 Centroid values against the known classifications and visually inspect where the natural divisions occur. This can often reveal clear cut-off points. Sensitivity analysis Perform a sensitivity analysis by varying the threshold values and observing the effect on the classification accuracy. Look for values that maximise the correct classification rate. Machine learning techniques Use machine learning algorithms to find the optimal threshold values. For example, you can treat it as an optimization problem where the goal is to maximise classification performance. Cross-validation Implement cross-validation by dividing your dataset into training and testing sets. Use the training set to determine the thresholds and validate them on the testing set to ensure they generalise well. Error metrics Use error metrics, such as confusion matrices, precision, recall, F1 score, to evaluate the performance of different thresholds. Choose thresholds that lead to the best balance of these metrics according to your systems requirements. Rule refinement Adjust your fuzzy rules if needed. If the centroids for certain classifications are consistently off, it might indicate that the rules or membership functions need tweaking. Iterative process Use an iterative approach where you start with initial threshold values based on your best understanding, then refine them based on the results of your analysis and system performance. User feedback If possible, incorporate user feedback into the threshold setting process. Users familiar with the system's domain can often provide valuable input on whether the classifications make sense. Setting these thresholds is not always a straightforward process, and it might require several iterations to get it right. Be prepared to adjust your approach based on the system's performance and any new insights you gain as you collect more data and feedback. I know how the detection error is being calculated because it's obviously wrong as it is. There's a bunch of other problems over there. So let's quickly reduce the default noise amplitude, maybe half at 2.25. Right, this is better. So for this square, we should be getting a square output. In the context of your fuzzy logic waveform detector system, detection error is typically calculated by comparing the expected output classification of a waveform with the actual output classification produced by the system. Here's a general approach to calculating detection error. Determine the expected output. For each waveform, you need to know the correct classification, e.g. square, triangle, trapezoid. Compare with actual output for each classification made by the system. Compare it to the expected classification. Count misclassifications. Each time the system's classification does not match the expected classification, it counts as an error. Calculate error rate. The detection error rate is then calculated as the number of misclassified waveforms divided by the total number of waveforms processed, often expressed as a percentage. Detection error rate equals opening parenthesis, number of misclassifications, total number of classifications, times 100. Detection error rate equals total number of classifications, number of misclassifications, times 100%. Given the snippets and information you've provided, it looks like your system could be implementing a similar method to calculate detection error. If you have a specific function in your code that calculates detection error, it would follow logic along these lines. If the actual implementation details or the code are available, I could provide more targeted insights into how detection error is being calculated in your specific scenario. Okay, I don't know. GitHub co-pilot website just went blank. We'll use co-pilot instead. So we have the function, text to arguments, original web classifier, classified wave. Yeah, another problem. At least on the same lens that our each element presents to the wave squared triangle. Okay, the problem is trapezoid has been detected as trapezoid. Square is not being detected as square. Square is also being detected as trapezoid. Basic functions first. Then we do the special cases of each. Have the full parts of the signal work like a charm. What's the problem with this one? Triangle doesn't work. All other waveforms to work. We added other causing issues after hitting triangle waveform. Ensure each waveform type is correctly implemented. For the triangle wave, the piecewise conditions and corresponding lambda functions should accurately represent a triangle wave. Mistakes here could cause incorrect signal generation. It appears you have a flask application designed to analyze waveforms using fuzzy logic, but you're facing some classification issues. The incorrect classifications of square and triangle waves as trapezoids, and the left triangle is unknown, suggest potential problems in either the fuzzy logic rules or the feature extraction, symmetry and peak sharpness methods. Review fuzzy rules ensure that the fuzzy rules accurately represent the characteristics of each waveform. For instance, square waves have high symmetry but low sharpness, while triangle waves have moderate values in both. Your rules should reflect these patterns. Adjust membership functions, the shapes of the membership functions, like mf underscore symmetry underscore low, mf underscore symmetry underscore mid, by not be capturing the features accurately. Experiment with different shapes and parameters. Feature extraction methods. Your methods for calculating symmetry and sharpness, calculate underscore symmetry underscore