addaptation of minimal_leastsq library

include/benchmark/benchmark_api.h

@@ -234,15 +234,14 @@ enum TimeUnit {
 // BigO is passed to a benchmark in order to specify the asymptotic computational 
 // complexity for the benchmark.
 enum BigO {
-  O_None,
-  O_1,
-  O_N,
-  O_M_plus_N,
-  O_N_Squared,
-  O_N_Cubed,
-  O_log_N,
-  O_N_log_N,
-  O_Auto
+	O_None,
+	O_1,
+	O_N,
+	O_N_Squared,
+	O_N_Cubed,
+	O_log_N,
+	O_N_log_N,
+	O_Auto
 };
 
 // State is passed to a running Benchmark and contains state for the

src/CMakeLists.txt

@@ -5,7 +5,7 @@ include_directories(${PROJECT_SOURCE_DIR}/src)
 set(SOURCE_FILES "benchmark.cc" "colorprint.cc" "commandlineflags.cc"
                  "console_reporter.cc" "csv_reporter.cc" "json_reporter.cc"
                  "log.cc" "reporter.cc" "sleep.cc" "string_util.cc"
-                 "sysinfo.cc" "walltime.cc")
+                 "sysinfo.cc" "walltime.cc" "minimal_leastsq.cc")
 # Determine the correct regular expression engine to use
 if(HAVE_STD_REGEX)
   set(RE_FILES "re_std.cc")

src/minimal_leastsq.cc

@@ -0,0 +1,113 @@
+// 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
+// Addapted to be used with google benchmark
+
+#include "minimal_leastsq.h"
+
+#include <math.h>
+
+// Internal function to calculate the different scalability forms
+double fittingCurve(double N, benchmark::BigO Complexity) {
+	if (Complexity == benchmark::O_N)
+		return N;
+	else if (Complexity == benchmark::O_N_Squared)
+		return pow(N, 2);
+	else if (Complexity == benchmark::O_N_Cubed)
+		return pow(N, 3);
+	else if (Complexity == benchmark::O_log_N)
+		return log2(N);
+	else if (Complexity == benchmark::O_N_log_N)
+		return N * log2(N);
+
+	return 1; // Default value for O_1
+}
+
+// Internal function to find the coefficient for the high-order term in the running time, by minimizing the sum of squares of relative error.
+//   - N          : Vector containing the size of the benchmark tests.
+//   - Time       : Vector containing the times for the benchmark tests.
+//   - Complexity : Fitting curve.
+// For a deeper explanation on the algorithm logic, look the README file at http://github.com/ismaelJimenez/Minimal-Cpp-Least-Squared-Fit
+
+LeastSq leastSq(const std::vector<int>& N, const std::vector<int>& Time, const benchmark::BigO Complexity) {
+	assert(N.size() == Time.size() && N.size() >= 2);
+	assert(Complexity != benchmark::O_None &&
+		Complexity != benchmark::O_Auto);
+
+	double sigmaGN = 0;
+	double sigmaGNSquared = 0;
+	double sigmaTime = 0;
+	double sigmaTimeGN = 0;
+
+	// Calculate least square fitting parameter
+	for (size_t i = 0; i < N.size(); ++i) {
+		double GNi = fittingCurve(N[i], Complexity);
+		sigmaGN += GNi;
+		sigmaGNSquared += GNi * GNi;
+		sigmaTime += Time[i];
+		sigmaTimeGN += Time[i] * GNi;
+	}
+
+	LeastSq result;
+	result.complexity = Complexity;
+
+	// Calculate complexity. 
+	// O_1 is treated as an special case
+	if (Complexity != benchmark::O_1)
+		result.coef = sigmaTimeGN / sigmaGNSquared;
+	else
+		result.coef = sigmaTime / N.size();
+
+	// Calculate RMS
+	double rms = 0;
+	for (size_t i = 0; i < N.size(); ++i) {
+		double fit = result.coef * fittingCurve(N[i], Complexity);
+		rms += pow((Time[i] - fit), 2);
+	}
+
+	double mean = sigmaTime / N.size();
+
+	result.rms = sqrt(rms) / mean; // Normalized RMS by the mean of the observed values
+
+	return result;
+}
+
+// Find the coefficient for the high-order term in the running time, by minimizing the sum of squares of relative error.
+//   - N          : Vector containing the size of the benchmark tests.
+//   - Time       : Vector containing the times for the benchmark tests.
+//   - Complexity : If different than O_Auto, the fitting curve will stick to this one. If it is O_Auto, it will be calculated 
+//                  the best fitting curve.
+
+LeastSq minimalLeastSq(const std::vector<int>& N, const std::vector<int>& Time, const benchmark::BigO Complexity) {
+	assert(N.size() == Time.size() && N.size() >= 2); // Do not compute fitting curve is less than two benchmark runs are given
+	assert(Complexity != benchmark::O_None);  // Check that complexity is a valid parameter. 
+
+	if(Complexity == benchmark::O_Auto) {
+		std::vector<benchmark::BigO> fitCurves = { benchmark::O_log_N, benchmark::O_N, benchmark::O_N_log_N, benchmark::O_N_Squared, benchmark::O_N_Cubed };
+
+		LeastSq best_fit = leastSq(N, Time, benchmark::O_1); // Take O_1 as default best fitting curve
+
+		// Compute all possible fitting curves and stick to the best one
+		for (const auto& fit : fitCurves) {
+			LeastSq current_fit = leastSq(N, Time, fit);
+			if (current_fit.rms < best_fit.rms)
+				best_fit = current_fit;
+		}
+
+		return best_fit;
+	}
+	else
+		return leastSq(N, Time, Complexity);
+}

