Upon trying to calculate precision#k, I get an exception. To what follows is the a simple code that reproduces the problem.
First the code defines the variable scope:
initializer = tf.random_uniform_initializer(-0.1, 0.1, seed=1234)
with tf.variable_scope("model", reuse=None, initializer=initializer)
Then it calls those lines:
predictions = tf.Variable(tf.ones([2, 10], tf.int64))
labels = tf.Variable(tf.ones([2, 1], tf.int64))
precision = tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, labels, 5)
tf.initialize_all_variables().run()
(I know this code is meaningless, and tries to calculate the precision given 2 fixed matrices...)
Then I get the following exception:
W tensorflow/core/framework/op_kernel.cc:936] Failed precondition:
Attempting to use uninitialized value
model/precision_at_5/false_positive_at_5 [[Node:
model/precision_at_5/false_positive_at_5/read = IdentityT=DT_DOUBLE,
_class=["loc:#model/precision_at_5/false_positive_at_5"], _device="/job:localhost/replica:0/task:0/gpu:0"]]
The same goes when I tried to invoke streaming_sparse_recall_at_k instead of streaming_sparse_precision_at_k.
The installed version is r0.10 on linux with python 2.7.
Please help... Thanks in advance :)
Unfortunately, tf.initialize_all_variables() doesn't initialize "local" variables (which tend to be internal implementation details for ops like tf.contrib.metrics.streaming_sparse_precision_at_k() and tf.train.string_input_producer(), as opposed to variables used as model weights).
You'll need to add a line to your program that runs tf.initialize_local_variables() before running the evaluation op:
sess.run(tf.initialize_local_variables()) # or `tf.initialize_local_variables().run()`
Related
So far, using Wolfram System Modeler 4.3 and 5.1 the following minimal example would compile without errors:
model UnitErrorModel
MyComponent c( hasUnit = "myUnit" );
block MyComponent
parameter String hasUnit = "1";
output Real y( unit = hasUnit );
equation
y = 10;
end MyComponent;
end UnitErrorModel;
But with the new release of WSM 12.0 (the jump in version is due to an alignment with the current release of Wolfram's flagship Mathematica) I am getting an error message:
Internal error: Codegen.getValueString: Non-constant expression:c.hasUnit
(Note: The error is given by WSMLink'WSMSimulate in Mathematica 12.0 which is running System Modeler 12.0 internally; here asking for the "InternalValues" property of the above model since I have not installed WSM 12.0 right now).
Trying to simulate the above model in OpenModelica [OMEdit v. 1.13.2 (64-bit)] reveals:
SimCodeUtil.mo: 8492:9-8492:218]: Internal error Unexpected expression (should have been handled earlier, probably in the front-end. Unit/displayUnit expression is not a string literal: c.hasUnit
So it seems that to set the unit attribute I cannot make use of a variable that has parameter variability? Why is this - after all shouldn't it suffice that the compiler can hard-wire the unit when compiling for runtime (after all the given model will run without any error in WSM 4.3 and 5.1)?
EDIT: From the answer to an older question of mine I had believed that at least final parameters might be used to set the unit-attribute. Making the modification final (e.g. c( final hasUnit = "myUnit" ) does not resolve the issue.
I have been given feedback on Wolfram Community by someone from Wolfram MathCore regarding this issue:
You are correct in that it's not in violation with the specification,
although making it a constant makes more sense since you would
invalidate all your static unit checking if you are allowed to change
the unit after building the simulation. We filed an issue on the
specification regarding this (Modelica Specification Issue # 2362).
So, MatheCore is a bit ahead of the game in proposing a Modelica specification change that they have already implemented. ;-)
Note: That in Wolfram System Modeler (12.0) using the annotation Evaluate = true will not cure the problem (cf. the comment above by #matth).
As a workaround variables used to set the unit attribute should have constant variability, but can nevertheless by included in user dialogs to be interactively changed using annotation(Dialog(group = "GroupName")).
I'm using the CPLEX solver to run my ILP model.The ILP model is implemented with Julia/MultiJuMP.
I would like to limit the time of optimization of the problem. If I were working with OPL, I would just have to add Cplex.tilimt=100
In Julia, I put the following code :
mmodel = MultiModel(solver = CplexSolver("CPLEX.tilim"=100), linear = true)
It doesn't work.
From the last section in https://github.com/JuliaOpt/CPLEX.jl/blob/master/README.md, it appears that Julia uses the legacy parameter names as they appear in the C API of CPLEX. For example, CplexSolver(CPX_PARAM_EPINT=1e-8).
Here's the link to the the CPLEX documentation for that parameter: https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/ilog.odms.cplex.help/CPLEX/Parameters/topics/EpInt.html. As you can see, the name appears as the first row in the 'Name prior to V12.6.0' column.
For the time limit, you should thus use CPX_PARAM_TILIM, as this is the name in https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/ilog.odms.cplex.help/CPLEX/Parameters/topics/TiLim.html.
I am new to sage and have got a code (link to code) which should run.
