I have a tricky matrix manipulation issue that I could really use some help with.
I need to reorganize a series of 2d matrices so that they align most effectively across subjects. Each matrix has ~50 rows (which are the observations) and 13 columns (which designate the 'weight' of each observation on a series of 13 outcome measures). Based on the manner in which the data are created, there is no inherent meaning in the order of the rows, however I need to reorganize each matrix such that the rows contain meaning between subjects.
Specifically, I want to be able to reorder the matrices such that the specific pattern of weightings in a given row aligns with a similar pattern in the same row across a group of 20 subjects. To make matters worse, some subjects have missing rows, although all have between 45 and 50 rows.
As an example:
subject 1:
[ 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5, 0.5, 0.6, 0.6, 0.7;
0.9, 0.8, 0.8, 0.7, 0.7, 0.6, 0.6, 0.5, 0.5, 0.4, 0.4, 0.3, 0.3]
subject 2:
[ 0.8, 0.7, 0.7, 0.6, 0.6, 0.5, 0.5, 0.4, 0.4, 0.3, 0.3, 0.2, 0.2;
0.0, 0.0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5, 0.6, 0.7]
problem: row 1 in subject 1 aligns best with row 2 in subject 2 (and v.v.) and I would like to reorganize them as such [note: the real life problem is much more convoluted than this].
I apologize ahead of time for how idiosyncratic this issue is, but I really appreciate any help that anyone can give.
Mac
Related
Right now I am doing a parameter sweep and I am trying to convert my data to a 3D graph to show the results in a very nice fashion. The problem is that I don't quite know how to plot it as I am having an issue with the result variable.
mute_rate = [0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625]
mute_step = linspace(-2.5, 2.5, 6)
results = [949.58, 293.53, 57.69, 53.65, 293.41, 1257.49;
279.19, 97.94, 32.60, 29.52, 90.52, 286.94;
32.96, 28.06, 19.56, 6.44, 13.47, 55.80;
2.01, 1.52, 5.38, 1.00, 0.89, 1.41;
0.61, 0.01, 18.59, 0.03, 0.56, 1.22;
1.85, 1.51, 18.64, 18.57, 18.54, 6.90]
So the first row in the result variable presents the results of the mute rate and mute step performed on the population from my genetic algorithm. For example:
0.5, -2.5 = 949.58,
0.5, -1.5 = 293.53,
0.5, -0.5 = 57.69
etc
It sounds like you want something akin to:
mesh(mute_step, mute_rate, results);
shading interp;
Other styles of plot would be surf or pcolor (for a 2d view).
i have some codes which i need to reimplement with Cairo::RefPtrCairo::Context...It is a bit confusing since i could not find good example which uses pattern while we have Cairo::RefPtrCairo::Context instead of cairo_t..
Cairo::RefPtr<Cairo::Surface> surface =
Cairo::ImageSurface::create(Cairo::FORMAT_ARGB32, width, height);
Cairo::RefPtr<Cairo::Context> cr = Cairo::Context::create(surface);
cairo_pattern_t *cp = cairo_pattern_create_radial(x_off, y_off, 0, x_off, y_off, cent_point_radius);
cairo_pattern_add_color_stop_rgba(cp, 0.0, 0.7, 0.7, 0.7, 0.8);
cairo_pattern_add_color_stop_rgba(cp, 1.0, 0.1, 0.1, 0.1, 0.8);
cairo_set_source(cr, cp);
How i can change "cp" to something which is recognizable for cr->set_resource().....cr used to be a cairo_t,but then i had to change it to Cairo::RefPtrCairo::Context
best regards
Since you've already decided to do it the C++ way, why not go all the way in?
// Create image surface.
Cairo::RefPtr <Cairo::Surface> refSurface =
Cairo::ImageSurface::create(Cairo::FORMAT_ARGB32,
nWidth,
nHeight);
// Create Cairo context for the image surface.
