 See if we have anything running at all, this should be a bit louder. Just moving the microphone around, see what difference it makes. Yeah, we should be here live, start in a second, yeah, I'll just assume all the streams are working. The microphone, yeah, we seem to be using the correct mic, that's good. This session is meant to be interactive, so please do comment, questions, suggestions, especially if you checked Bionicales.com, that will be highly appreciated. We'll start in a sec, start developing in a second. But yes, as you imagine, this session is meant to be interactive, so do make comment suggestions. So this last stream will actually be potentially better if you do minimize all the windows that I don't need anymore. What Chrome is using quite a bit of CPU, and that's a bit odd, I'll do, yeah. The last streaming on three platforms, YouTube, was where I always, that's the platform of choice, a priority. I'm still trying to get monetized on YouTube. Then we have Twitch, because someone suggested, said it's much better, I don't know, highly doubt it, but okay. And then I'm last streaming on LinkedIn, which is the least convenient one because, for example, it won't remember the stream settings. You can just go live in with one click of a button to do a whole process of generating a new event and stuff. We'll start developing in a second. Hopefully more people will join in. Why do I need to actually monitor the stream in various places just taking up bandwidth? But it seems like there's no option to turn it off and just leave it as is. Okay, just one technical, I'll start with the technical side of things. Just a comment on... So I'm using Visual Studio Code, even though it's a Microsoft product, obviously, using it in Linux. It doesn't really help. One thing with GitHub, co-pilot, chat. Let's start a new session. Just realize, open a new window. So this is... I used to have the whole project open like this. And then I will try to use GitHub chat, co-pilot. What will happen is if I use the workspace, which meant to look at multiple files, not just one, it will give me an error. So we'll try it now. Say, project structure. Yeah, this is opening the home, binary chaos, stuff, all the files and everything. So it's collecting workspace structure, deciding which information to collect. So essentially looking at which files to look at and then eventually was always giving me this array buffer location failed. So the way to fix it is actually select a smaller chunk of your project or not have big projects to begin with, will also help. And when you have a small project, if I do project structure on less number of files, it's again doing collecting structure, which workspace information to collect and actually working. So it's telling me that my structure is the follows. I have a Python flask application static folder in which I have two JavaScript files. We also have a template folder in which we have an index HTML. So it's done that it's done that the flask application file and folder structure. It gives you some suggestions. Where is the the if you're loading too many to large project too many files, then it just refuses to work with some buffer allocation issues, essentially tells you it's too much stuff. Close this for now. Eventually everything I do a piece on buying case.com. Mg to music conversion is still the last tool that we had. We also have that gesture to music conversion thing. But that one doesn't have been properly published yet. Still working with a description, thumbnails, things like that, the boring, boring stuff. Let's get into it. We have this flask application we are working on. So might as well run it if it does anything useful or not. We start off this game, ECG detection game. What you meant to do in this game is you have ECG waveforms scrolling on the screen and you're meant to click on ones that are abnormal. It should be pretty straightforward, especially when you know what normal ECG looks like. It doesn't look like that. This is abnormal ECG, so I keep getting hits. This is also abnormal if I don't click on it, I get a miss. This is also abnormal if I get another miss. This is abnormal if I click on it, I get a hit. So greens are good, reds are bad. Another hit. Waiting for a normal one. I don't remember what the ratio is. This should be 4060. I think normal to abnormal. Yeah, so that was normal. I correctly rejected it. Why do I get errors? Right, so what we want to do now and the advantage of having synthetic data like this, synthetic ECG is that you could design algorithm and AI machine learning or whatever that will play this game. And then you can compete with a machine to see who is better, especially if you are a cardiologist or ECG technician. So we'll be competing against the bot who can detect this thing better. This thing we also designed using a bot. We're currently using a fuzzy logic algorithm to train some machine. It's an explainable AI machine learning algorithm that we can actually tell what is it doing. And later we might be using neural networks as well, which pretty much everyone under the sun is using these days. Might do that as well. Just got a false alarm when clicking on a normal ECG. And my score went down. Okay, so we also started GPT-4. So currently we have this structure. We have both GPT-4 and GitHub co-pilot. What we do is use GPT or the general context of things. It seems to be better at keeping a broader