Changeset ea3d3da in tol for tol_pkg/MulVarFunAprox


Ignore:
Timestamp:
Nov 30, 2009, 10:58:14 AM (17 years ago)
Author:
Víctor de Buen <vdebuen@…>
Branches:
1941-docker-image-to-test, devel, master
Children:
eddd23a
Parents:
e1355dd
Message:

Class @BaseC2FunRn2R is moved to package FunRn2R and renamed as @BaseC2
Checking residuals of extra-sample evaluations

git-svn-id: ​https://www.tol-project.org/svn/tolp/trunk@1750 be4846ae-f051-483a-831f-b64e7d08527f

File:
1 edited

Legend:

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  • tol_pkg/MulVarFunAprox/test/test_0001/test.tol

    re1355dd rea3d3da  
    1414
    1515//Random seed setting
    16 Real PutRandomSeed(234234);
     16Real PutRandomSeed(0);
    1717Real rndSeed = GetRandomSeed(0);
    1818WriteLn("Current Random Seed = "<<rndSeed);
    … …  
    2626Real maxDeg = 4;
    2727
    28 
    29 MulVarFunAprox::@BaseC2FunRn2R.Monomial base =
    30   MulVarFunAprox::@BaseC2FunRn2R.Monomial::New(n,maxDeg);
     28FunRn2R::@BaseC2.Monomial base =
     29  FunRn2R::@BaseC2.Monomial::New(n,maxDeg);
    3130
    3231Real K = base::_.K;
    33 Real J = K*1000;
    34 
    35 VMatrix X = Group("ConcatColumns",For(1,n,VMatrix(Real i)
    36 {
    37   Real min = Rand(0,0);
    38   Real len = Rand(5,10)*(i^(1/n));
    39   Rand(J,1,min,min+len)
    40 }));
    41 
    42 
    43 VMatrix fun.eval.intra.base(VMatrix X)
    44 {
    45   Set x = For(1,n,VMatrix(Real i) { SubCol(X,[[i]]) });
    46   (x[1]*0+1)*Rand(-10,10)+
    47   x[1]*Rand(-10,10)+
    48   x[2]*Rand(-10,10)+
    49   x[1]^2*Rand(-10,10)+
    50   x[2]^2*Rand(-10,10)+
    51   x[1]$*x[2]*Rand(-10,10)
     32Real J = K*20;
     33
     34//////////////////////////////////////////////////////////////////////////////
     35VMatrix generate.nodal.points(Real J, Real n)
     36//////////////////////////////////////////////////////////////////////////////
     37{
     38  Group("ConcatColumns",For(1,n,VMatrix(Real i)
     39  {
     40    Real min = Rand(0,0);
     41    Real len = Rand(5,10)*(i^(1/n));
     42    Rand(J,1,min,min+len)
     43  }))
    5244};
    5345
     46//////////////////////////////////////////////////////////////////////////////
     47  NameBlock base.random.fun =
     48//////////////////////////////////////////////////////////////////////////////
     49[[
     50  Matrix coef = Rand(base::_.K, 1, -10, 10);
     51  VMatrix eval(VMatrix X)
     52  {
     53    SetSum(For(1,base::_.K, VMatrix(Real k)
     54    {
     55      base::function.v(k,X)*MatDat(coef,k,1)
     56    }))
     57  }
     58]];
     59
     60//////////////////////////////////////////////////////////////////////////////
    5461VMatrix fun.eval.extra.base(VMatrix X)
     62//////////////////////////////////////////////////////////////////////////////
    5563{
    5664  Set x = For(1,n,VMatrix(Real i) { SubCol(X,[[i]]) });
    … …  
    5866};
    5967
    60 VMatrix Fx = fun.eval.extra.base(X);
    61 
    62 Real lengthRatio = 0.5*(J/(K*n))^(1/n);
     68Code fun.eval = fun.eval.extra.base;
     69//Code fun.eval = base.random.fun::eval;
     70
     71
     72VMatrix X = generate.nodal.points(J,n);
     73VMatrix Fx = fun.eval(X);
     74
     75Real lengthRatio = 0.7*(J/(K*n))^(1/n);
    6376
    6477Set X.col.info = For(1,n,Set(Real i)
    … …  
    109122  ( X    << (Y*0));
    110123
