amueller

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

syhw

"""
A deep neural network with or w/o dropout in one file.
"""

import numpy
import theano
import sys
import math
from theano import tensor as T
from theano import shared

goldingn

# clear the workspace
rm(list = ls())

# load the relevant libraries

# install.packages(rCUR)
library(rCUR)  # for CUR decomposition
# install.packages(irlba)
library(irlba)  # for fast svd

GaelVaroquaux

import numpy as np
import time

from sklearn import cluster
from sklearn import datasets


lfw = datasets.fetch_lfw_people()
X_lfw = lfw.data[:, :5]
eps = 8. # This choice of EPS gives 44 clusters

amueller

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]