[2563] | 1 | /*************************************************************************
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| 2 | Copyright 2008 by Sergey Bochkanov (ALGLIB project).
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| 3 |
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| 4 | >>> SOURCE LICENSE >>>
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| 5 | This program is free software; you can redistribute it and/or modify
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| 6 | it under the terms of the GNU General Public License as published by
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| 7 | the Free Software Foundation (www.fsf.org); either version 2 of the
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| 8 | License, or (at your option) any later version.
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| 9 |
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| 10 | This program is distributed in the hope that it will be useful,
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| 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 13 | GNU General Public License for more details.
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| 14 |
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| 15 | A copy of the GNU General Public License is available at
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| 16 | http://www.fsf.org/licensing/licenses
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| 17 |
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| 18 | >>> END OF LICENSE >>>
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| 19 | *************************************************************************/
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| 20 |
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| 21 | using System;
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| 22 |
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| 23 | namespace alglib
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| 24 | {
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| 25 | public class bdss
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| 26 | {
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| 27 | public struct cvreport
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| 28 | {
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| 29 | public double relclserror;
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| 30 | public double avgce;
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| 31 | public double rmserror;
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| 32 | public double avgerror;
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| 33 | public double avgrelerror;
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| 34 | };
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 | /*************************************************************************
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| 40 | This set of routines (DSErrAllocate, DSErrAccumulate, DSErrFinish)
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| 41 | calculates different error functions (classification error, cross-entropy,
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| 42 | rms, avg, avg.rel errors).
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| 43 |
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| 44 | 1. DSErrAllocate prepares buffer.
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| 45 | 2. DSErrAccumulate accumulates individual errors:
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| 46 | * Y contains predicted output (posterior probabilities for classification)
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| 47 | * DesiredY contains desired output (class number for classification)
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| 48 | 3. DSErrFinish outputs results:
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| 49 | * Buf[0] contains relative classification error (zero for regression tasks)
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| 50 | * Buf[1] contains avg. cross-entropy (zero for regression tasks)
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| 51 | * Buf[2] contains rms error (regression, classification)
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| 52 | * Buf[3] contains average error (regression, classification)
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| 53 | * Buf[4] contains average relative error (regression, classification)
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| 54 |
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| 55 | NOTES(1):
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| 56 | "NClasses>0" means that we have classification task.
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| 57 | "NClasses<0" means regression task with -NClasses real outputs.
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| 58 |
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| 59 | NOTES(2):
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| 60 | rms. avg, avg.rel errors for classification tasks are interpreted as
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| 61 | errors in posterior probabilities with respect to probabilities given
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| 62 | by training/test set.
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| 63 |
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| 64 | -- ALGLIB --
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| 65 | Copyright 11.01.2009 by Bochkanov Sergey
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| 66 | *************************************************************************/
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| 67 | public static void dserrallocate(int nclasses,
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| 68 | ref double[] buf)
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| 69 | {
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| 70 | buf = new double[7+1];
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| 71 | buf[0] = 0;
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| 72 | buf[1] = 0;
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| 73 | buf[2] = 0;
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| 74 | buf[3] = 0;
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| 75 | buf[4] = 0;
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| 76 | buf[5] = nclasses;
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| 77 | buf[6] = 0;
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| 78 | buf[7] = 0;
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| 79 | }
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| 80 |
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| 81 |
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| 82 | /*************************************************************************
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| 83 | See DSErrAllocate for comments on this routine.
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| 84 |
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| 85 | -- ALGLIB --
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| 86 | Copyright 11.01.2009 by Bochkanov Sergey
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| 87 | *************************************************************************/
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| 88 | public static void dserraccumulate(ref double[] buf,
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| 89 | ref double[] y,
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| 90 | ref double[] desiredy)
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| 91 | {
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| 92 | int nclasses = 0;
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| 93 | int nout = 0;
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| 94 | int offs = 0;
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| 95 | int mmax = 0;
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| 96 | int rmax = 0;
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| 97 | int j = 0;
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| 98 | double v = 0;
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| 99 | double ev = 0;
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| 100 |
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| 101 | offs = 5;
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| 102 | nclasses = (int)Math.Round(buf[offs]);
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| 103 | if( nclasses>0 )
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| 104 | {
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| 105 |
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| 106 | //
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| 107 | // Classification
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| 108 | //
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| 109 | rmax = (int)Math.Round(desiredy[0]);
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| 110 | mmax = 0;
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| 111 | for(j=1; j<=nclasses-1; j++)
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| 112 | {
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| 113 | if( (double)(y[j])>(double)(y[mmax]) )
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| 114 | {
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| 115 | mmax = j;
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| 116 | }
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| 117 | }
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| 118 | if( mmax!=rmax )
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| 119 | {
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| 120 | buf[0] = buf[0]+1;
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| 121 | }
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| 122 | if( (double)(y[rmax])>(double)(0) )
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| 123 | {
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| 124 | buf[1] = buf[1]-Math.Log(y[rmax]);
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| 125 | }
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| 126 | else
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| 127 | {
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| 128 | buf[1] = buf[1]+Math.Log(AP.Math.MaxRealNumber);
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| 129 | }
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| 130 | for(j=0; j<=nclasses-1; j++)
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| 131 | {
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| 132 | v = y[j];
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| 133 | if( j==rmax )
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| 134 | {
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| 135 | ev = 1;
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| 136 | }
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| 137 | else
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| 138 | {
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| 139 | ev = 0;
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| 140 | }
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| 141 | buf[2] = buf[2]+AP.Math.Sqr(v-ev);
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| 142 | buf[3] = buf[3]+Math.Abs(v-ev);
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| 143 | if( (double)(ev)!=(double)(0) )
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| 144 | {
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| 145 | buf[4] = buf[4]+Math.Abs((v-ev)/ev);
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| 146 | buf[offs+2] = buf[offs+2]+1;
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| 147 | }
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| 148 | }
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| 149 | buf[offs+1] = buf[offs+1]+1;
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| 150 | }
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| 151 | else
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| 152 | {
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| 153 |
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| 154 | //
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| 155 | // Regression
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| 156 | //
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| 157 | nout = -nclasses;
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| 158 | rmax = 0;
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| 159 | for(j=1; j<=nout-1; j++)
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| 160 | {
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| 161 | if( (double)(desiredy[j])>(double)(desiredy[rmax]) )
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| 162 | {
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| 163 | rmax = j;
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| 164 | }
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| 165 | }
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| 166 | mmax = 0;
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| 167 | for(j=1; j<=nout-1; j++)
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| 168 | {
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| 169 | if( (double)(y[j])>(double)(y[mmax]) )
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| 170 | {
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| 171 | mmax = j;
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| 172 | }
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| 173 | }
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| 174 | if( mmax!=rmax )
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| 175 | {
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| 176 | buf[0] = buf[0]+1;
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| 177 | }
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| 178 | for(j=0; j<=nout-1; j++)
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| 179 | {
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| 180 | v = y[j];
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| 181 | ev = desiredy[j];
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| 182 | buf[2] = buf[2]+AP.Math.Sqr(v-ev);
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| 183 | buf[3] = buf[3]+Math.Abs(v-ev);
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| 184 | if( (double)(ev)!=(double)(0) )
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| 185 | {
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| 186 | buf[4] = buf[4]+Math.Abs((v-ev)/ev);
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| 187 | buf[offs+2] = buf[offs+2]+1;
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| 188 | }
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| 189 | }
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| 190 | buf[offs+1] = buf[offs+1]+1;
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| 191 | }
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| 192 | }
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| 193 |
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| 194 |
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| 195 | /*************************************************************************
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| 196 | See DSErrAllocate for comments on this routine.
