From 3eb46f6c26a352f7b4d7f859a7a3c30798e71343 Mon Sep 17 00:00:00 2001 From: Hannes Signer Date: Fri, 24 Jan 2025 16:28:56 +0100 Subject: [PATCH] further experiments --- POET_Training.ipynb | 701 +++++++++++++++++++++++++++++++++++++------- preprocessing.py | 2 +- 2 files changed, 604 insertions(+), 99 deletions(-) diff --git a/POET_Training.ipynb b/POET_Training.ipynb index 20450db..429255b 100644 --- a/POET_Training.ipynb +++ b/POET_Training.ipynb @@ -30,20 +30,11 @@ "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-01-23 14:37:53.766781: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-01-23 14:37:53.786741: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Running Keras in version 3.6.0\n" + "Running Keras in version 3.8.0\n" ] } ], @@ -65,9 +56,20 @@ "import os\n", "from preprocessing import *\n", "from sklearn import set_config\n", + "from importlib import reload\n", "set_config(transform_output = \"pandas\")" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -77,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -112,17 +114,17 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Model: \"sequential_2\"\n",
+       "
Model: \"sequential\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -134,11 +136,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_7 (Dense)                 │ (None, 128)            │         1,664 │\n",
+       "│ dense (Dense)                   │ (None, 128)            │         1,664 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_8 (Dense)                 │ (None, 128)            │        16,512 │\n",
+       "│ dense_1 (Dense)                 │ (None, 128)            │        16,512 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_9 (Dense)                 │ (None, 12)             │         1,548 │\n",
+       "│ dense_2 (Dense)                 │ (None, 12)             │         1,548 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -146,11 +148,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,664\u001b[0m │\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,664\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,548\u001b[0m │\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,548\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -319,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,8 +417,8 @@
     "design = data_file[\"design\"]\n",
     "results = data_file[\"result\"]\n",
     "\n",
-    "df_design = pd.DataFrame(np.array(design[\"data\"]).transpose(), columns = design[\"names\"].asstr())\n",
-    "df_results = pd.DataFrame(np.array(results[\"data\"]).transpose(), columns = results[\"names\"].asstr())\n",
+    "df_design = pd.DataFrame(np.array(design[\"data\"]).transpose(), columns = np.array(design[\"names\"].asstr()))\n",
+    "df_results = pd.DataFrame(np.array(results[\"data\"]).transpose(), columns = np.array(results[\"names\"].asstr()))\n",
     "\n",
     "data_file.close()"
    ]
@@ -439,14 +441,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/signer/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/base.py:1473: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
+      "/Users/hannessigner/miniforge3/envs/ai/lib/python3.12/site-packages/sklearn/base.py:1474: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
       "  return fit_method(estimator, *args, **kwargs)\n"
      ]
     },
@@ -463,7 +465,7 @@
     }
    ],
    "source": [
-    "X_train, X_val, X_test, y_train, y_val, y_test, scaler_X, scaler_y = preprocessing_training(df_design, df_results, func_dict_in, func_dict_out, \"over\", 'individual', 0.1)"
+    "X_train, X_val, X_test, y_train, y_val, y_test, scaler_X, scaler_y = preprocessing_training(df_design, df_results, func_dict_in, func_dict_out, \"over\", 'global', 0.1)"
    ]
   },
   {
@@ -475,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -506,46 +508,46 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 0.0018 - val_loss: 3.6601e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 0.0021 - val_loss: 3.4232e-05\n",
       "Epoch 2/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.6899e-05 - val_loss: 3.6822e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.5182e-05 - val_loss: 3.3009e-05\n",
       "Epoch 3/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.5005e-05 - val_loss: 3.5655e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.3553e-05 - val_loss: 3.1858e-05\n",
       "Epoch 4/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.4032e-05 - val_loss: 3.3455e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.2530e-05 - val_loss: 3.1686e-05\n",
       "Epoch 5/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.3279e-05 - val_loss: 3.3064e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1540e-05 - val_loss: 3.1268e-05\n",
       "Epoch 6/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.3023e-05 - val_loss: 3.3338e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1264e-05 - val_loss: 3.1947e-05\n",
       "Epoch 7/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2532e-05 - val_loss: 3.2765e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1342e-05 - val_loss: 3.1175e-05\n",
       "Epoch 8/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.2749e-05 - val_loss: 3.2730e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.1101e-05 - val_loss: 3.1003e-05\n",
       "Epoch 9/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.2961e-05 - val_loss: 3.2593e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.1035e-05 - val_loss: 3.0818e-05\n",
       "Epoch 10/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2573e-05 - val_loss: 3.2576e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.0654e-05 - val_loss: 3.0667e-05\n",
       "Epoch 11/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.2442e-05 - val_loss: 3.2507e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0462e-05 - val_loss: 3.0639e-05\n",
       "Epoch 12/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2135e-05 - val_loss: 3.2548e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.0563e-05 - val_loss: 3.0643e-05\n",
       "Epoch 13/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2451e-05 - val_loss: 3.2482e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.1567e-05 - val_loss: 3.0610e-05\n",
       "Epoch 14/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2296e-05 - val_loss: 3.2475e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0625e-05 - val_loss: 3.0598e-05\n",
       "Epoch 15/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2081e-05 - val_loss: 3.2470e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0962e-05 - val_loss: 3.0589e-05\n",
       "Epoch 16/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2440e-05 - val_loss: 3.2471e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1131e-05 - val_loss: 3.0598e-05\n",
       "Epoch 17/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2050e-05 - val_loss: 3.2460e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0939e-05 - val_loss: 3.0580e-05\n",
       "Epoch 18/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2444e-05 - val_loss: 3.2452e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1100e-05 - val_loss: 3.0580e-05\n",
       "Epoch 19/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.2259e-05 - val_loss: 3.2452e-05\n",