     124//////////////////////////////////////////////////////////////////////////////
    111125VMatrix closest.Y.index(VMatrix A)
     126//////////////////////////////////////////////////////////////////////////////
    112127{
    113128  Real m = VRows(A);
    … …  
    156171Real X.maxNeighbour = 1+2*n;
    157172
    158 VMatrix X.closest.Y.index = closest.Y.index(X);
    159 VMatrix X.closest.Y = SubRow(Y,VCol2Set(X.closest.Y.index));
    160 
    161 VMatrix X.j=DifEq(1/(1-B),Rand(J,1,1,1));
    162 
    163 VMatrix X.h=SubRow(Y.neighbourhood, VCol2Set(X.closest.Y.index));
    164 
    165 Set X.dif = For(1,X.maxNeighbour,VMatrix(Real i)
    166 {
    167   X - SubRow(Y,VCol2Set(SubCol(X.h,[[i]])))
    168 });
    169 
    170 Set X.tao = For(1,X.maxNeighbour,VMatrix(Real i)
    171 {
    172   VMatrix X.norm2 = RPow(X.dif[i],2)*Rand(n,1,1,1);
    173   RPow(X.norm2,0.5)/radius
    174 });
    175 
    176 Set X.tao2 = For(1,X.maxNeighbour,VMatrix(Real i)
    177 {
    178   RPow(X.tao[i],2)
    179 });
    180 
    181 Set X.match = For(1,X.maxNeighbour,VMatrix(Real i)
    182 {
    183   LE(X.tao[i],Rand(J,1,1,1))
    184 });
    185 
    186 Set X.lambda = For(1,X.maxNeighbour,VMatrix(Real i)
    187 {
    188   RPow(X.match[i] $* (Rand(J,1,1,1)-X.tao2[i]),4)
    189 });
    190 
    191 VMatrix X.lambda.sum = SetSum(X.lambda);
    192 
    193 Set X.weight = For(1,X.maxNeighbour,VMatrix(Real i)
    194 {
    195   X.lambda[i] $/ X.lambda.sum
    196 });
    197 
    198 Matrix QX.triplet = Group("ConcatRows", For(1,X.maxNeighbour,Matrix(Real i)
    199 {
    200   VMatrix w.i = X.weight[i];
    201   Set sel.rows = MatQuery::SelectMatch(NE(Drop(Abs(w.i),1.E-15),w.i*0));
    202   VMatrix wi = SubRow(w.i,sel.rows);
    203   VMatrix X.hiK = SubRow((SubCol(X.h,[[i]])-1)*K,sel.rows);
    204   VMatrix Xdif.i = SubRow(X.dif[i],sel.rows);
    205   VMatrix X.j.i = SubRow(X.j,sel.rows);
    206   Group("ConcatRows", For(1,K,Matrix(Real k)
    207   {
    208     VMatrix pk = base::function.v(k,Xdif.i);
    209     VMatrix qk = wi$*pk;
    210     VMat2Mat(X.j.i | (X.hiK+k) | qk)
    211   }))
    212 }));
    213 
    214 VMatrix QX = Pack(Convert(Triplet(QX.triplet, J, N),"Cholmod.R.Sparse"));
     173//////////////////////////////////////////////////////////////////////////////
     174VMatrix builds.obs.input(VMatrix X)
     175//////////////////////////////////////////////////////////////////////////////
     176{
     177  Real J = VRows(X);
     178 
     179  VMatrix X.closest.Y.index = closest.Y.index(X);
     180  VMatrix X.closest.Y = SubRow(Y,VCol2Set(X.closest.Y.index));
     181 
     182  VMatrix X.j=DifEq(1/(1-B),Rand(J,1,1,1));
     183 
     184  VMatrix X.h=SubRow(Y.neighbourhood, VCol2Set(X.closest.Y.index));
     185 
     186  Set X.dif = For(1,X.maxNeighbour,VMatrix(Real i)
     187  {
     188    X - SubRow(Y,VCol2Set(SubCol(X.h,[[i]])))
     189  });
     190 
     191  Set X.tao = For(1,X.maxNeighbour,VMatrix(Real i)
     192  {
     193    VMatrix X.norm2 = RPow(X.dif[i],2)*Rand(n,1,1,1);
     194    RPow(X.norm2,0.5)/radius
     195  });
     196 
     197  Set X.tao2 = For(1,X.maxNeighbour,VMatrix(Real i)
     198  {
     199    RPow(X.tao[i],2)
     200  });
     201 
     202  Set X.match = For(1,X.maxNeighbour,VMatrix(Real i)
     203  {
     204    LE(X.tao[i],Rand(J,1,1,1))
     205  });
     206 
     207  Set X.lambda = For(1,X.maxNeighbour,VMatrix(Real i)
