tansey

'''Pool adjacent violators algorithm for (column-)monotone matrix factorization.

Applies the PAV algorithm to column factors of a matrix factorization:
Given: M = W.V'
Returns: V_proj, a projected version of V such that M[i] is monotone decreasing
for all i.

Author: Wesley Tansey
Date: May 2019
'''

tansey

# Fit using a simple EM algorithm
# observations are x
# weights are w (must be same length as x)
# returns (r, p)
# r - dispersion parameter
# p - probability of success
weightedNegBinomFit <- function(x, w, maxsteps=30)
{
    sum.wx = sum(x*w)
    sum.w = sum(w)

tansey

import tensorflow as tf

def unpack_cholesky(q, ndims):
    # Build the lower-triangular Cholesky from the flat buffer (assumes q shape is [batchsize, cholsize])
    chol_diag = tf.nn.softplus(q[:,:ndims])
    chol_offdiag = q[:,ndims:]
    chol_rows = []
    chol_start = 0
    chol_end = 1
    for i in xrange(ndims):

tansey

#include <set>
#include <iostream>

class A
{
    std::set<int> s;
public:
    A() { s.insert(0); s.insert(1); }

    std::set<int> getSet() { return s; }

tansey

def plot_with_bands(graph_title, means, bands, series, xvals=None, xlabel=None, ylabel=None, subtitle=None, filename='results.pdf'):
    colors = ['blue','red','green', 'black', 'yellow', 'orange', 'purple', 'brown'] # max 8 lines
    print 'means: {0} bands: {1}'.format(means.shape, bands.shape)
    assert(means.shape == bands.shape)
    assert(xvals is None or xvals.shape[0] == means.shape[1])
    assert(means.shape[1] <= len(colors))
    if xvals is None:
        xvals = np.arange(means.shape[0])
    ax = plt.subplot(111)
    plt.ticklabel_format(axis='y', style='plain', useOffset=False)

tansey

import random
import math
import matplotlib.pyplot as plt
import numpy as np

def acf(x, length=35):
  return np.array([1] + [np.corrcoef(x[:-i], x[i:])[0,1] for i in range(1,length)])

# The number of samples to observe
n = 100

tansey

class A(object):
  def __init__(self, myvar = {}):
    self.myvar = myvar
    
a1 = A()
a1.myvar['foo'] = 1
 
a2 = A()
a2.myvar['bar'] = 2
 

tansey

class A(object):
  def __init__(self, myvar = {}):
    self.myvar = myvar
    
a1 = A()
a1.myvar['foo'] = 1

a2 = A()
a2.myvar['bar'] = 2

tansey

double[] inputs = ...
double[] desiredOutputs = ...

// Get the neural network
var network = ((FastCyclicNetwork)blackbox;

// Perform backpropagation through time
network.Train(inputs, desiredOutputs);

tansey

        /// <summary>
        /// Saves all phenotypic progress back to the genomes.
        /// </summary>
        private void PerformLamarkianEvolution(IList<TGenome> genomeList, Func<IAgent, FastCyclicNetwork> networkSelector)
        {
            for (int i = 0; i < _agents.Length; i++)
            {
                var agent = _agents[i];

                // Get the network for this teacher