From d517ca6540184b6b070187175a1654bac9419d5b Mon Sep 17 00:00:00 2001 From: Hannes Signer Date: Wed, 15 Jan 2025 17:23:58 +0100 Subject: [PATCH] add sampling function --- POET_Training.ipynb | 694 +++++++++----------------------------------- 1 file changed, 144 insertions(+), 550 deletions(-) diff --git a/POET_Training.ipynb b/POET_Training.ipynb index ba8f7b3..ccba69b 100644 --- a/POET_Training.ipynb +++ b/POET_Training.ipynb @@ -34,19 +34,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-01-15 14:03:45.613137: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1736946225.710416 5544 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1736946225.737063 5544 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2025-01-15 14:03:45.952881: 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: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + "2025-01-15 16:24:49.275664: 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", + "2025-01-15 16:24:49.404820: 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 3.7.0\n" + "Running Keras in version 3.6.0\n" ] } ], @@ -77,17 +74,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 141, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1736946248.679800 5544 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13393 MB memory: -> device: 0, name: NVIDIA A2, pci bus id: 0000:82:00.0, compute capability: 8.6\n" - ] - } - ], + "outputs": [], "source": [ "dtype = \"float32\"\n", "activation = \"relu\"\n", @@ -103,7 +92,9 @@ " staircase=True\n", ")\n", "\n", - "optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)\n", + "optimizer_simple = keras.optimizers.Adam(learning_rate=lr_schedule)\n", + "optimizer_large = keras.optimizers.Adam(learning_rate=lr_schedule)\n", + "\n", "loss = keras.losses.Huber()\n", "\n", "sample_fraction = 0.8" @@ -118,17 +109,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Model: \"sequential\"\n",
+       "
Model: \"sequential_5\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -140,11 +131,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense (Dense)                   │ (None, 128)            │         1,664 │\n",
+       "│ dense_17 (Dense)                │ (None, 128)            │         1,664 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (Dense)                 │ (None, 128)            │        16,512 │\n",
+       "│ dense_18 (Dense)                │ (None, 128)            │        16,512 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_2 (Dense)                 │ (None, 12)             │         1,548 │\n",
