Thank you for your correction, Jim.
In Apprentice.2, same approach with numpy version, I fixed as following.
```
Z = rand(100,2)
X, Y = Z[:,1], Z[:,2]
R = sqrt(X.^2 + Y.^2)
T = atan2(Y,X)
```
Also in Apprentice. 7, I fixed . position.
I understand Julia works well without anonymous function,
I think anonymous function usage and list comprehensions are separate issues.
Anonymous functions are not type-specialized, so they’re basically always slow.
I avoid them in any code that’s not one-off.
List comprehensions are type-specialized, but the inference for them isn’t
super robust,
In Apprentice.6, I don't think you want to use the sqrtm(). sqrt() is
already vectorized over the matrix. Also a couple of '.'s are misplaced,
so perhaps instead:
Z =rand(10,2)
D = sqrt((Z[:,1].-Z[:,1]').^2+(Z[:,2].-Z[:,2]').^2)
As a newbie myself, what surprised me is that this is faster
In Apprentice.6, I don't think you want to use the sqrtm(). sqrt() is
already vectorized over the matrix. Also a couple of '.'s are misplaced,
so perhaps instead:
Z =rand(10,2)
D = sqrt((Z[:,1].-Z[:,1]').^2+(Z[:,2].-Z[:,2]').^2)
As a newbie myself, what surprised me is that this is faster
Julia is not as performant with anonymous functions, and list
comprehension.
The compiler has a harder time with the optimization step. This is not a
surprise and is
known to the language designers. This is not a surprise.
On Saturday, July 5, 2014 8:01:46 PM UTC-5,
I solved Expert 4 by Alireza's approach like following. (I don't understand
how to use TensorOperations.jl in this case)
Thanks for Alireza!
```
p, n = 10, 20
M = ones(n,n,p)
V = ones(n,p)
S = reduce(+, [M[i,:,j]*V[i] for i = 1:n, j = 1:p])'
S
```
2014年6月27日金曜日 5時59分15秒 UTC+9 Steven G. Johnson:
In original numpy version as following, matrix and vector are 3dimension
arrays.
Is there any way to compute tensordot like numpy?
p, n = 10, 20M = np.ones((p,n,n))V = np.ones((p,n,1))S = np.tensordot(M, V,
axes=[[0, 2], [0, 1]])print S# returns #[[ 15.]# [ 15.]# [ 15.]# [ 15.]# [ 15.]]
On Thursday, June 26, 2014 9:54:34 AM UTC-4, Michiaki Ariga wrote:
In original numpy version as following, matrix and vector are 3dimension
arrays.
Is there any way to compute tensordot like numpy?
There is no built-in tensor contraction function at the moment
I'm not sure and array of tuple is what you want in the meshgrid case,
I mean can you use it to compute say cos(X*Y) (which is usually what you
want to use meshgrid for) ?
It's also linked to the problem of evaluating a Gaussian, the solution
given here use meshgrid:
X, Y =
Thank you for your kind replies.
I noticed that I'm not familiar with array comprehension style in Julia.
I added your solutions to my repos.
(If you have any problem to do it, please tell me)
As Alireza said, this is just a translation from numpy, but I believe there
are good questions
Actually that's not a bad idea; someone should start a Julia-specific
exercise repo.
About Expert.4, I'm not sure how you're running it, but matlist and veclist
should obviously be lists of matrices and vectors, respectively.
matlist = Matrix[rand(4,4), rand(4,4)]
veclist = Vector[rand(4),
Feel free to check out (and contribute!) to
https://github.com/quinnj/Rosetta-Julia. I started it when I first got
involved with julia and it's got a fair number of examples and exercises.
-Jacob
On Jun 23, 2014 5:52 PM, Alireza Nejati alireza@gmail.com wrote:
Actually that's not a bad
Actually, a slight modification. The way I wrote it, it will compute the
product of all matrices with all vectors (pxp mults), which is not what you
want. You just want each matrix to multiply its respective vector (p
mults). The solution to that is:
p = length(matlist)
reduce(+,
Very cool! Would be interesting to see some examples that utilize the
functional aspects of the Julia language.
On Sunday, June 22, 2014 10:43:32 AM UTC-4, Michiaki Ariga wrote:
Hi all,
I'm a Julia newbee, and I'm trying to learn Julia and wrote Julia version
of rougier's 100 numpy
I just had a quick look. Here are some ideas for a few exercices.
You can use list comprehension in some exercices e.g.
Checkerboard pattern
Float64[(i+j)%2 for i=1:8, j=1:8]
10x10 matrix with row values ranging from 0 to 9
Float64[j for i=0:9, j=0:9 ]
It seems that what is called
Same with Apprentice.4 :
[(x,y) for x in linspace(0,1,10), y in linspace(0,1,10)]
meshgrid() isn't included in Julia because it's almost never really needed.
Good work on these exercises, although I fear that the questions, being
designed for numpy, may not accurately reflect typical julia
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