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| 197 |
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| 198 | -- ALGLIB --
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| 199 | Copyright 11.01.2009 by Bochkanov Sergey
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| 200 | *************************************************************************/
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| 201 | public static void dserrfinish(ref double[] buf)
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| 202 | {
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| 203 | int nout = 0;
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| 204 | int offs = 0;
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| 205 |
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| 206 | offs = 5;
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| 207 | nout = Math.Abs((int)Math.Round(buf[offs]));
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| 208 | if( (double)(buf[offs+1])!=(double)(0) )
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| 209 | {
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| 210 | buf[0] = buf[0]/buf[offs+1];
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| 211 | buf[1] = buf[1]/buf[offs+1];
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| 212 | buf[2] = Math.Sqrt(buf[2]/(nout*buf[offs+1]));
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| 213 | buf[3] = buf[3]/(nout*buf[offs+1]);
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| 214 | }
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| 215 | if( (double)(buf[offs+2])!=(double)(0) )
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| 216 | {
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| 217 | buf[4] = buf[4]/buf[offs+2];
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| 218 | }
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| 219 | }
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| 220 |
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| 221 |
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| 222 | /*************************************************************************
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| 223 |
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| 224 | -- ALGLIB --
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| 225 | Copyright 19.05.2008 by Bochkanov Sergey
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| 226 | *************************************************************************/
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| 227 | public static void dsnormalize(ref double[,] xy,
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| 228 | int npoints,
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| 229 | int nvars,
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| 230 | ref int info,
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| 231 | ref double[] means,
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| 232 | ref double[] sigmas)
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| 233 | {
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| 234 | int i = 0;
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| 235 | int j = 0;
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| 236 | double[] tmp = new double[0];
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| 237 | double mean = 0;
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| 238 | double variance = 0;
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| 239 | double skewness = 0;
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| 240 | double kurtosis = 0;
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| 241 | int i_ = 0;
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| 242 |
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| 243 |
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| 244 | //
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| 245 | // Test parameters
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| 246 | //
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| 247 | if( npoints<=0 | nvars<1 )
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| 248 | {
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| 249 | info = -1;
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| 250 | return;
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| 251 | }
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| 252 | info = 1;
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| 253 |
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| 254 | //
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| 255 | // Standartization
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| 256 | //
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| 257 | means = new double[nvars-1+1];
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| 258 | sigmas = new double[nvars-1+1];
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| 259 | tmp = new double[npoints-1+1];
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| 260 | for(j=0; j<=nvars-1; j++)
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| 261 | {
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| 262 | for(i_=0; i_<=npoints-1;i_++)
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| 263 | {
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| 264 | tmp[i_] = xy[i_,j];
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| 265 | }
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| 266 | descriptivestatistics.calculatemoments(ref tmp, npoints, ref mean, ref variance, ref skewness, ref kurtosis);
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| 267 | means[j] = mean;
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| 268 | sigmas[j] = Math.Sqrt(variance);
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| 269 | if( (double)(sigmas[j])==(double)(0) )
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| 270 | {
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| 271 | sigmas[j] = 1;
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| 272 | }
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| 273 | for(i=0; i<=npoints-1; i++)
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| 274 | {
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| 275 | xy[i,j] = (xy[i,j]-means[j])/sigmas[j];
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| 276 | }
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| 277 | }
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| 278 | }
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| 279 |
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| 280 |
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| 281 | /*************************************************************************
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| 282 |
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| 283 | -- ALGLIB --
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| 284 | Copyright 19.05.2008 by Bochkanov Sergey
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| 285 | *************************************************************************/
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| 286 | public static void dsnormalizec(ref double[,] xy,
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| 287 | int npoints,
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| 288 | int nvars,
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| 289 | ref int info,
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| 290 | ref double[] means,
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| 291 | ref double[] sigmas)
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| 292 | {
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| 293 | int i = 0;
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| 294 | int j = 0;
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| 295 | double[] tmp = new double[0];
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| 296 | double mean = 0;
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| 297 | double variance = 0;
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| 298 | double skewness = 0;
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| 299 | double kurtosis = 0;
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| 300 | int i_ = 0;
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| 301 |
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| 302 |
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| 303 | //
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| 304 | // Test parameters
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| 305 | //
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| 306 | if( npoints<=0 | nvars<1 )
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| 307 | {
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| 308 | info = -1;
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| 309 | return;
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| 310 | }
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| 311 | info = 1;
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| 312 |
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| 313 | //
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| 314 | // Standartization
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| 315 | //
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| 316 | means = new double[nvars-1+1];
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| 317 | sigmas = new double[nvars-1+1];
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| 318 | tmp = new double[npoints-1+1];
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| 319 | for(j=0; j<=nvars-1; j++)
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| 320 | {
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| 321 | for(i_=0; i_<=npoints-1;i_++)
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| 322 | {
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| 323 | tmp[i_] = xy[i_,j];
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| 324 | }
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| 325 | descriptivestatistics.calculatemoments(ref tmp, npoints, ref mean, ref variance, ref skewness, ref kurtosis);
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| 326 | means[j] = mean;
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| 327 | sigmas[j] = Math.Sqrt(variance);
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| 328 | if( (double)(sigmas[j])==(double)(0) )
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| 329 | {
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| 330 | sigmas[j] = 1;
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| 331 | }
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| 332 | }
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| 333 | }
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| 334 |
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| 335 |
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| 336 | /*************************************************************************
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| 337 |
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| 338 | -- ALGLIB --
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| 339 | Copyright 19.05.2008 by Bochkanov Sergey
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| 340 | *************************************************************************/
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| 341 | public static double dsgetmeanmindistance(ref double[,] xy,
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| 342 | int npoints,
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| 343 | int nvars)
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| 344 | {
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| 345 | double result = 0;
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| 346 | int i = 0;
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| 347 | int j = 0;
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| 348 | double[] tmp = new double[0];
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| 349 | double[] tmp2 = new double[0];
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| 350 | double v = 0;
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| 351 | int i_ = 0;
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| 352 |
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| 353 |
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| 354 | //
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| 355 | // Test parameters
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| 356 | //
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| 357 | if( npoints<=0 | nvars<1 )
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| 358 | {
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| 359 | result = 0;
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| 360 | return result;
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| 361 | }
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| 362 |
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| 363 | //
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| 364 | // Process
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| 365 | //
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| 366 | tmp = new double[npoints-1+1];
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| 367 | for(i=0; i<=npoints-1; i++)
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| 368 | {
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| 369 | tmp[i] = AP.Math.MaxRealNumber;
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| 370 | }
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| 371 | tmp2 = new double[nvars-1+1];
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| 372 | for(i=0; i<=npoints-1; i++)
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| 373 | {
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| 374 | for(j=i+1; j<=npoints-1; j++)
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| 375 | {
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| 376 | for(i_=0; i_<=nvars-1;i_++)
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| 377 | {
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| 378 | tmp2[i_] = xy[i,i_];
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| 379 | }
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| 380 | for(i_=0; i_<=nvars-1;i_++)
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| 381 | {
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| 382 | tmp2[i_] = tmp2[i_] - xy[j,i_];
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| 383 | }
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| 384 | v = 0.0;
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| 385 | for(i_=0; i_<=nvars-1;i_++)
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| 386 | {
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| 387 | v += tmp2[i_]*tmp2[i_];
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| 388 | }
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| 389 | v = Math.Sqrt(v);
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| 390 | tmp[i] = Math.Min(tmp[i], v);
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| 391 | tmp[j] = Math.Min(tmp[j], v);
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| 392 | }
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| 393 | }
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| 394 | result = 0;
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| 395 | for(i=0; i<=npoints-1; i++)
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| 396 | {
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| 397 | result = result+tmp[i]/npoints;
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| 398 | }
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| 399 | return result;
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| 400 | }
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| 401 |
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| 402 |
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| 403 | /*************************************************************************
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| 404 |
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| 405 | -- ALGLIB --
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| 406 | Copyright 19.05.2008 by Bochkanov Sergey
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| 407 | *************************************************************************/
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| 408 | public static void dstie(ref double[] a,
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| 409 | int n,
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| 410 | ref int[] ties,
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| 411 | ref int tiecount,
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| 412 | ref int[] p1,
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| 413 | ref int[] p2)
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| 414 | {
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| 415 | int i = 0;
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| 416 | int k = 0;
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| 417 | int[] tmp = new int[0];
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| 418 |
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| 419 |
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| 420 | //
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| 421 | // Special case
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| 422 | //
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| 423 | if( n<=0 )
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| 424 | {
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| 425 | tiecount = 0;
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| 426 | return;
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| 427 | }
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| 428 |
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| 429 | //
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| 430 | // Sort A
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| 431 | //
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| 432 | tsort.tagsort(ref a, n, ref p1, ref p2);
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| 433 |
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| 434 | //
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| 435 | // Process ties
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| 436 | //
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| 437 | tiecount = 1;
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| 438 | for(i=1; i<=n-1; i++)
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| 439 | {
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| 440 | if( (double)(a[i])!=(double)(a[i-1]) )
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| 441 | {
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| 442 | tiecount = tiecount+1;
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| 443 | }
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| 444 | }
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| 445 | ties = new int[tiecount+1];
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| 446 | ties[0] = 0;
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| 447 | k = 1;
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| 448 | for(i=1; i<=n-1; i++)
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| 449 | {
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| 450 | if( (double)(a[i])!=(double)(a[i-1]) )
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| 451 | {
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| 452 | ties[k] = i;
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| 453 | k = k+1;
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| 454 | }
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| 455 | }
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| 456 | ties[tiecount] = n;
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| 457 | }
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| 458 |
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| 459 |
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| 460 | /*************************************************************************
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| 461 |
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| 462 | -- ALGLIB --
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| 463 | Copyright 11.12.2008 by Bochkanov Sergey
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| 464 | *************************************************************************/
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| 465 | public static void dstiefasti(ref double[] a,
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| 466 | ref int[] b,
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| 467 | int n,
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| 468 | ref int[] ties,
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| 469 | ref int tiecount)
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| 470 | {
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| 471 | int i = 0;
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| 472 | int k = 0;
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| 473 | int[] tmp = new int[0];
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| 474 |
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| 475 |
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| 476 | //
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| 477 | // Special case
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| 478 | //
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| 479 | if( n<=0 )
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| 480 | {
|
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| 481 | tiecount = 0;
|
---|
| 482 | return;
|
---|
| 483 | }
|
---|
| 484 |
|
---|
| 485 | //
|
---|
| 486 | // Sort A
|
---|
| 487 | //
|
---|
| 488 | tsort.tagsortfasti(ref a, ref b, n);
|
---|
| 489 |
|
---|
| 490 | //
|
---|
| 491 | // Process ties
|
---|
| 492 | //
|
---|
| 493 | ties[0] = 0;
|
---|
| 494 | k = 1;
|
---|
| 495 | for(i=1; i<=n-1; i++)
|
---|
| 496 | {
|
---|
| 497 | if( (double)(a[i])!=(double)(a[i-1]) )
|
---|
| 498 | {
|
---|
| 499 | ties[k] = i;
|
---|
| 500 | k = k+1;
|
---|
| 501 | }
|
---|
| 502 | }
|
---|
| 503 | ties[k] = n;
|
---|
| 504 | tiecount = k;
|
---|
| 505 | }
|
---|
| 506 |
|
---|
| 507 |
|
---|
| 508 | /*************************************************************************
|
---|
| 509 | Optimal partition, internal subroutine.
