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Format tests and fix one formatting issue in src
1 parent 518eb6b commit 1783cbf

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9 files changed

+318
-335
lines changed

9 files changed

+318
-335
lines changed

‎.gitignore‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -12,6 +12,7 @@ CMakeCache.txt
1212
CPackConfig.cmake
1313
CPackSourceConfig.cmake
1414
cmake_install.cmake
15+
.cmake/
1516
fann-config.cmake
1617
fann.pc
1718
install_manifest.txt

‎format.sh‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,4 @@
11
#!/bin/sh
22

33
find src -regex '.*\.\(cpp\|hpp\|c\|h\)' -exec clang-format -i {} \;
4+
find tests -regex '.*\.\(cpp\|hpp\|c\|h\)' -exec clang-format -i {} \;

‎src/fann.c‎

Lines changed: 5 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -1413,10 +1413,11 @@ void fann_update_stepwise(struct fann *ann) {
14131413
fann_min(ann->multiplier - (fann_type)(ann->multiplier / 100.0 + 1.0), ann->multiplier - 1);
14141414

14151415
for (i = 0; i < 6; i++) {
1416-
ann->sigmoid_values[i] = (fann_type)(
1417-
((log(ann->multiplier / (float)ann->sigmoid_results[i] - 1) * (float)ann->multiplier) /
1418-
-2.0) *
1419-
(float)ann->multiplier);
1416+
ann->sigmoid_values[i] =
1417+
(fann_type)(((log(ann->multiplier / (float)ann->sigmoid_results[i] - 1) *
1418+
(float)ann->multiplier) /
1419+
-2.0) *
1420+
(float)ann->multiplier);
14201421
ann->sigmoid_symmetric_values[i] =
14211422
(fann_type)(((log((ann->multiplier - (float)ann->sigmoid_symmetric_results[i]) /
14221423
((float)ann->sigmoid_symmetric_results[i] + ann->multiplier)) *

‎tests/fann_test.cpp‎

Lines changed: 96 additions & 97 deletions
Original file line numberDiff line numberDiff line change
@@ -4,172 +4,171 @@
44
using namespace std;
55

66
void FannTest::SetUp() {
7-
//ensure random generator is seeded at a known value to ensure reproducible results
8-
srand(0);
9-
fann_disable_seed_rand();
7+
// ensure random generator is seeded at a known value to ensure reproducible results
8+
srand(0);
9+
fann_disable_seed_rand();
1010
}
1111

1212
void FannTest::TearDown() {
13-
net.destroy();
14-
data.destroy_train();
13+
net.destroy();
14+
data.destroy_train();
1515
}
1616

1717
void FannTest::AssertCreate(neural_net &net, unsigned int numLayers, const unsigned int *layers,
1818
unsigned int neurons, unsigned int connections) {
19-
EXPECT_EQ(numLayers, net.get_num_layers());
20-
EXPECT_EQ(layers[0], net.get_num_input());
21-
EXPECT_EQ(layers[numLayers - 1], net.get_num_output());
22-
unsigned int *layers_res = new unsigned int[numLayers];
23-
net.get_layer_array(layers_res);
24-
for (unsigned int i = 0; i < numLayers; i++) {
25-
EXPECT_EQ(layers[i], layers_res[i]);
26-
}
27-
delete[] layers_res;
19+
EXPECT_EQ(numLayers, net.get_num_layers());
20+
EXPECT_EQ(layers[0], net.get_num_input());
21+
EXPECT_EQ(layers[numLayers - 1], net.get_num_output());
22+
unsigned int *layers_res = new unsigned int[numLayers];
23+
net.get_layer_array(layers_res);
24+
for (unsigned int i = 0; i < numLayers; i++) {
25+
EXPECT_EQ(layers[i], layers_res[i]);
26+
}
27+
delete[] layers_res;
2828

29-
EXPECT_EQ(neurons, net.get_total_neurons());
30-
EXPECT_EQ(connections, net.get_total_connections());
29+
EXPECT_EQ(neurons, net.get_total_neurons());
30+
EXPECT_EQ(connections, net.get_total_connections());
3131

32-
AssertWeights(net, -0.09, 0.09, 0.0);
32+
AssertWeights(net, -0.09, 0.09, 0.0);
3333
}
3434

35-
void FannTest::AssertCreateAndCopy(neural_net &net, unsigned int numLayers, const unsigned int *layers, unsigned int neurons,
35+
void FannTest::AssertCreateAndCopy(neural_net &net, unsigned int numLayers,
36+
const unsigned int *layers, unsigned int neurons,
3637
unsigned int connections) {
37-
AssertCreate(net, numLayers, layers, neurons, connections);
38-
neural_net net_copy(net);
39-
AssertCreate(net_copy, numLayers, layers, neurons, connections);
38+
AssertCreate(net, numLayers, layers, neurons, connections);
39+
neural_net net_copy(net);
40+
AssertCreate(net_copy, numLayers, layers, neurons, connections);
4041
}
4142

