mrgloom

#!/usr/bin/python
#
# K-means clustering using Lloyd's algorithm in pure Python.
# Written by Lars Buitinck. This code is in the public domain.
#
# The main program runs the clustering algorithm on a bunch of text documents
# specified as command-line arguments. These documents are first converted to
# sparse vectors, represented as lists of (index, value) pairs.

from collections import defaultdict

mrgloom

#test save and load of numpy matrix
#test matrix multiplication in memory and using memmap

#in case of memmap no need to use batch processing

#also can test hdf5 and pytables for matrix mult

#can use matrix mult for pca (more smart to use randompca)

#need to test it on x64 machine

mrgloom

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

mrgloom

#test save and load of numpy matrix
#test matrix multiplication in memory and using memmap

#in case of memmap no need to use batch processing

#also can test hdf5 and pytables for matrix mult

#can use matrix mult for pca (more smart to use randompca)

#need to test it on x64 machine

mrgloom

def pca(data,k):
	#data M x N
	#get mean
	mean= np.mean(data,axis=0) # N long
	# print mean.shape
	# print mean

	#M x N
	data_c= (data-mean) 
	print data_c.shape

mrgloom

import numpy as np
import time

def read_data():
	#M x N
	data= np.loadtxt("data_3d.txt",delimiter=" ", skiprows=1, usecols=(0,1,2))
	print data.shape
	# print data
	return data
	

mrgloom

import numpy as np
import time

def read_data():
	#M x N
	data= np.loadtxt("data_3d.txt",delimiter=" ", skiprows=1, usecols=(0,1,2))
	print data.shape
	# print data
	return data
	

mrgloom

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

mrgloom

# 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

mrgloom

>> a = [1 2 3; 2 5 7; 3 7 9]

a =

     1     2     3
     2     5     7
     3     7     9

>> [U S V] = svd(a)