diff --git a/README.md b/README.md
index a33a6e6..e1c8400 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 ![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 ![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
-![Static Badge](https://img.shields.io/badge/Coverage-95,8%25-green)
+![Static Badge](https://img.shields.io/badge/Coverage-97,1%25-green)
 
 Bayesian Network Classifiers using libtorch from scratch
 
diff --git a/bayesnet/utils/Mst.cc b/bayesnet/utils/Mst.cc
index b0897b2..b463205 100644
--- a/bayesnet/utils/Mst.cc
+++ b/bayesnet/utils/Mst.cc
@@ -1,3 +1,4 @@
+#include <sstream>
 #include <vector>
 #include <list>
 #include "Mst.h"
@@ -45,15 +46,6 @@ namespace bayesnet {
             }
         }
     }
-    void Graph::display_mst()
-    {
-        std::cout << "Edge :" << " Weight" << std::endl;
-        for (int i = 0; i < T.size(); i++) {
-            std::cout << T[i].second.first << " - " << T[i].second.second << " : "
-                << T[i].first;
-            std::cout << std::endl;
-        }
-    }
 
     void insertElement(std::list<int>& variables, int variable)
     {
diff --git a/bayesnet/utils/Mst.h b/bayesnet/utils/Mst.h
index 9fa951b..6c9b421 100644
--- a/bayesnet/utils/Mst.h
+++ b/bayesnet/utils/Mst.h
@@ -5,29 +5,28 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class MST {
-    private:
-        torch::Tensor weights;
-        std::vector<std::string> features;
-        int root = 0;
     public:
         MST() = default;
         MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
         std::vector<std::pair<int, int>> maximumSpanningTree();
+    private:
+        torch::Tensor weights;
+        std::vector<std::string> features;
+        int root = 0;
     };
     class Graph {
-    private:
-        int V;      // number of nodes in graph
-        std::vector <std::pair<float, std::pair<int, int>>> G; // std::vector for graph
-        std::vector <std::pair<float, std::pair<int, int>>> T; // std::vector for mst
-        std::vector<int> parent;
     public:
         explicit Graph(int V);
         void addEdge(int u, int v, float wt);
         int find_set(int i);
         void union_set(int u, int v);
         void kruskal_algorithm();
-        void display_mst();
         std::vector <std::pair<float, std::pair<int, int>>> get_mst() { return T; }
+    private:
+        int V;      // number of nodes in graph
+        std::vector <std::pair<float, std::pair<int, int>>> G; // std::vector for graph
+        std::vector <std::pair<float, std::pair<int, int>>> T; // std::vector for mst
+        std::vector<int> parent;
     };
 }
 #endif
\ No newline at end of file
diff --git a/tests/TestBayesClassifier.cc b/tests/TestBayesClassifier.cc
index 31fa660..c6b0031 100644
--- a/tests/TestBayesClassifier.cc
+++ b/tests/TestBayesClassifier.cc
@@ -54,6 +54,13 @@ TEST_CASE("Invalid feature name", "[Classifier]")
     REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), std::invalid_argument);
     REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), "feature [petallength] not found in states");
 }
+TEST_CASE("Invalid hyperparameter", "[Classifier]")
+{
+    auto model = bayesnet::KDB(2);
+    auto raw = RawDatasets("iris", true);
+    REQUIRE_THROWS_AS(model.setHyperparameters({ { "alpha", "0.0" } }), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.setHyperparameters({ { "alpha", "0.0" } }), "Invalid hyperparameters{\"alpha\":\"0.0\"}");
+}
 TEST_CASE("Topological order", "[Classifier]")
 {
     auto model = bayesnet::TAN();
diff --git a/tests/TestBayesEnsemble.cc b/tests/TestBayesEnsemble.cc
index 79c3228..215f293 100644
--- a/tests/TestBayesEnsemble.cc
+++ b/tests/TestBayesEnsemble.cc
@@ -3,6 +3,8 @@
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
 #include "bayesnet/ensembles/BoostAODE.h"
+#include "bayesnet/ensembles/AODE.h"
+#include "bayesnet/ensembles/AODELd.h"
 #include "TestUtils.h"
 
 
@@ -73,6 +75,15 @@ TEST_CASE("Graph", "[Ensemble]")
     clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
     auto graph = clf.graph();
     REQUIRE(graph.size() == 56);
+    auto clf2 = bayesnet::AODE();
+    clf2.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    graph = clf2.graph();
+    REQUIRE(graph.size() == 56);
+    raw = RawDatasets("glass", false);
+    auto clf3 = bayesnet::AODELd();
+    clf3.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    graph = clf3.graph();
+    REQUIRE(graph.size() == 261);
 }
 TEST_CASE("Compute ArgMax", "[Ensemble]")
 {
diff --git a/tests/TestBayesModels.cc b/tests/TestBayesModels.cc
index 8d071d8..a52df09 100644
--- a/tests/TestBayesModels.cc
+++ b/tests/TestBayesModels.cc
@@ -14,7 +14,7 @@
 #include "bayesnet/ensembles/BoostAODE.h"
 #include "TestUtils.h"
 
-const std::string ACTUAL_VERSION = "1.0.4";
+const std::string ACTUAL_VERSION = "1.0.4.1";
 
 TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
 {
@@ -52,6 +52,7 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
             auto score = clf->score(raw.Xt, raw.yt);
             INFO("Classifier: " + name + " File: " + file_name);
             REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
+            REQUIRE(clf->getStatus() == bayesnet::NORMAL);
         }
     }
     SECTION("Library check version")
@@ -61,7 +62,7 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
     }
     delete clf;
 }
-TEST_CASE("Models features", "[Models]")
+TEST_CASE("Models features & Graph", "[Models]")
 {
     auto graph = std::vector<std::string>({ "digraph BayesNet {\nlabel=<BayesNet Test>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n",
         "class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n",
@@ -70,15 +71,30 @@ TEST_CASE("Models features", "[Models]")
         "sepallength -> sepalwidth", "sepalwidth [shape=circle] \n", "sepalwidth -> petalwidth", "}\n"
         }
     );
-    auto raw = RawDatasets("iris", true);
-    auto clf = bayesnet::TAN();
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
-    REQUIRE(clf.getNumberOfNodes() == 5);
-    REQUIRE(clf.getNumberOfEdges() == 7);
-    REQUIRE(clf.getNumberOfStates() == 19);
-    REQUIRE(clf.getClassNumStates() == 3);
-    REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
-    REQUIRE(clf.graph("Test") == graph);
+    SECTION("Test TAN")
+    {
+        auto raw = RawDatasets("iris", true);
+        auto clf = bayesnet::TAN();
+        clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+        REQUIRE(clf.getNumberOfNodes() == 5);
+        REQUIRE(clf.getNumberOfEdges() == 7);
+        REQUIRE(clf.getNumberOfStates() == 19);
+        REQUIRE(clf.getClassNumStates() == 3);
+        REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
+        REQUIRE(clf.graph("Test") == graph);
+    }
+    SECTION("Test TANLd")
+    {
+        auto clf = bayesnet::TANLd();
+        auto raw = RawDatasets("iris", false);
+        clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+        REQUIRE(clf.getNumberOfNodes() == 5);
+        REQUIRE(clf.getNumberOfEdges() == 7);
+        REQUIRE(clf.getNumberOfStates() == 19);
+        REQUIRE(clf.getClassNumStates() == 3);
+        REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
+        REQUIRE(clf.graph("Test") == graph);
+    }
 }
 TEST_CASE("Get num features & num edges", "[Models]")
 {
@@ -222,6 +238,12 @@ TEST_CASE("KDB with hyperparameters", "[Models]")
     REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
     REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
 }
+TEST_CASE("Incorrect type of data for SPODELd", "[Models]")
+{
+    auto raw = RawDatasets("iris", true);
+    auto clf = bayesnet::SPODELd(0);
+    REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
+}
 TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
 {
     auto clf = bayesnet::AODE();
diff --git a/tests/TestBoostAODE.cc b/tests/TestBoostAODE.cc
index 3ba8303..73669b5 100644
--- a/tests/TestBoostAODE.cc
+++ b/tests/TestBoostAODE.cc
@@ -157,18 +157,13 @@ TEST_CASE("Bisection", "[BoostAODE]")
 TEST_CASE("Block Update", "[BoostAODE]")
 {
     auto clf = bayesnet::BoostAODE();
-    // auto raw = RawDatasets("mfeat-factors", true);
-    auto raw = RawDatasets("glass", true);
+    auto raw = RawDatasets("mfeat-factors", true);
     clf.setHyperparameters({
         {"bisection", true},
         {"block_update", true},
         {"maxTolerance", 3},
         {"convergence", true},
         });
-    // clf.setHyperparameters({
-    //     {"block_update", true},
-    //     });
-
     clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
     REQUIRE(clf.getNumberOfNodes() == 217);
     REQUIRE(clf.getNumberOfEdges() == 431);
diff --git a/tests/TestFeatureSelection.cc b/tests/TestFeatureSelection.cc
index 2feb723..159d3b8 100644
--- a/tests/TestFeatureSelection.cc
+++ b/tests/TestFeatureSelection.cc
@@ -1,6 +1,7 @@
 #include <catch2/catch_test_macros.hpp>
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
+#include <catch2/matchers/catch_matchers.hpp>
 #include "bayesnet/utils/BayesMetrics.h"
 #include "bayesnet/feature_selection/CFS.h"
 #include "bayesnet/feature_selection/FCBF.h"
@@ -68,4 +69,15 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
             delete featureSelector;
         }
     }
+}
+TEST_CASE("Oddities", "[FeatureSelection]")
+{
+    auto raw = RawDatasets("iris", true);
+    // FCBF Limits
+    REQUIRE_THROWS_AS(bayesnet::FCBF(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 1e-8), std::invalid_argument);
+    REQUIRE_THROWS_WITH(bayesnet::FCBF(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 1e-8), "Threshold cannot be less than 1e-7");
+    REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, -1e4), std::invalid_argument);
+    REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, -1e4), "Threshold has to be in [0, 0.5]");
+    REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 0.501), std::invalid_argument);
+    REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 0.501), "Threshold has to be in [0, 0.5]");
 }
\ No newline at end of file