ETphJASzYes.json

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        "text": " Hello, I'm back for another genetic algorithms video.",
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        "text": " Although this video actually that you're watching right now",
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        "text": " is not just a video about something you could do",
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        "text": " for genetic algorithms.",
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        "text": " more often than other things.",
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        "text": " And this is a thing that you do have to do.",
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        "text": " Where's my eraser?",
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        "text": " Over here.",
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        "text": " This is a thing that you have to do.",
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        "text": " down to the next generation.",
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        "text": " But let's erase our minds of the wonder",
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        "text": " that is genetic algorithms and just start",
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        "text": " with a very simple scenario.",
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        "text": " I'm going to create a JavaScript object.",
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        "text": " And this marker has a very interesting smell.",
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        "text": " And it's going to have things in it, like mango, five,",
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        "text": " blueberry, three, cherry, one, and apple, one.",
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        "text": " So what I want to be able to do is",
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        "text": " I want to be able to pick a fruit randomly.",
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        "text": " I want a function that says pick fruit.",
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        "text": " And I call that function.",
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        "text": " I'm going to get out of it returned a particular fruit.",
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        "text": " Maybe mango, maybe blueberry, maybe cherry, maybe apple.",
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        "text": " And I am going to get mango five times as often",
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        "text": " as I'm going to get apple.",
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        "text": " I'm going to get mango.",
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        "text": " And I wrote these numbers very specifically to add up to 10,",
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        "seek": 11474,
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        "temperature": 0,
        "text": " because it's very easy for me to just sort of do the math",
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        "text": " here.",
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        "text": " But I'm going to get mango 50% of the time,",
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        "text": " blueberry 30% of the time, cherry 10% of the time,",
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        "text": " and apple 10% of the time.",
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        "text": " This relates to so many different kinds of scenarios",
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        "text": " that you might do in computer programming,",
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        "text": " if that makes sense.",
        "tokens": [
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        "text": " So how do we do this?",
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        "text": " Now, if you've been watching some",
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        "text": " of my other genetic algorithm videos or other videos,",
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        "text": " I've done this a number of different ways.",
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        "text": " One way that I've done this is I've built a separate array.",
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        "text": " And what I've done with that array is I've put, I've just",
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        "text": " said, hey, let's put mango in it five times.",
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        "text": " I'm just going to write m.",
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        "text": " Mango's in this array five times.",
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        "text": " And then I'm going to put blueberry in this array three",
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        "text": " times.",
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        "text": " And then I'm going to put cherry in it once and apple in it",
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        "text": " once.",
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        "text": " So if I had a bucket full of all these letters,",
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        "text": " and there's five m's in it, three b's, one c, one a,",
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        "text": " and I shuffled around and pick one out randomly,",
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        "text": " I'm going to pick m more five times as often",
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        "text": " as I'm going to pick a.",
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        "text": " So that's one way of doing it.",
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        "text": " Now, there's a bit of an issue with this way of doing it.",
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        "text": " This works really nicely for this.",
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        "text": " But if I have thousands of elements and thousands",
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        "text": " of different and a vast array of probabilities, some of which",
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        "text": " are very, very, very, very small probabilities,",
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        "text": " I did have to build this array that just",
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        "text": " has so much stuff in it.",
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        "text": " It's not a really efficient performance, good performance",
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        "text": " solution.",
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        "text": " And actually, if you look at a lot of my examples,",
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        "text": " that's the solution I'm using.",
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        "text": " It doesn't work so great.",
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        "text": " So I'm not proposing this.",
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        "text": " I didn't invent this.",
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        "text": " I'm just talking about it.",
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        "text": " I'm going to look at another solution.",
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        "text": " Now, there is another solution to this problem,",
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        "text": " which is a way that you could pick random numbers twice.",
        "tokens": [
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        "text": " So two random numbers.",
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        "temperature": 0,
        "text": " So each one of these, 0, 1, 2, 3.",
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        "text": " So on the one hand, I'm just picking a random number, 0, 1,",
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        "end": 222.38,
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        "text": " 2, or 3.",
        "tokens": [
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        "avg_logprob": -0.26189584732055665,
        "compression_ratio": 1.6814159292035398,
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        "text": " And I could do that easily by saying,",
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        "compression_ratio": 1.6814159292035398,
        "end": 226.46,
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        "text": " it depends on in JavaScript or in processing,",
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        "text": " let's say floor random 4.",
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        "text": " And this will give me 0, 1, 2, or 3.",
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        "text": " So this is one way that I could do this.",
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        "text": " But if I do that, I'm just going to get each one of these",
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        "text": " 25% of the time.",
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        "text": " But if, in addition to picking one of these randomly,",
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        "text": " I picked another random number just between 0 and 1.",
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        "text": " So this is the fruit that I'm picking.",
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        "text": " And this is kind of my test number.",
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        "text": " If I pick another random number,",
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        "text": " that random number has to be less than the probability",
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        "text": " in order for this first random thing to qualify.",
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        "text": " So I might have to do this a bunch of times.",
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        "text": " Like, I might pick 3, then 0.7, and then 2, and 0.4,",
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        "temperature": 0,
        "text": " and then 1, and 0.8, and then 0, and 0.6, and then 3, and 0.01.",
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        "temperature": 0,
        "text": " You know, ah, Apple qualifies.",
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        "temperature": 0,
        "text": " So you can see, Mango is going to be able to qualify.",
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        "temperature": 0,
        "text": " If I pick 0, 50% of the time, this second random number",
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        "text": " is going to be fine.",
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        "temperature": 0,
        "text": " But if I pick Apple, only 10% of the time",
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        "text": " is this second random number going to be fine.",
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        "compression_ratio": 1.8235294117647058,
        "end": 294.38,
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        "no_speech_prob": 0.0006878477288410068,
        "seek": 28390,
        "start": 293.17999999999995,
        "temperature": 0,
        "text": " So that's one way.",
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        "temperature": 0,
        "text": " The problem with this way is, again,",
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        "avg_logprob": -0.2032904242077013,
        "compression_ratio": 1.8235294117647058,
        "end": 299.26,
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        "seek": 28390,
        "start": 296.85999999999996,
        "temperature": 0,
        "text": " with a lot of elements and a lot of probabilities,",
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        "text": " I can be stuck doing this over.",
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        "temperature": 0,
        "text": " That loop can take many, many, many, many times",
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        "text": " before it finds a qualifying random number.",
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        "text": " So this video, I'm going to program",
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        "text": " an entirely different algorithm.",
