| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Globalization;
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| 25 | using System.Linq;
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| 26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 27 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Tests;
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| 28 | using HeuristicLab.Random;
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| 29 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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| 30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Tests {
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| 31 |
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| 32 | [TestClass()]
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| 33 | public class SymbolicTimeSeriesPrognosisInterpreterTest {
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| 34 | private const int N = 1000;
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| 35 | private const int Rows = 100;
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| 36 | private const int Columns = 50;
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| 37 |
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| 38 | [TestMethod]
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| 39 | [TestCategory("Problems.DataAnalysis.Symbolic")]
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| 40 | [TestProperty("Time", "long")]
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| 41 | public void TimeSeriesPrognosisInterpreterTestTypeCoherentGrammarPerformance() {
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| 42 | TestTypeCoherentGrammarPerformance(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
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| 43 | }
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| 44 |
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| 45 | [TestMethod]
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| 46 | [TestCategory("Problems.DataAnalysis.Symbolic")]
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| 47 | [TestProperty("Time", "long")]
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| 48 | public void TimeSeriesPrognosisInterpreterTestFullGrammarPerformance() {
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| 49 | TestFullGrammarPerformance(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
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| 50 | }
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| 51 |
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| 52 | [TestMethod]
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| 53 | [TestCategory("Problems.DataAnalysis.Symbolic")]
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| 54 | [TestProperty("Time", "long")]
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| 55 | public void TimeSeriesPrognosisInterpreterTestArithmeticGrammarPerformance() {
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| 56 | TestArithmeticGrammarPerformance(new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter("y"), 12.5e6);
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| 57 | }
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| 58 |
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| 59 | private void TestTypeCoherentGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
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| 60 | var twister = new MersenneTwister(31415);
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| 61 | var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
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| 62 | var grammar = new TypeCoherentExpressionGrammar();
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| 63 | grammar.ConfigureAsDefaultRegressionGrammar();
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| 64 | grammar.MaximumFunctionArguments = 0;
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| 65 | grammar.MaximumFunctionDefinitions = 0;
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| 66 | grammar.MinimumFunctionArguments = 0;
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| 67 | grammar.MinimumFunctionDefinitions = 0;
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| 68 | var randomTrees = Util.CreateRandomTrees(twister, dataset, grammar, N, 1, 100, 0, 0);
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| 69 | foreach (ISymbolicExpressionTree tree in randomTrees) {
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| 70 | Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
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| 71 | }
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| 72 | double nodesPerSec = Util.CalculateEvaluatedNodesPerSec(randomTrees, interpreter, dataset, 3);
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| 73 | //mkommend: commented due to performance issues on the builder
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| 74 | //Assert.IsTrue(nodesPerSec > nodesPerSecThreshold); // evaluated nodes per seconds must be larger than 15mNodes/sec
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| 75 | }
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| 76 |
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| 77 | private void TestFullGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
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| 78 | var twister = new MersenneTwister(31415);
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| 79 | var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
