From 411f66a2540fa17c736116d865e0ceb0cfe5623b Mon Sep 17 00:00:00 2001
From: jeanne <jeanne@localhost.localdomain>
Date: Wed, 11 May 2022 09:54:38 -0700
Subject: Initial commit.

---
 src/lib/test/train_xor_test.c | 66 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 src/lib/test/train_xor_test.c

(limited to 'src/lib/test/train_xor_test.c')

diff --git a/src/lib/test/train_xor_test.c b/src/lib/test/train_xor_test.c
new file mode 100644
index 0000000..6ddc6e0
--- /dev/null
+++ b/src/lib/test/train_xor_test.c
@@ -0,0 +1,66 @@
+#include <neuralnet/train.h>
+
+#include <neuralnet/matrix.h>
+#include <neuralnet/neuralnet.h>
+#include "activation.h"
+#include "neuralnet_impl.h"
+
+#include "test.h"
+#include "test_util.h"
+
+#include <assert.h>
+
+TEST_CASE(neuralnet_train_xor_test) {
+  const int num_layers = 2;
+  const int layer_sizes[] = { 2, 2, 1 };
+  const nnActivation layer_activations[] = { nnRelu, nnIdentity };
+
+  nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
+  assert(net);
+
+  // Train.
+
+  #define N 4
+  const R inputs[N][2]  = { { 0., 0. }, { 0., 1. }, { 1., 0. }, { 1., 1. } };
+  const R targets[N] = { 0., 1., 1., 0. };
+
+  nnMatrix inputs_matrix  = nnMatrixMake(N, 2);
+  nnMatrix targets_matrix = nnMatrixMake(N, 1);
+  nnMatrixInit(&inputs_matrix, (const R*)inputs);
+  nnMatrixInit(&targets_matrix, targets);
+
+  nnTrainingParams params = {
+    .learning_rate = 0.1,
+    .max_iterations = 500,
+    .seed = 0,
+    .weight_init = nnWeightInit01,
+    .debug = false,
+  };
+
+  nnTrain(net, &inputs_matrix, &targets_matrix, &params);
+
+  // Test.
+
+  #define M 4
+
+  nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/M);
+
+  const R test_inputs[M][2] = { { 0., 0. }, { 1., 0. }, { 0., 1. }, { 1., 1. } };
+  nnMatrix test_inputs_matrix = nnMatrixMake(M, 2);
+  nnMatrixInit(&test_inputs_matrix, (const R*)test_inputs);
+  nnQuery(net, query, &test_inputs_matrix);
+
+  const R expected_outputs[M] = { 0., 1., 1., 0. };
+  for (int i = 0; i < M; ++i) {
+    const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
+    printf("\nInput: (%f, %f), Output: %f, Expected: %f\n",
+      test_inputs[i][0], test_inputs[i][1], test_output, expected_outputs[i]);
+  }
+  for (int i = 0; i < M; ++i) {
+    const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
+    TEST_TRUE(double_eq(test_output, expected_outputs[i], OUTPUT_EPS));
+  }
+
+  nnDeleteQueryObject(&query);
+  nnDeleteNet(&net);
+}
-- 
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