 So we're making a bot that will play this ECG game. So it's showing you an abnormality score, which later, yes, we can turn into an actual response of it making a decision. It's had an abnormality of 0.9. This wasn't 0.56. This is wrong. Okay. Yeah, but change something like this time. The abnormality for normal ECG, not like this, but with another way from there should be 0.1. Okay, this is great. But can I first share the entire code with you and then you make your comments and things on the whole thing that is except this HTML. There's an additional two JavaScript files and Java, Python, Bec, and Flask application. Okay, might want to turn the funny few notches down. Maybe keep it to one joke at a time. Yeah, I forgot about the colors. Originally the waveform was changing color if you were making a wrong decision. But I don't think we do that anymore. It's okay to leave it as is for now. Here is the Bec and Flask application. It's like finding Waldo, but in waveforms. Fuzzy logic for analysis. Using fuzzy logic for data analysis is like having a wise sage making decisions not always black and white, but often right. Okay, I just realized it's actually working correctly. It's just that there is a learning element of it is that the thresholds, so say the amplitude thresholds change over time as new waveforms come in. So I just realized I was wrong before the abnormalities score is actually working correctly. It is showing 0.1 for a normal ECG. It just seems like it needs some time for the thresholds to stabilize to work correctly. Could you explain that bit? And also there seems to be a problem that sometimes the normalized amplitude output is not showing a value. So even after the algorithm was running for a while and it got all the possible waveform types, I'm getting this output. Why is the normalized amplitude is not being calculated in this case? Well, it's a given that the maximum value of 349 returns a normalized amplitude of 1, but then for the lowest amplitude waveform with a raw amplitude of 199, I get no output for the normalization function. Would it help to not have the global mean amp and the global max amp variables to be plus and minus infinity? It will definitely work better than what we currently have. We get 0.6, 0.5 for 288, 0.1 for 349, which is the maximum amplitude that we have. We still get nothing for 199. Do we get any errors? No errors. I wonder if it meant to improve over time. So I still get no value for 199. I mean, I'm happy to assume that the minimum amplitude is zero. Shall we do that? Can you explain how is it different from what we are currently doing? New method. This is like always starting from the floor and seeing how high you can jump, ignoring how low you can go. We're just using the maximum value for normalization and assuming the minimum is zero, a bit like measuring how high you can stack pancakes without worrying about how thin they can get. Now you made me hungry. Okay, this is not cool. Don't use any emojis. Cape response is short. Can you rewrite the normalized function? Look at the original code. It should have three inputs. And what did you recommend? Every time you mention something like not to do, it then picks up on that words. It's as if it's surely understand the context of not doing something as in do not use emojis. So in our current data set, we don't have the edge cases. We probably should add them. Let's see if it may be better. Okay, now we get a value for 199, which is 0.57, 0.57. Okay, so this is better. Okay, this is abnormal. That's the highly abnormal of 0.9, 0.5. Yes, this bit. So just trying to make sure this makes sense. Otherwise we have to change it. If someone known like reason for like an unknown period of time, I guess until it got all the possible waveforms for a normal ECG, we get a 0.56, 0.56 abnormality value, which is a not right. I think later it's correcting itself to show 0.1. So for normal, a waveform, not like this. Yeah, that 0.56 values seem to be like some sort of default. Mean frequency could also be zero. Yeah, this 0.56, 424 should not be shown for normal ECG. Right, so not showing 0.1, which is what we want. So there is like a learning thing happening, but this learning thing is fake. It's just, it's a simple, there's no actual like machine learning there or anything, it's just adjusting those thresholds because they are none to begin with. I'm just thinking. So for some time in the beginning, we get this result for a normal ECG, whereas later the score of abnormality for normal ECG goes down to 0.1 as expected. Considering this is not a real machine learning, can we adjust the initial global minimum and maximum variables? I'm happy for all the minimums to be zero and all the maximum to say be 1,000 should that fix this problem. Okay, later when we add more waveform types of different abnormalities and different variations, it would be interesting to see if the fuzzy logic is still working correctly or not. Probably not. We currently have 356 lines of code, including spaces, including empty lines, carriage