/*
* Prune the dcache to remove unused children of the parent dentry.
*/
void shrink_dcache_parent(struct dentry * parent)
{
int found;
while ((found = select_parent(parent)) != 0)
prune_dcache(found);
}
void shrink_dcache_parent(struct dentry * parent)
{
int found;
while ((found = select_parent(parent)) != 0) {
prune_dcache(found);
conditional_schedule(); /* Typically sys_rmdir() */
}
}
/*
* evolution of the group, including the process of
* select parent, crossover and mutation
*/
void evolve(Group* grp)
{
select_parent(grp);
crossover(grp);
mutate(grp);
}
/**
* The E-means algorithm, uses a genetic algorithm to optimize the parameters
* for the K-means implemetation of clustering based Lloyds clustering algorithm.
*
* @return Status code, 0 for SUCCESS, 1 for ERROR
*/
int emeans(void)
{
gsl_matrix *data = NULL,
*bounds = NULL,
*parent1 = NULL,
*parent2 = NULL,
**population = NULL,
**new_population = NULL,
***clusters = NULL;
int status = SUCCESS;
double fitness[size],
probability[size];
// Initialize the PRNG
pcg32_random_t rng;
int rounds = 5;
pcg32_srandom_r(&rng, time(NULL) ^ (intptr_t)&printf, (intptr_t)&rounds);
// Allocate memory and load the data
data = gsl_matrix_alloc(data_rows, data_cols);
bounds = gsl_matrix_alloc(data_cols, 2);
parent1 = gsl_matrix_alloc(n_clusters, data_cols);
parent2 = gsl_matrix_alloc(n_clusters, data_cols);
population = (gsl_matrix **)calloc(size, sizeof(gsl_matrix **));
new_population = (gsl_matrix **)calloc(size, sizeof(gsl_matrix **));
clusters = (gsl_matrix ***)calloc(size, sizeof(gsl_matrix ***));
for (int i = 0; i < (int)size; ++i)
{
clusters[i] = (gsl_matrix **)calloc(n_clusters, sizeof(gsl_matrix **));
population[i] = gsl_matrix_alloc(n_clusters, data_cols);
new_population[i] = gsl_matrix_alloc(n_clusters, data_cols);
}
if ((status = load_data(data_file, data)) != SUCCESS)
{
fprintf(stderr, RED "Unable to load data!\n" RESET);
status = ERROR;
goto free;
}
// Calculate the bounds of the data
calc_bounds(data, bounds);
// Generate the initial population
printf(CYAN "Generating initial population...\n" RESET);
for (int i = 0; i < (int)size; ++i)
{
random_centroids(population[i], bounds, &rng);
}
// Perform the Genetic Algorithm
for (int iter = 0; iter < max_iter; ++iter)
{
// Compute the fitness of each chromosome
for (int i = 0; i < (int)size; ++i)
{
lloyd_defined(trials, population[i], data, n_clusters, clusters[i]);
fitness[i] = dunn_index(population[i], n_clusters, clusters[i]);
if (VERBOSE == 1)
printf(CYAN "chromsome[%d], fitness: %10.6f\n" RESET, i, fitness[i]);
}
// Generate the probabilities for roulette wheel selection
gen_probability(size, fitness, probability);
// Save the results if there is a new best solution
save_results(fitness_file, centroids_file, cluster_file, size, fitness,
population, n_clusters, clusters);
// Perform roulette wheel selection and GA operators
if (VERBOSE == 1)
printf(CYAN "Executing roulette wheel selection...\n" RESET);
// Select parents from the population and perform genetic operators
for (int i = 0; i < size; i += 2)
{
// Select parents
for (int j = 0, idx = 0; j < 2; ++j)
{
idx = select_parent(size, probability, &rng);
if (VERBOSE == 1)
printf(CYAN "Parent %d selected as chromsome %d from population\n" RESET, j, idx);
if (j == 0)
gsl_matrix_memcpy(parent1, population[idx]);
else
gsl_matrix_memcpy(parent2, population[idx]);
}
// Perform crossover and mutation with specified probabilities
if (VERBOSE == 1)
printf(CYAN "Performing crossover...\n" RESET);
if (pcg32_random_r(&rng) / (double)UINT32_MAX <= c_rate)
{
crossover(parent1, parent2, &rng);
}
if (VERBOSE == 1)
printf(CYAN "Performing background mutation...\n" RESET);
for (int j = 0; j < 2; ++j)
{
if (pcg32_random_r(&rng) / (double)UINT32_MAX <= m_rate)
{
if (j == 0)
mutate(parent1, bounds, &rng);
else
mutate(parent2, bounds, &rng);
}
}
// Copy each parent to the new population
if (VERBOSE == 1)
printf(CYAN "Copying parents to new population...\n" RESET);
gsl_matrix_memcpy(new_population[i], parent1);
gsl_matrix_memcpy(new_population[i+1], parent2);
}
// Copy the new population to the next population
if (VERBOSE == 1)
printf(CYAN "Copying current population as new population\n" RESET);
for (int i = 0; i < size; ++i)
{
gsl_matrix_memcpy(population[i], new_population[i]);
}
// Check if stop signal, terminate if present
if (access("./stop", F_OK) != -1)
{
printf(YELLOW "Stop signal received, shutting down!\n" RESET);
remove("./stop");
break;
}
}
printf(GREEN "Finished executing E-means, shutting down!\n" RESET);
free:
for (int i = 0; i < (int)size; ++i)
{
for (int j = 0; j < n_clusters; ++j)
{
gsl_matrix_free(clusters[i][j]);
}
free(clusters[i]);
}
free(clusters);
for (int i = 0; i < (int)size; ++i)
{
gsl_matrix_free(population[i]);
gsl_matrix_free(new_population[i]);
}
free(population);
free(new_population);
gsl_matrix_free(data);
gsl_matrix_free(bounds);
return status;
}
