# Source code for bumps.dream.gelman

"""
Convergence test statistic from Gelman and Rubin, 1992.

 Gelman, Andrew, and Donald B. Rubin.
"Inference from Iterative Simulation Using Multiple Sequences."
Statistical Science 7, no. 4 (November 1, 1992): 457-72.
https://doi.org/10.2307/2246093.
"""

from __future__ import division

__all__ = ["gelman"]

from numpy import var, mean, ones, sqrt

[docs]def gelman(sequences, portion=0.5):
"""
Calculates the R-statistic convergence diagnostic

Inference from Iterative Simulation Using Multiple Sequences,
Statistical Science, Volume 7, Issue 4, 457-472.
doi:10.1214/ss/1177011136
"""

# Find the size of the sample
chain_len, nchains, nvar = sequences.shape
#print sequences[:20, 0, 0]

# Only use the last portion of the sample
chain_len = int(chain_len*portion)
sequences = sequences[-chain_len:]

if chain_len < 2:
# Set the R-statistic to a large value
r_stat = -2 * ones(nvar)
else:
# Step 1: Determine the sequence means
mean_seq = mean(sequences, axis=0)

# Step 1: Determine the variance between the sequence means
b = chain_len * var(mean_seq, axis=0, ddof=1)

# Step 2: Compute the variance of the various sequences
var_seq = var(sequences, axis=0, ddof=1)

# Step 2: Calculate the average of the within sequence variances
w = mean(var_seq, axis=0)

# Step 3: Estimate the target mean
#mu = mean(mean_seq)

# Step 4: Estimate the target variance (Eq. 3)
sigma2 = ((chain_len - 1)/chain_len) * w + (1/chain_len) * b

# TODO: the second term, -(N-1)/(K N), doesn't appear in 
# Step 5: Compute the R-statistic
r_stat = sqrt((nchains + 1)/nchains * sigma2 / w
- (chain_len-1)/nchains/chain_len)
#par=2
#print chain_len,b[par],var_seq[...,par],w[par],r_stat[par]

return r_stat

def test():
from numpy import reshape, arange, transpose
from numpy.linalg import norm
# Targe values computed from octave:
#    format long
#    s = reshape([1:15*6*7],[15,6,7]);
#    r = gelman(s,struct('n',6,'seq',7))
s = reshape(arange(1.0, 15*6*7+1)**-2, (15, 6, 7), order='F')
s = transpose(s, [0, 2, 1])
target = [1.06169861367116, 2.75325774624905, 4.46256647696399,
6.12792266170178, 7.74538715553575, 9.31276519155232]
r = gelman(s, portion=1)
#print r
#print "target", array(target), "\nactual", r
assert norm(r-target) < 1e-14
r = gelman(s, portion=0.1)
assert norm(r - [-2, -2, -2, -2, -2, -2]) == 0

if __name__ == "__main__":
test()