Metadata-Version: 2.1
Name: py4tfidf
Version: 0.0.1
Summary: Pseudo Nearest Neighbors Python Library
Home-page: https://github.com/luthfi118/py4tfidf
Author: Mgs. M. Luthfi Ramadhan
Author-email: luthfir96@gmail.com
License: UNKNOWN
Description: # py4tfidf
        
        Term Frequencyâ€“Inverse Document Frequency (TF-IDF) Python Library
        
        ## Getting Started
        
        This project is simply implementation of TF-IDF algorithm in python programming language.
        
        ### Prerequisites
        
        Numpy
        
        
        ### Installing
        
        The easiest way to install py4tfidf is using pip
        
        ```
        pip install py4tfidf
        ```
        
        ### Usage
        There is 2 public method of tfidf class. It is vectorize_train and vectorize_test. vectorize_train used to build the corpus, calculate idf based on training text, and transform it into usable vector by multiplying it's tf and it's idf, while vectorize_test is just simply transforming the test text into usable vector by multiplying it's tf with previously obtained idf. vectorize_train and vectorize_test takes 1 argument namely x_train and x_text respectively. Because tokenizing is usually done in text preprocessing phase, we assume you tokenize your text by your own, so the argument for vectorize_train and vectorize_test should be list of tokenized text.
        ```
        from py4tfidf.vectorizer import tfidf
        vec = tfidf()
        x_train = [['i','love', 'python'],['natrual','language','processing','is','fun'],['python','is','fun']]
        x_test = [['python','language','is','fun'],['im','learning','natrual','language','processing']]
        x_train = vec.vectorize_train(x_train)
        x_test = vec.vectorize_test(x_test)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
