hello i have found a code that can (in solidity) divide an amount 50 /50 and send it off my problem is that i need to be able to change the amounts in percentages of a whole amount and i am not sure how to go about this. to clarify i take 10 percent of a transaction and it gets sent to say a tax wallet and I need part of this say 3% to go towards a burn wallet and 2% to a mint wallet and 5% to go to lp I don't need the mint or burn function or auto lp just the method of automatically dividing it up when its deposited into the initial 10%s wallet. thank you ahead of time .
i tried searching for several hours last night and I could only find a script that did 5050 but with no percentages that i could modify on my own .
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I am using the sensors (gyroscope/accelerometer) of smartphones to acquire raw X, Y, X values with time stamps via a sensordemo created. I would like to be able to compare these data points to predefined values.
For example, if a user (female) is standing on one leg for 30 seconds, if she looses her balance at 14 seconds then she is at risk for falling. If she first looses her balance at 19 seconds then she is not at risk for falling.
I need help writing a formula to be able to compare the raw data to predefined model in to a mobile application.
Given the variable 'points' which increases every time a variable 'player' collects a point, how do I logically find a way to reward user for finding 30 points inside a 5 minutes limit? There's no countdown timer.
e.g player may have 4 points but in 5 minutes if he has 34 points that also counts.
I was thinking about using timestamps but I don't really know how to do that.
What you are talking about is a "sliding window". Your window is time based. Record each point's timestamp and slide your window over these timestamps. You will need to pick a time increment to slide your window.
Upon each "slide", count your points. When you get the amount you need, "reward your user". The "upon each slide" means you need some sort of timer that calls a function each time to evaluate the result and do what you want.
For example, set a window for 5 minutes and a slide of 1 second. Don't keep a single variable called points. Instead, simply create an array of timestamps. Every timer tick (of 1 second in this case), count the number of timestamps that match t - 5 minutes to t now; if there are 30 or more, you've met your threshold and can reward your super-fast user. If you need the actual value, that may be 34, well, you've just computed it, so you can use it.
There may be ways to optimize this. I've provided the naive approach. Timestamps that have gone out of range can be deleted to save space.
If there are "points going into the window" that count, then just add them to the sum.
So this is really stumping me, and I think it should just be a linear equation.
Suppose I am charging my client $1000 for a good or service. The client wants to charge this to their credit card. I use Square as my payment processor, and they take a 2.75% cut of the transaction.
This cut is $27.50 for a $1000 sale, so the settlement ends up being only $972.50. Suppose I want the customer to pay the $27.50 fee, but they also want that fee charged to their card.
Well, if I charge $1027.50, Square will take a 2.75% cut of that amount, so their cut will be $28.26, and my settlement would be $999.24.
It's closer to $1000, but not exactly $1000.
I determined on a spreadsheet through trial and error that I'd need to charge $1028.28 to offset the processing fee and get my desired exact settlement.
I've been wracking my brains to think of a simple algebraic equation. It seems like it should just be a single variable, but maybe not?
Welcome to Stack Overflow. Not sure if it belongs on SO (not a programming question), but this is what my arithmetic teacher would say:
You charge X dollars. You want to get $1000 after fees. Fees are 2.75%; so you get 97.25%. Therefore X * .9725 = 1000. X = $1,028.28
I have a setup with a Beaglebone Black which communicates over I²C with his slaves every second and reads data from them. Sometimes the I²C readout fails though, and I want to get statistics about these fails.
I would like to implement an algorithm which displays the percentage of successful communications of the last 5 minutes (up to 24 hours) and updates that value constantly. If I would implement that 'normally' with an array where I store success/no success of every second, that would mean a lot of wasted RAM/CPU load for a minor feature (especially if I would like to see the statistics of the last 24 hours).
Does someone know a good way to do that, or can anyone point me in the right direction?
Why don't you just implement a low-pass filter? For every successfull transfer, you push in a 1, for every failed one a 0; the result is a number between 0 and 1. Assuming that your transfers happen periodically, this works well -- and you just have to adjust the cutoff frequency of that filter to your desired "averaging duration".
However, I can't follow your RAM argument: assuming you store one byte representing success or failure per transfer, which you say happens every second, you end up with 86400B per day -- 85KB/day is really negligible.
EDIT Cutoff frequency is something from signal theory and describes the highest or lowest frequency that passes a low or high pass filter.
Implementing a low-pass filter is trivial; something like (pseudocode):
new_val = 1 //init with no failed transfers
alpha = 0.001
while(true):
old_val=new_val
success=do_transfer_and_return_1_on_success_or_0_on_failure()
new_val = alpha * success + (1-alpha) * old_val
That's a single-tap IIR (infinite impulse response) filter; single tap because there's only one alpha and thus, only one number that is stored as state.
EDIT2: the value of alpha defines the behaviour of this filter.
EDIT3: you can use a filter design tool to give you the right alpha; just set your low pass filter's cutoff frequency to something like 0.5/integrationLengthInSamples, select an order of 0 for the IIR and use an elliptic design method (most tools default to butterworth, but 0 order butterworths don't do a thing).
I'd use scipy and convert the resulting (b,a) tuple (a will be 1, here) to the correct form for this feedback form.
