abridged version of the concatenation of the happiness dataset

concat-happiness-ds.py

def assemble(col1, col2, col3, col4):
  return np.concatenate((col1, col2, col3, col4), axis=1)

csd = assemble(cold[:s], slow[:s], dislike[:s], unhappy)
csl = assemble(cold[s:s*2], slow[s:s*2], like[:s], unhappy)
cfd = assemble(cold[s*2:s*3], fast[:s], dislike[s:s*2], unhappy)
cfl = assemble(cold[s*3:s*4], fast[s:s*2], like[s:s*2], happy)

# ... 
# We repeat the same for hot(hsd, hsl, hfd, hfl) and burning(bsd, bsl, bfd, bfl) tea temperatures

# tea internet book
tib = np.concatenate((csd, csl, cfd, cfl, hsd, hsl, hfd, hfl, bsd, bsl, bfd, bfl), axis=0)

# printing the first 5 rows and the shape of the dataset
tib[:5], tib.shape
'''
Out:
(array([[11.236, 12.965,  0.   ,  0.   ],
        [28.521,  3.448,  0.   ,  0.   ],
        [21.96 , 17.448,  0.   ,  0.   ],
        [17.96 , 12.262,  0.   ,  0.   ],
        [ 4.681,  3.144,  0.   ,  0.   ]]), (6000, 4))
'''