Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 1 | // Ceres Solver - A fast non-linear least squares minimizer |
| 2 | // Copyright 2012 Google Inc. All rights reserved. |
| 3 | // http://code.google.com/p/ceres-solver/ |
| 4 | // |
| 5 | // Redistribution and use in source and binary forms, with or without |
| 6 | // modification, are permitted provided that the following conditions are met: |
| 7 | // |
| 8 | // * Redistributions of source code must retain the above copyright notice, |
| 9 | // this list of conditions and the following disclaimer. |
| 10 | // * Redistributions in binary form must reproduce the above copyright notice, |
| 11 | // this list of conditions and the following disclaimer in the documentation |
| 12 | // and/or other materials provided with the distribution. |
| 13 | // * Neither the name of Google Inc. nor the names of its contributors may be |
| 14 | // used to endorse or promote products derived from this software without |
| 15 | // specific prior written permission. |
| 16 | // |
| 17 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| 18 | // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| 19 | // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| 20 | // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| 21 | // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| 22 | // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| 23 | // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| 24 | // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| 25 | // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| 26 | // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| 27 | // POSSIBILITY OF SUCH DAMAGE. |
| 28 | // |
| 29 | // Author: moll.markus@arcor.de (Markus Moll) |
| 30 | // sameeragarwal@google.com (Sameer Agarwal) |
| 31 | |
| 32 | #include "ceres/polynomial.h" |
| 33 | |
| 34 | #include <limits> |
| 35 | #include <cmath> |
| 36 | #include <cstddef> |
| 37 | #include <algorithm> |
| 38 | #include "gtest/gtest.h" |
| 39 | #include "ceres/test_util.h" |
| 40 | |
| 41 | namespace ceres { |
| 42 | namespace internal { |
| 43 | namespace { |
| 44 | |
| 45 | // For IEEE-754 doubles, machine precision is about 2e-16. |
| 46 | const double kEpsilon = 1e-13; |
| 47 | const double kEpsilonLoose = 1e-9; |
| 48 | |
| 49 | // Return the constant polynomial p(x) = 1.23. |
| 50 | Vector ConstantPolynomial(double value) { |
| 51 | Vector poly(1); |
| 52 | poly(0) = value; |
| 53 | return poly; |
| 54 | } |
| 55 | |
| 56 | // Return the polynomial p(x) = poly(x) * (x - root). |
| 57 | Vector AddRealRoot(const Vector& poly, double root) { |
| 58 | Vector poly2(poly.size() + 1); |
| 59 | poly2.setZero(); |
| 60 | poly2.head(poly.size()) += poly; |
| 61 | poly2.tail(poly.size()) -= root * poly; |
| 62 | return poly2; |
| 63 | } |
| 64 | |
| 65 | // Return the polynomial |
| 66 | // p(x) = poly(x) * (x - real - imag*i) * (x - real + imag*i). |
| 67 | Vector AddComplexRootPair(const Vector& poly, double real, double imag) { |
| 68 | Vector poly2(poly.size() + 2); |
| 69 | poly2.setZero(); |
| 70 | // Multiply poly by x^2 - 2real + abs(real,imag)^2 |
| 71 | poly2.head(poly.size()) += poly; |
| 72 | poly2.segment(1, poly.size()) -= 2 * real * poly; |
| 73 | poly2.tail(poly.size()) += (real*real + imag*imag) * poly; |
