bmr | Bitmessage reader , a bitmessage client | Dektop Application library
kandi X-RAY | bmr Summary
kandi X-RAY | bmr Summary
Bmr is a Bitmessage client written in [Node.js] using the [node-webkit] desktop application framework. It is a self-contained Bitmessage client for reading messages that you can run on your desktop and connect to your Bitmessage server. Bmr is available for Linux, Mac and Windows desktops. The goal of Bmr is to provide a better Bitmessage user experience than the default client.
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bmr Examples and Code Snippets
Community Discussions
Trending Discussions on bmr
QUESTION
I have a picker that updates an variable bmr. the picker in not updating the value. I put a test Text on the second screen to see if I can call the new value, its always showing the default.
...ANSWER
Answered 2022-Mar-26 at 18:18The issue is bmr is an Int? while your tags are Int. Since they are not the same thing, the selection won't update it. The trick is to cast your tag as an Int? like this:
QUESTION
I am trying to build a calorie calculator using HTML and JS and am currently struggling to show the output on screen (or via console.log). I know I'm doing something very basic quite wrong but can't currently pinpoint what that is.
Here's both my HTML and JS code below:
...ANSWER
Answered 2022-Jan-21 at 20:35Try this one, you are almost done, just by getting value from the input when user clicks the button.
But I have to notice you that submit button will immediately redirect to a new page, you should use click instead if you want to show yourself result.
QUESTION
Basically, I made a fragment with a few EditTexts where the user can insert data about himself, such as bodyweight, height, age, etc.. I also added a TextView which displays that person's basal metabolic rate depending on the data that was inserted, but the problem is, whenever I change the person's information, it doesn't automatically update.
I'm assuming this is because the calculations are made inside the onCreateView method, thus it only calculates BMR if I statically pre-insert the respective data.
So, how can I make it so whenever I change the value of a certain variable (f.e age) it will also automatically re-calculate the value of the person's BMR?
JAVA
...ANSWER
Answered 2022-Jan-17 at 13:36You can use TextWatcher for your EditTexts. When user change text in editText then calculate again
QUESTION
# look at my code below
class User:
def __init__(self,name, gender, age, hight, weight, activity):
self.name = name
self.gender = gender
self.age = age
self.hight = hight
self.weight = weight
self.activity = activity
def __repr__(self):
return f'Welcome back {self.name}'
def get_name(self):
return self.name
def get_gender(self):
return self.gender
def get_age(self):
return self.age
def get_hight(self):
return self.hight
def get_weight(self):
return self.weight
def get_activity(self):
return self.activity
def set_age(self, new_age):
self.age = new_age
def set_weight(self, new_weight):
self.weight = new_weight
def set_activity(self, new_activity):
self.activity = new_activity
def daily_calories(self):
if self.gender == 'men':
bmr = int(((66 + (13.8 * self.weight)) + (5 * self.hight)) - (6.8 * self.age))
elif self.gender == 'women':
bmr = int(((655 + (9.6 * self.weight)) + (1.8 * self.hight)) - (4.7 * self.age))
if int(self.activity) == 0:
calories_out = int((1.2 * bmr) * 1.1)
return print(f'\nHellow {self.name} Your body need {calories_out} calories per day')
elif int(self.activity) == 1:
calories_out = int((1.375 * bmr) * 1.1)
return print(f'\nHellow {self.name} Your body need {calories_out} calories per day')
elif int(self.activity) == 2:
calories_out = int((1.55 * bmr) * 1.1)
return print(f'\nHellow {self.name} Your body need {calories_out} calories per day')
elif int(self.activity) == 3:
calories_out = int((1.725 * bmr) * 1.1)
return print(f'\nHellow {self.name} Your body need {calories_out} calories per day')
else:
calories_out = int((1.9 * bmr) * 1.1)
return print(f'\nHellow {self.name} Your body need {calories_out} calories per day')
user1 = User('User1', 'men', 41, 163, 66, 0)
print(user1.daily_calories())
ANSWER
Answered 2022-Jan-01 at 06:38The print function returns None. You are returning that value.
QUESTION
I struggle to understand what exactly happens if I use commands like x <- task. As an example, here is what I do:
- I have a lists of tasks, learners and resamplings and I combine them for benchmarking. As I do the matchin manually, my list of tasks consists of multiple repeats of the same task, e.g. the second and third entry of my list are the same. So, I have no problem to understand what happens in the following lines:
This is understandable. As I refer to the same object, the third entry of my list must change as I manipulate the second (yes, they are the same object)
- Now, I use this lists to generate a benchmark design and execute the resampling:
ANSWER
Answered 2021-Nov-25 at 16:25The tasks, learners and resamplings are cloned when calling benchmark() i.e. a copy of each object is created. The entries in design and tab do not refer to the same objects in memory. Therefore, changing tab$resample_result[[2]]$task does not change list_of_tasks[[2]].
