Histogram.cc (7002:48a19d52d939) Histogram.cc (7039:bc0b6ea676b5)
1
2/*
3 * Copyright (c) 1999-2008 Mark D. Hill and David A. Wood
4 * All rights reserved.
5 *
6 * Redistribution and use in source and binary forms, with or without
7 * modification, are permitted provided that the following conditions are
8 * met: redistributions of source code must retain the above copyright
9 * notice, this list of conditions and the following disclaimer;

--- 12 unchanged lines hidden (view full) ---

22 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
23 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
24 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
25 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
26 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
27 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
28 */
29
1/*
2 * Copyright (c) 1999-2008 Mark D. Hill and David A. Wood
3 * All rights reserved.
4 *
5 * Redistribution and use in source and binary forms, with or without
6 * modification, are permitted provided that the following conditions are
7 * met: redistributions of source code must retain the above copyright
8 * notice, this list of conditions and the following disclaimer;

--- 12 unchanged lines hidden (view full) ---

21 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
22 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
23 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
24 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
25 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
26 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
27 */
28
30/*
31 * $Id$
32 *
33 */
34
35#include <cmath>
36#include <iomanip>
37
29#include <cmath>
30#include <iomanip>
31
32#include "base/intmath.hh"
38#include "mem/ruby/common/Histogram.hh"
39
40using namespace std;
41
42Histogram::Histogram(int binsize, int bins)
43{
33#include "mem/ruby/common/Histogram.hh"
34
35using namespace std;
36
37Histogram::Histogram(int binsize, int bins)
38{
44 m_binsize = binsize;
45 m_bins = bins;
46 clear();
39 m_binsize = binsize;
40 m_bins = bins;
41 clear();
47}
48
49Histogram::~Histogram()
50{
51}
52
42}
43
44Histogram::~Histogram()
45{
46}
47
53void Histogram::clear(int binsize, int bins)
48void
49Histogram::clear(int binsize, int bins)
54{
50{
55 m_binsize = binsize;
56 clear(bins);
51 m_binsize = binsize;
52 clear(bins);
57}
58
53}
54
59void Histogram::clear(int bins)
55void
56Histogram::clear(int bins)
60{
57{
61 m_bins = bins;
62 m_largest_bin = 0;
63 m_max = 0;
64 m_data.setSize(m_bins);
65 for (int i = 0; i < m_bins; i++) {
66 m_data[i] = 0;
67 }
68 m_count = 0;
69 m_max = 0;
58 m_bins = bins;
59 m_largest_bin = 0;
60 m_max = 0;
61 m_data.setSize(m_bins);
62 for (int i = 0; i < m_bins; i++) {
63 m_data[i] = 0;
64 }
65 m_count = 0;
66 m_max = 0;
70
67
71 m_sumSamples = 0;
72 m_sumSquaredSamples = 0;
68 m_sumSamples = 0;
69 m_sumSquaredSamples = 0;
73}
74
75
70}
71
72
76void Histogram::add(int64 value)
73void
74Histogram::add(int64 value)
77{
75{
78 assert(value >= 0);
79 m_max = max(m_max, value);
80 m_count++;
76 assert(value >= 0);
77 m_max = max(m_max, value);
78 m_count++;
81
79
82 m_sumSamples += value;
83 m_sumSquaredSamples += (value*value);
80 m_sumSamples += value;
81 m_sumSquaredSamples += (value*value);
84
82
85 int index;
86 if (m_binsize == -1) {
87 // This is a log base 2 histogram
88 if (value == 0) {
89 index = 0;
83 int index;
84 if (m_binsize == -1) {
85 // This is a log base 2 histogram
86 if (value == 0) {
87 index = 0;
88 } else {
89 index = floorLog2(value) + 1;
90 if (index >= m_data.size()) {
91 index = m_data.size() - 1;
92 }
93 }
90 } else {
94 } else {
91 index = int(log(double(value))/log(2.0))+1;
92 if (index >= m_data.size()) {
93 index = m_data.size()-1;
94 }
95 // This is a linear histogram
96 while (m_max >= (m_bins * m_binsize)) {
97 for (int i = 0; i < m_bins/2; i++) {
98 m_data[i] = m_data[i*2] + m_data[i*2 + 1];
99 }
100 for (int i = m_bins/2; i < m_bins; i++) {
101 m_data[i] = 0;
102 }
103 m_binsize *= 2;
104 }
105 index = value/m_binsize;
95 }
106 }
96 } else {
97 // This is a linear histogram
98 while (m_max >= (m_bins * m_binsize)) {
99 for (int i = 0; i < m_bins/2; i++) {
100 m_data[i] = m_data[i*2] + m_data[i*2 + 1];
101 }
102 for (int i = m_bins/2; i < m_bins; i++) {
103 m_data[i] = 0;
104 }
105 m_binsize *= 2;
106 }