returns, whatever they're called. Are we currently using the abnormality membership function? What membership functions are we using currently? Isn't the abnormality membership function an input or output? Also, how would you recommend using it? Ideally, it will make the decision. Currently, your system uses membership functions for four input variables, frequency, amplitude, positive peaks and negative peaks. Would you actually make a suggestion as to how we can use them? I'm thinking in the game logic JavaScript on the front end, in the same way we're doing a score calculation for the human clicking on the waveform, can we make a calculation of how well the fuzzy logic algorithm is playing the game and calculate its score in a similar way and display it on the front end, on the right side. So we turn it into a competition. Okay. Can you look at the code we have so far and make the necessary adjustments? If you prefer, you can generate the prompts for GitHub co-pullet to change the code. Everyone need to help integrating this thing. How do we do this? The idea just to be clear. Is in addition to the current game score, which is the human score, have a score by an AI, the machine learning bit. But it will be, well, the obvious placement is the right side. If the human score is on the left. So in JavaScript, we have score. They both start the same. Later we can give a human an advantage of whatever thousand points in this game. Yeah, human has no chance. The speed as well is important because the machine obviously can do it. They just say it is getting the raw data that the human is seeing on the screen. So the score should be calculated in the same way. Okay. So I added the initialization for the fuzzy logic system score. Can you look at the specific code that we have so far and give more specific responses? I need help integrating the changes you suggested into the code. Or shall I get the GitHub co-pullet to do it? I did upload the code before. I don't know why saying you don't have access to it. Here is the game logic again. Now, shall we plot the fuzzy logic? The machine learning a score in exactly the same way as the human one, just on the other side of the page. Also, are you going to be using the fuzzy membership function to make a yes, no decision equivalent to the human clicking on the waveform? Where is that thing being drawn? It's a control shift. See, it's being drawn on top of the canvas, right? It's actually happening in JavaScript. It's not the HTML bit. So this will be the human score, right? And on the other side of the screen, we should have a machine score the way. Are we doing the actual scoring business? It's different. We want it to be identical. And there's a limit of the score not to drop below minus 50. Then incorrect rejection and misses. It will do the fuzzy, the both scores separately as in in the code, but then it has to make sure we're doing everything exactly in the same fashion. I mean that the points are getting exactly the same. So the threshold, yeah, we split it because it says score, the abnormality score is between zero and one, we just split the threshold into 0.5. Well, our way to look at it is whether we have normal or abnormal. So we could have anything. No, no, no. Okay, so this is the big thing. Okay, one big important thing is that, I don't know, okay, I misunderstood. So the front end actually generating the waveform, so it knows what the result is. So essentially the JavaScript is the judge. Is that correct? Does that make sense? We need to decide what the threshold is. So essentially in our case, normal ECG is 0.1 and anything else. So anything above 0.1 should be considered abnormal ECG. How do we do that? So we receive the data, we display analysis result, that's the human analysis. So this would be display human analysis result. Now I would like the same function, but for the fuzzy logic algorithm, so for the machine. I don't understand why the display fuzzy analysis result need two inputs. To save, it's also receiving data. This is wrong. This is wrong. Go back, go back. Made a mistake. We receive data updating the fuzzy score, displaying analysis result. Okay, shouldn't this return something? Yeah, the machine seems to make some mistakes to begin with. Because for normal, it's still giving 0.36, but it's doing better later. Okay, the fuzzy logic score should be using the abnormality score taken from the fuzzy logic algorithm and displaying a score for the machine. Okay, how do we display the score on the canvas in the same way as the score for clicks is being displayed? Okay, so the first one is the human score. The second one is the machine score. Okay, now I'm playing against the machine. Obviously it's much faster, already lagging behind. Okay, that's 50-50. Oh, I'm winning. I'm winning. I'm winning. I'm winning the machine for now. It's abnormal. I'm way ahead. Should I 110? This is normal. I do not click on. Okay, I'm winning quite a bit, assuming I'm a human. I'm doing much better. Supposed to the algorithm. Okay, now it's becoming faster. It's normal. Most double the score. The machine is stuck at 100 and I'm almost at 300. Okay, the machine score is going down. 350, 270, 390, like winning by far. Okay, that's normal. Abnormal, abnormal, abnormal. Yeah, I'm winning the robot. Normal, abnormal. Okay, now it's becoming harder, like because it's super quick. Normal, normal, abnormal, normal, abnormal. Oops, the machine stuck at 200. Okay, I don't even have enough. Oops, time to click on it. Oops, I was clicking on a normal eG. I'm still at 800. 