matchms.similarity.vector_similarity_functions module¶
Collection of functions for calculating vector-vector similarities.
- matchms.similarity.vector_similarity_functions.cosine_similarity(u: ndarray, v: ndarray) float64 [source]¶
Calculate cosine similarity score.
- Parameters:
u – Input vector.
v – Input vector.
- Returns:
The Cosine similarity score between vectors u and v.
- Return type:
cosine_similarity
- matchms.similarity.vector_similarity_functions.cosine_similarity_matrix(references: ndarray, queries: ndarray) ndarray [source]¶
Returns matrix of cosine similarity scores between all-vs-all vectors of references and queries.
- Parameters:
references – Reference vectors as 2D numpy array. Expects that vector_i corresponds to references[i, :].
queries – Query vectors as 2D numpy array. Expects that vector_i corresponds to queries[i, :].
- Returns:
Matrix of all-vs-all similarity scores. scores[i, j] will contain the score between the vectors references[i, :] and queries[j, :].
- Return type:
scores
- matchms.similarity.vector_similarity_functions.dice_similarity(u: ndarray, v: ndarray) float64 [source]¶
Computes the Dice similarity coefficient (DSC) between two boolean 1-D arrays.
The Dice similarity coefficient between u and v, is
\[\begin{split}DSC(u,v) = \\frac{2|u \cap v|} {|u| + |v|}\end{split}\]- Parameters:
u – Input array. Expects boolean vector.
v – Input array. Expects boolean vector.
- Returns:
The Dice similarity coefficient between 1-D arrays u and v.
- Return type:
dice_similarity
- matchms.similarity.vector_similarity_functions.dice_similarity_matrix(references: ndarray, queries: ndarray) ndarray [source]¶
Returns matrix of dice similarity scores between all-vs-all vectors of references and queries.
- Parameters:
references – Reference vectors as 2D numpy array. Expects that vector_i corresponds to references[i, :].
queries – Query vectors as 2D numpy array. Expects that vector_i corresponds to queries[i, :].
- Returns:
Matrix of all-vs-all similarity scores. scores[i, j] will contain the score between the vectors references[i, :] and queries[j, :].
- Return type:
scores
- matchms.similarity.vector_similarity_functions.jaccard_index(u: ndarray, v: ndarray) float64 [source]¶
Computes the Jaccard-index (or Jaccard similarity coefficient) of two boolean 1-D arrays. The Jaccard index between 1-D boolean arrays u and v, is defined as
\[\begin{split}J(u,v) = \\frac{u \cap v} {u \cup v}\end{split}\]- Parameters:
u – Input array. Expects boolean vector.
v – Input array. Expects boolean vector.
- Returns:
The Jaccard similarity coefficient between vectors u and v.
- Return type:
jaccard_similarity
- matchms.similarity.vector_similarity_functions.jaccard_similarity_matrix(references: ndarray, queries: ndarray) ndarray [source]¶
Returns matrix of jaccard indices between all-vs-all vectors of references and queries.
- Parameters:
references – Reference vectors as 2D numpy array. Expects that vector_i corresponds to references[i, :].
queries – Query vectors as 2D numpy array. Expects that vector_i corresponds to queries[i, :].
- Returns:
Matrix of all-vs-all similarity scores. scores[i, j] will contain the score between the vectors references[i, :] and queries[j, :].
- Return type:
scores