Implementation of the Hungarian (Munkres) Algorithm. input_matrix is a List of Lists. input_matrix is assumed to be a cost m

Get results after calculation.

Returns expected value after calculation.

Find a row without a choice in it for the column indexed. If a row does not exist then return None.

Mark all columns not already marked which have zeros in marked rows.

Check if column or row is marked. If not marked then mark it.

Apply NMF to the specified document-term matrix X.

Apply topic modeling to the specific document-term matrix, using K topics.

centres, Xtocentre, distances = kmeans( X, initial centres ... ) in: X N x dim may be sparse centres k x dim: initial centres, e

Converts a profit matrix into a cost matrix. Expects NumPy objects as input.

Return the top ranked terms for the specified topic, using the centroids from the last run.

Return the top ranked terms for the specified topic, generated during the last NMF run.

Return the top ranked terms for the specified topic, generated during the last LDA run.

Computes a score for the specified rank, which is indexed from 1

Calculates minimum number of lines necessary to cover all zeros in a matrix. Algorithm based on: http://weber.ucsd.edu/~vcrawfor/hung

Return Boolean True if there is a choice in all marked columns. Returns boolean False otherwise.

Find a row index to use for the choice so that the column that needs to be changed is optimal. Return a random row and column if unab

Find a marked column that does not have a choice.

Returns rows and columns with matches in them.

Run the Mallet pre-processing step.

input_matrix is a List of Lists. input_matrix is assumed to be a cost matrix unless is_profit_matrix is True.

Mark all rows not already marked which have choices in marked columns.

Implements the term re-weighting method proposed by Blei and Lafferty.

Run the Mallet LDA step.

Set results during calculation.

Write documents to temporary file, for parsing by Mallet.

Subtract m from every uncovered number and add m to every element covered with two lines.

Construct the similarity matrix between the pairs of rankings in two different ranking sets.

-> |X| x |Y| cdist array, any cdist metric X or Y may be sparse -- best csr

Find all files in the specified directory and its subdirectories, and store them as strings in a list.

Return list of covered columns.

Return list of covered rows.

Solve the Hungarian matching problem to find the best matches between columns and rows based on values in the specified similarity matrix.

Read and normalize body text from the specified document file.

Selects row column combination with minimum number of zeros in it.

Calculate the overall agreement between two different ranking sets. This is given by the mean similarity values for all matched pairs.

Input a matrix and save it as a boolean matrix to designate zero locations. Run calculation procedure to generate results.

Apply NMF to the specified document-term matrix X.

Build a set of K centroids based on the specified partition memberships.

Utility function to compute a sequence of relevance scores using the specified function.

Convert a dictionary representing a clustering into a partition.

Format a list of multiple term rankings using PrettyTable.

Format a list of multiple term rankings using lists.

Load a pre-processed scikit-learn corpus and associated metadata using Joblib.

Load a NMF factorization result using Joblib.

Load a disjoint partition (clustering) result using Joblib.

Load stopwords from a file into a set.

Load a list of multiple term rankings using Joblib.

Preprocess a list containing text documents stored as strings.

Return the top ranked terms for the specified topic, generated during the last NMF run.