//本プログラムのmain関数です。
int main(void)
{
//配列変数を定義します。
int Array1[3][3] = {0}, Array2[3][3] = {0}, ArraySUM[3][3] = {0}, ArrayMULTIPLY[3][3] = {0};
//array_inputを呼び出し。ユーザから3x3型行列を受け取ります。
printf("１つ目の3x3型行列を設定します。(Array1)\n");
printf("//////////////////////////////////////////////////\n");
array_input(Array1);
printf("//////////////////////////////////////////////////\n\n");
printf("2つ目の3x3型行列を設定します。(Array2)\n");
printf("//////////////////////////////////////////////////\n");
array_input(Array2);
printf("//////////////////////////////////////////////////\n\n");
//array_printを呼び出し、設定した２つの行列を出力します。
printf("1つ目の行列(Array1)\n");
printf("//////////////////////////////////////////////////\n");
array_print(Array1);
printf("//////////////////////////////////////////////////\n\n");
printf("2つ目の行列(Array1)\n");
printf("//////////////////////////////////////////////////\n");
array_print(Array2);
printf("//////////////////////////////////////////////////\n\n");
//array_addを呼び出し、Array1とArray2の和の行列を計算し、出力します。
array_add(Array1, Array2, ArraySUM);
printf("Array1とArray2の和の行列(ArraySUM)\n");
printf("//////////////////////////////////////////////////\n");
array_print(ArraySUM);
printf("//////////////////////////////////////////////////\n\n");
//array_mulを呼び出し、Array1とArray2の積の行列を計算し、出力します。
array_mul(Array1, Array2, ArrayMULTIPLY);
printf("Array1とArray2の積の行列(ArrayMULTIPLY)\n");
printf("//////////////////////////////////////////////////\n");
array_print(ArrayMULTIPLY);
printf("//////////////////////////////////////////////////\n\n");
return 0;
}
void SparseVectorCompressed<T>::mul(const MyT& v, T s)
{
makeSimilar(v);
array_mul(vals,v.vals,s,num_entries);
}
void SparseVectorCompressed<T>::inplaceMul(T s)
{
array_mul(vals,s,num_entries);
}
int cec(struct cec_context * context)
{
struct cec_matrix * X = context->points;
struct cec_matrix * C = context->centers;
const int m = X->m;
const int k = C->m;
const int n = X->n;
const int max = context->max_iterations;
const int min_card = context->min_card;
cross_entropy_function * cross_entropy_functions = context->cross_entropy_functions;
struct cross_entropy_context ** cross_entropy_contexts = context->cross_entropy_contexts;
int _k = k;
int removed_clusters = 0;
double energy_sum = 0;
int * cluster = context->clustering_vector;
int * clusters_number = context->clusters_number;
int card[k], removed[k], cluster_map[k];
double clusters_energy[k];
double * energy = context->energy;
struct cec_matrix ** covariance_matrices = context->covriances;
struct cec_matrix * t_mean_matrix = context->temp_data->t_mean_matrix;
struct cec_matrix * t_matrix_nn = context->temp_data->t_matrix_nn;
struct cec_matrix * n_covariance_matrix =
context->temp_data->n_covariance_matrix;
struct cec_matrix ** t_covariance_matrices =
context->temp_data->t_covariance_matrices;
context->iterations = 0;
/*
* Assign points to its closest clusters and calculate clusters means.
*/
for (int i = 0; i < m; i++)
{
double dist = BIG_DOUBLE;
for (int j = 0; j < k; j++)
{
double dist_temp = dist2(cec_matrix_row(X, i),
cec_matrix_row(C, j), n);
if (dist > dist_temp)
{
dist = dist_temp;
cluster[i] = j;
}
}
}
for (int i = 0; i < k; i++)
{
card[i] = 0;
removed[i] = 0;
cluster_map[i] = i;
}
cec_matrix_set(C, 0.0);
for (int i = 0; i < m; i++)
{
int l = cluster[i];
card[l]++;
array_add(cec_matrix_row(C, l), cec_matrix_row(X, i), n);
}
for (int i = 0; i < k; i++)
{
if (card[i] < min_card)
{
removed[i] = 1;
array_fill(cec_matrix_row(C, i), NAN, n);
removed_clusters++;
continue;
}
array_mul(cec_matrix_row(C, i), 1.0 / card[i], n);
}
/*
* Compute initial covariances using maximum likelihood estimator.
*/
for (int i = 0; i < k; i++)
{
cec_matrix_set(covariance_matrices[i], 0.0);
cec_matrix_set(t_covariance_matrices[i], 0.0);
}
for (int i = 0; i < m; i++)
{
int l = cluster[i];
double t_vec[n];
array_copy(cec_matrix_row(X, i), t_vec, n);
array_sub(t_vec, cec_matrix_row(C, l), n);
cec_vector_outer_product(t_vec, t_matrix_nn, n);
cec_matrix_add(covariance_matrices[l], t_matrix_nn);
}
for (int i = 0; i < k; i++)
{
if (removed[i])
continue;
cec_matrix_mul(covariance_matrices[i], 1.0 / card[i]);
double hx = cross_entropy_functions[i](cross_entropy_contexts[i], covariance_matrices[i]);
if (isnan(hx))
return *(cross_entropy_contexts[i]->last_error);
clusters_energy[i] = cluster_energy(m, hx, card[i]);
energy_sum += clusters_energy[i];
}
/*
* Change cluster mapping for removed clusters.
