mirror of
https://github.com/sudoxnym/habitica.git
synced 2026-07-14 16:02:14 +00:00
autoAllocate: compactify some code (no reason, just for the ninja
feel). Add simulation script to show results for all auto-allocate methods for levels 1-20
This commit is contained in:
Tyler Renelle
parent
f4f6dcebdc
commit
2721800f37
4 changed files with 161 additions and 99 deletions
118
dist/habitrpg-shared.js
vendored
118
dist/habitrpg-shared.js
vendored
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@ -63,7 +63,7 @@ process.chdir = function (dir) {
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};
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},{}],3:[function(require,module,exports){
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var global=typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {};/**
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var global=self;/**
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* @license
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* Lo-Dash 2.4.1 (Custom Build) <http://lodash.com/>
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* Build: `lodash modern -o ./dist/lodash.js`
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@ -12059,69 +12059,66 @@ var process=require("__browserify_process");(function() {
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*/
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autoAllocate: function() {
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var diff, ideal, preference, suggested;
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switch (user.preferences.allocationMode) {
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case "flat":
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suggested = Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per);
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if (user.stats.int === suggested) {
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return "int";
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} else if (user.stats.per === suggested) {
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return "per";
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} else if (user.stats.str === suggested) {
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return "str";
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} else {
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return "con";
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}
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break;
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case "classbased":
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ideal = [user.stats.lvl / 7 * 3, user.stats.lvl / 7 * 2, user.stats.lvl / 7, user.stats.lvl / 7];
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switch (user.stats["class"]) {
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case "wizard":
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preference = ["int", "per", "con", "str"];
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break;
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case "rogue":
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preference = ["per", "str", "int", "con"];
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break;
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case "healer":
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preference = ["con", "int", "str", "per"];
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break;
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default:
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preference = ["str", "con", "per", "int"];
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}
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diff = [user.stats[preference[0]] - ideal[0], user.stats[preference[1]] - ideal[1], user.stats[preference[2]] - ideal[2], user.stats[preference[3]] - ideal[3]];
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suggested = _.findIndex(diff, (function(val) {
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if (val === _.min(diff)) {
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return true;
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return user.stats[(function() {
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var diff, ideal, preference, suggested;
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switch (user.preferences.allocationMode) {
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case "flat":
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switch (Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per)) {
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case user.stats.int:
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return "int";
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case user.stats.per:
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return "per";
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case user.stats.str:
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return "str";
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default:
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return "con";
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}
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}));
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if (suggested === -1) {
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return "str";
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} else {
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return preference[suggested];
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}
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break;
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case "taskbased":
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suggested = _.findKey(user.stats.training, (function(val) {
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if (val === _.max(user.stats.training)) {
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return val;
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break;
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case "classbased":
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ideal = [user.stats.lvl / 7 * 3, user.stats.lvl / 7 * 2, user.stats.lvl / 7, user.stats.lvl / 7];
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preference = (function() {
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switch (user.stats["class"]) {
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case "wizard":
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return ["int", "per", "con", "str"];
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case "rogue":
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return ["per", "str", "int", "con"];
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case "healer":
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return ["con", "int", "str", "per"];
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default:
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return ["str", "con", "per", "int"];
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}
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})();
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diff = [user.stats[preference[0]] - ideal[0], user.stats[preference[1]] - ideal[1], user.stats[preference[2]] - ideal[2], user.stats[preference[3]] - ideal[3]];
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suggested = _.findIndex(diff, (function(val) {
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if (val === _.min(diff)) {
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return true;
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}
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}));
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if (~suggested) {
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return preference[suggested];
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} else {
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return "str";
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}
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}));
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user.stats.training.str = 0;
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user.stats.training.int = 0;
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user.stats.training.con = 0;
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user.stats.training.per = 0;
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if (suggested === void 0) {
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case "taskbased":
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suggested = _.findKey(user.stats.training, (function(val) {
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if (val === _.max(user.stats.training)) {
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return val;
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}
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}));
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_.merge(user.stats.training, {
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str: 0,
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int: 0,
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con: 0,
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per: 0
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});
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return suggested || "str";
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default:
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return "str";
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} else {
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return suggested;
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}
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break;
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default:
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return "str";
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}
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}
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})()]++;
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},
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updateStats: function(stats) {
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var suggested, tnl;
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var tnl;
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if (stats.hp <= 0) {
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return user.stats.hp = 0;
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}
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@ -12140,8 +12137,7 @@ var process=require("__browserify_process");(function() {
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user.stats.lvl++;
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tnl = api.tnl(user.stats.lvl);
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if (user.preferences.automaticAllocation) {
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suggested = user.fns.autoAllocate();
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user.stats[suggested]++;
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user.fns.autoAllocate();
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} else {
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user.stats.points = user.stats.lvl - (user.stats.con + user.stats.str + user.stats.per + user.stats.int);
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}
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@ -1006,41 +1006,36 @@ api.wrap = (user) ->
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{update} if aggregated changes, pass in userObj as update. otherwise commits will be made immediately
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###
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autoAllocate: ->
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switch user.preferences.allocationMode
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when "flat"
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suggested = Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per)
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if user.stats.int is suggested # In case of ties, favor INT first, to get the next point sooner
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return "int"
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else if user.stats.per is suggested # Then favor PER, it's a god stat
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return "per"
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else if user.stats.str is suggested # Then favor STR, everyone loves crits
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return "str"
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else
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return "con" # CON, the unsexiest of attributes
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when "classbased"
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# Attributes get 3:2:1:1 per 7 levels.
