added constructor using iterators

Krelkal · Nov 8, 2015 · df4baad · df4baad
1 parent 6dfea22
commit df4baad
Show file tree

Hide file tree

Showing 5 changed files with 53 additions and 10 deletions.
diff --git a/src/include/fann_cpp.h b/src/include/fann_cpp.h
@@ -2,6 +2,7 @@
 #define FANN_CPP_H_INCLUDED
 
 #include <memory>
+#include <iterator>
 /*
  *  Fast Artificial Neural Network (fann) C++ Wrapper
  *  Copyright (C) 2004-2006 created by freegoldbar (at) yahoo dot com
@@ -106,6 +107,39 @@ namespace FANN {
             assert(ann != NULL);
         }
 
+        /* Constructor: neural_net(network_type_enum net_type, InputIterator layersBeginIterator, InputIterator layersEndIterator)
+
+            Creates a neural network of the desired <network_type_enum> net_type, based on iterator to the layers.
+
+            Parameters:
+                net_type - The desired network type of the neural network
+                layersBeginIterator - begin iterator to the collection of unsigned int layers
+                layersEndIterator - end iterator to the collection of unsigned int layers
+
+            Example:
+              >vector<unsigned int> layers{2, 3, 4, 5};
+              >neural_net net(LAYER, layers.begin(), layers.end());
+
+            This function appears in FANN >= 2.3.0.
+         */
+        template <class InputIterator>
+        neural_net(network_type_enum net_type, InputIterator layersBeginIterator, InputIterator layersEndIterator) {
+            unsigned int num_layers = static_cast<unsigned int>(std::distance(layersBeginIterator, layersEndIterator));
+            unsigned int *layers = new unsigned int[num_layers];
+            std::copy(layersBeginIterator, layersEndIterator, layers);
+
+            switch (net_type){
+                case LAYER:
+                    ann = fann_create_standard_array(num_layers, layers);
+                    break;
+                case SHORTCUT:
+                    ann = fann_create_shortcut_array(num_layers, layers);
+                    break;
+            }
+            delete[] layers;
+            assert(ann != NULL);
+        }
+
         /* Constructor: neural_net(network_type_enum net_type, unsigned int num_layers, ...)
 
 	        Creates a neural network of the desired <network_type_enum> net_type.

diff --git a/tests/fann_test.cpp b/tests/fann_test.cpp
@@ -1,5 +1,8 @@
+#include <vector>
 #include "fann_test.h"
 
+using namespace std;
+
 void FannTest::SetUp() {
     //ensure random generator is seeded at a known value to ensure reproducible results
     srand(0);
@@ -11,7 +14,7 @@ void FannTest::TearDown() {
     data.destroy_train();
 }
 
-void FannTest::AssertCreate(neural_net &net, unsigned int numLayers, unsigned int *layers,
+void FannTest::AssertCreate(neural_net &net, unsigned int numLayers, const unsigned int *layers,
                             unsigned int neurons, unsigned int connections) {
     EXPECT_EQ(numLayers, net.get_num_layers());
     EXPECT_EQ(layers[0], net.get_num_input());
@@ -29,7 +32,7 @@ void FannTest::AssertCreate(neural_net &net, unsigned int numLayers, unsigned in
     AssertWeights(net, -0.09, 0.09, 0.0);
 }
 
-void FannTest::AssertCreateAndCopy(neural_net &net, unsigned int numLayers, unsigned int *layers, unsigned int neurons,
+void FannTest::AssertCreateAndCopy(neural_net &net, unsigned int numLayers, const unsigned int *layers, unsigned int neurons,
                                    unsigned int connections) {
     AssertCreate(net, numLayers, layers, neurons, connections);
     neural_net net_copy(net);
@@ -79,6 +82,12 @@ TEST_F(FannTest, CreateStandardFourLayersArrayUsingCreateMethod) {
     AssertCreateAndCopy(net, 4, layers, 17, 50);
 }
 