|
---|
| 510 |
|
---|
| 511 | -- ALGLIB --
|
---|
| 512 | Copyright 22.05.2008 by Bochkanov Sergey
|
---|
| 513 | *************************************************************************/
|
---|
| 514 | public static void dsoptimalsplit2(double[] a,
|
---|
| 515 | int[] c,
|
---|
| 516 | int n,
|
---|
| 517 | ref int info,
|
---|
| 518 | ref double threshold,
|
---|
| 519 | ref double pal,
|
---|
| 520 | ref double pbl,
|
---|
| 521 | ref double par,
|
---|
| 522 | ref double pbr,
|
---|
| 523 | ref double cve)
|
---|
| 524 | {
|
---|
| 525 | int i = 0;
|
---|
| 526 | int t = 0;
|
---|
| 527 | double s = 0;
|
---|
| 528 | double pea = 0;
|
---|
| 529 | double peb = 0;
|
---|
| 530 | int[] ties = new int[0];
|
---|
| 531 | int tiecount = 0;
|
---|
| 532 | int[] p1 = new int[0];
|
---|
| 533 | int[] p2 = new int[0];
|
---|
| 534 | double v1 = 0;
|
---|
| 535 | double v2 = 0;
|
---|
| 536 | int k = 0;
|
---|
| 537 | int koptimal = 0;
|
---|
| 538 | double pak = 0;
|
---|
| 539 | double pbk = 0;
|
---|
| 540 | double cvoptimal = 0;
|
---|
| 541 | double cv = 0;
|
---|
| 542 |
|
---|
| 543 | a = (double[])a.Clone();
|
---|
| 544 | c = (int[])c.Clone();
|
---|
| 545 |
|
---|
| 546 |
|
---|
| 547 | //
|
---|
| 548 | // Test for errors in inputs
|
---|
| 549 | //
|
---|
| 550 | if( n<=0 )
|
---|
| 551 | {
|
---|
| 552 | info = -1;
|
---|
| 553 | return;
|
---|
| 554 | }
|
---|
| 555 | for(i=0; i<=n-1; i++)
|
---|
| 556 | {
|
---|
| 557 | if( c[i]!=0 & c[i]!=1 )
|
---|
| 558 | {
|
---|
| 559 | info = -2;
|
---|
| 560 | return;
|
---|
| 561 | }
|
---|
| 562 | }
|
---|
| 563 | info = 1;
|
---|
| 564 |
|
---|
| 565 | //
|
---|
| 566 | // Tie
|
---|
| 567 | //
|
---|
| 568 | dstie(ref a, n, ref ties, ref tiecount, ref p1, ref p2);
|
---|
| 569 | for(i=0; i<=n-1; i++)
|
---|
| 570 | {
|
---|
| 571 | if( p2[i]!=i )
|
---|
| 572 | {
|
---|
| 573 | t = c[i];
|
---|
| 574 | c[i] = c[p2[i]];
|
---|
| 575 | c[p2[i]] = t;
|
---|
| 576 | }
|
---|
| 577 | }
|
---|
| 578 |
|
---|
| 579 | //
|
---|
| 580 | // Special case: number of ties is 1.
|
---|
| 581 | //
|
---|
| 582 | // NOTE: we assume that P[i,j] equals to 0 or 1,
|
---|
| 583 | // intermediate values are not allowed.
|
---|
| 584 | //
|
---|
| 585 | if( tiecount==1 )
|
---|
| 586 | {
|
---|
| 587 | info = -3;
|
---|
| 588 | return;
|
---|
| 589 | }
|
---|
| 590 |
|
---|
| 591 | //
|
---|
| 592 | // General case, number of ties > 1
|
---|
| 593 | //
|
---|
| 594 | // NOTE: we assume that P[i,j] equals to 0 or 1,
|
---|
| 595 | // intermediate values are not allowed.
|
---|
| 596 | //
|
---|
| 597 | pal = 0;
|
---|
| 598 | pbl = 0;
|
---|
| 599 | par = 0;
|
---|
| 600 | pbr = 0;
|
---|
| 601 | for(i=0; i<=n-1; i++)
|
---|
| 602 | {
|
---|
| 603 | if( c[i]==0 )
|
---|
| 604 | {
|
---|
| 605 | par = par+1;
|
---|
| 606 | }
|
---|
| 607 | if( c[i]==1 )
|
---|
| 608 | {
|
---|
| 609 | pbr = pbr+1;
|
---|
| 610 | }
|
---|
| 611 | }
|
---|
| 612 | koptimal = -1;
|
---|
| 613 | cvoptimal = AP.Math.MaxRealNumber;
|
---|
| 614 | for(k=0; k<=tiecount-2; k++)
|
---|
| 615 | {
|
---|
| 616 |
|
---|
| 617 | //
|
---|
| 618 | // first, obtain information about K-th tie which is
|
---|
| 619 | // moved from R-part to L-part
|
---|
| 620 | //
|
---|
| 621 | pak = 0;
|
---|
| 622 | pbk = 0;
|
---|
| 623 | for(i=ties[k]; i<=ties[k+1]-1; i++)
|
---|
| 624 | {
|
---|
| 625 | if( c[i]==0 )
|
---|
| 626 | {
|
---|
| 627 | pak = pak+1;
|
---|
| 628 | }
|
---|
| 629 | if( c[i]==1 )
|
---|
| 630 | {
|
---|
| 631 | pbk = pbk+1;
|
---|
| 632 | }
|
---|
| 633 | }
|
---|
| 634 |
|
---|
| 635 | //
|
---|
| 636 | // Calculate cross-validation CE
|
---|
| 637 | //
|
---|
| 638 | cv = 0;
|
---|
| 639 | cv = cv-xlny(pal+pak, (pal+pak)/(pal+pak+pbl+pbk+1));
|
---|
| 640 | cv = cv-xlny(pbl+pbk, (pbl+pbk)/(pal+pak+1+pbl+pbk));
|
---|
| 641 | cv = cv-xlny(par-pak, (par-pak)/(par-pak+pbr-pbk+1));
|
---|
| 642 | cv = cv-xlny(pbr-pbk, (pbr-pbk)/(par-pak+1+pbr-pbk));
|
---|
| 643 |
|
---|
| 644 | //
|
---|
| 645 | // Compare with best
|
---|
| 646 | //
|
---|
| 647 | if( (double)(cv)<(double)(cvoptimal) )
|
---|
| 648 | {
|
---|
| 649 | cvoptimal = cv;
|
---|
| 650 | koptimal = k;
|
---|
| 651 | }
|
---|
| 652 |
|
---|
| 653 | //
|
---|
| 654 | // update
|
---|
| 655 | //
|
---|
| 656 | pal = pal+pak;
|
---|
| 657 | pbl = pbl+pbk;
|
---|
| 658 | par = par-pak;
|
---|
| 659 | pbr = pbr-pbk;
|
---|
| 660 | }
|
---|
| 661 | cve = cvoptimal;
|
---|
| 662 | threshold = 0.5*(a[ties[koptimal]]+a[ties[koptimal+1]]);
|
---|
| 663 | pal = 0;
|
---|
| 664 | pbl = 0;
|
---|
| 665 | par = 0;
|
---|
| 666 | pbr = 0;
|
---|
| 667 | for(i=0; i<=n-1; i++)
|
---|
| 668 | {
|
---|
| 669 | if( (double)(a[i])<(double)(threshold) )
|
---|
| 670 | {
|
---|
| 671 | if( c[i]==0 )
|
---|
| 672 | {
|
---|
| 673 | pal = pal+1;
|
---|
| 674 | }
|
---|
| 675 | else
|
---|
| 676 | {
|
---|
| 677 | pbl = pbl+1;
|
---|
| 678 | }
|
---|
| 679 | }
|
---|
| 680 | else
|
---|
| 681 | {
|
---|
| 682 | if( c[i]==0 )
|
---|
| 683 | {
|
---|
| 684 | par = par+1;
|
---|
| 685 | }
|
---|
| 686 | else
|
---|
| 687 | {
|
---|
| 688 | pbr = pbr+1;
|
---|
| 689 | }
|
---|
| 690 | }
|
---|
| 691 | }
|
---|
| 692 | s = pal+pbl;
|
---|
| 693 | pal = pal/s;
|
---|
| 694 | pbl = pbl/s;
|
---|
| 695 | s = par+pbr;
|
---|
| 696 | par = par/s;
|
---|
| 697 | pbr = pbr/s;
|
---|
| 698 | }
|
---|
| 699 |
|
---|
| 700 |
|
---|
| 701 | /*************************************************************************
|
---|
| 702 | Optimal partition, internal subroutine. Fast version.
