rmontanana/BayesNet
rmontanana/BayesNet
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Restore sample.cc

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This commit is contained in:
Ricardo Montañana Gómez 2023-09-29 18:50:25 +02:00
parent 66ec1b343b
commit fd04e78ad9
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
1 changed files with 0 additions and 46 deletions
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sample/sample.cc
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@ -58,52 +58,6 @@ pair<vector<vector<int>>, vector<int>> extract_indices(vector<int> indices, vect
int main(int argc, char** argv)
{
torch::Tensor weights_ = torch::full({ 10 }, 1.0 / 10, torch::kFloat64);
torch::Tensor y_ = torch::tensor({ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, torch::kInt32);
torch::Tensor ypred = torch::tensor({ 1, 1, 1, 0, 0, 1, 1, 1, 1, 0 }, torch::kInt32);
cout << "Initial weights_: " << endl;
for (int i = 0; i < 10; i++) {
cout << weights_.index({ i }).item<double>() << ", ";
}
cout << "end." << endl;
cout << "y_: " << endl;
for (int i = 0; i < 10; i++) {
cout << y_.index({ i }).item<int>() << ", ";
}
cout << "end." << endl;
cout << "ypred: " << endl;
for (int i = 0; i < 10; i++) {
cout << ypred.index({ i }).item<int>() << ", ";
}
cout << "end." << endl;
auto mask_wrong = ypred != y_;
auto mask_right = ypred == y_;
auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
double epsilon_t = masked_weights.sum().item<double>();
cout << "epsilon_t: " << epsilon_t << endl;
double wt = (1 - epsilon_t) / epsilon_t;
cout << "wt: " << wt << endl;
double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
cout << "alpha_t: " << alpha_t << endl;
// Step 3.2: Update weights for next classifier
// Step 3.2.1: Update weights of wrong samples
cout << "exp(alpha_t): " << exp(alpha_t) << endl;
cout << "exp(-alpha_t): " << exp(-alpha_t) << endl;
weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
// Step 3.2.2: Update weights of right samples
weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
// Step 3.3: Normalise the weights
double totalWeights = torch::sum(weights_).item<double>();
cout << "totalWeights: " << totalWeights << endl;
cout << "Before normalization: " << endl;
for (int i = 0; i < 10; i++) {
cout << weights_.index({ i }).item<double>() << endl;
}
weights_ = weights_ / totalWeights;
cout << "After normalization: " << endl;
for (int i = 0; i < 10; i++) {
cout << weights_.index({ i }).item<double>() << endl;
}
map<string, bool> datasets = {
{"diabetes", true},
{"ecoli", true},