array and calculate underscore peak underscore sharpness underscore array are crucial. Verify that these methods correctly compute the desired features for each waveform type. Thresholds in fuzzy logic, the decision thresholds, e.g., centroid less than 0.2 for square wave, may need adjustment. Find tuning these thresholds based on testing with a variety of input data can improve accuracy. Data normalization, if your input data varies significantly in amplitude or scale, consider normalizing it before processing. Error analysis, analyze the cases where the classification fails. Check if there's a pattern or specific characteristic in the input data that leads to incorrect classification. Enhance input data, if possible, include more features that could help differentiate waveforms. Testing and validation, use a diverse set of test data to validate your model. This will help identify weaknesses in your classification logic. Logging and debugging, implement extensive logging to track the entire decision-making process of your fuzzy logic. This will make it easier to identify where the classification is going wrong. Use a feedback, if applicable, incorporate user feedback into your system to improve the classification over time. It seems that the SK fuzzy module is not available in this environment, so I'm unable to execute the fuzzy logic code directly. However, I can still assist with conceptual advice or code review. Refine membership functions, the shapes and boundaries of a membership function are crucial. Adjust the parameters of the trapezoidal membership functions, fuzz, trap-mf, for symmetry and peak underscore sharpness. For example, you may need to narrow or widen the ranges to more accurately capture the characteristics of each waveform. Adjust fuzzy rules, the rules you define in your fuzzy logic system are key to its performance. Consider adding more nuanced rules or adjusting existing ones to better differentiate between waveforms. For example, square waves are characterized by low sharpness and high symmetry, whereas triangle waves have moderate sharpness and symmetry. Fine-tuning thresholds for classification, the decision thresholds used in classifying the output waveform based on the centroid need careful adjustment. These thresholds determine how the system classifies the waveform based on the aggregated fuzzy value. Experiment with different rule aggregation methods. You currently use NB, fmax for rule aggregation. Experiment with other methods like probabilistic OR or summing the rule outwards and see if they offer better classification. Centroid calculation method, you're using the mean of maximum, mom, method for defuzzification. Experiment with other methods like the center of gravity, cog, or bisector, as different methods can yield different results. Data normalization, ensure your input data is normalized so that the fuzzy logic system compresses it consistently. This is especially important if the amplitude of your waveforms varies. Let's quickly finish up. Let's quickly look at another tool to only have a JavaScript for getting an error that code. Let's actually start working. I don't know what's up with those errors. It's just a test thing for clear. This is another tool that hopefully will appear on bunnykills.com. It's JavaScript. It's the JavaScript for it. It's currently only in JavaScript. This one allows you to play music using your hands. This will be all JavaScript. So the source code will be in the browser. The idea to put it in here quickly. How it works, we'll get the co-pilot to explain it quickly. So essentially when I put my hands higher, the sound is louder. It's too separate. It's not mentioning it. Why should I mention two separate scales, musical scales? One is more annoying than the other. So this is confusing. Was it my right hand or my left hand? It's turning yellow. Yeah. Some sort of detection error. There's just not enough light in the room. Some more annoying. It's meant to be left hand. They overlap as well. I think. C5 is over there. This will be C4. C5. They have A4. A5. The other thing, if you close your hand... If you close your hand, the notes will be shorter. If you open it, they should be longer in theory. Yeah, it's the distance between the finger and the thumb. Yeah, it kind of works, could be more pronounced. The music is being synthesized. First, why is it so annoying? What controls the speed? Is it the distance between the thumb and the finger? If so, the difference is not very pronounced. It should have been longer. Why is it generating so much stuff? Why is it generating so much stuff? Yeah, so X coordinate is triggering the scale. So that works. I was asking about what happens. The note should be longer with the hand open. The ratio of note determining the distance between the thumb and the index finger. It's normalized between the range of 0 and 1, multiplied by 8. Okay, that's 8. Don't like magic numbers. Where is it at? 8, normalized distance. How to make the normalized distance more sensitive? Yeah, it's that way. How to make the direction more sensitive to the normalized distance? Conclusion in the current in the distance between the thumb index and normalized range 0 and 1. This means that the large change in the distance will result in a relatively small change in normalized distance. Can it have a sensitivity factor? It's still multiplied by 8. There's still a magic number. This is interesting. That quickly we do want that code. I'm most curious if we already have the sensitivity factor. The variable. Why do we still need to multiply by 