I'm new to both AudioKit and stack overflow, sorry if I'm not asking this in the correct way.
I'm trying to figure out how to modulate the frequency of one AKFMOscillator with the output of another oscillator, so that they would work more like the operators of an FM synth. If I understand correctly
, AKFMOscillators have their own built in modulation source, but I'd like to be able to stack multiple modulators so that I can set up algorithms like on an FM synth, however nothing I've tried so far works.
I've gotten some results doing this:
var oscillatorOne = AKFMOscillator()
var oscillatorTwo = AKFMOscillator()
oscillatorOne.modulatingMultiplier = oscillatorTwo
but that's not really doing the right thing. Is this even possible? Thanks for any advice.
Related
In my python neat code I am using opencv to downscale and covert into gray every frame of the environment. what I want a archive is that opencv opens a window displaying the frame/ video that is it processing.
In short I want to view the the neat algorithm learning and evolving.
Because there are 3 environment running in parallel i want opencv to display the frame/video that is best performing right now.
I am working with the python neat library to do some machine learning tasks. At the moment I am doing parallel learning with 3 threads with the environment of sonic the hedgehog. I have tried to do simple open CV frame commands, but its just opening a black window.
net = neat.nn.FeedForwardNetwork.create(self.genome, self.config)
fitness = 0
xpos = 0
xpos_max = 0
counter = 0
imgarray = []
while not done:
# self.env.render()
ob = cv2.resize(ob, (inx, iny))
ob = cv2.cvtColor(ob, cv2.COLOR_BGR2GRAY)
ob = np.reshape(ob, (inx, iny))
imgarray = np.ndarray.flatten(ob)
actions = net.activate(imgarray)
ob, rew, done, info = self.env.step(actions)
xpos = info['x']
This is the part of the code that downscales the frame and converts it to gray scale.
Bonus if it could only show the frame/worker that is doing the best based on the fitness value.
View full code here: https://gitlab.com/lucasrthompson/Sonic-Bot-In-OpenAI-and-NEAT/blob/master/neat-paralle-sonic.py
by lucasrthompson
The output that I expect is one window that shows the frame/ video of the environment. Awesome
The built it render
self.env.render()
Pops up many many windows with past and present versions of the environment.
thanks
I am writing my own NEAT implementation and I am also testing with OpenAi gym.
You can use wrappers to record the video for you, and this will be the real video, without downscaling or changing colors:
env_wrapped = gym.make('OpenAI-env-id')
env = wrappers.Monitor(env_wrapped, dir , video_callable=record_video_function)
Where the "record_video_function" is a callable which can return true or false when you desire the episode to be recorded.
What I usually do to see the best performing genomes is:
Sort the genomes by fitness
Run the evaluation loop
If a last species champion is next, I change a global variable to True
In the "record_video_function" I return the value of this global variable, so if it's true it will enable the video recording for the episode
After the episode is over, I return this global variable to False
So, with this I can see the best genome performers of last generation. You can't see the best of the current generation because there's no way to know how they will perform. If the environment is deterministic, you would be able to see the best performance in the next generation. If it's stochastic, then it may not be the best anymore.
I need to find the number of times the accelerometer value stream attains a maximum. I made a plot of the accelerometer values obtained from an iPhones against time, using CoreMotion method to obtain the DeviceMotionUpdates. When the data was being recorded, I shook the phone 9 times (where each extremity was one of the highest points of acceleration).
I have marked the 18 (i.e. 9*2) times when acceleration had attained maximum in red boxes on the plot.
But, as you see, there are some local maxima that I do not want to consider. Can someone direct me towards an idea that will help me achieve detecting only the maxima of importance to me?
Edit: I think I have to use a low pass filter. But, how do I implement this in Swift? How do I choose the frequency of cut-off?
Edit 2:
I implemented a low pass filter and passed the raw motion data through it and obtained the graph as shown below. This is a lot better. I still need a way to avoid the insignificant maxima that can be observed. I'll work in depth with the filter and probably fix it.
Instead of trying to find the maximas, I would try to look for cycles. Especially, we note that the (main) minimas seem to be a lot more consistent than the maximas.
I am not familiar with swift, so I'll layout my idea in pseudo code. Suppose we have our values in v[i] and the derivative in dv[i] = v[i] - v[i - 1]. You can use any other differentiation scheme if you get a better result.
I would try something like
cycles = [] // list of pairs
cstart = -1
cend = -1
v_threshold = 1.8 // completely guessing these figures looking at the plot
dv_threshold = 0.01
for i in v:
if cstart < 0 and
v[i] > v_threshold and
dv[i] < dv_threshold then:
// cycle is starting here
cstart = i
else if cstart > 0 and
v[i] < v_threshold and
dv[i] < dv_threshold then:
// cycle ended
cend = i
cycles.add(pair(cstart, cend))
cstart = -1
cend = -1
end if
Now you note in comments that the user should be able to shake with different force and you should be able to recognise the motion. I would start with a simple 'hard-coded' cases as the one above, and see if you can get it to work sufficiently well. There is a lot of things you could try to get a variable threshold, but you will nevertheless always need one. However, from the data you show I strongly suggest at least limiting yourself to looking at the minimas and not the maximas.
