Dev #13
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Dev #13
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* Fixed the wrapper for Calling Opticluster! * Fixing the .RD file * Fixed issue where opticluster was returning a matrix array and not a data.frame. * Changing documentation * Fixing the warnings from building the code. * Update to the test * Updating the gitignore
* Added Cpp testing structure! * Added Test Fixture Class * Forced the cluster to only return OTU data, and created the fix test fixture with test! * Created all of the test for the optimatrix adapter and removed the .dll from being tracked! * Created all the test in the testfixture for listvectors and now I am adding them to the test files. * Created base test for listVector * Basic test for listVector are completed! * Created the basic structure and test fixture for cluster commands * Cluster Testing suite being pushed up! * Fix cluster algorithm (#2) * Fixed a slew of issues by adding bidirection distances. * Fixed the clustering issue, clustering should be working correctly now! * Generated Definitions for OptiCluster Test * Finished creating unit test of the cluster command and opticluster * Added OptiData test fixture * Started creating the unit test for utils * Utils command cluster and test work as expected!! * Removed old code from one of the testing suites. * Added another test in utils. * Clustor has a 89% test coverage! Everything should be tested and ready to merge back!
…roper stats". I appears it is returning incorrect data on different computer architectures. It is also not needed in the code.
* This should be the fix for the failing cluster test. * Changed the test to look for equivalence rows instead of equal dataframes. Gives the same results, the only differences is that the cluster have different numbers.
…/false condition for shuffling
* Added to build ignore and properly documented the new R code. * Unstable, have to fix the mismatch header situation, but was able to return a vector with all the metrics! * Cluster now returns test metrics and added test to support it.
* Adding a single, complete, average, and weighted linkage algorithms * Adding cluster functions * Added the new clustering methods, but am getting issues when trying to format them. * Unstable cannot create proper phylip file with matrix data.. * More unstable code, but we are getting closer. The phylip file is correct for a small dataset * STABLE, the cluster does not fail and will actually cluster. However I am getting incorrect results. Will refactor the code to work. * All Clusters (Furthest, Nearest, average, and weighted) all work as they should! I just need to clean it up and add test! * Created R-facing function in R from cpp and cleaned up a bit of the code. * Cleaning up the main function * Small optimization to cluster (x2 speed_up) * Added optimization and cleaned up functions * Modifications to the namespace file and test! * Cluster works with windows now! * Fixing the round point error on windows and in R. It has something to do with the compilation of the objects...most likely it is related to the conversions of data. * Fixed the floating point issue, now I have to fix an issue where the cutoff amounts are decreasing. * Fixed the floating point issue! My adjust was not set! Now I need to figure out why the nearest algorithm is not returning any data. * Small code style changes * Cluster works as expected! Everything clusters. Now to add tests * Started adding more test for Clustor * Created Test fixtures for the new code * Completed the sparse distance matrix and read phylip test, need to fix some test errors. * Added test for phylip reader and matrix adapter. Adding the final test for rAbund now. * Added all the rAbund test, and the cluster feature should be working properly! * Adding include statements for windows and other operating systems, this should work as expected. RCMD check works.
* Adding a count table variation * Update Makevars * Preparation to Create a Shared File for the clustering output. * Added a simple make shared functionality. It works without the group information. Count_table + list_file = shared_file * Small changes, going to need to modify the make shared code soon. * Modifying the way cluster result data is stored. * Add a datastructure for returning and generating cluster data. * Cluster export works for Traditional Cluster Methods * Progress on making a shared file without a group. Have to make test soon. * Created a pseudo shared file successfully! * Changes to the abundance output * Delete F:\Opticluster\output_rabund.txt * Delete tests/testthat/F:\Opticluster\output_rabund.txt Removing werid paths. * Some example data and count table creation * Pushing lots of temp test files, and small changes * Adding the count table! * Count table structure has been created * Shared File with count tables and without work! * Count table can now be added to the cluster functions and will produce an output of abundance and clusters in dataframes. - Removed the exchanged of strings and read table between r and c++ and added a more modifiable approach. * Fixed test, now we have to add more test. * Fixes for linux and other computer architectures. * Removing dependencies that are not used. * Created test fixtures for unit test * Removed all of the negligent files that are not needed and added test to all of the new functions! --------- Co-authored-by: YoungKrug <[email protected]>
* Adding example data for mothur results. * Added support for similarity matrix functionality * Updates to phylip writing, and fixes to incorrect clustering! * Stable, Classic cluster and opticluster both are identical to that of mothur! + Names can be added * Results are the same as they are in mothur! * All the unit test work, and clustur prints out expected results!
* No warnings on mac! * Still fixing clang-tidy errors. Lots of comparisons between longs and ints that will cause problems. * Half way done, modified a lot of deduced type conversions. May need to go back in the future to remove them. * Should fix all the warnings on linux and windows. * Pushing through more deduced type fixes. * Should be the final fix for linux.
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Stable clustering, and clustering result match that of mothur. We need to allow users to input more than just traditional data frames to start clustering (like Phylip files).