     208  {
     209    RPow(X.match[i] $* (Rand(J,1,1,1)-X.tao2[i]),4)
     210  });
     211 
     212  VMatrix X.lambda.sum = SetSum(X.lambda);
     213 
     214  Set X.weight = For(1,X.maxNeighbour,VMatrix(Real i)
     215  {
     216    X.lambda[i] $/ X.lambda.sum
     217  });
     218 
     219  Matrix QX.triplet = Group("ConcatRows", For(1,X.maxNeighbour,Matrix(Real i)
     220  {
     221    VMatrix w.i = X.weight[i];
     222    Set sel.rows = MatQuery::SelectMatch(NE(Drop(Abs(w.i),1.E-15),w.i*0));
     223    VMatrix wi = SubRow(w.i,sel.rows);
     224    VMatrix X.hiK = SubRow((SubCol(X.h,[[i]])-1)*K,sel.rows);
     225    VMatrix Xdif.i = SubRow(X.dif[i],sel.rows);
     226    VMatrix X.j.i = SubRow(X.j,sel.rows);
     227    Group("ConcatRows", For(1,K,Matrix(Real k)
     228    {
     229      VMatrix pk = base::function.v(k,Xdif.i);
     230      VMatrix qk = wi$*pk;
     231      VMat2Mat(X.j.i | (X.hiK+k) | qk)
     232    }))
     233  }));
     234  Pack(Convert(Triplet(QX.triplet, J, N),"Cholmod.R.Sparse"))
     235};
     236
     237VMatrix QX = builds.obs.input(X);
    215238
    216239
    … …  
    334357Real E.ste = VMatStDs(E);
    335358
     359
     360VMatrix X2 = generate.nodal.points(J,n);
     361VMatrix Fx2 = fun.eval(X2);
     362VMatrix QX2 = builds.obs.input(X2);
     363VMatrix F2.frc = QX2*alpha;
     364VMatrix E2 = Fx2 - F2.frc;
     365Real E2.avr = VMatAvr(E2);
     366Real E2.ste = VMatStDs(E2);
     367
    336368Set View([[
    337 Real n, K, J, H,
    338 X.maxNeighbour, Z.maxNeighbour,
    339 T, N, Mz,M, E.avr, E.ste, "\n"]],"");
     369  Real n, K, J, H,
     370  X.maxNeighbour, Z.maxNeighbour,
     371  T, N, Mz,M,
     372  E.avr, E.ste,
     373  E2.avr, E2.ste,
     374  "\n"]],"");
    340375
    341376/*
    … …  
    367402/* */
    368403
    369 /*
    370 VMatrix Z.min = Group("ConcatColumns", For(1,n,VMatrix(Real i)
    371 {
    372   Real aux = range[i][1];
    373   Rand(2*H,1,aux,aux)
    374 }));
    375 
    376 VMatrix Z.max = Group("ConcatColumns", For(1,n,VMatrix(Real i)
    377 {
    378   Real aux = range[i][Card(range[i])];
    379   Rand(2*H,1,aux,aux)
    380 }));
    381 
    382 VMatrix Z.interior = EQ(
    383   And(GE(Z.all,Z.min), LE(Z.all,Z.max))*Rand(n,1,1,1),
    384   Rand(2*H,1,n,n));
    385 
    386 VMatrix Z = SubRow(Z.all,MatQuery::SelectMatch(Z.interior));
    387 VMatrix Z.Y.1 = SubRow(Z.Y.1.all,MatQuery::SelectMatch(Z.interior));
    388 VMatrix Z.Y.2 = SubRow(Z.Y.2.all,MatQuery::SelectMatch(Z.interior));
    389 VMatrix Z.h.1 = closest.Y.index(Z.Y.1);
    390 VMatrix Z.h.2 = closest.Y.index(Z.Y.2);
    391 */
    392 
    393 /*
    394 Real deg = 3;
    395 
    396 Real dim = 2;
    397 Real maxDeg = 3;
    398 VMatrix candidates = Mat2VMat(SetMat(Range(0,maxDeg,1)^dim));
    399 VMatrix match = LE(candidates*Rand(dim,1,1,1),Rand(VRows(candidates),1,maxDeg,maxDeg));
    400 VMatrix degrees = SubRow(candidates, MatQuery::SelectMatch(match));
    401 Real K = VRows(degrees);
    402 Class @PolynBasic
    403 {
    404   Real k;
    405   VMatrix fun (VMatrix x)
    406   {
    407     SetProd(For(1,dim,VMatrix(Real i)
    408     {
    409      
    410     })
    411   };
    412  
    413 };
    414 Set base = For(1,K,NameBlock(Real k)
    415 {
    416 });
    417 /* */
    418 
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