Also: the code I suggested is written assuming you have the full data set, however you will want to run this in real time. This will be no problem, and the algorithm will still work (that is, the idea will still work but you'll have to code it somewhat differently).
Is there a more efficient way to reorder columns in a Deedle frame than doing something like the following:
let fr2 = fr.Columns.[["colC"; "colA"; "colB"]]
It seems a bit inefficient, especially given that I have to iterate through a ton of these files every weekend. There is a related question here but doesn't seem to work for me.
How about this:
let df = Frame.ofColumns(df.Columns.Realign(newcolidx))
As this appears to me returning a new df, it might not be ideal for you.
And this Issue still remains open.
I'm pretty much a noob when it comes to this kind of thing, so if you guys could either help me or direct me to a place to learn what I need to know, I would greatly appreciate it.
Basically my problem is that I am using the libpruio library to continuously sample analog values from the board. 2 things are going wrong here.
The first is that whenever the BB is sampling the voltages, the voltage of the wire that is hooked up to the AIN pin goes up. I've observed this through hooking up an oscilloscope to the same wire the pin is sampling. What I see is that whenever the BB starts sampling, the entire signal (just a sound wave from an amplified mic) is shifted up .8-.9 volts. This is also reflected in the values that I get from the BB, which are around 30,000 (when they should be 0). Hooking the pin up to ground gets me 0, which is correct, and hooking it up to 1.8 volts gets me something like 65520, which is also correct. So maybe it has something to do with the signal being weak?
The second issue is that even though I am receiving values at a rate of like 500khz-900khz, the actual rate seems to be around 11khz. What I mean by this is I only get a new value every 88us, and the rest of the values I get are stay the same as the new value until the next 88us passes, when I get a new value. These times correspond to the voltage shift up, which I mentioned in the previous paragraph. So actually what I see on the oscilloscope is that whenever I sample with the BB, there is a saw wave, with the frequency at the 11khz I was mentioning earlier.
In conclusion, whenever the BB samples, it first increases the voltage at the pin by .9volts, takes a sample of that voltage, and the voltage dies down for the next 88us, all the while the BB spits back the sample it took at the beginning of the period. I do not want this. I want it to not affect the voltage significantly, and take new samples every time the code runs.
As for the code I'm using, it's basically a slightly modified version of the IO_Input example in the libpruio library, with the values being stored in an array for later use instead of being printed immediately.
If you guys need any more information, I will gladly post it here, but for now I'm wondering if it is something super obvious that I'm missing.
Hooking the pin up to ground gets me 0, which is correct, and hooking
it up to 1.8 volts gets me something like 65520, which is also
correct. So maybe it has something to do with the signal being weak?
The BBB and libpruio seem to work OK. Check your wiring.
Regarding the sampling rate, the io_input example uses IO mode. If you need accurate timing for the samples use MM mode or RB mode.
Your target isn't very clear, so I cannot give detailed advices. (Some code also would help to understand what you're trying to do.)
BR
After spending over 10 hours to compile tesseract using libc++ so it works with OpenCV, I've got issue getting any meaningful results. I'm trying to use it for digit recognition, the image data I'm passing is a small square (50x50) image with either one or no digits in it.
I've tried using both eng and equ tessdata (from google code), the results are different but both get guess 0 digits. Using eng data I get '4\n\n' or '\n\n' as a result most of the time (even when there's no digit in the image), with confidence anywhere from 1 to 99.
Using equ data I get '\n\n' with confidence 0-4.
I also tried binarizing the image and the results are more or less the same, I don't think there's a need for it though since images are filtered pretty good.
I'm assuming that there's something wrong since the images are pretty easy to recognize compared to even simplest of the example images.
Here's the code:
Initialization:
_tess = new TessBaseAPI();
_tess->Init([dataPath cStringUsingEncoding:NSUTF8StringEncoding], "eng");
_tess->SetVariable("tessedit_char_whitelist", "0123456789");
_tess->SetVariable("classify_bln_numeric_mode", "1");
Recognition:
char *text = _tess->TesseractRect(imageData, (int)bytes_per_pixel, (int)bytes_per_line, 0, 0, (int)imageSize.width, (int)imageSize.height);
I'm getting no errors. TESSDATA_PREFIX is set properly and I've tried different methods for recognition. imageData looks ok when inspected.
Here are some sample images:
http://imgur.com/a/Kg8ar
Should this work with the regular training data?
Any help is appreciated, my first time trying tessarect out and I could have missed something.
EDIT:
I've found this:
_tess->SetPageSegMode(PSM_SINGLE_CHAR);
I'm assuming it must be used in this situation, tried it but got the same results.
I think Tesseract is a bit overkill for this stuff. You would be better off with a simple neural network, trained explicitly for your images. At my company, recently we were trying to use Tesseract on iOS for an OCR task (scanning utility bills with the camera), but it was too slow and inaccurate for our purposes (scanning took more than 30 seconds on an iPhone 4 at a tremendously low FPS). At the end, I trained a neural-network specifically for our target font, and this solution not only beat Tesseract (it could scan stuff flawlessly even on an iPhone 3Gs), but also a commercial ABBYY OCR engine, which we were given a sample from the company.
This course's material would be a good start in machine learning.