src/minimal_leastsq.h

@@ -0,0 +1,46 @@
+// 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
+// Addapted to be used with google benchmark
+
+#if !defined(MINIMAL_LEASTSQ_H_)
+#define MINIMAL_LEASTSQ_H_
+
+#include "benchmark/benchmark_api.h"
+
+#include <vector>
+
+// This data structure will contain the result returned vy minimalLeastSq
+//   - coef        : Estimated coeficient for the high-order term as interpolated from data.
+//   - rms         : Normalized Root Mean Squared Error.
+//   - complexity  : Scalability form (e.g. O_N, O_N_log_N). In case a scalability form has been provided to minimalLeastSq
+//                   this will return the same value. In case BigO::O_Auto has been selected, this parameter will return the 
+//                   best fitting curve detected.
+
+struct LeastSq {
+	LeastSq() :
+		coef(0),
+		rms(0),
+		complexity(benchmark::O_None) {}
+
+	double coef;
+	double rms;
+	benchmark::BigO   complexity;
+};
+
+// Find the coefficient for the high-order term in the running time, by minimizing the sum of squares of relative error.
+LeastSq minimalLeastSq(const std::vector<int>& N, const std::vector<int>& Time, const benchmark::BigO Complexity = benchmark::O_Auto);
+
+#endif

src/reporter.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "benchmark/reporter.h"
+#include "minimal_leastsq.h"
 
 #include <cstdlib>
 #include <vector>

test/complexity_test.cc

@@ -38,15 +38,6 @@ static void BM_Complexity_O_N(benchmark::State& state) {
 }
 BENCHMARK(BM_Complexity_O_N) -> Range(1, 1<<10) -> Complexity(benchmark::O_N);
 BENCHMARK(BM_Complexity_O_N) -> Range(1, 1<<10) -> Complexity(benchmark::O_Auto);
-
-static void BM_Complexity_O_M_plus_N(benchmark::State& state) {
-  std::string s1(state.range_x(), '-');
-  std::string s2(state.range_x(), '-');
-  while (state.KeepRunning())
-    benchmark::DoNotOptimize(s1.compare(s2));
-}
-BENCHMARK(BM_Complexity_O_M_plus_N)
-	->RangeMultiplier(2)->Range(1<<10, 1<<18) -> Complexity(benchmark::O_M_plus_N);
     
 static void BM_Complexity_O_N_Squared(benchmark::State& state) {
   std::string s1(state.range_x(), '-');