Christiaan Swanepoel christiaanjs

## BFGSOptimizer.java

import org.ejml.data.DMatrixRMaj;
import org.ejml.dense.row.CommonOps_DDRM;
import org.ejml.simple.SimpleMatrix;

public class BFGSOptimizer implements Optimizer {

	private double decreaseTol = 0.001; //(0, 0.5)
	private double curvatureTol = 0.9; //(decreaseTol, 1.0)
	private double gradTol = 1e-6;

## latent-gp-poison.ipynb

      
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                christiaanjs
                / latent-gp-poison.ipynb
            
            
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              January 16, 2018 00:58
            
              
                Latent GP Poisson model
              
          
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## distribution-test.ipynb

      
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                christiaanjs
                / distribution-test.ipynb
            
            
              Created
              November 22, 2018 19:53
            
              
                Bootstrap multivariate two-sample test for distribution
              
          
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## operator-jacobian.ipynb

      
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                / operator-jacobian.ipynb
            
            
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              December 14, 2018 04:04
            
          
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## xml-sequence.py
from xml.etree import ElementTree

data_xml_filename = '311spp_5genes.xml'
xml_root = ElementTree.parse(data_xml_filename)
flat_dict = [{ 'alignment': alignment.attrib['id'], 'taxon': sequence.find('taxon').attrib['idref'], 'sequence': sequence.find('taxon').tail.strip() } for alignment in xml_root.findall('alignment') for sequence in alignment.findall('sequence') ]
taxa_dict = { taxon: { value['alignment']: value['sequence'] for value in values } for taxon, values in groupby(sorted(flat_dict, key=key_func), key=key_func) }
print(taxa_dict)

## skygrid-test.ipynb

      
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                christiaanjs
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              July 27, 2019 00:55
            
              
                Skygrid + Beagle test
              
          
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## bijectors.ipynb

      
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                christiaanjs
                / bijectors.ipynb
            
            
              Created
              September 18, 2019 23:47
            
              
                Truncated Lognormal distribution in Tensorflow Probability
              
          
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## quantile-test.ipynb

      
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                christiaanjs
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              January 16, 2020 03:37
            
          
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## phylo-model.py
model = tfd.JointDistributionNamed(dict(
        frequencies=tfd.Dirichlet(concentration=[4,4,4,4]),
        kappa=tfd.LogNormal(loc=0, scale=1),
        pop_size=tfd.LogNormal(loc=0, scale=1),
        tree=lambda pop_size: treeflow.coalescent.ConstantCoalescent(pop_size=pop_size, sampling_times=sampling_times),
        sequences=lambda tree, kappa, frequencies: treeflow.sequences.PhyloCTMC(tree=tree, q=HKY(frequencies=frequencies, kappa=kappa))
    ))

## tf-likelihood-test.ipynb

      
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              May 14, 2020 09:33
            
              
                Making Tensorflow likelihood gradients work in function mode
              
          
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	import org.ejml.data.DMatrixRMaj;
	import org.ejml.dense.row.CommonOps_DDRM;
	import org.ejml.simple.SimpleMatrix;

	public class BFGSOptimizer implements Optimizer {

	private double decreaseTol = 0.001; //(0, 0.5)
	private double curvatureTol = 0.9; //(decreaseTol, 1.0)
	private double gradTol = 1e-6;
	from xml.etree import ElementTree

	data_xml_filename = '311spp_5genes.xml'
	xml_root = ElementTree.parse(data_xml_filename)
	flat_dict = [{ 'alignment': alignment.attrib['id'], 'taxon': sequence.find('taxon').attrib['idref'], 'sequence': sequence.find('taxon').tail.strip() } for alignment in xml_root.findall('alignment') for sequence in alignment.findall('sequence') ]
	taxa_dict = { taxon: { value['alignment']: value['sequence'] for value in values } for taxon, values in groupby(sorted(flat_dict, key=key_func), key=key_func) }
	print(taxa_dict)
	model = tfd.JointDistributionNamed(dict(
	frequencies=tfd.Dirichlet(concentration=[4,4,4,4]),
	kappa=tfd.LogNormal(loc=0, scale=1),
	pop_size=tfd.LogNormal(loc=0, scale=1),
	tree=lambda pop_size: treeflow.coalescent.ConstantCoalescent(pop_size=pop_size, sampling_times=sampling_times),
	sequences=lambda tree, kappa, frequencies: treeflow.sequences.PhyloCTMC(tree=tree, q=HKY(frequencies=frequencies, kappa=kappa))
	))