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The bubbles are not reality but it's inside your mind, originally uploaded by sheetal_shundori.
We live in a bubble baby.
A bubble's not reality.
You gotta have a look outside.
Listen
I almost wish I hadn't gone down that rabbit-hole — and yet — and yet — it's rather curious, you know, this sort of life!
Today is the day to be Happy
Today is the day to be Free
Today is the day that will change everything
"I must not fear.
Fear is the mind-killer.
Fear is the little-death that brings total obliteration.
I will face my fear.
I will permit it to pass over me and through me.
And when it has gone past I will turn the inner eye to see its path.
Where the fear has gone there will be nothing.
Only I will remain."
Am I smart enough?
That's the wrong question. There is no such thing as ``smart enough''.
If you love playing the piano, play it. You don't expect to be Alfred Brendel or Vladimir Ashkenazy. But you are doing something productive for yourself and those around you. Research is the same. If you enjoy doing it, pursue it. You will usually find that you can contribute something. As you go on, you will discover your strengths and find you have something unique to offer. Maybe there are others who are smarter in some way; they can compute complex probabilities in their head faster than you can, or whatever. But your contributions may be valuable in other ways.
As discussed above, research has many components. Problem solving, identifying issues, presentation, modeling, and criticism are amongst them. See what aspects best suit your abilities and personality. There is probably a niche for you somewhere.
What is written above about kidding yourself does NOT refer to ability or perception of ability; it refers to motivation and honesty about motivation. I want to be sure you are pursuing research for the ``right'' reasons.
%number of examples N=size(X,2); %dimension of each example M=size(X,1); %mean meanX=mean(X,2); %centering the data Xm=X-meanX*ones(1,N); %covariance matrix C=(Xm*Xm')/N; %computing the eigenspace: [U D]=eig(C); %projecting the centered data over the eigenspace P=U'*Xm;
import Jama.*; public class KLTransform { //Matrix x[i,j]= jth feature in ith example public Matrix k_lTransform(Matrix x) { x = x.transpose(); int nExample = x.getColumnDimension(); //calculate mean Matrix mean = getMean(x); double[][] oneD = new double[1][nExample]; for(int i = 0; i < nExample; i++) oneD[0][i] = 1; Matrix ones = new Matrix(oneD); //center the data Matrix xm = x.minus(mean.times(ones)); //Calculate covariance matrix Matrix cov = xm.times(xm.transpose()); /* In the matlab code, the covariance matrix is divided with N (nExample). Now cov and cov/nExample have the same eigenvectors but different eigenvalues. In this code, the division doesn't make any difference as we are only considering the eigenvectors. But there are some cases, like in Kaiser-Guttman stopping rule where only the eigenvectors with eigenvalue > 1 are chosen, division might make a difference. */ cov = cov.times(1.0/nExample); //compute eigen vectors Matrix eigenVectors = cov.eig().getV(); //compute pca Matrix pca = eigenVectors.transpose().times(xm); return pca; } public Matrix getMean(Matrix x) { int nExample = x.getColumnDimension(); int nFeature = x.getRowDimension(); double[][] meanD = new double[nFeature][1]; Matrix mean = new Matrix(meanD); for(int i = 0; i < nFeature; i++) { double avg = 0.0; for(int j = 0; j < nExample; j++) { avg+=x.get( i,j); } mean.set(i, 0, avg/nExample); } return mean; } //test public static void main(String[] args) { KLTransform kl = new KLTransform(); double [][] d = new double[][]{{1, 2, 3},{4,5,6}}; Matrix x = new Matrix(d); Matrix pc = kl.k_lTransform(x); pc.print(pc.getRowDimension(), 2); } }
find <where-to-look> -name <name-of-the-file> | xargs /bin/rm -f
Example:find <main-directory-name> -name .svn | xargs /bin/rm -rf
If you gcc is <=3.4 --> add this #include <iostream>
#include <cstdlib>
CHECK THIS OUT ! |
From Sheetal (sheetal57@gmail.com) | |
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Check this out! this is so cool !! Want to know more ? Go to www.nabaztag.com ! |
তোমার আমার মাঝখানেতে
একটি বহে নদী ,
দুই তটেরে একই গান সে
শোনায় নিরবধি ।
যেমন ঢাকা ছিলে তুমি
তেমনি রইলে ঢাকা ,
তোমার কাছে যেমন ছিনু
তেমনি রইনু ফাঁকা !
তবে কিসের তরে
থামলে লীলাভরে
যেতে যেতে পাড়ার পথে
কলস লয়ে কাঁখে ?
একটুখানি ফিরে কেন
দেখলে ঘোমটা - ফাঁকে ?
#-----------------
# start svn server
#-----------------
sudo -u www /sw/bin/svnserve -d -r /Users/al/svnrepo
#-------------------
# checkout a project
#-------------------
svn checkout svn://localhost/project1
sudo apt-get install libpng3
sudo ln -s /usr/lib/libpng.so.3 /usr/lib/libpng.so.2
unable to open color database /usr/X11R6/lib/X11/rgb.txt :
No such file or directory (stage.c stg_lookup_color)
sudo ln -s /usr/X11/share/X11/rgb.txt /usr/X11R6/lib/X11/rgb.txt