342
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1 /*
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2 * Copyright 2001-2007 Sun Microsystems, Inc. All Rights Reserved.
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3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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4 *
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5 * This code is free software; you can redistribute it and/or modify it
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6 * under the terms of the GNU General Public License version 2 only, as
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7 * published by the Free Software Foundation.
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8 *
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9 * This code is distributed in the hope that it will be useful, but WITHOUT
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10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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12 * version 2 for more details (a copy is included in the LICENSE file that
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13 * accompanied this code).
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14 *
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15 * You should have received a copy of the GNU General Public License version
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16 * 2 along with this work; if not, write to the Free Software Foundation,
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17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
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18 *
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19 * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
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20 * CA 95054 USA or visit www.sun.com if you need additional information or
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21 * have any questions.
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22 *
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23 */
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24
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25 # include "incls/_precompiled.incl"
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26 # include "incls/_numberSeq.cpp.incl"
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27
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28 AbsSeq::AbsSeq(double alpha) :
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29 _num(0), _sum(0.0), _sum_of_squares(0.0),
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30 _davg(0.0), _dvariance(0.0), _alpha(alpha) {
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31 }
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32
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33 void AbsSeq::add(double val) {
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34 if (_num == 0) {
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35 // if the sequence is empty, the davg is the same as the value
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36 _davg = val;
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37 // and the variance is 0
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38 _dvariance = 0.0;
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39 } else {
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40 // otherwise, calculate both
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41 _davg = (1.0 - _alpha) * val + _alpha * _davg;
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42 double diff = val - _davg;
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43 _dvariance = (1.0 - _alpha) * diff * diff + _alpha * _dvariance;
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44 }
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45 }
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46
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47 double AbsSeq::avg() const {
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48 if (_num == 0)
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49 return 0.0;
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50 else
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51 return _sum / total();
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52 }
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53
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54 double AbsSeq::variance() const {
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55 if (_num <= 1)
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56 return 0.0;
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57
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58 double x_bar = avg();
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59 double result = _sum_of_squares / total() - x_bar * x_bar;
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60 if (result < 0.0) {
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61 // due to loss-of-precision errors, the variance might be negative
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62 // by a small bit
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63
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64 // guarantee(-0.1 < result && result < 0.0,
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65 // "if variance is negative, it should be very small");
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66 result = 0.0;
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67 }
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68 return result;
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69 }
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70
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71 double AbsSeq::sd() const {
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72 double var = variance();
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73 guarantee( var >= 0.0, "variance should not be negative" );
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74 return sqrt(var);
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75 }
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76
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77 double AbsSeq::davg() const {
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78 return _davg;
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79 }
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80
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81 double AbsSeq::dvariance() const {
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82 if (_num <= 1)
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83 return 0.0;
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84
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85 double result = _dvariance;
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86 if (result < 0.0) {
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87 // due to loss-of-precision errors, the variance might be negative
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88 // by a small bit
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89
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90 guarantee(-0.1 < result && result < 0.0,
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91 "if variance is negative, it should be very small");
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92 result = 0.0;
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93 }
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94 return result;
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95 }
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96
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97 double AbsSeq::dsd() const {
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98 double var = dvariance();
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99 guarantee( var >= 0.0, "variance should not be negative" );
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100 return sqrt(var);
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101 }
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102
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103 NumberSeq::NumberSeq(double alpha) :
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104 AbsSeq(alpha), _maximum(0.0), _last(0.0) {
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105 }
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106
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107 bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
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108 for (int i = 0; i < n; ++i) {
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109 if (parts[i] != NULL && total->num() != parts[i]->num())
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110 return false;
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111 }
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112 return true;
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113 }
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114
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115 NumberSeq::NumberSeq(NumberSeq *total, int n, NumberSeq **parts) {
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116 guarantee(check_nums(total, n, parts), "all seq lengths should match");
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117 double sum = total->sum();
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118 for (int i = 0; i < n; ++i) {
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119 if (parts[i] != NULL)
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120 sum -= parts[i]->sum();
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121 }
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122
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123 _num = total->num();
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124 _sum = sum;
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125
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126 // we do not calculate these...