|
---|
| 703 |
|
---|
| 704 | Accepts:
|
---|
| 705 | A array[0..N-1] array of attributes array[0..N-1]
|
---|
| 706 | C array[0..N-1] array of class labels
|
---|
| 707 | TiesBuf array[0..N] temporaries (ties)
|
---|
| 708 | CntBuf array[0..2*NC-1] temporaries (counts)
|
---|
| 709 | Alpha centering factor (0<=alpha<=1, recommended value - 0.05)
|
---|
| 710 |
|
---|
| 711 | Output:
|
---|
| 712 | Info error code (">0"=OK, "<0"=bad)
|
---|
| 713 | RMS training set RMS error
|
---|
| 714 | CVRMS leave-one-out RMS error
|
---|
| 715 |
|
---|
| 716 | Note:
|
---|
| 717 | content of all arrays is changed by subroutine
|
---|
| 718 |
|
---|
| 719 | -- ALGLIB --
|
---|
| 720 | Copyright 11.12.2008 by Bochkanov Sergey
|
---|
| 721 | *************************************************************************/
|
---|
| 722 | public static void dsoptimalsplit2fast(ref double[] a,
|
---|
| 723 | ref int[] c,
|
---|
| 724 | ref int[] tiesbuf,
|
---|
| 725 | ref int[] cntbuf,
|
---|
| 726 | int n,
|
---|
| 727 | int nc,
|
---|
| 728 | double alpha,
|
---|
| 729 | ref int info,
|
---|
| 730 | ref double threshold,
|
---|
| 731 | ref double rms,
|
---|
| 732 | ref double cvrms)
|
---|
| 733 | {
|
---|
| 734 | int i = 0;
|
---|
| 735 | int k = 0;
|
---|
| 736 | int cl = 0;
|
---|
| 737 | int tiecount = 0;
|
---|
| 738 | double cbest = 0;
|
---|
| 739 | double cc = 0;
|
---|
| 740 | int koptimal = 0;
|
---|
| 741 | int sl = 0;
|
---|
| 742 | int sr = 0;
|
---|
| 743 | double v = 0;
|
---|
| 744 | double w = 0;
|
---|
| 745 | double x = 0;
|
---|
| 746 |
|
---|
| 747 |
|
---|
| 748 | //
|
---|
| 749 | // Test for errors in inputs
|
---|
| 750 | //
|
---|
| 751 | if( n<=0 | nc<2 )
|
---|
| 752 | {
|
---|
| 753 | info = -1;
|
---|
| 754 | return;
|
---|
| 755 | }
|
---|
| 756 | for(i=0; i<=n-1; i++)
|
---|
| 757 | {
|
---|
| 758 | if( c[i]<0 | c[i]>=nc )
|
---|
| 759 | {
|
---|
| 760 | info = -2;
|
---|
| 761 | return;
|
---|
| 762 | }
|
---|
| 763 | }
|
---|
| 764 | info = 1;
|
---|
| 765 |
|
---|
| 766 | //
|
---|
| 767 | // Tie
|
---|
| 768 | //
|
---|
| 769 | dstiefasti(ref a, ref c, n, ref tiesbuf, ref tiecount);
|
---|
| 770 |
|
---|
| 771 | //
|
---|
| 772 | // Special case: number of ties is 1.
|
---|
| 773 | //
|
---|
| 774 | if( tiecount==1 )
|
---|
| 775 | {
|
---|
| 776 | info = -3;
|
---|
| 777 | return;
|
---|
| 778 | }
|
---|
| 779 |
|
---|
| 780 | //
|
---|
| 781 | // General case, number of ties > 1
|
---|
| 782 | //
|
---|
| 783 | for(i=0; i<=2*nc-1; i++)
|
---|
| 784 | {
|
---|
| 785 | cntbuf[i] = 0;
|
---|
| 786 | }
|
---|
| 787 | for(i=0; i<=n-1; i++)
|
---|
| 788 | {
|
---|
| 789 | cntbuf[nc+c[i]] = cntbuf[nc+c[i]]+1;
|
---|
| 790 | }
|
---|
| 791 | koptimal = -1;
|
---|
| 792 | threshold = a[n-1];
|
---|
| 793 | cbest = AP.Math.MaxRealNumber;
|
---|
| 794 | sl = 0;
|
---|
| 795 | sr = n;
|
---|
| 796 | for(k=0; k<=tiecount-2; k++)
|
---|
| 797 | {
|
---|
| 798 |
|
---|
| 799 | //
|
---|
| 800 | // first, move Kth tie from right to left
|
---|
| 801 | //
|
---|
| 802 | for(i=tiesbuf[k]; i<=tiesbuf[k+1]-1; i++)
|
---|
| 803 | {
|
---|
| 804 | cl = c[i];
|
---|
| 805 | cntbuf[cl] = cntbuf[cl]+1;
|
---|
| 806 | cntbuf[nc+cl] = cntbuf[nc+cl]-1;
|
---|
| 807 | }
|
---|
| 808 | sl = sl+(tiesbuf[k+1]-tiesbuf[k]);
|
---|
| 809 | sr = sr-(tiesbuf[k+1]-tiesbuf[k]);
|
---|
| 810 |
|
---|
| 811 | //
|
---|
| 812 | // Calculate RMS error
|
---|
| 813 | //
|
---|
| 814 | v = 0;
|
---|
| 815 | for(i=0; i<=nc-1; i++)
|
---|
| 816 | {
|
---|
| 817 | w = cntbuf[i];
|
---|
| 818 | v = v+w*AP.Math.Sqr(w/sl-1);
|
---|
| 819 | v = v+(sl-w)*AP.Math.Sqr(w/sl);
|
---|
| 820 | w = cntbuf[nc+i];
|
---|
| 821 | v = v+w*AP.Math.Sqr(w/sr-1);
|
---|
| 822 | v = v+(sr-w)*AP.Math.Sqr(w/sr);
|
---|
| 823 | }
|
---|
| 824 | v = Math.Sqrt(v/(nc*n));
|
---|
| 825 |
|
---|
| 826 | //
|
---|
| 827 | // Compare with best
|
---|
| 828 | //
|
---|
| 829 | x = (double)(2*sl)/((double)(sl+sr))-1;
|
---|
| 830 | cc = v*(1-alpha+alpha*AP.Math.Sqr(x));
|
---|
| 831 | if( (double)(cc)<(double)(cbest) )
|
---|
| 832 | {
|
---|
| 833 |
|
---|
| 834 | //
|
---|
| 835 | // store split
|
---|
| 836 | //
|
---|
| 837 | rms = v;
|
---|
| 838 | koptimal = k;
|
---|
| 839 | cbest = cc;
|
---|
| 840 |
|
---|
| 841 | //
|
---|
| 842 | // calculate CVRMS error
|
---|
| 843 | //
|
---|
| 844 | cvrms = 0;
|
---|
| 845 | for(i=0; i<=nc-1; i++)
|
---|
| 846 | {
|
---|
| 847 | if( sl>1 )
|
---|
| 848 | {
|
---|
| 849 | w = cntbuf[i];
|
---|
| 850 | cvrms = cvrms+w*AP.Math.Sqr((w-1)/(sl-1)-1);
|
---|
| 851 | cvrms = cvrms+(sl-w)*AP.Math.Sqr(w/(sl-1));
|
---|
| 852 | }
|
---|
| 853 | else
|
---|
| 854 | {
|
---|
| 855 | w = cntbuf[i];
|
---|
| 856 | cvrms = cvrms+w*AP.Math.Sqr((double)(1)/(double)(nc)-1);
|
---|
| 857 | cvrms = cvrms+(sl-w)*AP.Math.Sqr((double)(1)/(double)(nc));
|
---|
| 858 | }
|
---|
| 859 | if( sr>1 )
|
---|
| 860 | {
|
---|
| 861 | w = cntbuf[nc+i];
|
---|
| 862 | cvrms = cvrms+w*AP.Math.Sqr((w-1)/(sr-1)-1);
|
---|
| 863 | cvrms = cvrms+(sr-w)*AP.Math.Sqr(w/(sr-1));
|
---|
| 864 | }
|
---|
| 865 | else
|
---|
| 866 | {
|
---|
| 867 | w = cntbuf[nc+i];
|
---|
| 868 | cvrms = cvrms+w*AP.Math.Sqr((double)(1)/(double)(nc)-1);
|
---|
| 869 | cvrms = cvrms+(sr-w)*AP.Math.Sqr((double)(1)/(double)(nc));
|
---|
| 870 | }
|
---|
| 871 | }
|
---|
| 872 | cvrms = Math.Sqrt(cvrms/(nc*n));
|
---|
| 873 | }
|
---|
| 874 | }
|
---|
| 875 |
|
---|
| 876 | //
|
---|
| 877 | // Calculate threshold.
