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Implement "training" over the course of a level, for task-based attribute allocation

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This commit is contained in:
Sabe Jones 2013-12-31 09:05:03 -06:00
parent c4a82d4edd
commit 4662fe151b
2 changed files with 30 additions and 22 deletions
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35
dist/habitrpg-shared.js vendored
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@ -11688,6 +11688,9 @@ var process=require("__browserify_process");(function() {
currVal = task.value < -47.27 ? -47.27 : task.value > 21.27 ? 21.27 : task.value;
nextDelta = Math.pow(0.9747, currVal) * (direction === 'down' ? -1 : 1);
if (task.type !== 'reward') {
if (user.preferences.automaticAllocation === true && user.preferences.allocationMode === 'taskbased') {
user.stats.training[task.attribute] += nextDelta;
}
adjustAmt = nextDelta;
if (direction === 'up' && task.type !== 'reward' && !(task.type === 'habit' && !task.down)) {
adjustAmt = nextDelta * (1 + user._statsComputed.str * .004);
@ -11963,7 +11966,7 @@ var process=require("__browserify_process");(function() {
*/
autoAllocate: function() {
var diff, ideal, preference, suggested, tallies;
var diff, ideal, preference, suggested;
switch (user.preferences.allocationMode) {
case "flat":
suggested = Math.min(user.stats.str, user.stats.int, user.stats.con, user.stats.per);
@ -12005,23 +12008,21 @@ var process=require("__browserify_process");(function() {
}
break;
case "taskbased":
tallies = _.reduce(user.tasks, (function(m, v) {
m[v.attribute || 'str'] += v.value;
return m;
}), {
str: 0,
int: 0,
con: 0,
per: 0
});
suggested = _.reduce(tallies, (function(m, v, k) {
if (v > tallies[m]) {
return k;
} else {
return m;
suggested = _.findKey(user.stats.training, (function(val) {
if (val === _.max(user.stats.training)) {
return val;
}
}), 'str');
return suggested;
}));
user.stats.training.str = 0;
user.stats.training.int = 0;
user.stats.training.con = 0;
user.stats.training.per = 0;
if (suggested === void 0) {
return "str";
} else {
return suggested;
}
break;
default:
return "str";
}

17
script/index.coffee
View file

@ -687,6 +687,7 @@ api.wrap = (user) ->
else task.value
nextDelta = Math.pow(0.9747, currVal) * (if direction is 'down' then -1 else 1)
unless task.type is 'reward'
if (user.preferences.automaticAllocation is true and user.preferences.allocationMode is 'taskbased') then user.stats.training[task.attribute] += nextDelta
adjustAmt = nextDelta
# ===== STRENGTH =====
# (Only for up-scoring, ignore up-onlies and rewards)
@ -965,11 +966,17 @@ api.wrap = (user) ->
# Get the difference between the ideal attribute spread according to level, and the user's current spread.
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])]
suggested = _.findIndex(diff, ((val) -> if val is _.min(diff) then true)) # Returns the index of the first attribute that's furthest behind the ideal
if suggested is -1 then return "str" else return preference[suggested] # If _findIndex failed, we'd get a -1...
when "taskbased" # old logic, temp
tallies = _.reduce user.tasks, ((m,v)-> m[v.attribute or 'str'] += v.value;m), {str:0,int:0,con:0,per:0}
suggested = _.reduce tallies, ((m,v,k)-> if v>tallies[m] then k else m), 'str'
return suggested
if suggested is -1 then return "str" else return preference[suggested] # If _.findIndex failed, we'd get a -1...
when "taskbased"
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
# FIXME Reset training for this level. Tried _.each but couldn't get it to take.
user.stats.training.str = 0
user.stats.training.int = 0
user.stats.training.con = 0
user.stats.training.per = 0
if suggested is undefined then return "str" else return suggested # Failed _.findkey gives undefined
# tallies = _.reduce user.tasks, ((m,v)-> m[v.attribute or 'str'] += v.value;m), {str:0,int:0,con:0,per:0}
# suggested = _.reduce tallies, ((m,v,k)-> if v>tallies[m] then k else m), 'str'
else return "str" # if all else fails, dump into STR
updateStats: (stats) ->