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| 80 | var grammar = new FullFunctionalExpressionGrammar();
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| 81 | grammar.MaximumFunctionArguments = 0;
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| 82 | grammar.MaximumFunctionDefinitions = 0;
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| 83 | grammar.MinimumFunctionArguments = 0;
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| 84 | grammar.MinimumFunctionDefinitions = 0;
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| 85 | var randomTrees = Util.CreateRandomTrees(twister, dataset, grammar, N, 1, 100, 0, 0);
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| 86 | foreach (ISymbolicExpressionTree tree in randomTrees) {
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| 87 | Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
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| 88 | }
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| 89 | double nodesPerSec = Util.CalculateEvaluatedNodesPerSec(randomTrees, interpreter, dataset, 3);
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| 90 | //mkommend: commented due to performance issues on the builder
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| 91 | //Assert.IsTrue(nodesPerSec > nodesPerSecThreshold); // evaluated nodes per seconds must be larger than 15mNodes/sec
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| 92 | }
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| 93 |
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| 94 | private void TestArithmeticGrammarPerformance(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double nodesPerSecThreshold) {
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| 95 | var twister = new MersenneTwister(31415);
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| 96 | var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
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| 97 | var grammar = new ArithmeticExpressionGrammar();
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| 98 | grammar.MaximumFunctionArguments = 0;
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| 99 | grammar.MaximumFunctionDefinitions = 0;
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| 100 | grammar.MinimumFunctionArguments = 0;
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| 101 | grammar.MinimumFunctionDefinitions = 0;
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| 102 | var randomTrees = Util.CreateRandomTrees(twister, dataset, grammar, N, 1, 100, 0, 0);
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| 103 | foreach (SymbolicExpressionTree tree in randomTrees) {
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| 104 | Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
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| 105 | }
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| 106 |
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| 107 | double nodesPerSec = Util.CalculateEvaluatedNodesPerSec(randomTrees, interpreter, dataset, 3);
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| 108 | //mkommend: commented due to performance issues on the builder
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| 109 | //Assert.IsTrue(nodesPerSec > nodesPerSecThreshold); // evaluated nodes per seconds must be larger than 15mNodes/sec
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| 110 | }
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| 111 |
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| 112 |
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| 113 | /// <summary>
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| 114 | ///A test for Evaluate
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| 115 | ///</summary>
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| 116 | [TestMethod]
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| 117 | [TestCategory("Problems.DataAnalysis.Symbolic")]
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| 118 | [TestProperty("Time", "short")]
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| 119 | public void TimeSeriesPrognosisInterpreterTestEvaluation() {
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| 120 | Dataset ds = new Dataset(new string[] { "Y", "A", "B" }, new double[,] {
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| 121 | { 1.0, 1.0, 1.0 },
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| 122 | { 2.0, 2.0, 2.0 },
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| 123 | { 3.0, 1.0, 2.0 },
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| 124 | { 4.0, 1.0, 1.0 },
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| 125 | { 5.0, 2.0, 2.0 },
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| 126 | { 6.0, 1.0, 2.0 },
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| 127 | { 7.0, 1.0, 1.0 },
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| 128 | { 8.0, 2.0, 2.0 },
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| 129 | { 9.0, 1.0, 2.0 },
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| 130 | { 10.0, 1.0, 1.0 },
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| 131 | { 11.0, 2.0, 2.0 },
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| 132 | { 12.0, 1.0, 2.0 }
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| 133 | });
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| 134 |
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| 135 | var interpreter = new SymbolicDataAnalysisExpressionTreeInterpreter();