context of the project. We might have here, let's do quick format document. And comment at the end saying a more code is coming in the next prompt. Assuming GPT is aware what's the current prompt and what's the next prompt is. I really can tell we're having some errors coming through that's happening for this analysis result. So this analysis result is a result of fuzzy logic analysis. This code snippet demonstrates a fast application designed to analyze waveform data using fuzzy logic. It's well structured and modular, which is great for maintenance and scalability. Key points. Global variables. These are used to track the minimum and maximum values of frequency, peaks and amplitude. It's a sensible approach for normalization, although you need to be cautious about thread safety and concurrency issues in a web server context. Flask roots. You have defined two roots, a home root and an analysis root. Analyze underscore waveform. This structure is clear and follows Flask standard practices. Data processing. The process underscore data function uses FFT for frequency analysis, peak detection and amplitude calculation. This is a solid approach for waveform analysis. Just ensure your incoming data format is consistent with these expectations. Fuzzy logic analysis. You're using psychic fuzzy for fuzzy logic analysis, which is a good choice for Python. The definition of membership functions and rules seems appropriate for a basic system. However, the effectiveness of these rules will depend on the specific application and data characteristics. Overall, your code is well organized and seems fit for the purpose of waveform analysis in a biomedical context. You might want to add more error handling, especially if the data inputs are during the FFT and peak detection processes. Additionally, instead of security implications of receiving and processing data in a web application. Currently in the app Python file, it will find where is the file actually located. So I have the chppt response. It's talking about global variables. We have this analyze waveform. Flask roots. Pretty sure we have more of them. Check them in a second. In this signal, we're looking at three features at the moment. We're looking at frequency amplitude and the number of positive and negative peaks. So ideally we want to display them all in here. Just keep going to HTML. Next. The other problem with GPT-4 is that eventually it will give us a timeout. Your HTML code for the CardioQuest ECG scoring game is well structured and seems designed to provide a good user experience. Here are some observations and suggestions. Metatags. You've included meta tags for social media sharing, open graph tags, which is great for visibility and shareability. Sorry, I forget that it was actually talking to us. Sorry for that. I was talking over it. Both audio outputs were going into the stream. Unfortunately at the moment when I record the video locally as well. But unfortunately the two audio streams are just mixed together into one. That's not good. I'll try and correct it later. Let's do the waveforms next. So it knows what the waveforms are. Well then just so we don't stop overloading GPT-4. We will not be generating code with it. We'll be asking you to generate prompts from what we discussed. And then I'll use a GitHub co-pilot for actually doing the code. We have the game logic as well. Just format this document quickly. Let's give you some general stuff about the code. Which we already went through previous streams, but I'm happy to go over it again. We'll go over the relevant bits for what we're trying to do. Generally, yeah, explain what the game is doing. It's available on the site. You can go try it out, play with it. And now we're making a board that will play the game. And then potentially can compete with the board. So at the bottom here is meant to be the result of the fuzzy logic analysis. There's essentially a machine learning that will now be going over. I'll explain more about how it works. Yeah, this error here. Yeah, I'm pretty sure I know why it's happening. Just open this full screen. This patch. Yeah, we're using... Let's forget that Ctrl C is what happens with Windows users. If you press Ctrl C, in this case it will stop the execution of the Flask application. Instead of just copying the terminal results. So I'm looking at... What am I looking at? This bit. Ctrl Shift C. Let's pop it into... I think it might be too much for Copilot to handle. We'll check it anyway. Let's bring it back here. So we have the game logic. I did say canvas, click event handling, game loop, regression with background result display, performance. All right, we have this thing where as your score goes higher, the stuff on the screen starts moving faster. So I think from memory every 50 points, the speed increases. So now we minus 50, so it goes back. If you're losing points, then it goes back to be slower again. Okay, I might not use the Bluetooth headphones that are going low on battery. Air handling. It's always suggesting to do more air handling. So the error message indicates that the phasologic system is unable to calculate a crisp output because the system too sparse. We'll need more explanation on this one. I know vaguely what this means. My hypothesis is that the outputs are not normalized. So essentially the display of how the phasologic is doing its decisions. We also have... I thought we have a CSS, but it's