4243
void FannTest::AssertWeights(neural_net &net, fann_type min, fann_type max, fann_type avg) {
43-
connection *connections = new connection[net.get_total_connections()];
44-
net.get_connection_array(connections);
44+
connection *connections = new connection[net.get_total_connections()];
45+
net.get_connection_array(connections);
4546

46-
fann_type minWeight = connections[0].weight;
47-
fann_type maxWeight = connections[0].weight;
48-
fann_type totalWeight = 0.0;
49-
for (int i = 1; i < net.get_total_connections(); ++i) {
50-
if (connections[i].weight < minWeight)
51-
minWeight = connections[i].weight;
52-
if (connections[i].weight > maxWeight)
53-
maxWeight = connections[i].weight;
54-
totalWeight += connections[i].weight;
55-
}
47+
fann_type minWeight = connections[0].weight;
48+
fann_type maxWeight = connections[0].weight;
49+
fann_type totalWeight = 0.0;
50+
for (int i = 1; i < net.get_total_connections(); ++i) {
51+
if (connections[i].weight < minWeight) minWeight = connections[i].weight;
52+
if (connections[i].weight > maxWeight) maxWeight = connections[i].weight;
53+
totalWeight += connections[i].weight;
54+
}
5655

57-
EXPECT_NEAR(min, minWeight, 0.05);
58-
EXPECT_NEAR(max, maxWeight, 0.05);
59-
EXPECT_NEAR(avg, totalWeight / (fann_type) net.get_total_connections(), 0.5);
56+
EXPECT_NEAR(min, minWeight, 0.05);
57+
EXPECT_NEAR(max, maxWeight, 0.05);
58+
EXPECT_NEAR(avg, totalWeight / (fann_type)net.get_total_connections(), 0.5);
6059
}
6160

6261
TEST_F(FannTest, CreateStandardThreeLayers) {
63-
neural_net net(LAYER, 3, 2, 3, 4);
64-
unsigned int layers[] = {2, 3, 4};
65-
AssertCreateAndCopy(net, 3, layers, 11, 25);
62+
neural_net net(LAYER, 3, 2, 3, 4);
63+
unsigned int layers[] = {2, 3, 4};
64+
AssertCreateAndCopy(net, 3, layers, 11, 25);
6665
}
6766

6867
TEST_F(FannTest, CreateStandardThreeLayersUsingCreateMethod) {
69-
ASSERT_TRUE(net.create_standard(3, 2, 3, 4));
70-
unsigned int layers[] = {2, 3, 4};
71-
AssertCreateAndCopy(net, 3, layers, 11, 25);
68+
ASSERT_TRUE(net.create_standard(3, 2, 3, 4));
69+
unsigned int layers[] = {2, 3, 4};
70+
AssertCreateAndCopy(net, 3, layers, 11, 25);
7271
}
7372

7473
TEST_F(FannTest, CreateStandardFourLayersArray) {
75-
unsigned int layers[] = {2, 3, 4, 5};
76-
neural_net net(LAYER, 4, layers);
77-
AssertCreateAndCopy(net, 4, layers, 17, 50);
74+
unsigned int layers[] = {2, 3, 4, 5};
75+
neural_net net(LAYER, 4, layers);
76+
AssertCreateAndCopy(net, 4, layers, 17, 50);
7877
}
7978

8079
TEST_F(FannTest, CreateStandardFourLayersArrayUsingCreateMethod) {
81-
unsigned int layers[] = {2, 3, 4, 5};
82-
ASSERT_TRUE(net.create_standard_array(4, layers));
83-
AssertCreateAndCopy(net, 4, layers, 17, 50);
80+
unsigned int layers[] = {2, 3, 4, 5};
81+
ASSERT_TRUE(net.create_standard_array(4, layers));
82+
AssertCreateAndCopy(net, 4, layers, 17, 50);
8483
}
8584

8685
TEST_F(FannTest, CreateStandardFourLayersVector) {
87-
vector<unsigned int> layers{2, 3, 4, 5};
88-
neural_net net(LAYER, layers.begin(), layers.end());
89-
AssertCreateAndCopy(net, 4, layers.data(), 17, 50);
86+
vector<unsigned int> layers{2, 3, 4, 5};
87+
neural_net net(LAYER, layers.begin(), layers.end());
88+
AssertCreateAndCopy(net, 4, layers.data(), 17, 50);
9089
}
9190