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| 136 | EvaluateTerminals(interpreter, ds);
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| 137 | EvaluateOperations(interpreter, ds);
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| 138 | EvaluateAdf(interpreter, ds);
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| 139 | }
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| 140 |
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| 141 | private void EvaluateTerminals(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, Dataset ds) {
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| 142 | // constants
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| 143 | Evaluate(interpreter, ds, "(+ 1.5 3.5)", 0, 5.0);
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| 144 |
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| 145 | // variables
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| 146 | Evaluate(interpreter, ds, "(variable 2.0 a)", 0, 2.0);
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| 147 | Evaluate(interpreter, ds, "(variable 2.0 a)", 1, 4.0);
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| 148 | }
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| 149 |
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| 150 | private void EvaluateAdf(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, Dataset ds) {
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| 151 |
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| 152 | // ADF
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| 153 | Evaluate(interpreter, ds, @"(PROG
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| 154 | (MAIN
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| 155 | (CALL ADF0))
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| 156 | (defun ADF0 1.0))", 1, 1.0);
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| 157 | Evaluate(interpreter, ds, @"(PROG
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| 158 | (MAIN
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| 159 | (* (CALL ADF0) (CALL ADF0)))
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| 160 | (defun ADF0 2.0))", 1, 4.0);
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| 161 | Evaluate(interpreter, ds, @"(PROG
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| 162 | (MAIN
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| 163 | (CALL ADF0 2.0 3.0))
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| 164 | (defun ADF0
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| 165 | (+ (ARG 0) (ARG 1))))", 1, 5.0);
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| 166 | Evaluate(interpreter, ds, @"(PROG
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| 167 | (MAIN (CALL ADF1 2.0 3.0))
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| 168 | (defun ADF0
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| 169 | (- (ARG 1) (ARG 0)))
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| 170 | (defun ADF1
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| 171 | (+ (CALL ADF0 (ARG 1) (ARG 0))
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| 172 | (CALL ADF0 (ARG 0) (ARG 1)))))", 1, 0.0);
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| 173 | Evaluate(interpreter, ds, @"(PROG
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| 174 | (MAIN (CALL ADF1 (variable 2.0 a) 3.0))
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| 175 | (defun ADF0
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| 176 | (- (ARG 1) (ARG 0)))
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| 177 | (defun ADF1
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| 178 | (CALL ADF0 (ARG 1) (ARG 0))))", 1, 1.0);
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| 179 | Evaluate(interpreter, ds,
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| 180 | @"(PROG
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| 181 | (MAIN (CALL ADF1 (variable 2.0 a) 3.0))
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| 182 | (defun ADF0
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| 183 | (- (ARG 1) (ARG 0)))
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| 184 | (defun ADF1
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| 185 | (+ (CALL ADF0 (ARG 1) (ARG 0))
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| 186 | (CALL ADF0 (ARG 0) (ARG 1)))))", 1, 0.0);
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| 187 | }
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| 188 |
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| 189 | private void EvaluateOperations(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, Dataset ds) {
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| 190 | // addition
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| 191 | Evaluate(interpreter, ds, "(+ (variable 2.0 a ))", 1, 4.0);
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| 192 | Evaluate(interpreter, ds, "(+ (variable 2.0 a ) (variable 3.0 b ))", 0, 5.0);
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| 193 | Evaluate(interpreter, ds, "(+ (variable 2.0 a ) (variable 3.0 b ))", 1, 10.0);
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| 194 | Evaluate(interpreter, ds, "(+ (variable 2.0 a) (variable 3.0 b ))", 2, 8.0);
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| 195 | Evaluate(interpreter, ds, "(+ 8.0 2.0 2.0)", 0, 12.0);
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| 196 |
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| 197 | // subtraction
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| 198 | Evaluate(interpreter, ds, "(- (variable 2.0 a ))", 1, -4.0);