QUESTION
I have a large list of task/learner/resampling combinations. I execute the resampling via
...ANSWER
Answered 2021-Nov-25 at 16:09At least one learner predicted NAs. Search for NAs in the predictions to identify the failing learner.
QUESTION
I am using the benchmark() function in mlr3 to compare several ML algorithms. One of them is XGB with hyperparameter tuning. Thus, I have an outer resampling to evaluate the overall performance (hold-out sample) and an inner resampling for the hyper parameter tuning (5-fold Cross-validation). Besides having an estimate of the accuracy for all ML algorithms, I would like to see the feature importance of the tuned XGB. For that, I would have to access the tuned model (within the benchmark object). I do not know how to do that. The object returned by benchmark() is a deeply nested list and I do not understand its structure.
This answer on stackoverflow did not help me, because it uses a different setup (a learner in a pipeline rather than a benchmark object).
This answer on github did not help me, because it shows how to extract all the information about the benchmarking at once but not how to extract one (tuned) model of one of the learners in the benchmark.
Below is the code I am using to carry out the nested resampling. Following the benchmarking, I would like to estimate the feature importance as described here, which requires accessing the tuned XGB model.
...ANSWER
Answered 2021-Nov-03 at 16:54library(mlr3tuning)
library(mlr3learners)
library(mlr3misc)
learner = lrn("classif.xgboost", nrounds = to_tune(100, 500), eval_metric = "logloss")
at = AutoTuner$new(
learner = learner,
resampling = rsmp("cv", folds = 3),
measure = msr("classif.ce"),
terminator = trm("evals", n_evals = 5),
tuner = tnr("random_search"),
store_models = TRUE
)
design = benchmark_grid(task = tsk("pima"), learner = at, resampling = rsmp("cv", folds = 5))
bmr = benchmark(design, store_models = TRUE)
QUESTION
I would like to monitor the progress of benchmark() in mlr3. Benchmarking several models including hyperparameter tuning on a large data set can take hours or even days. I would like to be able to monitor the progress while benchmark is running, so that I can decide whether or not to abort the benchmark. In addition, if status messages are printed during the process, I can abort the process after some parts are completed and know how long certain steps took. For example, Naive Bayes might have already completed but the hyperparameter tuning for decisions trees is still running (and has been for hours...). That way I could make appropriate changes for the next benchmark run (e.g., limit the search space for decision trees or go with only Naive Bayes).
The problem is that only the first messages ("running resampling instances") is displayed during/at the beginning of the process. The rest only shows up after the full benchmark is completed. In other words, for hours or even days the only status messages displayed is the first one. If one aborts the process, all information about the progress (duration of individual steps) is lost.
This is a very short example. The relevant part of the code is adopted straight from the mlr3 book:
...ANSWER
Answered 2021-Sep-26 at 16:24The reason for this behaviour is the internal call to future.apply::future_mapply(). When using base::mapply(), the output is printed directly.
The latter can be enforced by setting options("mlr3.debug" = TRUE) as shown below.
I've opened a pull request to also force this behaviour in future.apply::future_mapply(), which is the default if options("mlr3.debug" = FALSE).
Here's a temporary workaround
QUESTION
I would like to compare several machine learning algorithms in a classification task using the benchmark_grid() function in mlr3. According to https://mlr3book.mlr-org.com/benchmarking.html benchmark_grid() takes a resampling scheme to partition the date in the task into training and test data. However, I would like to use a manual partitioning. How can I specify training and test set manually when using benchmark_grid()?
EDIT: Code example based on the suggestion by pat-s
...ANSWER
Answered 2021-Aug-16 at 07:24You can use the "custom_cv" resampling scheme.
QUESTION
binding.doneButton.setOnClickListener {
checkValid()
if (valid){
Intent(this, DailyActivity::class.java).also{
it.putExtra("EXTRA_BMI", calculateBmi().round(1))
it.putExtra("EXTRA_BMR", calculateBmr().round(2))
startActivity(it)
}
}
}
ANSWER
Answered 2021-Jun-26 at 04:40Try this, change this Activity with your own code
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