view pkg/common/nashsutcliffe.go @ 5095:e21cbb9768a2

Prevent duplicate fairway areas In principal, there can be only one or no fairway area at each point on the map. Since polygons from real data will often be topologically inexact, just disallow equal geometries. This will also help to avoid importing duplicates with concurrent imports, once the history of fairway dimensions will be preserved.
author Tom Gottfried <tom@intevation.de>
date Wed, 25 Mar 2020 18:10:02 +0100
parents 8c5df0f3562e
children 6270951dda28
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// This is Free Software under GNU Affero General Public License v >= 3.0
// without warranty, see README.md and license for details.
//
// SPDX-License-Identifier: AGPL-3.0-or-later
// License-Filename: LICENSES/AGPL-3.0.txt
//
// Copyright (C) 2019 by via donau
//   – Österreichische Wasserstraßen-Gesellschaft mbH
// Software engineering by Intevation GmbH
//
// Author(s):
//  * Sascha L. Teichmann <sascha.teichmann@intevation.de>

package common

import (
	"fmt"
	"time"
)

type (
	// TimedValue is a tuple of a point in time and an associated value.
	TimedValue struct {
		When  time.Time
		Value float64
	}

	// TimedValues is a slice of TimedValue tuples.
	TimedValues []TimedValue
)

func epsEquals(a, b time.Time) bool {
	d := a.Sub(b)
	return -10*time.Millisecond < d && d < 10*time.Millisecond
}

// Find scans to the tuples and compares the time with an
// epsilon of ten micro seconds. If they are equals the associated
// value is returned. The return bool flags indicated if the
// search was successful.
func (mvs TimedValues) Find(when time.Time) (float64, bool) {
	for i := range mvs {
		if epsEquals(when, mvs[i].When) {
			return mvs[i].Value, true
		}
	}
	return 0, false
}

// NashSutcliffe calculates the Nash-Sutcliffe coefficent for
// given predicted and observed values.
// See
//   https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient
// for details.
// The function panics if predicted and observed are of different lengths.
func NashSutcliffe(predicted, observed []float64) float64 {

	if len(predicted) != len(observed) {
		panic(fmt.Sprintf(
			"NashSutcliffe: predicted and observed len differ: %d != %d",
			len(predicted),
			len(observed)))
	}

	if len(observed) == 0 {
		return 0
	}

	var mo float64
	for _, v := range observed {
		mo += v
	}

	mo /= float64(len(observed))

	var num, denom float64
	for i, o := range observed {
		d1 := predicted[i] - o
		num += d1 * d1
		d2 := o - mo
		denom += d2 * d2
	}

	return 1 - num/denom
}