Create scoring module

score.js

@@ -0,0 +1,44 @@
+const currentScores = {};
+
+const rescore = (target, { name, jab, rgb }) => {
+  currentScores[name] = {
+    jab: {
+      total: applyMetrics(jab.total, target.jab),
+      clusters: jab.clusters.map(c => applyMetrics(c, target.jab)),
+    },
+    rgb: {
+      total: applyMetrics(rgb.total, target.rgb),
+      clusters: rgb.clusters.map(c => applyMetrics(c, target.rgb)),
+    },
+  };
+};
+
+const rescoreAll = target => pokemonData.forEach(pkmn => rescore(target, pkmn));
+
+const getBestClusterIndex = (pkmn, space, { sortMetric, scaleOption, sortOrder }) => {
+  // get the scales
+  const scales = scaleOption(pkmn);
+  // and multiply with the intended metric, and find the best value
+  return argComp(sortOrder)(
+    currentScores[pkmn.name][space].map((c, i) => c[sortMetric] * scales[i])
+  );
+}
+
+const getBest = (number, space, clusterSettings, { sortMetric, scaleOption, sortOrder }) => {
+  let valueExtractor;
+  if (clusterSettings) {
+    valueExtractor = pkmn => {
+      const index = getBestClusterIndex(pkmn, space, clusterSettings);
+      // and then get the *actual* score according to the sort metric
+      return scaleOption(pkmn)[index] * clusters[index][sortMetric];      
+    };
+  } else {
+    // ignore scaleOption if not using clusters
+    valueExtractor = pkmn => currentScores[pkmn.name][space].total[sortMetric];
+  }
+  
+  return pokemonData
+    .slice()
+    .sort((a, b) => sortOrder(valueExtractor(a), valueExtractor(b)))
+    .slice(0, number);
+};