ecclesia/dist/election/attribution/proportionalFactory.cjs

Framework for political and electoral simulations

"use strict";
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var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);

// src/election/attribution/proportionalFactory.ts
var proportionalFactory_exports = {};
__export(proportionalFactory_exports, {
  dHondt: () => dHondt,
  hamilton: () => hamilton,
  hareLargestRemainders: () => hareLargestRemainders,
  highestAverages: () => highestAverages,
  huntingtonHill: () => huntingtonHill,
  jefferson: () => jefferson,
  largestRemainders: () => largestRemainders,
  sainteLague: () => sainteLague,
  webster: () => webster
});
module.exports = __toCommonJS(proportionalFactory_exports);
var import_python4 = require("@gouvernathor/python");
var import_collections7 = require("@gouvernathor/python/collections");

// src/election/attribution/transform.ts
var import_collections6 = require("@gouvernathor/python/collections");

// src/election/attribution/base.ts
var AttributionFailure = class extends Error {
};

// src/election/attribution/proportionalBase.ts
var import_collections = require("@gouvernathor/python/collections");
function proportionalFromRankIndexFunction({ nSeats, rankIndexFunction }) {
  const attrib = (votes, rest = {}) => {
    const allVotes = votes.total;
    const fractions = new Map([...votes.entries()].map(([party, v]) => [party, v / allVotes]));
    const rankIndexValues = new Map([...fractions.entries()].map(([party, f]) => [party, rankIndexFunction(f, 0)]));
    const parties = [...votes.keys()].sort((a, b) => rankIndexValues.get(a) - rankIndexValues.get(b));
    const seats = new import_collections.Counter();
    s: for (let sn = 0; sn < nSeats; sn++) {
      const winner = parties.pop();
      seats.increment(winner);
      rankIndexValues.set(winner, rankIndexFunction(fractions.get(winner), seats.get(winner)));
      for (let pn = 0; pn < parties.length; pn++) {
        if (rankIndexValues.get(parties[pn]) >= rankIndexValues.get(winner)) {
          parties.splice(pn, 0, winner);
          continue s;
        }
      }
      parties.push(winner);
    }
    return seats;
  };
  attrib.nSeats = nSeats;
  return attrib;
}
function stationaryDivisorFunction(r) {
  return (k) => k + r;
}
function rankIndexFunctionFromDivisorFunction(divisorFunction) {
  return (t, a) => t / divisorFunction(a);
}
function proportionalFromDivisorFunction({ nSeats, divisorFunction }) {
  return proportionalFromRankIndexFunction({
    nSeats,
    rankIndexFunction: rankIndexFunctionFromDivisorFunction(divisorFunction)
  });
}

// src/election/attribution/majorityFactory.ts
var import_python = require("@gouvernathor/python");
var import_collections2 = require("@gouvernathor/python/collections");

// src/election/attribution/orderingFactory.ts
var import_python2 = require("@gouvernathor/python");
var import_collections3 = require("@gouvernathor/python/collections");
function condorcet({ nSeats, contingency = null }) {
  const attrib = (votes, rest = {}) => {
    const counts = new import_collections3.DefaultMap(() => new import_collections3.Counter());
    const majority = votes.length / 2;
    for (const ballot of votes) {
      for (const [i, party1] of (0, import_python2.enumerate)(ballot)) {
        for (const party2 of ballot.slice(i + 1)) {
          counts.get(party1).increment(party2);
        }
      }
    }
    const win = new Set(counts.keys());
    for (const [party, partyCounter] of counts) {
      for (const value of partyCounter.pos.values()) {
        if (value > majority) {
          win.delete(party);
          break;
        }
      }
    }
    if (win.size !== 1) {
      if (win.size !== 0) {
        throw new Error("Bad attribution");
      }
      if (contingency === null) {
        throw new condorcet.Standoff("No Condorcet winner");
      }
      return contingency(votes, rest);
    }
    const [winner] = win;
    return new import_collections3.Counter([[winner, nSeats]]);
  };
  attrib.nSeats = nSeats;
  return attrib;
}
condorcet.Standoff = class CondorcetStandoff extends AttributionFailure {
};

