guessAlgTime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Import section basically stolen from templates\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten,LSTM,Dropout,Embedding\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error \n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = pd.read_csv(\"algsWithTimesExtra.csv\",delimiter='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split(x):\n",
    "    return x.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "allMoves = set()\n",
    "\n",
    "for line in times[2].apply(split):\n",
    "    for move in line:\n",
    "        allMoves.add(move)\n",
    "\n",
    "allMoves = list(allMoves)\n",
    "\n",
    "moveMapping = {move:idx for idx,move in enumerate(allMoves)}\n",
    "maxline = times[2].apply(split).apply(len).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def processAlgForML(x):\n",
    "    ans = []\n",
    "    for move in x:\n",
    "        ans.append(moveMapping[move])\n",
    "    while len(ans) < maxline:\n",
    "        ans.append(len(moveMapping)+1)\n",
    "    return ans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(times[2].apply(split).apply(processAlgForML).tolist())\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,times[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inputSingleAlg(x):\n",
    "    ans = []\n",
    "    for move in x.split(' '):\n",
    "        ans.append(moveMapping[move])\n",
    "    while len(ans) < maxline:\n",
    "        ans.append(len(moveMapping)+1)\n",
    "    return pd.DataFrame([ans])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9   ...  11  12  13  14  15  16  17  \\\n",
       "0  25   8  10  16  17  10  29   1  17  28  ...  23  24  20  24  28  24  20   \n",
       "\n",
       "   18  19  20  \n",
       "0  16  99  99  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputSingleAlg(\"R2 F D B2 L D R B L D' R2 D2 R' U2 R' D' R' U2 B2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(moveMapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_4 (Embedding)     (None, 21, 64)            2368      \n",
      "                                                                 \n",
      " lstm_21 (LSTM)              (None, 21, 64)            33024     \n",
      "                                                                 \n",
      " lstm_22 (LSTM)              (None, 32)                12416     \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 47,841\n",
      "Trainable params: 47,841\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# No hyper paramter tuning at all.\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(moveMapping)+2, 64, input_length=maxline)) \n",
    "model.add(LSTM(64, activation='relu', return_sequences=True))\n",
    "model.add(LSTM(32, activation='relu', return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, kernel_initializer='normal',activation='linear'))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NN_model = Sequential()\n",
    "\n",
    "# # The Input Layer :\n",
    "# NN_model.add(Dense(128, kernel_initializer='normal',input_dim = maxline, activation='relu'))\n",
    "# NN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\n",
    "# NN_model.add(Dense(1, kernel_initializer='normal',activation='linear'))\n",
    "# NN_model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])\n",
    "# NN_model.summary()\n",
    "# # NN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\n",
    "# # NN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "8/8 [==============================] - 2s 50ms/step - loss: 0.8907 - val_loss: 0.9451\n",
      "Epoch 2/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.7783 - val_loss: 0.7201\n",
      "Epoch 3/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.4080 - val_loss: 0.2143\n",
      "Epoch 4/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.1795 - val_loss: 0.2219\n",
      "Epoch 5/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.1548 - val_loss: 0.1838\n",
      "Epoch 6/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.1259 - val_loss: 0.1795\n",
      "Epoch 7/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.1206 - val_loss: 0.1524\n",
      "Epoch 8/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0986 - val_loss: 0.1366\n",
      "Epoch 9/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0965 - val_loss: 0.1253\n",
      "Epoch 10/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0802 - val_loss: 0.0897\n",
      "Epoch 11/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0572 - val_loss: 0.0372\n",
      "Epoch 12/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0324 - val_loss: 0.0333\n",
      "Epoch 13/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0434 - val_loss: 0.0577\n",
      "Epoch 14/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0459 - val_loss: 0.0256\n",
      "Epoch 15/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0342 - val_loss: 0.0575\n",