|
---|
| 878 | // Code is a bit complicated because there can be such
|
---|
| 879 | // numbers that 0.5(A+B) equals to A or B (if A-B=epsilon)
|
---|
| 880 | //
|
---|
| 881 | threshold = 0.5*(a[tiesbuf[koptimal]]+a[tiesbuf[koptimal+1]]);
|
---|
| 882 | if( (double)(threshold)<=(double)(a[tiesbuf[koptimal]]) )
|
---|
| 883 | {
|
---|
| 884 | threshold = a[tiesbuf[koptimal+1]];
|
---|
| 885 | }
|
---|
| 886 | }
|
---|
| 887 |
|
---|
| 888 |
|
---|
| 889 | /*************************************************************************
|
---|
| 890 | Automatic non-optimal discretization, internal subroutine.
|
---|
| 891 |
|
---|
| 892 | -- ALGLIB --
|
---|
| 893 | Copyright 22.05.2008 by Bochkanov Sergey
|
---|
| 894 | *************************************************************************/
|
---|
| 895 | public static void dssplitk(double[] a,
|
---|
| 896 | int[] c,
|
---|
| 897 | int n,
|
---|
| 898 | int nc,
|
---|
| 899 | int kmax,
|
---|
| 900 | ref int info,
|
---|
| 901 | ref double[] thresholds,
|
---|
| 902 | ref int ni,
|
---|
| 903 | ref double cve)
|
---|
| 904 | {
|
---|
| 905 | int i = 0;
|
---|
| 906 | int j = 0;
|
---|
| 907 | int j1 = 0;
|
---|
| 908 | int k = 0;
|
---|
| 909 | int[] ties = new int[0];
|
---|
| 910 | int tiecount = 0;
|
---|
| 911 | int[] p1 = new int[0];
|
---|
| 912 | int[] p2 = new int[0];
|
---|
| 913 | int[] cnt = new int[0];
|
---|
| 914 | double v2 = 0;
|
---|
| 915 | int bestk = 0;
|
---|
| 916 | double bestcve = 0;
|
---|
| 917 | int[] bestsizes = new int[0];
|
---|
| 918 | double curcve = 0;
|
---|
| 919 | int[] cursizes = new int[0];
|
---|
| 920 |
|
---|
| 921 | a = (double[])a.Clone();
|
---|
| 922 | c = (int[])c.Clone();
|
---|
| 923 |
|
---|
| 924 |
|
---|
| 925 | //
|
---|
| 926 | // Test for errors in inputs
|
---|
| 927 | //
|
---|
| 928 | if( n<=0 | nc<2 | kmax<2 )
|
---|
| 929 | {
|
---|
| 930 | info = -1;
|
---|
| 931 | return;
|
---|
| 932 | }
|
---|
| 933 | for(i=0; i<=n-1; i++)
|
---|
| 934 | {
|
---|
| 935 | if( c[i]<0 | c[i]>=nc )
|
---|
| 936 | {
|
---|
| 937 | info = -2;
|
---|
| 938 | return;
|
---|
| 939 | }
|
---|
| 940 | }
|
---|
| 941 | info = 1;
|
---|
| 942 |
|
---|
| 943 | //
|
---|
| 944 | // Tie
|
---|
| 945 | //
|
---|
| 946 | dstie(ref a, n, ref ties, ref tiecount, ref p1, ref p2);
|
---|
| 947 | for(i=0; i<=n-1; i++)
|
---|
| 948 | {
|
---|
| 949 | if( p2[i]!=i )
|
---|
| 950 | {
|
---|
| 951 | k = c[i];
|
---|
| 952 | c[i] = c[p2[i]];
|
---|
| 953 | c[p2[i]] = k;
|
---|
| 954 | }
|
---|
| 955 | }
|
---|
| 956 |
|
---|
| 957 | //
|
---|
| 958 | // Special cases
|
---|
| 959 | //
|
---|
| 960 | if( tiecount==1 )
|
---|
| 961 | {
|
---|
| 962 | info = -3;
|
---|
| 963 | return;
|
---|
| 964 | }
|
---|
| 965 |
|
---|
| 966 | //
|
---|
| 967 | // General case:
|
---|
| 968 | // 0. allocate arrays
|
---|
| 969 | //
|
---|
| 970 | kmax = Math.Min(kmax, tiecount);
|
---|
| 971 | bestsizes = new int[kmax-1+1];
|
---|
| 972 | cursizes = new int[kmax-1+1];
|
---|
| 973 | cnt = new int[nc-1+1];
|
---|
| 974 |
|
---|
| 975 | //
|
---|
| 976 | // General case:
|
---|
| 977 | // 1. prepare "weak" solution (two subintervals, divided at median)
|
---|
| 978 | //
|
---|
| 979 | v2 = AP.Math.MaxRealNumber;
|
---|
| 980 | j = -1;
|
---|
| 981 | for(i=1; i<=tiecount-1; i++)
|
---|
| 982 | {
|
---|
| 983 | if( (double)(Math.Abs(ties[i]-0.5*(n-1)))<(double)(v2) )
|
---|
| 984 | {
|
---|
| 985 | v2 = Math.Abs(ties[i]-0.5*n);
|
---|
| 986 | j = i;
|
---|
| 987 | }
|
---|
| 988 | }
|
---|
| 989 | System.Diagnostics.Debug.Assert(j>0, "DSSplitK: internal error #1!");
|
---|
| 990 | bestk = 2;
|
---|
| 991 | bestsizes[0] = ties[j];
|
---|
| 992 | bestsizes[1] = n-j;
|
---|
| 993 | bestcve = 0;
|
---|
| 994 | for(i=0; i<=nc-1; i++)
|
---|
| 995 | {
|
---|
| 996 | cnt[i] = 0;
|
---|
| 997 | }
|
---|
| 998 | for(i=0; i<=j-1; i++)
|
---|
| 999 | {
|
---|
| 1000 | tieaddc(ref c, ref ties, i, nc, ref cnt);
|
