// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2014 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
//   this list of conditions and the following disclaimer in the documentation
//   and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
//   used to endorse or promote products derived from this software without
//   specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/program.h"

#include <limits>
#include <cmath>
#include <vector>
#include "ceres/sized_cost_function.h"
#include "ceres/problem_impl.h"
#include "ceres/residual_block.h"
#include "ceres/triplet_sparse_matrix.h"
#include "gtest/gtest.h"

namespace ceres {
namespace internal {

// A cost function that simply returns its argument.
class UnaryIdentityCostFunction : public SizedCostFunction<1, 1> {
 public:
  virtual bool Evaluate(double const* const* parameters,
                        double* residuals,
                        double** jacobians) const {
    residuals[0] = parameters[0][0];
    if (jacobians != NULL && jacobians[0] != NULL) {
      jacobians[0][0] = 1.0;
    }
    return true;
  }
};

// Templated base class for the CostFunction signatures.
template <int kNumResiduals, int N0, int N1, int N2>
class MockCostFunctionBase : public
SizedCostFunction<kNumResiduals, N0, N1, N2> {
 public:
  virtual bool Evaluate(double const* const* parameters,
                        double* residuals,
                        double** jacobians) const {
    for (int i = 0; i < kNumResiduals; ++i) {
      residuals[i] = kNumResiduals +  N0 + N1 + N2;
    }
    return true;
  }
};

class UnaryCostFunction : public MockCostFunctionBase<2, 1, 0, 0> {};
class BinaryCostFunction : public MockCostFunctionBase<2, 1, 1, 0> {};
class TernaryCostFunction : public MockCostFunctionBase<2, 1, 1, 1> {};

TEST(Program, RemoveFixedBlocksNothingConstant) {
  ProblemImpl problem;
  double x;
  double y;
  double z;

  problem.AddParameterBlock(&x, 1);
  problem.AddParameterBlock(&y, 1);
  problem.AddParameterBlock(&z, 1);
  problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
  problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
  problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);

  Program* program = problem.mutable_program();
  vector<double*> removed_parameter_blocks;
  double fixed_cost = 0.0;
  string message;
  EXPECT_TRUE(program->RemoveFixedBlocks(&removed_parameter_blocks,
                                         &fixed_cost,
                                         &message));
  EXPECT_EQ(program->NumParameterBlocks(), 3);
  EXPECT_EQ(program->NumResidualBlocks(), 3);
  EXPECT_EQ(removed_parameter_blocks.size(), 0);
  EXPECT_EQ(fixed_cost, 0.0);
}

TEST(Program, RemoveFixedBlocksAllParameterBlocksConstant) {
  ProblemImpl problem;
  double x = 1.0;

  problem.AddParameterBlock(&x, 1);
  problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
  problem.SetParameterBlockConstant(&x);

  Program* program = problem.mutable_program();
  vector<double*> removed_parameter_blocks;
  double fixed_cost = 0.0;
  string message;
  EXPECT_TRUE(program->RemoveFixedBlocks(&removed_parameter_blocks,
                                         &fixed_cost,
                                         &message));
  EXPECT_EQ(program->NumParameterBlocks(), 0);
  EXPECT_EQ(program->NumResidualBlocks(), 0);
  EXPECT_EQ(removed_parameter_blocks.size(), 1);
  EXPECT_EQ(removed_parameter_blocks[0], &x);
  EXPECT_EQ(fixed_cost, 9.0);
}


TEST(Program, RemoveFixedBlocksNoResidualBlocks) {
  ProblemImpl problem;
  double x;
  double y;
  double z;

  problem.AddParameterBlock(&x, 1);
  problem.AddParameterBlock(&y, 1);
  problem.AddParameterBlock(&z, 1);

  Program* program = problem.mutable_program();
  vector<double*> removed_parameter_blocks;
  double fixed_cost = 0.0;
  string message;
  EXPECT_TRUE(program->RemoveFixedBlocks(&removed_parameter_blocks,
                                         &fixed_cost,
                                         &message));
  EXPECT_EQ(program->NumParameterBlocks(), 0);
  EXPECT_EQ(program->NumResidualBlocks(), 0);
  EXPECT_EQ(removed_parameter_blocks.size(), 3);
  EXPECT_EQ(fixed_cost, 0.0);
}

TEST(Program, RemoveFixedBlocksOneParameterBlockConstant) {
  ProblemImpl problem;
  double x;
  double y;
  double z;

  problem.AddParameterBlock(&x, 1);
  problem.AddParameterBlock(&y, 1);
  problem.AddParameterBlock(&z, 1);

  problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
  problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
  problem.SetParameterBlockConstant(&x);

