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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" => General
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Sets how many lines of history VIM has to remember
set history=300

" Enable filetype plugin
filetype plugin on
filetype indent on

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#!/usr/bin/env python2 
from nltk.corpus.reader import TaggedCorpusReader
from nltk.tokenize import RegexpTokenizer
from shogun.Kernel import CommUlongStringKernel
from shogun.Features import StringUlongFeatures, StringCharFeatures, RAWBYTE
from shogun.PreProc import SortUlongString
from scikits.learn.cluster import affinity_propagation
import numpy as np

def read_reviews():

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import XMonad
import Data.List
import XMonad.Hooks.ManageHelpers
import XMonad.Util.EZConfig
import XMonad.Config.Gnome
import XMonad.Config.Desktop (desktopLayoutModifiers)
import XMonad.Layout.NoBorders (smartBorders)
import XMonad.Layout.PerWorkspace (onWorkspace)
import XMonad.Layout.CenteredMaster (centerMaster)
import XMonad.Layout.SimpleFloat (simpleFloat)

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#!/usr/bin/env python2

import random
import nltk
from sklearn.linear_model import LogisticRegression
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer

from nltk.corpus import movie_reviews
documents = [(movie_reviews.raw(fileid), category)

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from sklearn.datasets import load_svmlight_file
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm.sparse import LinearSVC
from sklearn.cross_validation import StratifiedKFold
from sklearn import metrics
import numpy as np

X, y = load_svmlight_file("fr.vec")
y[y == -1] = 0
kf = StratifiedKFold(y, k = 10, indices=True)

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from sklearn.datasets import load_svmlight_file
from sklearn.naive_bayes import MultinomialNB
from sklearn.cross_validation import StratifiedKFold
from sklearn import metrics
X, y = load_svmlight_file("mpqa_en.vec")
kf = StratifiedKFold(y, k = 10, indices=True)
clf = MultinomialNB()
for train_index, test_index in kf:
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

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from sklearn.naive_bayes import EMNB, MultinomialNB, BernoulliNB
from sklearn.cross_validation import KFold
from sklearn.datasets import load_svmlight_file
from scipy.sparse import vstack
import numpy as np

X, y = load_svmlight_file("mpqa_en.vec")
y = np.asarray(y, np.int32)
n_labeled = int(0.8 * X.shape[0])
X_labeled = X[:n_labeled]

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"""http://stackoverflow.com/questions/6282432/load-sparse-array-from-npy-file
"""
import random
import scipy.sparse as sparse
import scipy.io
import numpy as np

def save_sparse_matrix(filename, x):
    x_coo = x.tocoo()
    row = x_coo.row

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Programming Achievements: How to Level Up as a Developer

This gist is part of a blog post. Check it out at:

http://jasonrudolph.com/blog/2011/08/09/programming-achievements-how-to-level-up-as-a-developer

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#encoding:utf-8
from java.io import FileInputStream
from java.io import ObjectInputStream

import sys

jarfiles = ["/opt/lingpipe-segmenter/lingpipe-4.0.1.jar", "/opt/lingpipe-segmenter/zhToksDemo.jar"]
for jar in jarfiles:
    if jar not in sys.path:
        sys.path.append(jar)