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        "text": " It was suggested to use this algorithm in a GitHub issue.",
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        "text": " I will link to that GitHub issue in this video's description,",
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        "text": " which is, hello, description down there.",
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        "text": " I'm trying to get used to where things are.",
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        "text": " Over there is some, no, maybe over there is like an ad.",
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        "text": " I don't know what you, don't, I don't know if,",
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        "text": " don't click on it.",
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        "text": " Maybe click on it.",
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        "text": " I don't know.",
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        "text": " Now I'm all stressed out.",
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        "text": " OK, never mind all that.",
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        "text": " There's stuff around me.",
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        "text": " I'm a person in a YouTube video.",
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        "text": " But there was a point to all this, wasn't there?",
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        "text": " Ah, OK.",
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        "text": " So I will also, in a moment, bring up that GitHub issue",
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        "text": " to look at it on the computer, because I",
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        "text": " do have a computer over there, which I will get to eventually.",
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        "text": " So what is this other algorithm?",
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        "text": " Well, one way you could imagine is, let's say I created,",
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        "text": " I don't know what this is, a tube, a pipe, a rectangle,",
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        "text": " some sort of two-dimensional space.",
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        "text": " And I said, ah, 50% of that space is mangoes space.",
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        "text": " And 30% of that space is blueberries space.",
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        "text": " And 10% of that space is cherry.",
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        "text": " And 10% of that space is apple.",
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        "text": " And this down here is 0.",
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        "text": " And up here is 1.",
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        "text": " So what if I just pick a random number now between 0 and 1?",
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        "text": " And it's almost like throwing a dart.",
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        "text": " I could be over here.",
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        "text": " And I'm going to throw a dart.",
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        "text": " Let's do it.",
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        "text": " Ready?",
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        "text": " 1, 2, 3.",
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        "text": " I missed.",
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        "text": " But actually, it landed over here, blueberry.",
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        "text": " I'm going to be more likely, there's more space for me",
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        "text": " than there is for cherry and apple.",
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        "text": " Now, I'm not going to build some whole entire dart-throwing",
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        "text": " simulation thing in my sketch.",
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        "text": " So I need a mechanism to do this,",
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        "text": " to pick the random number and figure out where I am.",
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        "text": " And actually, the way that this could work",
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        "text": " is I can actually start up here.",
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        "text": " You can pick a random number and then start subtracting values",
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        "text": " and find out at what point do I sort of exit this space.",
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        "text": " So hopefully, I'm going to program that algorithm.",
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        "text": " We'll look at it.",
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        "text": " Hopefully, it'll work.",
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        "text": " And then we'll come back here and sort of see",
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        "text": " if this makes more sense to us then.",
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        "text": " I'm going over there now.",
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        "text": " OK, here I am back in this screen with my computer.",
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        "text": " So this is the GitHub issue that I was referencing.",
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        "text": " There's a link to it in the video's description.",
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        "text": " Spectron, thank you for this original suggestion.",
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        "text": " And then also, Sinclairz81.",
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        "text": " This is from a while ago.",
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        "text": " It took me a while to get to this.",
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        "text": " Has a very fancy, crazy, ooh, look at this,",
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        "text": " unicorn implementation of it that",
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        "text": " uses ES6 and all sorts of cool, interesting JavaScript syntax",
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        "text": " that, ooh, I don't fully understand.",
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        "text": " Oh, but I will understand someday.",
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        "text": " So I'm going to do my own.",
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        "text": " So let me close those.",
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        "text": " And let's write some code.",
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        "text": " So first of all, let's quickly, I",
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        "text": " should have done this in advance.",
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        "text": " OK, there's a little pause there where I decided, actually,",
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        "text": " what I want to do is write the information.",
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        "text": " I want to pick one of the, it's easier",
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        "text": " for me to put it in an array.",
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        "text": " Because what I want to do is pick one of these elements",
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        "text": " from the array.",
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        "text": " And then I want the probability of how",
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        "text": " I pick it to be something relative to its score.",
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        "text": " So the first thing that I need to do, by the way,",
        "tokens": [
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          286,
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        "text": " is I need to normalize all of these scores",
        "tokens": [
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        "text": " to values between 0 and 1.",
        "tokens": [
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        "avg_logprob": -0.20423916903409092,
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        "seek": 48110,
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        "temperature": 0,
        "text": " And so that they all add up to 1.",
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        "avg_logprob": -0.20423916903409092,
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        "text": " I want them to be a percentage.",
        "tokens": [
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        "text": " So the first thing that I'm going to do",
        "tokens": [
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          551,
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          286,
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      {
        "avg_logprob": -0.24073378245035806,
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        "seek": 51062,
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        "text": " is I'm going to just loop through every single fruit.",
        "tokens": [
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      {
        "avg_logprob": -0.24073378245035806,
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        "end": 527.34,
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        "seek": 51062,
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        "temperature": 0,
        "text": " And what I need to do is I need to add up all the scores.",
        "tokens": [
          51076,
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          437,
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          360,
          307,
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      {
        "avg_logprob": -0.24073378245035806,
        "compression_ratio": 1.808139534883721,
        "end": 529.3,
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        "seek": 51062,
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        "text": " I need to find out what do they all add up to.",
        "tokens": [
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        "avg_logprob": -0.24073378245035806,
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        "end": 530.78,
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        "seek": 51062,
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        "temperature": 0,
        "text": " Sum equals 0.",
        "tokens": [
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        "avg_logprob": -0.24073378245035806,
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        "end": 537.98,
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        "text": " And I'm going to say sum plus equal fruits index i dot score.",
        "tokens": [
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        "seek": 51062,
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        "temperature": 0,
        "text": " Then the next thing I'm going to do,",
        "tokens": [
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        "avg_logprob": -0.21998864726016396,
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        "text": " now, here's the thing.",
        "tokens": [
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        "no_speech_prob": 0.000030241913918871433,
        "seek": 53978,
        "start": 541.22,
        "temperature": 0,
        "text": " I might want to retain that score value.",
        "tokens": [
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        "end": 545.54,
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        "no_speech_prob": 0.000030241913918871433,
        "seek": 53978,
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        "temperature": 0,
        "text": " Or I could just overwrite it.",
        "tokens": [
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        "temperature": 0,
        "text": " But ultimately, what I want to do,",
        "tokens": [
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        "avg_logprob": -0.21998864726016396,
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        "end": 552.02,