| 74 | return poly2; |
| 75 | } |
| 76 | |
| 77 | // Sort the entries in a vector. |
| 78 | // Needed because the roots are not returned in sorted order. |
| 79 | Vector SortVector(const Vector& in) { |
| 80 | Vector out(in); |
| 81 | std::sort(out.data(), out.data() + out.size()); |
| 82 | return out; |
| 83 | } |
| 84 | |
| 85 | // Run a test with the polynomial defined by the N real roots in roots_real. |
| 86 | // If use_real is false, NULL is passed as the real argument to |
| 87 | // FindPolynomialRoots. If use_imaginary is false, NULL is passed as the |
| 88 | // imaginary argument to FindPolynomialRoots. |
| 89 | template<int N> |
| 90 | void RunPolynomialTestRealRoots(const double (&real_roots)[N], |
| 91 | bool use_real, |
| 92 | bool use_imaginary, |
| 93 | double epsilon) { |
| 94 | Vector real; |
| 95 | Vector imaginary; |
| 96 | Vector poly = ConstantPolynomial(1.23); |
| 97 | for (int i = 0; i < N; ++i) { |
| 98 | poly = AddRealRoot(poly, real_roots[i]); |
| 99 | } |
| 100 | Vector* const real_ptr = use_real ? &real : NULL; |
| 101 | Vector* const imaginary_ptr = use_imaginary ? &imaginary : NULL; |
| 102 | bool success = FindPolynomialRoots(poly, real_ptr, imaginary_ptr); |
| 103 | |
| 104 | EXPECT_EQ(success, true); |
| 105 | if (use_real) { |
| 106 | EXPECT_EQ(real.size(), N); |
| 107 | real = SortVector(real); |
| 108 | ExpectArraysClose(N, real.data(), real_roots, epsilon); |
| 109 | } |
| 110 | if (use_imaginary) { |
| 111 | EXPECT_EQ(imaginary.size(), N); |
| 112 | const Vector zeros = Vector::Zero(N); |
| 113 | ExpectArraysClose(N, imaginary.data(), zeros.data(), epsilon); |
| 114 | } |
| 115 | } |
| 116 | } // namespace |
| 117 | |
| 118 | TEST(Polynomial, InvalidPolynomialOfZeroLengthIsRejected) { |
| 119 | // Vector poly(0) is an ambiguous constructor call, so |
| 120 | // use the constructor with explicit column count. |
| 121 | Vector poly(0, 1); |
| 122 | Vector real; |
| 123 | Vector imag; |
| 124 | bool success = FindPolynomialRoots(poly, &real, &imag); |
| 125 | |
| 126 | EXPECT_EQ(success, false); |
| 127 | } |
| 128 | |
| 129 | TEST(Polynomial, ConstantPolynomialReturnsNoRoots) { |
| 130 | Vector poly = ConstantPolynomial(1.23); |
| 131 | Vector real; |
| 132 | Vector imag; |
| 133 | bool success = FindPolynomialRoots(poly, &real, &imag); |
| 134 | |
| 135 | EXPECT_EQ(success, true); |
| 136 | EXPECT_EQ(real.size(), 0); |
| 137 | EXPECT_EQ(imag.size(), 0); |
| 138 | } |
| 139 | |
| 140 | TEST(Polynomial, LinearPolynomialWithPositiveRootWorks) { |
| 141 | const double roots[1] = { 42.42 }; |
| 142 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 143 | } |
| 144 | |
| 145 | TEST(Polynomial, LinearPolynomialWithNegativeRootWorks) { |
| 146 | const double roots[1] = { -42.42 }; |
| 147 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 148 | } |
| 149 | |
| 150 | TEST(Polynomial, QuadraticPolynomialWithPositiveRootsWorks) { |
| 151 | const double roots[2] = { 1.0, 42.42 }; |
| 152 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 153 | } |
| 154 | |
| 155 | TEST(Polynomial, QuadraticPolynomialWithOneNegativeRootWorks) { |
| 156 | const double roots[2] = { -42.42, 1.0 }; |
| 157 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 158 | } |
| 159 | |
| 160 | TEST(Polynomial, QuadraticPolynomialWithTwoNegativeRootsWorks) { |
