madanh

{
  "name": "uniqueuntruemicrokernel",
  "version": "1.0.0",
  "description": "throw away for fcc",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "author": "Hennadii Madan",
  "license": "ISC"

madanh

import numpy as np
import matplotlib.pyplot as plt
import pymc3 as pm
import theano

print("numpy version",np.__version__)
print("theano version",theano.__version__)
print("pymc version",pm.__version__)

floatX = theano.config.floatX # "float32"

madanh

# Modified from original implementation by Dominik Wabersich (2013)

import numpy as np
import numpy.random as nr

from .arraystep import ArrayStep, Competence
from ..model import modelcontext
from ..theanof import inputvars
from ..vartypes import continuous_types

madanh

def lnl(theta,ndim,nparams):
    '''
    A dumb implementation of log-likelihood for the multigauss(tm) model.
    We follow separation strategy of Barnard and McCuloch and
    parametrize the covariance matrix through stds and correlation coeffs.
    Inversion to precision matrix is done in the loop [facepalm.jpg].
    '''

    # Unpack the theta
    ##infer the number of proper parameters and truth from data size