9291
TEST_F(FannTest, CreateSparseFourLayers) {
93-
neural_net net(0.5, 4, 2, 3, 4, 5);
94-
unsigned int layers[] = {2, 3, 4, 5};
95-
AssertCreateAndCopy(net, 4, layers, 17, 31);
92+
neural_net net(0.5, 4, 2, 3, 4, 5);
93+
unsigned int layers[] = {2, 3, 4, 5};
94+
AssertCreateAndCopy(net, 4, layers, 17, 31);
9695
}
9796

9897
TEST_F(FannTest, CreateSparseFourLayersUsingCreateMethod) {
99-
ASSERT_TRUE(net.create_sparse(0.5f, 4, 2, 3, 4, 5));
100-
unsigned int layers[] = {2, 3, 4, 5};
101-
AssertCreateAndCopy(net, 4, layers, 17, 31);
98+
ASSERT_TRUE(net.create_sparse(0.5f, 4, 2, 3, 4, 5));
99+
unsigned int layers[] = {2, 3, 4, 5};
100+
AssertCreateAndCopy(net, 4, layers, 17, 31);
102101
}
103102

104103
TEST_F(FannTest, CreateSparseArrayFourLayers) {
105-
unsigned int layers[] = {2, 3, 4, 5};
106-
neural_net net(0.5f, 4, layers);
107-
AssertCreateAndCopy(net, 4, layers, 17, 31);
104+
unsigned int layers[] = {2, 3, 4, 5};
105+
neural_net net(0.5f, 4, layers);
106+
AssertCreateAndCopy(net, 4, layers, 17, 31);
108107
}
109108

110109
TEST_F(FannTest, CreateSparseArrayFourLayersUsingCreateMethod) {
111-
unsigned int layers[] = {2, 3, 4, 5};
112-
ASSERT_TRUE(net.create_sparse_array(0.5f, 4, layers));
113-
AssertCreateAndCopy(net, 4, layers, 17, 31);
110+
unsigned int layers[] = {2, 3, 4, 5};
111+
ASSERT_TRUE(net.create_sparse_array(0.5f, 4, layers));
112+
AssertCreateAndCopy(net, 4, layers, 17, 31);
114113
}
115114

116115
TEST_F(FannTest, CreateSparseArrayWithMinimalConnectivity) {
117-
unsigned int layers[] = {2, 2, 2};
118-
neural_net net(0.01f, 3, layers);
119-
AssertCreateAndCopy(net, 3, layers, 8, 8);
116+
unsigned int layers[] = {2, 2, 2};
117+
neural_net net(0.01f, 3, layers);
118+
AssertCreateAndCopy(net, 3, layers, 8, 8);
120119
}
121120

122121
TEST_F(FannTest, CreateShortcutFourLayers) {
123-
neural_net net(SHORTCUT, 4, 2, 3, 4, 5);
124-
unsigned int layers[] = {2, 3, 4, 5};
125-
AssertCreateAndCopy(net, 4, layers, 15, 83);
126-
EXPECT_EQ(SHORTCUT, net.get_network_type());
122+
neural_net net(SHORTCUT, 4, 2, 3, 4, 5);
123+
unsigned int layers[] = {2, 3, 4, 5};
124+
AssertCreateAndCopy(net, 4, layers, 15, 83);
125+
EXPECT_EQ(SHORTCUT, net.get_network_type());
127126
}
128127

129128
TEST_F(FannTest, CreateShortcutFourLayersUsingCreateMethod) {
130-
ASSERT_TRUE(net.create_shortcut(4, 2, 3, 4, 5));
131-
unsigned int layers[] = {2, 3, 4, 5};
132-
AssertCreateAndCopy(net, 4, layers, 15, 83);
133-
EXPECT_EQ(SHORTCUT, net.get_network_type());
129+
ASSERT_TRUE(net.create_shortcut(4, 2, 3, 4, 5));
130+
unsigned int layers[] = {2, 3, 4, 5};
131+
AssertCreateAndCopy(net, 4, layers, 15, 83);
132+
EXPECT_EQ(SHORTCUT, net.get_network_type());
134133
}
135134

136135
TEST_F(FannTest, CreateShortcutArrayFourLayers) {
137-
unsigned int layers[] = {2, 3, 4, 5};
138-
neural_net net(SHORTCUT, 4, layers);
139-
AssertCreateAndCopy(net, 4, layers, 15, 83);
140-
EXPECT_EQ(SHORTCUT, net.get_network_type());
136+
unsigned int layers[] = {2, 3, 4, 5};
137+
neural_net net(SHORTCUT, 4, layers);
138+
AssertCreateAndCopy(net, 4, layers, 15, 83);
139+
EXPECT_EQ(SHORTCUT, net.get_network_type());
141140
}
142141