okay. We'll deal with CSS later. If no one wants to chat... Unfortunately, I'll have to chat with the stone cold soulless bot. Clear. I might turn this speaker on quickly. Clear. So we have this flask application to troubleshoot how the phasologic system is working. Essentially how it is making decisions regarding the output. Can you recap what the phasologic system meant to be detecting? Yep. Your flask application, which incorporates fuzzy logic analysis, is designed to analyze ECG, electrocardiogram, waveforms. The purpose of this system is to detect abnormalities in ECG patterns. Here's a recap of how it's meant to work and what it's detecting. Input waveforms. The system takes ECG waveforms as input. These waveforms represent the electrical activity of the heart and are typically characterized by distinct components like P-waves, QRS complexes, and T-waves. Dominant frequency, extracted using Fast Fourier Transform, FFT. It represents the most prominent frequency component in the waveform. Number of peaks, detected using peak detection algorithms in the Skidby Library. Peaks can correspond to the various waves in the ECG, like the P, Q, R, S, and T-waves. Amplitude, calculated as the difference between the maximum and minimum values in the waveform. It indicates the strength of the heart's electrical signal. Normalization, the extracted features are normalized against global minimum and maximum values to ensure consistency and comparability between different waveform samples. The application uses Psychic Fuzzy to implement the fuzzy logic system. Fuzzy sets and membership functions are defined for each feature, frequency, peaks, and amplitude, and for the output variable, which is the level of abnormality. Fuzzy rules are established to determine how the input features contribute to the output. For example, a rule might state that if the frequency is high and the number of peaks is low, then the likelihood of an abnormality is high. The system outputs a score or value indicating the likelihood of an abnormality in the ECG waveform. This score is derived from the fuzzy rules and membership functions, representing the system's assessment of how abnormal the waveform is. Purpose, detection of abnormal ECG patterns. The primary goal is to identify patterns that deviate from normal ECG characteristics, which might indicate various cardiac conditions or anomalies. To troubleshoot or fine-tune the system, you might need to adjust the membership functions, re-evaluate the fuzzy rules, or look into the normalization process to ensure it's handling the data correctly. It's also important to test the system with a wide range of ECG data, including both normal and abnormal waveforms, to assess its accuracy and reliability. Your system's effectiveness in detecting ECG abnormalities will largely depend on the accuracy of the feature extraction process, the appropriateness of the fuzzy logic rules, and the quality of the input data. Okay, so this is correct. Sounds legit. Just a recap of what we are doing. So, yes, we're looking at the dominant frequencies using FFT. The number of peaks then could indicate if this number is lower than PQRST 1, 2, 3, 4, 5, then it's highly likely that the ECG is abnormal. Amplitude could be tricky, because if the amplitude is way too high, it could mean there is some sort of noise, but if it's too low as well, it could mean that the system is just not working, that no ECG is being generated. This would not necessarily mean abnormal ECG, but if the amplitude is within a certain range, then it should be okay. Specifically, could somehow, by looking at the amplitude, it would be primarily looking at the R peak, or because that's the highest peak in the ECG signal. Now, normalization is very important, because membership functions will need to be designed accordingly. So the question is if normalization is working as expected, because from memory we had this adaptive normalization that will just get improved as you have more waveforms coming through. The main thing is that we want to be able to review on the front panel, on the HTML page how the fuzzy logic system is making the actual decision. So we need to display essentially all the parameters of the fuzzy logic system. Can you, shall we, would it be better for you to provide prompts for GitHub co-pilot to do the actual coding and improve the current script? Yep, sorry for that, it's just a long one, but... Certainly. Integrating a front-end display to show how the fuzzy logic system in your Flask app makes decisions is a great idea. This will improve transparency and understanding of the system for users. To achieve this, you'll need to modify both the back-end, Flask app, and the front-end, HTML, JavaScript. Here are prompts that can be used to guide further development, potentially with GitHub co-pilot or other coding assistance tools. Enhance the JSON response. Modify the analyze underscore waveform route to include more detailed information in the response. This should include the raw feature values, frequency, peaks, amplitude, their normalized values, and details about the fuzzy logic decision-making process, like the degree of membership in different fuzzy sets. Prompt. Modify Flask route to include detailed fuzzy logic analysis data in the response, such as raw and normalized feature values, and fuzzy set memberships. Prompt. Modify Flask route to include detailed fuzzy logic analysis data