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        "text": " I'm going to say fruits index i dot score equals fruits index",
        "tokens": [
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        "avg_logprob": -0.21998864726016396,
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        "temperature": 0,
        "text": " i.",
        "tokens": [
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        "avg_logprob": -0.21998864726016396,
        "compression_ratio": 1.837037037037037,
        "end": 554.1,
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        "temperature": 0,
        "text": " And I know I could say divide equals.",
        "tokens": [
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        "avg_logprob": -0.21998864726016396,
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        "end": 556.1,
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        "seek": 53978,
        "start": 554.1,
        "temperature": 0,
        "text": " But I'm just going to write this all the way out.",
        "tokens": [
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      {
        "avg_logprob": -0.21998864726016396,
        "compression_ratio": 1.837037037037037,
        "end": 559.5,
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        "temperature": 0,
        "text": " Fruits index i dot score divided by sum.",
        "tokens": [
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      {
        "avg_logprob": -0.21998864726016396,
        "compression_ratio": 1.837037037037037,
        "end": 563.06,
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        "seek": 53978,
        "start": 559.5,
        "temperature": 0,
        "text": " So the idea is I want to normalize all of those scores.",
        "tokens": [
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        "avg_logprob": -0.21998864726016396,
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        "end": 565.06,
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        "seek": 53978,
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        "temperature": 0,
        "text": " So all I need to do is add up the total",
        "tokens": [
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      {
        "avg_logprob": -0.21998864726016396,
        "compression_ratio": 1.837037037037037,
        "end": 566.5799999999999,
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        "seek": 53978,
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        "text": " and divide each one by the total.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21998864726016396,
        "compression_ratio": 1.837037037037037,
        "end": 568.6999999999999,
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        "seek": 53978,
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        "temperature": 0,
        "text": " That's exactly what I would have done here.",
        "tokens": [
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      {
        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 569.62,
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        "no_speech_prob": 0.0004238781984895468,
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        "temperature": 0,
        "text": " I get 10.",
        "tokens": [
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        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 570.94,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
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        "temperature": 0,
        "text": " 5 divided by 10 is 50%.",
        "tokens": [
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      {
        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 572.22,
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        "start": 570.94,
        "temperature": 0,
        "text": " 3 divided by 30%.",
        "tokens": [
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        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 574.7,
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        "seek": 56870,
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        "temperature": 0,
        "text": " And the math is never going to work out this perfectly.",
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        "avg_logprob": -0.24759853129484216,
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        "temperature": 0,
        "text": " But who cares?",
        "tokens": [
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        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 577.7,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 575.9000000000001,
        "temperature": 0,
        "text": " Because the computer's doing it, not us.",
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        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 580.6600000000001,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 577.7,
        "temperature": 0,
        "text": " OK, so what I'm going to do here is",
        "tokens": [
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      },
      {
        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 583.5400000000001,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 580.6600000000001,
        "temperature": 0,
        "text": " I am going to go back to the browser.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.24759853129484216,
        "compression_ratio": 1.6407407407407408,
        "end": 585.1400000000001,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 583.5400000000001,
        "temperature": 0,
        "text": " And I'm going to refresh.",
        "tokens": [
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      },
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        "compression_ratio": 1.6407407407407408,
        "end": 588.1,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 585.1400000000001,
        "temperature": 0,
        "text": " This canvas is here for no reason.",
        "tokens": [
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        "compression_ratio": 1.6407407407407408,
        "end": 590.7,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 588.1,
        "temperature": 0,
        "text": " And I'm going to look at fruits.",
        "tokens": [
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        "seek": 56870,
        "start": 590.7,
        "temperature": 0,
        "text": " And we can see here that now the scores are normalized 0.5, 0.3.",
        "tokens": [
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        "end": 597.94,
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        "no_speech_prob": 0.0004238781984895468,
        "seek": 56870,
        "start": 595.6600000000001,
        "temperature": 0,
        "text": " And I could, if I want to, it might make sense",
        "tokens": [
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        "avg_logprob": -0.2595770828367218,
        "compression_ratio": 1.8484848484848484,
        "end": 600.46,
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        "no_speech_prob": 0.00018814094073604792,
        "seek": 59794,
        "start": 597.94,
        "temperature": 0,
        "text": " to say actually I'm going to add a new property to each one",
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        "seek": 59794,
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        "text": " of these objects.",
        "tokens": [
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      {
        "avg_logprob": -0.2595770828367218,
        "compression_ratio": 1.8484848484848484,
        "end": 603.98,
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        "seek": 59794,
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        "text": " I'm going to say like norm score for like,",
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      {
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        "text": " I'm just going to call it probability for probability.",
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      {
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        "seek": 59794,
        "start": 606.3000000000001,
        "temperature": 0,
        "text": " Fruits index i dot probability.",
        "tokens": [
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        "compression_ratio": 1.8484848484848484,
        "end": 609.3800000000001,
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        "seek": 59794,
        "start": 608.0600000000001,
        "temperature": 0,
        "text": " So now I'm going to hit refresh.",
        "tokens": [
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        "avg_logprob": -0.2595770828367218,
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        "text": " I'm going to look at that fruits array.",
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        "seek": 59794,
        "start": 610.98,
        "temperature": 0,
        "text": " And I'm going to see here now it retained the score",
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        "avg_logprob": -0.2595770828367218,
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        "temperature": 0,
        "text": " and has a probability of 0.5.",
        "tokens": [
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        "compression_ratio": 1.8484848484848484,
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        "seek": 59794,
        "start": 615.86,
        "temperature": 0,
        "text": " And as you can see, if I were to go here and add one other fruit",
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        "text": " like melon and give it a score of 7.7,",
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        "end": 632.58,
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        "seek": 62186,
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        "temperature": 0,
        "text": " then if I hit refresh and look at fruits,",
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        "end": 633.78,
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        "text": " you can see now I've got five.",
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        "text": " And it's worked it out.",
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        "text": " The math isn't so clean because it doesn't add up to 10.",
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        "text": " But mango now has a 29% chance of being picked.",
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        "text": " And melon has a 41% chance of being picked.",
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        "text": " So this is good.",
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        "temperature": 0,
        "text": " So now, how do I pick one of these randomly",
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        "text": " according to its probability?",
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      },
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        "compression_ratio": 1.9382716049382716,
        "end": 654.98,
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        "temperature": 0,
        "text": " Now, it's time for me to write an algorithm that",
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        "compression_ratio": 1.9382716049382716,
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        "text": " implements this particular, write",
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        "end": 659.1,
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        "text": " some code that implements this particular idea,",
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        "text": " this particular algorithm.",
        "tokens": [
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        "avg_logprob": -0.24437351364025967,
        "compression_ratio": 1.9382716049382716,
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        "temperature": 0,
        "text": " Throw a dart and find a place in here.",
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        "text": " So the way that I'm going to do that is first,",
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        "text": " I'm going to write a function.",
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        "text": " I'm going to call it pick one.",
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        "text": " And it's going to receive a list.",
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        "temperature": 0,
        "text": " So I'm going to have a generic function that could",
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        "text": " work with any list of things.",
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        "temperature": 0,
        "text": " Now, it's going to be set up to work in such a way",
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        "temperature": 0,
        "text": " that the list of things is made up",
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        "start": 682.1,
