search for: dstd

...logr, order = c(5, 0, 0), include.mean = T) logr<- ar5$res acf(logr) #fit GARCH distribution hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD') norm_fit <- normFit(logr) curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE, col=2) t_fit <- stdFit(logr) curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add = TRUE, col=6) snorm_fit <- snormFit(logr) curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]), -.25, .15, add = TRUE, col=4) st_fit <- sstdFit(logr) curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3], st...

...; Operand ImmOd, ImmLeaf imm_type> >> { >> .... >> def IMM_MEM_MEM : SetABDIn<asmstr, ImmOd, memhx, memhx, >> [(directStore (dstType (OpNode >> imm_type:$srcA, (srcBType (load addr16:$srcB)))), addr16:$dstD)]>; >> ..... >> } >> >> class SetABDIn<string asmstr, DAGOperand srcA, DAGOperand srcB, >> DAGOperand >> dstD, list<dag> pattern> >> : A_B_D<(outs), (ins srcA:$srcA, srcB:$srcB, dstD:$dstD), >> !strconc...

...cAType, ValueType srcBType, ValueType dstType, Operand ImmOd, ImmLeaf imm_type> { .... def IMM_MEM_MEM : SetABDIn<asmstr, ImmOd, memhx, memhx, [(directStore (dstType (OpNode imm_type:$srcA, (srcBType (load addr16:$srcB)))), addr16:$dstD)]>; ..... } class SetABDIn<string asmstr, DAGOperand srcA, DAGOperand srcB, DAGOperand dstD, list<dag> pattern> : A_B_D<(outs), (ins srcA:$srcA, srcB:$srcB, dstD:$dstD), !strconcat(asmstr, "\t$srcA, $srcB, $dstD"), pattern, IIAlu> { l...

...rom fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns: data(EuStockMarkets) eps = diff(log(EuStockMarkets[,"CAC"])) library(fSeries) g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd") s = g at fit$series All the coefficients are ok (checked with SAS 9.1) except nu (degrees of freedom of the student) and the log-likelyhood. I've really checked everything and can't find the estimated series sigma (volatility) and eta, such that eps = sigma * eta and eta is centered...

...T, T(neg33), T(abs31), SRC1, T(neg3b), T(fi2) }, { 0x0340000000000001ull, 0x37c0000000000003ull, N("mul"), T(ftz2f), T(sat35), T(frm2a), T(neg3b), N("f32"), DST, SRC1, T(fi2) }, + { 0x0380000000000001ull, 0x37c0000000000003ull, N("add"), T(frm2a), N("f64"), DSTD, T(neg33), T(abs31), SRC1D, T(neg3b), T(di2) }, { 0x0400000000000001ull, 0x37c0000000000003ull, N("mul"), T(frm2a), T(neg3b), N("f64"), DSTD, SRC1D, T(di2) }, { 0x0500000000000001ull, 0x37c0000000000003ull, N("selp"), DST, SRC1, T(i3bi2), T(pnot2d), PSRC3 }, { 0x...

It appears that it's impossible to duplicate a node in the dag. For example, there is some code: b = a * a; // a is a global int A LD node is generated for A and it goes into both Operand 0 and 1 of the MUL node. The issue is I'm trying to match a pattern of: set dstReg:$dstD (OpNode (srcAType (load addr32:$srcA)), (srcBType (load addr32:$srcB))) so basically a mem, mem, reg operation. The issue is this pattern won't match in the above example because there is only one LD generated for 'a'. I tried to duplicate the LD in the dag but it doesn't show up...

...c(5, 0, 0), include.mean = T) logr<- ar5$res #remove mean acf(logr) #fit GARCH distribution hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD') norm_fit <- normFit(logr) curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE, col=2) t_fit <- stdFit(logr) curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add = TRUE, col=6) snorm_fit <- snormFit(logr) curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]), -.25, .15, add = TRUE, col=4) st_fit <- sstdFit(logr) curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3], st...

Thanks for your reply. We are propagating sign info to tablegen currently using BinaryWithFlagsSDNode.Flags.hasNoSignedWrap atm. I imagine (I have not looked) they are printed according to instruction in AsmPrinter.cpp (pure speculation). I'm still confused as to why 0x7FFF is ok to match 16 bit int but not 0x8000? Thanks. On Wed, Feb 15, 2017 at 1:44 PM, Manuel Jacob <me at

...lass AD<string asmstr, SDPatternOperator OpNode, RegisterClass srcAReg, RegisterClass dstReg, ValueType srcAType, ValueType dstType, Operand ImmOd, ImmLeaf imm_type> { def REG_REG : SetADInOut<asmstr, srcAReg, dstReg, [(set dstReg:$dstD, (OpNode srcAReg:$srcA))]>; def IMM_REG : SetADInOut<asmstr, ImmOd, dstReg, [(set dstReg:$dstD, (OpNode imm_type:$srcA))]>; def IMM_MEM : SetADIn<asmstr, ImmOd, memhx, [(directStore (dstType (OpNode imm_type:$srcA)),...