---|
| 1001 | }
|
---|
| 1002 | bestcve = bestcve+getcv(ref cnt, nc);
|
---|
| 1003 | for(i=0; i<=nc-1; i++)
|
---|
| 1004 | {
|
---|
| 1005 | cnt[i] = 0;
|
---|
| 1006 | }
|
---|
| 1007 | for(i=j; i<=tiecount-1; i++)
|
---|
| 1008 | {
|
---|
| 1009 | tieaddc(ref c, ref ties, i, nc, ref cnt);
|
---|
| 1010 | }
|
---|
| 1011 | bestcve = bestcve+getcv(ref cnt, nc);
|
---|
| 1012 |
|
---|
| 1013 | //
|
---|
| 1014 | // General case:
|
---|
| 1015 | // 2. Use greedy algorithm to find sub-optimal split in O(KMax*N) time
|
---|
| 1016 | //
|
---|
| 1017 | for(k=2; k<=kmax; k++)
|
---|
| 1018 | {
|
---|
| 1019 |
|
---|
| 1020 | //
|
---|
| 1021 | // Prepare greedy K-interval split
|
---|
| 1022 | //
|
---|
| 1023 | for(i=0; i<=k-1; i++)
|
---|
| 1024 | {
|
---|
| 1025 | cursizes[i] = 0;
|
---|
| 1026 | }
|
---|
| 1027 | i = 0;
|
---|
| 1028 | j = 0;
|
---|
| 1029 | while( j<=tiecount-1 & i<=k-1 )
|
---|
| 1030 | {
|
---|
| 1031 |
|
---|
| 1032 | //
|
---|
| 1033 | // Rule: I-th bin is empty, fill it
|
---|
| 1034 | //
|
---|
| 1035 | if( cursizes[i]==0 )
|
---|
| 1036 | {
|
---|
| 1037 | cursizes[i] = ties[j+1]-ties[j];
|
---|
| 1038 | j = j+1;
|
---|
| 1039 | continue;
|
---|
| 1040 | }
|
---|
| 1041 |
|
---|
| 1042 | //
|
---|
| 1043 | // Rule: (K-1-I) bins left, (K-1-I) ties left (1 tie per bin); next bin
|
---|
| 1044 | //
|
---|
| 1045 | if( tiecount-j==k-1-i )
|
---|
| 1046 | {
|
---|
| 1047 | i = i+1;
|
---|
| 1048 | continue;
|
---|
| 1049 | }
|
---|
| 1050 |
|
---|
| 1051 | //
|
---|
| 1052 | // Rule: last bin, always place in current
|
---|
| 1053 | //
|
---|
| 1054 | if( i==k-1 )
|
---|
| 1055 | {
|
---|
| 1056 | cursizes[i] = cursizes[i]+ties[j+1]-ties[j];
|
---|
| 1057 | j = j+1;
|
---|
| 1058 | continue;
|
---|
| 1059 | }
|
---|
| 1060 |
|
---|
| 1061 | //
|
---|
| 1062 | // Place J-th tie in I-th bin, or leave for I+1-th bin.
|
---|
| 1063 | //
|
---|
| 1064 | if( (double)(Math.Abs(cursizes[i]+ties[j+1]-ties[j]-(double)(n)/(double)(k)))<(double)(Math.Abs(cursizes[i]-(double)(n)/(double)(k))) )
|
---|
| 1065 | {
|
---|
| 1066 | cursizes[i] = cursizes[i]+ties[j+1]-ties[j];
|
---|
| 1067 | j = j+1;
|
---|
| 1068 | }
|
---|
| 1069 | else
|
---|
| 1070 | {
|
---|
| 1071 | i = i+1;
|
---|
| 1072 | }
|
---|
| 1073 | }
|
---|
| 1074 | System.Diagnostics.Debug.Assert(cursizes[k-1]!=0 & j==tiecount, "DSSplitK: internal error #1");
|
---|
| 1075 |
|
---|
| 1076 | //
|
---|
| 1077 | // Calculate CVE
|
---|
| 1078 | //
|
---|
| 1079 | curcve = 0;
|
---|
| 1080 | j = 0;
|
---|
| 1081 | for(i=0; i<=k-1; i++)
|
---|
| 1082 | {
|
---|
| 1083 | for(j1=0; j1<=nc-1; j1++)
|
---|
| 1084 | {
|
---|
| 1085 | cnt[j1] = 0;
|
---|
| 1086 | }
|
---|
| 1087 | for(j1=j; j1<=j+cursizes[i]-1; j1++)
|
---|
| 1088 | {
|
---|
| 1089 | cnt[c[j1]] = cnt[c[j1]]+1;
|
---|
| 1090 | }
|
---|
| 1091 | curcve = curcve+getcv(ref cnt, nc);
|
---|
| 1092 | j = j+cursizes[i];
|
---|
| 1093 | }
|
---|
| 1094 |
|
---|
| 1095 | //
|
---|
| 1096 | // Choose best variant
|
---|
| 1097 | //
|
---|
| 1098 | if( (double)(curcve)<(double)(bestcve) )
|
---|
| 1099 | {
|
---|
| 1100 | for(i=0; i<=k-1; i++)
|
---|
| 1101 | {
|
---|
| 1102 | bestsizes[i] = cursizes[i];
|
---|
| 1103 | }
|
---|
| 1104 | bestcve = curcve;
|
---|
| 1105 | bestk = k;
|
---|
| 1106 | }
|
---|
| 1107 | }
|
---|
| 1108 |
|
---|
| 1109 | //
|
---|
| 1110 | // Transform from sizes to thresholds
|
---|
| 1111 | //
|
---|
| 1112 | cve = bestcve;
|
---|
| 1113 | ni = bestk;
|
---|
| 1114 | thresholds = new double[ni-2+1];
|
---|
| 1115 | j = bestsizes[0];
|
---|
| 1116 | for(i=1; i<=bestk-1; i++)
|
---|
| 1117 | {
|
---|
| 1118 | thresholds[i-1] = 0.5*(a[j-1]+a[j]);
|
---|
| 1119 | j = j+bestsizes[i];
|
---|
| 1120 | }
|
---|
| 1121 | }
|
---|
| 1122 |
|
---|
| 1123 |
|
---|
| 1124 | /*************************************************************************
|
---|
| 1125 | Automatic optimal discretization, internal subroutine.
