ubc-stat-ml / conifer Goto Github PK

Users/bouchard/Documents/workspace/conifer/src/main/java/conifer/PairProteinModel.java:14: package briefj.tomove does not exist
import briefj.tomove.Results;
                    ^
/Users/bouchard/Documents/workspace/conifer/src/main/java/conifer/PairProteinModel.java:52: cannot find symbol
symbol  : variable Results
location: class conifer.PairProteinModel
  private final PrintWriter treeWriter = BriefIO.output(Results.getFileInResultFolder("trees.newick"));
                                                        ^
Note: Some input files use or override a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
Note: /Users/bouchard/Documents/workspace/conifer/src/main/java/conifer/io/newick/NewickParser.java uses unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
2 errors
:compileJava FAILED

Error in exists(name, env) : argument "env" is missing, with no default
Calls: ggmcmc ... ggplot_build -> scales_add_missing -> find_global -> exists

Error in data.frame(..., check.names = FALSE) : 
  arguments imply differing number of rows: 1, 0
Calls: ggmcmc ... ggs_compare_partial -> rbind -> cbind -> cbind -> data.frame

FAILURE: Build failed with an exception.

* What went wrong:
Could not determine java version from '11.0.7'.

$ java --version
openjdk 11.0.7 2020-04-14
OpenJDK Runtime Environment (build 11.0.7+10-post-Ubuntu-3ubuntu1)
OpenJDK 64-Bit Server VM (build 11.0.7+10-post-Ubuntu-3ubuntu1, mixed mode, sharing)

FAILURE: Build completed with 2 failures.

1: Task failed with an exception.
-----------
* Where:
Build file '/home/mbiron/opt/conifer/build.gradle' line: 110

* What went wrong:
A problem occurred evaluating root project 'conifer'.
> No such property: archiveTask for class: org.gradle.api.internal.artifacts.dsl.LazyPublishArtifact

* Try:
Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output. Run with --scan to get full insights.
==============================================================================

2: Task failed with an exception.
-----------
* What went wrong:
A problem occurred configuring root project 'conifer'.
> Failed to notify project evaluation listener.
   > 'java.lang.String org.gradle.api.tasks.compile.CompileOptions.getBootClasspath()'
   > 'java.lang.String org.gradle.api.tasks.compile.CompileOptions.getBootClasspath()'

> Task :generateXtext
ERROR:Type mismatch: cannot convert from MultiCategorySubstitutionModel<ExpFamMixture> to SequenceAlignment (file:/home/mbiron/opt/conifer/src/main/java/conifer/GLMExample.bl line : 20 column : 5)
ERROR:Type mismatch: cannot convert from SequenceAlignment to MultiCategorySubstitutionModel<ExpFamMixture> (file:/home/mbiron/opt/conifer/src/main/java/conifer/GLMExample.bl line : 20 column : 24)
WARNING:The import 'conifer.io.TreeObservations' is never used. (file:/home/mbiron/opt/conifer/src/main/java/conifer/EvoGLM.bl line : 3 column : 1)
WARNING:The import 'conifer.moves.PhyloHMCMove' is never used. (file:/home/mbiron/opt/conifer/src/main/java/conifer/EvoGLM.bl line : 6 column : 1)

> Task :generateXtext FAILED

FAILURE: Build failed with an exception.

* What went wrong:
Execution failed for task ':generateXtext'.
> Xtext validation failed, see build log for details.

> Task :startScripts FAILED

FAILURE: Build failed with an exception.

* What went wrong:
A problem was found with the configuration of task ':startScripts'.
> No value has been specified for property 'mainClassName'.

$ ./gradlew installDist

> Task :generateXtext
WARNING:The import 'conifer.io.TreeObservations' is never used. (file:/home/mbiron/opt/conifer/src/main/java/conifer/EvoGLM.bl line : 3 column : 1)
WARNING:The import 'conifer.moves.PhyloHMCMove' is never used. (file:/home/mbiron/opt/conifer/src/main/java/conifer/EvoGLM.bl line : 6 column : 1)

> Task :compileJava
Note: Some input files use or override a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.

BUILD SUCCESSFUL in 30s
5 actionable tasks: 5 executed

package conifer.ctmc;
import org.ejml.data.DenseMatrix64F;
import org.ejml.data.Eigenpair;
import org.ejml.factory.DecompositionFactory;
import org.ejml.factory.EigenDecomposition;
import org.ejml.ops.EigenOps;
import org.ejml.simple.SimpleMatrix;

import tutorialj.Tutorial;
import bayonet.distributions.Multinomial;
import bayonet.math.EJMLUtils;
import bayonet.math.NumericalUtils;
import bayonet.math.EJMLUtils.SimpleEigenDecomposition;


import java.io.*;
import java.util.*;

import org.jblas.ComplexDoubleMatrix;
import org.jblas.DoubleMatrix;
import org.jblas.MatrixFunctions;
import org.junit.Assert;
import org.junit.Test;

import Jama.EigenvalueDecomposition;
import Jama.Matrix;