107 index = value/m_binsize;
108 }
109 assert(index >= 0);
110 m_data[index]++;
111 m_largest_bin = max(m_largest_bin, index);
107 assert(index >= 0);
108 m_data[index]++;
109 m_largest_bin = max(m_largest_bin, index);
112}
113
110}
111
114void Histogram::add(const Histogram& hist)
112void
113Histogram::add(const Histogram& hist)
115{
114{
116 assert(hist.getBins() == m_bins);
117 assert(hist.getBinSize() == -1); // assume log histogram
118 assert(m_binsize == -1);
115 assert(hist.getBins() == m_bins);
116 assert(hist.getBinSize() == -1); // assume log histogram
117 assert(m_binsize == -1);
119
118
120 for (int j = 0; j < hist.getData(0); j++) {
121 add(0);
122 }
123
124 for (int i = 1; i < m_bins; i++) {
125 for (int j = 0; j < hist.getData(i); j++) {
126 add(1<<(i-1)); // account for the + 1 index
119 for (int j = 0; j < hist.getData(0); j++) {
120 add(0);
127 }
121 }
128 }
129
122
123 for (int i = 1; i < m_bins; i++) {
124 for (int j = 0; j < hist.getData(i); j++) {
125 add(1<<(i-1)); // account for the + 1 index
126 }
127 }
130}
131
132// Computation of standard deviation of samples a1, a2, ... aN
133// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1)
134// std deviation equals square root of variance
128}
129
130// Computation of standard deviation of samples a1, a2, ... aN
131// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1)
132// std deviation equals square root of variance
135double Histogram::getStandardDeviation() const
133double
134Histogram::getStandardDeviation() const
136{
135{
137 double variance;
138 if(m_count > 1){
139 variance = (double)(m_sumSquaredSamples - m_sumSamples*m_sumSamples/m_count)/(m_count - 1);
140 } else {
141 return 0;
142 }
143 return sqrt(variance);
136 if (m_count <= 1)
137 return 0.0;
138
139 double variance =
140 (double)(m_sumSquaredSamples - m_sumSamples * m_sumSamples / m_count)
141 / (m_count - 1);
142 return sqrt(variance);
144}
145
143}
144
146void Histogram::print(ostream& out) const
145void
146Histogram::print(ostream& out) const
147{
147{
148 printWithMultiplier(out, 1.0);
148 printWithMultiplier(out, 1.0);
149}
150
149}
150
151void Histogram::printPercent(ostream& out) const
151void
152Histogram::printPercent(ostream& out) const
152{
153{
153 if (m_count == 0) {
154 printWithMultiplier(out, 0.0);
155 } else {
156 printWithMultiplier(out, 100.0/double(m_count));
157 }
154 if (m_count == 0) {
155 printWithMultiplier(out, 0.0);
156 } else {
157 printWithMultiplier(out, 100.0 / double(m_count));
158 }
158}
159
159}
160
160void Histogram::printWithMultiplier(ostream& out, double multiplier) const
161void
162Histogram::printWithMultiplier(ostream& out, double multiplier) const
161{
163{
162 if (m_binsize == -1) {
163 out << "[binsize: log2 ";
164 } else {
165 out << "[binsize: " << m_binsize << " ";
166 }
167 out << "max: " << m_max << " ";
168 out << "count: " << m_count << " ";
169 // out << "total: " << m_sumSamples << " ";
170 if (m_count == 0) {
171 out << "average: NaN |";
172 out << "standard deviation: NaN |";
173 } else {
174 out << "average: " << setw(5) << ((double) m_sumSamples)/m_count << " | ";
175 out << "standard deviation: " << getStandardDeviation() << " |";
176 }
177 for (int i = 0; i < m_bins && i <= m_largest_bin; i++) {
178 if (multiplier == 1.0) {
179 out << " " << m_data[i];
164 if (m_binsize == -1) {
165 out << "[binsize: log2 ";
180 } else {
166 } else {
181 out << " " << double(m_data[i]) * multiplier;
167 out << "[binsize: " << m_binsize << " ";
182 }
168 }
183 }
184 out << " ]";
169 out << "max: " << m_max << " ";
170 out << "count: " << m_count << " ";
171 // out << "total: " << m_sumSamples << " ";
172 if (m_count == 0) {
173 out << "average: NaN |";
174 out << "standard deviation: NaN |";
175 } else {
176 out << "average: " << setw(5) << ((double) m_sumSamples)/m_count
177 << " | ";
178 out << "standard deviation: " << getStandardDeviation() << " |";
179 }
180 for (int i = 0; i < m_bins && i <= m_largest_bin; i++) {
181 if (multiplier == 1.0) {
182 out << " " << m_data[i];
183 } else {
184 out << " " << double(m_data[i]) * multiplier;
185 }
186 }
187 out << " ]";
185}
186
188}
189
187bool node_less_then_eq(const Histogram* n1, const Histogram* n2)
190bool
191node_less_then_eq(const Histogram* n1, const Histogram* n2)
188{
192{
189 return (n1->size() > n2->size());
193 return (n1->size() > n2->size());
190}
194}