Adding Google benchmarking library.

third_party/google_benchmark/src/complexity.h

+// Copyright 2016 Ismael Jimenez Martinez. All rights reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Source project : https://github.com/ismaelJimenez/cpp.leastsq
+// Adapted to be used with google benchmark
+
+#ifndef COMPLEXITY_H_
+#define COMPLEXITY_H_
+
+#include <string>
+#include <vector>
+
+#include "benchmark/benchmark_api.h"
+#include "benchmark/reporter.h"
+
+namespace benchmark {
+
+// Return a vector containing the mean and standard devation information for
+// the specified list of reports. If 'reports' contains less than two
+// non-errored runs an empty vector is returned
+std::vector<BenchmarkReporter::Run> ComputeStats(
+    const std::vector<BenchmarkReporter::Run>& reports);
+
+// Return a vector containing the bigO and RMS information for the specified
+// list of reports. If 'reports.size() < 2' an empty vector is returned.
+std::vector<BenchmarkReporter::Run> ComputeBigO(
+    const std::vector<BenchmarkReporter::Run>& reports);
+
+// This data structure will contain the result returned by MinimalLeastSq
+//   - coef        : Estimated coeficient for the high-order term as
+//                   interpolated from data.
+//   - rms         : Normalized Root Mean Squared Error.
+//   - complexity  : Scalability form (e.g. oN, oNLogN). In case a scalability
+//                   form has been provided to MinimalLeastSq this will return
+//                   the same value. In case BigO::oAuto has been selected, this
+//                   parameter will return the best fitting curve detected.
+
+struct LeastSq {
+  LeastSq() : coef(0.0), rms(0.0), complexity(oNone) {}
+
+  double coef;
+  double rms;
+  BigO complexity;
+};
+
+// Function to return an string for the calculated complexity
+std::string GetBigOString(BigO complexity);
+
+}  // end namespace benchmark
+#endif  // COMPLEXITY_H_