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View dataset_readers.py
"""Basic dataset reader"""
# Author: Sudeep Pillai <spillai@csail.mit.edu>
# License: MIT
import cv2
import numpy as np
import os, fnmatch, time
import re
from itertools import izip, imap, chain
from collections import defaultdict, namedtuple
View test-tuple.cpp
// 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;
View gist:7780849
#!/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
View matplotlibrc
### 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).
View tmux.md

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

# remap prefix to Control + a
View fast_svd.py
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]
View fast_svd.py
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]
View learning_gabor_filters.py
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
View gist:5321478
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]