        "temperature": 0,
        "text": " of objects that have a property called prob for probability.",
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        "seek": 68066,
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        "temperature": 0,
        "text": " But obviously, it could make this more generic",
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        "text": " in other ways as well.",
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      },
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        "temperature": 0,
        "text": " OK, so the first thing that I'm actually going to do",
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        "text": " is I'm going to say var index equals 0.",
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        "temperature": 0,
        "text": " So I'm going to sort of assume that the one that I'm",
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        "text": " going to pick is the first one.",
        "tokens": [
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        "seek": 68066,
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        "text": " Then I'm going to pick a random number between 0 and 1.0.",
        "tokens": [
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        "seek": 70674,
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        "temperature": 0,
        "text": " And I'm going to say, as long as r is greater than 0,",
        "tokens": [
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          11,
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      {
        "avg_logprob": -0.18603277206420898,
        "compression_ratio": 1.503030303030303,
        "end": 723.98,
        "id": 274,
        "no_speech_prob": 0.000011478760825411882,
        "seek": 70674,
        "start": 714.0600000000001,
        "temperature": 0,
        "text": " then I'm going to say r equals r minus that particular object's",
        "tokens": [
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        "avg_logprob": -0.18603277206420898,
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        "no_speech_prob": 0.000011478760825411882,
        "seek": 70674,
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        "temperature": 0,
        "text": " probability.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18603277206420898,
        "compression_ratio": 1.503030303030303,
        "end": 729.74,
        "id": 276,
        "no_speech_prob": 0.000011478760825411882,
        "seek": 70674,
        "start": 726.02,
        "temperature": 0,
        "text": " So let's think about this for a second.",
        "tokens": [
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      {
        "avg_logprob": -0.18603277206420898,
        "compression_ratio": 1.503030303030303,
        "end": 731.78,
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        "no_speech_prob": 0.000011478760825411882,
        "seek": 70674,
        "start": 729.74,
        "temperature": 0,
        "text": " What I'm saying is that let's say",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18603277206420898,
        "compression_ratio": 1.503030303030303,
        "end": 736.42,
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        "no_speech_prob": 0.000011478760825411882,
        "seek": 70674,
        "start": 731.78,
        "temperature": 0,
        "text": " I have something with a probability of 0.9.",
        "tokens": [
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      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 740.02,
        "id": 279,
        "no_speech_prob": 0.000015446288671228103,
        "seek": 73642,
        "start": 736.42,
        "temperature": 0,
        "text": " And I have something with a probability of 0.1.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 742.2199999999999,
        "id": 280,
        "no_speech_prob": 0.000015446288671228103,
        "seek": 73642,
        "start": 740.02,
        "temperature": 0,
        "text": " I shouldn't draw this right.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 742.86,
        "id": 281,
        "no_speech_prob": 0.000015446288671228103,
        "seek": 73642,
        "start": 742.2199999999999,
        "temperature": 0,
        "text": " Time out.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 744.4799999999999,
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        "no_speech_prob": 0.000015446288671228103,
        "seek": 73642,
        "start": 742.86,
        "temperature": 0,
        "text": " I shouldn't draw it right next to this,",
        "tokens": [
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      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 746.9399999999999,
        "id": 283,
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        "seek": 73642,
        "start": 744.4799999999999,
        "temperature": 0,
        "text": " because I'm going to do something different now.",
        "tokens": [
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        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 750.6999999999999,
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        "seek": 73642,
        "start": 746.9399999999999,
        "temperature": 0,
        "text": " So I'm going to use, where is, I don't have any space.",
        "tokens": [
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        "avg_logprob": -0.22563770238090963,
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        "end": 751.9,
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        "no_speech_prob": 0.000015446288671228103,
        "seek": 73642,
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        "temperature": 0,
        "text": " I'm going to come over here.",
        "tokens": [
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        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 753.5,
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        "temperature": 0,
        "text": " This is fine.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 755.9399999999999,
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        "seek": 73642,
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        "temperature": 0,
        "text": " Let's just say a simplified arrangement,",
        "tokens": [
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      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 757.4599999999999,
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        "seek": 73642,
        "start": 755.9399999999999,
        "temperature": 0,
        "text": " where this is my bar here.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 761.8199999999999,
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        "seek": 73642,
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        "temperature": 0,
        "text": " And I have something that has a as a probability of 0.9",
        "tokens": [
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      {
        "avg_logprob": -0.22563770238090963,
        "compression_ratio": 1.852813852813853,
        "end": 764.2199999999999,
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        "seek": 73642,
        "start": 761.8199999999999,
        "temperature": 0,
        "text": " and b as a probability of 0.1.",
        "tokens": [
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      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 767.02,
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        "seek": 76422,
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        "temperature": 0,
        "text": " The first thing that I did is I picked a random number",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 769.58,
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        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
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        "temperature": 0,
        "text": " somewhere between 0 and 1.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 776.1800000000001,
        "id": 293,
        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
        "start": 769.58,
        "temperature": 0,
        "text": " And then I'm going to subtract 0.9 from it,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 778.02,
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        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
        "start": 776.1800000000001,
        "temperature": 0,
        "text": " because 0.9 is first.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 781.46,
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        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
        "start": 778.02,
        "temperature": 0,
        "text": " How often am I going to pick a number between 0 and 1,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 785.7,
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        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
        "start": 781.46,
        "temperature": 0,
        "text": " where I subtract 0.9, and now I have a value less than 0?",
        "tokens": [
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      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 787.34,
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        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
        "start": 785.7,
        "temperature": 0,
        "text": " 90% of the time, right?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 790.5400000000001,
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        "no_speech_prob": 0.0000037853221783734625,
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        "start": 787.34,
        "temperature": 0,
        "text": " Only if I pick a number greater than 0.9.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1633081905177382,
        "compression_ratio": 1.6559633027522935,
        "end": 793.02,
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        "no_speech_prob": 0.0000037853221783734625,
        "seek": 76422,
        "start": 790.5400000000001,
        "temperature": 0,
        "text": " If I pick 0.95 and I subtract 0.9,",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 796.06,
        "id": 300,
        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 793.02,
        "temperature": 0,
        "text": " I'm going to have a value of 0.05.",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 799.26,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 796.06,
        "temperature": 0,
        "text": " So 90% of the time, when I subtract this,",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 800.9399999999999,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 799.26,
        "temperature": 0,
        "text": " I'm going to be less than 0.",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 805.22,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 800.9399999999999,
        "temperature": 0,
        "text": " When I'm out, when I'm done, when I'm out, I'm out.",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 806.02,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 805.22,
        "temperature": 0,
        "text": " You're out.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 809.1,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 806.02,
        "temperature": 0,
        "text": " When I'm below 0, that's the thing I'm left with.",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 813.42,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 809.1,
        "temperature": 0,
        "text": " 10% of the time, I'll pick that number, like 0.95,",
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 816.3,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 813.42,
        "temperature": 0,
        "text": " and then I go and subtract 0.1, and I'm out.",
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      },
      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 819.18,
        "id": 308,
        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 816.3,
        "temperature": 0,
        "text": " So 10% of the time, I'll be left the last place I was.",
        "tokens": [
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      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 820.62,
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        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 819.18,
        "temperature": 0,
        "text": " I'm starting up here.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.16160082968936604,
        "compression_ratio": 1.8333333333333333,
        "end": 822.38,
        "id": 310,
        "no_speech_prob": 0.005139592103660107,
        "seek": 79302,
        "start": 820.62,
        "temperature": 0,
        "text": " I pick some random number somewhere.",
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 823.9399999999999,
        "id": 311,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 822.38,
        "temperature": 0,
        "text": " I subtract this.",
        "tokens": [
