// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2015 Google Inc. All rights reserved.

// http://ceres-solver.org/

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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/normal_prior.h"

#include <cstddef>

#include "gtest/gtest.h"

#include "ceres/internal/eigen.h"

#include "ceres/random.h"

namespace ceres {

namespace internal {

void RandomVector(Vector* v) {

  for (int r = 0; r < v->rows(); ++r)

    (*v)[r] = 2 * RandDouble() - 1;

}

void RandomMatrix(Matrix* m) {

  for (int r = 0; r < m->rows(); ++r) {

    for (int c = 0; c < m->cols(); ++c) {

      (*m)(r, c) = 2 * RandDouble() - 1;

    }

  }

}

TEST(NormalPriorTest, ResidualAtRandomPosition) {

  srand(5);

  for (int num_rows = 1; num_rows < 5; ++num_rows) {

    for (int num_cols = 1; num_cols < 5; ++num_cols) {

      Vector b(num_cols);

      RandomVector(&b);

      Matrix A(num_rows, num_cols);

      RandomMatrix(&A);

      double * x = new double[num_cols];

      for (int i = 0; i < num_cols; ++i)

        x[i] = 2 * RandDouble() - 1;

      double * jacobian = new double[num_rows * num_cols];

      Vector residuals(num_rows);

      NormalPrior prior(A, b);

      prior.Evaluate(&x, residuals.data(), &jacobian);

      // Compare the norm of the residual

      double residual_diff_norm =

          (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();

      EXPECT_NEAR(residual_diff_norm, 0, 1e-10);

      // Compare the jacobians

      MatrixRef J(jacobian, num_rows, num_cols);

      double jacobian_diff_norm = (J - A).norm();

      EXPECT_NEAR(jacobian_diff_norm, 0.0, 1e-10);

      delete []x;

      delete []jacobian;

    }

  }

}

TEST(NormalPriorTest, ResidualAtRandomPositionNullJacobians) {

  srand(5);

  for (int num_rows = 1; num_rows < 5; ++num_rows) {

    for (int num_cols = 1; num_cols < 5; ++num_cols) {

      Vector b(num_cols);

      RandomVector(&b);

      Matrix A(num_rows, num_cols);

      RandomMatrix(&A);

      double * x = new double[num_cols];

      for (int i = 0; i < num_cols; ++i)

        x[i] = 2 * RandDouble() - 1;

      double* jacobians[1];

      jacobians[0] = NULL;

      Vector residuals(num_rows);

      NormalPrior prior(A, b);

      prior.Evaluate(&x, residuals.data(), jacobians);

      // Compare the norm of the residual

      double residual_diff_norm =

          (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();

      EXPECT_NEAR(residual_diff_norm, 0, 1e-10);

      prior.Evaluate(&x, residuals.data(), NULL);

      // Compare the norm of the residual

      residual_diff_norm =

          (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();

      EXPECT_NEAR(residual_diff_norm, 0, 1e-10);

      delete []x;

    }

  }

}

}  // namespace internal

}  // namespace ceres