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| 199 | Evaluate(interpreter, ds, "(- (variable 2.0 a ) (variable 3.0 b))", 0, -1.0);
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| 200 | Evaluate(interpreter, ds, "(- (variable 2.0 a ) (variable 3.0 b ))", 1, -2.0);
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| 201 | Evaluate(interpreter, ds, "(- (variable 2.0 a ) (variable 3.0 b ))", 2, -4.0);
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| 202 | Evaluate(interpreter, ds, "(- 8.0 2.0 2.0)", 0, 4.0);
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| 203 |
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| 204 | // multiplication
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| 205 | Evaluate(interpreter, ds, "(* (variable 2.0 a ))", 0, 2.0);
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| 206 | Evaluate(interpreter, ds, "(* (variable 2.0 a ) (variable 3.0 b ))", 0, 6.0);
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| 207 | Evaluate(interpreter, ds, "(* (variable 2.0 a ) (variable 3.0 b ))", 1, 24.0);
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| 208 | Evaluate(interpreter, ds, "(* (variable 2.0 a ) (variable 3.0 b ))", 2, 12.0);
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| 209 | Evaluate(interpreter, ds, "(* 8.0 2.0 2.0)", 0, 32.0);
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| 210 |
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| 211 | // division
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| 212 | Evaluate(interpreter, ds, "(/ (variable 2.0 a ))", 1, 1.0 / 4.0);
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| 213 | Evaluate(interpreter, ds, "(/ (variable 2.0 a ) 2.0)", 0, 1.0);
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| 214 | Evaluate(interpreter, ds, "(/ (variable 2.0 a ) 2.0)", 1, 2.0);
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| 215 | Evaluate(interpreter, ds, "(/ (variable 3.0 b ) 2.0)", 2, 3.0);
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| 216 | Evaluate(interpreter, ds, "(/ 8.0 2.0 2.0)", 0, 2.0);
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| 217 |
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| 218 | // gt
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| 219 | Evaluate(interpreter, ds, "(> (variable 2.0 a) 2.0)", 0, -1.0);
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| 220 | Evaluate(interpreter, ds, "(> 2.0 (variable 2.0 a))", 0, -1.0);
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| 221 | Evaluate(interpreter, ds, "(> (variable 2.0 a) 1.9)", 0, 1.0);
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| 222 | Evaluate(interpreter, ds, "(> 1.9 (variable 2.0 a))", 0, -1.0);
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| 223 | Evaluate(interpreter, ds, "(> (log -1.0) (log -1.0))", 0, -1.0); // (> nan nan) should be false
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| 224 |
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| 225 | // lt
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| 226 | Evaluate(interpreter, ds, "(< (variable 2.0 a) 2.0)", 0, -1.0);
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| 227 | Evaluate(interpreter, ds, "(< 2.0 (variable 2.0 a))", 0, -1.0);
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| 228 | Evaluate(interpreter, ds, "(< (variable 2.0 a) 1.9)", 0, -1.0);
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| 229 | Evaluate(interpreter, ds, "(< 1.9 (variable 2.0 a))", 0, 1.0);
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| 230 | Evaluate(interpreter, ds, "(< (log -1.0) (log -1.0))", 0, -1.0); // (< nan nan) should be false
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| 231 |
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| 232 | // If
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| 233 | Evaluate(interpreter, ds, "(if -10.0 2.0 3.0)", 0, 3.0);
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| 234 | Evaluate(interpreter, ds, "(if -1.0 2.0 3.0)", 0, 3.0);
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| 235 | Evaluate(interpreter, ds, "(if 0.0 2.0 3.0)", 0, 3.0);
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| 236 | Evaluate(interpreter, ds, "(if 1.0 2.0 3.0)", 0, 2.0);
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| 237 | Evaluate(interpreter, ds, "(if 10.0 2.0 3.0)", 0, 2.0);
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| 238 | Evaluate(interpreter, ds, "(if (log -1.0) 2.0 3.0)", 0, 3.0); // if(nan) should return the else branch
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| 239 |
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| 240 | // NOT
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| 241 | Evaluate(interpreter, ds, "(not -1.0)", 0, 1.0);
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| 242 | Evaluate(interpreter, ds, "(not -2.0)", 0, 1.0);
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| 243 | Evaluate(interpreter, ds, "(not 1.0)", 0, -1.0);
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| 244 | Evaluate(interpreter, ds, "(not 2.0)", 0, -1.0);
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| 245 | Evaluate(interpreter, ds, "(not 0.0)", 0, 1.0);
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| 246 | Evaluate(interpreter, ds, "(not (log -1.0))", 0, 1.0);
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| 247 |
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| 248 | // AND
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| 249 | Evaluate(interpreter, ds, "(and -1.0 -2.0)", 0, -1.0);
