![]() ![]() * /two/discussion/18585/multiplying-vectors#Item_6 You can paste that class in a separate tab, so it gets the job done and gets outta your sight! :-bd /** [-O<įor now, I've made a custom PVectorPlus class, in order to get back the lost functionality for both mult() & div() methods. Fortran 90 provides two intrinsic functions for the multiplication of matrices and/or vectors: the scalar function DOTPRODUCT() for evaluating the inner. It's very hard to convince Processing's devs to do what we wish for the project. I'm sure there's a very good reason why this is not-done, but how should I approach this? Stick to the incredibly long x * x, y * y and z * z or is there some other method? PVector pixelPosition = PVector.mult(pixelSpaceSize.x * normalizedPosition.x, pixelSpaceSize.y * normalizedPosition.y, pixelSpaceSize.z * normalizedPosition.z) Right? But instead I now have to multiply the individual fields: PVector pixelPosition = PVector.mult(pixelSpaceSize, normalizedPosition) PVector normalizedPosition = new PVector(0.8, 0.2) Īnd I want to get the pixel space position of my normalised position? Ideally I'd do. PVector pixelSpaceSize = new PVector(400, 300) Can someone point me to the correct way of multiplying a normalised position PVector with a pixel space PVector? I've seen one or two threads on the forums about this, so I know that it apparently doesn't make much sense. MULTIPLY A SCALAR TO ONE VECTOR CODE UPDATEThen, in order to update this vector in-place, write rand!(state).Everytime I want to do multiplications on PVectors I get frustrated as PVector.mult(PVector) doesn't exist. The former creates a BitVector, while the latter makes a vector of Bools.įor a vector of random true/false use either state = rand(Bool, n) or state = Random.bitrand(n). In other words, you re-use the labels state and out, but you don’t re-use the arrays that those labels were applied to.Īlso, false(n) is very different from rand(, n). ![]() These pre-allocations have no effect: state = falses(n)īecause you over-write them later (not in-place, just new assignment), so they are wasted. You have to make your operations in-place, the view only helps with the slicing. But since Im retrieving the values of the vector from another function, I dont have the same access to the variables that form the vectors values. There is also no in-place copying into out going on here. Now if I was adjusting the values of the vector individually, it would simply be a matter of writing a line to multiply those values before they are assigned to the vector. But both view(w,3,:,:) * state will allocate a completely new array, and multiplication with 0.1 will also allocate a new array. So you don’t allocate anything in the slicing operation. In a vector space, it is a way to multiply vectors together, with the result of this multiplication. So the above is equivalent to out = 0.1 * view(w, 3, :, :) * state An inner product is a generalization of the dot product. + 0.0)) # safe viewĪlequa: out = only affect indexing expressions. You might do better to re-arrange the dimensions of w, and to take state = rand((1.0, 0.0),n) instead. Julia> mul!($out, w), $state, 0.1, 0.0) # no allocationīut the other thing to know is that you aren’t hitting the fast * here at all, because the element types don’t match, and because the view of w isn’t a happy one. Julia> 0.1 * $w) * $state) # allocates two vectors Julia> (0.1 * $w)) * $state # allocates a new matrix, then a vector ![]() (Soon, perhaps, may automate such things.) If out was pre-allocated, you could also write mul!(out, w, state, 0.1, 0). Scalar-Vector multiplication is a very important arithmetic operation in implementing signal or image processing algorithms. * w*state, or better lmul!(0.1, w*state). It would be more efficient from this perspective to write 0.1 * (w*state) which is the same as 0.1. *(a, b, c, xs.) in Base at operators.jl:538Īnd so this takes the view, multiplies it by a scalar to make a matrix, and then multiplies that by the vector. ![]() MULTIPLY A SCALAR TO ONE VECTOR CODE CODEMaybe the first thing to know is that * proceeds left to right: julia> 0.1*w*state # will show the code ![]()
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