| 161 | const double roots[2] = { -42.42, -1.0 }; |
| 162 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 163 | } |
| 164 | |
| 165 | TEST(Polynomial, QuadraticPolynomialWithCloseRootsWorks) { |
| 166 | const double roots[2] = { 42.42, 42.43 }; |
| 167 | RunPolynomialTestRealRoots(roots, true, false, kEpsilonLoose); |
| 168 | } |
| 169 | |
| 170 | TEST(Polynomial, QuadraticPolynomialWithComplexRootsWorks) { |
| 171 | Vector real; |
| 172 | Vector imag; |
| 173 | |
| 174 | Vector poly = ConstantPolynomial(1.23); |
| 175 | poly = AddComplexRootPair(poly, 42.42, 4.2); |
| 176 | bool success = FindPolynomialRoots(poly, &real, &imag); |
| 177 | |
| 178 | EXPECT_EQ(success, true); |
| 179 | EXPECT_EQ(real.size(), 2); |
| 180 | EXPECT_EQ(imag.size(), 2); |
| 181 | ExpectClose(real(0), 42.42, kEpsilon); |
| 182 | ExpectClose(real(1), 42.42, kEpsilon); |
| 183 | ExpectClose(std::abs(imag(0)), 4.2, kEpsilon); |
| 184 | ExpectClose(std::abs(imag(1)), 4.2, kEpsilon); |
| 185 | ExpectClose(std::abs(imag(0) + imag(1)), 0.0, kEpsilon); |
| 186 | } |
| 187 | |
| 188 | TEST(Polynomial, QuarticPolynomialWorks) { |
| 189 | const double roots[4] = { 1.23e-4, 1.23e-1, 1.23e+2, 1.23e+5 }; |
| 190 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 191 | } |
| 192 | |
| 193 | TEST(Polynomial, QuarticPolynomialWithTwoClustersOfCloseRootsWorks) { |
| 194 | const double roots[4] = { 1.23e-1, 2.46e-1, 1.23e+5, 2.46e+5 }; |
| 195 | RunPolynomialTestRealRoots(roots, true, true, kEpsilonLoose); |
| 196 | } |
| 197 | |
| 198 | TEST(Polynomial, QuarticPolynomialWithTwoZeroRootsWorks) { |
| 199 | const double roots[4] = { -42.42, 0.0, 0.0, 42.42 }; |
| 200 | RunPolynomialTestRealRoots(roots, true, true, kEpsilonLoose); |
| 201 | } |
| 202 | |
| 203 | TEST(Polynomial, QuarticMonomialWorks) { |
| 204 | const double roots[4] = { 0.0, 0.0, 0.0, 0.0 }; |
| 205 | RunPolynomialTestRealRoots(roots, true, true, kEpsilon); |
| 206 | } |
| 207 | |
| 208 | TEST(Polynomial, NullPointerAsImaginaryPartWorks) { |
| 209 | const double roots[4] = { 1.23e-4, 1.23e-1, 1.23e+2, 1.23e+5 }; |
| 210 | RunPolynomialTestRealRoots(roots, true, false, kEpsilon); |
| 211 | } |
| 212 | |
| 213 | TEST(Polynomial, NullPointerAsRealPartWorks) { |
| 214 | const double roots[4] = { 1.23e-4, 1.23e-1, 1.23e+2, 1.23e+5 }; |
| 215 | RunPolynomialTestRealRoots(roots, false, true, kEpsilon); |
| 216 | } |
| 217 | |
| 218 | TEST(Polynomial, BothOutputArgumentsNullWorks) { |
| 219 | const double roots[4] = { 1.23e-4, 1.23e-1, 1.23e+2, 1.23e+5 }; |
| 220 | RunPolynomialTestRealRoots(roots, false, false, kEpsilon); |
| 221 | } |
| 222 | |
| 223 | TEST(Polynomial, DifferentiateConstantPolynomial) { |
| 224 | // p(x) = 1; |
| 225 | Vector polynomial(1); |
| 226 | polynomial(0) = 1.0; |
| 227 | const Vector derivative = DifferentiatePolynomial(polynomial); |
Sameer Agarwal | c89ea4b | 2013-01-09 16:09:35 -0800 | [diff] [blame] | 228 | EXPECT_EQ(derivative.rows(), 1); |
| 229 | EXPECT_EQ(derivative(0), 0); |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 230 | } |
| 231 | |
| 232 | TEST(Polynomial, DifferentiateQuadraticPolynomial) { |
| 233 | // p(x) = x^2 + 2x + 3; |
| 234 | Vector polynomial(3); |
| 235 | polynomial(0) = 1.0; |
| 236 | polynomial(1) = 2.0; |
| 237 | polynomial(2) = 3.0; |
| 238 | |
| 239 | const Vector derivative = DifferentiatePolynomial(polynomial); |