// src/election/attribution/scoreFactory.ts
var import_python3 = require("@gouvernathor/python");
var import_collections4 = require("@gouvernathor/python/collections");
var import_statistics = require("@gouvernathor/python/statistics");

// src/election/ballots.ts
var Scores;
((Scores2) => {
  function get(key) {
    const value = this.get(key);
    if (value === void 0) {
      return Array(this.ngrades).fill(0);
    }
    return value;
  }
  function fromEntries(elements) {
    if (elements.length === 0) {
      throw new Error("Use the fromGrades method to create an empty Scores instance");
    }
    const ths = new Map(elements);
    ths.ngrades = elements[0][1].length;
    ths.get = get.bind(ths);
    return ths;
  }
  Scores2.fromEntries = fromEntries;
  function fromGrades(ngrades) {
    const ths = /* @__PURE__ */ new Map();
    ths.ngrades = ngrades;
    ths.get = get.bind(ths);
    return ths;
  }
  Scores2.fromGrades = fromGrades;
})(Scores || (Scores = {}));

// src/election/attribution/randomFactory.ts
var import_collections5 = require("@gouvernathor/python/collections");

// src/utils.ts
var import_rng = __toESM(require("@gouvernathor/rng"), 1);

// src/election/attribution/transform.ts
function addThresholdToSimpleAttribution({ threshold, attribution, contingency = attribution }) {
  const attrib = (votes, rest = {}) => {
    if (threshold > 0) {
      const original_votes = votes;
      const votes_threshold = threshold * votes.total;
      votes = new import_collections6.Counter([...votes.entries()].filter(([_, v]) => v >= votes_threshold));
      if (votes.size === 0) {
        if (contingency === null) {
          throw new AttributionFailure("No party reached the threshold");
        }
        return contingency(original_votes, rest);
      }
    }
    return attribution(votes, rest);
  };
  return attrib;
}

// src/election/attribution/proportionalFactory.ts
var divisor1 = stationaryDivisorFunction(1);
function jefferson({ nSeats }) {
  return proportionalFromDivisorFunction({
    nSeats,
    divisorFunction: divisor1
  });
}
var dHondt = jefferson;
var divisorPoint5 = (k) => 2 * k + 1;
function webster({ nSeats }) {
  return proportionalFromDivisorFunction({
    nSeats,
    divisorFunction: divisorPoint5
  });
}
var sainteLague = webster;
function hamilton({ nSeats }) {
  const attrib = (votes, rest = {}) => {
    const seats = new import_collections7.Counter();
    const remainders = /* @__PURE__ */ new Map();
    const sumVotes = votes.total;
    for (const [party, scores] of votes) {
      const [i, r] = (0, import_python4.divmod)(scores * nSeats, sumVotes);
      seats.set(party, i);
      remainders.set(party, r);
    }
    seats.update([...remainders.keys()].sort((a, b) => remainders.get(b) - remainders.get(a)).slice(0, nSeats - seats.total));
    return seats;
  };
  attrib.nSeats = nSeats;
  return attrib;
}
var hareLargestRemainders = hamilton;
var huntingtonHillBaseRankIndexFunction = rankIndexFunctionFromDivisorFunction((k) => Math.sqrt(k * (k + 1)));
var huntingtonHillRankIndexFunction = (t, a) => {
  if (a <= 0) {
    return Infinity;
  }
  return huntingtonHillBaseRankIndexFunction(t, a);
};
function huntingtonHill({ nSeats, threshold, contingency = null }) {
  const attrib = addThresholdToSimpleAttribution({
    threshold,
    contingency,
    attribution: proportionalFromRankIndexFunction({
      nSeats,
      rankIndexFunction: huntingtonHillRankIndexFunction
    })
  });
  attrib.nSeats = nSeats;
  return attrib;
}
var highestAverages = webster;
var largestRemainders = hamilton;
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
  dHondt,
  hamilton,
  hareLargestRemainders,
  highestAverages,
  huntingtonHill,
  jefferson,
  largestRemainders,
  sainteLague,
  webster
});
//# sourceMappingURL=proportionalFactory.cjs.map