Predictor | Crown Width/Root Flare Predictor
kandi X-RAY | Predictor Summary
kandi X-RAY | Predictor Summary
To use this tool maven is required, and a species.xml file. This xml file holds the species definitions. The path to the file should be set using the SPECIES_FILE_PATH environmental variable. Example:.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Calculate the collision
- Gets the name
- Find a species by its name
- Gets the root cost
- Performs DBH calculation
- Returns the value in bits
- Gets the crown width
- Get the list of species
- Finds all the species
- Returns the species with the given name
- Calculate the crown width of a species
- Calculates the pit flare for a species
- Get value in hex format
- Gets the valueInches property
- This method is called after all fields have been set
- Deserialize the species list
- Serializes the species list to a file
- Gets the home page
Predictor Key Features
Predictor Examples and Code Snippets
Community Discussions
Trending Discussions on Predictor
QUESTION
When I try to get the console.log with
...ANSWER
Answered 2022-Mar-16 at 18:52I basically fixed it with an additional js
QUESTION
I am checking out the mgcv
package in R
and I would like to know
how to update a model based on new data. For example, suppose I have the
following data and I am interested in fitting a cubic regression spline.
ANSWER
Answered 2022-Mar-07 at 00:46Here is a brief example
- Create your
smoothCon
object, usingx
QUESTION
I am working in R but have been validating my results in Stata and through doing so have observed that predict
in R is not ignoring my offset from my Poisson model. Let me explain:
I have fitted the following model in R - to model excess mortality as opposed to simply mortality (ExpDeaths is the expected deaths given each subjects age, sex, and period based on the general population and logExpDeaths in the Stata code shown next is just the natural log of ExpDeaths):
...ANSWER
Answered 2022-Feb-25 at 14:06When you call nooffset
you are simply subtracting the offset from the linear predictor.
QUESTION
I have two GAMs which have the same predictor variables but different independent variables. I would like to combine the two GAMs to a set of plots where the smooth component (partial residuals) of each predictor variable are in the same panel (differentiated with e.g. color). Reproducible example:
...ANSWER
Answered 2022-Feb-18 at 17:55If you want them in the same plot, you can pull the data from your fit with trt_fit1[["plots"]][[1]]$data$fit
and plot them yourself. I looked at the plot style from the mgcViz
github. You can add a second axis or scale as necessary.
QUESTION
I am benchmarking the following code for (T& x : v) x = x + x;
where T is int
.
When compiling with mavx2
Performance fluctuates 2 times depending on some conditions.
This does not reproduce on sse4.2
I would like to understand what's happening.
How does the benchmark workI am using Google Benchmark. It spins the loop until the point it is sure about the time.
The main benchmarking code:
...ANSWER
Answered 2022-Feb-12 at 20:11Yes, data misalignment could explain your 2x slowdown for small arrays that fit in L1d. You'd hope that with every other load/store being a cache-line split, it might only slow down by a factor of 1.5x, not 2, if a split load or store cost 2 accesses to L1d instead of 1.
But it has extra effects like replays of uops dependent on the load result that apparently account for the rest of the problem, either making out-of-order exec less able to overlap work and hide latency, or directly running into bottlenecks like "split registers".
ld_blocks.no_sr
counts number of times cache-line split loads are temporarily blocked because all resources for handling the split accesses are in use.
When a load execution unit detects that the load splits across a cache line, it has to save the first part somewhere (apparently in a "split register") and then access the 2nd cache line. On Intel SnB-family CPUs like yours, this 2nd access doesn't require the RS to dispatch the load uop to the port again; the load execution unit just does it a few cycles later. (But presumably can't accept another load in the same cycle as that 2nd access.)
- https://chat.stackoverflow.com/transcript/message/48426108#48426108 - uops waiting for the result of a cache-split load will get replayed.
- Are load ops deallocated from the RS when they dispatch, complete or some other time? But the load itself can leave the RS earlier.
- How can I accurately benchmark unaligned access speed on x86_64? general stuff on split load penalties.
The extra latency of split loads, and also the potential replays of uops waiting for those loads results, is another factor, but those are also fairly direct consequences of misaligned loads. Lots of counts for ld_blocks.no_sr
tells you that the CPU actually ran out of split registers and could otherwise be doing more work, but had to stall because of the unaligned load itself, not just other effects.
You could also look for the front-end stalling due to the ROB or RS being full, if you want to investigate the details, but not being able to execute split loads will make that happen more. So probably all the back-end stalling is a consequence of the unaligned loads (and maybe stores if commit from store buffer to L1d is also a bottleneck.)
On a 100KB I reproduce the issue: 1075ns vs 1412ns. On 1 MB I don't think I see it.