in the response, such as raw and normalized feature values, and fuzzy set memberships. Logging or debug mode. Implement a logging or debug mode in the Flask app that provides detailed console outputs of the fuzzy logic processing steps. Prompt. Create a debug mode in Flask app to log the steps in fuzzy logic analysis for troubleshooting. Display analysis data. Extend the front-end to display the additional data received from the back-end. This could be in the form of a table or a series of divs that update with each analysis. Prompt. Write JavaScript code to dynamically update the HTML page with detailed analysis data received from the Flask back-end. Prompt. Write JavaScript code to dynamically update the HTML page with detailed analysis data received from the Flask back-end. Interactive visualization. Implement an interactive way to visualize the fuzzy logic data, perhaps using a library like D3, JS. This could include graphs showing the degree of membership in different fuzzy sets for the current waveform. Prompt. Develop an interactive visualization for the fuzzy logic analysis data using D3, JS or a similar JavaScript library. User control for debug information. Add a toggle on the web page that allows users to turn on, off the detailed analysis display. This enhances the user experience by providing control over the amount of information displayed. Prompt. Document a feature on the web page allowing users to toggle the visibility of detailed fuzzy logic analysis information. Test with diverse data. Ensure that the system is tested with a wide range of ECG waveforms to validate the effectiveness of the fuzzy logic analysis and normalization. Okay, let's see if it's legit. Particularly, so in the back-end, yes, it makes sense to include the other details about the fuzzy logic system in the JSON response. What do we currently... Where's the JSON? Right, so only including... Okay, can we do this one step at a time? Can we modify the back-end first? What prompts do we need to modify the back-end? Yep, let's do it one by one. Certainly, let's break down the task of modifying the back-end step by step. You will need to enhance the Flask application to provide more detailed information about the fuzzy logic analysis in its response. Here are the specific prompts to guide the modification of the back-end. Expand the data processing function, modify. Modify the process underscore data function to return not only the normalized values, but also the raw values of frequency, peaks, and amplitude. Update the fuzzy underscore logic underscore analysis function to include details about how the input values map onto the fuzzy sets. This can involve capturing the degree of membership in each fuzzy set for each input feature. Okay, okay, let's do this one by one. So updating the process data function. It already returns just the normalized peaks. I want to see how the normalization works. Just do that. So it's returning the raw and normalized. That's fine. The thing is, you do ctrl-I update to return both raw and normalized frequency, peaks, and amplitude. Okay, let's accept that. So expect the thing to stop working. It's not running at all. It's still working, but not producing any analysis result. That's okay. Now fuzzy logic analysis. We want to enhance the fuzzy logic analysis function to include and return the degree of membership for each input feature in its respective fuzzy set. So doing ctrl-I, that prompt, change anything there. So instead of return result does more. Right, so we have frequency membership, low, medium, high, amplitude, low, medium, high, and a number of peaks, a few, moderate, and many. Yeah, okay, maybe, okay. See how we go. So where the test should be a separate thing, shouldn't it? Let me try it out quickly. So the unit test should be a separate file, is it? Asking the test module, this test will bother the output of the fuzzy logic analysis with a variety of SCG waveforms. And let's do a test-py. How does testing work? Test, okay. It's not giving me much, is it? Yeah, because it doesn't even have the test waveforms. So the test waveforms actually come from waveforms.js. So this would not work. Okay, let's leave it for now. Okay, the testing doesn't work because the actual waveforms are in waveforms. The actual waveforms are in waveforms JavaScript file. So Python testing would not work. Let's move on, though, to the front end changes. Understood. Since the waveforms are generated and processed within the JavaScript environment, Python-based testing wouldn't be directly applicable. Let's move on to modifying the front end to display the detailed fuzzy logic analysis from the flask back end. Here are the steps and corresponding prompts for making these front end changes. Create HTML elements for displaying analysis data, add. Add new elements in your HTML page where the analysis data will be displayed. This could be a series of divs at a table. Update JavaScript to handle extended response. Modify the JavaScript that handles the response from the flask back end to extract and display the additional analysis data. Okay, let's do one at a time and I wonder if it will understand the context. So, for example, I'm wondering if I'm in the Python code and I pop this prompt in. What would it do? No, it's giving it the... Okay, let's actually go HTML. Let's do Ctrl I over here. Delete that bit. Okay, that's not it though. Instead of that, yeah, the