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/neuralnet_test.c | 92 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 src/lib/test/neuralnet_test.c

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

diff --git a/src/lib/test/neuralnet_test.c b/src/lib/test/neuralnet_test.c
new file mode 100644
index 0000000..14d9438
--- /dev/null
+++ b/src/lib/test/neuralnet_test.c
@@ -0,0 +1,92 @@
+#include <neuralnet/neuralnet.h>
+
+#include <neuralnet/matrix.h>
+#include "activation.h"
+#include "neuralnet_impl.h"
+
+#include "test.h"
+#include "test_util.h"
+
+#include <assert.h>
+
+TEST_CASE(neuralnet_perceptron_test) {
+  const int num_layers = 1;
+  const int layer_sizes[] = { 1, 1 };
+  const nnActivation layer_activations[] = { nnSigmoid };
+  const R weights[] = { 0.3 };
+
+  nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
+  assert(net);
+  nnSetWeights(net, weights);
+
+  nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/1);
+
+  const R input[] = { 0.9 };
+  R output[1];
+  nnQueryArray(net, query, input, output);
+
+  const R expected_output = sigmoid(input[0] * weights[0]);
+  printf("\nOutput: %f, Expected: %f\n", output[0], expected_output);
+  TEST_TRUE(double_eq(output[0], expected_output, EPS));
+
+  nnDeleteQueryObject(&query);
+  nnDeleteNet(&net);
+}
+
+TEST_CASE(neuralnet_xor_test) {
+  const int num_layers = 2;
+  const int layer_sizes[] = { 2, 2, 1 };
+  const nnActivation layer_activations[] = { nnRelu, nnIdentity };
+  const R weights[] = {
+    1, 1, 1, 1,  // First (hidden) layer.
+    1, -2        // Second (output) layer.
+  };
+  const R biases[] = {
+    0, -1,  // First (hidden) layer.
+    0       // Second (output) layer.
+  };
+
+  nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
+  assert(net);
+  nnSetWeights(net, weights);
+  nnSetBiases(net, biases);
+
+  // First layer weights.
+  TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 0), 1);
+  TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 1), 1);
+  TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 2), 1);
+  TEST_EQUAL(nnMatrixAt(&net->weights[0], 0, 3), 1);
+  // Second layer weights.
+  TEST_EQUAL(nnMatrixAt(&net->weights[1], 0, 0), 1);
+  TEST_EQUAL(nnMatrixAt(&net->weights[1], 0, 1), -2);
+  // First layer biases.
+  TEST_EQUAL(nnMatrixAt(&net->biases[0], 0, 0), 0);
+  TEST_EQUAL(nnMatrixAt(&net->biases[0], 0, 1), -1);
+  // Second layer biases.
+  TEST_EQUAL(nnMatrixAt(&net->biases[1], 0, 0), 0);
+
+  // 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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