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0586e-05 - val_loss: 3.0579e-05\n",
       "Epoch 20/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.2442e-05 - val_loss: 3.2448e-05\n",
-      "Training took 276.5459449291229 seconds\n"
+      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0749e-05 - val_loss: 3.0576e-05\n",
+      "Training took 295.5790858268738 seconds\n"
      ]
     }
    ],
@@ -567,19 +569,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
+      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 595us/step\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -589,13 +591,13 @@
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       ""
+       "
"
       ]
      },
-     "execution_count": 69,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     },
     {
      "data": {
@@ -609,49 +611,559 @@
     }
    ],
    "source": [
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "species = \"O\"\n",
+    "species = \"H\"\n",
     "iterations = 1000\n",
-    "cell_offset = 60\n",
+    "cell_offset = 1\n",
     "y_design = []\n",
     "y_results = []\n",
+    "y_differences = []\n",
+    "\n",
+    "\n",
+    "df_design_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_design))\n",
+    "df_results_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_results))\n",
+    "\n",
     "for i in range(0,iterations):\n",
     "    idx = i*50*50 + cell_offset -1\n",
-    "    y_design.append(df_design.iloc[idx, :])\n",
-    "    y_results.append(df_results.iloc[idx,:])\n",
+    "    y_design.append(df_design_transformed_scaled.iloc[idx, :])\n",
+    "    y_results.append(df_results_transformed_scaled.iloc[idx,:])\n",
     "    \n",
     "y_design = pd.DataFrame(y_design)\n",
     "y_results = pd.DataFrame(y_results)\n",
-    "plt.plot(np.arange(0,iterations), y_design[species], label = \"Design\")\n",
+    "# plt.plot(np.arange(0,iterations), y_design[species], label = \"Design\")\n",
     "plt.plot(np.arange(0,iterations), y_results[species], label = \"Results\")\n",
     "\n",
-    "y = FuncTransform(func_dict_in, func_dict_out).fit_transform(y_design)\n",
-    "y = scaler_X.transform(y)\n",
-    "prediction = model_large.predict(y.iloc[:, y.columns != \"Class\"])\n",
-    "prediction = pd.DataFrame(prediction, columns = y.columns[y.columns != \"Class\"])\n",
-    "prediction_back = pd.DataFrame(scaler_X.inverse_transform(prediction), columns=prediction.columns)\n",
-    "prediction_back = FuncTransform(func_dict_out, func_dict_in).inverse_transform(prediction_back.iloc[:, prediction_back.columns != \"Class\"])\n",
+    "prediction = model_simple.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n",
+    "prediction = pd.DataFrame(prediction, columns = y_results.columns)\n",
     "\n",
-    "plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n",
+    "y_results_back = FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(y_results), columns=df_results.columns))\n",
+    "prediction_back = FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(prediction), columns=df_results.columns))\n",
+    "\n",
+    "plt.plot(np.arange(0,iterations), prediction[species], label = \"Prediction\")\n",
     "plt.xlabel('Iteration')\n",
     "plt.ylabel(species)\n",
     "plt.title(species+' Concentration over Iterations')\n",
     "plt.show()\n",
     "\n",
+    "plt.plot(np.arange(0,iterations), y_results_back[species], label = \"Results\")\n",
+    "plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
     "timestep = 1000\n",
-    "plt.imshow(np.array(df_results[\"Barite\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower')"
+    "plt.imshow(np.array(df_results[\"Barite\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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aelUAALAKQSUIyUk2dXI23NldwoRaAAAsQ1AJEvNUAACwHkElSLksow8AgOUIKkFi0TcAAKxHUAmSd9G3UoIKAACWIagEKTutYY5KCfv9AABgGYJKkJijAgCA9QgqQcpJZxl9AACsRlAJEvv9AABgPYJKkFhHBQAA6xFUguS964c5KgAAWIegEqTDh37YmBAAAGsQVILkvT3ZY0oVNXURrg0AAPGJoBIkZ5L90MaETKgFAMASBJUOOLToG0EFAAArEFQ6gGX0AQCwFkGlA3wbE9KjAgCAJZKCedOXX36pt99+W19++aWqqqrUtWtXDRkyREVFRUpJSQl1HaNWbpp3LRWCCgAAVggoqCxcuFAPPfSQ1q1bp27duqlHjx5KTU1VSUmJtm/frpSUFE2ZMkW33XabevfubVWdo8ahZfRZ9A0AACv4HVROPPFE2Ww2XX311XrhhRfUq1evJq+7XC699957WrRokYYOHapHHnlEl156acgrHE1y2JgQAABL+R1U7rnnHp177rmtvu50OjVq1CiNGjVK9957r3bu3BmSCkYzb48Kc1QAALCG35NpvSGlrq5OCxYs0L59+1o9t0uXLjr55JM7Xrsol5vmveuHoR8AAKwQ8F0/SUlJuuGGG+RyuayoT0zJ8a6jwmRaAAAsEdTtycOGDdOmTZtCXJXYk8M6KgAAWCqo25N//vOfa8aMGdq9e7dOOukkpaenN3n9+OOPD0nlol3uYXf9eDymbDYjwjUCACC+BBVULr/8cknSzTff7DtmGIZM05RhGKqvrw9N7aKcdwn9eo+pipo6ZTU+BwAAoRFUUEmEO3r84UyyKz3Zrsraeh2oqiWoAAAQYkEFlURYzM1fOenJqqytVklVrY5SevtvAAAAfgt6r5//+7//04gRI1RQUKBdu3ZJkh588EH985//DFnlYgGLvgEAYJ2ggsrcuXM1Y8YMjR8/XqWlpb45KdnZ2XrwwQdDWb+oxzL6AABYJ6ig8te//lVPPPGE7rzzTtntdt/xoUOHavPmzSGrXCzwrqVCjwoAAKEXVFDZuXOnhgwZ0uy40+lUZWVlhysVS7xDPyz6BgBA6AUVVPr06dPigm+vvvqq+vfv39E6xZRcFn0DAMAyQd318+tf/1rTp09XTU2NTNPUunXr9Nxzz+m+++7T3//+91DXMar5ltFn6AcAgJALKqhcc801qqur029+8xtVVVVp8uTJ6tGjh/73f/9XkyZNCnUdoxqTaQEAsE5QQUWSrrvuOl133XUqLi6Wx+NRt27dQlmvmJHL7ckAAFgm6KAiSfv379fWrVtlGIYMw1DXrl1DVa+Yke0NKsxRAQAg5IKaTFteXq6pU6eqoKBAp59+uk477TQVFBToyiuvVFlZWajrGNUO35jQNM0I1wYAgPgSVFD5yU9+ovfff1/Lli1TaWmpysrK9O9//1sffPCBrrvuulDXMaodvjFheU1dhGsDAEB8CWroZ9myZXrttdc0cuRI37FzzjlHTzzxhMaOHRuyysWCFIddacl2VdXW60BlrbJS2ZgQAIBQCapHpXPnzsrKymp2PCsrSzk5OR2uVKzJYZ4KAACWCCqo3HXXXZoxY4b27t3rO7Zv3z79+te/1t133x2yysWKnPTGZfQJKgAAhJTfQz9DhgyRYRi+59u2bVPv3r3Vq1cvSdJXX30lp9Op7777Tj/72c9CX9Mo5ltGv5K1VAAACCW/g8rEiRMtrEZsYxl9AACs4XdQmTlzppX1iGmHelQIKgAAhFJQc1TQFJNpAQCwRlC3J9tstibzVY5UX18fdIViUa53Mi1zVAAACKmggsrixYubPHe73dq4caMWLFig2bNnh6RiscS7jH4JPSoAAIRUUEHlggsuaHbskksu0YABA/T888/r2muv7XDFYgmTaQEAsEZI56gMGzZMb7zxRiiLjAncngwAgDVCFlSqq6v117/+VT179gzq/ffdd58Mw9Att9wSqiqFjXfBt9KqWjYmBAAghIIa+snJyWkymdY0TVVUVCgtLU3PPPNMwOWtX79ejz/+uI4//vhgqhNx3h6VOo+pCledMlPY7wcAgFAIKqj85S9/aRJUbDabunbtqmHDhgW818/Bgwc1ZcoUPfHEE7r33nuDqU7EHbkxIUEFAIDQCCqoXH311SGrwPTp03Xuuefq7LPPjtmgIjX0qlTVVquksla9O6dHujoAAMSFgILKxx9/7Nd5/g7hLFq0SBs2bND69ev9Ot/lcsnlcvmel5eXS2q4PdrtDu1EVm95/pabnZakb0ql4opqud2dQloXqwXa1liWSG2VEqu9tDV+JVJ7E6WtgbTPMAOY/eld6M37Fu/wz+FFGIbh14Jvu3fv1tChQ/X6669r8ODBkqRRo0bphBNO0IMPPtjie2bNmtXiOi0LFy5UWlqav82wxCOf2bS1zKYpx9TrlK5MqAUAoDVVVVWaPHmyysrKlJmZ2ea5AQWVXbt2+b42TVMDBw7UK6+8ot69ezc578jnLVmyZIkuvPBC2e1237H6+noZhiGbzSaXy9XkNanlHpXCwkIVFxe329BAud1urVixQqNHj5bD0f6ck1++8LH+vXmf/mdsX/14xFEhrYvVAm1rLEuktkqJ1V7aGr8Sqb2J0tby8nJ16dLFr6AS0NDPkQHEMAz17NnTr2BypLPOOkubN29ucuyaa65Rv379dNtttzULKZLkdDrldDqbHXc4HJZdUH/L7pKRIkkqd9XH7A+Xld/HaJNIbZUSq720NX4lUnvjva2BtC2oybShkJGRoYEDBzY5lp6ers6dOzc7Hguy0xq+6Sz6BgBA6LB7coiwjD4AAKHX4R6VtnZRDtSqVatCVla4HVpGn6ACAECoBBRUhgwZ0iSYVFdXa8KECUpOTm5y3oYNG0JTuxjiDSoH6FEBACBkAgoqEydObPK8pV2UE5V3v58DVcxRAQAgVAIKKjNnzrSqHjHPO0flQGXDxoShHBIDACBRMZk2RI7cmBAAAHSc30Fl7NixWrNmTbvnVVRUaM6cOfrb3/7WoYrFmhSHXamOhrVfSrlFGQCAkPB76OfSSy/VZZddpoyMDJ1//vkaOnSoCgoKlJKSogMHDuizzz7TO++8o1deeUXnnXee/vSnP1lZ76iUk+ZQdVm9Sqpq1atzZJf0BwAgHvgdVK699lpNnTpV//jHP/T888/riSeeUGlpqaSGW5T79++vc845Rx9++KGOO+44q+ob1XLSk7WnrEYHuEUZAICQCGgybXJysiZPnqzJkydLksrKylRdXa3OnTvH9VK//vJNqOUWZQAAQqJDC75lZWUpKysrVHWJedks+gYAQEhx108I5aZ511IhqAAAEAoElRDK8Q39cNcPAAChQFAJId8y+gz9AAAQEgSVEMphMi0AACEVVFDZvXu3vv76a9/zdevW6ZZbbtHjjz8esorFolxfjwpDPwAAhEJQQWXy5MlauXKlJGnfvn0aPXq01q1bpzvuuEO/+93vQlrBWJLdOJm2hB4VAABCIqig8sknn+iUU06RJL3wwgsaOHCg1qxZo4ULF+qpp54KZf1iincdldKqho0JAQBAxwQVVNxut5xOpyTpjTfe0Pnnny9J6tevn/bu3Ru62sUY72Rad72pg2xMCABAhwUVVAYMGKBHH31Ub7/9tlasWKGxY8dKkvbs2aPOnTuHtIKxJDXZrhRHw7eUeSoAAHRcUEFlzpw5euyxxzRq1ChdccUVGjx4sCRp6dKlviGhROWbUMs8FQAAOiyoJfRHjRql4uJilZeXKycnx3f8pz/9qdLSEnvX4Oy0ho0JmVALAEDHBdWjUl1dLZfL5Qspu3bt0oMPPqitW7eqW7duIa1grPFtTMiibwAAdFhQQeWCCy7Q008/LUkqLS3VsGHD9Oc//1kTJ07U3LlzQ1rBWMMy+gAAhE5QQWXDhg069dRTJUn/+Mc/1L17d+3atUtPP/20HnrooZBWMNbkeDcmpEcFAIAOCyqoVFVVKSMjQ5L0+uuv66KLLpLNZtPw4cO1a9eukFYw1uQwmRYAgJAJKqgcc8wxWrJkiXbv3q3XXntNY8aMkSTt379fmZmZIa1grMllvx8AAEImqKDy29/+VrfeequOOuoonXLKKSoqKpLU0LsyZMiQkFYw1viW0WfoBwCADgvq9uRLLrlEI0eO1N69e31rqEjSWWedpQsvvDBklYtFh5bRZzItAAAdFVRQkaS8vDzl5eXp66+/lmEY6tGjR8Iv9iYdmqNCjwoAAB0X1NCPx+PR7373O2VlZal3797q1auXsrOzdc8998jj8YS6jjEl57A5KmxMCABAxwTVo3LnnXdq3rx5uv/++zVixAiZpql3331Xs2bNUk1NjX7/+9+Hup4xI/ewjQkra+vVyRl0pxUAAAkvqH9FFyxYoL///e++XZMlafDgwerRo4d+/vOfJ3RQSU22y5lkk6vOowOVtQQVAAA6IKihn5KSEvXr16/Z8X79+qmkpKTDlYp13gm1zFMBAKBjggoqgwcP1sMPP9zs+MMPP9zkLqBExaJvAACERlDjEn/84x917rnn6o033lBRUZEMw9CaNWu0e/duvfLKK6GuY8zJSW9cRp+gAgBAhwTVo3L66afr888/14UXXqjS0lKVlJTooosu0tatW317ACWyQ7cos5YKAAAdEfRMz4KCgmaTZnfv3q0f//jHevLJJztcsVh2aNE3elQAAOiIoHpUWlNSUqIFCxaEssiYlM2ibwAAhERIgwoa5Dbu98My+gAAdAxBxQI53J4MAEBIEFQswO3JAACERkCTaS+66KI2Xy8tLe1IXeJGbjpBBQCAUAgoqGRlZbX7+lVXXdWhCsUD38aElW6ZpinDMCJcIwAAYlNAQWX+/PlW1SOu5DROpq2t97AxIQAAHcAcFQukOho2JpSkA0yoBQAgaAQVCxiGwYRaAABCgKBiEW5RBgCg4wgqFslNZ9E3AAA6iqBiEZbRBwCg4wgqFslNY2NCAAA6iqBiEd8cFYIKAABBI6hYxLuWyoFK5qgAABAsgopFWEYfAICOI6hYJIfJtAAAdBhBxSIs+AYAQMcRVCyS07iOyoGqho0JAQBA4AgqFvH2qNTWeVRVWx/h2gAAEJsiGlTmzp2r448/XpmZmcrMzFRRUZFeffXVSFYpZNKS7Upu3JiQeSoAAAQnokGlZ8+euv/++/XBBx/ogw8+0JlnnqkLLrhAn376aSSrFRKGYRy26Bu3KAMAEIyIBpUJEyZo/Pjx6tu3r/r27avf//736tSpk9auXRvJaoVMduNaKiz6BgBAcJIiXQGv+vp6vfjii6qsrFRRUVGkqxMSvrVUGPoBACAoEQ8qmzdvVlFRkWpqatSpUyctXrxY/fv3b/Fcl8sll8vle15eXi5JcrvdcrtDO7ziLa8j5WanNnx7iyuqQ16/UApFW2NFIrVVSqz20tb4lUjtTZS2BtI+w4zwvbO1tbX66quvVFpaqpdeekl///vftXr16hbDyqxZszR79uxmxxcuXKi0tLRwVDcgL+yw6d1vbTqnh0fje3kiXR0AAKJCVVWVJk+erLKyMmVmZrZ5bsSDypHOPvtsHX300XrssceavdZSj0phYaGKi4vbbWig3G63VqxYodGjR8vhcARVxoP/+UJ/W7VDU04p1KwJPwxp/UIpFG2NFYnUVimx2ktb41citTdR2lpeXq4uXbr4FVQiPvRzJNM0m4SRwzmdTjmdzmbHHQ6HZRe0I2V3yUiRJJXW1MXED5yV38dok0htlRKrvbQ1fiVSe+O9rYG0LaJB5Y477tC4ceNUWFioiooKLVq0SKtWrdLy5csjWa2Q8S2jz2RaAACCEtGg8u2332rq1Knau3evsrKydPzxx2v58uUaPXp0JKsVMjm+HZTje1IUAABWiWhQmTdvXiQ/3nK59KgAANAh7PVjocMXfIuyOcsAAMQEgoqFvAu+1dZ5VO1mY0IAAAJFULFQWrJdyXY2JgQAIFgEFQsZhqGc9IbhnwOVTKgFACBQBBWL+W5RZmNCAAACRlCxGEEFAIDgEVQsxg7KAAAEj6BiMe8clRIWfQMAIGAEFYuxjD4AAMEjqFiMOSoAAASPoGIx3xwVggoAAAEjqFjMt4w+66gAABAwgorFvD0qpfSoAAAQMIKKxbxzVEoq2ZgQAIBAEVQsltPYo+JiY0IAAAJGULFY+mEbEx5gLRUAAAJCULGYYRi+CbWspQIAQGAIKmHgnVBbQlABACAgBJUwYNE3AACCQ1AJA+9+Pwz9AAAQGIJKGBzqUWEyLQAAgSCohAHL6AMAEByCShhkpzGZFgCAYBBUwiC3cY5KKUM/AAAEhKASBjn0qAAAEBSCShhwezIAAMEhqIQBk2kBAAgOQSUMvEvo17g9qq5lY0IAAPxFUAmDTs4kOeyGJKmEXhUAAPxGUAkDwzAOzVNhQi0AAH4jqIQJE2oBAAgcQSVMfPv9sJYKAAB+I6iEie/OH4Z+AADwG0ElTFhGHwCAwBFUwiS3MaiUMkcFAAC/EVTCJKdx6KeEOSoAAPiNoBImOY2LvjFHBQAA/xFUwiSHZfQBAAgYQSVMWPANAIDAEVTCxDuZliX0AQDwH0ElTLwLvrExIQAA/iOohEknZ5KSbA0bEzJPBQAA/xBUwsQwjEO3KDNPBQAAvxBUwujQom+spQIAgD8IKmGU3biWChNqAQDwD0EljLwbE7KMPgAA/iGohBFzVAAACAxBJYxYRh8AgMAQVMLItzotk2kBAPALQSWMctnvBwCAgBBUwsjbo8IcFQAA/ENQCaOcdNZRAQAgEASVMPJOpqVHBQAA/xBUwsjbo1LtrleNm40JAQBoD0EljDLYmBAAgIAQVMLIMAxlM6EWAAC/EVTCLDe9YZ4KE2oBAGhfRIPKfffdp5NPPlkZGRnq1q2bJk6cqK1bt0aySpbjFmUAAPwX0aCyevVqTZ8+XWvXrtWKFStUV1enMWPGqLKyMpLVstSh1WkJKgAAtCcpkh++fPnyJs/nz5+vbt266cMPP9Rpp50WoVpZy3vnz4FKhn4AAGhPRIPKkcrKyiRJubm5Lb7ucrnkcrl8z8vLyyVJbrdbbndo/+H3lhfqcrNT7JKk4oM1IS87WFa1NRolUlulxGovbY1fidTeRGlrIO0zTNM0LayL30zT1AUXXKADBw7o7bffbvGcWbNmafbs2c2OL1y4UGlpaVZXMSRW7jG0ZJddJ3b2aFpfT6SrAwBA2FVVVWny5MkqKytTZmZmm+dGTVCZPn26li1bpnfeeUc9e/Zs8ZyWelQKCwtVXFzcbkMD5Xa7tWLFCo0ePVoOhyNk5S7euEe/efkTjTyms+ZPOylk5XaEVW2NRonUVimx2ktb41citTdR2lpeXq4uXbr4FVSiYujnpptu0tKlS/XWW2+1GlIkyel0yul0NjvucDgsu6ChLrtLZookqbTaHXU/hFZ+H6NNIrVVSqz20tb4lUjtjfe2BtK2iAYV0zR10003afHixVq1apX69OkTyeqEhe+uHybTAgDQrogGlenTp2vhwoX65z//qYyMDO3bt0+SlJWVpdTU1EhWzTK56dyeDACAvyK6jsrcuXNVVlamUaNGKT8/3/d4/vnnI1ktS3mX0K+qZWNCAADaE/Ghn0STnmyXzZA8pvSfLd9q7MB82Rs3KgQAAE2x108YLf9kr07940p5GvPZ9IUbNXLOm1r+yd7IVgwAgChFUAmT5Z/s1Q3PbNDespomx/eV1eiGZzYQVgAAaAFBJQzqPaZm/+sztTTQ5T02+1+fqd6TeENhAAC0haASBut2ljTrSTmcKWlvWY3W7SwJX6UAAIgBBJUw2F/RekgJ5jwAABIFQSUMumWk+HXeis++1d6yaotrAwBA7CCohMEpfXKVn5Wi9m5C/vfHe3XqnJX61Qsfaeu+irDUDQCAaEZQCQO7zdDMCf0lqVlYMRof0884WsP65KrOY+qlDV/rnAff0jXz12ntju8Tcr0ZAACkKNmUMBGMHZivuVeeqNn/+qzJxNq8rBTNnNBfYwfmS5I27S7V429t16uf7NPKrd9p5dbvNLgwW9ef9gONGZDH4nAAgIRCUAmjsQPzNbp/ntbtLNH+ihp1y0jRKX1ym4SPEwqz9ciUk/RlcaX+/s4OvfjB1/pod6lueHaDjuqcpp+c+gNdclJPpTjsEWwJAADhQVAJM7vNUNHRnds976gu6bp34iDdcnZfPb3mSz29dpe+/L5Kdy35RH9Z8bmm/egoTR3eWzmNmxx61XvMNoMQAACxhKAS5bp0cmrGmON0/aij9cL63Xri7Z36prRaD6z4XHNXbdflJxfq2pF9VJibpuWf7G02tJR/xNASAACxhKASI9KSk3T1iD66cnhvLdu8V4+/tUOf7inXU2u+1P+t3aUhhdn6YNeBZu/zLtE/98oTCSsAgJjDXT8xJslu0wUn9NC/bxqpZ64dplOP7aJ6j9liSJFYoh8AENsIKjHKMAyNPLaL/u/aYfrDhQPbPJcl+gEAsYqhnziQ7vTvMt7+0scadVxXHd8zW4MLs/SDLp1kC3CiLZN1AQDhRFCJA/4u0b+rpEoL3tslaZckqZMzSQN7ZGpgQabqvjc06ECV+nTNlGG0HDyYrAsACDeCShzwLtG/r6xGLc1CMSR1yXDqjnH9tPmbcm3+plSffFOug646rd1RorU7SiTZ9dQD7yg3PVmDemRpcM8sHd8zW8cXZqlbRoqWf7JXNzyzoVn5TNYFAFiJoBIHvEv03/DMBhlSkzDh7Ru554IBGjswXxee2PC8rt6jL747qI93l2njVyV657Pd2ldjU0llrVZ//p1Wf/6dr4y8TKcOVLlbDEFm42fM/tdnGt2/4yvnMrQEADgcQSVO+LtEv1eS3aZ+eZnql5epC0/I0ytJX+qs0Wfri+9r9PHXpfpod5k+/rpUX3x3UPvKXW1+tney7uNvbdfo/nnqkZ2q1OTAV85laAkAcCSCShzxZ4n+tjgddp1QmK0TCrOlooZjla46PbZ6ux5684t23z9n+VbNWb5VktQ5PVk9c1LVIydVPbIbHj1z0hqe56QqM8XR5L3hGFqq95h6f2eJPiw21HlniYqO6UZvDQBEOYJKnPF3iX5/pTuTVHR0F7+CSmFOqg5UuXXQVafvK2v1fWWtPvq6rMVzM1OS1CMnTT2yU1WQnaLFG7+xdGipaW+NXU9v+4DeGgCIAQQVtMufybp5WSla9eszZDOk8uo67T5QpW9Kq/XNgWp9faBa35Q2PP/6QLVKq9wqr6lT+d5ybdlb3u7ne4eWfvXCJg3plaMunZzqmnHokZ5sb/VOJSk8vTUS82sAwAoEFbTLn8m6Myf09/2jnJXmUFZalgb2yGqxvEpX3WEhpkqrPv9O/9myv916LNm0R0s27Wl2PMVhawgtnZzNQkxuWrLuWvKJ5ROBwzG/hiAEIBERVOCXQCfrtiXdmaS+3TPUt3uGJOmYbhl+BZUx/bvLbjP0XYVL3x10qbjCpcraetW4PdpdUq3dJdUBt+vw3pr+BZnKTHEoK7Xhkdn4Z1aaQxnOpDbXl7G6x4YgBCBREVTgt45O1m2Nv0NLc688qdlnVbrqVHzQpeKDroYAU+HSdwdrfV9v+7ZCu0qq2q1Da701XjZDh4KLN8ikONQpJUn//mhPqz02kjRz6ac6s193JScFt2NFPAQhq0OQ1ROlraw/ARFoG0EFAQn1ZF1vmYEMLR0u3ZmkdGeSendOb7Hs97Z/ryueWNtuHc4Z0F1pyUkqq3Y3eZRXu+Wq88hjSqVVbpVWuQNu37flLvW961WlOuxKdyapk9OuTilJSk9OUidnUsPXzsavnUmHznE6lOqw6c7F1g5dWR2ErA5BVk+UtrL+8dBTFsshNB6+N7Fcvr8IKogKoRxaOpy/vTWPTGneW+NV465X+REBxvt4f2eJln+yz6+6VLvrVe2uV/HBoJrSIu/Q1cg5byo3PVmpDrtSfA+bnEmG9n9j08fLtyo9xfu6zXdest2mO9u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HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestite
0111.01243455.508700-1.216415e-090.02.217711e-134.495355e-071.532249e-120.00.0006210.0006200.0010001.000000
1111.01243455.508700-1.222504e-090.01.312902e-124.500359e-071.044603e-080.00.0006210.0006200.0010001.000000
2111.01243455.508699-1.220407e-090.01.614098e-124.500563e-074.907802e-070.00.0006210.0006200.0010001.000000
3111.01243455.508695-1.216831e-090.02.293739e-124.504482e-074.772370e-060.00.0006200.0006220.0010001.000000
4111.01243455.508679-1.216842e-090.02.641545e-124.534070e-072.200220e-050.00.0006160.0006260.0010001.000000
.......................................
