Ch2 Ex2-Programming Collective Intelligence

from pydelicious import DeliciousAPI
from pydelicious import *
from getpass import getpass
from time import sleep

def invert_dict(d):
    inverse = dict()
    for key in d:
        val = d[key]
        if val not in inverse:
            inverse[val] = [key]
        else:
            inverse[val].append(key)
    return inverse

def main():
    username = str(raw_input('Username:'))
    pwd = getpass('Pwd:')
    a = DeliciousAPI(username, pwd)

    # Get list of tagged urls
    frogs = get_tagposts('programming')
    sleep(2)

    # Put tagged urls into list
    list1 = []
    for a in range(len(frogs)):
        count = 0
        for b in frogs[a]:
            count += 1
            if count == 4:
                list1.append(frogs[a][b])

    # Get all instances of urls
    dic2 = {}
    for tadpoles in list1:
        spawn = get_urlposts(tadpoles)

    # Put tags into histogram
        for a in range(len(spawn)):
            count = 0
            for b in spawn[a]:
                count += 1
                if count == 3:
                    if (spawn[a][b] not in dic2):
                        dic2[spawn[a][b]] = 1
                    else:
                        dic2[spawn[a][b]] = dic2[spawn[a][b]] + 1

    # Invert the histogram and print most often first
    dic3 = invert_dict(dic2)
    # Print results
    res = dic3.keys()
    res.sort()
    res.reverse()
    print res
    for z in res:
        print dic3[z],"appears",z,"times"

if __name__ == "__main__":
    main()

"""
 Tag similarity. Using the del.icio.us API, create a dataset of tags and items. Use this to calculate similarity between tags and see if you can find any that are almost identical. Find some items that could have been tagged programming but were not.
Ps Dont know why I used frog related variables
"""