From 6bf0a5cb5034a7e684dcc3500e841785237ce2dd Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Sun, 7 Apr 2024 19:32:43 +0200
Subject: Adding upstream version 1:115.7.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 third_party/jpeg-xl/lib/jxl/enc_optimize_test.cc | 109 +++++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 third_party/jpeg-xl/lib/jxl/enc_optimize_test.cc

(limited to 'third_party/jpeg-xl/lib/jxl/enc_optimize_test.cc')

diff --git a/third_party/jpeg-xl/lib/jxl/enc_optimize_test.cc b/third_party/jpeg-xl/lib/jxl/enc_optimize_test.cc
new file mode 100644
index 0000000000..1c6699f99e
--- /dev/null
+++ b/third_party/jpeg-xl/lib/jxl/enc_optimize_test.cc
@@ -0,0 +1,109 @@
+// Copyright (c) the JPEG XL Project Authors. All rights reserved.
+//
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+#include "lib/jxl/enc_optimize.h"
+
+#include <stdio.h>
+
+#include "lib/jxl/testing.h"
+
+namespace jxl {
+namespace optimize {
+namespace {
+
+// The maximum number of iterations for the test.
+static const size_t kMaxTestIter = 100000;
+
+// F(w) = (w - w_min)^2.
+struct SimpleQuadraticFunction {
+  typedef Array<double, 2> ArrayType;
+  explicit SimpleQuadraticFunction(const ArrayType& w0) : w_min(w0) {}
+
+  double Compute(const ArrayType& w, ArrayType* df) const {
+    ArrayType dw = w - w_min;
+    *df = -2.0 * dw;
+    return dw * dw;
+  }
+
+  ArrayType w_min;
+};
+
+// F(alpha, beta, gamma| x,y) = \sum_i(y_i - (alpha x_i ^ gamma + beta))^2.
+struct PowerFunction {
+  explicit PowerFunction(const std::vector<double>& x0,
+                         const std::vector<double>& y0)
+      : x(x0), y(y0) {}
+
+  typedef Array<double, 3> ArrayType;
+  double Compute(const ArrayType& w, ArrayType* df) const {
+    double loss_function = 0;
+    (*df)[0] = 0;
+    (*df)[1] = 0;
+    (*df)[2] = 0;
+    for (size_t ind = 0; ind < y.size(); ++ind) {
+      if (x[ind] != 0) {
+        double l_f = y[ind] - (w[0] * pow(x[ind], w[1]) + w[2]);
+        (*df)[0] += 2.0 * l_f * pow(x[ind], w[1]);
+        (*df)[1] += 2.0 * l_f * w[0] * pow(x[ind], w[1]) * log(x[ind]);
+        (*df)[2] += 2.0 * l_f * 1;
+        loss_function += l_f * l_f;
+      }
+    }
+    return loss_function;
+  }
+
+  std::vector<double> x;
+  std::vector<double> y;
+};
+
+TEST(OptimizeTest, SimpleQuadraticFunction) {
+  SimpleQuadraticFunction::ArrayType w_min;
+  w_min[0] = 1.0;
+  w_min[1] = 2.0;
+  SimpleQuadraticFunction f(w_min);
+  SimpleQuadraticFunction::ArrayType w(0.);
+  static const double kPrecision = 1e-8;
+  w = optimize::OptimizeWithScaledConjugateGradientMethod(f, w, kPrecision,
+                                                          kMaxTestIter);
+  EXPECT_NEAR(w[0], 1.0, kPrecision);
+  EXPECT_NEAR(w[1], 2.0, kPrecision);
+}
+
+TEST(OptimizeTest, PowerFunction) {
+  std::vector<double> x(10);
+  std::vector<double> y(10);
+  for (int ind = 0; ind < 10; ++ind) {
+    x[ind] = 1. * ind;
+    y[ind] = 2. * pow(x[ind], 3) + 5.;
+  }
+  PowerFunction f(x, y);
+  PowerFunction::ArrayType w(0.);
+
+  static const double kPrecision = 0.01;
+  w = optimize::OptimizeWithScaledConjugateGradientMethod(f, w, kPrecision,
+                                                          kMaxTestIter);
+  EXPECT_NEAR(w[0], 2.0, kPrecision);
+  EXPECT_NEAR(w[1], 3.0, kPrecision);
+  EXPECT_NEAR(w[2], 5.0, kPrecision);
+}
+
+TEST(OptimizeTest, SimplexOptTest) {
+  auto f = [](const std::vector<double>& x) -> double {
+    double t1 = x[0] - 1.0;
+    double t2 = x[1] + 1.5;
+    return 2.0 + t1 * t1 + t2 * t2;
+  };
+  auto opt = RunSimplex(2, 0.01, 100, f);
+  EXPECT_EQ(opt.size(), 3u);
+
+  static const double kPrecision = 0.01;
+  EXPECT_NEAR(opt[0], 2.0, kPrecision);
+  EXPECT_NEAR(opt[1], 1.0, kPrecision);
+  EXPECT_NEAR(opt[2], -1.5, kPrecision);
+}
+
+}  // namespace
+}  // namespace optimize
+}  // namespace jxl
-- 
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