d1 = m.document(name="lion")
d2 = m.document(name="tiger")
d3 = m.document(name="dolphin")
d4 = m.document(name="shark")
d5 = m.document(name="parakeet")
print("lion-tiger:", m.similarity(d1, d2))
print("lion-dolphin:", m.similarity(d1, d3))
print("dolphin-shark:", m.similarity(d3, d4))
print("dolphin-parakeet:", m.similarity(d3, d5))
print()
print("Related to tiger:")
print(m.neighbors(d2, top=3)) # Top three most similar.
print()
print("Related to a search query ('water'):")
print(m.search("water", top=10))
# In summary:
# A Document:
# - takes a string of text,
# - counts the words in the text,
# - constructs a vector of words (features) and normalized word count (weight).
# A Model:
# - groups multiple vectors in a matrix,
# - tweaks the weight with TF-IDF to find "unique" words in each document,
# - computes cosine similarity (= distance between vectors),
# - compares documents using cosine similatity.
# is called cosine similarity. This is what a Model uses:
d1 = m.document(name="lion")
d2 = m.document(name="tiger")
d3 = m.document(name="dolphin")
d4 = m.document(name="shark")
d5 = m.document(name="parakeet")
print "lion-tiger:", m.similarity(d1, d2)
print "lion-dolphin:", m.similarity(d1, d3)
print "dolphin-shark:", m.similarity(d3, d4)
print "dolphin-parakeet:", m.similarity(d3, d5)
print
print "Related to tiger:"
print m.neighbors(d2, top=3) # Top three most similar.
print
print "Related to a search query ('water'):"
print m.search("water", top=10)
# In summary:
# A Document:
# - takes a string of text,
# - counts the words in the text,
# - constructs a vector of words (features) and normalized word count (weight).
# A Model:
# - groups multiple vectors in a matrix,
# - tweaks the weight with TF-IDF to find "unique" words in each document,
# - computes cosine similarity (= distance between vectors),
# - compares documents using cosine similatity.
# is called cosine similarity. This is what a Model uses:
d1 = m.document(name="lion")
d2 = m.document(name="tiger")
d3 = m.document(name="dolphin")
d4 = m.document(name="shark")
d5 = m.document(name="parakeet")
print "lion-tiger:", m.similarity(d1, d2)
print "lion-dolphin:", m.similarity(d1, d3)
print "dolphin-shark:", m.similarity(d3, d4)
print "dolphin-parakeet:", m.similarity(d3, d5)
print
print "Related to tiger:"
print m.neighbors(d2, top=3) # Top three most similar.
print
print "Related to a search query ('water'):"
print m.search("water", top=10)
# In summary:
# A Document:
# - takes a string of text,
# - counts the words in the text,
# - constructs a vector of words (features) and normalized word count (weight).
# A Model:
# - groups multiple vectors in a matrix,
# - tweaks the weight with TF-IDF to find "unique" words in each document,
# - computes cosine similarity (= distance between vectors),
# - compares documents using cosine similatity.
