i use Ucinet to manipulate some data and i would plot it with NetDraw, but i have a problem
after normalization and affiliation i have a dataset as this:
A B C
A 0.0 0.5 0.5
B 0.5 0.0 0.5
C 0.5 0.5 0.0
In doing so, i do Visualize->NetDraw and, after that, File->Open->Ucinet dataset->Network, selecting the file above and the mode: "1-mode Network(s)". But when i try to do this i found the error "Access violation at address 0000000040C76E in module netdraw.exe. Write of address 0000000000000."
Can anyone help me?
Thank you
I solve this problem installing the 32-bit version of Ucinet. In doing so i solve all problem encountred before.
Related
I hope you can help me with what seems to be an easy problem but I cannot figure it out.
I have a variable (a) in a dataframe that ranges from 0-10 with continous values. I now want to recode this variable into a new variable where all values correspond to their distance from a fixed value = 5.
In an example, I would like for a to be recoded to the values of b.
a= c(1,2,3,4.5,6,7,9,10,3.4,5.5,7.3)
b= 4.0 3.0 2.0 0.5 1.0 2.0 4.0 5.0 1.6 0.5 1.3
I appreciate any help. Thank you!
I am trying to fit a non-linear model using 3 independent variables (light, temperature and vapor pressure deficit (VPD)) to predict net ecosystem CO2 exchange (NEE).
I know how to use the nlinfit function, but my problem is that I want to use 2 criteria:-
1. if VPD < 1.3
NEE = (Param(1).*Param(2).*Ind_var(:,1))./(Param(1).*Ind_var(:,1)+Param(2)) + Param(3).*(1.6324.^((Ind_var(:,2)-18)./10));
2. if VPD >= 1.3
NEE = (Param(1).*(Param(2).*exp(-Param(4).*(Ind_var(:,3)-1.3))).*Ind_var(:,1))./(Param(1).*Ind_var(:,1)+(Param(2).*exp(-Param(4).*(Ind_var(:,3)-1.3))))+Param(3).*(1.6324.^((Ind_var(:,2)-18)./10));
Basically, if the independent variable VPD (Vapor pressure deficit) is below 1.3, I want to force my Param(4) = 0.
But I don't know how to do that.
Could you help me?
Thanks,
Alexis
You can replace Param(4) by Param(4)*(VPD<1.3).
Conditional expressions are evaluated to 1 if true and 0 if false in Matlab.
I am using GLPK for solving a minimization linear programming problem in octave. its giving me some variable values like 0.0000000000277 or 0.999999999999. I want to get that 0.0000000000277 as 0 and that 0.999999999999 as 1. Does GLPK has any option for doing this? Any help will be really appreciated.
GLPK has an option called Solution Rounding which you can set to round off tiny values 0. Look for Solution Rounding among these options in GLPK.
However, for other values you will have to do a bit of post-processing by writing some code.
If 0.9 < x < 1.1, set x = 1 etc.
I'm attempting to use the fscanf() command to read a text file. The text file contains a table of years (in the first column) and across the row is the rainfall for each month in that year. Nothing is in scientific notation. The file is called "OctavilleRainfall.txt", containing following:
1988 9.2 5.4 5.6 1.2 2.2 0.1 0.0 0.0 0.1 0.0 0.4 2.3
1989 12.3 3.4 2.1 1.9 1.2 0.5 0.1 0.0 0.3 0.3 0.5 2.1
1990 10.2 6.7 3.3 1.3 1.1 0.2 0.2 0.0 0.1 0.2 0.3 1.9
1991 9.0 2.3 4.8 0.7 0.6 1.1 0.0 0.0 0.0 0.1 0.6 3.4
My current code reads as follows:
file = fopen('OctavilleRainfall.txt');
A = fscanf(file,'%f',[13 inf])
But when A displays, it is in scientific notation.
I'm not sure why it is in scientific notation, as I'm using the format command f not e. I need it not in scientific notation to be able to do my work. Any suggestions on how to fix this? It would be much appreciated.
The problem is not with fscanf(), but rather with the display format Matlab uses. Try changing it to a different format. Simply type:
format shortG
Note that the display format does not change any of the calculations performed, so it shouldn't affect your work.
I'd like to make a piece of music and there are 16 sounds possible, but not every sound can follow the other (for example sound 'A' can be followed by sound 'B', 'D' and 'F' but not by 'c' and 'E' etc). I have substituted this as a vector with 16 state spaces (values between 0 and 9) and I'd like to use them as inputs for neural network. Let's say I get new values as random (from 0 to 9) for the 16 elements of my vector. Now what I'd like my network to decide is which is the best suited value from the 16 based on the value of the last output, because as I already mentioned, each element has a certain number of possible elements that can come afterwards. DO YOU THINK IT COULD BE SOLVED BY NEURAL NETWORK and WHAT KIND OF NN WOULD BE BEST SUITED. Does anyone have similar examples. THANKS!
Rather than using a neural network, you may just want to create a table of probabilities that mark transitions between your notes. Imagining you could only play A, C, and E:
A C E
A 0.2 0.1 0.7
C 0.4 0.2 0.4
E 0.2 0.5 0.3
Using that table as an example, you could start on a random note (let's say a C). Then, using the table and starting from the row for 'C', we see that with a 0.4 probability we go to A, a 0.2 probability we go to C again, and a 0.4 probability we go to E. Then you just keep transitioning between your notes. A lot simpler than a neural network, and it may be closer to what you're looking for?