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| 250 | Evaluate(interpreter, ds, "(and -1.0 2.0)", 0, -1.0);
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| 251 | Evaluate(interpreter, ds, "(and 1.0 -2.0)", 0, -1.0);
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| 252 | Evaluate(interpreter, ds, "(and 1.0 0.0)", 0, -1.0);
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| 253 | Evaluate(interpreter, ds, "(and 0.0 0.0)", 0, -1.0);
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| 254 | Evaluate(interpreter, ds, "(and 1.0 2.0)", 0, 1.0);
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| 255 | Evaluate(interpreter, ds, "(and 1.0 2.0 3.0)", 0, 1.0);
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| 256 | Evaluate(interpreter, ds, "(and 1.0 -2.0 3.0)", 0, -1.0);
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| 257 | Evaluate(interpreter, ds, "(and (log -1.0))", 0, -1.0); // (and NaN)
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| 258 | Evaluate(interpreter, ds, "(and (log -1.0) 1.0)", 0, -1.0); // (and NaN 1.0)
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| 259 |
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| 260 |
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| 261 | // OR
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| 262 | Evaluate(interpreter, ds, "(or -1.0 -2.0)", 0, -1.0);
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| 263 | Evaluate(interpreter, ds, "(or -1.0 2.0)", 0, 1.0);
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| 264 | Evaluate(interpreter, ds, "(or 1.0 -2.0)", 0, 1.0);
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| 265 | Evaluate(interpreter, ds, "(or 1.0 2.0)", 0, 1.0);
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| 266 | Evaluate(interpreter, ds, "(or 0.0 0.0)", 0, -1.0);
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| 267 | Evaluate(interpreter, ds, "(or -1.0 -2.0 -3.0)", 0, -1.0);
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| 268 | Evaluate(interpreter, ds, "(or -1.0 -2.0 3.0)", 0, 1.0);
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| 269 | Evaluate(interpreter, ds, "(or (log -1.0))", 0, -1.0); // (or NaN)
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| 270 | Evaluate(interpreter, ds, "(or (log -1.0) 1.0)", 0, -1.0); // (or NaN 1.0)
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| 271 |
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| 272 | // sin, cos, tan
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| 273 | Evaluate(interpreter, ds, "(sin " + Math.PI.ToString(NumberFormatInfo.InvariantInfo) + ")", 0, 0.0);
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| 274 | Evaluate(interpreter, ds, "(sin 0.0)", 0, 0.0);
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| 275 | Evaluate(interpreter, ds, "(cos " + Math.PI.ToString(NumberFormatInfo.InvariantInfo) + ")", 0, -1.0);
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| 276 | Evaluate(interpreter, ds, "(cos 0.0)", 0, 1.0);
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| 277 | Evaluate(interpreter, ds, "(tan " + Math.PI.ToString(NumberFormatInfo.InvariantInfo) + ")", 0, Math.Tan(Math.PI));
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| 278 | Evaluate(interpreter, ds, "(tan 0.0)", 0, Math.Tan(Math.PI));
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| 279 |
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| 280 | // exp, log
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| 281 | Evaluate(interpreter, ds, "(log (exp 7.0))", 0, Math.Log(Math.Exp(7)));
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| 282 | Evaluate(interpreter, ds, "(exp (log 7.0))", 0, Math.Exp(Math.Log(7)));
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| 283 | Evaluate(interpreter, ds, "(log -3.0)", 0, Math.Log(-3));
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| 284 |
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| 285 | // power
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| 286 | Evaluate(interpreter, ds, "(pow 2.0 3.0)", 0, 8.0);
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| 287 | Evaluate(interpreter, ds, "(pow 4.0 0.5)", 0, 1.0); // interpreter should round to the nearest integer value value (.5 is rounded to the even number)
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| 288 | Evaluate(interpreter, ds, "(pow 4.0 2.5)", 0, 16.0); // interpreter should round to the nearest integer value value (.5 is rounded to the even number)
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| 289 | Evaluate(interpreter, ds, "(pow -2.0 3.0)", 0, -8.0);
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| 290 | Evaluate(interpreter, ds, "(pow 2.0 -3.0)", 0, 1.0 / 8.0);
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| 291 | Evaluate(interpreter, ds, "(pow -2.0 -3.0)", 0, -1.0 / 8.0);
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| 292 |
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| 293 | // root
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| 294 | Evaluate(interpreter, ds, "(root 9.0 2.0)", 0, 3.0);
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| 295 | Evaluate(interpreter, ds, "(root 27.0 3.0)", 0, 3.0);
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| 296 | Evaluate(interpreter, ds, "(root 2.0 -3.0)", 0, Math.Pow(2.0, -1.0 / 3.0));
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| 297 |
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| 298 | // mean
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| 299 | Evaluate(interpreter, ds, "(mean -1.0 1.0 -1.0)", 0, -1.0 / 3.0);