|
---|
| 1126 |
|
---|
| 1127 | -- ALGLIB --
|
---|
| 1128 | Copyright 22.05.2008 by Bochkanov Sergey
|
---|
| 1129 | *************************************************************************/
|
---|
| 1130 | public static void dsoptimalsplitk(double[] a,
|
---|
| 1131 | int[] c,
|
---|
| 1132 | int n,
|
---|
| 1133 | int nc,
|
---|
| 1134 | int kmax,
|
---|
| 1135 | ref int info,
|
---|
| 1136 | ref double[] thresholds,
|
---|
| 1137 | ref int ni,
|
---|
| 1138 | ref double cve)
|
---|
| 1139 | {
|
---|
| 1140 | int i = 0;
|
---|
| 1141 | int j = 0;
|
---|
| 1142 | int s = 0;
|
---|
| 1143 | int jl = 0;
|
---|
| 1144 | int jr = 0;
|
---|
| 1145 | double v1 = 0;
|
---|
| 1146 | double v2 = 0;
|
---|
| 1147 | double v3 = 0;
|
---|
| 1148 | double v4 = 0;
|
---|
| 1149 | int[] ties = new int[0];
|
---|
| 1150 | int tiecount = 0;
|
---|
| 1151 | int[] p1 = new int[0];
|
---|
| 1152 | int[] p2 = new int[0];
|
---|
| 1153 | double cvtemp = 0;
|
---|
| 1154 | int[] cnt = new int[0];
|
---|
| 1155 | int[] cnt2 = new int[0];
|
---|
| 1156 | double[,] cv = new double[0,0];
|
---|
| 1157 | int[,] splits = new int[0,0];
|
---|
| 1158 | int k = 0;
|
---|
| 1159 | int koptimal = 0;
|
---|
| 1160 | double cvoptimal = 0;
|
---|
| 1161 |
|
---|
| 1162 | a = (double[])a.Clone();
|
---|
| 1163 | c = (int[])c.Clone();
|
---|
| 1164 |
|
---|
| 1165 |
|
---|
| 1166 | //
|
---|
| 1167 | // Test for errors in inputs
|
---|
| 1168 | //
|
---|
| 1169 | if( n<=0 | nc<2 | kmax<2 )
|
---|
| 1170 | {
|
---|
| 1171 | info = -1;
|
---|
| 1172 | return;
|
---|
| 1173 | }
|
---|
| 1174 | for(i=0; i<=n-1; i++)
|
---|
| 1175 | {
|
---|
| 1176 | if( c[i]<0 | c[i]>=nc )
|
---|
| 1177 | {
|
---|
| 1178 | info = -2;
|
---|
| 1179 | return;
|
---|
| 1180 | }
|
---|
| 1181 | }
|
---|
| 1182 | info = 1;
|
---|
| 1183 |
|
---|
| 1184 | //
|
---|
| 1185 | // Tie
|
---|
| 1186 | //
|
---|
| 1187 | dstie(ref a, n, ref ties, ref tiecount, ref p1, ref p2);
|
---|
| 1188 | for(i=0; i<=n-1; i++)
|
---|
| 1189 | {
|
---|
| 1190 | if( p2[i]!=i )
|
---|
| 1191 | {
|
---|
| 1192 | k = c[i];
|
---|
| 1193 | c[i] = c[p2[i]];
|
---|
| 1194 | c[p2[i]] = k;
|
---|
| 1195 | }
|
---|
| 1196 | }
|
---|
| 1197 |
|
---|
| 1198 | //
|
---|
| 1199 | // Special cases
|
---|
| 1200 | //
|
---|
| 1201 | if( tiecount==1 )
|
---|
| 1202 | {
|
---|
| 1203 | info = -3;
|
---|
| 1204 | return;
|
---|
| 1205 | }
|
---|
| 1206 |
|
---|
| 1207 | //
|
---|
| 1208 | // General case
|
---|
| 1209 | // Use dynamic programming to find best split in O(KMax*NC*TieCount^2) time
|
---|
| 1210 | //
|
---|
| 1211 | kmax = Math.Min(kmax, tiecount);
|
---|
| 1212 | cv = new double[kmax-1+1, tiecount-1+1];
|
---|
| 1213 | splits = new int[kmax-1+1, tiecount-1+1];
|
---|
| 1214 | cnt = new int[nc-1+1];
|
---|
| 1215 | cnt2 = new int[nc-1+1];
|
---|
| 1216 | for(j=0; j<=nc-1; j++)
|
---|
| 1217 | {
|
---|
| 1218 | cnt[j] = 0;
|
---|
| 1219 | }
|
---|
| 1220 | for(j=0; j<=tiecount-1; j++)
|
---|
| 1221 | {
|
---|
| 1222 | tieaddc(ref c, ref ties, j, nc, ref cnt);
|
---|
| 1223 | splits[0,j] = 0;
|
---|
| 1224 | cv[0,j] = getcv(ref cnt, nc);
|
---|
| 1225 | }
|
---|
| 1226 | for(k=1; k<=kmax-1; k++)
|
---|
| 1227 | {
|
---|
| 1228 | for(j=0; j<=nc-1; j++)
|
---|
| 1229 | {
|
---|
| 1230 | cnt[j] = 0;
|
---|
| 1231 | }
|
---|
| 1232 |
|
---|
| 1233 | //
|
---|
| 1234 | // Subtask size J in [K..TieCount-1]:
|
---|
| 1235 | // optimal K-splitting on ties from 0-th to J-th.
|
---|
| 1236 | //
|
---|
| 1237 | for(j=k; j<=tiecount-1; j++)
|
---|
| 1238 | {
|
---|
| 1239 |
|
---|
| 1240 | //
|
---|
| 1241 | // Update Cnt - let it contain classes of ties from K-th to J-th
|
---|
| 1242 | //
|
---|
| 1243 | tieaddc(ref c, ref ties, j, nc, ref cnt);
|
---|
| 1244 |
|
---|
| 1245 | //
|
---|
| 1246 | // Search for optimal split point S in [K..J]
|
---|
| 1247 | //
|
---|
| 1248 | for(i=0; i<=nc-1; i++)
|
---|
| 1249 | {
|
---|
| 1250 | cnt2[i] = cnt[i];
|
---|
| 1251 | }
|
---|
| 1252 | cv[k,j] = cv[k-1,j-1]+getcv(ref cnt2, nc);
|
---|
| 1253 | splits[k,j] = j;
|
---|
| 1254 | for(s=k+1; s<=j; s++)
|
---|
| 1255 | {
|
---|
| 1256 |
|
---|
| 1257 | //
|
---|
| 1258 | // Update Cnt2 - let it contain classes of ties from S-th to J-th
|
---|
| 1259 | //
|
---|
| 1260 | tiesubc(ref c, ref ties, s-1, nc, ref cnt2);
|
---|
| 1261 |
|
---|
| 1262 | //
|
---|
| 1263 | // Calculate CVE
|
---|
| 1264 | //
|
---|
| 1265 | cvtemp = cv[k-1,s-1]+getcv(ref cnt2, nc);
|
---|
| 1266 | if( (double)(cvtemp)<(double)(cv[k,j]) )
|
---|
| 1267 | {
|
---|
| 1268 | cv[k,j] = cvtemp;
|
---|
| 1269 | splits[k,j] = s;
|
---|
| 1270 | }
|
---|
| 1271 | }
|
---|
| 1272 | }
|
---|
| 1273 | }
|
---|
| 1274 |
|
---|
| 1275 | //
|
---|
| 1276 | // Choose best partition, output result
|
---|
| 1277 | //
|
---|
| 1278 | koptimal = -1;
|
---|
| 1279 | cvoptimal = AP.Math.MaxRealNumber;
|
---|
| 1280 | for(k=0; k<=kmax-1; k++)
|
---|
| 1281 | {
|
---|
| 1282 | if( (double)(cv[k,tiecount-1])<(double)(cvoptimal) )
|
---|
| 1283 | {
|
---|
| 1284 | cvoptimal = cv[k,tiecount-1];
|
---|
| 1285 | koptimal = k;
|
---|
| 1286 | }
|
---|
| 1287 | }
|
---|
| 1288 | System.Diagnostics.Debug.Assert(koptimal>=0, "DSOptimalSplitK: internal error #1!");
|
---|
| 1289 | if( koptimal==0 )
|
---|
| 1290 | {
|
---|
| 1291 |
|
---|
| 1292 | //
|
---|
| 1293 | // Special case: best partition is one big interval.
|
---|
| 1294 | // Even 2-partition is not better.
|
---|
| 1295 | // This is possible when dealing with "weak" predictor variables.
|
---|
| 1296 | //
|
---|
| 1297 | // Make binary split as close to the median as possible.
|
---|
| 1298 | //
|
---|
| 1299 | v2 = AP.Math.MaxRealNumber;
|
---|
| 1300 | j = -1;
|
---|
| 1301 | for(i=1; i<=tiecount-1; i++)
|
---|
| 1302 | {
|
---|
| 1303 | if( (double)(Math.Abs(ties[i]-0.5*(n-1)))<(double)(v2) )
|
---|
| 1304 | {
|
---|
| 1305 | v2 = Math.Abs(ties[i]-0.5*(n-1));
|
---|
| 1306 | j = i;
|
---|
| 1307 | }
|
---|
| 1308 | }
|
---|
| 1309 | System.Diagnostics.Debug.Assert(j>0, "DSOptimalSplitK: internal error #2!");
|
---|
| 1310 | thresholds = new double[0+1];
|
---|
| 1311 | thresholds[0] = 0.5*(a[ties[j-1]]+a[ties[j]]);
|
---|
| 1312 | ni = 2;
|
---|
| 1313 | cve = 0;
|
---|
| 1314 | for(i=0; i<=nc-1; i++)
|
---|
| 1315 | {
|
---|
| 1316 | cnt[i] = 0;
|
---|
| 1317 | }
|
---|
| 1318 | for(i=0; i<=j-1; i++)
|
---|
| 1319 | {
|
---|
| 1320 | tieaddc(ref c, ref ties, i, nc, ref cnt);
|
---|
| 1321 | }
|
---|
| 1322 | cve = cve+getcv(ref cnt, nc);
|
---|
| 1323 | for(i=0; i<=nc-1; i++)
|
---|
| 1324 | {
|
---|
| 1325 | cnt[i] = 0;
|
---|
| 1326 | }
|
---|
| 1327 | for(i=j; i<=tiecount-1; i++)
|
---|
| 1328 | {
|
---|
| 1329 | tieaddc(ref c, ref ties, i, nc, ref cnt);
|
---|
| 1330 | }
|
---|
| 1331 | cve = cve+getcv(ref cnt, nc);
|
---|
| 1332 | }
|
---|
| 1333 | else
|
---|
| 1334 | {
|
---|
| 1335 |
|
---|
| 1336 | //
|
---|
| 1337 | // General case: 2 or more intervals
|
---|
| 1338 | //
|
---|
| 1339 | thresholds = new double[koptimal-1+1];
|
---|
| 1340 | ni = koptimal+1;
|
---|
| 1341 | cve = cv[koptimal,tiecount-1];
|
---|
| 1342 | jl = splits[koptimal,tiecount-1];
|
---|
| 1343 | jr = tiecount-1;
|
---|
| 1344 | for(k=koptimal; k>=1; k--)
|
---|
| 1345 | {
|
---|
| 1346 | thresholds[k-1] = 0.5*(a[ties[jl-1]]+a[ties[jl]]);
|
---|
| 1347 | jr = jl-1;
|
---|
| 1348 | jl = splits[k-1,jl-1];
|
---|
| 1349 | }
|
---|
| 1350 | }
|
---|
| 1351 | }
|
---|
| 1352 |
|
---|
| 1353 |
|
---|
| 1354 | /*************************************************************************
|
---|
| 1355 | Subroutine prepares K-fold split of the training set.