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127 _sum_of_squares = -1.0;
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128 _maximum = -1.0;
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129 _davg = -1.0;
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130 _dvariance = -1.0;
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131 }
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132
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133 void NumberSeq::add(double val) {
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134 AbsSeq::add(val);
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135
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136 _last = val;
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137 if (_num == 0) {
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138 _maximum = val;
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139 } else {
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140 if (val > _maximum)
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141 _maximum = val;
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142 }
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143 _sum += val;
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144 _sum_of_squares += val * val;
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145 ++_num;
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146 }
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147
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148
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149 TruncatedSeq::TruncatedSeq(int length, double alpha):
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150 AbsSeq(alpha), _length(length), _next(0) {
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151 _sequence = NEW_C_HEAP_ARRAY(double, _length);
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152 for (int i = 0; i < _length; ++i)
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153 _sequence[i] = 0.0;
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154 }
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155
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156 void TruncatedSeq::add(double val) {
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157 AbsSeq::add(val);
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158
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159 // get the oldest value in the sequence...
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160 double old_val = _sequence[_next];
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161 // ...remove it from the sum and sum of squares
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162 _sum -= old_val;
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163 _sum_of_squares -= old_val * old_val;
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164
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165 // ...and update them with the new value
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166 _sum += val;
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167 _sum_of_squares += val * val;
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168
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169 // now replace the old value with the new one
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170 _sequence[_next] = val;
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171 _next = (_next + 1) % _length;
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172
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173 // only increase it if the buffer is not full
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174 if (_num < _length)
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175 ++_num;
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176
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177 guarantee( variance() > -1.0, "variance should be >= 0" );
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178 }
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179
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180 // can't easily keep track of this incrementally...
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181 double TruncatedSeq::maximum() const {
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182 if (_num == 0)
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183 return 0.0;
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184 double ret = _sequence[0];
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185 for (int i = 1; i < _num; ++i) {
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186 double val = _sequence[i];
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187 if (val > ret)
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188 ret = val;
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189 }
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190 return ret;
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191 }
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192
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193 double TruncatedSeq::last() const {
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194 if (_num == 0)
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195 return 0.0;
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196 unsigned last_index = (_next + _length - 1) % _length;
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197 return _sequence[last_index];
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198 }
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199
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200 double TruncatedSeq::oldest() const {
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201 if (_num == 0)
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202 return 0.0;
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203 else if (_num < _length)
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204 // index 0 always oldest value until the array is full
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205 return _sequence[0];
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206 else {
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207 // since the array is full, _next is over the oldest value
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208 return _sequence[_next];
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209 }
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210 }
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211
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212 double TruncatedSeq::predict_next() const {
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213 if (_num == 0)
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214 return 0.0;
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215
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216 double num = (double) _num;
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217 double x_squared_sum = 0.0;
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218 double x_sum = 0.0;
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219 double y_sum = 0.0;
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220 double xy_sum = 0.0;
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221 double x_avg = 0.0;
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222 double y_avg = 0.0;
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223
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224 int first = (_next + _length - _num) % _length;
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225 for (int i = 0; i < _num; ++i) {
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226 double x = (double) i;
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227 double y = _sequence[(first + i) % _length];
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228
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229 x_squared_sum += x * x;
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230 x_sum += x;
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231 y_sum += y;
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232 xy_sum += x * y;
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233 }
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234 x_avg = x_sum / num;
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235 y_avg = y_sum / num;
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236
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237 double Sxx = x_squared_sum - x_sum * x_sum / num;
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238 double Sxy = xy_sum - x_sum * y_sum / num;
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239 double b1 = Sxy / Sxx;
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240 double b0 = y_avg - b1 * x_avg;
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241
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242 return b0 + b1 * num;
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243 }
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