+       "│ dense_19 (Dense)                │ (None, 12)             │         1,548 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -152,11 +143,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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 (\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_17 (\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_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",
+       "│ dense_18 (\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_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",
+       "│ dense_19 (\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"
       ]
      },
@@ -213,23 +204,23 @@
     "    ]\n",
     ")\n",
     "\n",
-    "model_simple.compile(optimizer=optimizer, loss = loss)\n",
+    "model_simple.compile(optimizer=optimizer_simple, loss = loss)\n",
     "model_simple.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
Model: \"sequential_1\"\n",
+       "
Model: \"sequential_6\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -241,13 +232,13 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_3 (Dense)                 │ (None, 512)            │         6,656 │\n",
+       "│ dense_20 (Dense)                │ (None, 512)            │         6,656 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_4 (Dense)                 │ (None, 1024)           │       525,312 │\n",
+       "│ dense_21 (Dense)                │ (None, 1024)           │       525,312 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_5 (Dense)                 │ (None, 512)            │       524,800 │\n",
+       "│ dense_22 (Dense)                │ (None, 512)            │       524,800 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_6 (Dense)                 │ (None, 12)             │         6,156 │\n",
+       "│ dense_23 (Dense)                │ (None, 12)             │         6,156 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -255,13 +246,13 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m6,656\u001b[0m │\n",
+       "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m6,656\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │       \u001b[38;5;34m525,312\u001b[0m │\n",
+       "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │       \u001b[38;5;34m525,312\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m524,800\u001b[0m │\n",
+       "│ dense_22 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m524,800\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m6,156\u001b[0m │\n",
+       "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m6,156\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -317,7 +308,7 @@
     "     keras.layers.Dense(12, dtype=dtype)\n",
     "     ])\n",
     "\n",
-    "model_large.compile(optimizer=optimizer, loss = loss)\n",
