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import numpy as np
from scipy import linalg

def lse(A, b, B, d, cond=None):
    """
    Equality-contrained least squares.
    
    The following algorithm minimizes ||Ax - b|| subject to the
    constrain Bx = d.
    

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class AlwaysFive:
    def __iter__(self):
        while True:
            yield 5

a = AlwaysFive()

it = iter(a)

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# ====================================================================================
# = Koala sightings example from Link and Barker 2009. Original WinBUGS model below. =
# ====================================================================================

import numpy as np
from numpy.ma import masked_array
from pymc import *

# Prior model weights, derived from Bayes factors   
pi = (0.016656, 0.052853, 0.045682, 0.087367, 0.091975, 0.221741, 0.19819,

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'''
Gibbs sampler for function:

f(x,y) = x x^2 \exp(-xy^2 - y^2 + 2y - 4x)

using conditional distributions:

x|y \sim Gamma(3, y^2 +4)
y|x \sim Normal(\frac{1}{1+x}, \frac{1}{2(1+x)})
'''

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"""

A random walk Metropolis algorithm with tuning, implemented for a linear model that
predicts price as a function of age.

"""

import numpy as np
from scipy.stats import distributions
rnorm = np.random.normal

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from pymc import *
from numpy import array, resize, diag, exp, zeros, prod, concatenate
import pdb

# Data
Releases = array([12,12,10,14,17,21,9,30,25,41,45,79,97,80,62,80,74,74,46,45])
NRCperiods = len(Releases) 
recdata = resize((
8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,
0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,

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'''
Gibbs sampler for function:

f(x,y) = x x^2 \exp(-xy^2 - y^2 + 2y - 4x)

using conditional distributions:

x|y \sim Gamma(3, y^2 +4)
y|x \sim Normal(\frac{1}{1+x}, \frac{1}{2(1+x)})
'''

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species,eggs,parasite,lat,lon
WOTH, 2, 0, -82.32, 25.10
TOSO, 2, 0, -80.56, 26.66

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