5e329a5e3e9453ae0800ab438d2fa9f991f35a00,pymc3/examples/LKJ_correlation.py,,,#,8

Before Change

// In order to convert the upper triangular correlation values to a complete
// correlation matrix, we need to construct an index matrix:
n_elem = int(n_var * (n_var - 1) / 2)
tri_index = np.zeros([n_var, n_var], dtype=int)
tri_index[np.triu_indices(n_var, k=1)] = np.arange(n_elem)
tri_index[np.triu_indices(n_var, k=1)[::-1]] = np.arange(n_elem)

with pm.Model() as model:

After Change

    cov = tt.dot(chol, chol.T)

    // Extract the standard deviations etc
    sd = pm.Deterministic("sd", tt.sqrt(tt.diag(cov)))
    corr = tt.diag(sd**-1).dot(cov.dot(tt.diag(sd**-1)))
    r = pm.Deterministic("r", corr[np.triu_indices(n_var, k=1)])

    like = pm.MvNormal("likelihood", mu=mu, chol=chol, observed=dataset)

Instances