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ideal = [(user.stats.lvl / 7 * 3), (user.stats.lvl / 7 * 2), (user.stats.lvl / 7), (user.stats.lvl / 7)]
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# Primary, secondary etc. attributes aren't explicitly defined, so hardcode them. In order as above
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switch user.stats.class
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when "wizard" then preference = ["int", "per", "con", "str"]
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when "rogue" then preference = ["per", "str", "int", "con"]
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when "healer" then preference = ["con", "int", "str", "per"]
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else preference = ["str", "con", "per", "int"]
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# Get the difference between the ideal attribute spread according to level, and the user's current spread.
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diff = [(user.stats[preference[0]]-ideal[0]),(user.stats[preference[1]]-ideal[1]),(user.stats[preference[2]]-ideal[2]),(user.stats[preference[3]]-ideal[3])]
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suggested = _.findIndex(diff, ((val) -> if val is _.min(diff) then true)) # Returns the index of the first attribute that's furthest behind the ideal
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if suggested is -1 then return "str" else return preference[suggested] # If _.findIndex failed, we'd get a -1...
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when "taskbased"
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suggested = _.findKey(user.stats.training, ((val) -> if val is _.max(user.stats.training) then val)) # Returns the stat that's been trained up the most this level
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# FIXME Reset training for this level. Tried _.each but couldn't get it to take.
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user.stats.training.str = 0
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user.stats.training.int = 0
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user.stats.training.con = 0
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user.stats.training.per = 0
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if suggested is undefined then return "str" else return suggested # Failed _.findkey gives undefined
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# tallies = _.reduce user.tasks, ((m,v)-> m[v.attribute or 'str'] += v.value;m), {str:0,int:0,con:0,per:0}
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# suggested = _.reduce tallies, ((m,v,k)-> if v>tallies[m] then k else m), 'str'
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else return "str" # if all else fails, dump into STR
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user.stats[(->
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switch user.preferences.allocationMode
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when "flat"
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switch Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per)
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when user.stats.int then "int" # In case of ties, favor INT first, to get the next point sooner
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when user.stats.per then "per" # Then favor PER, it's a god stat
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when user.stats.str then "str" # Then favor STR, everyone loves crits
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else "con" # CON, the unsexiest of attributes
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when "classbased"
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# Attributes get 3:2:1:1 per 7 levels.
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ideal = [(user.stats.lvl / 7 * 3), (user.stats.lvl / 7 * 2), (user.stats.lvl / 7), (user.stats.lvl / 7)]
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# Primary, secondary etc. attributes aren't explicitly defined, so hardcode them. In order as above
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preference = switch user.stats.class
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when "wizard" then ["int", "per", "con", "str"]
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when "rogue" then ["per", "str", "int", "con"]
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when "healer" then ["con", "int", "str", "per"]
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else ["str", "con", "per", "int"]
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# Get the difference between the ideal attribute spread according to level, and the user's current spread.
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diff = [(user.stats[preference[0]]-ideal[0]),(user.stats[preference[1]]-ideal[1]),(user.stats[preference[2]]-ideal[2]),(user.stats[preference[3]]-ideal[3])]
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suggested = _.findIndex(diff, ((val) -> if val is _.min(diff) then true)) # Returns the index of the first attribute that's furthest behind the ideal
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return if ~suggested then preference[suggested] else "str" # If _.findIndex failed, we'd get a -1...