995111.01243455.5064102.170844e-080.04.777415e-111.550089e-049.784038e-020.00.0000480.0488140.1494760.846327
996111.01243455.5064102.166504e-080.04.808612e-111.559012e-049.785750e-020.00.0000480.0488210.1517080.844093
997111.01243455.5064092.162167e-080.04.811205e-111.567226e-049.787459e-020.00.0000480.0488290.1539450.841856
998111.01243455.5064092.157995e-080.04.815004e-111.574812e-049.789167e-020.00.0000480.0488360.1561850.839614
999111.01243455.5064092.153938e-080.04.815067e-111.581835e-049.790872e-020.00.0000480.0488440.1584280.837370
\n",
+       "
1000 rows × 12 columns
\n",
+       "
"
+      ],
+      "text/plain": [
+       "              H          O        Charge  H_0_          O_0_            Ba  \\\n",
+       "0    111.012434  55.508700 -1.216415e-09   0.0  2.217711e-13  4.495355e-07   \n",
+       "1    111.012434  55.508700 -1.222504e-09   0.0  1.312902e-12  4.500359e-07   \n",
+       "2    111.012434  55.508699 -1.220407e-09   0.0  1.614098e-12  4.500563e-07   \n",
+       "3    111.012434  55.508695 -1.216831e-09   0.0  2.293739e-12  4.504482e-07   \n",
+       "4    111.012434  55.508679 -1.216842e-09   0.0  2.641545e-12  4.534070e-07   \n",
+       "..          ...        ...           ...   ...           ...           ...   \n",
+       "995  111.012434  55.506410  2.170844e-08   0.0  4.777415e-11  1.550089e-04   \n",
+       "996  111.012434  55.506410  2.166504e-08   0.0  4.808612e-11  1.559012e-04   \n",
+       "997  111.012434  55.506409  2.162167e-08   0.0  4.811205e-11  1.567226e-04   \n",
+       "998  111.012434  55.506409  2.157995e-08   0.0  4.815004e-11  1.574812e-04   \n",
+       "999  111.012434  55.506409  2.153938e-08   0.0  4.815067e-11  1.581835e-04   \n",
+       "\n",
+       "               Cl  S_2_      S_6_        Sr    Barite  Celestite  \n",
+       "0    1.532249e-12   0.0  0.000621  0.000620  0.001000   1.000000  \n",
+       "1    1.044603e-08   0.0  0.000621  0.000620  0.001000   1.000000  \n",
+       "2    4.907802e-07   0.0  0.000621  0.000620  0.001000   1.000000  \n",
+       "3    4.772370e-06   0.0  0.000620  0.000622  0.001000   1.000000  \n",
+       "4    2.200220e-05   0.0  0.000616  0.000626  0.001000   1.000000  \n",
+       "..            ...   ...       ...       ...       ...        ...  \n",
+       "995  9.784038e-02   0.0  0.000048  0.048814  0.149476   0.846327  \n",
+       "996  9.785750e-02   0.0  0.000048  0.048821  0.151708   0.844093  \n",
+       "997  9.787459e-02   0.0  0.000048  0.048829  0.153945   0.841856  \n",
+       "998  9.789167e-02   0.0  0.000048  0.048836  0.156185   0.839614  \n",
+       "999  9.790872e-02   0.0  0.000048  0.048844  0.158428   0.837370  \n",
+       "\n",
+       "[1000 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(y_results), columns=df_results.columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "
HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestite
0111.01242855.508682-1.193141e-093.397941e-152.128961e-13-0.0000120.000021-1.191799e-170.0006200.0006300.0009851.000231
1111.01242855.508682-1.202185e-092.918474e-151.019832e-12-0.0000160.000008-7.920033e-180.0006200.0006300.0009461.000121
2111.01242855.508682-1.203471e-091.785468e-152.398433e-12-0.000015-0.000013-9.158671e-180.0006200.0006270.0009130.999977
3111.01242855.508682-1.199235e-091.746077e-152.357316e-12-0.000016-0.000011-9.246642e-180.0006190.0006290.0009160.999936
4111.01242855.508682-1.197043e-091.533956e-152.670053e-12-0.0000190.000003-9.144569e-180.0006150.0006350.0009430.999769
.......................................
995111.01242855.5064162.189995e-08-3.792428e-154.785985e-110.0002790.0976421.182626e-160.0000510.0487380.1490160.844585
996111.01242855.5064162.188781e-08-3.910681e-154.822730e-110.0002790.0976651.169111e-160.0000510.0487510.1512410.842272
997111.01242855.5064162.184875e-08-3.749360e-154.824932e-110.0002790.0976871.146696e-160.0000510.0487610.1534770.840015
998111.01242855.5064162.180415e-08-3.500642e-154.816323e-110.0002790.0977101.119656e-160.0000510.0487700.1557200.837773
999111.01242855.5064162.177183e-08-3.375572e-154.822685e-110.0002790.0977321.095921e-160.0000500.0487800.1579620.835504
\n",
+       "
1000 rows × 12 columns
\n",
+       "
"
+      ],
+      "text/plain": [
+       "              H          O        Charge          H_0_          O_0_  \\\n",
+       "0    111.012428  55.508682 -1.193141e-09  3.397941e-15  2.128961e-13   \n",
+       "1    111.012428  55.508682 -1.202185e-09  2.918474e-15  1.019832e-12   \n",
+       "2    111.012428  55.508682 -1.203471e-09  1.785468e-15  2.398433e-12   \n",
+       "3    111.012428  55.508682 -1.199235e-09  1.746077e-15  2.357316e-12   \n",
+       "4    111.012428  55.508682 -1.197043e-09  1.533956e-15  2.670053e-12   \n",
+       "..          ...        ...           ...           ...           ...   \n",
+       "995  111.012428  55.506416  2.189995e-08 -3.792428e-15  4.785985e-11   \n",
+       "996  111.012428  55.506416  2.188781e-08 -3.910681e-15  4.822730e-11   \n",
+       "997  111.012428  55.506416  2.184875e-08 -3.749360e-15  4.824932e-11   \n",
+       "998  111.012428  55.506416  2.180415e-08 -3.500642e-15  4.816323e-11   \n",
+       "999  111.012428  55.506416  2.177183e-08 -3.375572e-15  4.822685e-11   \n",
+       "\n",
+       "           Ba        Cl          S_2_      S_6_        Sr    Barite  Celestite  \n",
+       "0   -0.000012  0.000021 -1.191799e-17  0.000620  0.000630  0.000985   1.000231  \n",
+       "1   -0.000016  0.000008 -7.920033e-18  0.000620  0.000630  0.000946   1.000121  \n",
+       "2   -0.000015 -0.000013 -9.158671e-18  0.000620  0.000627  0.000913   0.999977  \n",
+       "3   -0.000016 -0.000011 -9.246642e-18  0.000619  0.000629  0.000916   0.999936  \n",
+       "4   -0.000019  0.000003 -9.144569e-18  0.000615  0.000635  0.000943   0.999769  \n",
+       "..        ...       ...           ...       ...       ...       ...        ...  \n",
+       "995  0.000279  0.097642  1.182626e-16  0.000051  0.048738  0.149016   0.844585  \n",