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          13,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 827.18,
        "id": 312,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 823.9399999999999,
        "temperature": 0,
        "text": " Am I already below 0, or should I keep going?",
        "tokens": [
          50442,
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          11,
          420,
          820,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 828.9,
        "id": 313,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 827.18,
        "temperature": 0,
        "text": " If I subtract this, then I'm about last.",
        "tokens": [
          50604,
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          16390,
          341,
          11,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 831.1,
        "id": 314,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 828.9,
        "temperature": 0,
        "text": " So this idea of just I keep subtracting these values,",
        "tokens": [
          50690,
          407,
          341,
          1558,
          295,
          445,
          286,
          1066,
          16390,
          278,
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          11,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 833.1,
        "id": 315,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 831.1,
        "temperature": 0,
        "text": " and it doesn't matter what order, right?",
        "tokens": [
          50800,
          293,
          309,
          1177,
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          437,
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          11,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 836.14,
        "id": 316,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 833.1,
        "temperature": 0,
        "text": " Even if this was B was first, and I'm always",
        "tokens": [
          50900,
          2754,
          498,
          341,
          390,
          363,
          390,
          700,
          11,
          293,
          286,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 838.7,
        "id": 317,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 836.14,
        "temperature": 0,
        "text": " subtracting 0.1 first, I'm only going",
        "tokens": [
          51052,
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          278,
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          13,
          16,
          700,
          11,
          286,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 842.06,
        "id": 318,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 838.7,
        "temperature": 0,
        "text": " to be less than 0 if I happen to pick a random number that's",
        "tokens": [
          51180,
          281,
          312,
          1570,
          813,
          1958,
          498,
          286,
          1051,
          281,
          1888,
          257,
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          1230,
          300,
          311,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 843.18,
        "id": 319,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 842.06,
        "temperature": 0,
        "text": " less than 0.1.",
        "tokens": [
          51348,
          1570,
          813,
          1958,
          13,
          16,
          13,
          51404
        ]
      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 845.66,
        "id": 320,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 843.18,
        "temperature": 0,
        "text": " So hopefully, this kind of makes some intuitive sense to you.",
        "tokens": [
          51404,
          407,
          4696,
          11,
          341,
          733,
          295,
          1669,
          512,
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          13,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 847.54,
        "id": 321,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 845.66,
        "temperature": 0,
        "text": " I feel like there's a very smart way to diagram this",
        "tokens": [
          51528,
          286,
          841,
          411,
          456,
          311,
          257,
          588,
          4069,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 849.58,
        "id": 322,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 847.54,
        "temperature": 0,
        "text": " that I'm not doing, but I've said it enough times.",
        "tokens": [
          51622,
          300,
          286,
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          884,
          11,
          457,
          286,
          600,
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      },
      {
        "avg_logprob": -0.20979669993509076,
        "compression_ratio": 1.71875,
        "end": 850.66,
        "id": 323,
        "no_speech_prob": 0.0006462009041570127,
        "seek": 82238,
        "start": 849.58,
        "temperature": 0,
        "text": " I'm hoping it makes sense.",
        "tokens": [
          51724,
          286,
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        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 853.02,
        "id": 324,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 850.66,
        "temperature": 0,
        "text": " Let's come back to the code.",
        "tokens": [
          50364,
          961,
          311,
          808,
          646,
          281,
          264,
          3089,
          13,
          50482
        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 854.2199999999999,
        "id": 325,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 853.02,
        "temperature": 0,
        "text": " So what do I need to do?",
        "tokens": [
          50482,
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          437,
          360,
          286,
          643,
          281,
          360,
          30,
          50542
        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 859.06,
        "id": 326,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 854.2199999999999,
        "temperature": 0,
        "text": " Let's say, so if, so what I need to do now is say index plus plus.",
        "tokens": [
          50542,
          961,
          311,
          584,
          11,
          370,
          498,
          11,
          370,
          437,
          286,
          643,
          281,
          360,
          586,
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          13,
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      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 860.6,
        "id": 327,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 859.06,
        "temperature": 0,
        "text": " So I'm going to just keep doing this.",
        "tokens": [
          50784,
          407,
          286,
          478,
          516,
          281,
          445,
          1066,
          884,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 865.18,
        "id": 328,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 860.6,
        "temperature": 0,
        "text": " I'm going to go through the list until I have now gone below 0.",
        "tokens": [
          50861,
          286,
          478,
          516,
          281,
          352,
          807,
          264,
          1329,
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          286,
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      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 869.5799999999999,
        "id": 329,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 865.18,
        "temperature": 0,
        "text": " Now, there's a little bit, and then I want to say return list",
        "tokens": [
          51090,
          823,
          11,
          456,
          311,
          257,
          707,
          857,
          11,
          293,
          550,
          286,
          528,
          281,
          584,
          2736,
          1329,
          51310
        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 873.3,
        "id": 330,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 869.5799999999999,
        "temperature": 0,
        "text": " index, except for the fact that this isn't exactly right.",
        "tokens": [
          51310,
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          11,
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          337,
          264,
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          300,
          341,
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        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 876.18,
        "id": 331,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 873.3,
        "temperature": 0,
        "text": " This isn't exactly right because,",
        "tokens": [
          51496,
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          380,
          2293,
          558,
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          11,
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        ]
      },
      {
        "avg_logprob": -0.2712043821811676,
        "compression_ratio": 1.7108433734939759,
        "end": 878.74,
        "id": 332,
        "no_speech_prob": 0.0001511816371930763,
        "seek": 85066,
        "start": 876.18,
        "temperature": 0,
        "text": " I like how this bracket is highlighted down here.",
        "tokens": [
          51640,
          286,
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          307,
          17173,
          760,
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      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 881.54,
        "id": 333,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 878.82,
        "temperature": 0,
        "text": " But anyway, forget about that.",
        "tokens": [
          50368,
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          11,
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          466,
          300,
          13,
          50504
        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 888.46,
        "id": 334,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 881.54,
        "temperature": 0,
        "text": " This isn't exactly right because I actually want to,",
        "tokens": [
          50504,
          639,
          1943,
          380,
          2293,
          558,
          570,
          286,
          767,
          528,
          281,
          11,
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        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 891.1800000000001,
        "id": 335,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 888.46,
        "temperature": 0,
        "text": " I want, if I say index plus plus,",
        "tokens": [
          50850,
          286,
          528,
          11,
          498,
          286,
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          8186,
          1804,
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          11,
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        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 894.82,
        "id": 336,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 891.1800000000001,
        "temperature": 0,
        "text": " I'm going to go one past the one that was actually",
        "tokens": [
          50986,
          286,
          478,
          516,
          281,
          352,
          472,
          1791,
          264,
          472,
          300,
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          767,
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        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 896.82,
        "id": 337,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 894.82,
        "temperature": 0,
        "text": " the one that I wanted to pick.",
        "tokens": [
          51168,
          264,
          472,
          300,
          286,
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          281,
          1888,
          13,
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        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 897.82,
        "id": 338,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 896.82,
        "temperature": 0,
        "text": " It makes sense.",
        "tokens": [
          51268,
          467,
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          2020,
          13,
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        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 900.34,
        "id": 339,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 897.82,
        "temperature": 0,
        "text": " So I've got to very quickly just say index minus minus.",
        "tokens": [
          51318,
          407,
          286,
          600,
          658,
          281,
          588,
          2661,
          445,
          584,
          8186,
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          3175,
          13,