| 240 | EXPECT_EQ(derivative.rows(), 2); |
| 241 | EXPECT_EQ(derivative(0), 2.0); |
| 242 | EXPECT_EQ(derivative(1), 2.0); |
| 243 | } |
| 244 | |
| 245 | TEST(Polynomial, MinimizeConstantPolynomial) { |
| 246 | // p(x) = 1; |
| 247 | Vector polynomial(1); |
| 248 | polynomial(0) = 1.0; |
| 249 | |
| 250 | double optimal_x = 0.0; |
| 251 | double optimal_value = 0.0; |
| 252 | double min_x = 0.0; |
| 253 | double max_x = 1.0; |
| 254 | MinimizePolynomial(polynomial, min_x, max_x, &optimal_x, &optimal_value); |
| 255 | |
| 256 | EXPECT_EQ(optimal_value, 1.0); |
| 257 | EXPECT_LE(optimal_x, max_x); |
| 258 | EXPECT_GE(optimal_x, min_x); |
| 259 | } |
| 260 | |
| 261 | TEST(Polynomial, MinimizeLinearPolynomial) { |
| 262 | // p(x) = x - 2 |
| 263 | Vector polynomial(2); |
| 264 | |
| 265 | polynomial(0) = 1.0; |
| 266 | polynomial(1) = 2.0; |
| 267 | |
| 268 | double optimal_x = 0.0; |
| 269 | double optimal_value = 0.0; |
| 270 | double min_x = 0.0; |
| 271 | double max_x = 1.0; |
| 272 | MinimizePolynomial(polynomial, min_x, max_x, &optimal_x, &optimal_value); |
| 273 | |
| 274 | EXPECT_EQ(optimal_x, 0.0); |
| 275 | EXPECT_EQ(optimal_value, 2.0); |
| 276 | } |
| 277 | |
| 278 | |
| 279 | TEST(Polynomial, MinimizeQuadraticPolynomial) { |
| 280 | // p(x) = x^2 - 3 x + 2 |
| 281 | // min_x = 3/2 |
| 282 | // min_value = -1/4; |
| 283 | Vector polynomial(3); |
| 284 | polynomial(0) = 1.0; |
| 285 | polynomial(1) = -3.0; |
| 286 | polynomial(2) = 2.0; |
| 287 | |
| 288 | double optimal_x = 0.0; |
| 289 | double optimal_value = 0.0; |
| 290 | double min_x = -2.0; |
| 291 | double max_x = 2.0; |
| 292 | MinimizePolynomial(polynomial, min_x, max_x, &optimal_x, &optimal_value); |
| 293 | EXPECT_EQ(optimal_x, 3.0/2.0); |
| 294 | EXPECT_EQ(optimal_value, -1.0/4.0); |
| 295 | |
| 296 | min_x = -2.0; |
| 297 | max_x = 1.0; |
| 298 | MinimizePolynomial(polynomial, min_x, max_x, &optimal_x, &optimal_value); |
| 299 | EXPECT_EQ(optimal_x, 1.0); |
| 300 | EXPECT_EQ(optimal_value, 0.0); |
| 301 | |
| 302 | min_x = 2.0; |
| 303 | max_x = 3.0; |
| 304 | MinimizePolynomial(polynomial, min_x, max_x, &optimal_x, &optimal_value); |
| 305 | EXPECT_EQ(optimal_x, 2.0); |
| 306 | EXPECT_EQ(optimal_value, 0.0); |
| 307 | } |
| 308 | |
| 309 | TEST(Polymomial, ConstantInterpolatingPolynomial) { |
| 310 | // p(x) = 1.0 |
| 311 | Vector true_polynomial(1); |
| 312 | true_polynomial << 1.0; |
| 313 | |
| 314 | vector<FunctionSample> samples; |
| 315 | FunctionSample sample; |
| 316 | sample.x = 1.0; |
| 317 | sample.value = 1.0; |
| 318 | sample.value_is_valid = true; |
| 319 | samples.push_back(sample); |
| 320 | |
| 321 | const Vector polynomial = FindInterpolatingPolynomial(samples); |
| 322 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-15); |
| 323 | } |
| 324 | |
| 325 | TEST(Polynomial, LinearInterpolatingPolynomial) { |
| 326 | // p(x) = 2x - 1 |
| 327 | Vector true_polynomial(2); |
| 328 | true_polynomial << 2.0, -1.0; |
| 329 | |
| 330 | vector<FunctionSample> samples; |
| 331 | FunctionSample sample; |
| 332 | sample.x = 1.0; |
| 333 | sample.value = 1.0; |
| 334 | sample.value_is_valid = true; |
| 335 | sample.gradient = 2.0; |
| 336 | sample.gradient_is_valid = true; |
| 337 | samples.push_back(sample); |
| 338 | |
| 339 | const Vector polynomial = FindInterpolatingPolynomial(samples); |