8? This will be essentially multiplying by 16, is it? It doesn't look like there's much difference to ratio. Let's say a multiplication codex is used to convert the normalized distance to duration. That's suitable. I did not change the duration now. It should be longer. Then quickly we need to restart this. Alright, that's why the camera is blinking. It seems like it's going the other way. I think we need this magic number. Supposed to the fuzzy logic stuff, this one just works. This is better. Then we have the two scales that overlap. This works. How about the two scales? Can we make them sound more musical? Can we play chords or something? Also, do the scales overlap? Do something with a mouth as well, something else we can move. We can dance to it. The scale defines the code C major. For the left hand and A minor. For the right hand. A minor is the relative minor. What? A minor is the relative minor of C major. Meaning they share the same key signature however the scales are defined. Do overlap. They include the notes C4 and C5. Both of them. That one doesn't have C4. To make that sound more musical you could consider using different scales that do not overlap. And that's what before you told me the opposite. This is using actual chords. You could define a set of chords for each hand to play. Here's an example of how I might do that. The chord function in this example does not exist in the tone.js library. So you tell me this would not work, would it? If it won't work. So this chord would not work. Play chords. Especially when moving the fingers or something. Could play one note, four notes together something. We check how many fingers are shown on camera. And based on the number of fingers play a corresponding number of notes simultaneously. Also, how do we make sure the notes play simultaneously are matched as in chords. So obviously what was provided before is not the complete function. Now this is the same stuff. Get up. Copilot is playing up. So what we want to do is generate multiple notes. How many fingers are shown? So four fingers will play a chord of four notes, hopefully of matched notes as well. So existing chords, not made up chords. So it's suggesting something, suggested something over here, but obviously it would not do that. And so we're writing up. Wrong. Check how many fingers are shown on camera and based on the number of fingers. Corresponding number of notes simultaneously to make sure the notes play simultaneously in chords. So get up. It's incredible to do this for us. Let's try GPT quickly. Yeah, with the whole chord. I mean that's what Kid Up Copilot does anyway. Just sending the whole chord on. So those will be all short notes. Open palm. Level will be volume. That should be intuitive. So if we start from here. Open hand will play longer notes. And yeah, there's the scale. So I don't know, can train to be a conductor or something, if you like. Yes, you can check the number of fingers shown on camera and play corresponding musical notes simultaneously, essentially forming chords. To achieve this, you can use the landmark provided by the media pipe hands model. Each hand is represented by 21 landmarks, with specific landmarks for each fingertip. By analyzing these landmarks, you can determine if a finger is extended or not. Detect extended fingers, you'll need to determine which fingers are extended. This typically involves comparing the positions of the fingertip landmarks to other landmarks on the same finger. For example, if the tip of the index finger is farther away from the base of the finger than a certain threshold, you can consider it extended. Chord mapping, define mappings for the number of extended fingers to specific chords. For instance, if one finger is extended, you can play a C major chord, two fingers for D minor, and so on. That's not what I meant, but... Simultaneous note playing, when you detect a specific number of extended fingers, trigger the corresponding chord. This involves playing multiple notes simultaneously. With tone, JS, you can use tone, polysynth to play chords. Github-copilot just told me that tone JS does not have an option to play chords. Let's go confront Github-copilot. Just a pseudo-code. I'm pretty sure that last response was not relevant, but the one... It's detective, it's doing false detections of hands on my head. It's not cool, just because it's too dark in the room or something. Continuing from where we left off, I extend the existing code to include the functionality for detecting extended fingers and playing chords. This includes defining the GetExtendedFingers function and integrating it with the OnResults function to play chords based on the number of extended fingers. I have to finish up, might leave you with some... generated music. Here is the continuation of your existing JS code. And landmarks, 8, represent the bass, middle, and tip of the index finger, respectively. Function to play chords based on extended fingers function play chord, extended fingers. Const chords equals, 1, C4, E4 feet, G4 feet, C major. 2, D4, F4 feet, A4 feet, D minor. Add more chords for different numbers of extended fingers. Yes, I'm playing those, those, those legit chords. C major, D minor, should be. Modify the OnResults function to include chord playing function on results, results. Existing code for processing hand results. Polish synths. Initialize polysynth for playing chords cons polysynth, equals new tone. Polysynth, tone, synth, destination. Anyway, hopefully, you will be able to... play around with it soon. Onbodycares.com. And I'll see you later. Bye.