Commit 68038a92 authored by Peter Jentsch's avatar Peter Jentsch
Browse files

univariate plots

parent 3e5f3663
......@@ -128,7 +128,6 @@ function weighted_degree_and_join_count(t,node,network::TimeDepMixingGraph,u_inf
node_is_immunized = u_inf[node] == Immunized
for g in network.graph_list[t]
<<<<<<< HEAD
for j in neighbors(g,node)
weight = get_weight(g,GraphEdge(node,j))
weighted_degree += weight
......@@ -137,10 +136,6 @@ function weighted_degree_and_join_count(t,node,network::TimeDepMixingGraph,u_inf
join_count_11 += ifelse(node_is_immunized && j_is_immunized,weight,0)
join_count_01 += ifelse(xor(node_is_immunized, j_is_immunized),weight,0)
join_count_00 += ifelse(!(node_is_immunized) && !(j_is_immunized),weight,0)
=======
for he in neighbors_and_weights(g,node)
weighted_degree += he.weight[]
>>>>>>> 082c742 (graph idea final)
end
end
return weighted_degree,(join_count_11,join_count_01,join_count_00)
......
......@@ -107,20 +107,9 @@ end
@test mean(mixing_dist[i]) expected_dist_mean[i] atol = 0.05
@test mean(mixing_dist[i]) mean(dist[i]) atol = 0.2
end
<<<<<<< HEAD
for i in 1:3, j in 1:i
@test mean(mixing_weights[i,j]) contact_time_distributions.ws[i,j].μ atol = 0.4
end
=======
mixing_weights = [Variance() for _ in 1:3, _ in 1:3]
for (sampler,weight) in g.weight_sample_list
indexofsampler = findfirst(==(sampler), contact_time_distributions.ws)
fit!(mixing_weights[indexofsampler],weight)
end
display(mean.(mixing_weights))
display(map(d -> d.μ,contact_time_distributions.ws))
>>>>>>> 082c742 (graph idea final)
end
end
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