apatil

#cloud-config

coreos:
  units:
    - name: test.service
      content: "[Unit]\nDescription=Test\n\n[Service]\nExecStart=/usr/bin/echo \"hi\"\nType=oneshot\n\n[Install]\nWantedBy=multi-user.target\n"

apatil

f = (cb) =>
  fibrous.run =>
    a.sync()
    b.sync()
  , cb

apatil

curl -v -H "Content-Type: application/json" -X POST \
  -d '{"Cmd":["/bin/sleep","infinity"],"Image":"ubuntu:14.04", "ExposedPorts":{"22/tcp":{}}}'  \
  http://127.0.0.1:4243/containers/create?name=testcontainer
  
curl -v -H "Content-Type: application/json" -X POST \
  -d '{"PortBindings": { "22/tcp": [{ "HostPort": "11022" }]} }' \
  http://127.0.0.1:4243/containers/testcontainer/start

apatil

@api {get} /engines Request Available Engines
@apiVersion 1.0.0
@apiName GetEngines
@apiGroup Engines

@apiDescription Gets a list of the currently available engines on Sense.

@apiError (404) user not found.

@apiSuccess (200) {Object[]} engines List of available engines.

apatil

# Illustrates why log-sums are better than direct sums for probability integrals
# when probabilities are initially computed on the log scale

import pymc
import numpy 

lp_big = numpy.random.normal(loc=0,scale=2,size=1000)
print numpy.log(numpy.mean(numpy.exp(lp_big)))
print pymc.flib.logsum(lp_big)-numpy.log(len(lp_big))

apatil

import numpy as np
import pylab as pl
import pymc as pm

Tau_test = np.matrix([[ 209.47883244,   10.88057915,   13.80581557],
                       [  10.88057915,  213.58694978,   11.18453854],
                       [  13.80581557,   11.18453854,  209.89396417]])
                       
C_test = Tau_test.I

apatil

from __future__ import division
import numpy as np

# A boat is a dict with keys
# mass, center_of_mass, cm_depth, front_depth, back_depth, pitch, length, width
# units are radians (angles), meters (depths and lengths), tons (mass)
# Boats are idealized as rectangular prisms

water_density = 1

apatil

#!/usr/bin/env python

import pymc
from pymc import gp
from pymc.gp.cov_funs import matern,gaussian
from pylab import *

# Load some data generated from a GP with mean=0, scale=1, amp=1
xdata,ydata = loadtxt('train.txt', unpack=1)

apatil

#!/usr/bin/env python
import numpy as npy 
from pymc.gp import Mean, Covariance, Realization, observe, plot_envelope, NearlyFullRankCovariance, FullRankCovariance
from pymc.gp.cov_funs import matern #, thinplate1d
import matplotlib
matplotlib.rcParams['axes.facecolor']=[1,1,1]

__all__ = ['surface_mean', 'M', 'C']
def surface_mean(x, val):
    """docstring for parabolic_fun"""

apatil

import pymc as pm
import numpy as np

# FIXME: Need to store duplicates too, when jumps are rejected. That means some mechanism
# for making sure the history is full-rank needs to be employed.

class HistoryCovarianceStepper(pm.StepMethod):
    
    _state = ['n_points','history','tally','verbose']