// Applies previously trained QDA classifier to new data matrix x
//
// Inputs:
// i_x - N x p data matrix of N samples in p dimensions
// i_means - K x p matrix of class centroids
// i_icovmats - K x p x p array of inverse covariance matrices
// i_bias - vector of length containing the bias term for each discriminant function
// i_N - number of samples in x
// i_p - dimensionality of x
// i_K - number of classes
//
// Outputs:
// o_labels - labels for all samples in i_x
//
// Memory allocation requirements for the outputs:
// o_labels must be of length N
void QDAtest(
double* i_x, double* i_means, double* i_icovmats, double* i_bias,
int* i_N, int* i_p, int* i_K, int* o_labels
)
{
// Create local storage
int t, k, i, j;
double temp;
double* x_c = make1D( *i_p );
double** x = make2D( *i_N, *i_p );
double** means = make2D( *i_K, *i_p );
double*** icovmats = make3D( *i_K, *i_p, *i_p );
double** delta = make2D( *i_N, *i_K );
// Copy and reshape the inputs to local storage
cp_vec_mat( *i_N, *i_p, i_x, x );
cp_vec_mat( *i_K, *i_p, i_means, means );
cp_vec_3D( *i_K, *i_p, *i_p, i_icovmats, icovmats );
// Compute the delta function values
for( t = 0; t < *i_N; t++ )
for( k = 0; k < *i_K; k++ )
{
add_vec_vec( *i_p, 1.0, -1.0, x[t], means[k], x_c );
delta[t][k] = 0.0;
for( i = 0; i < *i_p; i++ )
for( j = 0; j < *i_p; j++ )
delta[t][k] += icovmats[k][i][j] * x_c[i] * x_c[j];
delta[t][k] *= -0.5;
delta[t][k] += i_bias[k];
}
// Pick the highest discriminant function value for each point
for( t = 0; t < *i_N; t++ )
{
o_labels[t] = 1;
temp = delta[t][0];
for( k = 1; k < *i_K; k++ )
{
if( delta[t][k] > temp )
{
o_labels[t] = k + 1;
temp = delta[t][k];
}
}
}
// Free up memory
free( x_c );
free2D( x, *i_N );
free2D( means, *i_K );
free3D( icovmats, *i_K, *i_p );
free2D( delta, *i_N );
}
void histoBook::make( xmlConfig * config, string nodeName ){
if ( config && config->nodeExists( nodeName ) ){
string hName = config->tagName( nodeName );
if ( "" == hName )
hName = nodeName;
// store the path in the config file
configPath[ hName ] = nodeName;
string type = config->getString( nodeName + ":type", "1D" );
if ( "1D" == type ){
make1D( hName, config->getString( nodeName + ":title", hName ),
config->getInt( nodeName + ":nBinsX", 1 ), config->getDouble( nodeName + ":x1", 0 ),
config->getDouble( nodeName + ":x2", 1 ) );
} else if ( "2D" == type ){
make2D( hName, config->getString( nodeName + ":title", hName ),
config->getInt( nodeName + ":nBinsX", 1 ), config->getDouble( nodeName + ":x1", 0 ),
config->getDouble( nodeName + ":x2", 1 ),
config->getInt( nodeName + ":nBinsY", 1 ), config->getDouble( nodeName + ":y1", 0 ),
config->getDouble( nodeName + ":y2", 1 ) );
}
}
}
int main()
{
char *temp_arr = (char *)malloc(50 * sizeof(char));
int r, c, rd, cd;
int dim = getDim(temp_arr);
char **square = make2D(dim);
char **answer = make2D(dim);
moveTemp(square, temp_arr, dim);
fillSquare(square, dim);
/*printSquare(square, dim);*/
temp_arr = (char*)malloc(50 * sizeof(char));
while((fgets(temp_arr, 50, stdin)) != NULL )
{
if(strlen(temp_arr) > 1)
{
temp_arr[strlen(temp_arr) - 1] = '\0';
/*printf("searching for %s\n",temp_arr);*/
for(r = 0; r < dim; r++)
for(c = 0; c < dim; c++)
for(rd = -1; rd <= 1; rd++)
for(cd = -1; cd <= 1; cd++)
{
if(find(r, c, rd, cd, dim, square, temp_arr))
{
add(r, c, rd, cd, dim, square, answer, temp_arr);
cd = 2;
rd = 2;
c = dim + 1;
r = dim + 1;
}
}
}
}
free(temp_arr);
printSquare(answer, dim);
free2D(square, dim);
free2D(answer, dim);
return 0;
}
// Trains a QDA classifier
//
// Inputs:
// i_x - N x p data matrix of N samples in p dimensions
// i_y - N x 1 vector of labels