chat... GitHub Copilot Chat response was better. Okay, we'll sort this in a bit. Let's do the JavaScript next. So, the JavaScript fetch request. This will be in game logic. It's a fetch. And let's do it with Ctrl I. Analysis result, analysis data. Back for a sec, select the whole thing and put the prompt in the chat. I'm just trying to understand how it all works together. What doesn't. Ctrl Shift Z. Analyze waveform, content type, body chase and stringify. Okay, obviously that's better. Yeah, optional interactive visualisation. We'll do it later. Total feature on the web chat in Russian. Allowing users to show high-detail fuzzy logic analysis information. Yeah, we'll do that later. Okay, I don't understand how something like that... Like this is a good prompt. It's not running at the moment, is it? Yeah, because we're doing the test there now. It should be actually running. This number is to not produce anything. Let's click this to know which file is the problem with. Can we do slash workspace? And this error might need you to determine where the problem is. Or even better, explain it. There's a type mismatch. The frequency input. The data frequency is flawed, but the universe max is a dictionary. Yes, it should be a numerical value. So if it is a dictionary, so what should it be? Data frequency of raw. It's a good question. It's a good question. What keeps up? Have a better explanation? Normally does. The error you're encountering is a type error in the fuzzy logic analysis function of your flask application. It seems to be occurring when you're trying to set the input values for your fuzzy logic control system. The error message type error, less than, not supported between instances of float and dict, indicates that there's a mismatch in the data types being compared or assigned. The issue seems to be with this line, abnormality underscore analysis, input, frequency, equals data, frequency. The data frequency is expected to be a float, but it seems to be a dict instead. Let's address this. Check the process underscore data function, E. Verify data passed to fuzzy underscore logic underscore analysis. Before calling fuzzy underscore logic underscore analysis, processed underscore data, add a print statement to check the contents of processed underscore data. This will help you confirm that the data is as expected. If processed underscore data contains the correct values but in the wrong format, like a dict, you may need to extract the values and convert them into floats before passing them to the fuzzy logic system. Modify the way inputs are set in the fuzzy underscore logic underscore analysis function. Ensure that each input variable, like frequency, peaks, amplitude, is set to a float value. Add error handling in the fuzzy underscore logic underscore analysis function to catch type mismatches and log helpful error messages. By implementing these steps, you should be able to diagnose and fix the issue causing the type error in your flask application. Remember, the goal is to ensure that the data types being passed to and used within the fuzzy logic system are consistent and as expected. Let's go to those processed data. Okay, let's print it. Yet the normalization doesn't work. So we have frequency, rho is 0, normalize 0, peaks, rho is 2, normalize 0, amplitude. Yet those do not work, let's see. The issue is now clear from the debug information you've provided. The processed underscore data contains dictionaries for each feature, frequency, peaks, amplitude, with both rho and normalized values. However, the fuzzy underscore logic underscore analysis function expects float values, not dictionaries. Here's how you can resolve this. Update your fuzzy underscore logic underscore analysis function to handle the dictionary format of processed underscore data. You need to extract the normalized values, or rho values, depending on your fuzzy logic design, from each dictionary and pass them as floats to the fuzzy logic system. Extract the correct values. Before setting the inputs for the fuzzy logic system, make sure to extract the correct values, in this case, normalized values, from the processed underscore data dictionary. Ensure correct data types. Confirm that frequency underscore val, peaks underscore val, and amplitude underscore val are float values. This is crucial for the fuzzy logic system to function correctly. Update the backend accordingly. Apply these changes in your flask application, ensuring the fuzzy underscore logic underscore analysis function receives the data in the expected format. By implementing these changes, your application should correctly process the waveform data and avoid the type error you are encountering. Make sure to test with various data inputs to ensure robustness. Yeah, this doesn't make sense. This doesn't make sense. Yeah, probably. Maybe there's a problem with how I use GPT-4 and GitHub co-pilot, whereas GPT-4 is generating prompts for GitHub co-pilot. The problem is that the GPT doesn't have the... Okay, so I was using GitHub co-pilot to change the code. I don't know if it's a good idea or not. What do you think? Here is the updated Python script. The whole app by file. Using GitHub co-pilot for coding can be helpful, especially for automating repetitive tasks, generating boilerplate code, or getting suggestions on how to implement certain functionalities. However, there are a few things to keep in mind. Accuracy in context, co-pilot