test refactoring

Aspheria · Nov 1, 2015 · 6bc3459 · 6bc3459
1 parent 409c832
commit 6bc3459
Show file tree

Hide file tree

Showing 3 changed files with 64 additions and 35 deletions.
diff --git a/tests/fann_test.cpp b/tests/fann_test.cpp
@@ -11,7 +11,7 @@ void FannTest::TearDown() {
 }
 
 void FannTest::AssertCreate(FANN::neural_net net, unsigned int num_layers, unsigned int *layers,
-                             unsigned int neurons, unsigned int connections) {
+                            unsigned int neurons, unsigned int connections) {
     EXPECT_EQ(num_layers, net.get_num_layers());
     EXPECT_EQ(layers[0], net.get_num_input());
     EXPECT_EQ(layers[num_layers - 1], net.get_num_output());
@@ -44,14 +44,43 @@ void FannTest::AssertWeights(fann_type expected_min_weight, fann_type expected_m
     fann_type max_weight = connections[0].weight;
     fann_type total_weight = 0.0;
     for (int i = 1; i < net.get_total_connections(); ++i) {
-        if(connections[i].weight < min_weight)
+        if (connections[i].weight < min_weight)
             min_weight = connections[i].weight;
-        if(connections[i].weight > max_weight)
+        if (connections[i].weight > max_weight)
             max_weight = connections[i].weight;
         total_weight += connections[i].weight;
     }
 
     EXPECT_NEAR(expected_min_weight, min_weight, 0.01);
     EXPECT_NEAR(expected_max_weight, max_weight, 0.01);
-    EXPECT_NEAR(expected_avg_weight, total_weight/(fann_type)net.get_total_connections(), 0.1);
+    EXPECT_NEAR(expected_avg_weight, total_weight / (fann_type) net.get_total_connections(), 0.1);
 }
+
+
+void FannTest::InitializeTrainDataStructure(unsigned int num_data,
+                                            unsigned int num_input,
+                                            unsigned int num_output,
+                                            float input_value, float output_value,
+                                            fann_type **input,
+                                            fann_type **output) {
+    for (unsigned int i = 0; i < num_data; i++) {
+        input[i] = new fann_type[num_input];
+        output[i] = new fann_type[num_output];
+        for (unsigned int j = 0; j < num_input; j++)
+            input[i][j] = input_value;
+        for (unsigned int j = 0; j < num_output; j++)
+            output[i][j] = output_value;
+    }
+}
+
+
+void FannTest::AssertTrainData(unsigned int num_data, unsigned int num_input, unsigned int num_output,
+                               fann_type input_value, fann_type output_value) {
+    for (int i = 0; i < num_data; i++) {
+        for (int j = 0; j < num_input; j++)
+                EXPECT_EQ(input_value, this->data.get_input()[i][j]);
+        for (int j = 0; j < num_output; j++)
+                EXPECT_EQ(output_value, this->data.get_output()[i][j]);
+    }
+}
+
diff --git a/tests/fann_test.h b/tests/fann_test.h
@@ -22,10 +22,16 @@ class FannTest : public testing::Test {
     void AssertWeights(fann_type expected_min_weight, fann_type expected_max_weight,
                            fann_type expected_avg_weight);
 
+    void AssertTrainData(unsigned int num_data, unsigned int num_input, unsigned int num_output, fann_type input_value,
+                         fann_type output_value);
+
     virtual void SetUp();
 
     virtual void TearDown();
 
+    void InitializeTrainDataStructure(unsigned int num_data, unsigned int num_input, unsigned int num_output,
+                                      float input_value, float output_value, fann_type **input,
+                                      fann_type **output);
 };
 
 #endif //FANN_FANN_TESTFIXTURE_H
diff --git a/tests/train_data.cpp b/tests/train_data.cpp
@@ -25,37 +25,31 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 #include "fann_test.h"
 
 TEST_F(FannTest, CreateTrainDataFromPointerArrays) {
-    fann_type **input = new fann_type *[2];
-    fann_type **output = new fann_type *[2];
-    for (int i = 0; i < 2; i++) {
-        input[i] = new fann_type[3];
-        output[i] = new fann_type[1];
-        for (int j = 0; j < 3; j++) {
-            input[i][j] = 1.1f;
-        }
-        output[i][0] = 2.2f;
-    }
-
-    data.set_train_data(2, 3, input, 1, output);
-
-    for (int i = 0; i < 2; i++) {
-        for (int j = 0; j < 3; j++) {
-            EXPECT_EQ(1.1f, data.get_input()[i][j]);
-        }
-        EXPECT_EQ(2.2f, data.get_output()[i][0]);
-    }
+    unsigned int num_data = 2;
+    unsigned int num_input = 3;
+    unsigned int num_output = 1;
+    float input_value = 1.1f;
+    float output_value = 2.2f;
+    fann_type **input = new fann_type *[num_data];
+    fann_type **output = new fann_type *[num_data];
+
+    InitializeTrainDataStructure(num_data, num_input, num_output, input_value, output_value, input, output);
+
+    data.set_train_data(num_data, num_input, input, num_output, output);
+
+    AssertTrainData(num_data, num_input, num_output, input_value, output_value);
 }
 
 TEST_F(FannTest, CreateTrainDataFromArrays) {
-    fann_type input[] = {1.1f, 1.1f, 1.1f, 1.1f, 1.1f, 1.1f};
-    fann_type output[] = {2.2f, 2.2f};
-
-    data.set_train_data(2, 3, input, 1, output);
-
-    for (int i = 0; i < 2; i++) {
-        for (int j = 0; j < 3; j++) {
-            EXPECT_EQ(1.1f, data.get_input()[i][j]);
-        }
-        EXPECT_EQ(2.2f, data.get_output()[i][0]);
-    }
-}
+    unsigned int num_data = 2;
+    unsigned int num_input = 3;
+    unsigned int num_output = 1;
+    float input_value = 1.1f;
+    float output_value = 2.2f;
+
+    fann_type input[] = {input_value, input_value, input_value, input_value, input_value, input_value};
+    fann_type output[] = {output_value, output_value};
+    data.set_train_data(num_data, num_input, input, num_output, output);
+
+    AssertTrainData(num_data, num_input, num_output, input_value, output_value);
+}