+       "996  0.000279  0.097665  1.169111e-16  0.000051  0.048751  0.151241   0.842272  \n",
+       "997  0.000279  0.097687  1.146696e-16  0.000051  0.048761  0.153477   0.840015  \n",
+       "998  0.000279  0.097710  1.119656e-16  0.000051  0.048770  0.155720   0.837773  \n",
+       "999  0.000279  0.097732  1.095921e-16  0.000050  0.048780  0.157962   0.835504  \n",
+       "\n",
+       "[1000 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(prediction), columns=prediction.columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QILePG4xYSwoAgPYX8Mnmubm5ys3NdfnesmXLmmwbNmyYduzY0WKd48aN07hx47w+rivNzYvqKBJiWd0cAID2FvBHxMA34rm0BwBAuyNIhQjH6uZc2gMAoN0QpEJEIpf2AABodwSpEMFdewAAtD+CVIiI59IeAADtjiAVIux37VVWWx2PzQEAAP5FkAoR9kt7dQ02nbY2BLg1AACEB4JUiIiNjpQ5svEBy8yTAgCgfRCkQoTJZFJ8J/udewQpAADaA0EqhCTaJ5yzBAIAAO2CIBVCWJQTAID2RZAKIVzaAwCgfRGkQsjZESku7QEA0B4IUiHEvgRCJSNSAAC0C4JUCHGMSBGkAABoFwSpEBJvf3Axl/YAAGgXBKkQYl/+4BgjUgAAtAuCVAhJ6HT2eXsAAMD/CFIhhLv2AABoXwSpEBLficnmAAC0J4JUCLGPSNXW21RjbQhwawAACH0EqRBygSVKkREmSYxKAQDQHghSIcRkMjkW5WSeFAAA/keQCjH2y3vHTjEiBQCAvxGkQkzCmUU5KxmRAgDA7whSISaBO/cAAGg3BKkQE+9YS4ogBQCAvxGkQox9dXNGpAAA8D+CVIixTzZnjhQAAP5HkAoxjsfEMCIFAIDfEaRCjP2uvWPVjEgBAOBvBKkQw117AAC0H4JUiDk7R4ogBQCAvxGkQgx37QEA0H4IUiHGvo7UaWuDaqwNAW4NAAChjSAVYuIsUYowNX5fxeU9AAD8iiAVYiIiTIrvxOrmAAC0B4JUCEq0L4FwiiUQAADwJ4JUCOJ5ewAAtA+CVAiyryVVyZ17AAD4FUEqBNlXNz/O8/YAAPArglQIimd1cwAA2gVBKgQlMEcKAIB2QZAKQcyRAgCgfRCkQlBi5zPLH1QzRwoAAH8iSIUg5kgBANA+CFIhyH7XXiVzpAAA8CuCVAhKcIxIcWkPAAB/IkiFIPtde6fqGlRXbwtwawAACF0EqRAUF2OWydT4PZf3AADwH4JUCIqMMKlLzJklEFjdHAAAvyFIhajEM5f3jnHnHgAAfkOQClHx9uftEaQAAPAbglSI4s49AAD8jyAVoux37jHZHAAA/yFIhagEVjcHAMDvCFIhyjFHirv2AADwmyhPd/jqq6+0efNmffXVV6qurtZFF12kq6++WpmZmYqJifFHG+EFRqQAAPA/t0ek/vu//1uDBw/WpZdeqieffFJr167V5s2b9fLLL2vkyJFKSkpSbm6uvv76a48asGjRIqWlpSkmJkYZGRnavHlzi+U3bdqkjIwMxcTE6NJLL9WSJUualFm1apXS09NlsViUnp6uNWvWeHzc2bNnq0+fPurcubMSExP1H//xH9q2bZtHfQukxM4EKQAA/M2tIHXNNdfohRde0L333quvvvpK5eXl2r59u7Zs2aI9e/aoqqpKb7/9tmw2mwYOHKg333zTrYOvXLlSkydP1vTp01VSUqKhQ4dq1KhROnDggMvypaWlGj16tIYOHaqSkhI988wzeuyxx7Rq1SpHmeLiYmVnZysnJ0e7du1STk6Oxo8f7xSC3DnuFVdcoQULFmj37t3asmWLLrnkEmVlZem7775zq2+BltCJS3sAAPibyTAMo7VC77zzjn70ox+5VWFFRYVKS0t17bXXtlp20KBBuuaaa7R48WLHtr59++r222/X3Llzm5R/+umntW7dOu3du9exbdKkSdq1a5eKi4slSdnZ2aqqqtLf//53R5mRI0cqMTFRy5cv9+q4klRVVaX4+Hht3LhRt9xyi8sytbW1qq2tddqnR48eqqioUJcuXVr9PDxhtVpVVFSk4cOHy2w2N3m/5OBxjV/6oS5OiNG7T9zo02O3t9b6GkrCqa9SePWXvoaucOpvuPS1qqpK3bp1U2VlZau/v92aI2UPUfX19XrjjTc0YsQIJScnuyzbrVs3devWrdU66+rqtH37dk2dOtVpe1ZWlrZu3epyn+LiYmVlZTltGzFihAoKCmS1WmU2m1VcXKwpU6Y0KZOfn+/1cevq6rR06VLFx8drwIABzfZp7ty5mjNnTpPtGzZsUGxsbLP7tUVRUZHL7YdPS1KUKk6cVmFhoV+O3d6a62soCqe+SuHVX/oausKpv6He1+rqarfLejTZPCoqSo888ojTiJC3Kioq1NDQoKSkJKftSUlJKi8vd7lPeXm5y/L19fWqqKhQSkpKs2XsdXpy3L/97W+66667VF1drZSUFBUVFbUYEqdNm6a8vDzHa/uIVFZWVruPSB09Vafndr6nmgaTho8YKXNk8N6gGS5/AUnh1VcpvPpLX0NXOPU3XPpaVVXldlmP79obNGiQdu7cqV69enm6q0smk8nptWEYTba1Vv787e7U6U6ZH/7wh9q5c6cqKir00ksvOeZade/e3WXbLBaLLBZLk+1ms9lvP3DN1d01LtLx/el6KTYm+H/g/fk5djTh1FcpvPpLX0NXOPU31PvqSd88DlK5ubnKy8vTwYMHlZGRoc6dOzu9//3vf9+terp166bIyMgmo0CHDx9uMlpkl5yc7LJ8VFSUunbt2mIZe52eHLdz58667LLLdNlll2nw4MG6/PLLVVBQoGnTprnVx0CKioxQXEyUTtTU6/hpq7pe0DTgAQCAtvH4ek92drZKS0v12GOP6frrr9cPfvADXX311Y7/uis6OloZGRlNrrMWFRVpyJAhLvfJzMxsUn7Dhg0aOHCgIz02V8ZepzfHtTMMw2kyeUeX6HhwMXfuAQDgDx6PSJWWlvrs4Hl5ecrJydHAgQOVmZmppUuX6sCBA5o0aZKkxjlH3377rV5//XVJjXfoLViwQHl5eZo4caKKi4tVUFDguBtPkh5//HHdeOONmj9/vsaMGaO3335bGzdu1JYtW9w+7qlTp/Tcc8/ptttuU0pKio4cOaJFixbpm2++0Z133umz/vtbQqxZB46ylhQAAP7icZDy1dwoqXF068iRI3r22WdVVlam/v37q7Cw0HGMsrIyp7Wd0tLSVFhYqClTpmjhwoVKTU3Viy++qLFjxzrKDBkyRCtWrNCMGTM0c+ZM9e7dWytXrtSgQYPcPm5kZKQ+//xzvfbaa6qoqFDXrl117bXXavPmzerXr5/P+u9v8axuDgCAX3kcpCTpv/7rv7RkyRKVlpaquLhYvXr1Un5+vtLS0jRmzBiP6srNzVVubq7L95YtW9Zk27Bhw7Rjx44W6xw3bpzGjRvn9XFjYmK0evXqFvcPBgmO5+0RpAAA8AeP50gtXrxYeXl5Gj16tI4fP66GhgZJUkJCgmOtJnQM9uftVTJHCgAAv/A4SP3pT3/SSy+9pOnTpysy8uwt9gMHDtTu3bt92ji0TULsmUt7jEgBAOAXHgep0tJSl3fnWSwWnTp1yieNgm8wRwoAAP/yOEilpaVp586dTbb//e9/V3p6ui/aBB+xL39wjEt7AAD4hceTzZ988kn94he/UE1NjQzD0Icffqjly5dr7ty5evnll/3RRnjJfmmvkkt7AAD4hcdB6mc/+5nq6+v11FNPqbq6Wvfcc4++973v6Y9//KPuuusuf7QRXnLMkeLSHgAAfuHV8gcTJ07UxIkTVVFRIZvN1uyz5xBY8Z1Y2RwAAH/yKkhJjc+m27dvn0wmk0wmky666CJftgs+YB+RqqqpV4PNUGRE8w+DBgAAnvN4snlVVZVycnKUmpqqYcOG6cYbb1RqaqruvfdeVVZW+qON8JL9rj1JqmKeFAAAPudxkJowYYK2bdumd955R8ePH1dlZaX+9re/6eOPP9bEiRP90UZ4yRwZoQssjYOOrCUFAIDveXxp75133tH69et1ww03OLaNGDFCL730kkaOHOnTxqHtEmLNOllbr2PVdUpT50A3BwCAkOLxiFTXrl0VHx/fZHt8fLwSExN90ij4jmMJBO7cAwDA5zwOUjNmzFBeXp7Kysoc28rLy/Xkk09q5syZPm0c2i7Bfufeae7cAwDA19y6tHf11VfLZDp7x9f+/fvVq1cv9ezZU5J04MABWSwWfffdd3r44Yf901J4JZ61pAAA8Bu3gtTtt9/u52bAXxJ43h4AAH7jVpCaNWuWv9sBP+ExMQAA+I/Hc6QQXBJY3RwAAL/xePmDiIgIp/lS52toaGhTg+Bb9hGpY1zaAwDA5zwOUmvWrHF6bbVaVVJSotdee01z5szxWcPgGwmx9rv2CFIAAPiax0FqzJgxTbaNGzdO/fr108qVK/XQQw/5pGHwjbPrSHFpDwAAX/PZHKlBgwZp48aNvqoOPuK4a48RKQAAfM4nQer06dP605/+pIsvvtgX1cGH4s+5a89mMwLcGgAAQovHl/YSExOdJpsbhqETJ04oNjZWf/nLX3zaOLRd/JkRKcOQTtTUO4IVAABoO4+D1B/+8AenIBUREaGLLrpIgwYN4ll7HZAlKlKx0ZGqrmvQ8dN1BCkAAHzI4yD1wAMP+KEZ8KeETmZV1zXoWLVVvboGujUAAIQOt4PUJ5984la573//+143Bv6REButQ5U1LMoJAICPuR2kfvCDH8hkMskwGics2y/v2V/bt7EgZ8fDY2IAAPAPt4NUaWmp43vDMNS/f38VFhaqV69efmkYfMcepHhwMQAAvuV2kDo/MJlMJl188cUEqSAQ73jeHkEKAABf4qHFYcAxInWaOVIAAPgSQSoM2Fc3r2RECgAAn2pTkDp3PSl0XPYRqWPctQcAgE+5PUfq6quvdgpOp0+f1q233qro6Gincjt27PBd6+ATCbFn5khx1x4AAD7ldpC6/fbbnV6PGTPG122Bn3BpDwAA/3A7SM2aNcuf7YAfMSIFAIB/MNk8DJxdR6pONpvRSmkAAOAut4LUyJEjtXXr1lbLnThxQvPnz9fChQvb3DD4TvyZS3s2QzpZVx/g1gAAEDrcurR35513avz48YqLi9Ntt92mgQMHKjU1VTExMTp27Jj27NmjLVu2qLCwUD/+8Y/1u9/9zt/thgdizJGKMUeoxmpTZbVVXWLMgW4SAAAhwa0g9dBDDyknJ0dvvfWWVq5cqZdeeknHjx+X1LgEQnp6ukaMGKHt27fryiuv9Gd74aWETtEqt9boWHWdelwYG+jmAAAQEtyebB4dHa177rlH99xzjySpsrJSp0+fVteuXWU2M8LR0SXEmlVeVcNjYgAA8CG3g9T54uPjFR8f78u2wI/OPiaGIAUAgK9w116YSDjz4OJKVjcHAMBnCFJh4uwSCIxIAQDgKwSpMBHPpT0AAHyOIBUm7Jf2GJECAMB3PA5SBw8e1DfffON4/eGHH2ry5MlaunSpTxsG3zp3dXMAAOAbHgepe+65R++++64kqby8XMOHD9eHH36oZ555Rs8++6zPGwjfSOTSHgAAPudxkPr000913XXXSZL+53/+R/3799fWrVv13//931q2bJmv2wcfiXdc2mNECgAAX/E4SFmtVlksFknSxo0bddttt0mS+vTpo7KyMt+2Dj5jv7RXyYgUAAA+43GQ6tevn5YsWaLNmzerqKhII0eOlCQdOnRIXbt29XkD4RvnLn9gGEaAWwMAQGjwOEjNnz9ff/7zn3XTTTfp7rvv1oABAyRJ69atc1zyQ8djv2uv3mboVF1DgFsDAEBo8PgRMTfddJMqKipUVVWlxMREx/af//znio3lYbgdVYw5QtFREaqrt+l4dZ0usHj9dCAAAHCGxyNSp0+fVm1trSNEff3118rPz9e+ffvUvXt3nzcQvmEymZTQidXNAQDwJY+D1JgxY/T6669Lko4fP65Bgwbp97//vW6//XYtXrzY5w2E7yTGsignAAC+5HGQ2rFjh4YOHSpJeuutt5SUlKSvv/5ar7/+ul588UWfNxC+c/YxMSyBAACAL3gcpKqrqxUXFydJ2rBhg+644w5FRERo8ODB+vrrr33eQPgOl/YAAPAtj4PUZZddprVr1+rgwYNav369srKyJEmHDx9Wly5dfN5A+A5rSQEA4FseB6lf//rX+tWvfqVLLrlE1113nTIzMyU1jk5dffXVHjdg0aJFSktLU0xMjDIyMrR58+YWy2/atEkZGRmKiYnRpZdeqiVLljQps2rVKqWnp8tisSg9PV1r1qzx6LhWq1VPP/20rrrqKnXu3Fmpqam67777dOjQIY/715EkxLK6OQAAvuRxkBo3bpwOHDigjz/+WOvXr3dsv+WWW/SHP/zBo7pWrlypyZMna/r06SopKdHQoUM1atQoHThwwGX50tJSjR49WkOHDlVJSYmeeeYZPfbYY1q1apWjTHFxsbKzs5WTk6Ndu3YpJydH48eP17Zt29w+bnV1tXbs2KGZM2dqx44dWr16tb744gvHKu7BKp5LewAA+JTHQUqSkpOTdfXVV+vQoUP69ttvJUnXXXed+vTp41E9L7zwgh566CFNmDBBffv2VX5+vnr06NHs3X9LlixRz549lZ+fr759+2rChAl68MEH9fzzzzvK5Ofna/jw4Zo2bZr69OmjadOm6ZZbblF+fr7bx42Pj1dRUZHGjx+vK6+8UoMHD9af/vQnbd++vdmQFwwSeHAxAAA+5fGqjDabTb/5zW/0+9//XidPnpQkxcXF6YknntD06dMVEeFeNqurq9P27ds1depUp+1ZWVnaunWry32Ki4sdc7LsRowYoYKCAlmtVpnNZhUXF2vKlClNytiDlDfHlaTKysrGtZgSEpotU1tbq9raWsfrqqoqSY2XCq1W34YXe32e1BsX3Xhujp2q9Xl7/MmbvgarcOqrFF79pa+hK5z6Gy599aR/Hgep6dOnq6CgQPPmzdP1118vwzD0/vvva/bs2aqpqdFzzz3nVj0VFRVqaGhQUlKS0/akpCSVl5e73Ke8vNxl+fr6elVUVCglJaXZMvY6vTluTU2Npk6dqnvuuafFCfVz587VnDlzmmzfsGGD31Z9LyoqcrvsF5UmSZH65vAxFRYW+qU9/uRJX4NdOPVVCq/+0tfQFU79DfW+VldXu13W4yD12muv6eWXX3aaLzRgwAB973vfU25urttBys5kMjm9NgyjybbWyp+/3Z063T2u1WrVXXfdJZvNpkWLFrXQE2natGnKy8tzvK6qqlKPHj2UlZXl8zsarVarioqKNHz4cJnNZrf2uaSsSgv3fKCGSItGj77Jp+3xJ2/6GqzCqa9SePWXvoaucOpvuPTVfkXJHR4HqaNHj7qcC9WnTx8dPXrU7Xq6deumyMjIJqNAhw8fbjJaZJecnOyyfFRUlLp27dpiGXudnhzXarVq/PjxKi0t1T//+c9Ww5DFYpHFYmmy3Ww2++0HzpO6u3VpHBWrPF2vqKioFgNrR+TPz7GjCae+SuHVX/oausKpv6HeV0/65vFk8wEDBmjBggVNti9YsEADBgxwu57o6GhlZGQ0GR4sKirSkCFDXO6TmZnZpPyGDRs0cOBAR6ebK2Ov093j2kPU/v37tXHjRkdQC2b2BTnrGmw6bW0IcGsAAAh+Ho9I/fa3v9WPfvQjbdy4UZmZmTKZTNq6dasOHjzo8bybvLw85eTkaODAgcrMzNTSpUt14MABTZo0SVLjpbJvv/3W8Wy/SZMmacGCBcrLy9PEiRNVXFysgoICLV++3FHn448/rhtvvFHz58/XmDFj9Pbbb2vjxo3asmWL28etr6/XuHHjtGPHDv3tb39TQ0ODYwTrwgsvVHR0tKcfW4cQGx0pc6RJ1gZDx6utio32+PQDAIBzePybdNiwYfriiy+0cOFCff755zIMQ3fccYdyc3OVmprqUV3Z2dk6cuSInn32WZWVlal///4qLCxUr169JEllZWVOyw2kpaWpsLBQU6ZM0cKFC5WamqoXX3xRY8eOdZQZMmSIVqxYoRkzZmjmzJnq3bu3Vq5cqUGDBrl93G+++Ubr1q2TJP3gBz9wavO7776rm266yaN+dhQmk0nxnaJVcbJWx6utSk3oFOgmAQAQ1LwakkhNTW0yqfzgwYN68MEH9corr3hUV25urnJzc12+t2zZsibbhg0bph07drRY57hx4zRu3Divj3vJJZc4JrGHmsRY85kgxermAAC0lVcLcrpy9OhRvfbaa76qDn7CopwAAPiOz4IUgkN8J/vz9ghSAAC0FUEqzJwdkeLSHgAAbUWQCjP2JRAqGZECAKDN3J5sfscdd7T4/vHjx9vaFrQDx4gUQQoAgDZzO0jFx8e3+v59993X5gbBv+Jjz8yR4tIeAABt5naQevXVV/3ZDrSTxDMjUscYkQIAoM2YIxVmEs7ctcccKQAA2o4gFWa4aw8AAN8hSIWZ+E5MNgcAwFcIUmHGPiJVW29TjbUhwK0BACC4EaTCzAWWKEVGmCQxKgUAQFsRpMKMyWRyLMrJPCkAANqGIBWG7Jf3jp1iRAoAgLYgSIWhhDOLclYyIgUAQJsQpMJQAnfuAQDgEwSpMBTvWEuKIAUAQFsQpMKQfXVzRqQAAGgbglQYsk82Z44UAABtQ5AKQ47HxDAiBQBAmxCkwpD9rr1j1YxIAQDQFgSpMMRdewAA+AZBKgydnSNFkAIAoC0IUmGIu/YAAPANglQYsq8jddraoBprQ4BbAwBA8CJIhaE4S5QiTI3fV3F5DwAArxGkwlBEhEnxnVjdHACAtiJIhalE+xIIp1gCAQAAbxGkwhTP2wMAoO0IUmHKvpZUJXfuAQDgNYJUmLKvbn6c5+0BAOA1glSYimd1cwAA2owgFaYSmCMFAECbEaTCFHOkAABoO4JUmErsfGb5g2rmSAEA4C2CVJhijhQAAG1HkApT9rv2KpkjBQCA1whSYSrBMSLFpT0AALxFkApT9rv2TtU1qK7eFuDWAAAQnAhSYSouxiyTqfF7Lu8BAOAdglSYiowwqUvMmSUQWN0cAACvEKTCWOKZy3vHuHMPAACvEKTCWLz9eXsEKQAAvEKQCmPcuQcAQNsQpMKY/c49JpsDAOAdglQYS2B1cwAA2oQgFcYcc6S4aw8AAK8QpMIYI1IAALQNQSqMJXYmSAEA0BYEqTCW0IlLewAAtAVBKozFxzIiBQBAWxCkwph9jlQlQQoAAK8QpMJYwpm79k7U1svaYAtwawAACD4EqTDWJSbK8X0Vi3ICAOAxglQYi4qMUNyZMHWcIAUAgMcIUmEu0fHgYu7cAwDAUwSpMJfAnXsAAHiNIBXm4lndHAAArwU8SC1atEhpaWmKiYlRRkaGNm/e3GL5TZs2KSMjQzExMbr00ku1ZMmSJmVWrVql9PR0WSwWpaena82aNR4fd/Xq1RoxYoS6desmk8mknTt3tqmfHVWC43l7BCkAADwV0CC1cuVKTZ48WdOnT1dJSYmGDh2qUaNG6cCBAy7Ll5aWavTo0Ro6dKhKSkr0zDPP6LHHHtOqVascZYqLi5Wdna2cnBzt2rVLOTk5Gj9+vLZt2+bRcU+dOqXrr79e8+bN898H0AGcXUuKOVIAAHgqqvUi/vPCCy/ooYce0oQJEyRJ+fn5Wr9+vRYvXqy5c+c2Kb9kyRL17NlT+fn5kqS+ffvq448/1vPPP6+xY8c66hg+fLimTZsmSZo2bZo2bdqk/Px8LV++3O3j5uTkSJK++uort/tTW1ur2tpax+uqqipJktVqldXq2xEfe31trTfOEilJOnqq1udt9BVf9TUYhFNfpfDqL30NXeHU33Dpqyf9C1iQqqur0/bt2zV16lSn7VlZWdq6davLfYqLi5WVleW0bcSIESooKJDVapXZbFZxcbGmTJnSpIw9fHlzXHfNnTtXc+bMabJ9w4YNio2NbVPdzSkqKmrT/ocOmSRFas//fq3CwlLfNMpP2trXYBJOfZXCq7/0NXSFU39Dva/V1dVulw1YkKqoqFBDQ4OSkpKcticlJam8vNzlPuXl5S7L19fXq6KiQikpKc2WsdfpzXHdNW3aNOXl5TleV1VVqUePHsrKylKXLl3aVPf5rFarioqKNHz4cJnNZq/rqSn5Vmu//kyxiRdp9OgMH7bQd3zV12AQTn2Vwqu/9DV0hVN/w6Wv9itK7gjopT1JMplMTq8Nw2iyrbXy5293p05Pj+sOi8Uii8XSZLvZbPbbD1xb6+4W10mSVFVT3+H/Ufjzc+xowqmvUnj1l76GrnDqb6j31ZO+BWyyebdu3RQZGdlkFOjw4cNNRovskpOTXZaPiopS165dWyxjr9Ob44Yy1pECAMB7AQtS0dHRysjIaHKdtaioSEOGDHG5T2ZmZpPyGzZs0MCBAx3psbky9jq9OW4oi+/EyuYAAHgroJf28vLylJOTo4EDByozM1NLly7VgQMHNGnSJEmNc46+/fZbvf7665KkSZMmacGCBcrLy9PEiRNVXFysgoICx914kvT444/rxhtv1Pz58zVmzBi9/fbb2rhxo7Zs2eL2cSXp6NGjOnDggA4dOiRJ2rdvn6TGEa/k5GS/fzbtxT4iVVVTrwabociItl3eBAAgnAQ0SGVnZ+vIkSN69tlnVVZWpv79+6uwsFC9evWSJJWVlTmt7ZSWlqbCwkJNmTJFCxcuVGpqql588UXH0geSNGTIEK1YsUIzZszQzJkz1bt3b61cuVKDBg1y+7iStG7dOv3sZz9zvL7rrrskSbNmzdLs2bP99ZG0O/vK5pJUddqqxM7RAWwNAADBJeCTzXNzc5Wbm+vyvWXLljXZNmzYMO3YsaPFOseNG6dx48Z5fVxJeuCBB/TAAw+0WEcoMEdG6AJLlE7W1us4QQoAAI8E/BExCDz7qNQx5kkBAOARghSU2Nn+mBju3AMAwBMEKSjBfufeaUakAADwBEEKimctKQAAvEKQghI6EaQAAPAGQQqOtaQqTxOkAADwBEEKZ+dIcdceAAAeIUjBMUfqGJf2AADwCEEKSoy137VHkAIAwBMEKZydI8WlPQAAPEKQwtm79hiRAgDAIwQpOOZIVZ62ymYzAtwaAACCB0EKjmftGYZ0oqY+wK0BACB4EKQgS1SkYqMjJfGYGAAAPEGQgqSz86RYAgEAAPcRpCBJSohlUU4AADxFkIIkHhMDAIA3CFKQdDZI8eBiAADcR5CCJCne8bw9ghQAAO4iSEHSOSNS3LUHAIDbCFKQdPauvUpGpAAAcBtBCpLOjkgd4649AADcRpCCpHOWP+CuPQAA3EaQgiQu7QEA4A2CFCQxIgUAgDcIUpB07jpSdbLZjAC3BgCA4ECQgiQp/sylPZshnayrD3BrAAAIDgQpSJJizJGKMTf+ODBPCgAA9xCk4JBwZnVzlkAAAMA9BCk48Lw9AAA8Q5CCw9nHxBCkAABwB0EKDvZLe5Vc2gMAwC0EKThwaQ8AAM8QpOAQz6U9AAA8QpCCg/3SHiNSAAC4hyAFh3NXNwcAAK0jSMEhkUt7AAB4hCAFh3jHpT1GpAAAcAdBCg72S3uVjEgBAOAWghQczl3+wDCMALcGAICOjyAFB/tde/U2Q6fqGgLcGgAAOj6CFBxizBGKjmr8kWCeFAAArSNIwcFkMimhE6ubAwDgLoIUnCTGsignAADuIkjBydnHxHBpDwCA1hCk4IRLewAAuI8gBSesJQUAgPsIUnCSEMvq5gAAuIsgBSfxXNoDAMBtBCk4sV/aO0aQAgCgVQQpOLEvf1DJXXsAALSKIAUn3LUHAID7CFJwcnYdKYIUAACtIUjBif2uvcpqqwzDCHBrAADo2KIC3QB0LHGWxh+JugabNn3xnYZefpEiI0w+q7/BZujD0qM6fKJG3eNidF3ahdQPAAhaBCk4/OPTMs3+6x7H6wde/Ugp8TGadWu6RvZP8Un9c/66R2WVNY5t1H9Wg83QttKj2l5hUtfSo8q8rHtQhUB/1h/MbbfXH6znNhQ++2A9t6Hw2QTrz6UnCFKQ1BgSHvnLDp1/Ma+8skaP/GWHFt97TZvCAvW3Xv/ZkBap1/d/HFQh0J/1B3Pbm9YfXOc2tD57f9fv23MbWp+Nb+v3d9s9xRwpqMFmaM5f9zQJCZJknPmasfZT7Tp4XHsOVWlf+Qn97+ET+vd3J/VVxSkdPFqtb45Vq6zytA5X1ei7E7U6eqpOldVWVdVYVXXaqtnrPmu2fkma89c9arB5NyertfZ39PrtIe3c/ylIZ0PaPz4t86reUKg/mNse7PUHc9uDvf5gbru/6/d3270R8BGpRYsW6Xe/+53KysrUr18/5efna+jQoc2W37Rpk/Ly8vTZZ58pNTVVTz31lCZNmuRUZtWqVZo5c6b+/e9/q3fv3nruuef0k5/8xKPjGoahOXPmaOnSpTp27JgGDRqkhQsXql+/fr79ADqAD0uPNvmhPF/FyTqNWfi+X45vSCqrrFH/Wf+QxRypSJNJEREmRZpMiowwyWSSak5H6o/7tygyIkKRESZFnHkvIsKk6lpri+2315/952J1u8CiiAjJpMZ6I0wmRZgkk+m81zI1ljOZdLiqxq36J68s0cWJsTJJMtnrOPPi/G2mM8e0GYaWbvqyxZD21Fuf6NvjpxVpMjnaadKZCiWnuu3v2V/bZGhu4ect1j919W6drmtQRERj/fY6dU495xzOUb8k2WzSM2t3t1j/M6t3N57LSHvdZytqqK/X3mMmXbC/QlFRUU7HNgxDz6z5tMW6p6/5VBdYohrb7tjzbPvOra9x+9lXNsPQ9NbqX/upLuwcfeaSgevLBiYXm01qDOCt1T9j7afqHhfjuCRxfl2m84557vsNNkMz1rZef2pCp7P1N9OH8+t3p+6Zaz9TjwtjG/+Nnldvfb1Vh6ql/f93UlHmpr9mbO7U//ZnSuvW+bzLNc23//zPZqYb9fe+6IJmLweZXJ1Yp/pb/uPw129/psu7x3l8ucnduq9Iarnu5s61u/X3Se7isn5rvVVHaqSDx6pljjK7rv9t7+tvSWt1m9T4R+3w9OR2vcxnMgJ4a9bKlSuVk5OjRYsW6frrr9ef//xnvfzyy9qzZ4969uzZpHxpaan69++viRMn6uGHH9b777+v3NxcLV++XGPHjpUkFRcXa+jQofrP//xP/eQnP9GaNWv061//Wlu2bNGgQYPcPu78+fP13HPPadmyZbriiiv0m9/8Rv/617+0b98+xcXFudW/qqoqxcfHq7KyUl26dPHRp9bIarWqsLBQo0ePltnc9IfZE2/v/FaPr9jZarn4TmZFR0XIZjNkMww12AzZDJ35r/M2AAACYfnEwcrs3bVNdXjy+zugQWrQoEG65pprtHjxYse2vn376vbbb9fcuXOblH/66ae1bt067d2717Ft0qRJ2rVrl4qLiyVJ2dnZqqqq0t///ndHmZEjRyoxMVHLly9367iGYSg1NVWTJ0/W008/LUmqra1VUlKS5s+fr4cffthlf2pra1VbW+t4XVVVpR49eqiiosIvQaqoqEjDhw9vc5DaVnpU977ycavl/vLgQA1Ku7DVcoZhyDCkBsOQzWZoW+kxPfRfO1rd77d39Ff/73WRzWac2bexjro6qz748ENlZAyUKTJSDTadDW02Q3vLT+iP//x3q/X/bEhP9era2dG+xvDX+J49CBqGnN4zDEMHj57W6p2HWq1/VL8kJXWxNF4OPbOv43vZ65Mk+/GlA0dP6cOvjrda9w8u7qKU+E5n6jMcf5HZ//Wef6zG/0qHq2q1t/xEq/Vf3r2zul1gcSx5Ya/L/r39GOcyDOnoqTqVHqlutf4eiZ2UGGtu0m6bYVNV1QnFxcXJZDI5HbPqtFWHWhkplaTkLhZdYDk76nFuK52bbDhtP1lbr+9Otr6Cf9fOZnU+t34X/8d09T/R6tp6HXVjYduETmbFRkc2qaPJ533e+6frGlRVU99q/V1iohRjjmyyvaX/9ddYG3SitqHVui+wRMoSdbZux0+mIdXV1Sk6OtrlZ1NXb9OputbrjzVHKjqq6QwUw2WtzvWfttparb+TOULmSFf1t8zaYFONG/Vbopqrv/kj1DfYVFvf+q9lS6RJUS7qbk19g021Da3XH91M/YYhNTTUKzIyyuVobH2DTXVu1G+ONCmqmVGj5vZusBmyulH3C3depVu/37a5UlVVVerWrZtbQSpgl/bq6uq0fft2TZ061Wl7VlaWtm7d6nKf4uJiZWVlOW0bMWKECgoKZLVaZTabVVxcrClTpjQpk5+f7/ZxS0tLVV5e7nQsi8WiYcOGaevWrc0Gqblz52rOnDlNtm/YsEGxsbEu92mroqKiNtdhM6SE6Egdr5NcD50bSoiWvtvzgQr3unjbR/WbD+3U/mYub/fuIh3f7zrsXeJm/d+3famICs/bf0mM9E836s+K+/bspMPmrwQ52R9r0odq+kvufDd0OabLuxx1u82O+qNM2lveev0julXp8njP/6baX2nSgiOt1z8m5WQr9R93XXdl63WPu7ja+7bvab3+u3vV+LX+e9P8W/99l9Z6XL+7dT/Qu66Vuk+3qf4HL2+tftfcrf+hy61+rX/iFZ7X73bdV9b7te0/b7V+10HY3fof9qL97tb95Wc7VfhNiUd1n6+6uvU/EO0CFqQqKirU0NCgpKQkp+1JSUkqLy93uU95ebnL8vX19aqoqFBKSkqzZex1unNc+39dlfn666+b7dO0adOUl5fneG0fkcrKyurQI1KSZL7k//TLFbskOf81YJ9Z8Zs7BmhEvyQXe/q/fnf62pHb35IGm6G3fv8v/V9Vrcu/wkySkuMtejT7Rq+u+Xf0+ls6tx297aFcvz/Pq7/bHuz1d/S2B/Lc+vuzOVdVVZXbZQM+2fz8CX2GYbQ4yc9V+fO3u1Onr8qcy2KxyGKxNNluNpt9EnZc8VXdP/7BxYqKimxyS2myj24p9UX9LfU1GNrvilnS7Nv66ZG/7JBJrkKaNOvWfoqxRId0/a7ObbC0PRTr9+d59Xfbg73+YGl7IM6tvz8bp2N58Hs1YMsfdOvWTZGRkU1Gnw4fPtxkJMguOTnZZfmoqCh17dq1xTL2Ot05bnJysiR51LZQMLJ/irY8fbOWTxysP971Ay2fOFhbnr7ZZ+tyUH/z9S6+9xolx8c4bU+Oj2nz+lTBXn8wtz3Y6w/mtgd7/cHcdn/X7++2e8UIoOuuu8545JFHnLb17dvXmDp1qsvyTz31lNG3b1+nbZMmTTIGDx7seD1+/Hhj1KhRTmVGjhxp3HXXXW4f12azGcnJycb8+fMd79fW1hrx8fHGkiVL3O5fZWWlIcmorKx0ex931dXVGWvXrjXq6up8XndHEy59rW+wGZv3lRszX37b2Lyv3KhvsPm8/q3/W2GsLfnG2Pq/FR2ifnfPbUdsu6f1B+u59ed59bZ+TwTzue2In01HObf+/mw8+f0d0CC1YsUKw2w2GwUFBcaePXuMyZMnG507dza++uorwzAMY+rUqUZOTo6j/JdffmnExsYaU6ZMMfbs2WMUFBQYZrPZeOuttxxl3n//fSMyMtKYN2+esXfvXmPevHlGVFSU8cEHH7h9XMMwjHnz5hnx8fHG6tWrjd27dxt33323kZKSYlRVVbndP4KUb9DX0BVO/aWvoSuc+hsuffXk93dA50hlZ2fryJEjevbZZ1VWVqb+/fursLBQvXr1kiSVlZXpwIEDjvJpaWkqLCzUlClTtHDhQqWmpurFF190rCElSUOGDNGKFSs0Y8YMzZw5U71799bKlSsda0i5c1xJeuqpp3T69Gnl5uY6FuTcsGGD22tIAQCA0Bfwyea5ubnKzc11+d6yZcuabBs2bJh27Gh5TaJx48Zp3LhxXh9XapxoPnv2bM2ePbvFegAAQPjiWXsAAABeIkgBAAB4iSAFAADgJYIUAACAlwhSAAAAXiJIAQAAeIkgBQAA4KWAryMVyowzD1T25CnS7rJaraqurlZVVZXfHojcUdDX0BVO/aWvoSuc+hsufbX/3rb/Hm8JQcqPTpw4IUnq0aNHgFsCAAA8deLECcXHx7dYxmS4E7fgFZvNpkOHDikuLk4mk8mndVdVValHjx46ePCgunTp4tO6Oxr6GrrCqb/0NXSFU3/Dpa+GYejEiRNKTU1VRETLs6AYkfKjiIgIXXzxxX49RpcuXUL6h/lc9DV0hVN/6WvoCqf+hkNfWxuJsmOyOQAAgJcIUgAAAF4iSAUpi8WiWbNmyWKxBLopfkdfQ1c49Ze+hq5w6m849dVdTDYHAADwEiNSAAAAXiJIAQAAeIkgBQAA4CWCFAAAgJcIUh3UokWLlJaWppiYGGVkZGjz5s0tlt+0aZMyMjIUExOjSy+9VEuWLGmnlrbN3Llzde211youLk7du3fX7bffrn379rW4z3vvvSeTydTk6/PPP2+nVntn9uzZTdqcnJzc4j7Bel4l6ZJLLnF5nn7xi1+4LB9M5/Vf//qXbr31VqWmpspkMmnt2rVO7xuGodmzZys1NVWdOnXSTTfdpM8++6zVeletWqX09HRZLBalp6drzZo1fuqB+1rqq9Vq1dNPP62rrrpKnTt3Vmpqqu677z4dOnSoxTqXLVvm8lzX1NT4uTeta+3cPvDAA03aPXjw4FbrDbZzK8nlOTKZTPrd737XbJ0d+dz6C0GqA1q5cqUmT56s6dOnq6SkREOHDtWoUaN04MABl+VLS0s1evRoDR06VCUlJXrmmWf02GOPadWqVe3ccs9t2rRJv/jFL/TBBx+oqKhI9fX1ysrK0qlTp1rdd9++fSorK3N8XX755e3Q4rbp16+fU5t3797dbNlgPq+S9NFHHzn1taioSJJ05513trhfMJzXU6dOacCAAVqwYIHL93/729/qhRde0IIFC/TRRx8pOTlZw4cPdzx/05Xi4mJlZ2crJydHu3btUk5OjsaPH69t27b5qxtuaamv1dXV2rFjh2bOnKkdO3Zo9erV+uKLL3Tbbbe1Wm+XLl2cznNZWZliYmL80QWPtHZuJWnkyJFO7S4sLGyxzmA8t5KanJ9XXnlFJpNJY8eObbHejnpu/cZAh3PdddcZkyZNctrWp08fY+rUqS7LP/XUU0afPn2ctj388MPG4MGD/dZGfzl8+LAhydi0aVOzZd59911DknHs2LH2a5gPzJo1yxgwYIDb5UPpvBqGYTz++ONG7969DZvN5vL9YD2vkow1a9Y4XttsNiM5OdmYN2+eY1tNTY0RHx9vLFmypNl6xo8fb4wcOdJp24gRI4y77rrL52321vl9deXDDz80JBlff/11s2VeffVVIz4+3reN8wNX/b3//vuNMWPGeFRPqJzbMWPGGDfffHOLZYLl3PoSI1IdTF1dnbZv366srCyn7VlZWdq6davLfYqLi5uUHzFihD7++GNZrVa/tdUfKisrJUkXXnhhq2WvvvpqpaSk6JZbbtG7777r76b5xP79+5Wamqq0tDTddddd+vLLL5stG0rnta6uTn/5y1/04IMPtvoA72A8r+cqLS1VeXm507mzWCwaNmxYs/+GpebPd0v7dESVlZUymUxKSEhosdzJkyfVq1cvXXzxxfrxj3+skpKS9mmgD7z33nvq3r27rrjiCk2cOFGHDx9usXwonNv/+7//0zvvvKOHHnqo1bLBfG69QZDqYCoqKtTQ0KCkpCSn7UlJSSovL3e5T3l5ucvy9fX1qqio8Ftbfc0wDOXl5emGG25Q//79my2XkpKipUuXatWqVVq9erWuvPJK3XLLLfrXv/7Vjq313KBBg/T6669r/fr1eumll1ReXq4hQ4boyJEjLsuHynmVpLVr1+r48eN64IEHmi0TrOf1fPZ/p578G7bv5+k+HU1NTY2mTp2qe+65p8UH2vbp00fLli3TunXrtHz5csXExOj666/X/v3727G13hk1apTeeOMN/fOf/9Tvf/97ffTRR7r