(C-x means ctrl+x, M-x means alt+x)
The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf
:
// Compile with: | |
// clang++ -std=c++11 -shared -l boost_python3 -I /usr/include/python3.2mu -fPIC -o bptuple.so tuple-test.cpp | |
#include <tuple> | |
#include <string> | |
#include <boost/python.hpp> | |
namespace py = boost::python; | |
using std::string; |
#!/bin/bash | |
sudo apt-get update | |
sudo apt-get install gcc | |
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_5.5-0_amd64.deb | |
sudo dpkg -i cuda-repo-ubuntu1204_5.5-0_amd64.deb | |
sudo apt-get update | |
sudo apt-get install cuda | |
export PATH=/usr/local/cuda-5.5/bin:$PATH | |
export LD_LIBRARY_PATH=/usr/local/cuda-5.5/lib64:$LD_LIBRARY_PATH | |
sudo apt-get install opencl-headers python-pip python-dev python-numpy python-mako |
### MATPLOTLIBRC FORMAT | |
# This is a sample matplotlib configuration file - you can find a copy | |
# of it on your system in | |
# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it | |
# there, please note that it will be overridden in your next install. | |
# If you want to keep a permanent local copy that will not be | |
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux | |
# like systems) and C:\Documents and Settings\yourname\.matplotlib | |
# (win32 systems). |
import numpy as np, numpy.linalg as linalg | |
def fast_svd(M, k): | |
p = k+5 | |
Y = np.dot(M, np.random.normal(size=(M.shape[1],p))) | |
Q,r = linalg.qr(Y) | |
B = np.dot(Q.T,M) | |
Uhat, s, v = linalg.svd(B, full_matrices=False) | |
U = np.dot(Q, Uhat) | |
return U.T[:k].T, s[:k], v[:k] |
import numpy as np, numpy.linalg as linalg | |
def fast_svd(M, k): | |
p = k+5 | |
Y = np.dot(M, np.random.normal(size=(M.shape[1],p))) | |
Q,r = linalg.qr(Y) | |
B = np.dot(Q.T,M) | |
Uhat, s, v = linalg.svd(B, full_matrices=False) | |
U = np.dot(Q, Uhat) | |
return U.T[:k].T, s[:k], v[:k] |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.datasets import fetch_mldata | |
from sklearn.decomposition import FastICA, PCA | |
from sklearn.cluster import KMeans | |
# fetch natural image patches | |
image_patches = fetch_mldata("natural scenes data") | |
X = image_patches.data |
import numpy as np | |
import matplotlib.pyplot as plt | |
from itertools import product | |
from sklearn.decomposition import RandomizedPCA | |
from sklearn.datasets import fetch_mldata | |
from sklearn.utils import shuffle | |
mnist = fetch_mldata("MNIST original") | |
X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000] |