      "Epoch 16/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0361 - val_loss: 0.0863\n",
      "Epoch 17/500\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 0.0271 - val_loss: 0.0602\n",
      "Epoch 18/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0331 - val_loss: 0.0883\n",
      "Epoch 19/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0364 - val_loss: 0.1149\n",
      "Epoch 20/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0555 - val_loss: 0.0849\n",
      "Epoch 21/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0477 - val_loss: 0.0868\n",
      "Epoch 22/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0370 - val_loss: 0.0348\n",
      "Epoch 23/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0226 - val_loss: 0.0373\n",
      "Epoch 24/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0272 - val_loss: 0.0564\n",
      "Epoch 25/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0273 - val_loss: 0.0386\n",
      "Epoch 26/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0267 - val_loss: 0.0460\n",
      "Epoch 27/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0196 - val_loss: 0.0313\n",
      "Epoch 28/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0274 - val_loss: 0.0868\n",
      "Epoch 29/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0483 - val_loss: 0.0519\n",
      "Epoch 30/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0392 - val_loss: 0.0540\n",
      "Epoch 31/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0271 - val_loss: 0.0512\n",
      "Epoch 32/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0348 - val_loss: 0.0221\n",
      "Epoch 33/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0287 - val_loss: 0.0232\n",
      "Epoch 34/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0304 - val_loss: 0.0660\n",
      "Epoch 35/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0361 - val_loss: 0.0389\n",
      "Epoch 36/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0274 - val_loss: 0.0422\n",
      "Epoch 37/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0187 - val_loss: 0.0251\n",
      "Epoch 38/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0273 - val_loss: 0.0218\n",
      "Epoch 39/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0224 - val_loss: 0.0478\n",
      "Epoch 40/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0215 - val_loss: 0.0322\n",
      "Epoch 41/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0233 - val_loss: 0.0300\n",
      "Epoch 42/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0202 - val_loss: 0.0328\n",
      "Epoch 43/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0234 - val_loss: 0.0446\n",
      "Epoch 44/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0243 - val_loss: 0.0399\n",
      "Epoch 45/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0265 - val_loss: 0.0405\n",
      "Epoch 46/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0185 - val_loss: 0.0461\n",
      "Epoch 47/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0213 - val_loss: 0.0395\n",
      "Epoch 48/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0273 - val_loss: 0.0662\n",
      "Epoch 49/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0214 - val_loss: 0.0487\n",
      "Epoch 50/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0179 - val_loss: 0.0398\n",
      "Epoch 51/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0197 - val_loss: 0.0263\n",
      "Epoch 52/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0497 - val_loss: 0.0789\n",
      "Epoch 53/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0341 - val_loss: 0.0572\n",
      "Epoch 54/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0346 - val_loss: 0.0420\n",
      "Epoch 55/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0232 - val_loss: 0.0650\n",
      "Epoch 56/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0214 - val_loss: 0.0344\n",
      "Epoch 57/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0311 - val_loss: 0.0616\n",
      "Epoch 58/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0244 - val_loss: 0.0578\n",
      "Epoch 59/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0239 - val_loss: 0.0386\n",
      "Epoch 60/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0192 - val_loss: 0.0570\n",
      "Epoch 61/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0218 - val_loss: 0.0430\n",
      "Epoch 62/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0177 - val_loss: 0.0548\n",
      "Epoch 63/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0309 - val_loss: 0.0485\n",
      "Epoch 64/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0195 - val_loss: 0.0399\n",
      "Epoch 65/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0185 - val_loss: 0.0367\n",
      "Epoch 66/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0170 - val_loss: 0.0397\n",
