akelleh

library(CRF)

# there are 10 nodes.  Each node can take on one of 3 states.
nNodes <- 10
nStates <- 3

# make the adjacency matrices for each graph

# a fully-connected graph of 10 nodes.  Loopiest possible.
# we expect a lot of error: the inferred node beliefs should

akelleh

library(CRF)

# there are 10 nodes.  Each node can take on one of 3 states.
nNodes <- 10
nStates <- 3

# make the adjacency matrices for each graph

# a fully-connected graph of 10 nodes.  Loopiest possible.
# we expect a lot of error: the inferred node beliefs should

akelleh

print "test"

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akelleh

from causality.nonparametric.causal_reg import MutualInformation

import numpy as np
import pandas as pd

K = [[1., 0.5, 0.25],
     [0.5, 1., 0.5],
     [0.25, 0.5, 1.]]
X = np.random.multivariate_normal(mean=[0,0,0],cov=K, size=1000)
X = pd.DataFrame(X,columns=['x1','x2','x3'])

akelleh