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from numpy import *
from scipy.stats import beta


class BetaBandit(object):
    def __init__(self, num_options=2, prior=(1.0,1.0)):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options
        self.prior = prior

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from numpy import *
from scipy.stats import beta
import random


class BetaBandit(object):
    def __init__(self, num_options=2, prior=None):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options

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# -*- coding: utf-8 -*-
"""
example use of pandas with oracle mysql postgresql sqlite
    - updated 9/18/2012 with better column name handling; couple of bug fixes.
    - used ~20 times for various ETL jobs.  Mostly MySQL, but some Oracle. 

    to do:  
            save/restore index (how to check table existence? just do select count(*)?), 
            finish odbc, 
            add booleans?, 

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#!/usr/bin/env python

# calling : python snap_to_comp_coloring.py <snap_components> <snap_singletons> <comp_coloring>
# in: [size] [node ID] [node ID] [...] and [singleton ID]
# out: [node ID]  [comp ID]
#      [node ID]  [comp ID]
#      ...
#      [singleton ID]  [comp ID]

# IMPORT MODULES

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%% Read Netflix dataset
A = readSMAT('/scratch/dgleich/netflix/netflix.smat');

k = [10 25 50 100 150 200];
l = size(k,2);

%% Matlab's SVDS
for i= 1:l
    tic; 
    [U,S,V] = svds(A,k(i)); 

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from numpy import *
from scipy.stats import beta
import random


class BetaBandit(object):
    def __init__(self, num_options=2, prior=None):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options

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import theano
from pylearn2.models import mlp
from pylearn2.train_extensions import best_params
from pylearn2.training_algorithms import sgd, learning_rule
from pylearn2.utils import serial
from pylearn2.termination_criteria import MonitorBased
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from sklearn.preprocessing import StandardScaler
import numpy as np
from random import randint

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### 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).

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import numpy as np
from sklearn.feature_extraction import image
from sklearn.cluster import MiniBatchKMeans
from sklearn import cross_validation, svm, datasets
from sklearn.datasets import fetch_olivetti_faces, fetch_mldata
from matplotlib import pylab as pl

def HIK_kernel(X,Y):
    return np.array([[np.sum(np.minimum(x,y)) for y in Y] for x in X]) 
    

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from scipy.stats import rv_continuous
from scipy.special import gammaln, gammaincinv, gammainc
from numpy import log,exp

class igamma_gen(rv_continuous):
  def _pdf(self, x, a, b):
      return exp(self._logpdf(x,a,b))
  def _logpdf(self, x, a, b):
      return a*log(b) - gammaln(a) -(a+1)*log(x) - b/x
  def _cdf(self, x, a, b):