      "Epoch 67/500\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 0.0200 - val_loss: 0.0451\n",
      "Epoch 68/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0167 - val_loss: 0.0464\n",
      "Epoch 69/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0186 - val_loss: 0.0514\n",
      "Epoch 70/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0277 - val_loss: 0.0598\n",
      "Epoch 71/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0250 - val_loss: 0.0523\n",
      "Epoch 72/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0307 - val_loss: 0.0385\n",
      "Epoch 73/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0194 - val_loss: 0.0368\n",
      "Epoch 74/500\n",
      "8/8 [==============================] - 0s 15ms/step - loss: 0.0186 - val_loss: 0.0430\n",
      "Epoch 75/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0163 - val_loss: 0.0466\n",
      "Epoch 76/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0198 - val_loss: 0.0463\n",
      "Epoch 77/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0154 - val_loss: 0.0296\n",
      "Epoch 78/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0177 - val_loss: 0.0559\n",
      "Epoch 79/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0236 - val_loss: 0.0555\n",
      "Epoch 80/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0174 - val_loss: 0.0654\n",
      "Epoch 81/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0224 - val_loss: 0.0497\n",
      "Epoch 82/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0235 - val_loss: 0.0305\n",
      "Epoch 83/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0219 - val_loss: 0.0532\n",
      "Epoch 84/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0199 - val_loss: 0.0388\n",
      "Epoch 85/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0215 - val_loss: 0.0385\n",
      "Epoch 86/500\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.0223 - val_loss: 0.0582\n",
      "Epoch 87/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0213 - val_loss: 0.0498\n",
      "Epoch 88/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0199 - val_loss: 0.0379\n",
      "Epoch 89/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0177 - val_loss: 0.0761\n",
      "Epoch 90/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0302 - val_loss: 0.0536\n",
      "Epoch 91/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0329 - val_loss: 0.0765\n",
      "Epoch 92/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0245 - val_loss: 0.0356\n",
      "Epoch 93/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0179 - val_loss: 0.0293\n",
      "Epoch 94/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0197 - val_loss: 0.0465\n",
      "Epoch 95/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0208 - val_loss: 0.0452\n",
      "Epoch 96/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0219 - val_loss: 0.0553\n",
      "Epoch 97/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0194 - val_loss: 0.0624\n",
      "Epoch 98/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0201 - val_loss: 0.0476\n",
      "Epoch 99/500\n",
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      "8/8 [==============================] - 0s 22ms/step - loss: 0.0127 - val_loss: 0.0489\n",
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      "Epoch 255/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0104 - val_loss: 0.0609\n",
      "Epoch 256/500\n",
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      "8/8 [==============================] - 0s 18ms/step - loss: 0.0173 - val_loss: 0.0604\n",
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      "8/8 [==============================] - 0s 19ms/step - loss: 0.0195 - val_loss: 0.0803\n",
      "Epoch 263/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0150 - val_loss: 0.0608\n",
      "Epoch 264/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0110 - val_loss: 0.0546\n",
      "Epoch 265/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0119 - val_loss: 0.0415\n",
      "Epoch 266/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0149 - val_loss: 0.0556\n",
      "Epoch 267/500\n",
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      "8/8 [==============================] - 0s 21ms/step - loss: 0.0180 - val_loss: 0.1038\n",
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      "8/8 [==============================] - 0s 21ms/step - loss: 0.0187 - val_loss: 0.0965\n",
      "Epoch 438/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0148 - val_loss: 0.0841\n",
      "Epoch 439/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0081 - val_loss: 0.0603\n",
      "Epoch 440/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0111 - val_loss: 0.0768\n",
      "Epoch 441/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0101 - val_loss: 0.0765\n",
