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

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

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

# 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))