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when "taskbased"
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suggested = _.findKey(user.stats.training, ((val) -> if val is _.max(user.stats.training) then val)) # Returns the stat that's been trained up the most this level
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# Reset training for this level.
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_.merge user.stats.training, {str:0,int:0,con:0,per:0}
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return suggested or "str" # Failed _.findkey gives undefined
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# tallies = _.reduce user.tasks, ((m,v)-> m[v.attribute or 'str'] += v.value;m), {str:0,int:0,con:0,per:0}
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# suggested = _.reduce tallies, ((m,v,k)-> if v>tallies[m] then k else m), 'str'
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else "str" # if all else fails, dump into STR
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)()]++
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updateStats: (stats) ->
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# Game Over
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@ -1067,8 +1062,7 @@ api.wrap = (user) ->
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# Auto-allocate a point, or give them a new manual point
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if user.preferences.automaticAllocation
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suggested = user.fns.autoAllocate()
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user.stats[suggested]++
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user.fns.autoAllocate()
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else
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# add new allocatable points. We could do user.stats.points++, but this does a fail-safe just in case
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user.stats.points = user.stats.lvl - (user.stats.con + user.stats.str + user.stats.per + user.stats.int);
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72
tests/simulations/autoAllocate.coffee
Normal file
72
tests/simulations/autoAllocate.coffee
Normal file
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@ -0,0 +1,72 @@
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###
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1) clone the repo
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2) npm install
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3) coffee ./tests/math_samples.coffee
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Current results at https://gist.github.com/lefnire/8049676
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###
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shared = require '../../script/index.coffee'
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_ = require 'lodash'
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$w = (s)->s.split(' ')
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id = shared.uuid()
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user =
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stats:
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class: 'warrior'
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lvl:1, hp:50, gp:0, exp:10
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per:0, int:0, con:0, str:0
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buffs: {per:0, int:0, con:0, str:0}
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training: {int:0,con:0,per:0,str:0}
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preferences: automaticAllocation: false
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party: quest: key:'evilsanta', progress: {up:0,down:0}
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achievements: {}
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items:
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eggs: {}
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hatchingPotions: {}
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food: {}
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gear:
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equipped:
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weapon: 'weapon_warrior_4'
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armor: 'armor_warrior_4'
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shield: 'shield_warrior_4'
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head: 'head_warrior_4'
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habits: [{value:1,type:'habit'}]
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dailys: []
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todos: []
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rewards: []
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modes =
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flat: _.cloneDeep user
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classbased_warrior: _.cloneDeep user
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classbased_rogue: _.cloneDeep user
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classbased_wizard: _.cloneDeep user
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classbased_healer: _.cloneDeep user
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taskbased: _.cloneDeep user
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modes.classbased_warrior.stats.class = 'warrior'
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modes.classbased_rogue.stats.class = 'rogue'
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modes.classbased_wizard.stats.class = 'wizard'
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modes.classbased_healer.stats.class = 'healer'
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_.each $w('flat classbased_warrior classbased_rogue classbased_wizard classbased_healer taskbased'), (mode) ->
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_.merge modes[mode].preferences,
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automaticAllocation: true
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allocationMode: if mode.indexOf('classbased') is 0 then 'classbased' else mode
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shared.wrap(modes[mode])
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console.log "\n\n================================================"
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console.log "New Simulation"
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console.log "================================================\n\n"
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_.times [20], (lvl) ->
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console.log ("[lvl #{lvl}]\n--------------\n")
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_.each $w('flat classbased_warrior classbased_rogue classbased_wizard classbased_healer taskbased'), (mode) ->
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u = modes[mode] #local var
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u.stats.exp = shared.tnl(lvl)+1 # level up
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u.habits[0].attribute = u.fns.randomVal({str:'str',int:'int',per:'per',con:'con'})
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u.ops.score {params:{id:u.habits[0].id},direction:'up'}
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u.fns.updateStats(u.stats) # trigger stats update
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str = mode + (if mode is 'taskbased' then " (#{u.habits[0].attribute})" else "")
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console.log str, _.pick(u.stats, $w 'per int con str')
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@ -6,7 +6,7 @@
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Current results at https://gist.github.com/lefnire/8049676
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###
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shared = require '../script/index.coffee'
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shared = require '../../script/index.coffee'
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_ = require 'lodash'
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id = shared.uuid()
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