#include <neuralnet/train.h>

#include "activation.h"
#include "neuralnet_impl.h"
#include <neuralnet/matrix.h>
#include <neuralnet/neuralnet.h>

#include "test.h"
#include "test_util.h"

#include <assert.h>

TEST_CASE(neuralnet_train_xor_test) {
  const int     num_layers = 3;
  const int     input_size = 2;
  const nnLayer layers[]   = {
      {.type = nnLinear, .linear = {.input_size = 2, .output_size = 2}},
      {.type = nnRelu},
      {.type = nnLinear, .linear = {.input_size = 2, .output_size = 1}}
  };

  nnNeuralNetwork* net = nnMakeNet(layers, num_layers, input_size);
  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, 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);
}