Changeset 16694
 Timestamp:
 03/19/19 11:02:52 (11 months ago)
 File:

 1 edited
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branches/2994AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/Interpreter.cs
r16693 r16694 27 27 break; 28 28 } 29 // case OpCodes.Constant:we initialize constants in Compile. The value never changes afterwards29 case OpCodes.Constant: { break; } // we initialize constants in Compile. The value never changes afterwards 30 30 case OpCodes.Add: { 31 31 instr.value.Assign(code[c].value); … … 67 67 break; 68 68 } 69 69 case OpCodes.Square: { 70 instr.value.AssignIntPower(code[c].value, 2); 71 break; 72 } 70 73 case OpCodes.Exp: { 71 74 instr.value.AssignExp(code[c].value); … … 77 80 break; 78 81 } 82 default: throw new ArgumentException($"Unknown opcode {code[c].opcode}"); 79 83 } 80 84 } … … 337 341 var u = code[0].value.UpperBound; 338 342 for (int i = 0; i < paramNodes.Length; ++i) { 343 if (paramNodes[i] == null) continue; 339 344 lowerGradient[i] = l.Gradient.Elements[paramNodes[i]]; 340 345 upperGradient[i] = u.Gradient.Elements[paramNodes[i]]; … … 502 507 Array.Copy(data, rowIndex, arr, 0, Math.Min(arr.Length, data.Length  rowIndex)); 503 508 } 504 505 509 public void CopyRowTo(double[,] dest, int row) { 506 510 for (int j = 0; j < arr.Length; ++j) dest[row, j] = arr[j]; … … 657 661 } 658 662 } 659 660 663 661 664 public class AlgebraicInterval : IAlgebraicType<AlgebraicInterval> { … … 887 890 where V : IAlgebraicType<V> { 888 891 private V v; 892 public V Value => v; 893 889 894 private V dv; 890 891 public V Value => v;892 895 public V Derivative => dv; 893 896 894 895 public Dual(V v, V dv) { 896 this.v = v; 897 this.dv = dv; 898 } 899 900 public Dual<V> Clone() { 901 return new Dual<V>(v.Clone(), dv.Clone()); 902 } 897 public Dual(V v, V dv) { this.v = v; this.dv = dv; } 903 898 904 899 public Dual<V> Zero => new Dual<V>(Value.Zero, Derivative.Zero); 905 900 906 public Dual<V> Add(Dual<V> a) { 907 v.Add(a.v); 908 dv.Add(a.dv); 909 return this; 910 } 911 912 public Dual<V> Assign(Dual<V> a) { 913 v.Assign(a.v); 914 dv.Assign(a.dv); 915 return this; 916 } 917 918 public Dual<V> AssignCos(Dual<V> a) { 919 v.AssignCos(a.v); 920 dv.AssignNeg(dv.AssignSin(a.v)); 921 return this; 922 } 923 924 public Dual<V> Div(Dual<V> a) { 925 Mul(a.Inv()); 926 return this; 927 } 928 929 public Dual<V> AssignExp(Dual<V> a) { 930 v.AssignExp(a.v); 931 dv.Assign(a.dv).Mul(v); // exp(f(x)) = exp(f(x))*f(x)' 932 return this; 933 } 934 935 public Dual<V> AssignIntPower(Dual<V> a, int p) { 936 v.AssignIntPower(a.v, p); 937 dv.Assign(a.dv).Scale(p).Mul(a.v.IntPower(p  1)); 938 return this; 939 } 940 941 public Dual<V> AssignIntRoot(Dual<V> a, int r) { 942 v.AssignIntRoot(a.v, r); 943 dv.Assign(a.dv).Scale(1.0 / r).Mul(a.v.IntRoot(r  1)); 944 return this; 945 } 946 947 public Dual<V> AssignInv(Dual<V> a) { 948 v.AssignInv(a.v); 949 dv.AssignNeg(a.dv).Mul(v).Mul(v); // (1/f(x))' =  f(x)' / f(x)^2 950 return this; 951 } 952 953 public Dual<V> AssignLog(Dual<V> a) { 954 v.AssignLog(a.v); 955 dv.Assign(a.dv).Div(a.v); // log(x)' = 1/f(x) * f(x)' 956 return this; 957 } 958 901 public Dual<V> Assign(Dual<V> a) { v.Assign(a.v); dv.Assign(a.dv); return