| 340 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-15); |
| 341 | } |
| 342 | |
| 343 | TEST(Polynomial, QuadraticInterpolatingPolynomial) { |
| 344 | // p(x) = 2x^2 + 3x + 2 |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 345 | Vector true_polynomial(3); |
| 346 | true_polynomial << 2.0, 3.0, 2.0; |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 347 | |
| 348 | vector<FunctionSample> samples; |
| 349 | { |
| 350 | FunctionSample sample; |
| 351 | sample.x = 1.0; |
| 352 | sample.value = 7.0; |
| 353 | sample.value_is_valid = true; |
| 354 | sample.gradient = 7.0; |
| 355 | sample.gradient_is_valid = true; |
| 356 | samples.push_back(sample); |
| 357 | } |
| 358 | |
| 359 | { |
| 360 | FunctionSample sample; |
| 361 | sample.x = -3.0; |
| 362 | sample.value = 11.0; |
| 363 | sample.value_is_valid = true; |
| 364 | samples.push_back(sample); |
| 365 | } |
| 366 | |
| 367 | Vector polynomial = FindInterpolatingPolynomial(samples); |
| 368 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-15); |
| 369 | } |
| 370 | |
| 371 | TEST(Polynomial, DeficientCubicInterpolatingPolynomial) { |
| 372 | // p(x) = 2x^2 + 3x + 2 |
| 373 | Vector true_polynomial(4); |
| 374 | true_polynomial << 0.0, 2.0, 3.0, 2.0; |
| 375 | |
| 376 | vector<FunctionSample> samples; |
| 377 | { |
| 378 | FunctionSample sample; |
| 379 | sample.x = 1.0; |
| 380 | sample.value = 7.0; |
| 381 | sample.value_is_valid = true; |
| 382 | sample.gradient = 7.0; |
| 383 | sample.gradient_is_valid = true; |
| 384 | samples.push_back(sample); |
| 385 | } |
| 386 | |
| 387 | { |
| 388 | FunctionSample sample; |
| 389 | sample.x = -3.0; |
| 390 | sample.value = 11.0; |
| 391 | sample.value_is_valid = true; |
| 392 | sample.gradient = -9; |
| 393 | sample.gradient_is_valid = true; |
| 394 | samples.push_back(sample); |
| 395 | } |
| 396 | |
| 397 | const Vector polynomial = FindInterpolatingPolynomial(samples); |
| 398 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-14); |
| 399 | } |
| 400 | |
| 401 | |
| 402 | TEST(Polynomial, CubicInterpolatingPolynomialFromValues) { |
| 403 | // p(x) = x^3 + 2x^2 + 3x + 2 |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 404 | Vector true_polynomial(4); |
| 405 | true_polynomial << 1.0, 2.0, 3.0, 2.0; |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 406 | |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 407 | vector<FunctionSample> samples; |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 408 | { |
| 409 | FunctionSample sample; |
| 410 | sample.x = 1.0; |
| 411 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 412 | sample.value_is_valid = true; |
| 413 | samples.push_back(sample); |
| 414 | } |
| 415 | |
| 416 | { |
| 417 | FunctionSample sample; |
| 418 | sample.x = -3.0; |
| 419 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 420 | sample.value_is_valid = true; |
| 421 | samples.push_back(sample); |
| 422 | } |
| 423 | |
| 424 | { |
| 425 | FunctionSample sample; |
| 426 | sample.x = 2.0; |
| 427 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 428 | sample.value_is_valid = true; |
| 429 | samples.push_back(sample); |
| 430 | } |
| 431 | |
| 432 | { |
| 433 | FunctionSample sample; |
| 434 | sample.x = 0.0; |
| 435 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 436 | sample.value_is_valid = true; |
