Mercurial > gemma

annotate pkg/common/nashsutcliffe.go @ 5688:6281c18b109f sr-v2

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Finsh serializing v2 meshes.
author Sascha L. Teichmann <sascha.teichmann@intevation.de>
date Mon, 12 Feb 2024 02:27:41 +0100
parents 8c5df0f3562e
children 6270951dda28
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1 // This is Free Software under GNU Affero General Public License v >= 3.0
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2 // without warranty, see README.md and license for details.
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3 //
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4 // SPDX-License-Identifier: AGPL-3.0-or-later
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5 // License-Filename: LICENSES/AGPL-3.0.txt
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6 //
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7 // Copyright (C) 2019 by via donau
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8 // – Österreichische Wasserstraßen-Gesellschaft mbH
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9 // Software engineering by Intevation GmbH
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10 //
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11 // Author(s):
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12 // * Sascha L. Teichmann <sascha.teichmann@intevation.de>
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13
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14 package common
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15
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16 import (
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17 "fmt"
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18 "time"
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19 )
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20
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21 type (
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22 // TimedValue is a tuple of a point in time and an associated value.
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23 TimedValue struct {
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24 When time.Time
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25 Value float64
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26 }
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27
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28 // TimedValues is a slice of TimedValue tuples.
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29 TimedValues []TimedValue
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30 )
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31
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32 func epsEquals(a, b time.Time) bool {
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33 d := a.Sub(b)
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34 return -10*time.Millisecond < d && d < 10*time.Millisecond
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35 }
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36
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37 // Find scans to the tuples and compares the time with an
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38 // epsilon of ten micro seconds. If they are equals the associated
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39 // value is returned. The return bool flags indicated if the
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40 // search was successful.
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41 func (mvs TimedValues) Find(when time.Time) (float64, bool) {
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42 for i := range mvs {
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43 if epsEquals(when, mvs[i].When) {
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44 return mvs[i].Value, true
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45 }
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46 }
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47 return 0, false
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48 }
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49
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50 // NashSutcliffe calculates the Nash-Sutcliffe coefficent for
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51 // given predicted and observed values.
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52 // See
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53 // https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient
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54 // for details.
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55 // The function panics if predicted and observed are of different lengths.
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56 func NashSutcliffe(predicted, observed []float64) float64 {
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57
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58 if len(predicted) != len(observed) {
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59 panic(fmt.Sprintf(
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60 "NashSutcliffe: predicted and observed len differ: %d != %d",
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61 len(predicted),
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62 len(observed)))
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63 }
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64
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65 if len(observed) == 0 {
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66 return 0
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67 }
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68
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69 var mo float64
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70 for _, v := range observed {
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71 mo += v
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72 }
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73
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74 mo /= float64(len(observed))
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75
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76 var num, denom float64
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77 for i, o := range observed {
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78 d1 := predicted[i] - o
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79 num += d1 * d1
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80 d2 := o - mo
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81 denom += d2 * d2
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82 }
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83
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84 return 1 - num/denom
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85 }