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import * as maps from './maps/maps';
import * as metadata from '../data/2d73896c/metadata.json';
const POPULAR_THRESHOLD = 1.0; // percent
const PICKS_THRESHOLD = 300; // picks
const VARIANCE_THRESHOLD = 0.25; // % minimum accuracy
const report = require('../data/2d73896c/report.json')
.filter((entry) => entry.Actor != undefined); // bad data from API downtime
const reports = new Map();
const totalPicks = new Map();
const top10Relevancy = new Map();
const topPicks = new Map();
const topWins = new Map();
const topRareWins = new Map();
const topEpicWins = new Map();
const topLegendaryWins = new Map();
const topLowLevel = new Map();
const topScaling = new Map();
const topUnpopularWins = new Map();
let totalMatches = 0;
let actors = [];
const modes = metadata.config.api.modes;
for(let mode of modes) {
const relevancy = (entry) => (entry.TotalPicks / totalPicks.get(mode)) * entry.TotalWinner;
const playersPerMatch = maps.playersPerMatch(mode);
const reportFilterMode = report.filter((entry) => entry.Mode == mode);
const baseLevelForEntry = (entry) => reportFilterMode
.filter((entry2) => entry2.Actor == entry.Actor &&
entry2.Talent == entry.Talent)
.map((entry2) => entry2.LevelBucket)
.reduce((agg, cur) => cur < agg? cur : agg);
const reportFilterModeBaseLevel = reportFilterMode.filter((entry) => entry.LevelBucket == baseLevelForEntry(entry));
// using the "base level" (bucket 0) report, add information about diff to 'NoTalent' and scaling
const reportFilterModeRegressed = reportFilterModeBaseLevel.map((entry) => {
const sum = (agg, cur) => agg + cur;
const range = (n) => [...Array(n).keys()];
// perform a weighted linear regression. x: level bucket (scaled 0 - 1) y: win rate weights: picks
const actorReport = reportFilterMode
.filter((entry2) =>
entry2.Actor == entry.Actor &&
entry2.Talent == entry.Talent &&
entry2.LevelBucket >= 0);
const buckets = actorReport.map((entry) => entry.LevelBucket).sort();
const findActorWithBucket = (bucket) => actorReport.find((entry) => entry.LevelBucket == bucket);
const weights = buckets.map((bucket) => findActorWithBucket(bucket).Count);
const sum_weights = weights.reduce(sum, 0);
const xs = [].concat(...buckets.map((bucket, index) => range(weights[index]).map((_) => bucket / metadata.config.self.levelBuckets)));
const ys = [].concat(...buckets.map((bucket, index) => range(weights[index]).map((_) => findActorWithBucket(bucket).Winner)));
const n = xs.length;
const sum_x = xs.reduce(sum, 0);
const sum_y = ys.reduce(sum, 0);
const sum_xy = xs.map((x, index) => x * ys[index]).reduce(sum, 0);
const sum_xx = xs.map((x) => x * x).reduce(sum, 0);
const sum_yy = ys.map((y) => y * y).reduce(sum, 0);
const slope = (n * sum_xy - sum_x * sum_y) / (n * sum_xx - sum_x * sum_x);
const intercept = (sum_y - slope * sum_x) / n;
// sum (actual - average)^2
const sstot = ys.map((y) => Math.pow(y - sum_y / n, 2)).reduce(sum, 0);
// sum (actual - estimated)^2
const ssres = ys.map((y, index) => Math.pow((intercept + slope * xs[index]) - y, 2)).reduce(sum, 0);
// r^2 is also correlation coefficient ^ 2
const rsquare = 1 - ssres / sstot;
return Object.assign({}, entry, {
TalentWinrateBase: intercept,
TalentWinrateScaling: slope, // to 1 = max level
TalentWinrateMax: intercept + slope,
TalentWinrateLevelScaling: slope / maps.getMaxLevel(entry),
TalentWinrateVariance: rsquare,
TotalPicks: sum_weights || entry.Count, // NoTalent has no weights
TotalWinner: sum_y / sum_weights || entry.Winner,
SampleTooSmall: sum_weights < PICKS_THRESHOLD,
VarianceTooLarge: rsquare < VARIANCE_THRESHOLD,
});
});
reports.set(mode, reportFilterModeRegressed);
totalPicks.set(mode, reports.get(mode)
.map((entry) => entry.TotalPicks)
.reduce((agg, cur) => agg + cur, 0));
totalMatches += totalPicks.get(mode) / maps.playersPerMatch(mode);
top10Relevancy.set(mode, reports.get(mode)
.sort((entry1, entry2) => relevancy(entry2) - relevancy(entry1))
.slice(0, 10));
topPicks.set(mode, reports.get(mode).sort((entry1, entry2) => entry2.TotalPicks - entry1.TotalPicks)[0]);
topWins.set(mode, reports.get(mode)
.filter((entry) => 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode) > POPULAR_THRESHOLD)
.sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
topUnpopularWins.set(mode, reports.get(mode)
.filter((entry) => 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode) <= POPULAR_THRESHOLD &&
entry.TotalPicks > PICKS_THRESHOLD)
.sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
topRareWins.set(mode, reports.get(mode)
.filter((entry) => maps.getTalentRarity(entry.Talent) == 'Rare' && 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode))
.sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
topEpicWins.set(mode, reports.get(mode)
.filter((entry) => maps.getTalentRarity(entry.Talent) == 'Epic' && 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode))
.sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
topLegendaryWins.set(mode, reports.get(mode)
.filter((entry) => maps.getTalentRarity(entry.Talent) == 'Legendary' && 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode))
.sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
topLowLevel.set(mode, reports.get(mode)
.filter((entry) => !entry.SampleTooSmall && !entry.VarianceTooLarge)
.sort((entry1, entry2) => entry2.TalentWinrateBase - entry1.TalentWinrateBase)[0]);
topScaling.set(mode, reports.get(mode)
.filter((entry) => !entry.SampleTooSmall && !entry.VarianceTooLarge)
.sort((entry1, entry2) => entry2.TalentWinrateLevelScaling - entry1.TalentWinrateLevelScaling)[0]);
if (actors.length == 0) {
actors = [...new Set(reports.get(mode).map((entry) => entry.Actor))];
}
}
export default {
getReport(mode) {
return reports.get(mode);
},
getTotalPicks(mode) {
return totalPicks.get(mode);
},
getTopPick(mode) {
return topPicks.get(mode);
},
getTop10Picks(mode) {
return top10Relevancy.get(mode);
},
getTopLowLevel(mode) {
return topLowLevel.get(mode);
},
getTopScaling(mode) {
return topScaling.get(mode);
},
getTopWin(mode) {
return topWins.get(mode);
},
getTopUnpopularWin(mode) {
return topUnpopularWins.get(mode);
},
getTopRareWins(mode) {
return topRareWins.get(mode);
},
getTopEpicWins(mode) {
return topEpicWins.get(mode);
},
getTopLegendaryWins(mode) {
return topLegendaryWins.get(mode);
},
getBestUnpopular(mode) {
return topUnpopular.get(mode);
},
getActors() {
return actors;
},
getModes() {
return modes;
},
getTotalMatches() {
return totalMatches;
},
getLastUpdate() {
return metadata.lastUpdate;
},
getLevelBuckets() {
return metadata.config.self.levelBuckets;
},
};
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