Mike Hughes michaelchughes

Goal

Install working tensorflow or pytorch via standard conda environment workflow.

The recommended conda-based install process works smoothly:

$ # Create a fresh environment

We assume that there are two users (A and B). A is gonna delete the bad files. B is gonna receive the changes.

At the start, we need A and B to have the SAME version history.

We'll assume there are no branches other than master.

	'''
	Computing marginal log likelihood of data for unknown mean, fixed variance model.

	Running Demo
	------------
	$ python marg_lik_of_normal.py

	This will compare numerical and exact methods for calculating marg. lik.
	By visual inspection, user can see the exact method matches numerical.

	import numpy as np
	import pandas as pd
	import argparse

	def convert_ordered_dx_df_to_indicator_df(
	ordered_dx_df,
	list_of_possible_icd_code_values=None,
	list_of_possible_icd_code_names=None):
	''' Convert rank-ordered diagnosis codes into indicator format

	import pandas as pd
	import numpy as np
	import time

	prng = np.random.RandomState(0)

	n_subj = 20000
	n_rows = 1000000

	# Randomly assign to one of 20000 subjects

	S = 10 # num samples throughout

	## FIRST, REMEMBER THE COMMON PARAMETERIZATION
	# x ~ Normal(m, sigma^2)
	# m is a real number
	# sigma >= 0 is a standard deviation

	m = 2
	sigma = 0.1


	## Using notation from HDP-HMM paper in NeurIPS 2015 (Hughes, Stephenson, Sudderth)

	## Start for a single sequence of interest,
	## Compute the following as in the tutorial here:
	https://bnpy.readthedocs.io/en/latest/examples/08_mocap6/plot-03-demo=interpret_hdphmm_params_and_run_viterbi.html#sphx-glr-examples-08-mocap6-plot-03-demo-interpret-hdphmm-params-and-run-viterbi-py

	# start_prob_K : 1D array size K, sums to one
	# trans_proba_KK : 2D array, size K x K, rows sum to one
	# log_lik_seq_TK : 2D array, size T x K

	import numpy as np
	import scipy.stats

	from scipy.special import logsumexp, softmax

	np.set_printoptions(precision=3, suppress=1)

	## Settings
	## --------