Linalg.eig | Python: Find Eigenvalues & Eigenvectors Via Numpy.linalg.eig


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Python: Find Eigenvalues & Eigenvectors Via Numpy.linalg.eig


Hello, everyone, This video will show you how to find. Eigenvalues and eigenvector via numpy doubt lean. LG DOT E IJ. So we have a matrix a you can buy four one six three. We are interested to find the eigenvalue and eigenvector, so the X is equal to Lambda X, So we’re gonna find Lambda and the corresponding Eigen Vector. Okay, so we are going to use the the dump. I mean, algebra, the EIG. So the first thing is very import. Numpy has empty set of the matrix and then call linear algebra on E IG and passing the matrix. How comes the eigenvalue and eigenvector? So even for the print of the eigenvalue you again six and one, they will point out the eigen vector corresponding. I go back. There will be zero point. Four, four, seven, two one, three, six and zero point, eight, nine, four, four, two seven one nine and minus zero point, three one, six, two, two, seven seven, then zero point, nine, four, eight, six, eight, three three. So there are two eigen eigen value One is six and they always point that there was one point zero, and then the corresponding eigen fact that it goes with six would be this one and the one corresponding with one would be. Listen, okay, so we are going to do. We test this piece so a times. X, which is equal to four one six three times. The eigen vector this one! Yeah, you will see that the answer would be you. Can, hmm, two point? Six, eight, three, two eight da da da and five point three, six, six, five six da da da, okay. Now let us check to see now. We have the eigenvalue. Which is this one multiply with corresponding? I go back to this one, and you see that you can. The musical. Putting Lambda Times X Lambda X Lambda in this case is six. The corresponding eigen vector will be this one in the value When you multiply this you. Can you get the same thing above, okay. That was okay. It’s their first eigenvalue. Now we’re gonna do the second piece, saying, please use the X 4 1 6 3 and then the the other I go back. There would be this one here. Then you see? The answer will be minus zero point, three, one, six, two, two seven seven dardo and then zero point nine, four, eight, six, eight, three three. Now, you know, check with the other lambda times. The Eigen Vector Lambda here is the 1 and corresponding eigen vector would be this one. Yeah, and then when you multiply, you will get this one same sure. She shows that hey. X is about the longer X. Similarly arms up here will be OK next. You could have known that. X, okay, I’m going to. I’m going to attach this to the description. So you guys can try it by yourself. Okay, thanks, guys. I hope you guys liked the video. You feel new to the channel? Don’t forget to subscribe and before you leave, Don’t forget the heat or smash the like button. Thanks, guys, see you next time. Bye bye!

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