...tternOperator OpNode, RegisterClass > srcAReg, > RegisterClass dstReg, ValueType srcAType, > ValueType dstType, Operand ImmOd, ImmLeaf imm_type> > { > def REG_REG : SetADInOut<asmstr, srcAReg, dstReg, > [(set dstReg:$dstD, (OpNode > srcAReg:$srcA))]>; > def IMM_REG : SetADInOut<asmstr, ImmOd, dstReg, > [(set dstReg:$dstD, (OpNode > imm_type:$srcA))]>; > def IMM_MEM : SetADIn<asmstr, ImmOd, memhx, > [(directStore (dstT...

...gisterClass >> srcAReg, >> RegisterClass dstReg, ValueType srcAType, >> ValueType dstType, Operand ImmOd, ImmLeaf imm_type> >> { >> def REG_REG : SetADInOut<asmstr, srcAReg, dstReg, >> [(set dstReg:$dstD, (OpNode >> srcAReg:$srcA))]>; >> def IMM_REG : SetADInOut<asmstr, ImmOd, dstReg, >> [(set dstReg:$dstD, (OpNode >> imm_type:$srcA))]>; >> def IMM_MEM : SetADIn<asmstr, ImmOd, memhx, >>...

...srcAReg, >>> RegisterClass dstReg, ValueType srcAType, >>> ValueType dstType, Operand ImmOd, ImmLeaf imm_type> >>> { >>> def REG_REG : SetADInOut<asmstr, srcAReg, dstReg, >>> [(set dstReg:$dstD, (OpNode >>> srcAReg:$srcA))]>; >>> def IMM_REG : SetADInOut<asmstr, ImmOd, dstReg, >>> [(set dstReg:$dstD, (OpNode >>> imm_type:$srcA))]>; >>> def IMM_MEM : SetADIn<asmstr, ImmOd, memhx, >>>...

...ula.var = formula.var, ): object "alpha" not found I tried to estimate this model with different series, but I always get this error message. For example, data(EuStockMarkets) dax <- diff(log(EuStockMarkets))[,"DAX"] g=garchFit(formula=~garch(0,1),data=dax,cond.dist="dstd",include.mean=FALSE) Series Initialization: ARMA model: arma Formula mean: ~ arma(0, 0) GARCH model: garch Formula var: ~ garch(0, 1) ARMA Order: 0 0 Max ARMA Order: 0 GARCH Order: 0 1 Max...

...t;> RegisterClass dstReg, ValueType srcAType, >>>> ValueType dstType, Operand ImmOd, ImmLeaf imm_type> >>>> { >>>> def REG_REG : SetADInOut<asmstr, srcAReg, dstReg, >>>> [(set dstReg:$dstD, (OpNode >>>> srcAReg:$srcA))]>; >>>> def IMM_REG : SetADInOut<asmstr, ImmOd, dstReg, >>>> [(set dstReg:$dstD, (OpNode >>>> imm_type:$srcA))]>; >>>> def IMM_MEM : SetADIn<asmstr, ImmOd, m...

Hello all, I am trying to use the "garchFit" function in the fSeries Package to fit a Garch(1,1) Model with t distribution. I am using the following codes. fit <- garchFit(~garch(1,1),data,cond.dist="dstd") fitted(fit) I was expecting the fitted(fit) would return the fitted volatility, but the result turns out to be a series of repeated same value. I tried to change the distribution to normal, and the same thing happened. Could anyone give me some advice? Many thanks. -- View this message in...

...ead.table("H://Daten//Zeitreihen//dax_1.csv", sep=";", header=T) DAX.kurs<-daten DAX.kurs<-ts(DAX.kurs,names="DAX-Kurs") DAX.rendite<-diff(DAX.kurs)/DAX.kurs[1:length(DAX.kurs)-1] g<-garchFit(data=DAX.rendite,formula=~garch(1,1),const.dist="dstd") g.predict<-predict(g,n.ahead=1) Best regards, Andy [[alternative HTML version deleted]]

> On Jun 15, 2015, at 7:05 AM, Ryan Taylor <ryta1203 at gmail.com> wrote: > > To be more specific: > > We have some operators that we are currently implementing as intrinsics, for example things like: abs, min, max, etc..... > > for some code: > > int a; > > int food() > { > return abs(a); > } > > the corresponding operator should

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Hi Ryan, > On Aug 24, 2015, at 6:49 PM, Ryan Taylor <ryta1203 at gmail.com> wrote: > > Quentin, > > I apologize for the spamming here but in getVR (where VReg is assigned an RC), it calls: > > const TargetRegisterClass *RC = TLI->getRegClassFor(Op.getSimpleValueType()); > VReg = MRI->createVirtualRegister(RC); > > My question is why is it using the

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