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def create_rdict(d):
    """
    Return a dictionary with each value mapping to a set of keys.
    """
    rd = {}
    for (key,value) in d.items():
        v = rd.setdefault(value, set([]))
        v.add(key)

    return rd

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def create_rdict(d):
    """
    Return a dictionary with each value mapping to a set of keys.
    """
    rd = {}
    for (key,value) in d.items():
        if rd.has_key(value):
            rd[value].add(key)
        else:
            rd[value] = set([key])

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def flat_dict(d):
    """
    Return a dict where sets are replaced by a single element of each
    set. Compose with create_rdict to reverse a 1-1 dict.
    """
    nd = {}
    for (key, value) in d.items():
        nd[key] = value.pop()

    return nd

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import random as pr

def flip(p):
    """ Flip a biased coin. """
    if pr.random() < p:
        return True
    else:
        return False

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# J. Stober
# May 13, 2011

import numpy as np
import numpy.random as nr

class EGreedy(object):

    def __init__(self, k = 10, epsilon = 0.1):

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#!/usr/bin/python
"""
Author: Jeremy M. Stober
Program: GMM.PY
Date: Thursday, September  4 2008
Description: Fit a Gaussian Mixture Model with EM.
"""

import os, sys, getopt, pdb, string
from numpy import *

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class A:
    pass

if __name__ == '__main__':

    import pickle

    pickle.dump(A(), open("A.pck","w"))
    a = pickle.load(open("A.pck"))
    print a

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#! /usr/bin/env python
"""
Author: Jeremy M. Stober
Program: NORMAL.PY
Date: Friday, July 7, 2011
Description: Manipulating normal distributions.
"""

import numpy as np
import numpy.linalg as la

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import numpy as np

# Unlike other implementations, this can handle very high dimensional data (like images).

def compute_pca(data):
    m = np.mean(data, axis=0)
    datac = np.array([obs - m for obs in data])
    T = np.dot(datac, datac.T)
    [u,s,v] = np.linalg.svd(T)

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#!/usr/bin/python
"""
Author: Jeremy M. Stober
Program: TILES.PY
Date: Monday, March 31 2008
Description: A simple CMAC implementation.
"""

import os, sys, getopt, pdb
from numpy import *