*/
for (int i = k; i > 0; i--)
{
if (removed[i - 1])
{
cluster_map[i - 1] = cluster_map[_k - 1];
_k--;
}
}
if (_k == 0)
{
return ALL_CLUSTERS_REMOVED_ERROR;
}
clusters_number[0] = _k;
energy[0] = energy_sum;
/*
* Special case when a cluster was removed before the first iteration.
*/
int handle_removed_flag = (k == _k) ? 0 : 1;
/*
* Main loop.
*/
for (int iter = (handle_removed_flag ? -1 : 0); iter < max; iter++)
{
int transfer_flag = 0;
int removed_last_iteration_flag = 0;
for (int i = 0; i < m; i++)
{
int l = cluster[i];
if (handle_removed_flag && !removed[l])
continue;
/*
* Energy and mean vector of cluster 'l' after removing point 'i'.
* Initializing to NAN to get rid of compiler warning.
*/
double n_l_energy = NAN;
double n_l_mean[n];
double energy_gain;
if (!removed[l])
{
/*
* Compute mean of group 'l' after removing data point 'i' and store
* its value in the n_mean.
*/
mean_remove_point(cec_matrix_row(C, l), n_l_mean,
cec_matrix_row(X, i), card[l], n);
/*
* Compute covariance of group 'l' after removing data point 'i' and store
* its value in n_covariance_matrix.
*/
cec_cov_remove_point(covariance_matrices[l],
n_covariance_matrix, cec_matrix_row(C, l),
cec_matrix_row(X, i), card[l], t_matrix_nn);
/*
* Compute energy of group 'l' after removing data point 'i'.
*/
double n_l_hx = cross_entropy_functions[l](cross_entropy_contexts[l], n_covariance_matrix);
if (isnan(n_l_hx))
return *(cross_entropy_contexts[l]->last_error);
n_l_energy = cluster_energy(m, n_l_hx, card[l] - 1);
energy_gain = 0;
} else
{
energy_gain = INFINITY;
}
int idx = -1;
double best_energy;
for (int _j = 0; _j < _k; _j++)
{
int j = cluster_map[_j];
if ((j == l) || (removed[j] == 1))
continue;
mean_add_point(cec_matrix_row(C, j),
cec_matrix_row(t_mean_matrix, j), cec_matrix_row(X, i),
card[j], n);
cec_cov_add_point(covariance_matrices[j],
t_covariance_matrices[j], cec_matrix_row(C, j),
cec_matrix_row(X, i), card[j], t_matrix_nn);
double t_hx = cross_entropy_functions[j](cross_entropy_contexts[j], t_covariance_matrices[j]);
if (isnan(t_hx))
return *(cross_entropy_contexts[j]->last_error);
double t_energy = cluster_energy(m, t_hx, card[j] + 1);
if (removed[l] == 1)
{
/*
* Since the energy of cluster 'l' was subtracted from the energy sum (when 'l' was removed),
* gain is only the change of the energy of cluster 'j' by adding point 'i'.
*/
double gain = (t_energy - clusters_energy[j]);
if (gain < energy_gain)
{
idx = j;
energy_gain = gain;
best_energy = t_energy;
}
} else
{
double gain = (n_l_energy + t_energy)
- (clusters_energy[l] + clusters_energy[j]);
if (gain < energy_gain)
{
idx = j;
energy_gain = gain;
best_energy = t_energy;
}
}
}
/*
* Transfer point 'i' to cluster 'idx'.
*/
if (idx != -1)
{
cluster[i] = idx;
card[idx]++;
clusters_energy[idx] = best_energy;
cec_matrix_copy_data(t_covariance_matrices[idx],
covariance_matrices[idx]);
array_copy(cec_matrix_row(t_mean_matrix, idx),
cec_matrix_row(C, idx), n);
if (!removed[l])
{
cec_matrix_copy_data(n_covariance_matrix,
covariance_matrices[l]);
clusters_energy[l] = n_l_energy;
card[l]--;
array_copy(n_l_mean, cec_matrix_row(C, l), n);
if (card[l] < min_card)
{
removed_last_iteration_flag = 1;
/*
* If the cluster is being removed, subtract its energy from the energy sum.
*/
energy_sum -= clusters_energy[l];
array_fill(cec_matrix_row(C, l), NAN, n);
cec_matrix_set(covariance_matrices[l], NAN);
removed[l] = 1;
}
}
energy_sum += energy_gain;
transfer_flag = 1;
}
/*
* Change the mapping and _k for removed clusters.
*/
for (int _j = _k; _j > 0; _j--)
{
int j = cluster_map[_j - 1];
if (removed[j])
{
cluster_map[_j - 1] = cluster_map[_k - 1];
_k--;
}
}
}
energy[iter + 1] = energy_sum;
clusters_number[iter + 1] = _k;
context->iterations = iter + 1;
if (!transfer_flag)
return NO_ERROR;
/*
* If cluster was removed in this iteration, we need to perform another iteration
* considering points that are not assigned. It will prevent energy dips.
*/
if (removed_last_iteration_flag)
{
handle_removed_flag = 1;
iter--;
} else
handle_removed_flag = 0;
}
return NO_ERROR;
}
