<commit_before>DataAnalysis
============

These programs analyze data using mathematical models and produces useful data/results. 

Instructions: Download Files and Run files individually using any Python IDE or Command Line Interpreter.

**Interpolation.py:** This program uses Langrang, Hermite, Linear Spline, and Cubic spline to interpolate data. 
                    
                       Data: To represent data points create a matrix where the 
                          first row represents your x-points and your second row
                          represents your y points.
                          EX: data = [ [1.0,2,3], [4,5.5,6.3] ]
                    
      Interpolating Methods: All methods return a interpolating function 
                             in array format. Which can be used to evaluate any 
                             point. In spline the case of spline methods, 
                             the methods will return a matrix where each row 
                             represents a linear equation based on interval. 
                                                      
                            Langrange:  LMethod(data)
                            Hermite:    HMethod(data)
                            Linear Spline: LSMethod(data)
                            Cubic Spline: CSMethod(data)

             Other Methods: showMatrix(mat)   #shows any matrix
                            printPoly(Poly)   #prints polynomial in regular form
                            evalPoly(Poly, x) #evaluates a Polynomial in array 
                                               format with point x
              Example Code:
                            data = [[1.1,2.2,3.3],[4.4,5.5,6.6]]
                            lp = LMethod(data)
                            print "\nLangrange P(x) = "
                            printPoly(lp)
                            print("P(0.58) = %f \n"% (evalPoly(lp,7.0/12)))<commit_msg>Fixed mispelling<commit_after>DataAnalysis
============

These programs analyze data using mathematical models and produces useful data/results. 

Instructions: Download Files and Run files individually using any Python IDE or Command Line Interpreter.

**Interpolation.py:** This program uses Langrang, Hermite, Linear Spline, and Cubic spline to interpolate data. 
                    
                       Data: To represent data points create a matrix where the 
                          first row represents your x-points and your second row
                          represents your y points.
                          EX: data = [ [1.0,2,3], [4,5.5,6.3] ]
                    
      Interpolating Methods: All methods return a interpolating function 
                             in array format. Which can be used to evaluate any 
                             point. In spline the case of spline methods, 
                             the methods will return a matrix where each row 
                             represents an equation based on interval. 
                                                      
                            Langrange:  LMethod(data)
                            Hermite:    HMethod(data)
                            Linear Spline: LSMethod(data)
                            Cubic Spline: CSMethod(data)

             Other Methods: showMatrix(mat)   #shows any matrix
                            printPoly(Poly)   #prints polynomial in regular form
                            evalPoly(Poly, x) #evaluates a Polynomial in array 
                                               format with point x
              Example Code:
                            data = [[1.1,2.2,3.3],[4.4,5.5,6.6]]
                            lp = LMethod(data)
                            print "\nLangrange P(x) = "
                            printPoly(lp)
                            print("P(0.58) = %f \n"% (evalPoly(lp,7.0/12)))
{'fraction_non_alphanumeric': 0.06350832266325224, 'fraction_numerical': 0.014340588988476313, 'mean_word_length': 1.1402739726027398, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 12, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3884620', 'n_tokens_mistral': 891, 'n_tokens_neox': 846, 'n_words': 313}