55ptVW1vb7D6hcG5fe+01xcXF6Y477mixXDCfW29FBboBcO38v9oNw2jxL3lX5V1t78geffRRffLJJ9qyZUuL5a688kpdeeWVjteZmZk6ePCgnn/+ed14443+bqbXRo0a5fj+qquuUmZmpnr37q3XXntNeXl5LvcJhfMqSQUFBRo1apRSU1ObLROs57U5nv4b9nafjsJqtequu+6SzWbTokWLWiw7ePBgpwna119/va655hr96U9/0osvvujvprZJdna24/v+/ftr4MCB6tWrl955550WQ0Ywn1tJeuWVV/TTn/601blOwXxuvcWIVAfTrVs3RUZGNvlL5fDhw03+orFLTk52WT4qKkpdu3b1W1t96Ze//KXWrVund999VxdffLHH+w8ePDjo/uLp3LmzrrrqqmbbHQrnVZK+/vprbdy4URMmTPB432A8r/Y7MT35N2zfz9N9Ogqr1arx48ertLRURUVFLY5GuRIREaFrr7026M611DiS2qtXrxbbHsznVpI2b96sffv2efVvOJjPrbsIUh1MdHS0MjIyHHc42RUVFWnIkCEu98nMzGxSfsOGDRo4cKDMZrPf2uoLhmHo0Ucf1erVq/XPf/5TaWlpXtVTUlKilJQUH7fOv2pra7V3795m2x3M5/Vcr776qrp3764f/ehHHu8bjOc1LS1NycnJTueurq5OmzZtavbfsNT8+W5pn47AHqL279+vjRs3ehXyDcPQzp07g+5cS9KRI0d08ODBFtserOfWrqCgQBkZGRowYIDH+wbzuXVboGa5o3krVqwwzGazUVBQYOzZs8eYPHmy0blzZ+Orr74yDMMwpk6dauTk5DjKf/nll0ZsbKwxZcoUY8+ePUZBQYFhNpuNt956K1BdcNsjjzxixMfHG++9955RVlbm+KqurnaUOb+/f/jDH4w1a9YYX3zxhfHpp58aU6dONSQZq1atCkQX3PbEE08Y7733nvHll18aH3zwgfHjH//YiIuLC8nzatfQ0GD07NnTePrpp5u8F8zn9cSJE0ZJSYlRUlJiSDJeeOEFo6SkxHGn2rx584z4+Hhj9erVxu7du427777bSElJMaqqqhx15OTkON2J+/777xuRkZHGvHnzjL179xrz5s0zoqKijA8++KDd+3eulvpqtVqN2267zbj44ouNnTt3Ov0brq2tddRxfl9nz55t/OMf/zD+/e9/GyUlJcbPfvYzIyoqyti2bVsguuikpf6eOHHCeOKJJ4ytW7capaWlxrvvvmtkZmYa3/ve90Lu3NpVVlYasbGxxuLFi13WEUzn1l8IUh3UwoULjV69ehnR0dHGNddc47QcwP33328MGzbMqfx7771nXH311UZ0dLRxySWXNPtD39FIcvn16quvOsqc39/58+cbvXv3NmJiYozExETjhhtuMN555532b7yHsrOzjZSUFMNsNhupqanGHXfcYXz22WeO90PpvNqtX7/ekGTs27evyXvBfF7tSzWc/3X//fcbhtG4BMKsWbOM5ORkw2KxGDfeeKOxe/dupzqGDRvmKG/35ptvGldeeaVhNpuNPn36dIgQ2VJfS0tLm/03/O677zrqOL+vkydPNnr27GlER0cbF110kZGVlWVs3bq1/TvnQkv9ra6uNrKysoyLLrrIMJvNRs+ePY3777/fOHDggFMdoXBu7f785z8bnTp1Mo4fP+6yjmA6t/5iMowzs1cBAADgEeZIAQAAeIkgBQAA4CWCFAAAgJcIUgAAAF4iSAEAAHiJIAUAAOAlghQAAICXCFIAAABeIkgBQDsymUxau3ZtoJsBwEcIUgDCxgMPPCCTydTka+TIkYFuGoAgFRXoBgBAexo5cqReffVVp20WiyVArQEQ7BiRAhBWLBaLkpOTnb4SExMlNV52W7x4sUaNGqVOnTopLS1Nb775ptP+u3fv1s0336xOnTqpa9eu+vnPf66TJ086lXnllVfUr18/WSwWpaSk6NFHH3V6v6KiQj/5yU8UGxuryy+/XOvWrfNvpwH4DUEKAM4xc+ZMjR07Vrt27dK9996ru+++W3v37pUkVVdXa+TIkUpMTNRHH32kN998Uxs3bnQKSosXL9YvfvEL/fznP9fu3bu1bt06XXbZZU7HmDNnjsaPH69PPvlEo0eP1k9/+lMdPXq0XfsJwEcMAAgT999/vxEZGWl07tzZ6evZZ581DMMwJBmTJk1y2mfQoEHGI488YhiGYSxdutRITEw0Tp486Xj/nXfeMSIiIozy8nLDMAwjNTXVmD59erNtkGTMmDHD8frkyZOGyWQy/v73v/usnwDaD3OkAISVH/7wh1q8eLHTtgsvvNDxfWZmptN7mZmZ2rlzpyRp7969GjBggDp37ux4//rrr5fNZtO+fftkMpl06NAh3XLLLS224fvf/77j+86dOysuLk6HDx/2tksAAoggBSCsdO7cucmlttaYTCZJkmEYju9dlenUqZNb9ZnN5ib72mw2j9oEoGNgjhQAnOODDz5o8rpPnz6SpPT0dO3cuVOnTp1yvP/+++8rIiJCV1xxheLi4nTJJZfo//2//9eubQYQOIxIAQgrtbW1Ki8vd9oWFRWlbt26SZLefPNNDRw4UDfccIPeeOMNffjhhyooKJAk/fSnP9WsWbN0//33a/bs2fruu+/0y1/+Ujk5OUpKSpIkzZ49W5MmTVL37t01atQonThxQu+//75++ctftm9HAbQLghSAsPKPf/xDKSkpTtuuvPJKff7555Ia76hbsWKFcnNzlZycrDfeeEPp6emSpNjYWK1fv16PP/64rr32WsXGxmrs2LF64YUXHHXdf//9qqmp0R/+8Af96le/Urdu3TRu3Lj26yCAdmUyDMMIdCMAoCMwmUxas2aNbr/99kA3BUCQYI4UAACAlwhSAAAAXmKOFACcwUwHAJ5iRAoAAMBLBCkAAAAvEaQAAAC8RJACAADwEkEKAADASwQpAAAALxGkAAAAvESQAgAA8NL/D3qRss4b1+VXAAAAAElFTkSuQmCC",
       "text/plain": [
        "
"
       ]
@@ -704,93 +1216,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m  20/7821\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m44s\u001b[0m 6ms/step - loss: 5.1914e-06"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 4ms/step - loss: 1.0395e-06\n"
+      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 395us/step - loss: 2.1143e-06\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "9.875676596493577e-07"
+       "1.9805663669103524e-06"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on all test data\n",
-    "model_large.evaluate(X_test.iloc[:,X_test.columns != \"Class\"], y_test.iloc[:, y_test.columns != \"Class\"])"
+    "model_simple.evaluate(X_test.iloc[:,X_test.columns != \"Class\"], y_test.iloc[:, y_test.columns != \"Class\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m7727/7727\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 4ms/step - loss: 5.4493e-07\n"
+      "\u001b[1m7727/7727\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 369us/step - loss: 1.0423e-06\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "5.075861508885282e-07"
+       "9.472264537180308e-07"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on non-reactive data\n",
-    "model_large.evaluate(X_test[X_test['Class'] == 0].iloc[:,:-1], y_test[X_test['Class'] == 0].iloc[:,:-1])"
+    "model_simple.evaluate(X_test[X_test['Class'] == 0].iloc[:,:-1], y_test[X_test['Class'] == 0].iloc[:,y_test.columns != \"Class\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m94/94\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.0710e-05\n"
+      "\u001b[1m94/94\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 542us/step - loss: 8.8564e-05\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "4.047931361128576e-05"
+       "8.700142643647268e-05"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on reactive data\n",
-    "model_large.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1].iloc[:, :-1])"
+    "model_simple.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1].iloc[:, y_test.columns != \"Class\"])"
    ]
   },
   {
@@ -889,7 +1394,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "training",
+   "display_name": "ai",
    "language": "python",
    "name": "python3"
   },
@@ -903,7 +1408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/preprocessing.py b/preprocessing.py
index 2de066a..6eeb98f 100644
--- a/preprocessing.py
+++ b/preprocessing.py
@@ -174,7 +174,7 @@ def preprocessing_training(df_design, df_targets, func_dict_in, func_dict_out, s
         
     elif scaling == 'global':
         scaler_X.fit(pd.concat([X_train.iloc[:, X_train.columns != "Class"], y_train.iloc[:, y_train.columns != "Class"]], axis=0))
-        scaler_y = clone(scaler_X)
+        scaler_y = scaler_X
     
     X_train = pd.concat([scaler_X.transform(X_train.loc[:, X_train.columns != "Class"]), X_train.loc[:, "Class"]], axis=1)
     X_test = pd.concat([scaler_X.transform(X_test.loc[:, X_test.columns != "Class"]), X_test.loc[:, "Class"]], axis=1)