|
---|
| 1356 |
|
---|
| 1357 | NOTES:
|
---|
| 1358 | "NClasses>0" means that we have classification task.
|
---|
| 1359 | "NClasses<0" means regression task with -NClasses real outputs.
|
---|
| 1360 |
|
---|
| 1361 | -- ALGLIB --
|
---|
| 1362 | Copyright 11.01.2009 by Bochkanov Sergey
|
---|
| 1363 | *************************************************************************/
|
---|
| 1364 | private static void dskfoldsplit(ref double[,] xy,
|
---|
| 1365 | int npoints,
|
---|
| 1366 | int nclasses,
|
---|
| 1367 | int foldscount,
|
---|
| 1368 | bool stratifiedsplits,
|
---|
| 1369 | ref int[] folds)
|
---|
| 1370 | {
|
---|
| 1371 | int i = 0;
|
---|
| 1372 | int j = 0;
|
---|
| 1373 | int k = 0;
|
---|
| 1374 |
|
---|
| 1375 |
|
---|
| 1376 | //
|
---|
| 1377 | // test parameters
|
---|
| 1378 | //
|
---|
| 1379 | System.Diagnostics.Debug.Assert(npoints>0, "DSKFoldSplit: wrong NPoints!");
|
---|
| 1380 | System.Diagnostics.Debug.Assert(nclasses>1 | nclasses<0, "DSKFoldSplit: wrong NClasses!");
|
---|
| 1381 | System.Diagnostics.Debug.Assert(foldscount>=2 & foldscount<=npoints, "DSKFoldSplit: wrong FoldsCount!");
|
---|
| 1382 | System.Diagnostics.Debug.Assert(!stratifiedsplits, "DSKFoldSplit: stratified splits are not supported!");
|
---|
| 1383 |
|
---|
| 1384 | //
|
---|
| 1385 | // Folds
|
---|
| 1386 | //
|
---|
| 1387 | folds = new int[npoints-1+1];
|
---|
| 1388 | for(i=0; i<=npoints-1; i++)
|
---|
| 1389 | {
|
---|
| 1390 | folds[i] = i*foldscount/npoints;
|
---|
| 1391 | }
|
---|
| 1392 | for(i=0; i<=npoints-2; i++)
|
---|
| 1393 | {
|
---|
| 1394 | j = i+AP.Math.RandomInteger(npoints-i);
|
---|
| 1395 | if( j!=i )
|
---|
| 1396 | {
|
---|
| 1397 | k = folds[i];
|
---|
| 1398 | folds[i] = folds[j];
|
---|
| 1399 | folds[j] = k;
|
---|
| 1400 | }
|
---|
| 1401 | }
|
---|
| 1402 | }
|
---|
| 1403 |
|
---|
| 1404 |
|
---|
| 1405 | /*************************************************************************
|
---|
| 1406 | Internal function
|
---|
| 1407 | *************************************************************************/
|
---|
| 1408 | private static double xlny(double x,
|
---|
| 1409 | double y)
|
---|
| 1410 | {
|
---|
| 1411 | double result = 0;
|
---|
| 1412 |
|
---|
| 1413 | if( (double)(x)==(double)(0) )
|
---|
| 1414 | {
|
---|
| 1415 | result = 0;
|
---|
| 1416 | }
|
---|
| 1417 | else
|
---|
| 1418 | {
|
---|
| 1419 | result = x*Math.Log(y);
|
---|
| 1420 | }
|
---|
| 1421 | return result;
|
---|
| 1422 | }
|
---|
| 1423 |
|
---|
| 1424 |
|
---|
| 1425 | /*************************************************************************
|
---|
| 1426 | Internal function,
|
---|
| 1427 | returns number of samples of class I in Cnt[I]
|
---|
| 1428 | *************************************************************************/
|
---|
| 1429 | private static double getcv(ref int[] cnt,
|
---|
| 1430 | int nc)
|
---|
| 1431 | {
|
---|
| 1432 | double result = 0;
|
---|
| 1433 | int i = 0;
|
---|
| 1434 | double s = 0;
|
---|
| 1435 |
|
---|
| 1436 | s = 0;
|
---|
| 1437 | for(i=0; i<=nc-1; i++)
|
---|
| 1438 | {
|
---|
| 1439 | s = s+cnt[i];
|
---|
| 1440 | }
|
---|
| 1441 | result = 0;
|
---|
| 1442 | for(i=0; i<=nc-1; i++)
|
---|
| 1443 | {
|
---|
| 1444 | result = result-xlny(cnt[i], cnt[i]/(s+nc-1));
|
---|
| 1445 | }
|
---|
| 1446 | return result;
|
---|
| 1447 | }
|
---|
| 1448 |
|
---|
| 1449 |
|
---|
| 1450 | /*************************************************************************
|
---|
| 1451 | Internal function, adds number of samples of class I in tie NTie to Cnt[I]
|
---|
| 1452 | *************************************************************************/
|
---|
| 1453 | private static void tieaddc(ref int[] c,
|
---|
| 1454 | ref int[] ties,
|
---|
| 1455 | int ntie,
|
---|
| 1456 | int nc,
|
---|
| 1457 | ref int[] cnt)
|
---|
| 1458 | {
|
---|
| 1459 | int i = 0;
|
---|
| 1460 |
|
---|
| 1461 | for(i=ties[ntie]; i<=ties[ntie+1]-1; i++)
|
---|
| 1462 | {
|
---|
| 1463 | cnt[c[i]] = cnt[c[i]]+1;
|
---|
| 1464 | }
|
---|
| 1465 | }
|
---|
| 1466 |
|
---|
| 1467 |
|
---|
| 1468 | /*************************************************************************
|
---|
| 1469 | Internal function, subtracts number of samples of class I in tie NTie to Cnt[I]
|
---|
| 1470 | *************************************************************************/
|
---|
| 1471 | private static void tiesubc(ref int[] c,
|
---|
| 1472 | ref int[] ties,
|
---|
| 1473 | int ntie,
|
---|
| 1474 | int nc,
|
---|
| 1475 | ref int[] cnt)
|
---|
| 1476 | {
|
---|
| 1477 | int i = 0;
|
---|
| 1478 |
|
---|
| 1479 | for(i=ties[ntie]; i<=ties[ntie+1]-1; i++)
|
---|
| 1480 | {
|
---|
| 1481 | cnt[c[i]] = cnt[c[i]]-1;
|
---|
| 1482 | }
|
---|
| 1483 | }
|
---|
| 1484 |
|
---|
| 1485 |
|
---|
| 1486 | /*************************************************************************
|
---|
| 1487 | Internal function,
|
---|
| 1488 | returns number of samples of class I in Cnt[I]
|
---|
| 1489 | *************************************************************************/
|
---|
| 1490 | private static void tiegetc(ref int[] c,
|
---|
| 1491 | ref int[] ties,
|
---|
| 1492 | int ntie,
|
---|
| 1493 | int nc,
|
---|
| 1494 | ref int[] cnt)
|
---|
| 1495 | {
|
---|
| 1496 | int i = 0;
|
---|
| 1497 |
|
---|
| 1498 | for(i=0; i<=nc-1; i++)
|
---|
| 1499 | {
|
---|
| 1500 | cnt[i] = 0;
|
---|
| 1501 | }
|
---|
| 1502 | for(i=ties[ntie]; i<=ties[ntie+1]-1; i++)
|
---|
| 1503 | {
|
---|
| 1504 | cnt[c[i]] = cnt[c[i]]+1;
|
---|
| 1505 | }
|
---|
| 1506 | }
|
---|
| 1507 | }
|
---|
| 1508 | }
|
---|