  Program* program = problem.mutable_program();
  vector<double*> removed_parameter_blocks;
  double fixed_cost = 0.0;
  string message;
  EXPECT_TRUE(program->RemoveFixedBlocks(&removed_parameter_blocks,
                                         &fixed_cost,
                                         &message));
  EXPECT_EQ(program->NumParameterBlocks(), 1);
  EXPECT_EQ(program->NumResidualBlocks(), 1);
}

TEST(Program, RemoveFixedBlocksNumEliminateBlocks) {
  ProblemImpl problem;
  double x;
  double y;
  double z;

  problem.AddParameterBlock(&x, 1);
  problem.AddParameterBlock(&y, 1);
  problem.AddParameterBlock(&z, 1);
  problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
  problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
  problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
  problem.SetParameterBlockConstant(&x);

  Program* program = problem.mutable_program();
  vector<double*> removed_parameter_blocks;
  double fixed_cost = 0.0;
  string message;
  EXPECT_TRUE(program->RemoveFixedBlocks(&removed_parameter_blocks,
                                         &fixed_cost,
                                         &message));
  EXPECT_EQ(program->NumParameterBlocks(), 2);
  EXPECT_EQ(program->NumResidualBlocks(), 2);
}

TEST(Program, RemoveFixedBlocksFixedCost) {
  ProblemImpl problem;
  double x = 1.23;
  double y = 4.56;
  double z = 7.89;

  problem.AddParameterBlock(&x, 1);
  problem.AddParameterBlock(&y, 1);
  problem.AddParameterBlock(&z, 1);
  problem.AddResidualBlock(new UnaryIdentityCostFunction(), NULL, &x);
  problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
  problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
  problem.SetParameterBlockConstant(&x);

  Program* program = problem.mutable_program();

  double expected_fixed_cost;
  ResidualBlock *expected_removed_block = program->residual_blocks()[0];
  scoped_array<double> scratch(
      new double[expected_removed_block->NumScratchDoublesForEvaluate()]);
  expected_removed_block->Evaluate(true,
                                   &expected_fixed_cost,
                                   NULL,
                                   NULL,
                                   scratch.get());

  vector<double*> removed_parameter_blocks;
  double fixed_cost = 0.0;
  string message;
  EXPECT_TRUE(program->RemoveFixedBlocks(&removed_parameter_blocks,
                                         &fixed_cost,
                                         &message));

  EXPECT_EQ(program->NumParameterBlocks(), 2);
  EXPECT_EQ(program->NumResidualBlocks(), 2);
  EXPECT_DOUBLE_EQ(fixed_cost, expected_fixed_cost);
}

TEST(Program, CreateJacobianBlockSparsityTranspose) {
  ProblemImpl problem;
  double x[2];
  double y[3];
  double z;

  problem.AddParameterBlock(x, 2);
  problem.AddParameterBlock(y, 3);
  problem.AddParameterBlock(&z, 1);

  problem.AddResidualBlock(new MockCostFunctionBase<2, 2, 0, 0>(), NULL, x);
  problem.AddResidualBlock(new MockCostFunctionBase<3, 1, 2, 0>(), NULL, &z, x);
  problem.AddResidualBlock(new MockCostFunctionBase<4, 1, 3, 0>(), NULL, &z, y);
  problem.AddResidualBlock(new MockCostFunctionBase<5, 1, 3, 0>(), NULL, &z, y);
  problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 1, 0>(), NULL, x, &z);
  problem.AddResidualBlock(new MockCostFunctionBase<2, 1, 3, 0>(), NULL, &z, y);
  problem.AddResidualBlock(new MockCostFunctionBase<2, 2, 1, 0>(), NULL, x, &z);
  problem.AddResidualBlock(new MockCostFunctionBase<1, 3, 0, 0>(), NULL, y);

  TripletSparseMatrix expected_block_sparse_jacobian(3, 8, 14);
  {
    int* rows = expected_block_sparse_jacobian.mutable_rows();
    int* cols = expected_block_sparse_jacobian.mutable_cols();
    double* values = expected_block_sparse_jacobian.mutable_values();
    rows[0] = 0;
    cols[0] = 0;

    rows[1] = 2;
    cols[1] = 1;
    rows[2] = 0;
    cols[2] = 1;

    rows[3] = 2;
    cols[3] = 2;
    rows[4] = 1;
    cols[4] = 2;

    rows[5] = 2;
    cols[5] = 3;
    rows[6] = 1;
    cols[6] = 3;

    rows[7] = 0;
    cols[7] = 4;
    rows[8] = 2;
    cols[8] = 4;

    rows[9] = 2;
    cols[9] = 5;
    rows[10] = 1;
    cols[10] = 5;

    rows[11] = 0;
    cols[11] = 6;
    rows[12] = 2;
    cols[12] = 6;

    rows[13] = 1;
    cols[13] = 7;
    fill(values, values + 14, 1.0);
    expected_block_sparse_jacobian.set_num_nonzeros(14);
  }

