Fix int type

Discretizer.cpp

@@ -20,7 +20,7 @@ namespace mdlp {
     {
         auto num_elements = X_.numel();
         samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
-        labels_t y(y_.data_ptr<int64_t>(), y_.data_ptr<int64_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
         fit(X, y);
     }
     torch::Tensor Discretizer::transform_t(torch::Tensor& X_)
@@ -28,14 +28,14 @@ namespace mdlp {
         auto num_elements = X_.numel();
         samples_t X(X_.data_ptr<float>(), X_.data_ptr<float>() + num_elements);
         auto result = transform(X);
-        return torch::tensor(result, torch::kInt64);
+        return torch::tensor(result, torch::kInt32);
     }
     torch::Tensor Discretizer::fit_transform_t(torch::Tensor& X_, torch::Tensor& y_)
     {
         auto num_elements = X_.numel();
         samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
-        labels_t y(y_.data_ptr<int64_t>(), y_.data_ptr<int64_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
         auto result = fit_transform(X, y);
-        return torch::tensor(result, torch::kInt64);
+        return torch::tensor(result, torch::kInt32);
     }
 }

sample/sample.cpp

@@ -139,12 +139,12 @@ void process_file(const string& path, const string& file_name, bool class_last,
         std::cout << std::fixed << std::setprecision(1) << X[0][i] << " " << data[i] << std::endl;
     }
     auto Xt = torch::tensor(X[0], torch::kFloat32);
-    auto yt = torch::tensor(y, torch::kInt64);
+    auto yt = torch::tensor(y, torch::kInt32);
     //test.fit_t(Xt, yt);
     auto result = test.fit_transform_t(Xt, yt);
     std::cout << "Transformed data (torch)...: " << std::endl;
     for (int i = 130; i < 135; i++) {
-        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << result[i].item<int64_t>() << std::endl;
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << result[i].item<int>() << std::endl;
     }
     auto disc = mdlp::BinDisc(3);
     auto res_v = disc.fit_transform(X[0], y);
@@ -152,7 +152,7 @@ void process_file(const string& path, const string& file_name, bool class_last,
     auto res_t = disc.transform_t(Xt);
     std::cout << "Transformed data (BinDisc)...: " << std::endl;
     for (int i = 130; i < 135; i++) {
-        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << res_v[i] << " " << res_t[i].item<int64_t>() << std::endl;
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << res_v[i] << " " << res_t[i].item<int>() << std::endl;
     }
 }
 

sonar-project.properties

@@ -3,7 +3,7 @@ sonar.organization=rmontanana
 
 # This is the name and version displayed in the SonarCloud UI.
 sonar.projectName=mdlp
-sonar.projectVersion=1.1.3
+sonar.projectVersion=1.2.1
 # sonar.test.exclusions=tests/**
 # sonar.tests=tests/
 # sonar.coverage.exclusions=tests/**,sample/**
@@ -11,4 +11,4 @@ sonar.projectVersion=1.1.3
 #sonar.sources=.
 
 # Encoding of the source code. Default is default system encoding
-sonar.sourceEncoding=UTF-8
+sonar.sourceEncoding=UTF-8

tests/BinDisc_unittest.cpp

@@ -335,10 +335,10 @@ namespace mdlp {
         auto Xtt = fit_transform(X[0], file.getY());
         EXPECT_EQ(expected, Xtt);
         auto Xt_t = torch::tensor(X[0], torch::kFloat32);
-        auto y_t = torch::tensor(file.getY(), torch::kInt64);
+        auto y_t = torch::tensor(file.getY(), torch::kInt32);
         auto Xtt_t = fit_transform_t(Xt_t, y_t);
         for (int i = 0; i < expected.size(); i++)
-            EXPECT_EQ(expected[i], Xtt_t[i].item<int64_t>());
+            EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
     }
     TEST_F(TestBinDisc4Q, irisQuantile)
     {
@@ -352,13 +352,13 @@ namespace mdlp {
         auto Xtt = fit_transform(X[0], file.getY());
         EXPECT_EQ(expected, Xtt);
         auto Xt_t = torch::tensor(X[0], torch::kFloat32);
-        auto y_t = torch::tensor(file.getY(), torch::kInt64);
+        auto y_t = torch::tensor(file.getY(), torch::kInt32);
         auto Xtt_t = fit_transform_t(Xt_t, y_t);
         for (int i = 0; i < expected.size(); i++)
-            EXPECT_EQ(expected[i], Xtt_t[i].item<int64_t>());
+            EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
         fit_t(Xt_t, y_t);
         auto Xt_t2 = transform_t(Xt_t);
         for (int i = 0; i < expected.size(); i++)
-            EXPECT_EQ(expected[i], Xt_t2[i].item<int64_t>());
+            EXPECT_EQ(expected[i], Xt_t2[i].item<int>());
     }
 }