generates code based on patterns it has learned from a vast corpus of code. It might not always understand the specific context or requirements of your project. Therefore, it's crucial for review and understand the code it suggests thoroughly. Customization and optimization, the code generated by co-pilot may need to be customized or optimized for your specific use case. It's important to adapt the suggestions to fit your application's architecture and performance needs. Debugging and validation, like any code, what co-pilot generates... Okay, okay, okay, okay. Yes, it might be an issue if using it this way. Regarding your updated Python script for the Flask application, it looks like you've made significant changes to handle the fuzzy logic analysis and the data returned. The script now appears to process the ECG data, apply fuzzy logic analysis, and then extract the degree of membership for each input feature. This is a good approach for providing more detailed analysis results. However, ensure that in the fuzzy underscore logic underscore analysis function, the data passed abnormality underscore analysis in better floats. You might need to modify these lines. These lines should extract the normalized float values from the data dictionary, similar to what was discussed in the previous solution. Okay, we need to do the same. We need to do the same for the game logic JavaScript file. Share the updated script in the next prompt. Please let me know how to change it as I'm still getting errors. This is the output of the terminal. Yep. Yeah, I'm really going to hit the 54 limit. The area you're encountering, object of type state per simulation is not JSON serializable. Typically occurs when trying to convert an object to JSON that isn't natively serializable by Python's JSON module. In your flask app, this is likely happening because the return value from the fuzzy underscore logic underscore analysis function includes objects that can't be directly converted to JSON. The issue probably lies in the attempt to include the fuzzy logic system state or other complex objects in the JSON response. Python's JSON module can handle basic data types like strings, numbers, lists, and dictionaries, but it cannot serialize custom objects like those from scikit fuzzy without some additional processing. You need to extract only the serializable data from these objects before including them in the JSON response. Typically, this means converting the relevant information into standard Python data types like floats, ints, lists, or dicks. Here's how you can modify the fuzzy underscore logic underscore analysis function. Extract relevant data. Instead of trying to return the entire state objects, extract only the necessary information, like the degree of membership values, and store them in a serializable format, like a dictionary. Return serializable data. Ensure the function returns a dictionary or a similar serializable structure. After making these changes, ensure that the rest of your flask app, especially the root handling the analysis, is updated to handle the modified return structure of fuzzy underscore logic underscore analysis. This should resolve the serialization error and allow your flask app to return a proper JSON response. To ensure that your flask app handles the modified return structure of fuzzy underscore logic underscore analysis, you need to update the root that calls this function. Here's a step-by-step guide on how to do this. Update the root to handle the new root. This should resolve the serialization error and allow your flask app to return a proper JSON response. Can you actually do this for me instead of... To ensure that your flask app handles the modified return structure of the fuzzy underscore logic underscore analysis function correctly, you need to update the root that calls this function. Update the root to handle the new return structure. The analyze underscore waveform root should be modified to handle the dictionary returned by the fuzzy underscore logic underscore analysis function. Extract and format the response data. Extract the relevant data from the dictionary returned by fuzzy underscore logic underscore analysis and format it into a JSON serializable format. Here's an updated version of the analyze underscore waveform root incorporating these changes. Error handling, the try accept block will catch any exceptions, providing a useful error message and a 500 status code. This is crucial for debugging and ensuring your app doesn't crash on unexpected inputs. Debugging, the print statement for process data is helpful for debugging. You can remove it or comment it out in a production version of your app. Data validation, ensure that the data being processed and returned is validated. This is important for both the security and stability of your application. Testing, after making these changes, test the endpoint with various inputs to ensure it behaves as expected and the front end receives the data in the correct format. By updating the root as shown, your flask app should correctly handle the modified structure returned by the fuzzy underscore logic underscore analysis function and provide the necessary information to the front end for display and further processing. Okay, so I think that error actually went away. Yeah, now we're actually having trouble with the action with the numbers themselves. Yeah, because if we have, I think we don't have