      "Epoch 442/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0124 - val_loss: 0.0725\n",
      "Epoch 443/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0100 - val_loss: 0.0747\n",
      "Epoch 444/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0102 - val_loss: 0.0690\n",
      "Epoch 445/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0076 - val_loss: 0.0774\n",
      "Epoch 446/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0081 - val_loss: 0.0865\n",
      "Epoch 447/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0112 - val_loss: 0.0766\n",
      "Epoch 448/500\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 0.0087 - val_loss: 0.0724\n",
      "Epoch 449/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0069 - val_loss: 0.0660\n",
      "Epoch 450/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0076 - val_loss: 0.0703\n",
      "Epoch 451/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0092 - val_loss: 0.0640\n",
      "Epoch 452/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0058 - val_loss: 0.0700\n",
      "Epoch 453/500\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.0055 - val_loss: 0.0668\n",
      "Epoch 454/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0081 - val_loss: 0.0637\n",
      "Epoch 455/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0060 - val_loss: 0.0632\n",
      "Epoch 456/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0089 - val_loss: 0.0632\n",
      "Epoch 457/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0063 - val_loss: 0.0681\n",
      "Epoch 458/500\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 0.0096 - val_loss: 0.0631\n",
      "Epoch 459/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0102 - val_loss: 0.0662\n",
      "Epoch 460/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0072 - val_loss: 0.0722\n",
      "Epoch 461/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0068 - val_loss: 0.0761\n",
      "Epoch 462/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0060 - val_loss: 0.0788\n",
      "Epoch 463/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0082 - val_loss: 0.0647\n",
      "Epoch 464/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0074 - val_loss: 0.0685\n",
      "Epoch 465/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0073 - val_loss: 0.0696\n",
      "Epoch 466/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0074 - val_loss: 0.0698\n",
      "Epoch 467/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0081 - val_loss: 0.0646\n",
      "Epoch 468/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0091 - val_loss: 0.0772\n",
      "Epoch 469/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0101 - val_loss: 0.0790\n",
      "Epoch 470/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0065 - val_loss: 0.0808\n",
      "Epoch 471/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0075 - val_loss: 0.0729\n",
      "Epoch 472/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0049 - val_loss: 0.0736\n",
      "Epoch 473/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0042 - val_loss: 0.0728\n",
      "Epoch 474/500\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 0.0054 - val_loss: 0.0758\n",
      "Epoch 475/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0058 - val_loss: 0.0729\n",
      "Epoch 476/500\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 0.0117 - val_loss: 0.0679\n",
      "Epoch 477/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0078 - val_loss: 0.0726\n",
      "Epoch 478/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0062 - val_loss: 0.0663\n",
      "Epoch 479/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0051 - val_loss: 0.0719\n",
      "Epoch 480/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0046 - val_loss: 0.0734\n",
      "Epoch 481/500\n",
      "8/8 [==============================] - 0s 18ms/step - loss: 0.0073 - val_loss: 0.0674\n",
      "Epoch 482/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0052 - val_loss: 0.0681\n",
      "Epoch 483/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0070 - val_loss: 0.0784\n",
      "Epoch 484/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0117 - val_loss: 0.0719\n",
      "Epoch 485/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0064 - val_loss: 0.0730\n",
      "Epoch 486/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0050 - val_loss: 0.0679\n",
      "Epoch 487/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0074 - val_loss: 0.0875\n",
      "Epoch 488/500\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 0.0084 - val_loss: 0.0798\n",
      "Epoch 489/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0059 - val_loss: 0.0764\n",
      "Epoch 490/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0074 - val_loss: 0.0716\n",
      "Epoch 491/500\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.0066 - val_loss: 0.0788\n",
