I would like to remesh my model using filter called "Remeshing: Isotropic Explicit Remeshing". In that filter, I'd like to adjust the target length. There are two types of target length, abs and %. But in the filter script available in PyMeshLab, there is only filter script for target length in terms of %. I don't want to adjust the percentage as I want to apply the same script for different size of models, where abs target length (in world unit format) will make more similar edge length rather than % form.
Is there anyway I can write the script for target length in abs form?
I think that documentation for remeshing_isotropic_explicit_remeshing is not very clear. I'm supposing you are using current version of pymeshlab (21.10)
if you pass a float value as parameter, it should be interpreted as an absolute value. You can force this behavior using arguments of type pymeshlab.AbsoluteValue(x). If you need to interpreter arguments as percentage value, use pymeshlab.Percentage(x).
You can find an example here
import pymeshlab
ms = pymeshlab.MeshSet()
ms.load_new_mesh('bunny10k.ply')
#Apply absolute value 3 as argument
ms.remeshing_isotropic_explicit_remeshing(targetlen = pymeshlab.AbsoluteValue(3))
ms.save_current_mesh('output1.ply')
ms.load_new_mesh('bunny10k.ply')
#Apply percentage 3% as argument
ms.remeshing_isotropic_explicit_remeshing(targetlen = pymeshlab.Percentage(3))
ms.save_current_mesh('output2.ply')
Related
Suppose I am using some twoway graph command in Stata. Without any action on my part Stata will choose some reasonable values for the ranges of both y and x axes, based both upon the minimum and maximum y and x values in my data, but also upon some algorithm that decides when it would be prettier for the range to extend instead to a number like '0' instead of '0.0139'. Wonderful! Great.
Now suppose that after (or while) I draw my graph, I want to slap some very important text onto it, and I want to be choosy about precisely where the text appears. Having the minimum and maximum values of the displayed axes would be useful: how can I get these min and max numbers? (Either before or while calling the graph command.)
NB: I am not asking how to set the y or x axis ranges.
Since this issue has been a bit of a headache for me for quite some time and I believe there is no good solution out there yet I wanted to write up two ways in which I was able to solve a similar problem to the one described in the post. Specifically, I was able to solve the issue of gray shading for part of the graph using these.
Define a global macro in the code generating the axis labels This is the less elegant way to do it but it works well. Locate the tickset_g.class file in your ado path. The graph twoway command uses this to draw the axes of any graph. There, I defined a global macro in the draw program that takes the value of the omin and omax locals after they have been set to the minimum between the axis range and data range (the command that does this is local omin = min(.scale.min,omin) and analogously for the max), since the latter sometimes exceeds the former. You could also define the global further up in that code block to only get the axis extent. You can then access the axis range using the globals after the graph command (and use something like addplot to add to the previously drawn graph). Two caveats for this approach: using global macros is, as far as I understand, bad practice and can be dangerous. I used names I was sure wouldn't be included in any program with the prefix userwritten. Also, you may not have administrator privileges that allow you to alter this file based on your organization's decisions. However, it is the simpler way. If you prefer a more elegant approach along the lines of what Nick Cox suggested, then you can:
Use the undocumented gdi natscale command to define your own axis labels The gdi commands are the internal commands that are used to generate what you see as graph output (cf. https://www.stata.com/meeting/dcconf09/dc09_radyakin.pdf). The tickset_g.class uses the gdi natscale command to generate the nice numbers of the axes. Basic documentation is available with help _natscale, basically you enter the minimum and maximum, e.g. from a summarize return, and a suggested number of steps and the command returns a min, max, and delta to be used in the x|ylabel option (several possible ways, all rather straightforward once you have those numbers so I won't spell them out for brevity). You'd have to adjust this approach in case you use some scale transformation.
Hope this helps!
I like Nick's suggestion, but if you're really determined, it seems that you can find these values by inspecting the output after you set trace on. Here's some inefficient code that seems to do exactly what you want. Three notes:
when I import the log file I get this message:
Note: Unmatched quote while processing row XXXX; this can be due to a formatting problem in the file or because a quoted data element spans multiple lines. You should carefully inspect your data after importing. Consider using option bindquote(strict) if quoted data spans multiple lines or option bindquote(nobind) if quotes are not used for binding data.
Sometimes the data fall outside of the min and max range values that are chosen for the graph's axis labels (but you can easily test for this).
The log linesize is actually important to my code below because the key values must fall on the same line as the strings that I use to identify the helpful rows.
* start a log (critical step for my solution)
cap log close _all
set linesize 255
log using "log", replace text
* make up some data:
clear
set obs 3
gen xvar = rnormal(0,10)
gen yvar = rnormal(0,.01)
* turn trace on, run the -twoway- call, and then turn trace off
set trace on
twoway scatter yvar xvar
set trace off
cap log close _all
* now read the log file in and find the desired info
import delimited "log.log", clear
egen my_string = concat(v*)
keep if regexm(my_string,"forvalues yf") | regexm(my_string,"forvalues xf")
drop if regexm(my_string,"delta")
split my_string, parse("=") gen(new)
gen axis = "vertical" if regexm(my_string,"yf")
replace axis = "horizontal" if regexm(my_string,"xf")
keep axis new*
duplicates drop
loc my_regex = "(.*[0-9]+)\((.*[0-9]+)\)(.*[0-9]+)"
gen min = regexs(1) if regexm(new3,"`my_regex'")
gen delta = regexs(2) if regexm(new3,"`my_regex'")
gen max_temp= regexs(3) if regexm(new3,"`my_regex'")
destring min max delta , replace
gen max = min + delta* int((max_temp-min)/delta)
*here is the info you want:
list axis min delta max
There are 100+ incoming fields for a target transformation in IICS. NULLs can appear in any of these columns. But the end goal is to convert the NULLs in each of the incoming fields to * so that the data in the target consists of * instead of NULL.