828, 30. Oops, missed it. It is crazy fast now. Yeah, I reached at 1,000. Oops, clicked on the normal. I didn't doing 1300. Now, obviously one advantage for the machine is that it can go forever. And it's picking up score as well. Oops, and I'm making mistakes because I'm out of focus already. And I should know this data set because I created it. Oops, just way too fast. I think I actually beat my own score. Oops, clicked on normal. Yes, you can see my breakdown of all the mistakes and things. I'm making a bunch of normals. Okay, I'm getting all died. My eyes hurt. The bot is picking up score. It's making mistakes as well. So what if the human just can't handle it anymore? It is something actually wrong with the bot. There are no point ones. Should be point ones for normal. There should be point ones for normal waveform. Yeah, no point ones, which is odd. Okay, now the bot is winning. But then, yeah, the bot is getting it incorrect every time. There is a normal because the number is not going down. As much as it should. Let's start again. So now it will go 30. It's abnormal. It will go 30, 40, sorry. Now the question is, for normal ECG, it's getting point three. Pretty sure it was getting point one before. Now the question is, is it getting bad with time? I don't know. No, that point three is not correct. Yeah, point three, six. Yeah, normal, it's point three, six. Yeah, if I make this value point three, six instead of point one. What's the exact number? Point six, point three, six, one or something. It is a magic number. It's abnormal, the same. Yeah. So if I tune this thing, yeah, it won't be making any mistakes anymore. That was a quick tune. And I think the human is Tansky because once it becomes faster, yeah, the machine is not making mistakes anymore. Just by adjusting the abnormality score threshold, I can try, but I won't be able to bid it. It's okay. My score is a bit delayed, but it's still the same. Did I really make a mistake? No. Yeah, once I start making mistakes, once these reds go, counters go up, I'm done. Humanity, zero. Robots, one. Well, haven't happened yet, but it's fairly obvious. Yeah, because every 50 points, it starts going faster for me. The pot making much quicker decisions. And once I make the first mistake, I won't be able to recover because the pot is not making any mistakes with the current setup. Let's focus because we will need a lot of focus. Okay. Now it's becoming significantly faster for me. The pot is getting the same information. Just getting the way through. Oh, yeah, it's getting the waveform. Yeah, I made one miss. That's making decisions much faster. It can go forever, and it doesn't make any mistakes. That was tricky. It was a bunch of normals, and then the slightly abnormal one. Oops, false alarm. Okay, crap. Okay, now it's just way too quick for me even to click on it. Yeah, the pot is winning. Doesn't make any mistakes. Yeah, I need to slow down before I can start again, as in before I can even see the waveforms. We'll see the pot just getting them in raw form. It's looking at the features, frequency, positive peaks, negative peaks, and amplitude. It's extracting the features by itself, and normalizing all the values between zero and one, using a fuzzy logic algorithm to generate an abnormality score. And then we just set the threshold to this 0.365. And that's it. Humans go out the door in terms of reviewing ECG. Or if we still review ECG, we'll need a lot of luck. Or at least we won't be able to do it this fast. We can do it slowly, but yeah. So the pot currently has doubled the score already, because it's not making any false alarms and misses, and I already made quite a lot. Well, yeah. So I can have my food now, just watch the pot doing its job. Just sit back and relax. I think it's pretty obvious for this round who won, well first, because I just gave up. Now it's slow again, so I can start labeling. But the robot doesn't make any mistakes. It kept going, so it doesn't get tired. And it's already reached a score of 7,000, which is several folds higher than what I could do. So even if I start labeling again, I made all these mistakes for false alarms and 332 misses. Whereas the fuzzy score, the machine, the robot, only did hits and correct rejections. Let's start a brush. The fuzzy count is draw waveform and draw score. Okay, so this bit, let's make it 40. They will overlap. And that one, let's make it say 100,000. Oops, got to save, make it outside the canvas. Should we make it easier for the human to make the canvas larger? Yeah, so much of how it works on different screens. Make it 500, so it fits the screen. Then this hit should be the same blue at the field text, hit, correct rejection, false alarm misses. Can the bot do this kind of stuff for me? Or should I do it manually? 