| 437 | samples.push_back(sample); |
| 438 | } |
| 439 | |
| 440 | const Vector polynomial = FindInterpolatingPolynomial(samples); |
| 441 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-14); |
| 442 | } |
| 443 | |
| 444 | TEST(Polynomial, CubicInterpolatingPolynomialFromValuesAndOneGradient) { |
| 445 | // p(x) = x^3 + 2x^2 + 3x + 2 |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 446 | Vector true_polynomial(4); |
| 447 | true_polynomial << 1.0, 2.0, 3.0, 2.0; |
| 448 | Vector true_gradient_polynomial = DifferentiatePolynomial(true_polynomial); |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 449 | |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 450 | vector<FunctionSample> samples; |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 451 | { |
| 452 | FunctionSample sample; |
| 453 | sample.x = 1.0; |
| 454 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 455 | sample.value_is_valid = true; |
| 456 | samples.push_back(sample); |
| 457 | } |
| 458 | |
| 459 | { |
| 460 | FunctionSample sample; |
| 461 | sample.x = -3.0; |
| 462 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 463 | sample.value_is_valid = true; |
| 464 | samples.push_back(sample); |
| 465 | } |
| 466 | |
| 467 | { |
| 468 | FunctionSample sample; |
| 469 | sample.x = 2.0; |
| 470 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 471 | sample.value_is_valid = true; |
| 472 | sample.gradient = EvaluatePolynomial(true_gradient_polynomial, sample.x); |
| 473 | sample.gradient_is_valid = true; |
| 474 | samples.push_back(sample); |
| 475 | } |
| 476 | |
| 477 | const Vector polynomial = FindInterpolatingPolynomial(samples); |
| 478 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-14); |
| 479 | } |
| 480 | |
| 481 | TEST(Polynomial, CubicInterpolatingPolynomialFromValuesAndGradients) { |
| 482 | // p(x) = x^3 + 2x^2 + 3x + 2 |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 483 | Vector true_polynomial(4); |
| 484 | true_polynomial << 1.0, 2.0, 3.0, 2.0; |
| 485 | Vector true_gradient_polynomial = DifferentiatePolynomial(true_polynomial); |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 486 | |
Sameer Agarwal | 509f68c | 2013-02-20 01:39:03 -0800 | [diff] [blame] | 487 | vector<FunctionSample> samples; |
Sameer Agarwal | e7295c2 | 2012-11-23 18:56:50 -0800 | [diff] [blame] | 488 | { |
| 489 | FunctionSample sample; |
| 490 | sample.x = -3.0; |
| 491 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 492 | sample.value_is_valid = true; |
| 493 | sample.gradient = EvaluatePolynomial(true_gradient_polynomial, sample.x); |
| 494 | sample.gradient_is_valid = true; |
| 495 | samples.push_back(sample); |
| 496 | } |
| 497 | |
| 498 | { |
| 499 | FunctionSample sample; |
| 500 | sample.x = 2.0; |
| 501 | sample.value = EvaluatePolynomial(true_polynomial, sample.x); |
| 502 | sample.value_is_valid = true; |
| 503 | sample.gradient = EvaluatePolynomial(true_gradient_polynomial, sample.x); |
| 504 | sample.gradient_is_valid = true; |
| 505 | samples.push_back(sample); |
| 506 | } |
| 507 | |
| 508 | const Vector polynomial = FindInterpolatingPolynomial(samples); |
| 509 | EXPECT_NEAR((true_polynomial - polynomial).norm(), 0.0, 1e-14); |
| 510 | } |
| 511 | |
| 512 | } // namespace internal |
| 513 | } // namespace ceres |