UPDATE In light of the comment by the OP 'determine a trend of which devices are failing' I would recommend the geometric average that Marcus Müller ꕺꕺ put forward.
ACCURATE METHOD
The method below is aimed at obtaining 'well defined' statistics for performance over time that are also useful for 'after the fact' analysis.
Notice that geometric average has a 'look back' over recent messages rather than fixed time period.
Maintain a rolling array of 24*60/5 = 288 'prior success rates' (SR[i] with i=-1, -2,...,-288) each representing a 5 minute interval in the preceding 24 hours.
That will consume about 2.5K if the elements are 64-bit doubles.
To 'effect' constant updating use an Estimated 'Current' Success Rate as follows:
ECSR = (t*S/M+(300-t)*SR[-1])/300
Where S and M are the count of errors and messages in the current (partially complete period. SR[-1] is the previous (now complete) bucket.
t is the number of seconds expired of the current bucket.
NB: When you start up you need to use 300*S/M/t.
In essence the approximation assumes the error rate was steady over the preceding 5 - 10 minutes.
To 'effect' a 24 hour look back you can either 'shuffle' the data down (by copy or memcpy()) at the end of each 5 minute interval or implement a 'circular array by keeping track of the current bucket index'.
NB: For many management/diagnostic purposes intervals of 15 minutes are often entirely adequate. You might want to make the 'grain' configurable.
I am writing an application which is recording some 'basic' stats -- page views, and unique visitors. I don't like the idea of storing every single view, so have thought about storing totals with a hour/day resolution. For example, like this:
Tuesday 500 views 200 unique visitors
Wednesday 400 views 210 unique visitors
Thursday 800 views 420 unique visitors
Now, I want to be able to query this data set on chosen time periods -- ie, for a week. Calculating views is easy enough: just addition. However, adding unique visitors will not give the correct answer, since a visitor may have visited on multiple days.
So my question is how do I determine or estimate unique visitors for any time period without storing each individual hit. Is this even possible? Google Analytics reports these values -- surely they don't store every single hit and query the data set for every time period!?
I can't seem to find any useful information on the net about this. My initial instinct is that I would need to store 2 sets of values with different resolutions (ie day and half-day), and somehow interpolate these for all possible time ranges. I've been playing with the maths, but can't get anything to work. Do you think I may be on to something, or on the wrong track?
Thanks,
Brendon.
If you are OK with approximations, I think tom10 is onto something, but his notion of random subsample is not the right one or needs a clarification. If I have a visitor that comes on day1 and day2, but is sampled only on day2, that is going to introduce a bias in the estimation. What I would do is to store full information for a random subsample of users (let's say, all users whose hash(id)%100 == 1). Then you do the full calculations on the sampled data and multiply by 100. Yes tom10 said about just that, but there are two differences: he said "for example" sample based on the ID and I say that's the only way you should sample because you are interested in unique visitors. If you were interested in unique IPs or unique ZIP codes or whatever you would sample accordingly. The quality of the estimation can be assessed using the normal approximation to the binomial if your sample is big enough. Beyond this, you can try and use a model of user loyalty, like you observe that over 2 days 10% of visitors visit on both days, over three days 11% of visitors visit twice and 5% visit once and so forth up to a maximum number of day. These numbers unfortunately can depend on time of the week, season and even modeling those, loyalty changes over time as the user base matures, changes in composition and the service changes as well, so any model needs to be re-estimated. My guess is that in 99% of practical situations you'd be better served by the sampling technique.
You could store a random subsample of the data, for example, 10% of the visitor IDs, then compare these between days.
The easiest way to do this is to store a random subsample of each day for future comparisons, but then, for the current day, temporarily store all your IDs and compare them to the subsampled historical data and determine the fraction of repeats. (That is, you're comparing the subsampled data to a full dataset for a given day and not comparing two subsamples -- it's possible to compare two subsamples and get an estimate for the total but the math would be a bit trickier.)
You don't need to store every single view, just each unique session ID per hour or day depending on the resolution you need in your stats.
You can keep these log files containing session IDs sorted to count unique visitors quickly, by merging multiple hours/days. One file per hour/day, one unique session ID per line.
In *nix, a simple one-liner like this one will do the job:
$ sort -m sorted_sid_logs/2010-09-0[123]-??.log | uniq | wc -l
It counts the number of unique visitors during the first three days of September.
You can calculate the uniqueness factor (UF) on each day and use it to calculate the composite (week by example) UF.
Let's say that you counted:
100 visits and 75 unique session id's on monday (you have to store the sessions ID's at least for a day, or the period you use as unit).
200 visits and 100 unique session id's on tuesday.
If you want to estimate the UF for the period Mon+Tue you can do:
UV = UVmonday + UVtuesday = TVmonday*UFmonday + TVtuesday*UFtuesday
being:
UV = Unique Visitors
TV = Total Visits
UF = Uniqueness Factor
So...
UV = (Sum(TVi*UFi))
UF = UV / TV
TV = Sum(TVi)
I hope it helps...
This math counts two visits of the same person as two unique visitors. I think it's ok if the only way you have to identify somebody is via the session ID.