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| 300 |
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| 301 | // lag
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| 302 | Evaluate(interpreter, ds, "(lagVariable 1.0 a -1) ", 1, ds.GetDoubleValue("A", 0));
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| 303 | Evaluate(interpreter, ds, "(lagVariable 1.0 a -1) ", 2, ds.GetDoubleValue("A", 1));
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| 304 | Evaluate(interpreter, ds, "(lagVariable 1.0 a 0) ", 2, ds.GetDoubleValue("A", 2));
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| 305 | Evaluate(interpreter, ds, "(lagVariable 1.0 a 1) ", 0, ds.GetDoubleValue("A", 1));
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| 306 |
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| 307 | // integral
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| 308 | Evaluate(interpreter, ds, "(integral -1.0 (variable 1.0 a)) ", 1, ds.GetDoubleValue("A", 0) + ds.GetDoubleValue("A", 1));
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| 309 | Evaluate(interpreter, ds, "(integral -1.0 (lagVariable 1.0 a 1)) ", 1, ds.GetDoubleValue("A", 1) + ds.GetDoubleValue("A", 2));
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| 310 | Evaluate(interpreter, ds, "(integral -2.0 (variable 1.0 a)) ", 2, ds.GetDoubleValue("A", 0) + ds.GetDoubleValue("A", 1) + ds.GetDoubleValue("A", 2));
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| 311 | Evaluate(interpreter, ds, "(integral -1.0 (* (variable 1.0 a) (variable 1.0 b)))", 1, ds.GetDoubleValue("A", 0) * ds.GetDoubleValue("B", 0) + ds.GetDoubleValue("A", 1) * ds.GetDoubleValue("B", 1));
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| 312 | Evaluate(interpreter, ds, "(integral -2.0 3.0)", 1, 9.0);
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| 313 |
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| 314 | // derivative
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| 315 | // (f_0 + 2 * f_1 - 2 * f_3 - f_4) / 8; // h = 1
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| 316 | Evaluate(interpreter, ds, "(diff (variable 1.0 a)) ", 5, (ds.GetDoubleValue("A", 5) + 2 * ds.GetDoubleValue("A", 4) - 2 * ds.GetDoubleValue("A", 2) - ds.GetDoubleValue("A", 1)) / 8.0);
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| 317 | Evaluate(interpreter, ds, "(diff (variable 1.0 b)) ", 5, (ds.GetDoubleValue("B", 5) + 2 * ds.GetDoubleValue("B", 4) - 2 * ds.GetDoubleValue("B", 2) - ds.GetDoubleValue("B", 1)) / 8.0);
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| 318 | Evaluate(interpreter, ds, "(diff (* (variable 1.0 a) (variable 1.0 b)))", 5, +
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| 319 | (ds.GetDoubleValue("A", 5) * ds.GetDoubleValue("B", 5) +
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| 320 | 2 * ds.GetDoubleValue("A", 4) * ds.GetDoubleValue("B", 4) -
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| 321 | 2 * ds.GetDoubleValue("A", 2) * ds.GetDoubleValue("B", 2) -
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| 322 | ds.GetDoubleValue("A", 1) * ds.GetDoubleValue("B", 1)) / 8.0);
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| 323 | Evaluate(interpreter, ds, "(diff -2.0 3.0)", 5, 0.0);
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| 324 |
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| 325 | // timelag
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| 326 | Evaluate(interpreter, ds, "(lag -1.0 (lagVariable 1.0 a 2)) ", 1, ds.GetDoubleValue("A", 2));
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| 327 | Evaluate(interpreter, ds, "(lag -2.0 (lagVariable 1.0 a 2)) ", 2, ds.GetDoubleValue("A", 2));
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| 328 | Evaluate(interpreter, ds, "(lag -1.0 (* (lagVariable 1.0 a 1) (lagVariable 1.0 b 2)))", 1, ds.GetDoubleValue("A", 1) * ds.GetDoubleValue("B", 2));
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| 329 | Evaluate(interpreter, ds, "(lag -2.0 3.0)", 1, 3.0);
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| 330 | }
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| 331 |
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| 332 | private void Evaluate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, Dataset ds, string expr, int index, double expected) {
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| 333 | var importer = new SymbolicExpressionImporter();
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| 334 | ISymbolicExpressionTree tree = importer.Import(expr);
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| 335 |
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| 336 | double actual = interpreter.GetSymbolicExpressionTreeValues(tree, ds, Enumerable.Range(index, 1)).First();
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| 337 |
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| 338 | Assert.IsFalse(double.IsNaN(actual) && !double.IsNaN(expected));
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| 339 | Assert.IsFalse(!double.IsNaN(actual) && double.IsNaN(expected));
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| 340 | if (!double.IsNaN(actual) && !double.IsNaN(expected))
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| 341 | Assert.AreEqual(expected, actual, 1.0E-12, expr);
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| 342 | }
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| 343 | }
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| 344 | }
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