#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);
}