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Refactor Xaode

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-27 10:08:27 +01:00
parent f51d5b5e40
commit 2dcd073299
1 changed files with 28 additions and 62 deletions
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90
src/experimental_clfs/Xaode.hpp
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@@ -38,43 +38,6 @@ namespace platform {
// Classifiers interface
// all parameter decide if the model is initialized with all the parents active or none of them
//
void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bool all_parents)
{
int num_instances = X[0].size();
int n_features_ = X.size();
significance_models_.resize(n_features_, (all_parents ? 1.0 : 0.0));
std::vector<int> statesv;
for (int i = 0; i < n_features_; i++) {
if (all_parents) active_parents.push_back(i);
statesv.push_back(*max_element(X[i].begin(), X[i].end()) + 1);
}
statesv.push_back(*max_element(y.begin(), y.end()) + 1);
// std::cout << "* States: " << statesv << std::endl;
// std::cout << "* Weights: " << weights_ << std::endl;
// std::cout << "* Instances: " << num_instances << std::endl;
// std::cout << "* Attributes: " << n_features_ +1 << std::endl;
// std::cout << "* y: " << y << std::endl;
// std::cout << "* x shape: " << X.size() << "x" << X[0].size() << std::endl;
// for (int i = 0; i < n_features_; i++) {
// std::cout << "* " << features[i] << ": " << instances[i] << std::endl;
// }
// std::cout << "Starting to build the model" << std::endl;
init(statesv);
std::vector<int> instance(n_features_ + 1);
for (int n_instance = 0; n_instance < num_instances; n_instance++) {
for (int feature = 0; feature < n_features_; feature++) {
instance[feature] = X[feature][n_instance];
}
instance[n_features_] = y[n_instance];
addSample(instance, weights[n_instance].item<double>());
}
computeProbabilities();
}
// -------------------------------------------------------
// init
// -------------------------------------------------------
//
// states.size() = nFeatures + 1,
// where states.back() = number of class states.
//
@@ -84,29 +47,24 @@ namespace platform {
//
// Internally, in COUNTS mode, data_ accumulates raw counts, then
// computeProbabilities(...) normalizes them into conditionals.
//
void init(const std::vector<int>& states)
void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bool all_parents)
{
//
// Check Valid input data
//
if (matrixState_ != MatrixState::EMPTY) {
throw std::logic_error("Xaode: already initialized.");
}
states_ = states;
nFeatures_ = static_cast<int>(states_.size()) - 1;
if (nFeatures_ < 1) {
throw std::invalid_argument("Xaode: need at least 1 feature plus class states.");
int num_instances = X[0].size();
nFeatures_ = X.size();
significance_models_.resize(nFeatures_, (all_parents ? 1.0 : 0.0));
for (int i = 0; i < nFeatures_; i++) {
if (all_parents) active_parents.push_back(i);
states_.push_back(*max_element(X[i].begin(), X[i].end()) + 1);
}
states_.push_back(*max_element(y.begin(), y.end()) + 1);
//
statesClass_ = states_.back();
if (statesClass_ <= 0) {
throw std::invalid_argument("Xaode: class states must be > 0.");
}
//
// Initialize data structures
//
active_parents.resize(nFeatures_);
int totalStates = std::accumulate(states.begin(), states.end(), 0) - statesClass_;
int totalStates = std::accumulate(states_.begin(), states_.end(), 0) - statesClass_;
// For p(x_i=si | c), we store them in a 1D array classFeatureProbs_ after we compute.
// We'll need the offsets for each feature i in featureClassOffset_.
@@ -140,14 +98,19 @@ namespace platform {
classCounts_.resize(statesClass_, 0.0);
matrixState_ = MatrixState::COUNTS;
//
// Add samples
//
std::vector<int> instance(nFeatures_ + 1);
for (int n_instance = 0; n_instance < num_instances; n_instance++) {
for (int feature = 0; feature < nFeatures_; feature++) {
instance[feature] = X[feature][n_instance];
}
instance[nFeatures_] = y[n_instance];
addSample(instance, weights[n_instance].item<double>());
}
computeProbabilities();
}
// Returns current mode: INIT, COUNTS or PROBS
MatrixState state() const
{
return matrixState_;
}
// Optional: print a quick summary
void show() const
{
@@ -171,7 +134,6 @@ namespace platform {
for (double d : data_) std::cout << d << " "; std::cout << std::endl;
std::cout << "--------------------------------" << std::endl;
}
// -------------------------------------------------------
// addSample (only in COUNTS mode)
// -------------------------------------------------------
@@ -424,7 +386,11 @@ namespace platform {
probs[i] /= sum;
}
}
// Returns current mode: INIT, COUNTS or PROBS
MatrixState state() const
{
return matrixState_;
}
int statesClass() const
{
return statesClass_;