hkraemer

function [a_out, b_out]=dl_kelo(varargin)
% DL_KELO   Mean of the diagonal line lengths and their distribution - 
% Correction for border lines: KEep LOngest diagonal line (kelo)
%    A=dl_kelo(X) computes the mean of the length of the diagonal 
%    line structures in a recurrence plot X. In this correction, just the 
%    longest border line (in each triangle) of the RP is counted. All other 
%    border lines are discarded.
%
%    A=dl_kelo(X,'semi') computes the mean of the length of the diagonal 
%    line structures in a recurrence plot X using the mentionded correction. 

hkraemer

function [X_new,dl_new] = rp_diagonal(varargin)
% RP_DIAGONAL  RP with corrected lines ...
% 
%    [RP_new, dl_new] = rp_diagonal(RP) 
%    computes a new recurrence plot 'RP_new' by altering diagonal line structures 
%    in the input recurrence plot 'RP': Slubs, but also block structures, 
%    are deleted in favour of the longest diagonal lines (skeletonization). 
%    Whenever a diagonal line (starting with the longest lines contained in 
%    the diagonal line length histogram) encounters an adjacent diagonal 
%    line, this adjacent line and - recursively - all its consecutive 

hkraemer

### exercise 4 2te teil
language, text = hamlets.iloc[0]

counted_text = count_words_fast(text)

data = pd.DataFrame({
    "word": list(counted_text.keys()),
    "count": list(counted_text.values())
})