I am learning about Embedded Coding on my own and the video I watched said that you could do loop faster if you did two increments in 1 loop then the last increment outside of the loop since it would check the while condition less often.
So my question is, would it be faster to increment by 2 always, and then subtract or add 1 at the end if you exceed your amount.
An example: Let's say I want to perform a method that repeats (i.e. approximate the minimum value of a function by something like newtons method) Instead of doing newtons method once per loop, we do it twice per loop. And let's say we want to do it 21 times because we were able to calculate that our method should converge close enough by roughly that many iterations. Therefore we loop 10x doing it twice per loop, then one last time outside the loop to finish.
Follow up question would be to assume that this is scalable? i.e. (n+1) times per loop > (n) times per loop + one outside of the loop
Related
I'm trying to implement eligibility traces (forward looking), whose pseudocode can be found in the following image
I'm uncertain what the For all s, a means (5th line from below). Where do they get that collection of s, a from?
If it's forward-looking, do loop forward from the current state to observe s'?
Do you adjust every single e(s, a)?
It's unfortunate that they've reused the variables s and a in two different scopes here, but yes, you adjust all e(s,a) values, e.g.,
for every state s in your state space
for every action a in your action space
update Q(s,a)
update e(s,a)
Note what's happening here. e(s,a) is getting incremented by an exponentially decreasing amount. But right before you go into that loop, you increment the single e(s,a) corresponding to the state/action pair just visited. So that pair gets "reset" in a way -- it doesn't get the exponentially smaller update, and on the next iteration, it's update will continue to be larger than all the pairs you haven't recently visited. Every time you visit a state/action pair, you're increasing the weight it contributes to the update of Q for a few iterations.
array={}
size=10
math.randomseed(os.time())
for i=1,size do
array[i]=math.random(size)
if(i>1) then
for j=1,i do
if array[j]==array[i] then
i=i-1
break
end
end
end
end
for i=1,size do
print(array[i])
end
The code above was meant to generate an array of random numbers from 1 to 'size' avoiding repeating values. I tried to achieve that by repeating top level 'for' loop once more if newly generated value was present before somewhere in array - by decreasing its iterator. Somehow it doesn't work. Why?
Is modifying iterator value from inside of loop not possible?
Example output with repeating values in array:
>lua5.1 "pairsss.lua"
2
1
10
6
5
2
5
7
7
4
>Exit code: 0
The solution to your problem is to shuffle the array, like Random iteration to fill a table in Lua.
To answer your question, from Lua 5.1 reference manual:
§2.4.5 – For Statement
All three control expressions are evaluated only once, before the loop starts. They must all result in numbers.
That means, no matter how you change the value of i inside the for loop, it doesn't affect how the iteration is done.
You can use a set instead of an array, as done by the author of question Randomize numbers in Lua with no repeats. As one of the answers points out, as your set gets closer in size to your range of randome numbers (say, you have random numbers 1 to 100 and your set is size 50) it will be more and more difficult to find a number that hasn't already been picked. You can see that for a set of size 50 and picking a random # from 1 to 100, then by the time you have the set half full, you have a 25-50 % chance of finding the random pick is already in use in your set. In that case, shuffling is the way to go, as explained in one of the answers to that post (Randomize numbers in Lua with no repeats).
I have a function that is called three times in quick succession, and it needs to generate a pseudorandom integer between 1 and 6 on each pass. However I can't manage to get enough entropy out of the function.
I've tried seeding math.randomseed() with all of the following, but there's never enough variation to affect the outcome.
os.time()
tonumber(tostring(os.time()):reverse():sub(1,6))
socket.gettime() * 1000
I've also tried this snippet, but every time my application runs, it generates the same pattern of numbers in the same order. I need different(ish) numbers every time my application runs.
Any suggestions?
Bah, I needed another zero when multiplying socket.gettime(). Multiplied by 10000 there is sufficient distance between the numbers to give me a good enough seed.
I have a 2 part question regarding downsampling on OpenTSDB.
The first is I was wondering if anyone knows whether OpenTSDB takes the last end point inclusive or exclusive when it calculates downsampling, or does it count the end data point twice?
For example, if my time interval is 12:30pm-1:30pm and I get DPs every 5 min starting at 12:29:44pm and my downsample interval is summing every 10 minute block, does the system take the DPs from 12:30-12:39 and summing them, 12:40-12:49 and sum them, etc or does it take the DPs from 12:30-12:40, then from 12:40-12:50, etc. Yes, I know my data is off by 15 sec but I don't control that.
I've tried to calculate it by hand but the data I have isn't helping me. The numbers I'm calculating aren't adding up to the above, nor is it matching what the graph is showing. I don't have access to the system that's pushing numbers into OpenTSDB so I can't setup dummy data to check.
The second question is how does downsampling plot its points on the graph from my time range and downsample interval? I set downsample to sum 10 min blocks. I set my range to be 12:30pm-1:30pm. The graph shows the first point of the downsampled graph to start at 12:35pm. That makes logical sense.I change the range to be 12:24pm-1:29pm and expected the first point to start at 12:30 but the first point shown is 12:25pm.
Hopefully someone can answer these questions for me. In the meantime, I'll continue trying to find some data in my system that helps show/prove how downsampling should work.
Thanks in advance for your help.
Downsampling isn't currently working the way you expect, although since this is a reasonable and commonly made expectations, we are thinking of changing this in a later release of OpenTSDB.
You're assuming that if you ask for a "10 min sum", the data points will be summed up within each "round" (or "aligned") 10 minute block (e.g. 12:30-12:39 then 12:40-12:49 in your example), but that's not what happens. What happens is that the code will start a 10-minute block from whichever data point is the first one it finds. So if the first one is at time 12:29:44, then the code will sum all subsequent data points until 600 seconds later, meaning until 12:39:44.
Within each 600 second block, there may be a varying number of data points. Some blocks may have more data points than others. Some blocks may have unevenly spaced data points, e.g. maybe all the data points are within one second of each other at the beginning of the 600s block. So in order to decide what timestamp will result from the downsampling operation, the code uses the average timestamp of all the data points of the block.
So if all your data points are evenly spaced throughout your 600s block, the average timestamp will fall somewhere in the middle of the block. But if you have, say, all the data points are within one second of each other at the beginning of the 600s block, then the timestamp returned will reflect that by virtue of being an average. Just to be clear, the code takes an average of the timestamps regardless of what downsampling function you picked (sum, min, max, average, etc.).
If you want to experiment quickly with OpenTSDB without writing to your production system, consider setting up a single-node OpenTSDB instance. It's very easy to do as is shown in the getting started guide.
I am trying to find out a way to increase the computation time of a function to 1 second without using the sleep function in xilinx microblaze, using the xilkernel.
Hence, may i know how many iterations do i need to do in a simple for loop to increase the computation time to 1 second?
You can't do this reliably and accurately. If you want do a bodge like this, you'll have to calibrate it yourself for your particular system as Microblaze is so configurable, there isn't one right answer. The bodgy way is:
Set up a GPIO peripheral, set one of the pins to '1', run a loop of 1000 iterations (make sure the compiler doesn't optimise it away!) set the pins to '0'. Hang a scope off that pin (you're doing work on embedded systems, you do have a scope, right?) and see how long it takes to run the loop.
But the right way to do it is to use a hardware timer peripheral. Even at a very simple level, you could clear the timer at the start of the function, then poll it at the end until it reaches whatever value corresponds to 1 second. This will still have some imperfections, but given that you haven't specified how close to 1 sec you need to be, it is probably adequate.