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These are the user uploaded subtitles that are being translated: 1 00:00:00,540 --> 00:00:04,250 Hello everyone and welcome to the lecture on matrix operations. 2 00:00:04,270 --> 00:00:09,120 Now that we learn how to create a matrix Let's go ahead and learn how to use functions and perform operations 3 00:00:09,120 --> 00:00:10,140 on that matrix. 4 00:00:10,320 --> 00:00:12,240 Let's jump to our studio. 5 00:00:12,240 --> 00:00:12,560 All right. 6 00:00:12,570 --> 00:00:14,450 So here are our studio. 7 00:00:14,550 --> 00:00:19,860 Make sure you've saved the code from the previous lecture or you can reference the notebook or notes 8 00:00:19,860 --> 00:00:25,410 for this lecture to go ahead and copy and paste this code which creates the stock matrix from the creating 9 00:00:25,410 --> 00:00:27,630 matrices lecture. 10 00:00:27,960 --> 00:00:30,830 Now that you have your stock matrix Let's go ahead and just run this. 11 00:00:30,840 --> 00:00:32,110 Make sure we have it. 12 00:00:32,190 --> 00:00:37,790 So we have our stock Matrix where the rows are the ticker symbols for Google and Microsoft stock and 13 00:00:37,790 --> 00:00:42,310 the columns represent some made up stock price for those days of the week. 14 00:00:42,330 --> 00:00:47,690 Let's go ahead and show how we can perform just some basic functions across columns and rows. 15 00:00:47,730 --> 00:00:53,640 So imagine you wanted to get the total sum of these stocks across the columns. 16 00:00:53,640 --> 00:00:56,520 You can use call some to do that. 17 00:00:56,610 --> 00:01:02,910 So that in through the consul and raise it up a bit since we already have these values in our environment 18 00:01:03,000 --> 00:01:07,170 I can go ahead and use call some's. 19 00:01:07,170 --> 00:01:14,640 Notice that the ES capitalized and then I can just pass in my stock matrix and I'm using our CDs kind 20 00:01:14,640 --> 00:01:20,340 of autocomplete here and that's going to sum the columns together. 21 00:01:20,370 --> 00:01:26,190 So I have the value sum for Monday Tuesday Wednesday Thursday Friday so you can imagine this is useful 22 00:01:26,190 --> 00:01:32,010 if you happen to have one stock of Google on one stock of Microsoft and you wanted to get your total 23 00:01:32,010 --> 00:01:39,370 portfolio value for each of those days if you wanted to get the rose some's even though it doesn't make 24 00:01:39,440 --> 00:01:41,460 too much sense for the data we have. 25 00:01:41,940 --> 00:01:50,190 You can do row sums as well and that will come across those rows so it's good to sum all those days 26 00:01:50,190 --> 00:01:53,980 together and across all those stock prices. 27 00:01:54,180 --> 00:01:59,500 OK so we have column some's and Rose sums and we can also do mathematical operations. 28 00:01:59,700 --> 00:02:04,550 So for example I could say row means. 29 00:02:04,890 --> 00:02:13,290 And then again just puts stock matrix and that will give me the mean value for those ROEs across both 30 00:02:13,290 --> 00:02:15,050 Google stock and Microsoft stock. 31 00:02:15,150 --> 00:02:20,460 So this is making a little more sense for the data we have because we can get the mean or average value 32 00:02:20,460 --> 00:02:24,990 for the week of Google stock and Microsoft's stock. 33 00:02:24,990 --> 00:02:30,900 And similarly you can actually use call means to do the same operation just across the columns. 34 00:02:30,900 --> 00:02:36,750 So something to note here is that there's a reference link in the notes for this lecture where it's 35 00:02:36,750 --> 00:02:40,380 going to have a reference to all the available functions for Matrix. 36 00:02:40,530 --> 00:02:44,430 And you'll notice that a lot of these functions are actually very similar to the built in functions 37 00:02:44,430 --> 00:02:49,290 for vectors except they just specify whether you're performing the action across the rows or across 38 00:02:49,290 --> 00:02:50,900 the columns. 39 00:02:50,910 --> 00:02:51,460 All right. 40 00:02:51,540 --> 00:02:55,070 So let's go ahead and see how we can add columns and rows to a matrix. 41 00:02:55,080 --> 00:03:00,810 We can use the C bind function to buy the new column and the our bind to bind a new row. 42 00:03:01,320 --> 00:03:07,330 Let's go ahead and console and create a new vector or call f b. 43 00:03:07,410 --> 00:03:13,750 And this is just going to be some made up Facebook stock values for the five days. 44 00:03:13,770 --> 00:03:16,470 Let's say there's a jump. 45 00:03:16,470 --> 00:03:16,730 All right. 46 00:03:16,740 --> 00:03:18,890 So I have my vector F. B. 47 00:03:18,990 --> 00:03:26,580 Now let's say I want to bind this to my original stock Matrix I can go ahead and make a new variable 48 00:03:26,580 --> 00:03:26,760 . 49 00:03:26,910 --> 00:03:35,580 I'll call it tech stocks and I'm going to have it be equal to our binds in our bine is going to take 50 00:03:35,580 --> 00:03:41,850 in our original matrix as the first arguments and the next argument is going to be what else we want 51 00:03:41,850 --> 00:03:48,120 to buy into it then in this case I want to bind SB to it and notice that I'm using our bind because 52 00:03:48,120 --> 00:03:50,610 I want to bind it as a new row. 53 00:03:51,440 --> 00:03:53,250 And so now if I check out tech stocks 54 00:03:55,950 --> 00:04:02,750 I get this nice output with Google Microsoft and our new Vektor Facebook stock binded as a new row. 55 00:04:03,870 --> 00:04:10,480 Something to note here is that the variable name F B was actually used as the new Wrone. 56 00:04:10,590 --> 00:04:13,250 So that's a useful feature to keep in mind. 57 00:04:13,260 --> 00:04:17,670 Let's go ahead and see how we can add a new column to our matrix to do this. 58 00:04:17,670 --> 00:04:23,250 I want to go ahead and make a variable called a Viji and I'm going to have that be equal to the row 59 00:04:23,250 --> 00:04:28,300 means of the stock matrix. 60 00:04:28,710 --> 00:04:32,350 Actually the tech stocks matrix since we added SB to it. 61 00:04:33,210 --> 00:04:38,550 So we have this average and that's the average weekly average of all those stocks. 62 00:04:38,550 --> 00:04:41,780 Let's go ahead and buying that as a new column next to Friday. 63 00:04:41,950 --> 00:04:47,700 So we'll have the prices for each week of the day Monday Tuesday Wednesday and then finally we'll have 64 00:04:47,730 --> 00:04:51,080 a column consisting of the average price for that week. 65 00:04:51,300 --> 00:04:54,510 So I'm going to go ahead and say tech stocks 66 00:04:57,440 --> 00:05:04,770 see binds which stands for Columbines and I'm going to go ahead and say tech stocks and then pasan that 67 00:05:04,860 --> 00:05:06,950 average factor. 68 00:05:07,590 --> 00:05:12,630 So if you go ahead and clear this and check out what tech stocks is equal to no notice we have that 69 00:05:12,630 --> 00:05:13,940 nice average. 70 00:05:14,250 --> 00:05:15,750 So that's it for this lecture. 71 00:05:15,750 --> 00:05:21,300 Later on we're going to learn even more useful features of matrices in our thanks everyone and I'll 72 00:05:21,300 --> 00:05:22,200 see you at the next lecture 7752