this; } 902 public Dual<V> Scale(double s) { v.Scale(s); dv.Scale(s); return this; } 903 public Dual<V> Add(Dual<V> a) { v.Add(a.v); dv.Add(a.dv); return this; } 904 public Dual<V> Sub(Dual<V> a) { v.Sub(a.v); dv.Sub(a.dv); return this; } 905 public Dual<V> AssignNeg(Dual<V> a) { v.AssignNeg(a.v); dv.AssignNeg(a.dv); return this; } 906 public Dual<V> AssignInv(Dual<V> a) { v.AssignInv(a.v); dv.AssignNeg(a.dv).Mul(v).Mul(v); return this; } // (1/f(x))' =  f(x)' / f(x)^2 907 908 // (a(x) * b(x))' = b(x)*a(x)' + b(x)'*a(x); 959 909 public Dual<V> Mul(Dual<V> a) { 960 // (a(x) * b(x))' = b(x)*a(x)' + b(x)'*a(x); 961 962 V t1 = default(V); 963 t1.Assign(a.dv).Mul(v); 964 965 var t2 = default(V); 966 t2.Assign(dv).Mul(a.v); 967 910 var t1 = a.dv.Clone().Mul(v); 911 var t2 = dv.Clone().Mul(a.v); 968 912 dv.Assign(t1).Add(t2); 969 913 … … 971 915 return this; 972 916 } 973 974 public Dual<V> AssignNeg(Dual<V> a) { 975 v.AssignNeg(a.v); 976 dv.AssignNeg(a.dv); 977 return this; 978 } 979 980 public Dual<V> Scale(double s) { 981 v.Scale(s); 982 dv.Scale(s); 983 return this; 984 } 985 986 public Dual<V> AssignSin(Dual<V> a) { 987 v.AssignSin(a.v); 988 dv.Assign(a.dv).Mul(a.v.Clone().Cos()); 989 return this; 990 } 991 992 public Dual<V> Sub(Dual<V> a) { 993 v.Sub(a.v); 994 dv.Sub(a.dv); 995 return this; 996 } 997 998 public Dual<V> AssignAbs(Dual<V> a) { 999 v.AssignAbs(a.v); 1000 // abs(f(x))' = f(x)*f'(x) / f(x) 1001 dv.Assign(a.dv).Mul(a.v.Clone().Sgn()); 1002 return this; 1003 } 1004 1005 public Dual<V> AssignSgn(Dual<V> a) { 1006 throw new NotImplementedException(); 1007 } 917 public Dual<V> Div(Dual<V> a) { Mul(a.Inv()); return this; } 918 919 public Dual<V> AssignExp(Dual<V> a) { v.AssignExp(a.v); dv.Assign(a.dv).Mul(v); return this; } // exp(f(x)) = exp(f(x))*f(x)' 920 public Dual<V> AssignLog(Dual<V> a) { v.AssignLog(a.v); dv.Assign(a.dv).Div(a.v); return this; } // log(x)' = 1/f(x) * f(x)' 921 922 public Dual<V> AssignIntPower(Dual<V> a, int p) { v.AssignIntPower(a.v, p); dv.Assign(a.dv).Scale(p).Mul(a.v.Clone().IntPower(p  1)); return this; } 923 public Dual<V> AssignIntRoot(Dual<V> a, int r) { v.AssignIntRoot(a.v, r); dv.Assign(a.dv).Scale(1.0 / r).Mul(a.v.IntRoot(r  1)); return this; } 924 925 public Dual<V> AssignSin(Dual<V> a) { v.AssignSin(a.v); dv.Assign(a.dv).Mul(a.v.Clone().Cos()); return this; } 926 public Dual<V> AssignCos(Dual<V> a) { v.AssignCos(a.v); dv.AssignNeg(a.dv).Mul(a.v.Clone().Sin()); return this; } 927 928 public Dual<V> AssignAbs(Dual<V> a) { v.AssignAbs(a.v); dv.Assign(a.dv).Mul(a.v.Clone().Sgn()); return this; } // abs(f(x))' = f(x)*f'(x) / f(x) 929 public Dual<V> AssignSgn(Dual<V> a) { v.AssignSgn(a.v); dv.Assign(a.dv.Zero); return this; } 930 931 public Dual<V> Clone() { return new Dual<V>(v.Clone(), dv.Clone()); } 932 1008 933 } 1009 934 … … 1019 944 public SparseVector<object, V> Gradient => dv; // <key,value> partial derivative identified via the key 1020 945 1021 private MultivariateDual(MultivariateDual<V> orig) { 1022 this.v = orig.v.Clone(); 1023 this.dv = orig.dv.Clone(); 1024 } 946 private MultivariateDual(MultivariateDual<V> orig) { this.v = orig.v.Clone(); this.dv = orig.dv.Clone(); } 1025 947 1026 948 /// <summary> … … 