#generate minhash aignitures for new text, and add new texts to LSH model
new_text = [
'Jupiter is primarily composed of hydrogen with a quarter of its mass being helium',
'Jupiter moving out of the inner Solar System would have allowed the formation of inner planets.',
]
new_labels = ['new_doc1', 'new_doc2']
#1.create minhash signitues for new text
new_minhash = MinHash(new_text,
n_gram=n_gram,
permutations=permutations,
hash_bits=hash_bits,
seed=seed)
#2.update lsh model with new hash signitures and verify lsh model updates reflected
lsh.update(new_minhash, new_labels)
print(lsh.contains())
#print the adjacency_list of all docs
print(lsh.adjacency_list())
#print the edge list of all docs that are flagged as duplicates to plot in text similarity graph
print(lsh.edge_list())
#remove text and label from model (if its not there , you will get an error returned)
lsh.remove(6)
print(lsh.contains())
#get matrix(n*m) of text signatures generated by minhash function (n=text row, m=selected permutations)
minhash.signatures.shape
def test_lsh_contains():
lsh = LSH(minhash, labels)
assert lsh.contains() == labels
