diff --git a/similarity.ipynb b/similarity.ipynb
index 2ed5af1fe39cc012524336c102ff841e829e8076..502c9c1859245a0310e611c9e30f7a7a03cbda2a 100644
--- a/similarity.ipynb
+++ b/similarity.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
@@ -55,7 +55,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
@@ -76,7 +76,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
@@ -118,18 +118,28 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"#count the number of cards in each deck and put in in a dictionary\n",
"\n",
"def count(decks_,decks2cards):\n",
- "\n",
+ " \n",
+ " deck_1 = decks_[decks_['equivalent'] >= 0]\n",
+ " #print(deck_1)\n",
+ " mask = decks2cards['deckuid'].isin(decks_['uid'])\n",
+ " \n",
+ " \n",
+ " decks2cards_ = decks2cards.loc[mask]\n",
+ " \n",
" decks_['count'] = 0\n",
+ " #print(decks2cards['deckuid'])\n",
+ " #print(decks_['uid'])\n",
+ " \n",
+ " #decks_['count'].loc[decks_['uid'] == decks2cards['deckuid'][1]] += d2c['amount']\n",
"\n",
- "\n",
- " for j,d2c in decks2cards.iterrows():\n",
+ " for j,d2c in decks2cards_.iterrows():\n",
"\n",
"\n",
" #decks_['count'].loc[decks_['uid'] == d2c['deckuid']] += d2c['amount']\n",
@@ -140,12 +150,14 @@
"\n",
"\n",
" decks_\n",
- " return(decks_)\n"
+ " return(decks_)\n",
+ "\n",
+ "\n"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
@@ -165,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
@@ -202,8 +214,8 @@
" if count > best_count:\n",
" best_count = count\n",
" best_card = card['uid']\n",
- " print(best_count)\n",
- " print(best_card)\n",
+ " #print(best_count)\n",
+ " #print(best_card)\n",
" \n",
" return best_card\n",
" \n",
@@ -215,32 +227,268 @@
"execution_count": null,
"metadata": {},
"outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>uid</th>\n",
+ " <th>count</th>\n",
+ " <th>equivalent</th>\n",
+ " <th>similarity</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1284241</td>\n",
+ " <td>71</td>\n",
+ " <td>6</td>\n",
+ " <td>0.084507</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1284242</td>\n",
+ " <td>72</td>\n",
+ " <td>2</td>\n",
+ " <td>0.027778</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1284385</td>\n",
+ " <td>72</td>\n",
+ " <td>9</td>\n",
+ " <td>0.125000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>1284246</td>\n",
+ " <td>72</td>\n",
+ " <td>2</td>\n",
+ " <td>0.027778</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>1284248</td>\n",
+ " <td>71</td>\n",
+ " <td>2</td>\n",
+ " <td>0.028169</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51394</th>\n",
+ " <td>1457421</td>\n",
+ " <td>91</td>\n",
+ " <td>4</td>\n",
+ " <td>0.043956</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51402</th>\n",
+ " <td>1457427</td>\n",
+ " <td>91</td>\n",
+ " <td>3</td>\n",
+ " <td>0.032967</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51411</th>\n",
+ " <td>1457433</td>\n",
+ " <td>72</td>\n",
+ " <td>3</td>\n",
+ " <td>0.041667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51424</th>\n",
+ " <td>1457441</td>\n",
+ " <td>74</td>\n",
+ " <td>1</td>\n",
+ " <td>0.013514</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51431</th>\n",
+ " <td>1457400</td>\n",
+ " <td>72</td>\n",
+ " <td>4</td>\n",
+ " <td>0.055556</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>14205 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " uid count equivalent similarity\n",
+ "0 1284241 71 6 0.084507\n",
+ "1 1284242 72 2 0.027778\n",
+ "2 1284385 72 9 0.125000\n",
+ "5 1284246 72 2 0.027778\n",
+ "7 1284248 71 2 0.028169\n",
+ "... ... ... ... ...\n",
+ "51394 1457421 91 4 0.043956\n",
+ "51402 1457427 91 3 0.032967\n",
+ "51411 1457433 72 3 0.041667\n",
+ "51424 1457441 74 1 0.013514\n",
+ "51431 1457400 72 4 0.055556\n",
+ "\n",
+ "[14205 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
"source": [
- "#get your best new card here\n",
"\n",
- "decks_ = create_decks_(decks)\n",
+ "def get_new_card(input):\n",
+ "\n",
+ " decks_ = create_decks_(decks)\n",
"\n",
- "decks_ = calc_equivalent(decks_,input,decks2cards)\n",
+ " decks_ = calc_equivalent(decks_,input,decks2cards)\n",
"\n",
- "decks_ = count(decks_,decks2cards)\n",
+ " decks_ = count(decks_,decks2cards)\n",
"\n",
- "decks_ = calc_similarity(decks_)\n",
+ " decks_ = calc_similarity(decks_)\n",
"\n",
"\n",
"\n",
- "largest_similarity = decks_.sort_values(by=[\"similarity\"],ascending=False)[\"similarity\"] [1]\n",
- "most_similar_deck = decks_.sort_values(by=[\"similarity\"],ascending=False)[\"uid\"][1]\n",
+ " largest_similarity = decks_.sort_values(by=[\"similarity\"],ascending=False)[\"similarity\"] [1]\n",
+ " most_similar_deck = decks_.sort_values(by=[\"similarity\"],ascending=False)[\"uid\"][1]\n",
"\n",
- "print(most_similar_deck)\n",
- "print(largest_similarity)\n",
+ " #print(most_similar_deck)\n",
+ " #print(largest_similarity)\n",
"\n",
- "best_deck = decks2cards.loc[decks2cards['deckuid'] == most_similar_deck]\n",
+ " best_deck = decks2cards.loc[decks2cards['deckuid'] == most_similar_deck]\n",
"\n",
"\n",
"\n",
"\n",
- "new_card = find_best_card(best_deck,input)\n",
- "print(new_card)\n",
+ " new_card = find_best_card(best_deck,input)\n",
+ " print(new_card)\n",
+ " \n",
+ " return new_card, most_similar_deck"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "21\n"
+ ]
+ }
+ ],
+ "source": [
+ "##start main here\n",
+ "\n",
+ "number_of_cards = 2\n",
+ "\n",
+ "list_of_new_cards = []\n",
+ "best_deck = 0\n",
+ "\n",
+ "\n",
+ "\n",
+ "for i in range(number_of_cards):\n",
+ " \n",
+ " new_card, best_deck = get_new_card(input)\n",
+ " \n",
+ " df = pandas.DataFrame({\"uid\":[len(input['uid'] + 2)],\n",
+ " \"carduid\":[new_card],\n",
+ " \"amount\":[1]})\n",
+ "\n",
+ " input = pandas.concat([input,df])\n",
+ " \n",
+ " list_of_new_cards.append(new_card)\n",
+ " \n",
+ "\n",
+ " \n",
+ "print(list_of_new_cards)\n",
+ "print(best_deck)\n",
+ "\n",
+ "##end main here\n",
"\n"
]
},
@@ -272,6 +520,41 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 108,