  Program* program = problem.mutable_program();
  program->SetParameterOffsetsAndIndex();

  scoped_ptr<TripletSparseMatrix> actual_block_sparse_jacobian(
      program->CreateJacobianBlockSparsityTranspose());

  Matrix expected_dense_jacobian;
  expected_block_sparse_jacobian.ToDenseMatrix(&expected_dense_jacobian);

  Matrix actual_dense_jacobian;
  actual_block_sparse_jacobian->ToDenseMatrix(&actual_dense_jacobian);
  EXPECT_EQ((expected_dense_jacobian - actual_dense_jacobian).norm(), 0.0);
}

template <int kNumResiduals, int kNumParameterBlocks>
class NumParameterBlocksCostFunction : public CostFunction {
 public:
  NumParameterBlocksCostFunction() {
    set_num_residuals(kNumResiduals);
    for (int i = 0; i < kNumParameterBlocks; ++i) {
      mutable_parameter_block_sizes()->push_back(1);
    }
  }

  virtual ~NumParameterBlocksCostFunction() {
  }

  virtual bool Evaluate(double const* const* parameters,
                        double* residuals,
                        double** jacobians) const {
    return true;
  }
};

TEST(Program, ReallocationInCreateJacobianBlockSparsityTranspose) {
  // CreateJacobianBlockSparsityTranspose starts with a conservative
  // estimate of the size of the sparsity pattern. This test ensures
  // that when those estimates are violated, the reallocation/resizing
  // logic works correctly.

  ProblemImpl problem;
  double x[20];

  vector<double*> parameter_blocks;
  for (int i = 0; i < 20; ++i) {
    problem.AddParameterBlock(x + i, 1);
    parameter_blocks.push_back(x + i);
  }

  problem.AddResidualBlock(new NumParameterBlocksCostFunction<1, 20>(),
                           NULL,
                           parameter_blocks);

  TripletSparseMatrix expected_block_sparse_jacobian(20, 1, 20);
  {
    int* rows = expected_block_sparse_jacobian.mutable_rows();
    int* cols = expected_block_sparse_jacobian.mutable_cols();
    for (int i = 0; i < 20; ++i) {
      rows[i] = i;
      cols[i] = 0;
    }

    double* values = expected_block_sparse_jacobian.mutable_values();
    fill(values, values + 20, 1.0);
    expected_block_sparse_jacobian.set_num_nonzeros(20);
  }

  Program* program = problem.mutable_program();
  program->SetParameterOffsetsAndIndex();

  scoped_ptr<TripletSparseMatrix> actual_block_sparse_jacobian(
      program->CreateJacobianBlockSparsityTranspose());

  Matrix expected_dense_jacobian;
  expected_block_sparse_jacobian.ToDenseMatrix(&expected_dense_jacobian);

  Matrix actual_dense_jacobian;
  actual_block_sparse_jacobian->ToDenseMatrix(&actual_dense_jacobian);
  EXPECT_EQ((expected_dense_jacobian - actual_dense_jacobian).norm(), 0.0);
}

TEST(Program, ProblemHasNanParameterBlocks) {
  ProblemImpl problem;
  double x[2];
  x[0] = 1.0;
  x[1] = std::numeric_limits<double>::quiet_NaN();
  problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 0, 0>(), NULL, x);
  string error;
  EXPECT_FALSE(problem.program().ParameterBlocksAreFinite(&error));
  EXPECT_NE(error.find("has at least one invalid value"),
            string::npos) << error;
}

TEST(Program, InfeasibleParameterBlock) {
  ProblemImpl problem;
  double x[] = {0.0, 0.0};
  problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 0, 0>(), NULL, x);
  problem.SetParameterLowerBound(x, 0, 2.0);
  problem.SetParameterUpperBound(x, 0, 1.0);
  string error;
  EXPECT_FALSE(problem.program().IsFeasible(&error));
  EXPECT_NE(error.find("infeasible bound"), string::npos) << error;
}

TEST(Program, InfeasibleConstantParameterBlock) {
  ProblemImpl problem;
  double x[] = {0.0, 0.0};
  problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 0, 0>(), NULL, x);
  problem.SetParameterLowerBound(x, 0, 1.0);
  problem.SetParameterUpperBound(x, 0, 2.0);
  problem.SetParameterBlockConstant(x);
  string error;
  EXPECT_FALSE(problem.program().IsFeasible(&error));
  EXPECT_NE(error.find("infeasible value"), string::npos) << error;
}

}  // namespace internal
}  // namespace ceres