          51444
        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 901.9,
        "id": 340,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 900.34,
        "temperature": 0,
        "text": " Let me talk you through that.",
        "tokens": [
          51444,
          961,
          385,
          751,
          291,
          807,
          300,
          13,
          51522
        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 903.82,
        "id": 341,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 901.9,
        "temperature": 0,
        "text": " In the scenario where I just have 0.9,",
        "tokens": [
          51522,
          682,
          264,
          9005,
          689,
          286,
          445,
          362,
          1958,
          13,
          24,
          11,
          51618
        ]
      },
      {
        "avg_logprob": -0.2852505322160392,
        "compression_ratio": 1.5949367088607596,
        "end": 907.14,
        "id": 342,
        "no_speech_prob": 0.000078437733463943,
        "seek": 87874,
        "start": 903.82,
        "temperature": 0,
        "text": " is the probability of index 0 is 0.9.",
        "tokens": [
          51618,
          307,
          264,
          8482,
          295,
          8186,
          1958,
          307,
          1958,
          13,
          24,
          13,
          51784
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 908.26,
        "id": 343,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 907.14,
        "temperature": 0,
        "text": " So I pick a random number.",
        "tokens": [
          50364,
          407,
          286,
          1888,
          257,
          4974,
          1230,
          13,
          50420
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 909.66,
        "id": 344,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 908.26,
        "temperature": 0,
        "text": " I pick 0.8.",
        "tokens": [
          50420,
          286,
          1888,
          1958,
          13,
          23,
          13,
          50490
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 912.6999999999999,
        "id": 345,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 909.66,
        "temperature": 0,
        "text": " I say r equals 0.8 minus 0.9.",
        "tokens": [
          50490,
          286,
          584,
          367,
          6915,
          1958,
          13,
          23,
          3175,
          1958,
          13,
          24,
          13,
          50642
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 914.14,
        "id": 346,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 912.6999999999999,
        "temperature": 0,
        "text": " So that's negative 0.1.",
        "tokens": [
          50642,
          407,
          300,
          311,
          3671,
          1958,
          13,
          16,
          13,
          50714
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 915.26,
        "id": 347,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 914.14,
        "temperature": 0,
        "text": " Then I say index plus plus.",
        "tokens": [
          50714,
          1396,
          286,
          584,
          8186,
          1804,
          1804,
          13,
          50770
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 916.78,
        "id": 348,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 915.26,
        "temperature": 0,
        "text": " So I go to 1.",
        "tokens": [
          50770,
          407,
          286,
          352,
          281,
          502,
          13,
          50846
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 917.6999999999999,
        "id": 349,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 916.78,
        "temperature": 0,
        "text": " And I go back to loop.",
        "tokens": [
          50846,
          400,
          286,
          352,
          646,
          281,
          6367,
          13,
          50892
        ]
      },
      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 919.86,
        "id": 350,
        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 917.6999999999999,
        "temperature": 0,
        "text": " I don't loop again because I'm now below 0.",
        "tokens": [
          50892,
          286,
          500,
          380,
          6367,
          797,
          570,
          286,
          478,
          586,
          2507,
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          13,
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        "compression_ratio": 1.76,
        "end": 921.42,
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        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
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        "temperature": 0,
        "text": " So I don't want to pick 1.",
        "tokens": [
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      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 922.3,
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        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 921.42,
        "temperature": 0,
        "text": " I want to pick 0.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.1979095458984375,
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        "end": 924.78,
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        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 922.3,
        "temperature": 0,
        "text": " So going up is just if I'm continuing.",
        "tokens": [
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      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 927.06,
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        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 924.78,
        "temperature": 0,
        "text": " But if I'm not continuing, I've got to back up 1.",
        "tokens": [
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        "avg_logprob": -0.1979095458984375,
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        "end": 931.3,
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        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
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        "temperature": 0,
        "text": " So this should work, I hope.",
        "tokens": [
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      {
        "avg_logprob": -0.1979095458984375,
        "compression_ratio": 1.76,
        "end": 933.18,
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        "no_speech_prob": 0.0007554033654741943,
        "seek": 90714,
        "start": 931.3,
        "temperature": 0,
        "text": " And so I'm going to hit refresh here.",
        "tokens": [
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      {
        "avg_logprob": -0.1979095458984375,
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        "end": 936.58,
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        "no_speech_prob": 0.0007554033654741943,
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        "temperature": 0,
        "text": " And I'm going to say pick one fruits.",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
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        "temperature": 0,
        "text": " And I got mango.",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 939.82,
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        "start": 938.3000000000001,
        "temperature": 0,
        "text": " And I got mango.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 941.1,
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        "seek": 93658,
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        "temperature": 0,
        "text": " And I got melon.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 942.46,
        "id": 361,
        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
        "start": 941.1,
        "temperature": 0,
        "text": " And I got cherry.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 943.14,
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        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
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        "temperature": 0,
        "text": " And I got melon.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 945.9000000000001,
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        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
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        "temperature": 0,
        "text": " I'm going to get melon most of the time.",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 948.4200000000001,
        "id": 364,
        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
        "start": 945.9000000000001,
        "temperature": 0,
        "text": " And so what I could do right now just to sort of verify",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
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        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
        "start": 948.4200000000001,
        "temperature": 0,
        "text": " that this is working, I am going to say,",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 957.1800000000001,
        "id": 366,
        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
        "start": 952.4200000000001,
        "temperature": 0,
        "text": " I'm going to write a little loop here for var j equals 0.",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
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        "seek": 93658,
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        "temperature": 0,
        "text": " I don't know.",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 959.1,
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        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
        "start": 957.74,
        "temperature": 0,
        "text": " I could use i again.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 960.1,
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        "no_speech_prob": 0.0020189823117107153,
        "seek": 93658,
        "start": 959.1,
        "temperature": 0,
        "text": " i equals 0.",
        "tokens": [
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      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 961.74,
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        "seek": 93658,
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        "temperature": 0,
        "text": " i is less than 100.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2544254596416767,
        "compression_ratio": 1.805,
        "end": 963.0200000000001,
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        "seek": 93658,
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        "temperature": 0,
        "text": " i plus plus.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 967.46,
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        "seek": 96302,
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        "temperature": 0,
        "text": " And I'm going to say var fruit equals pick one fruits.",
        "tokens": [
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        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 973.46,
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        "no_speech_prob": 0.014503364451229572,
        "seek": 96302,
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        "temperature": 0,
        "text": " And I'm going to say, I'm going to do var melon count.",
        "tokens": [
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        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 984.38,
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        "no_speech_prob": 0.014503364451229572,
        "seek": 96302,
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        "temperature": 0,
        "text": " And if fruit.name equals melon.",
        "tokens": [
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        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 985.98,
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        "temperature": 0,
        "text": " Melon count.",
        "tokens": [
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        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 987.86,
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        "seek": 96302,
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        "temperature": 0,
        "text": " Actually, you know what I'm just going to do?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 988.98,
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        "no_speech_prob": 0.014503364451229572,
        "seek": 96302,
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        "temperature": 0,
        "text": " That's fine.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27305917961652887,
        "compression_ratio": 1.7515923566878981,
        "end": 991.48,
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        "no_speech_prob": 0.014503364451229572,
