Approach:
I would like to Generate a performance/bandwidth graph between X and Y algorithms in Cooja Contiki Simulator. I will run both algorithms on the server by assuming Node A will run the X algorithm on Dataset-A and Node B will use the Dataset-B for Y Algorithm. Is it possible to capture the performance graph while running both algorithms?
I am not much familiar with the cooja simulator that is why I need help with this.
Your responses will be appreciated.
Thanks
Related
I'm estimating a linear OLS regression using some software, and I have three variables: Y (dependent), X1 (independent), and Intercept (a column of "1"s I manually created). I created Intercept because this particular software doesn't have a function to add a constant term.
The coefficients of X and Intercept are almost perfectly inverse (i.e. Intercept-coefficient = 1.5 and X-coefficient = negative 1.51). Both Y and X are columns of very small percentage changes (i.e. 0.0001). I've tried adding some other independent variables, and quickly run into multicollinearity issues - not sure if that's simply because the variables are highly similar, though.
I'm not very experienced with stats, are the coefficients a dead giveaway from statistical issues with the regression? Any advice is much appreciated, thank you!
I have installed Point Cloud Library(PCL) package for using Iterative closest point (icp) my question is: this package could be used for 2D data or not ?I want to align two TSNE data which are 2D.Link of the icp method
It is not a problem even if it supported only 3D. You can add a z=0 coordinate to your data points and the z component will always be zero in your solution.
PCL does not have a paired data ICP to the best of my knowledge, but it should be pretty trivial to write as the cost function just considers squared distance between each pair.
I would create clouds of the two sets of points, setting z to zero, write your cost function (sum of squared distances of each pair), and then cycle the icp through xy translation steps and z rotation steps using reverse half-ing style icp stepping.
I am trying to simulate a number of different distribution types for a project using Excel. Right now, I have generated a normal distribution with a mean of 35 and a standard deviation of 3.33. So far so good.
I would like to also generate some other distribution types.
One I have tried is a lognormal. To get that, I am using the following code:
=(LOGNORM.INV(RAND(),LN(45^2/SQRT(45^2+3.33^2)),SQRT(LN((45^2+3.33^2)/4.5^2))
It produces some output, but I would welcome anyone's input on the syntax.
What I really want to try to do is a power law distribution. From what I can tell, Excel does not have a built-in function to randomly generate this data. Does anyone know of a way to do it, besides switching software packages?
Thanks for any help you can provide.
E
For the (type I) Pareto distribution, if the parameters are a min value xm and an exponent alpha then the cdf is given by
p = 1 - (xm/x)^alpha
This gives the probability, p, that the random variable takes on a value which is <= x. This is easy to invert, so you can use inverse sampling to generate random variables which follow that distribution:
x = xm/(1-p)^(1/alpha) = xm*(1-p)^(-1/alpha)
If p is uniform over [0,1] then so is 1-p, so in the above you can just use RAND() to simulate 1/p. Thus, in Excel if you wanted to e.g. simulate a type-1 Pareto distribution with xm = 2 and alpha = 3, you would use the formula:
= 2 * RAND()^(-1/3)
If you are going to be doing this sort of thing a lot with different distributions, you might want to consider using R, which can be called directly from Excel using the REXcel add-in. R has a very large number of built-in distributions that it can directly sample from (and it also uses a better underlying random number generator than Excel does).
I am running the partial SVD of a large (120k x 600k) and sparse (0.1 of non-zero values) matrix on a 3,5GHz/3,9GHz (6 cores / 12 threads) server with 128GB of RAM using SVDLIBC.
Is it possible to speed up the process a little bit using multithreading so as to take full advantage of my server configuration?
I have no experience of multithreading; therefore I am asking for friendly advices and/or pointer to manuals/tutorials.
[EDIT] I am open to alternatives too (matlab/octave, r, etc.)
In Matlab, for sparse matrices, you have svds. This implementation benefits from multithreaded computation (1)
See irlba: Fast partial SVD by implicitly-restarted Lanczos bidiagonalization in R. It just calculates the first user-specified no. of dimensions. Had good experience with it in past. But, then I used on commercial version of R which was complied to take advantage of multi-threading so can't vouch for speed-improvement due because of multi-threading.
Let X1, X2,...,Xn be discrete random variables. I'm looking for a way to prove the random variables are independent but not identically distributed.
Can anyone suggest some ideas ?
Independence test is simple, and works for any distributions, X and Y are independent iff
P(X,Y)=P(X)P(Y)
testing whether they come from different distributions requires more advanced statistical tests - for example test whether the differences between these distributions are statistically significant using t-test
Both can be easily tested using R statistical software or any other similar tool (Matlab, Octave)