sjqtentacles

(fn table-insert [tab val]
  (var t tab)
  (table.insert t val)
  t)

(fn table-remove [tab indx]
  (var t tab)
  (table.remove t indx)
  t)

sjqtentacles

(define (zip lis1 lis2)
    (if (and (not (equal? lis1 '())) (not (equal? lis2 '())))
        (cons (cons (car lis1) (car lis2)) (zip (cdr lis1) (cdr lis2)))
        '()))

(define (take ls count)
    (if (and (> count 0) (not (equal? ls '())))
        (cons (car ls) (take (cdr ls) (- count 1)))
        '()))

sjqtentacles

-module(flatten_ints).
-export([flatten/1, test/0]).

% I also wrote a scala one from the prolog 99 problems set - but this was a while back
% located here: https://github.com/Nixonite/scala-99/blob/master/p07.scala


% base cases here
flatten([]) -> [];
flatten([[]|T]) -> flatten(T);

sjqtentacles

import scipy.io
import numpy as np

data = scipy.io.loadmat("subject.mat")

for i in data:
	if '__' not in i and 'readme' not in i:
		np.savetxt(("filesforyou/"+i+".csv"),data[i],delimiter=',')

sjqtentacles

def CH_index(X, labels, centroids):
    #X being a pandas dataframe, so X = dataframe.values
    #labels being the (KMeans(n_clusters=i).fit(X)).labels_
    #centroids being the KMeans centroid values of a fitted kmeans model
    '''
    https://github.com/scampion/scikit-learn/blob/master/scikits/learn/cluster/__init__.py
    '''
    mean = np.mean(X,axis=0) 
    B = np.sum([ np.sum(labels==i)*(c - mean)**2 for i,c in enumerate(centroids)])
    W = np.sum([ (x-centroids[labels[i]])**2 for i, x in enumerate(X)])