the technical issue anymore. But now we have a problem with the numbers themselves, particularly with how the normalization works. Well, it's actually doesn't work very well. Here is the terminal output really going to hit that limit. The issues you're encountering seem to stem from two main areas, the normalization process and the handling of fuzzy logic analysis in your flask application. Let's address these one by one. The normalization process seems to be not working as expected, with some values being normalized to zero or one, which might not be accurate. The normalization function is crucial as it ensures that the input values for the fuzzy logic system are scaled appropriately. Revisit normalization logic, check the logic in your normalize function. Ensure it correctly handles the range of values you expect for frequency, peaks, and amplitude. Be particularly cautious about how the minimum and maximum values are updated and used. Data range consideration, consider the range of values your ECG data might have. Ensure that the global min max values are being updated in a way that makes sense for your data. For example, if the range of your data is not wide enough, it might lead to incorrect normalization. The error float argument must be a string or a real number, not state per simulation indicates a mismatch in the expected data type for the fuzzy logic systems inputs. The other error regarding crisp output calculation suggests that the fuzzy rules might not be adequately defined for the given inputs. Correct data types for fuzzy inputs ensure that the inputs to the fuzzy system are floats. The error suggests that one of the inputs might still be a state per simulation object. Double check the fuzzy underscore logic underscore analysis function to ensure that all inputs to the fuzzy system are being correctly extracted and converted to floats. Okay, I might have to say bye-bye for now. Please leave comments, likes, go check bodykills.com. You know what to do. And might just continue what we are formally done with the stream. And the normalize function to be corrected def normalize value. Pretty sure we changed that last time. Right, someone is asking, yeah, we could probably sum up by explaining again what is it we are trying to do. Basically, we have this game on bodykills.com. You can go on the main page and play it yourself. It looks like this to click on abnormal ECG like this. And then your score goes up and this will measure your performance. This looks very much abnormal. Remember the ratio between normal. No, this looks normally G. So if I ECG, so when I do not click on it, it will give me a correct rejection, which is good. This should be another correct rejection. So my score went to 10. This is abnormal. So that's I clicked on it. So that's a heat. This is normal. Now correct rejection. Normal again. Correct rejection before. This one is normal. They're coming in a row. And once you get to 50, it will start going faster just because you're an expert in detecting ECG types. This is obviously abnormal. Every 50 points will start going faster. That was abnormal as well. This is abnormal. It was missing one of the peaks. This is abnormal. It's missing the last peak. This is abnormal. It's missing this downward peak after the R peak. And so on so forth. So you can go play this game. We have a video where I play this game and get to about 1,000 points. So see if you can beat me. It will get faster and faster. It should be easy once you know what the normal ECG signal looks like. Then it's easy. You can easily detect the ones that are missing certain bits. Now what we're trying to do in the last couple of streams is make a version of this that will look at the frequency, number of peaks, amplitude. And use the machine learning in the form of fuzzy logic, fuzzy set membership functions. And we'll tell you if the abnormality is likely possible or unlikely in this particular waveform that you have scrolling on the screen. So that's what we're trying to do. Obviously have some trouble. We get off GPT-4. We have both GPT-4 and GitHub co-pilot trying to help us. GPT-4 has a limit of currently 40 messages per 3 hours. So we're trying to just get it to generate prompts for GitHub co-pilot. And co-pilot will actually modify the code. We're doing this because GPT-4 seems to hold a better context for the whole project. Whereas co-pilot has the, you know, better, it's set up better for actually modifying the code. So I don't know if this is a good idea or not. What do you think? Yep. Using GPT-4 in conjunction with GitHub co-pilot for your project is a creative and effective strategy. Here's a breakdown of how these tools can complement each other. GPT-4's strengths, GPT-4 excels in understanding context, providing explanations, generating ideas, and offering prompts or high level guidance. It can give a broader perspective on your project, suggest best practices, and help in troubleshooting by generating targeted prompts for coding tasks. GitHub co-pilot strengths, GitHub co-pilot is more focused on code generation and completion. It can take the prompts or ideas generated by GPT-4 and turn them into concrete code snippets. It's particularly useful for quickly producing boilerplate code, implementing standard algorithms, and filling in gaps in your code based on context. Combining their capabilities Use GPT-4 for guidance and problem solving. You can leverage GPT-4 