1028 950 /// </summary> 1029 951 /// <param name="v"></param> 1030 public MultivariateDual(V v) { 1031 this.v = v.Clone(); 1032 this.dv = new SparseVector<object, V>(); 1033 } 952 public MultivariateDual(V v) { this.v = v.Clone(); this.dv = new SparseVector<object, V>(); } 1034 953 1035 954 /// <summary> … … 1039 958 /// <param name="keys"></param> 1040 959 /// <param name="dv"></param> 1041 public MultivariateDual(V v, object[] keys, V[] dv) { 1042 this.v = v.Clone(); 1043 this.dv = new SparseVector<object, V>(keys, dv); 1044 } 960 public MultivariateDual(V v, object[] keys, V[] dv) { this.v = v.Clone(); this.dv = new SparseVector<object, V>(keys, dv); } 1045 961 1046 962 /// <summary> … … 1050 966 /// <param name="key"></param> 1051 967 /// <param name="dv"></param> 1052 public MultivariateDual(V v, object key, V dv) { 1053 this.v = v.Clone(); 1054 this.dv = new SparseVector<object, V>(new[] { key }, new[] { dv }); 1055 } 968 public MultivariateDual(V v, object key, V dv) { this.v = v.Clone(); this.dv = new SparseVector<object, V>(new[] { key }, new[] { dv }); } 1056 969 1057 970 /// <summary> 1058 /// 971 /// Constructor with a given value and gradient. For internal use. 1059 972 /// </summary> 1060 /// <param name="v">not cloned</param> 1061 /// <param name="gradient">not cloned</param> 1062 internal MultivariateDual(V v, SparseVector<object, V> gradient) { 1063 this.v = v; 1064 this.dv = gradient; 1065 } 1066 1067 public MultivariateDual<V> Clone() { 1068 return new MultivariateDual<V>(this); 1069 } 1070 973 /// <param name="v">The value (not cloned).</param> 974 /// <param name="gradient">The gradient (not cloned).</param> 975 internal MultivariateDual(V v, SparseVector<object, V> gradient) { this.v = v; this.dv = gradient; } 976 977 public MultivariateDual<V> Clone() { return new MultivariateDual<V>(this); } 1071 978 1072 979 public MultivariateDual<V> Zero => new MultivariateDual<V>(Value.Zero, Gradient.Zero); 1073 980 1074 1075 public MultivariateDual<V> Add(MultivariateDual<V> a) { 1076 v.Add(a.v); 1077 dv.Add(a.dv); 1078 return this; 1079 } 1080 1081 public MultivariateDual<V> Assign(MultivariateDual<V> a) { 1082 v.Assign(a.v); 1083 dv.Assign(a.dv); 1084 return this; 1085 } 1086 1087 public MultivariateDual<V> AssignCos(MultivariateDual<V> a) { 1088 throw new NotImplementedException(); 1089 } 1090 1091 public MultivariateDual<V> AssignExp(MultivariateDual<V> a) { 1092 throw new NotImplementedException(); 1093 } 1094 1095 public MultivariateDual<V> AssignIntPower(MultivariateDual<V> a, int p) { 1096 throw new NotImplementedException(); 1097 } 1098 1099 public MultivariateDual<V> AssignIntRoot(MultivariateDual<V> a, int r) { 1100 throw new NotImplementedException(); 1101 } 1102 1103 public MultivariateDual<V> AssignInv(MultivariateDual<V> a) { 1104 throw new NotImplementedException(); 1105 } 1106 1107 public MultivariateDual<V> AssignLog(MultivariateDual<V> a) { 1108 throw new NotImplementedException(); 1109 } 1110 1111 public MultivariateDual<V> AssignNeg(MultivariateDual<V> a) { 1112 throw new NotImplementedException(); 1113 } 1114 1115 public MultivariateDual<V> AssignSin(MultivariateDual<V> a) { 1116 throw new NotImplementedException(); 1117 } 1118 1119 public MultivariateDual<V> Div(MultivariateDual<V> a) { 1120 throw new NotImplementedException(); 1121 } 1122 981 public MultivariateDual<V> Scale(double s) { v.Scale(s); dv.Scale(s); return this; } 982 983 public MultivariateDual<V> Add(MultivariateDual<V> a) { v.Add(a.v); dv.Add(a.dv); return this; } 984 public MultivariateDual<V> Sub(MultivariateDual<V> a) { v.Sub(a.v); dv.Sub(a.dv); return this; } 985 public MultivariateDual<V> Assign(MultivariateDual<V> a) { v.Assign(a.v); dv.Assign(a.dv); return this; } 1123 986 public MultivariateDual<V> Mul(MultivariateDual<V> a) { 1124 987 // (a(x) * b(x))' = b(x)*a(x)' + b(x)'*a(x); 1125 1126 988 var t1 = a.dv.Clone().Scale(v); 1127 989 var t2 = dv.Clone().Scale(a.v); … … 1131 993 return this; 1132 994 } 1133 1134 public MultivariateDual<V> Scale(double s) { 1135 v.Scale(s); 1136 dv.Scale(s); 1137 return this; 1138 } 1139 1140 public MultivariateDual<V> Sub(MultivariateDual<V> a) { 1141 v.Sub(a.v); 1142 dv.Sub(a.dv); 1143 return this; 1144 } 1145 1146 public MultivariateDual<V> AssignAbs(MultivariateDual<V> a) { 1147 v.AssignAbs(a.v); 1148 // abs(f(x))' = f(x)*f'(x) / f(x) doesn't work for intervals 1149 1150 dv.Assign(a.dv).Scale(a.v.Clone().Sgn()); 1151 return this; 1152 } 1153 1154 public MultivariateDual<V> AssignSgn(MultivariateDual<V> a) { 1155 v.AssignSgn(a.v); 1156 // sign(f(x)) = 0; 1157 dv = a.dv.Zero; 1158 return this; 1159 } 995 public MultivariateDual<V> Div(MultivariateDual<V> a) { v.Div(a.v); dv.Mul(a.dv.Inv()); return this; } 996 public MultivariateDual<V> AssignNeg(MultivariateDual<V> a) { v.AssignNeg(a.v); dv.AssignNeg(a.dv); return this; } 997 public MultivariateDual<V> AssignInv(MultivariateDual<V> a) { v.AssignInv(a.v); dv.AssignNeg(a.dv).Scale(v).Scale(v); return this; } // (1/f(x))' =  f(x)' / f(x)^2 998 999 public MultivariateDual<V> AssignSin(MultivariateDual<V> a) { v.AssignSin(a.v); dv.Assign(a.dv).Scale(a.v.Clone().Cos()); return this; } 1000 public MultivariateDual<V> AssignCos(MultivariateDual<V> a) { v.AssignCos(a.v); dv.AssignNeg(a.dv).Scale(a.v.Clone().Sin()); return this; } 1001 1002 public MultivariateDual<V> AssignIntPower(MultivariateDual<V> a, int p) { v.AssignIntPower(a.v, p); dv.Assign(a.dv).Scale(p).Scale(a.v.Clone().IntPower(p  1)); return this; } 1003 public MultivariateDual<V> AssignIntRoot(MultivariateDual<V> a, int r) { v.AssignIntRoot(a.v, r); dv.Assign(a.dv).Scale(1.0 / r).Scale(a.v.IntRoot(r  1)); return this; } 1004 1005 public MultivariateDual<V> AssignExp(MultivariateDual<V> a) { v.AssignExp(a.v); dv.Assign(a.dv).Scale(v); return this; } // exp(f(x)) = exp(f(x))*f(x)' 1006 public MultivariateDual<V> AssignLog(MultivariateDual<V> a) { v.AssignLog(a.v); dv.Assign(a.dv).Scale(a.v.Clone().Inv()); return this; } // log(x)' = 1/f(x) * f(x)' 1007 1008 public MultivariateDual<V> AssignAbs(MultivariateDual<V> a) { v.AssignAbs(a.v); dv.Assign(a.dv).Scale(a.v.Clone().Sgn()); return this; } // abs(f(x))' = f(x)*f'(x) / f(x) doesn't work for intervals 1009 public MultivariateDual<V> AssignSgn(MultivariateDual<V> a) { v.AssignSgn(a.v); dv = a.dv.Zero; return this; } // sign(f(x))' = 0; 1160 1010 } 1161 1011 }
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