Histogram.cc (7454:3a3e8e8cce1b) | Histogram.cc (9497:2759161b9d7f) |
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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; --- 20 unchanged lines hidden (view full) --- 29#include <cmath> 30#include <iomanip> 31 32#include "base/intmath.hh" 33#include "mem/ruby/common/Histogram.hh" 34 35using namespace std; 36 | 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; --- 20 unchanged lines hidden (view full) --- 29#include <cmath> 30#include <iomanip> 31 32#include "base/intmath.hh" 33#include "mem/ruby/common/Histogram.hh" 34 35using namespace std; 36 |
37Histogram::Histogram(int binsize, int bins) | 37Histogram::Histogram(int binsize, uint32_t bins) |
38{ 39 m_binsize = binsize; | 38{ 39 m_binsize = binsize; |
40 m_bins = bins; 41 clear(); | 40 clear(bins); |
42} 43 44Histogram::~Histogram() 45{ 46} 47 48void | 41} 42 43Histogram::~Histogram() 44{ 45} 46 47void |
49Histogram::clear(int binsize, int bins) | 48Histogram::clear(int binsize, uint32_t bins) |
50{ 51 m_binsize = binsize; 52 clear(bins); 53} 54 55void | 49{ 50 m_binsize = binsize; 51 clear(bins); 52} 53 54void |
56Histogram::clear(int bins) | 55Histogram::clear(uint32_t bins) |
57{ | 56{ |
58 m_bins = bins; | |
59 m_largest_bin = 0; 60 m_max = 0; | 57 m_largest_bin = 0; 58 m_max = 0; |
61 m_data.resize(m_bins); 62 for (int i = 0; i < m_bins; i++) { | 59 m_data.resize(bins); 60 for (uint32_t i = 0; i < bins; i++) { |
63 m_data[i] = 0; 64 } | 61 m_data[i] = 0; 62 } |
63 |
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65 m_count = 0; 66 m_max = 0; | 64 m_count = 0; 65 m_max = 0; |
67 | |
68 m_sumSamples = 0; 69 m_sumSquaredSamples = 0; 70} 71 | 66 m_sumSamples = 0; 67 m_sumSquaredSamples = 0; 68} 69 |
70void 71Histogram::doubleBinSize() 72{ 73 assert(m_binsize != -1); 74 uint32_t t_bins = m_data.size(); |
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72 | 75 |
76 for (uint32_t i = 0; i < t_bins/2; i++) { 77 m_data[i] = m_data[i*2] + m_data[i*2 + 1]; 78 } 79 for (uint32_t i = t_bins/2; i < t_bins; i++) { 80 m_data[i] = 0; 81 } 82 83 m_binsize *= 2; 84} 85 |
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73void 74Histogram::add(int64 value) 75{ 76 assert(value >= 0); 77 m_max = max(m_max, value); 78 m_count++; 79 80 m_sumSamples += value; 81 m_sumSquaredSamples += (value*value); 82 | 86void 87Histogram::add(int64 value) 88{ 89 assert(value >= 0); 90 m_max = max(m_max, value); 91 m_count++; 92 93 m_sumSamples += value; 94 m_sumSquaredSamples += (value*value); 95 |
83 int index; | 96 uint32_t index; 97 |
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 } 94 } else { 95 // This is a linear histogram | 98 if (m_binsize == -1) { 99 // This is a log base 2 histogram 100 if (value == 0) { 101 index = 0; 102 } else { 103 index = floorLog2(value) + 1; 104 if (index >= m_data.size()) { 105 index = m_data.size() - 1; 106 } 107 } 108 } else { 109 // 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 } | 110 uint32_t t_bins = m_data.size(); 111 112 while (m_max >= (t_bins * m_binsize)) doubleBinSize(); |
105 index = value/m_binsize; 106 } | 113 index = value/m_binsize; 114 } |
107 assert(index >= 0); | 115 116 assert(index < m_data.size()); |
108 m_data[index]++; 109 m_largest_bin = max(m_largest_bin, index); 110} 111 112void | 117 m_data[index]++; 118 m_largest_bin = max(m_largest_bin, index); 119} 120 121void |
113Histogram::add(const Histogram& hist) | 122Histogram::add(Histogram& hist) |
114{ | 123{ |
115 assert(hist.getBins() == m_bins); 116 assert(hist.getBinSize() == -1); // assume log histogram 117 assert(m_binsize == -1); | 124 uint32_t t_bins = m_data.size(); |
118 | 125 |
119 for (int j = 0; j < hist.getData(0); j++) { 120 add(0); | 126 if (hist.getBins() != t_bins) { 127 fatal("Histograms with different number of bins cannot be combined!"); |
121 } 122 | 128 } 129 |
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 | 130 m_max = max(m_max, hist.getMax()); 131 m_count += hist.size(); 132 m_sumSamples += hist.getTotal(); 133 m_sumSquaredSamples += hist.getSquaredTotal(); 134 135 // Both histograms are log base 2. 136 if (hist.getBinSize() == -1 && m_binsize == -1) { 137 for (int j = 0; j < hist.getData(0); j++) { 138 add(0); |
126 } | 139 } |
140 141 for (uint32_t i = 1; i < t_bins; i++) { 142 for (int j = 0; j < hist.getData(i); j++) { 143 add(1<<(i-1)); // account for the + 1 index 144 } 145 } 146 } else if (hist.getBinSize() >= 1 && m_binsize >= 1) { 147 // Both the histogram are linear. 148 // We are assuming that the two histograms have the same 149 // minimum value that they can store. 150 151 while (m_binsize > hist.getBinSize()) hist.doubleBinSize(); 152 while (hist.getBinSize() > m_binsize) doubleBinSize(); 153 154 assert(m_binsize == hist.getBinSize()); 155 156 for (uint32_t i = 0; i < t_bins; i++) { 157 m_data[i] += hist.getData(i); 158 159 if (m_data[i] > 0) m_largest_bin = i; 160 } 161 } else { 162 fatal("Don't know how to combine log and linear histograms!"); |
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127 } 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 133double 134Histogram::getStandardDeviation() const --- 37 unchanged lines hidden (view full) --- 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 } | 163 } 164} 165 166// Computation of standard deviation of samples a1, a2, ... aN 167// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1) 168// std deviation equals square root of variance 169double 170Histogram::getStandardDeviation() const --- 37 unchanged lines hidden (view full) --- 208 if (m_count == 0) { 209 out << "average: NaN |"; 210 out << "standard deviation: NaN |"; 211 } else { 212 out << "average: " << setw(5) << ((double) m_sumSamples)/m_count 213 << " | "; 214 out << "standard deviation: " << getStandardDeviation() << " |"; 215 } |
180 for (int i = 0; i < m_bins && i <= m_largest_bin; i++) { | 216 217 for (uint32_t i = 0; 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 << " ]"; 188} 189 190bool 191node_less_then_eq(const Histogram* n1, const Histogram* n2) 192{ 193 return (n1->size() > n2->size()); 194} | 218 if (multiplier == 1.0) { 219 out << " " << m_data[i]; 220 } else { 221 out << " " << double(m_data[i]) * multiplier; 222 } 223 } 224 out << " ]"; 225} 226 227bool 228node_less_then_eq(const Histogram* n1, const Histogram* n2) 229{ 230 return (n1->size() > n2->size()); 231} |