 1, params.weights->rows );
            cvConvert( params.weights, weights );
            cvReshape( weights, weights, 1, 1 );
            cvMaxS( weights, 0., weights );
            cvMinMaxLoc( weights, 0, &maxval );
            if( maxval < FLT_EPSILON )
                cvSet( weights, cvScalar(1./nclusters) );
            cvNormalize( weights, weights, 1., 0, CV_L1 );
            for( i = 0; i < nclusters; i++ )
                CV_CALL( cvConvert( params.covs[i], covs[i] ));
            force_symm_plus = 1;
        }
        else
            init_auto( train_data );
    }

    CV_CALL( tcov = cvCreateMat( dims, dims, CV_64FC1 ));
    CV_CALL( w = cvCreateMat( dims, dims, CV_64FC1 ));
    if( params.cov_mat_type == COV_MAT_GENERIC )
        CV_CALL( u = cvCreateMat( dims, dims, CV_64FC1 ));

    for( i = 0; i < nclusters; i++ )
    {
        if( force_symm_plus )
        {
            cvTranspose( covs[i], tcov );
            cvAddWeighted( covs[i], 0.5, tcov, 0.5, 0, tcov );
        }
        else
            cvCopy( covs[i], tcov );
        cvSVD( tcov, w, u, 0, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
        if( params.cov_mat_type == COV_MAT_SPHERICAL )
            cvSetIdentity( covs[i], cvScalar(cvTrace(w).val[0]/dims) );
        else if( params.cov_mat_type == COV_MAT_DIAGONAL )
            cvCopy( w, covs[i] );
        else
        {
            // generic case: covs[i] = (u')'*max(w,0)*u'
            cvGEMM( u, w, 1, 0, 0, tcov, CV_GEMM_A_T );
            cvGEMM( tcov, u, 1, 0, 0, covs[i], 0 );
        }
    }

    __END__;

    cvReleaseMat( &w );
    cvReleaseMat( &u );
    cvReleaseMat( &tcov );
}