      "Epoch 492/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0066 - val_loss: 0.0673\n",
      "Epoch 493/500\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 0.0040 - val_loss: 0.0644\n",
      "Epoch 494/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0062 - val_loss: 0.0636\n",
      "Epoch 495/500\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 0.0055 - val_loss: 0.0625\n",
      "Epoch 496/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0057 - val_loss: 0.0680\n",
      "Epoch 497/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0049 - val_loss: 0.0717\n",
      "Epoch 498/500\n",
      "8/8 [==============================] - 0s 21ms/step - loss: 0.0055 - val_loss: 0.0713\n",
      "Epoch 499/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0075 - val_loss: 0.0741\n",
      "Epoch 500/500\n",
      "8/8 [==============================] - 0s 20ms/step - loss: 0.0056 - val_loss: 0.0747\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb9201d9d50>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=500, batch_size=32, validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 5ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.02736572863905062"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(model.predict(x_test),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step\n",
      "0.9620359 0.98\n",
      "0.65571344 0.65\n",
      "1.0130925 0.98\n",
      "0.83231354 0.96\n",
      "0.9396909 0.95\n",
      "0.8013079 0.84\n",
      "0.53621125 0.56\n",
      "0.9605049 0.91\n",
      "0.85696745 0.79\n",
      "0.938923 0.92\n",
      "0.814461 0.72\n",
      "1.0192145 0.97\n",
      "0.7542268 0.64\n",
      "0.9354408 0.89\n",
      "0.89068925 0.98\n",
      "0.32674718 0.33\n",
      "0.84660006 0.93\n",
      "0.751292 0.83\n",
      "0.8751446 0.96\n",
      "0.91530985 0.92\n",
      "0.6917831 0.68\n",
      "0.75342345 0.67\n",
      "0.9720441 0.99\n",
      "0.8364713 0.8\n",
      "0.9410148 0.98\n",
      "0.5850965 0.59\n",
      "0.7406585 0.63\n",
      "0.79603803 0.67\n",
      "0.8041312 0.83\n",
      "0.9267742 0.94\n",
      "0.96275437 0.92\n",
      "0.8978194 0.78\n",
      "0.8773111 0.93\n",
      "0.67410815 0.79\n",
      "0.9238855 0.88\n",
      "0.908491 0.88\n",
      "0.62307554 0.75\n",
      "0.859894 0.87\n",
      "0.96216613 0.92\n",
      "1.0539441 1.07\n",
      "1.0299475 1.08\n",
      "0.92717814 0.97\n",
      "0.9454717 0.99\n",
      "0.75088364 0.77\n",
      "0.896951 0.98\n",
      "0.8763039 0.94\n",
      "0.94447005 0.93\n",
      "3.5083063 4.47\n",
      "0.70272774 0.73\n",
      "0.6266368 0.53\n",
      "0.8830613 0.99\n",
      "0.9439922 0.85\n",
      "0.8759584 0.85\n",
      "0.97771186 0.96\n",
      "1.0293658 0.91\n",
      "0.9160373 0.82\n",
      "3.0938952 4.19\n",
      "0.959686 0.94\n",
      "0.896453 0.99\n",
      "0.6866103 0.67\n",
      "0.9598051 0.95\n",
      "0.7889077 0.96\n",
      "0.9390869 0.89\n",
      "0.9795846 0.99\n",
      "1.3806396 0.81\n",
      "0.9436206 0.97\n",
      "0.8186211 0.85\n",
      "0.91427445 0.85\n",
      "1.0030708 0.94\n",
      "0.6900606 0.78\n",
      "0.971761 0.95\n",
      "0.9830069 0.92\n",
      "0.80656993 0.71\n",
      "0.8925276 0.82\n",
      "0.9101093 0.96\n",
      "0.8950931 0.88\n",
      "0.88018143 0.78\n",
      "0.8674053 0.85\n",
      "0.5366551 0.56\n",
      "0.8682952 0.95\n",
      "0.96623623 0.98\n",
      "0.8381589 0.87\n",
      "1.0532758 1.12\n",
      "0.88088703 0.88\n",
      "0.86414623 0.75\n",
      "0.8333975 0.88\n",
      "0.8576859 0.89\n",
      "0.8432938 0.81\n",
      "0.912681 0.78\n",
      "0.90012646 0.97\n",
      "1.0016919 0.96\n",
      "0.70473665 0.66\n",
      "0.9211674 0.95\n",
      "0.90213144 0.96\n",
      "0.97285914 0.89\n",
      "0.70970005 0.73\n",
      "0.983941 0.95\n",
      "0.92442214 0.9\n",
      "0.928037 0.95\n",
      "0.75684637 0.82\n",
      "1.0027342 0.89\n",
      "0.9691956 0.97\n",
      "1.043571 1.16\n",
      "0.93880713 1.03\n",
      "0.8408699 0.8\n",
      "0.90473926 0.89\n"
     ]
    }
   ],
   "source": [
    "for xt,yt in zip(model.predict(x_test),y_test):\n",
    "    print(xt[0],yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2.3915875]], dtype=float32)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(inputSingleAlg(\"R2 F D B2 L D R B L D' R2 D2 R' U2 R' D' R' U2 B2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.3424716]], dtype=float32)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(inputSingleAlg(\"R U R' U R U2 R'\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[0.88126934]], dtype=float32), array([[0.9245973]], dtype=float32))"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(inputSingleAlg(\"F U R U' R' U R U2 R' U' R U R' F'\")), model.predict(inputSingleAlg(\"R' U R U R' U R U' R2 D' R U' R' D R U' R\")) # This one should actually be like 0.80"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}