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#
# Copyright 2007-2021 by the individuals mentioned in the source code history
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
require(OpenMx)
if (mxOption(NULL,"Default optimizer")!='NPSOL') stop("SKIP")
if (0) {
mxOption(NULL,"Print level",20)
mxOption(NULL,"Print file",1)
mxOption(NULL,"Verify level",3)
}
set.seed(1611150)
x <- matrix(rnorm(1000))
colnames(x) <- "x"
m1 <- mxModel(
"m1",
mxData(observed=x,type="raw"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=0,labels="mu",name="Mu"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=1,labels="sigma2",name="Sigma",lbound=0),
mxExpectationNormal(covariance="Sigma",means="Mu",dimnames=c("x")),
mxFitFunctionML()
)
run1 <- mxRun(m1)
hess.ini <- matrix(-2*c(-1000/1,0,0,-1000/2),nrow=2,ncol=2)
ws <- chol(hess.ini)
plan <- mxComputeSequence(steps=list(
mxComputeGradientDescent(engine="NPSOL",warmStart=ws),
mxComputeNumericDeriv(analytic=FALSE),
mxComputeStandardError(),
mxComputeHessianQuality(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
m2 <- mxModel(
"m2",
plan,
mxData(observed=x,type="raw"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=0,labels="mu",name="Mu"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=1,labels="sigma2",name="Sigma",lbound=0),
mxExpectationNormal(covariance="Sigma",means="Mu",dimnames=c("x")),
mxFitFunctionML()
)
if (0) {
m2 <- mxOption(m2,"Always Checkpoint","Yes")
m2 <- mxOption(m2,"Checkpoint Units","evaluations")
m2 <- mxOption(m2,"Checkpoint Count",1)
m2 <- mxOption(m2, "Checkpoint Fullpath", "/dev/fd/2")
}
run2 <- mxRun(m2)
omxCheckTrue(run2$output$evaluations < run1$output$evaluations)
plan2 <- mxComputeSequence(steps=list(
mxComputeGradientDescent(engine="NPSOL",warmStart=matrix(1,1,1)),
mxComputeNumericDeriv(analytic=FALSE),
mxComputeStandardError(),
mxComputeHessianQuality(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
m3 <- mxModel(
"m3",
plan2,
mxData(observed=x,type="raw"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=-0.3,labels="mu",name="Mu"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=0.97,labels="sigma2",name="Sigma",lbound=0),
mxExpectationNormal(covariance="Sigma",means="Mu",dimnames=c("x")),
mxFitFunctionML()
)
omxCheckWarning(mxRun(m3),"MxComputeGradientDescent: warmStart size 1 does not match number of free parameters 2 (ignored)")
#Test "internal" warm start:
plan3 <- mxComputeSequence(steps=list(
mxComputeOnce(from="m4.fitfunction",what="hessian"),
mxComputeGradientDescent(engine="NPSOL"),
mxComputeNumericDeriv(analytic=FALSE),
mxComputeStandardError(),
mxComputeHessianQuality(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
m4 <- mxModel(
"m4",
mxModel(
"sub",
mxData(observed=x,type="raw"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=0,labels="mu",name="Mu"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=1,labels="sigma2",name="Sigma",lbound=0),
mxExpectationNormal(covariance="Sigma",means="Mu",dimnames=c("x")),
mxFitFunctionML()
),
plan3,
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=1,labels="sigma2",name="Sigma",lbound=0),
mxAlgebra(-2*rbind(
cbind(-1000/Sigma, 0),
cbind(0, -1000/(2*Sigma^2))
), name="hess",dimnames=list(c("mu","sigma2"),c("mu","sigma2")) ),
mxFitFunctionAlgebra(algebra="sub.fitfunction",hessian="hess",numObs=1000)
)
if (0) {
m4 <- mxOption(m4,"Always Checkpoint","Yes")
m4 <- mxOption(m4,"Checkpoint Units","evaluations")
m4 <- mxOption(m4,"Checkpoint Count",1)
m4 <- mxOption(m4, "Checkpoint Fullpath", "/dev/fd/2")
}
run4 <- mxRun(m4)
omxCheckTrue(run4$output$evaluations < run1$output$evaluations)
# There are two additional evaluations:
# 1 To initialize sub.fitfunction
# 2 to evaluate sub.fitfunction
omxCheckCloseEnough(run4$output$evaluations, run2$output$evaluations+2)
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