### univariate plots

parent 3e5f3663
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 ... ... @@ -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 \ No newline at end of file
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