Changeset 17602
 Timestamp:
 06/16/20 11:21:34 (4 weeks ago)
 Location:
 branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4
 Files:

 2 edited
Legend:
 Unmodified
 Added
 Removed

branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/VectorTreeSimplifier.cs
r17597 r17602 62 62 private static readonly Mean meanSymbol = new Mean(); 63 63 private static readonly Length lengthSymbol = new Length(); 64 private static readonly StandardDeviation standardDeviationSymbol = new StandardDeviation(); 65 private static readonly Variance varianceSymbol = new Variance(); 64 66 65 67 private readonly SymbolicDataAnalysisExpressionTreeVectorInterpreter interpreter; … … 271 273 private static bool IsLength(ISymbolicExpressionTreeNode node) { 272 274 return node.Symbol is Length; 275 } 276 277 private static bool IsStandardDeviation(ISymbolicExpressionTreeNode node) { 278 return node.Symbol is StandardDeviation; 279 } 280 281 private static bool IsVariance(ISymbolicExpressionTreeNode node) { 282 return node.Symbol is Variance; 273 283 } 274 284 #endregion … … 351 361 } else if (IsLength(original)) { 352 362 return SimplifyLengthAggregation(original); 363 } else if (IsStandardDeviation(original)) { 364 return SimplifyStandardDeviationAggregation(original); 365 } else if (IsVariance(original)) { 366 return SimplifyVarianceAggregation(original); 353 367 } else { 354 368 return SimplifyAny(original); … … 554 568 private ISymbolicExpressionTreeNode SimplifyLengthAggregation(ISymbolicExpressionTreeNode original) { 555 569 return MakeLengthAggregation(GetSimplifiedTree(original.GetSubtree(0))); 570 } 571 572 private ISymbolicExpressionTreeNode SimplifyStandardDeviationAggregation(ISymbolicExpressionTreeNode original) { 573 return MakeStandardDeviationAggregation(GetSimplifiedTree(original.GetSubtree(0))); 574 } 575 576 private ISymbolicExpressionTreeNode SimplifyVarianceAggregation(ISymbolicExpressionTreeNode original) { 577 return MakeVarianceAggregation(GetSimplifiedTree(original.GetSubtree(0))); 556 578 } 557 579 #endregion … … 1561 1583 } 1562 1584 1585 private ISymbolicExpressionTreeNode MakeStandardDeviationAggregation(ISymbolicExpressionTreeNode node) { 1586 if (IsConstant(node)) { 1587 return MakeConstant(0.0); 1588 } else if (IsMultiplication(node)  IsDivision(node)) { 1589 var factors = node.Subtrees; 1590 if (IsDivision(node)) factors = InvertNodes(factors, Invert); 1591 1592 var scalarFactors = factors.Where(IsScalarNode).ToList(); 1593 var remainingFactors = factors.Except(scalarFactors).ToList(); 1594 1595 if (scalarFactors.Any() && remainingFactors.Any()) { 1596 var scalarNode = scalarFactors.Aggregate(MakeProduct); 1597 var vectorNode = remainingFactors.Aggregate(MakeProduct); 1598 1599 var stdevNode = MakeStandardDeviationAggregation(vectorNode); 1600 1601 return MakeProduct(scalarNode, stdevNode); 1602 } else if (scalarFactors.Any()) { 1603 var scalarNode = scalarFactors.Aggregate(MakeProduct); 1604 return scalarNode; 1605 } else if (remainingFactors.Any()) { 1606 var vectorNode = remainingFactors.Aggregate(MakeProduct); 1607 var stdevNode = standardDeviationSymbol.CreateTreeNode(); 1608 stdevNode.AddSubtree(vectorNode); 1609 return stdevNode; 1610 } else 1611 throw new InvalidOperationException("Multiplication does not contain any terms to simplify."); 1612 } else if (IsVariableBase(node)) { // weight is like multiplication 1613 var variableNode = (VariableTreeNodeBase)node; 1614 var weight = variableNode.Weight; 1615 variableNode.Weight = 1.0; 1616 var stdevNode = standardDeviationSymbol.CreateTreeNode(); 1617 stdevNode.AddSubtree(node); 1618 return MakeProduct(MakeConstant(weight), stdevNode); 1619 } else { 1620 var stdevNode = standardDeviationSymbol.CreateTreeNode(); 1621 stdevNode.AddSubtree(node); 1622 return stdevNode; 1623 } 1624 } 1625 1626 private ISymbolicExpressionTreeNode MakeVarianceAggregation(ISymbolicExpressionTreeNode node) { 1627 if (IsConstant(node)) { 1628 return MakeConstant(0.0); 1629 } else if (IsMultiplication(node)  IsDivision(node)) { 1630 var factors = node.Subtrees; 1631 if (IsDivision(node)) factors = InvertNodes(factors, Invert); 1632 1633 var scalarFactors = factors.Where(IsScalarNode).ToList(); 1634 var remainingFactors = factors.Except(scalarFactors).ToList(); 1635 1636 if (scalarFactors.Any() && remainingFactors.Any()) { 1637 var scalarNode = scalarFactors.Aggregate(MakeProduct); 1638 var vectorNode = remainingFactors.Aggregate(MakeProduct); 1639 1640 var varNode = MakeVarianceAggregation(vectorNode); 1641 1642 return MakeProduct(MakeSquare(scalarNode), varNode); 1643 } else if (scalarFactors.Any()) { 1644 var scalarNode = scalarFactors.Aggregate(MakeProduct); 1645 return MakeSquare(scalarNode); 1646 } else if (remainingFactors.Any()) { 1647 var vectorNode = remainingFactors.Aggregate(MakeProduct); 1648 var varNode = varianceSymbol.CreateTreeNode(); 1649 varNode.AddSubtree(vectorNode); 1650 return varNode; 1651 } else 1652 throw new InvalidOperationException("Multiplication does not contain any terms to simplify."); 1653 } else if (IsVariableBase(node)) { // weight is like multiplication 1654 var variableNode = (VariableTreeNodeBase)node; 1655 var weight = variableNode.Weight; 1656 variableNode.Weight = 1.0; 1657 var varNode = varianceSymbol.CreateTreeNode(); 1658 varNode.AddSubtree(node); 1659 return MakeProduct(MakeSquare(MakeConstant(weight)), varNode); 1660 } else { 1661 var varNode = varianceSymbol.CreateTreeNode(); 1662 varNode.AddSubtree(node); 1663 return varNode; 1664 } 1665 } 1563 1666 #endregion 1564 1667 
branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeVectorInterpreter.cs
r17593 r17602 415 415 var cur = Evaluate(dataset, ref row, state); 416 416 return AggregateApply(cur, 417 s => double.NaN,418 v => Statistics. StandardDeviation(v));417 s => 0, 418 v => Statistics.PopulationStandardDeviation(v)); 419 419 } 420 420 case OpCodes.Length: { … … 439 439 var cur = Evaluate(dataset, ref row, state); 440 440 return AggregateApply(cur, 441 s => 0, 442 v => Statistics.PopulationVariance(v)); 443 } 444 case OpCodes.Skewness: { 445 var cur = Evaluate(dataset, ref row, state); 446 return AggregateApply(cur, 441 447 s => double.NaN, 442 v => Statistics. Variance(v));443 } 444 case OpCodes. Skewness: {448 v => Statistics.PopulationSkewness(v)); 449 } 450 case OpCodes.Kurtosis: { 445 451 var cur = Evaluate(dataset, ref row, state); 446 452 return AggregateApply(cur, 447 453 s => double.NaN, 448 v => Statistics.Skewness(v)); 449 } 450 case OpCodes.Kurtosis: { 451 var cur = Evaluate(dataset, ref row, state); 452 return AggregateApply(cur, 453 s => double.NaN, 454 v => Statistics.Kurtosis(v)); 454 v => Statistics.PopulationKurtosis(v)); 455 455 } 456 456 case OpCodes.EuclideanDistance: { … … 470 470 //(s1, v2) => 0, 471 471 //(v1, s2) => 0, 472 vvFunc: (v1, v2) => v1.Count == v2.Count ? Statistics. Covariance(v1, v2) : double.NaN);472 vvFunc: (v1, v2) => v1.Count == v2.Count ? Statistics.PopulationCovariance(v1, v2) : double.NaN); 473 473 } 474 474 case OpCodes.Variable: {
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