Cairo::RefPtr <Cairo::Context> refContext =
Cairo::Context::create(refSurface);
// Create a radial gradient (pattern)
Cairo::RefPtr <Cairo::RadialGradient> refPattern =
Cairo::RadialGradient::create(x_off,
y_off,
0,
x_off,
y_off,
cent_point_radius);
// Add color stops to the pattern
refPattern->add_color_stop_rgba(0.0,
0.7,
0.7,
0.7,
0.8);
refPattern->add_color_stop_rgba(1.0,
0.1,
0.1,
0.1,
0.8);
// Set the pattern as the source for the context.
refContext->set_source(refPattern);
// Add a closed path and fill...
I have an adjacency matrix with the non zero elements indicating the weights of the link.The weights are decimals below 1 but are positive. For example, consider the below matrix as the weighted adjacency matrix a
array([[0. , 0.93, 0.84, 0.76],
[0.93, 0. , 0.93, 0.85],
[0.84, 0.93, 0. , 0.92],
[0.76, 0.85, 0.92, 0. ]])
I would like to obtain the node betweenness centrality of all the nodes in it. It is to be noted that my actual adjacency matrix is 2000 X 2000. I am new to networkx and hence any help will be highly appreciable.
You can try this
import networkx as nx
import numpy as np
A=np.matrix([[0. , 0.93, 0.84, 0.76, 0.64],
[0.93, 0. , 0.93, 0.85, 0 ],
[0.84, 0.93, 0. , 0.92, 0.32],
[0.76, 0.85, 0.92, 0. , 0.55],
[0.64, 0 , 0.32, 0.55, 0]])
G=nx.from_numpy_matrix(A)
betweeness_dict = nx.centrality.betweenness_centrality(G,weight='weight')
The betweeness_dict will contain the betweeness centrality of all the nodes
{0: 0.0, 1: 0.0, 2: 0.13888888888888887, 3: 0.0, 4: 0.13888888888888887}
You can read more about the documentation at this link.
I'm using the curve fitting app in MATLAB. If I understand correctly the "b1" component in the left box is the mean of function i.e. the x point where y=50% and my x data is [-0.8 -0.7 -0.5 0 0.3 0.5 0.7], so why is this number in this example so big (631)?
General model Gauss1:
f(x) = a1*exp(-((x-b1)/c1)^2)
Coefficients (with 95% confidence bounds):
a1 = 3.862e+258 (-Inf, Inf)
b1 = 631.2 (-1.117e+06, 1.119e+06)
c1 = 25.83 (-2.287e+04, 2.292e+04)
Your data looks like cdf and not pdf. You can use this code for your solution
xi=[-0.8,-0.7,-0.5, 0.0, 0.3, 0.5, 0.7];
yi= [0.2, 0.0, 0.2, 0.2, 0.5, 1.0, 1.0];
fun=#(v) normcdf(xi,v(1),v(2))-yi;
[v]=lsqnonlin(fun,[1,1]); %[1,2]
mu=v(1); sigma=v(2);
x=linspace(-1.5,1.5,100);
y=normcdf(x,mu,sigma);
figure(1);clf;plot(xi,yi,'x',x,y);
annotation('textbox',[0.2,0.7,0.1,0.1], 'String',sprintf('mu=%f\nsigma=%f',mu,sigma),'FitBoxToText','on','FontSize',16);
you will get: mu=0.24537, sigma=0.213
And if you still want to fit to pdf, just change the function 'normcdf' in 'fun' (and 'y') to 'normpdf'.
I am trying to generate a Numeric Range of Double by val arrayOfDoubles = (0.0 to 1.0 by 0.1).toArray, but the resultant is not what I expected it to be. the result is something like Array(0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6, 0.7, 0.7999999999999999, 0.8999999999999999, 0.9999999999999999). Why is it like this. I could use this code to get what I expect it to be:
val roundedArray = for (x <- arrayOfDoubles) yield BigDecimal(x).setScale(1, BigDecimal.RoundingMode.HALF_UP).toDouble
Which results in Array[Double] = Array(0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)But this looks really cumbersome and expensive for BigDecimal converts the double to String and then parse it.
Is there a way I could get the NumericRange already rounded to one decimal place?
Thanks.
0 to 10 map (_ / 10.0)
should do the trick
Floating point arithmetic is not exact, i.e. some decimal numbers cannot be represented exactly, and are represented by the closest available numbers. See also Is floating point math broken?