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refactor: search is faster

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This commit is contained in:
Justin Lin
2022-03-22 07:59:54 +08:00
parent d38c3b91fc
commit d2a9b4be93
1 changed files with 7 additions and 4 deletions
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11
src/experimental/_impl/_tiles_wfc_impl.scad
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@@ -5,6 +5,7 @@ use <util/map/hashmap.scad>;
use <util/map/hashmap_put.scad>;
use <util/map/hashmap_get.scad>;
use <util/map/hashmap_keys.scad>;
use <util/map/hashmap_entries.scad>;
use <util/set/hashset.scad>;
use <util/set/hashset_elems.scad>;
@@ -13,7 +14,7 @@ function weights_of_tiles(sample) =
symbols = [for(row = sample) each row],
leng = len(symbols)
)
_weights_of_tiles(hashmap(number_of_buckets = leng), symbols, leng);
hashmap_entries(_weights_of_tiles(hashmap(number_of_buckets = leng), symbols, leng));
function _weights_of_tiles(weights, symbols, leng, i = 0) =
i == leng ? weights :
@@ -42,7 +43,7 @@ function wave_function(width, height, weights) =
function _initialEigenstates(width, height, weights) =
let(
keys = hashmap_keys(weights),
keys = [for(weight = weights) weight[0]],
row = [for(x = [0:width - 1]) keys]
)
[for(y = [0:height - 1]) row];
@@ -53,11 +54,13 @@ function wf_weights(wf) = wf[2];
function wf_eigenstates(wf) = wf[3];
function wf_eigenstates_at(wf, x, y) = wf_eigenstates(wf)[y][x];
function get_state_weight(weights, state) = weights[search([state], weights)[0]][1];
function wf_collapse(wf, x, y, weights) =
let(
states = wf_eigenstates_at(wf, x, y),
wets = is_undef(weights) ?
let(all_weights = wf_weights(wf)) [for(state = states) hashmap_get(all_weights, state)] :
let(all_weights = wf_weights(wf)) [for(state = states) get_state_weight(all_weights, state)] :
weights,
threshold = rand() * sum(wets)
)
@@ -74,7 +77,7 @@ function _wf_collapse(wf, x, y, states, weights, leng, threshold, i = 0) =
function wf_entropy_weights(wf, x, y) =
let(
all_weights = wf_weights(wf),
weights = [for(state = wf_eigenstates_at(wf, x, y)) hashmap_get(all_weights, state)],
weights = [for(state = wf_eigenstates_at(wf, x, y)) get_state_weight(all_weights, state)],
sumOfWeights = sum(weights),
sumOfWeightLogWeights = sum([for(w = weights) w * ln(w)])
)