        "seek": 96302,
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        "temperature": 0,
        "text": " Something I could do is I could actually just create a count.",
        "tokens": [
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 994.12,
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        "temperature": 0,
        "text": " Fruit.count++.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 996.28,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
        "start": 994.12,
        "temperature": 0,
        "text": " So I'm going to give all these.",
        "tokens": [
          50496,
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      },
      {
        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1001.96,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
        "start": 996.28,
        "temperature": 0,
        "text": " While I'm over here, I'm also going to say fruits index.",
        "tokens": [
          50604,
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      {
        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1003.84,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
        "start": 1001.96,
        "temperature": 0,
        "text": " I'm going to give them all a count of 0.",
        "tokens": [
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1006.84,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
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        "temperature": 0,
        "text": " So I'm going to count how many times they've picked.",
        "tokens": [
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1010.9200000000001,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
        "start": 1006.84,
        "temperature": 0,
        "text": " And if this is correct, doing it 100 is a small sample size.",
        "tokens": [
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1013.36,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
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        "temperature": 0,
        "text": " We should see that they're picked proportionally",
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1014.72,
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        "no_speech_prob": 0.00031503793434239924,
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        "temperature": 0,
        "text": " to their probability.",
        "tokens": [
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      },
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1017.36,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
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        "temperature": 0,
        "text": " So let's take a look at that.",
        "tokens": [
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      },
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        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1018.16,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
        "start": 1017.36,
        "temperature": 0,
        "text": " Let's run it.",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.19373937291423166,
        "compression_ratio": 1.6906779661016949,
        "end": 1020.6,
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        "no_speech_prob": 0.00031503793434239924,
        "seek": 99148,
        "start": 1018.16,
        "temperature": 0,
        "text": " And let's look at fruits.",
        "tokens": [
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        ]
      },
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        "avg_logprob": -0.22861922733367435,
        "compression_ratio": 1.672,
        "end": 1023.08,
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        "no_speech_prob": 0.00048029416939243674,
        "seek": 102060,
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        "temperature": 0,
        "text": " So fruits, the first one is mango,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22861922733367435,
        "compression_ratio": 1.672,
        "end": 1025.76,
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        "no_speech_prob": 0.00048029416939243674,
        "seek": 102060,
        "start": 1023.08,
        "temperature": 0,
        "text": " which was picked 30 times at a probability of 29.",
        "tokens": [
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        "end": 1027.04,
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        "seek": 102060,
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        "text": " That makes sense.",
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        "text": " Blueberry was picked 10 times.",
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        "text": " Its probability was 17.",
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        "text": " Cherry was picked 7.",
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        "text": " Its probability was 5%.",
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        "text": " Melon was picked 47.",
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        "text": " So this to me is working.",
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        "text": " Obviously, with just picking it 100 times,",
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        "text": " it's not going to map so perfectly.",
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        "text": " I would suspect if I made this 100,000,",
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        "text": " then those probabilities are going to, over time,",
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        "text": " going to even out and the counts are",
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        "text": " going to be much closer to the probabilities.",
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        "text": " So hopefully, you have enjoyed this particular video, which",
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        "text": " is just about how to pick elements",
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        "text": " from an array with different probabilities.",
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        "text": " And this might work.",
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        "text": " The nice thing about this particular algorithm",
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        "text": " is two things.",
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        "text": " One is I didn't have to create a separate array",
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        "text": " with lots of duplicate versions of all these objects in it.",
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        "text": " And I didn't have to do this thing",
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        "text": " where I have to pick random numbers multiple times",
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        "text": " and hope that one qualifies.",
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        "text": " And I could get stuck in an infinite loop.",
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        "text": " This is going to happen every single time.",
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        "text": " This is going to always pick and return a value.",
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        "text": " Sometimes it's going to have to run through this loop a few",
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        "text": " times, sometimes just once.",
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        "text": " But this is a pretty efficient and fast algorithm",
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        "text": " for doing this.",
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        "text": " So what did I miss?",
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        "text": " What questions do you have?",
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      },
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        "text": " Did you apply this in something?",
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      },
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        "text": " I'm going to apply this in something in just a moment.",
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        "temperature": 0,
        "text": " So if you look at this video's description,",
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        "text": " there's a link to a video where I program a solution",
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      {
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        "text": " to the traveling salesperson problem.",
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        "temperature": 0,
        "text": " The solution's a little bit strong.",
        "tokens": [
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        "temperature": 0,
        "text": " I don't know if it's really going to be a solution.",
        "tokens": [
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        "no_speech_prob": 0.007576703559607267,
        "seek": 110496,
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        "temperature": 0,
        "text": " An attempt at a solution to the traveling salesperson problem",
        "tokens": [
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        "text": " in JavaScript with a genetic algorithm.",
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    "transcription": " Hello, I'm back for another genetic algorithms video. Although this video actually that you're watching right now is not just a video about something you could do for genetic algorithms. This video is ultimately about picking from a list of things randomly, but picking some things more often than other things. And this is a thing that you do have to do. Where's my eraser? Over here. I'm coming, I'm coming. This is a thing that you have to do. With a genetic algorithm, because we want to have a population of elements, and we want to pick certain members of that population that have scored a higher fitness score to have their genetic information more likely passed down to the next generation. But let's erase our minds of the wonder that is genetic algorithms and just start with a very simple scenario. I'm going to create a JavaScript object. And this marker has a very interesting smell. And it's going to have things in it, like mango, five, blueberry, three, cherry, one, and apple, one. So what I want to be able to do is I want to be able to pick a fruit randomly. I want a function that says pick fruit. And I call that function. I'm going to get out of it returned a particular fruit. Maybe mango, maybe blueberry, maybe cherry, maybe apple. And I am going to get mango five times as often as I'm going to get apple. I'm going to get mango. And I wrote these numbers very specifically to add up to 10, because it's very easy for me to just sort of do the math here. But I'm going to get mango 50% of the time, blueberry 30% of the time, cherry 10% of the time, and apple 10% of the time. This relates to so many different kinds of scenarios that you might do in computer programming, if that makes sense. So how do we do this? Now, if you've been watching some of my other genetic algorithm videos or other videos, I've done this a number of different ways. One way that I've done this is I've built a separate array. And what I've done with that array is I've put, I've just said, hey, let's put mango in it five times. I'm just going to write m. Mango's in this array five times. And then I'm going to put blueberry in this array three times. And then I'm going to put cherry in it once and apple in it once. So if I had a bucket full of all these letters, and there's five m's in it, three b's, one c, one a, and I shuffled around and pick one out randomly, I'm going to pick m more five times as often as I'm going to pick a. So that's one way of doing it. Now, there's a bit of an issue with this way of doing it. This works really nicely for this. But if I have thousands of elements and thousands of different and a vast array of probabilities, some of which are very, very, very, very small probabilities, I did have to build this array that just has so much stuff in it. It's not a really efficient performance, good performance solution. And actually, if you look at a lot of my examples, that's the solution I'm using. It doesn't work so great. So I'm not proposing this. I didn't invent this. I'm just talking about it. I'm going to look at another solution. Now, there is another solution to this problem, which is a way that you could pick random numbers twice. So two random numbers. So each one of these, 0, 1, 2, 3. So on the one hand, I'm just picking a random number, 0, 1, 2, or 3. And I could do that easily by saying, it depends on in JavaScript or in processing, let's say floor random 4. And this will give me 0, 1, 2, or 3. So this is one way that I could do this. But if I do that, I'm just going to get