Data alignment doesn't normally make that much difference for large arrays (except with 512-bit vectors). With a cache line (2x YMM vectors) arriving less frequently, the back-end has time to work through the extra overhead of unaligned loads / stores and still keep up. HW prefetch does a good enough job that it can still max out the per-core L3 bandwidth. Seeing a smaller effect for a size that fits in L2 but not L1d (like 100kiB) is expected.
Of course, most kinds of execution bottlenecks would show similar effects, even something as simple as un-optimized code that does some extra store/reloads for each vector of array data. So this alone doesn't prove that it was misalignment causing the slowdowns for small sizes that do fit in L1d, like your 10 KiB. But that's clearly the most sensible conclusion.
Code alignment or other front-end bottlenecks seem not to be the problem; most of your uops are coming from the DSB, according to idq.dsb_uops
. (A significant number aren't, but not a big percentage difference between slow vs. fast.)
How can I mitigate the impact of the Intel jcc erratum on gcc? can be important on Skylake-derived microarchitectures like yours; it's even possible that's why your idq.dsb_uops
isn't closer to your uops_issued.any
.
QUESTION
I am reading this book by Fedor Pikus and he has some very very interesting examples which for me were a surprise.
Particularly this benchmark caught me, where the only difference is that in one of them we use || in if and in another we use |.
ANSWER
Answered 2022-Feb-08 at 19:57Code readability, short-circuiting and it is not guaranteed that Ord will always outperform a ||
operand.
Computer systems are more complicated than expected, even though they are man-made.
There was a case where a for loop with a much more complicated condition ran faster on an IBM. The CPU didn't cool and thus instructions were executed faster, that was a possible reason. What I am trying to say, focus on other areas to improve code than fighting small-cases which will differ depending on the CPU and the boolean evaluation (compiler optimizations).
QUESTION
ANSWER
Answered 2022-Feb-08 at 09:02You are mixing up the order of y_true
and y_pred
in brier_score
. Here is a working example:
QUESTION
How can I determine variable importance (vip package in r) for categorical predictors when they have been one-hot encoded? It seems impossible for r to do this when the model is built on the dummy variables rather than the original categorical predictor.
I will demonstrate what I mean with the Ames Housing dataset. I am going to use two categorical predictors. Street (two levels) and Sale.Type (ten levels). I converted them from characters to factors.
...ANSWER
Answered 2022-Jan-19 at 20:03From the caret
documentation, we see that variable importance in linear models corresponds to the absolute value of the t-statistic for each covariate. So, we can manually compute it, as I do in the code below.
lm()
automatically converts categorical variables as dummies. So, to get the importance of each covariate, we have to sum over dummies. I did not find a way to automate this, so if you want to apply my solution to a different set of variables, you need to be careful in choosing the items of t.stats
to be summed.
Finally, we can use results for plotting. I just used the baseline function for a bar plot, but you can customize it as you want (maybe also using the ggplot2
package for better visualization).
Ps when you provide a reproducible example, remember to load all the needed packages.
Pps summing over dummies may be sensitive to the base level of the dummy we are using (i.e., the level we omit from the regression). I do not know if that could be an issue.
QUESTION
there has been a similar question to mine 6 years+ ago and it hasn't been solve (R -- Can I apply the train function in caret to a list of data frames?) This is why I am bringing up this topic again.
I'm writing my own functions for my big R project at the moment and I'm wondering if there is an opportunity to sum up the model training function train()
of the pakage caret
for different dataframes with different predictors.
My function should look like this:
ANSWER
Answered 2022-Jan-14 at 11:43By writing predictor_iris <- "Species"
, you are basically saving a string object in predictor_iris
. Thus, when you run lda_ex
, I guess you incur in some error concerning the formula
object in train()
, since you are trying to predict a string using vectors of covariates.
Indeed, I tried the following toy example:
QUESTION
I am new to R. I am hoping to write a function that will scale all numeric columns in my data frame except for specific numeric columns (in the example below, I do not want to scale the column 'estimate'). Because of the particular context this function is being used in, I actually want to scale the data using another data frame. Below is an attempt that did not work. In this attempt original.df represents the data frame which needs to be scaled, and scaling.data represents the data used for scaling. I am trying to center the numeric original.df columns on the mean of the corresponding scaling.data columns, and divide by 2 standard deviations of scaling.data columns.
Additional information that may not be essential to a working solution:
This function will be nested in a larger function. In the larger function there is an argument called predictors, which represents the column names which need to be included in the new data frame, and are also found in the scaling data frame. This could be the vector used to iterate over for the scaling function, though this is not necessarily a requirement. (Note: This vector includes column names which reference columns that are both character and numeric, again I want the function to scale numeric columns only. The final product should include the unscaled 'estimate' column from original.df).
...ANSWER
Answered 2021-Dec-19 at 22:24We can do the following (I'm using dplyr
1.0.7 but anything >= 1.0.0 should work):
Create a function that scales
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Predictor
You can use Predictor like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Predictor component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page