// i_N - number of samples
// i_p - data dimensionality
// i_K - number of classes
// i_debug_output - level of debug output
//
// Output:
// o_priors - K x 1 vector of Bayesian priors, computed as fraction of points from each class
// o_means - K x p matrix of means approximated from the data
// o_covmats - K x p x p array of covariance matrices approximated from the data
// o_icovmats - K x p x p array of inverse covariance matrices
// o_bias - K x 1 vector of bias terms for discriminant function computations
//
// Memory allocation requirements for outputs:
// o_priors must be of length K
// o_means must be of length K*p
// o_covmat must be of length K*p*p
// o_icovmat must be of length K*p*p
// o_bias must be of lenght K
// o_status must be of length 1
// o_info must be of length 1
//
// Meaning of o_status:
// 0 - Everything is OK
// 1 - The covariance matrix for class *o_info is singular
void QDAtrain(
double* i_x, int* i_y, int* i_N, int* i_p, int* i_K, double* i_cov_reg,
int* i_debug_output,
double* o_priors, double* o_means, double* o_covmats, double* o_icovmats,
double* o_bias, int* o_status, int* o_info
)
{
if( *i_debug_output > 0 )
printf( "Currently running C version of the QDA.train code\n" );
// Create local storage
int i, j, t, k;
int* Nk = (int*)malloc( *i_K * sizeof(int) );
double** x = make2D( *i_N, *i_p );
double** means = make2D( *i_K, *i_p );
double*** covmats = make3D( *i_K, *i_p, *i_p );
double*** icovmats = make3D( *i_K, *i_p, *i_p );
// Copy and reshape the inputs
cp_vec_mat( *i_N, *i_p, i_x, x );
// Init the counts and means to 0
for( k = 0; k < *i_K; k++ )
{
Nk[k] = 0;
for( i = 0; i < *i_p; i++ )
means[k][i] = 0.0;
}
// Init the covariance matrices to 0
for( k = 0; k < *i_K; k++ )
for( i = 0; i < *i_p; i++ )
for( j = 0; j < *i_p; j++ )
covmats[k][i][j] = 0.0;
// Traverse one sample at a time to compute mean contributions
for( t = 0; t < *i_N; t++ )
{
k = i_y[t] - 1; // account for 0-based index
// Count the sample
Nk[k]++;
// Add contribution to the mean
add_vec_vec( *i_p, 1.0, 1.0, means[k], x[t], means[k] );
}
// Compute the means and the priors
for( k = 0; k < *i_K; k++ )
{
o_priors[k] = ((double)Nk[k]) / ((double)*i_N);
mult_const_vec( *i_p, 1.0 / ((double)Nk[k]), means[k] );
}
// Subtract the means from the samples and compute the covariance matrices
for( t = 0; t < *i_N; t++ )
{
k = i_y[t] - 1; // account for 0-based index
add_vec_vec( *i_p, 1.0, -1.0, x[t], means[k], x[t] );
for( i = 0; i < *i_p; i++ )
for( j = 0; j < *i_p; j++ )
covmats[k][i][j] += x[t][i] * x[t][j];
}
for( k = 0; k < *i_K; k++ )
for( i = 0; i < *i_p; i++ )
for( j = 0; j < *i_p; j++ )
covmats[k][i][j] /= (Nk[k] - 1);
// Regularize the covariance matrices
if( *i_cov_reg > 0 )
{
for( k = 0; k < *i_K; k++ )
for( i = 0; i < *i_p; i++ )
for( j = 0; j < *i_p; j++ )
{
covmats[k][i][j] *= (1 - *i_cov_reg);
if( i == j )
covmats[k][i][j] += *i_cov_reg;
}
}
// Compute the bias terms
for( k = 0; k < *i_K; k++ )
o_bias[k] = log( o_priors[k] ) - 0.5 * covmat_logdet( *i_p, covmats[k] );
// Compute the covariance matrix inverses
for( k = 0; k < *i_K; k++ )
{
*o_status = covmat_inverse( *i_p, covmats[k], icovmats[k] );
if( *o_status != 0 )
{
free2D( x, *i_N );
free2D( means, *i_K );
free3D( covmats, *i_K, *i_p );
free3D( icovmats, *i_K, *i_p );
*o_info = k;
return;
}
}
// Copy the results from local storage to outputs
cp_mat_vec( *i_K, *i_p, means, o_means );
cp_3D_vec( *i_K, *i_p, *i_p, covmats, o_covmats );
cp_3D_vec( *i_K, *i_p, *i_p, icovmats, o_icovmats );
// Free memory
free2D( x, *i_N );
free2D( means, *i_K );
free3D( covmats, *i_K, *i_p );
free3D( icovmats, *i_K, *i_p );
}