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;
10 * redistributions in binary form must reproduce the above copyright
11 * notice, this list of conditions and the following disclaimer in the
12 * documentation and/or other materials provided with the distribution;
13 * neither the name of the copyright holders nor the names of its
14 * contributors may be used to endorse or promote products derived from
15 * this software without specific prior written permission.
16 *
17 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
18 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
19 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
20 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
21 * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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
30/*
31 * $Id$
32 *
33 */
34
| 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;
10 * redistributions in binary form must reproduce the above copyright
11 * notice, this list of conditions and the following disclaimer in the
12 * documentation and/or other materials provided with the distribution;
13 * neither the name of the copyright holders nor the names of its
14 * contributors may be used to endorse or promote products derived from
15 * this software without specific prior written permission.
16 *
17 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
18 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
19 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
20 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
21 * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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
30/*
31 * $Id$
32 *
33 */
34
|
37Histogram::Histogram(int binsize, int bins)
38{
39 m_binsize = binsize;
40 m_bins = bins;
41 clear();
42}
43
44Histogram::~Histogram()
45{
46}
47
48void Histogram::clear(int binsize, int bins)
49{
50 m_binsize = binsize;
51 clear(bins);
52}
53
54void Histogram::clear(int bins)
55{
56 m_bins = bins;
57 m_largest_bin = 0;
58 m_max = 0;
59 m_data.setSize(m_bins);
60 for (int i = 0; i < m_bins; i++) {
61 m_data[i] = 0;
62 }
63 m_count = 0;
64 m_max = 0;
65
66 m_sumSamples = 0;
67 m_sumSquaredSamples = 0;
68}
69
70
71void Histogram::add(int64 value)
72{
73 assert(value >= 0);
74 m_max = max(m_max, value);
75 m_count++;
76
77 m_sumSamples += value;
78 m_sumSquaredSamples += (value*value);
79
80 int index;
81 if (m_binsize == -1) {
82 // This is a log base 2 histogram
83 if (value == 0) {
84 index = 0;
85 } else {
86 index = int(log(double(value))/log(2.0))+1;
87 if (index >= m_data.size()) {
88 index = m_data.size()-1;
89 }
90 }
91 } else {
92 // This is a linear histogram
93 while (m_max >= (m_bins * m_binsize)) {
94 for (int i = 0; i < m_bins/2; i++) {
95 m_data[i] = m_data[i*2] + m_data[i*2 + 1];
96 }
97 for (int i = m_bins/2; i < m_bins; i++) {
98 m_data[i] = 0;
99 }
100 m_binsize *= 2;
101 }
102 index = value/m_binsize;
103 }
104 assert(index >= 0);
105 m_data[index]++;
106 m_largest_bin = max(m_largest_bin, index);
107}
108
109void Histogram::add(const Histogram& hist)
110{
111 assert(hist.getBins() == m_bins);
112 assert(hist.getBinSize() == -1); // assume log histogram
113 assert(m_binsize == -1);
114
115 for (int j = 0; j < hist.getData(0); j++) {
116 add(0);
117 }
118
119 for (int i = 1; i < m_bins; i++) {
120 for (int j = 0; j < hist.getData(i); j++) {
121 add(1<<(i-1)); // account for the + 1 index
122 }
123 }
124
125}
126
127// Computation of standard deviation of samples a1, a2, ... aN
128// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1)
129// std deviation equals square root of variance
130double Histogram::getStandardDeviation() const
131{
132 double variance;
133 if(m_count > 1){
134 variance = (double)(m_sumSquaredSamples - m_sumSamples*m_sumSamples/m_count)/(m_count - 1);
135 } else {
136 return 0;
137 }
138 return sqrt(variance);
139}
140
141void Histogram::print(ostream& out) const
142{
143 printWithMultiplier(out, 1.0);
144}
145
146void Histogram::printPercent(ostream& out) const
147{
148 if (m_count == 0) {
149 printWithMultiplier(out, 0.0);
150 } else {
151 printWithMultiplier(out, 100.0/double(m_count));
152 }
153}
154
155void Histogram::printWithMultiplier(ostream& out, double multiplier) const
156{
157 if (m_binsize == -1) {
158 out << "[binsize: log2 ";
159 } else {
160 out << "[binsize: " << m_binsize << " ";
161 }
162 out << "max: " << m_max << " ";
163 out << "count: " << m_count << " ";
164 // out << "total: " << m_sumSamples << " ";
165 if (m_count == 0) {
166 out << "average: NaN |";
167 out << "standard deviation: NaN |";
168 } else {
169 out << "average: " << setw(5) << ((double) m_sumSamples)/m_count << " | ";
170 out << "standard deviation: " << getStandardDeviation() << " |";
171 }
172 for (int i = 0; i < m_bins && i <= m_largest_bin; i++) {
173 if (multiplier == 1.0) {
174 out << " " << m_data[i];
175 } else {
176 out << " " << double(m_data[i]) * multiplier;
177 }
178 }
179 out << " ]";
180}
181
182bool node_less_then_eq(const Histogram* n1, const Histogram* n2)
183{
184 return (n1->size() > n2->size());
185}
| 42Histogram::Histogram(int binsize, int bins)
43{
44 m_binsize = binsize;
45 m_bins = bins;
46 clear();
47}
48
49Histogram::~Histogram()
50{
51}
52
53void Histogram::clear(int binsize, int bins)
54{
55 m_binsize = binsize;
56 clear(bins);
57}
58
59void Histogram::clear(int bins)
60{
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;
70
71 m_sumSamples = 0;
72 m_sumSquaredSamples = 0;
73}
74
75
76void Histogram::add(int64 value)
77{
78 assert(value >= 0);
79 m_max = max(m_max, value);
80 m_count++;
81
82 m_sumSamples += value;
83 m_sumSquaredSamples += (value*value);
84
85 int index;
86 if (m_binsize == -1) {
87 // This is a log base 2 histogram
88 if (value == 0) {
89 index = 0;
90 } 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 }
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);
112}
113
114void Histogram::add(const Histogram& hist)
115{
116 assert(hist.getBins() == m_bins);
117 assert(hist.getBinSize() == -1); // assume log histogram
118 assert(m_binsize == -1);
119
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
127 }
128 }
129
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
135double Histogram::getStandardDeviation() const
136{
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);
144}
145
146void Histogram::print(ostream& out) const
147{
148 printWithMultiplier(out, 1.0);
149}
150
151void Histogram::printPercent(ostream& out) const
152{
153 if (m_count == 0) {
154 printWithMultiplier(out, 0.0);
155 } else {
156 printWithMultiplier(out, 100.0/double(m_count));
157 }
158}
159
160void Histogram::printWithMultiplier(ostream& out, double multiplier) const
161{
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];
180 } else {
181 out << " " << double(m_data[i]) * multiplier;
182 }
183 }
184 out << " ]";
185}
186
187bool node_less_then_eq(const Histogram* n1, const Histogram* n2)
188{
189 return (n1->size() > n2->size());
190}
|