143142
TEST_F(FannTest, CreateShortcutArrayFourLayersUsingCreateMethod) {
144-
unsigned int layers[] = {2, 3, 4, 5};
145-
ASSERT_TRUE(net.create_shortcut_array(4, layers));
146-
AssertCreateAndCopy(net, 4, layers, 15, 83);
147-
EXPECT_EQ(SHORTCUT, net.get_network_type());
143+
unsigned int layers[] = {2, 3, 4, 5};
144+
ASSERT_TRUE(net.create_shortcut_array(4, layers));
145+
AssertCreateAndCopy(net, 4, layers, 15, 83);
146+
EXPECT_EQ(SHORTCUT, net.get_network_type());
148147
}
149148

150149
TEST_F(FannTest, CreateFromFile) {
151-
ASSERT_TRUE(net.create_standard(3, 2, 3, 4));
152-
neural_net netToBeSaved(LAYER, 3, 2, 3, 4);
153-
ASSERT_TRUE(netToBeSaved.save("tmpfile"));
150+
ASSERT_TRUE(net.create_standard(3, 2, 3, 4));
151+
neural_net netToBeSaved(LAYER, 3, 2, 3, 4);
152+
ASSERT_TRUE(netToBeSaved.save("tmpfile"));
154153

155-
neural_net netToBeLoaded("tmpfile");
156-
unsigned int layers[] = {2, 3, 4};
157-
AssertCreateAndCopy(netToBeLoaded, 3, layers, 11, 25);
154+
neural_net netToBeLoaded("tmpfile");
155+
unsigned int layers[] = {2, 3, 4};
156+
AssertCreateAndCopy(netToBeLoaded, 3, layers, 11, 25);
158157
}
159158

160159
TEST_F(FannTest, CreateFromFileUsingCreateMethod) {
161-
ASSERT_TRUE(net.create_standard(3, 2, 3, 4));
162-
neural_net inputNet(LAYER, 3, 2, 3, 4);
163-
ASSERT_TRUE(inputNet.save("tmpfile"));
160+
ASSERT_TRUE(net.create_standard(3, 2, 3, 4));
161+
neural_net inputNet(LAYER, 3, 2, 3, 4);
162+
ASSERT_TRUE(inputNet.save("tmpfile"));
164163

165-
ASSERT_TRUE(net.create_from_file("tmpfile"));
164+
ASSERT_TRUE(net.create_from_file("tmpfile"));
166165

167-
unsigned int layers[] = {2, 3, 4};
168-
AssertCreateAndCopy(net, 3, layers, 11, 25);
166+
unsigned int layers[] = {2, 3, 4};
167+
AssertCreateAndCopy(net, 3, layers, 11, 25);
169168
}
170169

171170
TEST_F(FannTest, RandomizeWeights) {
172-
neural_net net(LAYER, 2, 20, 10);
173-
net.randomize_weights(-1.0, 1.0);
174-
AssertWeights(net, -1.0, 1.0, 0);
171+
neural_net net(LAYER, 2, 20, 10);
172+
net.randomize_weights(-1.0, 1.0);
173+
AssertWeights(net, -1.0, 1.0, 0);
175174
}

‎tests/fann_test.h‎

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -9,21 +9,21 @@
99
using namespace FANN;
1010

1111
class FannTest : public testing::Test {
12-
protected:
13-
neural_net net;
14-
training_data data;
12+
protected:
13+
neural_net net;
14+
training_data data;
1515

16-
void AssertCreateAndCopy(neural_net &net, unsigned int numLayers, const unsigned int *layers, unsigned int neurons,
17-
unsigned int connections);
16+
void AssertCreateAndCopy(neural_net &net, unsigned int numLayers, const unsigned int *layers,
17+
unsigned int neurons, unsigned int connections);
1818

19-
void AssertCreate(neural_net &net, unsigned int numLayers, const unsigned int *layers,
20-
unsigned int neurons, unsigned int connections);
19+
void AssertCreate(neural_net &net, unsigned int numLayers, const unsigned int *layers,
20+
unsigned int neurons, unsigned int connections);
2121

22-
void AssertWeights(neural_net &net, fann_type min, fann_type max, fann_type avg);
22+
void AssertWeights(neural_net &net, fann_type min, fann_type max, fann_type avg);
2323

24-
virtual void SetUp();
24+
virtual void SetUp();
2525

26-
virtual void TearDown();
26+
virtual void TearDown();
2727
};
2828

2929
#endif

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