 start of this game, ECG detection game. What you're meant to do in this game is you have ECG waveforms scrolling on the screen and you're meant to click on once the ab normal. So it should be pretty straightforward especially when you know what the normal ECG looks like. It's not 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. 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 40-60. I think normal to abnormal. So yeah, so that was normal. I correctly rejected it. 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 see who is better especially if you would be especially interesting if you are cardiologist or ECG technician. So we'll be competing against the bot scene. 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 this day so 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-pallet. What we do is use GPD for the general context of things. It seems to be better at keeping a broader context of the project. You might have here let's do quick format document. Okay in the 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. Yeah I already can tell we're having some errors coming through that's happening for this analysis result. So this is this analysis result is a result of fuzzy logic in the app Python file. It will find where is the file actually located. So I had the chat GPT response. It's talking about global variables. We have this analyze waveform. Flust 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. Let's do the waveforms next. Just so it knows what the waveforms are. Well then just we don't stop overloading GPT4. 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. She 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, live 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 it's 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 oh. Let's forget that control C is what happens with Windows users. As if you press control C in this case it will stop execution of the flask application instead of just copying the terminal results. So I'm looking at what am I looking at this bit. A control shift C. Let's pop it into. I think it might be too much for co-pilot to handle. So I have the game logic, I did say canvas, click event handling, game loop, regression with background result display, performance. Alright 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. Error handling. Yeah it's always suggesting to do more error handling. Just okay. Okay so the error message indicates that the fuzzy logic system is unable to calculate a crisp output because the system too sparse. Okay we'll need more explanation in this one. I know vaguely what this means. It's essentially my hypothesis is that the outputs are not normalized. So essentially the display of how the fuzzy logic is doing its decisions. So we have this flask application we need to troubleshoot how the fuzzy logic system is working. Essentially how it is making decisions regards the output. Can you recap what this fuzzy logic system meant to be detecting? 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 SkiBee 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 and anomalies. 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 then 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, 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 phasic logic system is making the actual decision. So we need to display essentially all the parameters of the fuzzy logic system. Would it be better for you to provide prompts for GitHub co-pilot to do the actual coding and improve the current script? 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安全 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 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 D3JS. 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 D3JS 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 implement 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. So in the back end yes it makes sense to include the other details about the fuzzy logic system in the JSON response. And let's do this one by one. So updating the process data function, it's already returns just the normalized peaks. And we'll see how the normalization works. Okay, should we just do that. So it's returning the raw and normalized. That's fine. Thing is, can we do control I update to return both raw and normalized frequency peaks and amplitude. 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. Right, so we have frequency membership low, medium, high, amplitude, low, medium, high, and the number of peaks, a few moderate and many. So the unit test should be a separate file, is it? Ask unit test module, this test will validate the output of the fuzzy logic analysis with a variety of ECG waveforms. That's okay. It's not giving me much is it? Yeah, because it doesn't even have the test waveforms. The test waveforms actually come from waveforms JS. Get the testing doesn't work because the actual waveforms are in waveforms JavaScript file. So Python testing would not work. Let's move on, though, to the front and the changes. Since the waveforms are generated and processed within a 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 fast back end. So the JavaScript fetch request. This will be game logic. It's a fetch. Let's do it with control I, analysis result analysis data. Let's go 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 or doesn't. To control shift z, analyze waveforms, content type, body chase and stringify. Okay, obviously that's better. 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 process data. Okay, let's print it out. Yet the normalization, obviously it doesn't work. So we have frequency, row is zero, normalize zero, peaks, rows two, normalize zero, amplitude. Yet those do not work. Let's see. The issue is now clear from the debug information you provided. The process underscore data contains dictionaries for each feature, frequency, peaks, amplitude, with both raw 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. Does that make sense? 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 pass to our normality underscore analysis, inverter 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. We need to do the same for the game logic JavaScript file. I'll 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. 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. 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. Here's a step-by-step guide on how to do this. 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 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 except 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. Yes, so I think that error actually went away. And 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. 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. 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, 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 system's inputs. The other error regarding crisp output calculations suggests that the fuzzy rules might not be adequately defined for the given inputs. Basically, we have this game on balicures.com, which you can go on the main page and play it yourself, and you need to click on abnormal ECG like this, and then your score goes up, and this will measure your performance. This looks very much abnormal. Don't remember the ratio between normal and normal. This looks normal ECG, 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 I clicked on it. So that's a hit. This is normal, now a correct rejection. Normal again, correct rejection, 40. This one is normal coming in a row, and once you get to 50, it will start going faster, just because you are an expert in detecting ECG types. This is obviously abnormal. Yeah, every 50 points, it 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 and 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 should be easy once you know what the normal ECG is. The signal looks like, then 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 frequency, number of peaks, amplitude, and use 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 have both GPT-4 and GitHub co-pilot trying to help us. We're doing this because GPT-4 seem to hold a better context for the whole project. Whereas co-pilot has the better for actually modifying the code. So I don't know if this is a good idea or not. What do you think? 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. Right, I didn't realize we had... Yeah, the normalized function has 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. Okay. Without normalization... 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? 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, processes 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 l zero. Might have to say bye, bye for now. Please leave comments, likes, go check bodykills.com. You know what to do.