Fix typo in inference HOWTO

This fixes issue #683

doc/demos/inference/_edge_weights.rst

@@ -108,7 +108,7 @@ the weights, as follows:
          
 .. testcode:: weighted-model
 
-   g = gt.collection.konect_data["moreno_train"]
+   g = gt.collection.ns["train_terrorists"]
 
    # This network contains an internal edge property map with name
    # "weight" that contains the strength of interactions. The values
@@ -168,7 +168,7 @@ follows:
          
 .. testcode:: food-web
 
-   g = gt.collection.konect_data["foodweb-baywet"]
+   g = gt.collection.ns["foodweb_baywet"]
 
    # This network contains an internal edge property map with name
    # "weight" that contains the energy flow between species. The values
@@ -224,7 +224,7 @@ can fit this alternative model simply by using the transformed weights:
                                                                   rec_types=["real-normal"]))
 
    # improve solution with merge-split
-   state_ln = state.copy(bs=state.get_bs() + [np.zeros(1)] * 4, sampling=True)
+   state_ln = state.copy(bs=state_ln.get_bs() + [np.zeros(1)] * 4, sampling=True)
 
    for i in range(100):
        ret = state_ln.multiflip_mcmc_sweep(niter=10, beta=np.inf)
@@ -276,9 +276,9 @@ Therefore, we can compute the posterior odds ratio between both models as:
 .. testoutput:: food-web
    :options: +NORMALIZE_WHITESPACE
 
-   ln Λ:  16490.463643...
+   ln Λ:  16506.096401...
 
-A value of :math:`\Lambda \approx \mathrm{e}^{16490} \approx 10^{7161}`
+A value of :math:`\Lambda \approx \mathrm{e}^{16506} \approx 10^{7168}`
 in favor the log-normal model indicates that the exponential model does
 not provide a better fit for this particular data. Based on this, we
 conclude that the log-normal model should be preferred in this case.
@@ -293,7 +293,7 @@ initialization, e.g..
 
 .. testcode:: weighted-model
 
-   g = gt.collection.konect_data["foodweb-baywet"]
+   g = gt.collection.ns["foodweb_baywet"]
 
    state = gt.NestedBlockState(g, state_args=dict(recs=[g.ep.weight], rec_types=["real-exponential"]))
 

doc/demos/inference/_layers.rst

@@ -37,7 +37,7 @@ separating these two types of interactions in two layers:
          
 .. testcode:: layered-model
 
-   g = gt.collection.konect_data["ucidata-gama"]
+   g = gt.collection.ns["new_guinea_tribes"]
 
    # The edge types are stored in the edge property map "weights".
 

doc/demos/inference/_reconstruction.rst

@@ -158,7 +158,7 @@ simple example, using
    state = gt.MeasuredBlockState(g, n=n, n_default=1, x=x, x_default=0)
 
    # We will first equilibrate the Markov chain
-   gt.mcmc_equilibrate(state, wait=1000, mcmc_args=dict(niter=10))
+   gt.mcmc_equilibrate(state, wait=10000, mcmc_args=dict(niter=10))
 
    # Now we collect the marginals for exactly 100,000 sweeps, at
    # intervals of 10 sweeps:
@@ -187,9 +187,9 @@ Which yields the following output:
    
 .. testoutput:: measured
 
-   Posterior probability of edge (11, 36): 0.768976...
-   Posterior probability of non-edge (15, 73): 0.039203...
-   Estimated average local clustering: 0.571939 ± 0.003534...
+   Posterior probability of edge (11, 36): 0.829782...
+   Posterior probability of non-edge (15, 73): 0.058105...
+   Estimated average local clustering: 0.572087 ± 0.003632...
 
 We have a successful reconstruction, where both ambiguous adjacency
 matrix entries are correctly recovered. The value for the average
@@ -310,9 +310,9 @@ Which yields:
    
 .. testoutput:: measured
 
-   Posterior probability of edge (11, 36): 0.631563...
-   Posterior probability of non-edge (15, 73): 0.022402...
-   Estimated average local clustering: 0.570065 ± 0.007145...
+   Posterior probability of edge (11, 36): 0.655165...
+   Posterior probability of non-edge (15, 73): 0.013301...
+   Estimated average local clustering: 0.553358 ± 0.01615...
 
 The results are very similar to the ones obtained with the uniform model
 in this case, but can be quite different in situations where a large