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+#TextMining Project: KeyWord Search
+#This script will search through a given paper or papers for the occurences of several keywords
+#it will then tell the user the most relevant paper based on the maximum number of occurences of a keyword or keywords
+#Claire Kincaid
+#April 18, 2016 (revised for MiniProject 5)
+import doctest
+import string
+import random
+import math
+from random import randint
+import requests
+
+
+
+Knight_full_text = requests.get('http://www.gutenberg.org/files/55708/55708-8.txt').text
+America_full_text = requests.get('http://www.gutenberg.org/files/55713/55713-0.txt').text
+Wolf_full_text = requests.get('http://www.gutenberg.org/files/55720/55720-8.txt').text
+#first remove all punctuation from the texts
+s1 = Knight_full_text # do you just put the whole text here?
+#out1 = s1.translate(string.punctuation)
+s2 = America_full_text # Sample string
+#out2 = s2.translate(string.punctuation)
+s3 = Wolf_full_text
+exclude = set(string.punctuation)
+s1 = ''.join(ch for ch in s1 if ch not in exclude)
+s2 = ''.join(ch for ch in s2 if ch not in exclude)
+s3 = ''.join(ch for ch in s3 if ch not in exclude)
+
+#make all letters lowercase
+Knight_text = str.lower(s1)
+America_text = str.lower(s2)
+Wolf_text = str.lower(s3)
+whole_text = Knight_text + America_text + Wolf_text
+#print(whole_text)
+
+word_list = whole_text.split(' ')
+
+#make an index of all words in Herland & Crusoe
+new_dict = {}
+""" The following code creates a dictionary (new_dict) that contains all of the
+words in both texts as keys and then the word that
+follows the key word stored in a dictionary. If the word already exists in the
+dictionary the code simply adds the following word in the list to the dictionary.
+ """
+for index,word in enumerate(word_list[:-1]):
+     if word not in new_dict:
+         new_dict[word] = [word_list[index + 1]]
+     else:
+         new_dict[word].append(word_list[index + 1])
+#print(new_dict)
+
+def quote(data,length_quote):
+     """ This function generated a random sentence/quote from the dictionary
+     created above. this code randomly chooses an index and then finds the key
+     with that index in the dictionary and then randomly chooses a value of that
+     key. That value is then added to a string and then becomes the next key.
+     This process is repeted until the desired length of quote is reached.
+     """
+     new_string = ''
+     num_words = 0
+     x = random.choice(list(data.keys()))
+     while num_words < length_quote:
+         if num_words > 0:
+             new_string += ' '
+             #print(new_string)
+         next_word = random.choice(data[x])
+         new_string = new_string + next_word
+         x = next_word
+         num_words = num_words + 1
+     new_string += '."'
+     #print(new_string)
+     return new_string
+
+
+
+Knight_America_Wolf = quote(new_dict, 10000)
+print(Knight_America_Wolf)
+
+
+
+def make_data(data):
+    """Takes a string, removes all punctuation, makes all letters lowercase and puts words of string into a list
+    >>> make_data("I'm hilarious")
+    ['im', 'hilarious']
+    """
+    listdata = data.split(data)
+    return listdata
+
+def word_count(data):
+    """Takes a string, uses make_data to turn it into an analyzable list
+    creates a dictionary that counts all words within that list
+    >>> word_count("I'm hilarious")
+    {'im': 1, 'hilarious': 1}
+    """
+    words = dict()
+    for word in make_data(data):
+        words[word] = words.get(word, 0) + 1
+    return words
+
+def word_find(data, keyword):
+    """ Takes a string, uses word_count to create dict counting all words in string
+    returns frequency of word specified as a keyword
+    >>> word_find("I'm hilarious", "hilarious")
+    1
+    """
+    hist = word_count(data)
+    return hist.get(keyword, 0)
+
+def multi_keywords_find(data, keywords):
+    """ Takes a string data and a list of keywords and returns dict w/ word count of those words
+    >>> multi_keywords_find("I'm hilarious", ['im', 'hilarious'])
+    {'im': 1, 'hilarious': 1}
+    """
+    all_keywords = dict()
+    for i in keywords:
+        all_keywords[i] = (word_find(data, i))
+    return all_keywords
+
+def multi_paper_word_find(data, keyword):
+    """takes string keyword, uses word_find to find the occurences of keyword in three datasets in a list
+    returns dictionary of papers in order of highest occurences of word to lowest"""
+    data_keyword = dict()
+    for i in data:
+        data_keyword[i] = word_find(i, keyword)
+    return data_keyword
+
+def relevance(data, keyword):
+    data_keyword = multi_paper_word_find(data, keyword)
+    most_relevant = max(data_keyword.get(data, 0))
+    return most_relevant
+
+
+keywords = ['kingdom', 'wild', 'cold', 'learning', 'war', 'violence', 'journey', 'love','hero','freedom','morning','people','interested','the']
+
+
+print (multi_keywords_find(Knight_America_Wolf, keywords))
+print (relevance(Knight_America_Wolf, keywords[1]))