// Added by Tingting
import org.jblas.Eigen;
/**
 * A continuous time Markov chain. The main functionalities consists
 * in computing a marginal transition probability and a stationary distibution
 * (see below).
 * 
 * This implementation is based on caching the eigendecomposition
 * of the provided rate matrix.
 * 
 * @author Alexandre Bouchard ([email protected])
 *
 */
public class EigenCTMC implements CTMC
{
  private static final double THRESHOLD = 1e-6;
  private final SimpleEigenDecomposition eigenDecomp; 
  private final double [] stationaryDistribution;
  private final double [][] rates;

  /**
   * Note: if the RateMatrix is changed in place,
   * these changes will not be mirrored by this class.
   * 
   * It should be recreated each time a likelihood 
   * calculation is performed.
   * @param rateMatrix
   */

 public EigenCTMC(double [][] rates)
  {
    RateMatrixUtils.checkValidRateMatrix(rates);
     this.eigenDecomp = SimpleEigenDecomposition(rates); 
     this.stationaryDistribution = computeStationary();
     this.rates = rates;
     }

 public static SimpleEigenDecomposition SimpleEigenDecomposition(double [][] rates)
 {
   Matrix ratesMatrix = new Matrix(rates);
   EigenvalueDecomposition ed = new EigenvalueDecomposition(ratesMatrix);
   Matrix V = ed.getV();
   Matrix D =ed.getD();
   Matrix Vinverse = V.inverse();
   Matrix resultMatrix = V.times(D).times(V.inverse());
   //check if result and rates are close enough
   SimpleMatrix trueMatrix = new SimpleMatrix(rates);
   SimpleMatrix calculatedMatrix = new SimpleMatrix(resultMatrix.getArray()) ;
   if(EJMLUtils.isClose(trueMatrix, calculatedMatrix, THRESHOLD))
   {
     return new  SimpleEigenDecomposition(V, D, Vinverse);
   }else{
     throw new RuntimeException();
     }
   }




 public static class SimpleEigenDecomposition
 {
   public final Matrix V, D, Vinverse, calculatedRateMtx;

   public SimpleEigenDecomposition(Matrix v, Matrix d,
       Matrix vinverse)
   {
     V = v;
     D = d;
     Vinverse = vinverse;
     calculatedRateMtx=V.times(D).times(V.inverse());
   }

 }




  /**
   * Compute and cache the stationary (called by the constructor)
   * @return
   */
  private double[] computeStationary()
  {
    Matrix marginal = new Matrix(marginalTransitionProbability(1.0));
    Matrix exponentiatedTranspose = marginal.transpose();

    double [] result = null;
    final int dim = marginal.getColumnDimension();
    EigenvalueDecomposition eigenDecomp = new EigenvalueDecomposition(exponentiatedTranspose);
    // Find an eigen value equal to one (up to numerical precision)
    // result will hold the corresponding eigenvalue
    double []  realEigenvalues=eigenDecomp.getRealEigenvalues();
    double []  imageEigenvalues=eigenDecomp.getImagEigenvalues();
    double [][] eigenVectorTranspose = eigenDecomp.getV().transpose().getArray();
    for (int i = 0; i < dim; i++)
      if (NumericalUtils.isClose(0.0, imageEigenvalues[i], NumericalUtils.THRESHOLD)&& 
          NumericalUtils.isClose(1.0, realEigenvalues[i], NumericalUtils.THRESHOLD))
      {
        result = eigenVectorTranspose[i];
      }
    if (result == null)
      throw new RuntimeException("Could not find an eigenvalue equal to one. Not a proper rate matrix?");
    double sum = NumericalUtils.getNormalization(result);
    if (sum < 0.0)
    {
      for (int i = 0; i < result.length; i++)
        {
        result[i] *= -1;
        }
      }
    Multinomial.normalize(result);
    return result;
  }

  /**
   * 
   * Fill in ``marginalTransitionProbability()``.
   * 
   * Use the diagonalization method covered in class, using 
   * the eigen-decomposition functionalities provided by EJML.
   */
  @Tutorial(showSource = false, showLink = true)
  public double [][] marginalTransitionProbability(double branchLength)
  {

    DoubleMatrix rateMtx = new DoubleMatrix(eigenDecomp.calculatedRateMtx.getArray());
    rateMtx.muli(branchLength);
    double [][] result = MatrixFunctions.expm(rateMtx).toArray2();
    NumericalUtils.checkIsTransitionMatrix(result);

    for (int row = 0; row < result.length; row++)
      {
        Multinomial.normalize(result[row]);
      }
    return result;
  }


  public double [] stationaryDistribution()
  {
    return stationaryDistribution;
  }

  @Override
  public double[][] getRateMatrix()
  {
    return rates;
  }


//  public static void main(String [] args)
//  {
//    SimpleRateMatrix tryMtx = RateMatrices.accordance();
//    SimpleMatrix trySimpleMtx =new SimpleMatrix(tryMtx.getRateMatrix());
//    RateMatrixUtils.checkValidRateMatrix(tryMtx.getRateMatrix());
//    EJMLUtils.simpleEigenDecomposition(trySimpleMtx);
//    System.out.println("finished");
 //   }

}