80, 120, 200, It's 160, right. Notice numbers should be 500. And yes, we could. Yeah, I have no idea how it will be displayed on a mobile, actually. So that's how it will look like on my screen. It doesn't look too bad on a mobile. At least on my mobile. So yeah, it makes sense for it to only go high, to go faster when the human is doing better. I mean, for the fuzzy logic algorithm, it doesn't make any difference whatsoever. It doesn't make any mistakes with the current setup. Generally, we don't like magic numbers. So ideally this would be automated. Yeah, so essentially this could show you how machines are better at these specific tasks at annotating time, serious waveforms. If you are human and you are engaged at the task, it will then become faster and faster. The bot will just keep doing its thing. It doesn't mind. It's making the decision essentially immediately. So it doesn't mind how fast or quick the scrolling is on the screen. It gets the same waveform as you are. So it doesn't know that the algorithm, this score here, the abnormality, this prediction is being made by the backend that doesn't have access to the waveform generation business. And yes, we can make it harder for the bot by introducing more types of waveforms. So if you have any suggestions as to what did I forget? What type of abnormal ECG I should include, do let me know. Definitely the machine is winning by miles as opposed to human assuming I'm human. You should not make this assumption because I can talk like this as well. Okay, let's go back a couple prompts. Okay, so it's working. Okay, working. Okay, now I still get this for normal ECG, normal activity from your system. So the rule might be too sensitive or not accurately reflecting the nuances of a normal ECG. We did go over the rules several times. I thought they were working well. Yes, we need to find a threshold for classifying normal versus abnormal ECG. Now we actually manually look at the number and we set the threshold as a magic number in the code. But we don't want to do this. This is the updated game logic and I'll be sharing the Flask Python script in the next prompt. You've updated the game logic to include scoring for both the human player and the fuzzy logic system. The setup for handling scores, hits, misses and other counters for both players looks well structured. You've also included a threshold for the fuzzy logic system to determine abnormal waveforms, which is key for making the comparison fair and interesting. What do you think about actually using the membership functions from the back end from Python code to actually return a decision? Yes, no, normal, abnormal. In addition to the abnormalities core, would it make more sense to do it in the back end instead of the front end? Why not use the abnormality membership function? Isn't that what it's there for? Yes, we have the abnormality membership function. The membership functions are unlikely, possible, likely. All we need to decide for the yes and no is what do we do with the possible and unlikely is essentially 0 to 0.3, a possible is 0.2 to 0.8, which is a problem because currently for the normal thing, we still get 0.36. So ideally the algorithm the bot shouldn't be dealing with magic numbers, should be dealing with concepts. I mean, if you have three membership functions, you might as well split them, you know, third, split 0 to 1, 3 ways. Yeah, the human score, if it goes minus 50, then it stops displaying the negative value. Jeez, that's kind of, well right now it recovered from it. So that's how well I'm doing at scoring ECG waveforms as opposed to a robot. Yes, we need to check how generalizable the robot's algorithm is. So essentially if we add a more different types of waveforms, we want to see what happens to this performance. That would be a next stage. Currently we're just trying to remove any unnecessary magic numbers from the code. This 0.365 is currently 0.365 is the perfect threshold. If we make it 0.4 something, 0.37, we'll start making mistakes. Let it run for a while. Okay, because I'm getting 0.364 normal, but I think one of the abnormals has a very similar number. Yeah, I think it's the one that missing that negative peak. No, that one is getting 0.5. Right, this one getting 0.46, 0.37. Yeah, so if we go 0.377, we'll start making mistakes, but it will always do the one waveform type incorrectly. Yeah, we expect for one of the waveforms for it to do a miss, yeah, for this one. And I already did two miss as well because I wasn't paying attention. So the robot will always miss that one waveform type. So yes, ideally those fuzzy rules and membership functions should be just designed based on textbook knowledge and not actually looking at any waveforms. That's enough backend. Okay, there's no questions in the chat, never mind, finish up and I'll see you next time. Bye.