"--cleansvd", "true" }; ToolRunner.run(getConfiguration(), new DistributedLanczosSolver().new DistributedLanczosSolverJob(), args); "--cleansvd", "true" }; ToolRunner.run(getConfiguration(), new DistributedLanczosSolver().new DistributedLanczosSolverJob(), args); Path cleanEigenvectors2 = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS); Matrix eigenVectors2 = new DenseMatrix(7, corpus.numCols());
"--workingDir", workingDir.toString() }; ToolRunner.run(getConfiguration(), new DistributedLanczosSolver().new DistributedLanczosSolverJob(), args); "--workingDir", workingDir.toString() }; ToolRunner.run(getConfiguration(), new DistributedLanczosSolver().new DistributedLanczosSolverJob(), args);
@Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption("numRows", "nr", "Number of rows of the input matrix"); addOption("numCols", "nc", "Number of columns of the input matrix"); addOption("rank", "r", "Desired decomposition rank (note: only roughly 1/4 to 1/3 " + "of these will have the top portion of the spectrum)"); addOption("symmetric", "sym", "Is the input matrix square and symmetric?"); addOption("workingDir", "wd", "Working directory path to store Lanczos basis vectors " + "(to be used on restarts, and to avoid too much RAM usage)"); // options required to run cleansvd job addOption("cleansvd", "cl", "Run the EigenVerificationJob to clean the eigenvectors after SVD", false); addOption("maxError", "err", "Maximum acceptable error", "0.05"); addOption("minEigenvalue", "mev", "Minimum eigenvalue to keep the vector for", "0.0"); addOption("inMemory", "mem", "Buffer eigen matrix into memory (if you have enough!)", "false"); DistributedLanczosSolver.this.parsedArgs = parseArguments(args); if (DistributedLanczosSolver.this.parsedArgs == null) { return -1; } else { return DistributedLanczosSolver.this.run(args); } } }
@Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption("numRows", "nr", "Number of rows of the input matrix"); addOption("numCols", "nc", "Number of columns of the input matrix"); addOption("rank", "r", "Desired decomposition rank (note: only roughly 1/4 to 1/3 " + "of these will have the top portion of the spectrum)"); addOption("symmetric", "sym", "Is the input matrix square and symmetric?"); addOption("workingDir", "wd", "Working directory path to store Lanczos basis vectors " + "(to be used on restarts, and to avoid too much RAM usage)"); // options required to run cleansvd job addOption("cleansvd", "cl", "Run the EigenVerificationJob to clean the eigenvectors after SVD", false); addOption("maxError", "err", "Maximum acceptable error", "0.05"); addOption("minEigenvalue", "mev", "Minimum eigenvalue to keep the vector for", "0.0"); addOption("inMemory", "mem", "Buffer eigen matrix into memory (if you have enough!)", "false"); DistributedLanczosSolver.this.parsedArgs = parseArguments(args); if (DistributedLanczosSolver.this.parsedArgs == null) { return -1; } else { return DistributedLanczosSolver.this.run(args); } } }
@Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption("numRows", "nr", "Number of rows of the input matrix"); addOption("numCols", "nc", "Number of columns of the input matrix"); addOption("rank", "r", "Desired decomposition rank (note: only roughly 1/4 to 1/3 " + "of these will have the top portion of the spectrum)"); addOption("symmetric", "sym", "Is the input matrix square and symmetric?"); addOption("workingDir", "wd", "Working directory path to store Lanczos basis vectors " + "(to be used on restarts, and to avoid too much RAM usage)"); // options required to run cleansvd job addOption("cleansvd", "cl", "Run the EigenVerificationJob to clean the eigenvectors after SVD", false); addOption("maxError", "err", "Maximum acceptable error", "0.05"); addOption("minEigenvalue", "mev", "Minimum eigenvalue to keep the vector for", "0.0"); addOption("inMemory", "mem", "Buffer eigen matrix into memory (if you have enough!)", "false"); DistributedLanczosSolver.this.parsedArgs = parseArguments(args); if (DistributedLanczosSolver.this.parsedArgs == null) { return -1; } else { return DistributedLanczosSolver.this.run(args); } } }
public DistributedLanczosSolverJob job() { return new DistributedLanczosSolverJob(); }
public DistributedLanczosSolverJob job() { return new DistributedLanczosSolverJob(); }
public DistributedLanczosSolverJob job() { return new DistributedLanczosSolverJob(); }