to understand complex issues, get advice on best practices, and generate prompts to describe what you want to achieve in your code. Use GitHub co-pilot for code implementation. Once you have a clear idea or prompt from GPT-4, you can use GitHub co-pilot to actually write the code. Co-pilot can rapidly generate code snippets based on the prompts, which you can then fine-tune or integrate into your project. Advantages of this approach Efficiency, this method can save time, as you get the best of both worlds, strategic guidance and practical implementation. The normalization process seems to be not working as expected, with some values being normalized to 0 or 1, which might not be accurate. The normalization function is crucial as it ensures that the input values for the fuzzy logic system are scaled appropriately. I didn't realize we had yet a normalized function. It's been funny. Do we actually have to normalize? Can we actually try not normalizing? This will probably mean we need to change the membership functions, particularly the range, obviously. The range, the range, range. Deciding not to normalize your data before feeding it into the fuzzy logic system is a significant change and comes with its own set of considerations. Here's a breakdown of what this entails and the steps you might need to take. Impact on membership functions. If you choose not to normalize, your membership functions in the fuzzy logic system must be defined to accommodate the actual range of your data. This means that the universe of discourse, the range over which your fuzzy sets are defined, for each input variable must align with the real-world range of these variables. Potential loss of generality. Normalization often helps in making a system more general and robust to variations in input scales. Without normalization, your system might become more sensitive to specific ranges of data and might not perform well if the input data characteristics change over time. Risk of misclassification. The effectiveness of fuzzy logic heavily relies on how well the membership functions represent the data. Oh, come on, just stop the talking. Okay, you convinced me. Let's do the normalization. Can you describe how the normalization currently working and give suggestions as to how to modify it? Yep. Certainly. Let's review how your current normalization process works and explore potential modifications to improve its effectiveness. Your current normalization function appears to linearly scale each value based on the global minimum and maximum values for each feature, frequency, peaks, amplitude. The process is as follows. Calculation. The function calculates the normalized value by subtracting the minimum value from the current value and then dividing by the range, max, min, python copy code def normalize, value, min underscore val, max underscore val. Return, value, min underscore val, max underscore val, min underscore val, if max underscore val equals min underscore val L0. Global min, max updates, whenever a new data point is processed, the global minimum and maximum values for each feature are updated if the new data point exceeds the current ranges. Edge cases. The function handles the edge case where the maximum and minimum values are the same, to avoid division by zero, returning zero in such cases. Static range problem. If the initial waveforms are not representative of the full range of possible data, the normalization might not scale new data correctly. Sensitivity to outliers. The normalization is sensitive to outliers, which can skew the global min, max values, leading to ineffective normalization for subsequent data points. Initial values. Setting the initial global min to float, inf, and max to float, inf, works well for updating with actual data, but it assumes the first data points are representative. Predefined range. If you have prior knowledge of the expected range of your data, consider setting initial min, max values based on this range instead of using the first data points. Handling outliers. Implement a method to detect and possibly exclude outliers from impacting the global min, max values. This could involve statistical methods like calculating the standard deviation and ignoring values that fall too far from the mean. Smoothing or windowing. Instead of updating global min, max with each data point, consider using a rolling window or a smoothing technique to update these values. This can help in reducing the impact of sudden spikes or drops in the data. Dynamic adjustment. Implement a dynamic adjustment mechanism where the normalization range can be updated based on the distribution of a certain number of recent data points, not just the global extremes. Avoid zero division. Enhance the edge case handling where min and max are equal. For instance, you might return the midpoint of your expected range instead of zero, or handle this case based on the specific requirements of your application. Logging and monitoring. Implement logging to monitor how the normalization values change over time. This can provide insights and help in fine tuning the process. Here's a simple example of a dynamic adjustment mechanism. In this example, rolling underscore window keeps track of the last window underscore size data points, and the global min, max are updated based on this window. This approach can help in mitigating the impact of outliers and provide a more stable normalization over time.