+TEST_F(FannTest, CreateStandardFourLayersVector) {
+    vector<unsigned int> layers{2, 3, 4, 5};
+    neural_net net(LAYER, layers.begin(), layers.end());
+    AssertCreateAndCopy(net, 4, layers.data(), 17, 50);
+}
+
 TEST_F(FannTest, CreateSparseFourLayers) {
     neural_net net(0.5, 4, 2, 3, 4, 5);
     AssertCreateAndCopy(net, 4, (unsigned int[]){2, 3, 4, 5}, 17, 31);

diff --git a/tests/fann_test.h b/tests/fann_test.h
@@ -13,10 +13,10 @@ class FannTest : public testing::Test {
     neural_net net;
     training_data data;
 
-    void AssertCreateAndCopy(neural_net &net, unsigned int numLayers, unsigned int *layers, unsigned int neurons,
+    void AssertCreateAndCopy(neural_net &net, unsigned int numLayers, const unsigned int *layers, unsigned int neurons,
                              unsigned int connections);
 
-    void AssertCreate(neural_net &net, unsigned int numLayers, unsigned int *layers,
+    void AssertCreate(neural_net &net, unsigned int numLayers, const unsigned int *layers,
                       unsigned int neurons, unsigned int connections);
 
     void AssertWeights(neural_net &net, fann_type min, fann_type max, fann_type avg);

diff --git a/tests/fann_test_data.cpp b/tests/fann_test_data.cpp
@@ -37,7 +37,7 @@ void FannTestData::InitializeTrainDataStructure(unsigned int numData,
     }
 }
 
-void FannTestData::AssertTrainData(FANN::training_data &trainingData, unsigned int numData, unsigned int numInput,
+void FannTestData::AssertTrainData(training_data &trainingData, unsigned int numData, unsigned int numInput,
                                    unsigned int numOutput, fann_type inputValue, fann_type outputValue) {
     EXPECT_EQ(numData, trainingData.length_train_data());
     EXPECT_EQ(numInput, trainingData.num_input_train_data());
@@ -68,15 +68,15 @@ TEST_F(FannTestData, CreateTrainDataFromArrays) {
 
 TEST_F(FannTestData, CreateTrainDataFromCopy) {
     data.set_train_data(numData, numInput, inputData, numOutput, outputData);
-    FANN::training_data dataCopy(data);
+    training_data dataCopy(data);
 
     AssertTrainData(dataCopy, numData, numInput, numOutput, inputValue, outputValue);
 }
 
 TEST_F(FannTestData, CreateTrainDataFromFile) {
     data.set_train_data(numData, numInput, inputData, numOutput, outputData);
     data.save_train("tmpFile");
-    FANN::training_data dataCopy;
+    training_data dataCopy;
     dataCopy.read_train_from_file("tmpFile");
 
     AssertTrainData(dataCopy, numData, numInput, numOutput, inputValue, outputValue);
@@ -104,7 +104,7 @@ TEST_F(FannTestData, ShuffleTrainData) {
 
 TEST_F(FannTestData, MergeTrainData) {
     data.set_train_data(numData, numInput, inputData, numOutput, outputData);
-    FANN::training_data dataCopy(data);
+    training_data dataCopy(data);
     data.merge_train_data(dataCopy);
     AssertTrainData(data, numData*2, numInput, numOutput, inputValue, outputValue);
 }

diff --git a/tests/fann_test_train.cpp b/tests/fann_test_train.cpp
@@ -12,7 +12,7 @@ void FannTestTrain::TearDown() {
 }
 
 TEST_F(FannTestTrain, TrainOnDateSimpleXor) {
-    net.create_standard(3, 2, 3, 1);
+    neural_net net(LAYER, 3, 2, 3, 1);
 
     data.set_train_data(4, 2, xorInput, 1, xorOutput);
     net.train_on_data(data, 100, 100, 0.001);
@@ -22,7 +22,7 @@ TEST_F(FannTestTrain, TrainOnDateSimpleXor) {
 }
 
 TEST_F(FannTestTrain, TrainSimpleIncrementalXor) {
-    net.create_standard(3, 2, 3, 1);
+    neural_net net(LAYER, 3, 2, 3, 1);
 
     for(int i = 0; i < 100000; i++) {
         net.train((fann_type[]) {0.0, 0.0}, (fann_type[]) {0.0});