I am still getting an error message in the decoding part. The error trace looks like this:
in decode(y)
--> sigma[i+1+1] = sigma[i+1]*(z)\
-(delta[i+1]/delta[mu+1])*z^(i-mu)*sigma[mu+1]*(z);
in sage.structure.element.Element.__mul__
if BOTH_ARE_ELEMNT(cl):
--> return coercion_model.bin_op(left, right, mul)
in sage.structure.coerce.CoercionModel_cache_maps.bin_op
--> action = self.get_action(xp,yp,op,x,y)
...... some more traces (don't actually know if they are important)
TypeError: positive characteristics not allowed in symbolic computations
Does anybody know if there is something wrong in this code snipped? Due to previous errors, I changed the following to get to where I am at the moment:
.coeffs() changed to .coefficients(sparse=False) due to a warning message.
in the code line sigma[i+1+1] = sigma[i+1](z)\
-(delta[i+1]/delta[mu+1])*z^(i-mu)*sigma[mu+1](z); where the error occurs, i needed to insert * eg. sigma[i+1]*(z)
I would be grateful for any guess what could be wrong!
Your issue is that you are multiplying things not of characteristic zero (like elements related to Phi.<x> = GF(2^m)) with elements of symbolic computation like z which you have explicitly defined as a symbolic variable
Phi.<x> = GF(2^m)
PR = PolynomialRing(Phi,'z')
z = var('z')
Basically, the z you get from PR is not the same one as from var('z'). I recommend naming it something else. You should be able to access this with PR.gen() or maybe PR(z).
I'd be able to be more detailed, but I encourage you next time to paste a fully (non-)working example; trying to slog through a big worksheet is not the easiest thing to track all this down. Finally, good luck, hope Sage ends up being useful for you!
I am trying to use R for the first time to do some probit analaysis.
I get the following error:
Error in if (!(validmu(mu) && valideta(eta))) stop("cannot find valid starting values: please specify some", :
missing value where TRUE/FALSE needed
This is the command I am using:
m1=glm(Good~Stg.Days+Dev.Deployments+Check.Ins+NoOfDevelopers,family=poisson(link = "probit"),data=deploy[1:4,])
My data deploy[1:4,] are as loaded in from a CSV file follows:
Good,Application Type,Project,Start Date,End Date,Stg Days,Dev Deployments,Check Ins,NoOfDevelopers
1,DocumentPlatform,ZCP,11/08/2010,11/11/2010,0.6,0,12,4
1,DocumentPlatform,ZCP,11/11/2010,09/12/2010,0.4,0,4,1
0,DocumentPlatform,ZCP,09/12/2010,07/03/2011,10,0,7,3
1,FactsheetPlatform,Process.ARCH,28/06/2010,09/03/2011,7.1,0,18,2
deploy is in reality a much bigger vector than 1:4 I am just using a subset of the data to help determine the problem.
Any ideas what is wrong?
As i commented: Using ?glm I found tha the poisoon family supports the following links: log, identity, and sqrt.
Testing on another link:
test <- data.frame('Good'=c(1,1,0,1),'Stg Days'=c(0.6,0.4,10,7.1),'Dev Deployments'=c(0,0,0,0),'Check Ins'=c(12,4,7,18),'NoOfDevelopers'=c(4,1,3,2))
m1=glm(Good~ . ,family=poisson(link = "log"),data=test)
Gives no errors. So I think your link = "probit" is the problem.
I am trying to run the cspade function in the arulesSequences package in R. After I successfully read in my transactions using read_baskets, I try to execute the cspade function against the transactions object I read in.
However, when I execute the command, I obtain an error: system invocation failed. Specifically, here is the output.
preprocessing ... 1 partition(s), 1.2 MB [0.23s]
mining transactions ...Error in cspade(table, parameter = list(support = 0.1), control = list (verbose = TRUE)) :
system invocation failed
The presence of "mining transactions" indicates that the following function call in the cspade code is failing.
if (system2(file.path(exe, "spade"), args = c("-i", file,
"-s", parameter#support, opt, "-e", nop, "-o"), stdout = out))
stop("system invocation failed").
For reference, I can successfully generate sequences using the example zaki dataset.
Does anyone have any idea why that command might be failing?
Thanks,
Stewart
http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Sequence_Mining/SPADE#Caveats
See link above, caveats section and lower your support threshold.
I have the same problem, I decreased the support ( put 0), still didn't work. But came up with another solution:
I changed the data size (less rows) and it works.
so if for example you can devide your data set to 2 parts of half size and make transcation data of each part as x_1 and x_2,
frequent_pattern_1 <- cspade(x_1,parameter = list(support = 0 ))
frequent_pattern_2 <- cspade(x_2,parameter = list(support = 0 ))
then get the absolute support of analysing each dataset to get result of all.
get the absolute support value
support_x_1<-support(frequent_pattern_1 ,x_1, type= c("absolute"), control = NULL)
support_x_2<-support(frequent_pattern_2 ,x_2, type= c("absolute"), control = NULL)
then finding the matching sequence and summing the supports of the matched ones.