each one of these 25% of the time. But if, in addition to picking one of these randomly, I picked another random number just between 0 and 1. So this is the fruit that I'm picking. And this is kind of my test number. If I pick another random number, that random number has to be less than the probability in order for this first random thing to qualify. So I might have to do this a bunch of times. Like, I might pick 3, then 0.7, and then 2, and 0.4, and then 1, and 0.8, and then 0, and 0.6, and then 3, and 0.01. You know, ah, Apple qualifies. So you can see, Mango is going to be able to qualify. If I pick 0, 50% of the time, this second random number is going to be fine. But if I pick Apple, only 10% of the time is this second random number going to be fine. So that's one way. The problem with this way is, again, with a lot of elements and a lot of probabilities, I can be stuck doing this over. That loop can take many, many, many, many times before it finds a qualifying random number. So this video, I'm going to program an entirely different algorithm. It was suggested to use this algorithm in a GitHub issue. I will link to that GitHub issue in this video's description, which is, hello, description down there. I'm trying to get used to where things are. Over there is some, no, maybe over there is like an ad. I don't know what you, don't, I don't know if, don't click on it. Maybe click on it. I don't know. Now I'm all stressed out. OK, never mind all that. There's stuff around me. I'm a person in a YouTube video. But there was a point to all this, wasn't there? Ah, OK. So I will also, in a moment, bring up that GitHub issue to look at it on the computer, because I do have a computer over there, which I will get to eventually. So what is this other algorithm? Well, one way you could imagine is, let's say I created, I don't know what this is, a tube, a pipe, a rectangle, some sort of two-dimensional space. And I said, ah, 50% of that space is mangoes space. And 30% of that space is blueberries space. And 10% of that space is cherry. And 10% of that space is apple. And this down here is 0. And up here is 1. So what if I just pick a random number now between 0 and 1? And it's almost like throwing a dart. I could be over here. And I'm going to throw a dart. Let's do it. Ready? 1, 2, 3. I missed. But actually, it landed over here, blueberry. I'm going to be more likely, there's more space for me to hit with mango than there is for blueberry than there is for cherry and apple. Now, I'm not going to build some whole entire dart-throwing simulation thing in my sketch. So I need a mechanism to do this, to pick the random number and figure out where I am. And actually, the way that this could work is I can actually start up here. You can pick a random number and then start subtracting values and find out at what point do I sort of exit this space. So hopefully, I'm going to program that algorithm. We'll look at it. Hopefully, it'll work. And then we'll come back here and sort of see if this makes more sense to us then. I'm going over there now. OK, here I am back in this screen with my computer. So this is the GitHub issue that I was referencing. There's a link to it in the video's description. Spectron, thank you for this original suggestion. And then also, Sinclairz81. This is from a while ago. It took me a while to get to this. Has a very fancy, crazy, ooh, look at this, unicorn implementation of it that uses ES6 and all sorts of cool, interesting JavaScript syntax that, ooh, I don't fully understand. Oh, but I will understand someday. So I'm going to do my own. So let me close those. And let's write some code. So first of all, let's quickly, I should have done this in advance. OK, there's a little pause there where I decided, actually, what I want to do is write the information. I want to pick one of the, it's easier for me to put it in an array. Because what I want to do is pick one of these elements from the array. And then I want the probability of how I pick it to be something relative to its score. So the first thing that I need to do, by the way, is I need to normalize all of these scores to values between 0 and 1. And so that they all add up to 1. I want them to be a percentage. So the first thing that I'm going to do is I'm going to just loop through every single fruit. And what I need to do is I need to add up all the scores. I need to find out what do they all add up to. Sum equals 0. And I'm going to say sum plus equal fruits index i dot score. Then the next thing I'm going to do, now, here's the thing. I might want to retain that score value. Or I could just overwrite it. But ultimately, what I want to do, I'm going to say fruits index i dot score equals fruits index i. And I know I could say divide equals. But I'm just going to write this all the way out. Fruits index i dot score divided by sum. So the idea is I want to normalize all of those scores. So all I need to do is add up the total and divide each one by the total. That's exactly what I would have done here. I get 10. 5 divided by 10 is 50%. 3 divided by 30%. And the math is never going to work out this perfectly. But who cares? Because the computer's doing it, not us. OK, so what I'm going to do here is I am going to go back to the browser. And I'm going to refresh. This canvas is here for no reason. And I'm going to look at fruits. And we can see here that now the scores are normalized 0.5, 0.3. And I could, if I want to, it might make sense to say actually I'm going to add a new property to each one of these objects. I'm going to say like norm score for like, I'm just going to call it probability for probability. Fruits index i dot probability. So now I'm going to hit refresh. I'm going to look at that fruits array. And I'm going to see here now it retained the score and has a probability of 0.5. And as you can see, if I were to go here and add one other fruit like melon and give it a score of 7.7, then if I hit refresh and look at fruits, you can see now I've got five. And it's worked it out. The math isn't so clean because it doesn't add up to 10. But mango now has a 29% chance of being picked. And melon has a 41% chance of being picked. So this is good. So now, how do I pick one of these randomly according to its probability? Now, it's time for me to write an algorithm that implements this particular, write some code that implements this particular idea, this particular algorithm. Throw a dart and find a place in here. So the way that I'm going to do that is first, I'm going to write a function. I'm going to call it pick one. And it's going to receive a list. So I'm going to have a generic function that could work with any list of things. Now, it's going to be set up to work in such a way that the list of things is made up of objects that have a property called prob for probability. But obviously, it could make this more generic in other ways as well. OK, so the first thing that I'm actually going to do is I'm going to say var index equals 0. So I'm going to sort of assume that the one that I'm going to pick is the first one. Then I'm going to pick a random number between 0 and 1.0. And I'm going to say, as long as r is greater than 0, then I'm going to say r equals r minus that particular object's probability. So let's think about this for a second. What I'm saying is that let's say I have something with a probability of 0.9. And I have something with a probability of 0.1. I shouldn't draw this right. Time out. I shouldn't draw it right next to this, because I'm going to do something different now. So I'm going to use, where is, I don't have any space. I'm going to come over here. This is fine. Let's just say a simplified arrangement, where this is my bar here. And I have something that has a as a probability of 0.9 and b as a probability of 0.1. The first thing that I did is I picked a random number somewhere between 0 and 1. And then I'm going to subtract 0.9 from it, because 0.9 is first. How often am I going to pick a number between 0 and 1, where I subtract 0.9, and now I have a value less than 0? 90% of the time, right? Only if I pick a number greater than 0.9. If I pick 0.95 and I subtract 0.9, I'm going to have a value of 0.05. So 90% of the time, when I subtract this, I'm going to be less than 0. When I'm out, when I'm done, when I'm out, I'm out. You're out. When I'm below 0, that's the thing I'm left with. 10% of the time, I'll pick that number, like 0.95, and then I go and subtract 0.1, and I'm out. So 10% of the time, I'll be left the last place I was. I'm starting up here. I pick some random number somewhere. I subtract this. Am I already below 0, or should I keep going? If I subtract this, then I'm about last. So this idea of just I keep subtracting these values, and it doesn't matter what order, right? Even if this was B was first, and I'm always subtracting 0.1 first, I'm only going to be less than 0 if I happen to pick a random number that's less than 0.1. So hopefully, this kind of makes some intuitive sense to you. I feel like there's a very smart way to diagram this that I'm not doing, but I've said it enough times. I'm hoping it makes sense. Let's come back to the code. So what do I need to do? Let's say, so if, so what I need to do now is say index plus plus. So I'm going to just keep doing this. I'm going to go through the list until I have now gone below 0. Now, there's a little bit, and then I want to say return list index, except for the fact that this isn't exactly right. This isn't exactly right because, I like how this bracket is highlighted down here. But anyway, forget about that. This isn't exactly right because I actually want to, I want, if I say index plus plus, I'm going to go one past the one that was actually the one that I wanted to pick. It makes sense. So I've got to very quickly just say index minus minus. Let me talk you through that. In the scenario where I just have 0.9, is the probability of index 0 is 0.9. So I pick a random number. I pick 0.8. I say r equals 0.8 minus 0.9. So that's negative 0.1. Then I say index plus plus. So I go to 1. And I go back to loop. I don't loop again because I'm now below 0. So I don't want to pick 1. I want to pick 0. So going up is just if I'm continuing. But if I'm not continuing, I've got to back up 1. So this should work, I hope. And so I'm going to hit refresh here. And I'm going to say pick one fruits. And I got mango. And I got mango. And I got melon. And I got cherry. And I got melon. I'm going to get melon most of the time. And so what I could do right now just to sort of verify that this is working, I am going to say, I'm going to write a little loop here for var j equals 0. I don't know. I could use i again. i equals 0. i is less than 100. i plus plus. And I'm going to say var fruit equals pick one fruits. And I'm going to say, I'm going to do var melon count. And if fruit.name equals melon. Melon count. Actually, you know what I'm just going to do? That's fine. Something I could do is I could actually just create a count. Fruit.count++. So I'm going to give all these. While I'm over here, I'm also going to say fruits index. I'm going to give them all a count of 0. So I'm going to count how many times they've picked. And if this is correct, doing it 100 is a small sample size. We should see that they're picked proportionally to their probability. So let's take a look at that. Let's run it. And let's look at fruits. So fruits, the first one is mango, which was picked 30 times at a probability of 29. That makes sense. Blueberry was picked 10 times. Its probability was 17. Cherry was picked 7. Its probability was 5%. Melon was picked 47. So this to me is working. Obviously, with just picking it 100 times, it's not going to map so perfectly. I would suspect if I made this 100,000, then those probabilities are going to, over time, going to even out and the counts are going to be much closer to the probabilities. So hopefully, you have enjoyed this particular video, which is just about how to pick elements from an array with different probabilities. And this might work. The nice thing about this particular algorithm is two things. One is I didn't have to create a separate array with lots of duplicate versions of all these objects in it. And I didn't have to do this thing where I have to pick random numbers multiple times and hope that one qualifies. And I could get stuck in an infinite loop. This is going to happen every single time. This is going to always pick and return a value. Sometimes it's going to have to run through this loop a few times, sometimes just once. But this is a pretty efficient and fast algorithm for doing this. So what did I miss? What questions do you have? Did you apply this in something? I'm going to apply this in something in just a moment. So if you look at this video's description, there's a link to a video where I program a solution to the traveling salesperson problem. The solution's a little bit strong. I don't know if it's really going to be a solution. An attempt at a solution to the traveling salesperson problem in JavaScript with a genetic algorithm. So thanks for watching. I will see you in another video. Goodbye. Thank you.",
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