| import nltk | |
| import string | |
| from collections import Counter | |
| def untokenize(ngram): | |
| tokens = list(ngram) | |
| return "".join([" "+i if not i.startswith("'") and \ | |
| i not in string.punctuation and \ | |
| i != "n't" | |
| else i for i in tokens]).strip() |
This bookmarklet allows you to access content like papers, reports from scientific sites or any other website which can be opened easily when you are in UIUC network. The bookmarklet removes the hassle of finding the library proxy URL and then opening the site through that. It does that part automatically.
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| # coding=UTF-8 | |
| import nltk | |
| from nltk.corpus import brown | |
| # This is a fast and simple noun phrase extractor (based on NLTK) | |
| # Feel free to use it, just keep a link back to this post | |
| # http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/ | |
| # Create by Shlomi Babluki | |
| # May, 2013 |
| ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' | |
| ' SAMPLE: Encryption and decryption using DPAPI functions. | |
| ' | |
| ' To run this sample, create a new Visual Basic.NET project using the Console | |
| ' Application template and replace the contents of the Module1.vb file with | |
| ' the code below. | |
| ' | |
| ' Go to the folder %APPDATA%\Subversion\auth\svn.simple | |
| ' Copy the Encrypted Value of the password denoted by the line below 2 lines below the line saying password | |
| ' Compile the code and put this value in a new file called InputFile.txt and in the same location where you are executing the code. |
| """ | |
| Author: Shubhanshu Mishra | |
| Posted this on the keras issue tracker at: https://github.com/fchollet/keras/issues/108 | |
| Implementing a linear regression using Keras. | |
| """ | |
| from keras.models import Sequential | |
| from keras.layers.core import Dense, Activation | |
| model = Sequential() |
| df = pd.DataFrame(np.random.randn(10,2)) | |
| df.columns = ['Column 1', 'Column 2'] | |
| latexify() | |
| ax = df.plot() | |
| ax.set_xlabel("X label") | |
| ax.set_ylabel("Y label") | |
| ax.set_title("Title") | |
| plt.tight_layout() | |
| format_axes(ax) |
| import logging | |
| logger = logging.getLogger('anagram') | |
| logger.setLevel(logging.DEBUG) | |
| def anagram(string_set): | |
| if len(string_set) == 1: | |
| logger.info("Reached Length 1") | |
| return string_set # If the set has only one element then return that element. | |
| logger.debug("Input: %s" % string_set) | |
| temp = set() # Use a set to store all the possible anagrams. |
| """ | |
| This code snippet explains the implementation of a dictionary using a hashing function. | |
| Problem: Store the marks of student with given roll numbers in a data structure for quick access of marks based on roll number. | |
| Concept: Use a dictionary data structure. | |
| """ | |
| roll_numbers = [9,8,7,2,1,3,4,5] | |
| marks = [10,20,5,13,77,33,2,99] | |
| HASH_CONST=11 # Constant used for hashing. For practical purposes more sophesticated and optimized hash functions are used. |
| max_n = 1200 | |
| [k for k in range(1,max_n) if k % 10**int(np.log10(k)) == 0] | |
| """ | |
| [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000] | |
| """ | |
| #More computationally effective solution for large max_n |