A laborious way to do this is to define an expression for each column. That 100+ expressions to cover each and every column. The task of the expression is to convert NULL into *. But that is difficult in terms of maintenance.
In Informatica Power center there is a property on the target object that converts all the NULL values to * as shown in the below screenshot.
Tried setting the property Replacement Character on IICS for the target transformation. But that didn't help. The data is still coming in as NULL.
Do we have a similar functionality or property for target transformation on IICS? If so how to use it?
i think i find easier to create a reusable exp transformation with 10 input and 10 putput. Then copy it 10 times for 100 fields.
create an input, output port like below -
in_col
out_col = IIF(isnull(in_col) OR is_spaces(in_col),'*',in_col)
Then copy in_col - 10 times. And copy out_col 10 times. You need to adjust/fix the formula though.
Save it and make it reusable'
Then copy that reusable widget 10 times.
This has flexibility - if formula changes, you just have to change only 1 widget and viola, everything changed.
Try using Vertical macro. It allows writing a function that will affect a set of indicated ports. Follow the link for full documentation with examples.
Gurobi 9.0.0 // C++
I am trying to get the shadow price of variables without explicitly generating a constraint for them.
I am generating variables the following way:
GRBModel* milp_model
milp_model->addVar(lb, up, type, 0, name)
Now I would like to get the shadow price (dual) for these variables.
I found this article which says that for "a linear program with lower and upper bounds on a variable, i.e., l ≤ x ≤ u" [...] "Gurobi gives access to the reduced cost of x, which corresponds to sl+su".
To get the shadow price of a constraint one would use the GRB functions according to the following answer (python but same idea) using the Pi constraint attribute.
What would be the GRB function that returns the previously mentioned reduced cost of x / shadow price of a variable?
I tried gurobi_var.get(GRB_DoubleAttr_Pi) which works for gurobi_constr.get(GRB_DoubleAttr_Pi)
but it returns: Not right attribute. Error code = 10003
Can anyone help me with this?
I suppose you are referring to the reduced costs of the variables. You can get them via the variable attribute RC as explained here. And then you need to figure out whether these dual values are corresponding to the upper or lower bound as discussed here.
The command model.most_similar(positive=['france'], topn=100) gives the top 100 most similar words to "france". However, I would like to know if there is a method which will output the most similar words above a similarity threshold to a given word. Is there a method like the following?:
model.most_similar(positive=['france'], threshold=0.9)
No, you'd have to request a large number (or all, with topn=0) then apply the cutoff yourself.
What you request could theoretically be added as an option.
However, the cosine-similarity absolute magnitudes don't necessarily have a stable meaning, like "90% similar" across different model runs. Their distribution can vary based on model training parameters, such as the vector size, and they are most-often interpreted only in ranked-comparison to other pairwise values from the same model.
For example, the composition of the top-100 most-similar words for 'cold' may be very similar in models with different training parameters, but the range of absolute similarity values for the #1 to #100 words can be quite different. So if you were picking an absolute threshold, you'd likely want to vary the cutoff based on observing the model, or along with other model training metaparameters.
Well, let's say you can. Try the following code:
def find_most_similar(model, wrd, threshold=0.75):
res = [item for item in model.wv.most_similar(wrd, topn=len(model.wv.vocab)) if item[1] > threshold]
return res
I am trying to estimate a maximum likelihood model and it is running into convergence problems in Stata. The actual model is quite complicated, but it converges with no troubles in R when it is supplied with appropriate starting values. I however cannot seem to get Stata to accept the starting values I provide.
I have included a simple example below estimating the mean of a poisson distribution. This is not the actual model I am trying to estimate, but it demonstrates my problem. I set the trace variable, which allows you to see the parameters as Stata searches the likelihood surface.
Although I use init to set a starting value of 0.5, the first iteration still shows that Stata is trying a coefficient of 4.
Why is this? How can I force the estimation procedure to use my starting values?
Thanks!
generate y = rpoisson(4)
capture program drop mypoisson
program define mypoisson
args lnf mu
quietly replace `lnf' = $ML_y1*ln(`mu') - `mu' - lnfactorial($ML_y1)
end
ml model lf mypoisson (mean:y=)
ml init 0.5, copy
ml maximize, iterations(2) trace
Output:
Iteration 0:
Parameter vector:
mean:
_cons
r1 4
Added: Stata doesn't ignore the initial value. If you look at the output of the ml maximize command, the first line in the listing will be titled
initial: log likelihood =
Following the equal sign is the value of the likelihood for the parameter value set in the init statement.
I don't know how the search(off) or search(norescale) solutions affect the subsequent likelihood calculations, so these solution might still be worthwhile.
Original "solutions":
To force a start at your initial value, add the search(off) option to ml maximize:
ml maximize, iterate(2) trace search(off)
You can also force a use of the initial value with search(norescale). See Jeff Pitblado's post at http://www.stata.com/statalist/archive/2006-07/msg00499.html.