+    "model_large.compile(optimizer=optimizer_large, loss = loss)\n",
     "model_large.summary()\n"
    ]
   },
@@ -400,11 +391,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
-    "os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n",
+    "# os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n",
     "data_file = h5py.File(\"Barite_50_Data_training.h5\")\n",
     "\n",
     "design = data_file[\"design\"]\n",
@@ -425,14 +416,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/mnt/beegfs/home/signer/.conda/envs/ai/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",
+      "/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",
       "  return fit_method(estimator, *args, **kwargs)\n"
      ]
     }
@@ -463,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -496,22 +487,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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RGzdu1N69e9XZ2ak77rhjQTccALD4zTlCR44c0Qc/+MHm1/fff78k6a677tLf/M3f6IEHHtDU1JTuuecenTlzRjfddJMOHTqk7u7uhdtqAMCSYJxzzvdGtBobG1OpVNJ23a7QRL43BwAwR4mr6Tl9R6Ojoxc9xLKgp2gvKGPy5Vzt1UwAwDy0b4TeyrlhIkoAsGi1b4SMzZfZuKzlfS1RIkgAsKi0bYRMEMiYYGZw6lxmz11R/yaCBACLSRtHyMgYK+fOmX7LnExQ/3s9PrNGiSABQNtr2wgpiiQTyWQtIyHnpEBSfV0jUMbUIzNblAgSALStto2QiSIZG0nZzMAoc2qcVW6yLI+KaYlSY6R0bpDOnbIjRgDgXftGqFCQsYX8C1cPT+Ykl8m0/L0Zpay+/twgWWIEAO2qbSPkOouSKTQjYZyT0vrIxzkpTZthMlnajJJLsxlBOnd0NGuMCBEAeNHGEYqVBbGU1QOU5aMeZZlMPUYmy/Iwpalclv9pgjxQypxcmrbEyEmZnX1kxKgIALxo2wilXQWZMJacZNJMxqkeICeXZTJJPUBZJpOk00FKEjmbn9pt0qA5YjJpOnNkRIwAwLu2jVCtuyAXRvX4OJlMsmmmsFbTL02ckbFOUiajLP+7SyWbydhUkqvHKKsfN8okk6kWGL2edUrGzR4jpugA4G3VthGq9gTKwkCmfq6ByZxMEujqsVH992P/7bJ/7ovRen2puF21zM4yMgrkGic8MCoCgCuufSPUbZRGdjpCqWRTp4oL9GahM39TvQ+m5e/5+pYv3PSLXVlV/6E2rIf1nL7YdYuqWf06pDSdESOm6ADg7dG+EeoxCgomj1A6HaJTXe/QrR//U9lEsjUnmzjZRAqqmUzi8nXVVDbJZGppviSZVEu0efw1fXHkSW2pDeuhyWf0xe4dqmWhnLUzjhkZ484/k44pOgBYcAv6eO+FVC05VVc6VUtSZaVUKUmVlWZ6KTX+tKqUrMorA1VLoaqlULVSpFpPQUl3rLS7qKwrlusq6pWrNuqhdb+tsgm1pTqsh87+L0WhlSlEMmEoRZFMEEjWSMbK2PooqHEj1dkeLQEAuGxtG6FaKVN1ZeviVC05VVblcaqsqkdpVX1ZaVVeaVRemQepsjJUdWWo6owgxTp29bv10DW/m4eo8mM9NH5IhdBI9RA1l8BKQSBjTR6j1hARIwBYEG0bIVOqSitrcitrylYmSlfVlKxKVFuVqroqqy9OldV5mCqrNCNIlZJRuRSoUspHSLWeULUVkdIVBR1d+2594V135CEqn9IXRv9JhUIgFSIpCvPRUBDIGCMFwfSoqPXREoQIAOatbY8J9ZSmZDucMmfyJTPKMqs0NcrSQFlilCRWqlmZxMhUjWzVyNakoGJka0ZBRQoqUlI2CitGQcUqLGeyFauXw3fr8+Hv60uvfktbpl7TF37xXf3pO35DNRPJGSOTmOaxIqe0eaErx4kAYOG0bYT6e8YUdFaUOaM0s0qcVZpZ1TKrWhooSfM/a7VASRIoqwZKqlamapVW8iAFFSNbMQrLUlqWgrJRWjAKy1ZZZPVy9G49GP6B/uuJv9WWqdf0tz/5uv4tXJmfTNe4PVB9OatI37Sb9SO7Mj+N21hCBADz1LYRuqbzFyqsiJQ5q0xGtSxQ4qxqWaBqFqiahiqnoSppqHISqlyNVKmFqlZDpZUgX8pWtmyUFo2CslE4JWUFKS0YRZGRC41eit6tz9m7tPf//q1WplNamU695TZdl/1cny3con8JVk+fPUeIAOCytW2E3tXxM0UdeYQkqeYCpc6q5gLVXKBKFmoqLWgqjTSRFjSZFDRRK2iiWtBkNVK5HCmphEqmQmVlqyw2ygr5EhSkLJLSyCosGx0J360/WHG/3vvGj2Xrp3UrSfJTu5NELk31WxPH9KvpaT1afSYPkVk9PT1HiADgsrRthH4p+oU6C/kjVFNnlcooc1ZVF6jmQtVcoLKLNJnGmswKGkuKmkhijdaKGq8WNRbHmqgUVI4j1aYiZXGgLLLKIqMgMsoioyyUstAoC41eD67S6a6VCsqpgnIiU05kqjWZak2qJTpaWK8vvfk/9avJT/Vo9RntiW/V/9MqQgQA89C2EVobvqmuMB8FNUZDqYxSNUZDocouUjmLNJHFGo+KOpsWdabWqbFCh85UOzRa6NB4IdbZQqxyHCmNQmWFQFnBNkdFWX1UlIVWYcEojKxcZBWEVrZspcDKWKuyMfq8/Yi+9It/1K8mP9VA5X8TIgCYp7aN0Gpb0Qo7fUp0mt+cR6kzqskqc0ZlF6psI5VdpLGsqMkg1oqgrNGkU11hRd1RRWfCTo1GNY1FsSajWJVCpKQQKIuC+mgoHxWlBaMscnJBfqzIWaMgsLKBkbVWskZlY/SFq/6z/vSNpwkRACyAto3QCmvUbWdei5M6JxmnVJlqTupUopqrquwCddqKJrNYnbaiTltVd1DWL4IudQVVdYad6gg7NBYlGo9iTRUi1Qr1Kbr6qCgo5PHJwvxYURbmo6LQGsna/JohYzRlCREALJS2jVCHCdRpWkdCrn6nUilzTqlxqjmnmklVdPXF1JrLuO1Qp62oOyjXR0VljRY69GahQ6NxUWfjWFNxQWkhn6ILCzaPUP1YkQuMXCA5G8oFRkHeojxEWkGIAGABtG2EIhMoaolQpPpISJKMlClToEyRnGpyskpl5VSwmSKTqGhq6rKVGSOjnrCs7rCiFVGHzkSdGouKOhvFqkSRalGoLAiUhaYlQFbOmjx+RpI1+S0mnCNEALAA2jZCtv6fGevq8ZEkOavISFZOgZwC4xS5TAWXKXKZiiZR0dXUaSrqslWVggmNBl0qhVPqCTvVHVX0RtSlN6MOvRkVNRnFSoNILgzkAqMsyI8LOSs5E8i13KbHSoyIAGABtG2E3kozTCZToEDWOWUmf4KqNVIgp8ikeYhcqrKpqstWNJHF6gnKKqVFlYIu9YTl+vGiquIw0S+CVOO2qFpQUM2GcvWpNxkjZ/Lf3NxdmbvgiIjriADg0iy6CDVY2XxKzpjzR0Vy+TRd/XhR1SXqMjWVXbk5Muq0FXUGFXUEVRVsosimCqzTmHWqWKfEhpKxcsZMj4JMS4gaU3PSeSFqvaBVSrnFDwC8hUUbIen8EKn+dyunrBEiOcUmU9Flil2qoknUZSvqshUVbS0fNZlUkclkjVNgM41ap7KREhPWw9P6+IY8RM4YhXrrqblmiBr3mgMAnGdRR0iaGaJAgVKXT5VlJpOVU2Tys+lq9WNGxfoxo8iksvWTGwI5WTNzedM4TZlYiYmU/0Sj+rycGk/AMPVRTXNqLuvS51dPX9D6aPUZfTb8oP7FrmJaDgBm0bYRsjKyMrO+lsmd8976nRUaoyJJctOnNTSDpDxGgcuPGeUnNGSy9aXx99Dkl8YaI01KShVJmn1ElP8CJ5tJck5l16XPr/wNfenMP+b3mkue1X3RTv2rXUGIAOAcbRuhC7Ey54UoX9/60Lms+dfWExisnKzyP2VrLe9xCurrAzP9s52TpkweInPuiMhZGRdI+TW0+cgoa4Tow9p35mltzN7QB7NT+h/Bv+dEBQA4x6KMkPTWIZp+/ZyHxracTZcPsDKpJURBy88KGrGqx8gYadJJSeuISKYennqEUidlmWyWyWSZylmXThTWamP5DYVW+fnlaWNb6iECgGVu0UZIuniIZr639SSG4LwQNSIU1E9QaMjqox7npClJiYvqIyAjkxmZTDKZlUkDmSzKY5RmMmmWh0eSjJExRi4IZJROn6jAaAjAMreoIzRXjRBJUiBTP/MtD1FQHxFZc+ERypQzSlyo+lWs9RBZmTQfDZk0lE0yKcskG9R/sZGCIH9UuLEzp+UAYBlbVhGS6tN0Jmue0t0IUVAfEdlZRiZZ/QSJ/KnfRlNOSlwok1nZ1NQDZGSTQDZ1Mkk+JdccCVkrE9THWs5JmVVzbo7REIBlbNFHaC5TctPfMx2i/DofScrUKScpkWy5+d7Mmfyhes4ocYEyZ5RlRpXMKEmNTH2xiWQTK5MEMrVQSp0U1E/lNlaKwvzXOCenVMYZTlIAsOwt+ghJOu9U7kuJUjNEkuSC+nVGmfLpuUSZqSizVmnjwXr1J7tmzijNrN7MjGqpUZIY2cTI1qZDZGuBTOrk6hFSYKUwlDInpWl+fKgxLZfOunkAsCwsiQid61KjNNutf1K5fETUcvq2JKWySusRSjKrNDMaTQOlqVGSBjKJkanHKKjm03JpmO/em8on9UT3FlWD+rScc/nIJxW39AGwrNmLv2Xxe6uLXvPX8v/kt/uxioxVZIxiI3XaRN22rJXBhK4Ox9Qbjak3HtOajnGt7pxSZ1dZpjNR0pkp7XBKOqWkw6jWaZV0BBrsf5/KNtKG6s/1hV98V4VAUhTmJymY/MQIYxsnSADA8rNsPv0uFKKGwBhFyp9jVDRGncap29a00k7pquCsrg7HtKYwpt54XFcVJ7Syo6y4sybXmSrpdEo7pKTTKCkaJR1Wr169Xg9e93GVTagtU6/pC29+X5FxMoGVgmD6xIUGc/FtBIClpG0jNNeTDear9eLWQGbWEK0OzurqcFxrCmNaE49rZXFKKzoqCjoTpZ2Zks7GaEhKikZpbHV0zbX6/Ht+Pw9R+ZQeGvsnRda1PDL8nNEQIQKwjLRthKQ8RK3LfF1sNNQ6LTdriIJJXRWc1epgQldFE7oqnlBPsayOjqrUkeZTco3RUEc+GkqKgY5efa2+8K478hBVfqyHxgdVsFn+vPBzR0MAsIy0dYTOtVAxupAZx4dapuaKxqnLJOq2U1oZTGp1OKHV0YRWxZNaUawo6qgp60iVFp3SYn5sKCkapUWrLA507Kp36aFrfjcPUXVYD008o8g6RkMAlrVFFaGG+YToUo4NNUJ0/jGiVF2mpm47pVIwoVXRpFYVplSKy+oo1mRbpuXSjpZpuaJVFoc6tvpdemjdb+chqg3r4alnFRnHaAjAsrUoIyTNP0SXMjUnqX79kFEko8hIsUnVY8v5o8KDKa2MJrUynlJ3saJCMZFrnZarj4aSolUaW7k40Culd+qhvo+prFBbkn/Vf6n9H0ZDAJatRRshaf4nL1wsRo0QWVlZY1QwRkWTqWgSdZqKSsGESuGUStGUeuKyOovTJyk0QpQWpTQ2ymKrLArkokCvdP87feWq/yhJ2pL86/SxIU7VBrDMLPpPvYU4TnSxEM02GsofEV6tj4amtLIwpZ5iRcViTSpOHxtKi0ZJnJ8plxWsXBRIUah/K15V/w1m+rohieuGACwrS+aOCZncJR3veSuX8nwia5wCJxXqo6Eum4+GJsOCJgqxJpOCpoqRqpVQtUqgtJxPxwWxlMRGQcEqKAQyFTt9Sx8pv42PtdN3UZh+gbsoAFjSlkyEpOnpufnE6GIiYxTJqWhSddqKeoJIk9mkxqOiJuKCJpKCJjsiJdVAWdkqKxulRaN0SspioyyyslGQX6wq5TdPDYLz7ynHox4ALANLct7nSp3GHagxLSdFJlOXqarTVNQdlFUKJ1WKplQqTKkrriqME2XFLJ+Wi6WsIKWFfEouK4RyYdDyg/N7ynHdEIDlZkmNhFrNd3qu1YxHPxinQEZFOdXqx4bKbkqlINZkGGuiEGu8UNREsaBaHOXhiYzSglEaOWWRkQtt8zEPajxcz5rpJ7AanjcEYHlYkiOhhisxImqeoCCpWL9uqKd+k9NSOKmecErdhbI6CzUFcSoXO6Wxq4+ETD1KVgpbjgk17yXXso4TFAAsA3zKtbjYyCmon8FmjcmPDdXPlOu0lXqIJtUTltUdVdRVqKpQSOSiTFkkpQUpi6QsMvnSiJBR/qwhWz9Djik5AMvIko/QQo2GWq8ZahwbKtavG+oyiTptRd02PzbUHZbVFVUVR4lMnCoruGaA0kjKIptPyTUE9fAEQXNKbsYoiAtXASxRSz5C0sJOy8147pCMiqYxLVdVVz1EPWFZnWFVxUJNNsrkGhEqtIyEgkZY8tGPaYyA7LL4RwIAkpZJhObiUu6g0AhR67Rc45TtLltRp62qK6iqGCYKw1QudMoipyxUc3Fh/fcY5dcMtY6C6kHiuBCApW7ZfMLNZTR0odv5nHtPudZTtgtKVbQ1dQYVdQQ1xUGiKEqlKJNrBKg+LeeClp8fBPmUXONBd7NNvzElB2AJWjYRkuY+LfdWMWo9PtR4HHh+AWuioqmpaGrqCKoqBomiIJUJs3wkFElZaPIlao2QnfGQO0nnHxcCgCVo2X3KXc7xobcK0cwH4OWjocgkikyqyKQqBIkKYSobOrnmUp+OC1qm46ydnpLjuBCAZeSKfdr9xV/8hTZs2KBisagbbrhB//zP/3ylftWcLVSImq8Zo0CSlVMgp8gkim2igk0V2kzWZvlxoZZjQrUw0OliST8v9OTXDBnTXJrHhcT1QgCWtivy6fb3f//32r17tx588EEdPXpUv/7rv67bbrtNP/7xj6/Er/PGnrP7ApMHqFAfCUWNCAWZZJ2cbYyCpNdWrdFv7XhQn9xybz4qso0QtVwndO71QhwXArDEXJEI7d+/X3/0R3+kP/7jP9Z73/tePfbYY1q/fr0ef/zxK/HrLstCnbbduIA1aAmENZkCkyk0maIgVRBkUuDkAsnZfCouC0z965YLVBuLRHAALAsLHqFqtaqXXnpJO3funLF+586deuGFFxb617WFoD5VF9Sn4wI5WZPli5ysdZKVXFD/s7mYfDFqjoQ4IQHAcrLgNzD9+c9/rjRN1dvbO2N9b2+vRkZGznt/pVJRpVJpfj02NrbQm+RFfmwoU2AzWeNkbEuAgpYQBfnoxzUCBADLyBX7v9znfqA652b9kB0YGFCpVGou69evv1Kb9LZoTMVZk9W/drLGScbJmXp4TEuEjOSMyf9JNE5OqE/JMSoCsNQt+CfcO97xDgVBcN6o5/Tp0+eNjiRpz549Gh0dbS7Dw8MLvUne5FNyTsa4/Eal9Rjlf1fLGXGzfTPxAbD0Lfh0XKFQ0A033KDBwUH95m/+ZnP94OCgbr/99vPeH8ex4jhufu3qz84ZP/v2PFV0Ls8cmu1khkyZai7TlMt0NnOayDJNJqlqtZqqUzXVJqtKJytyU1aunCqtWCVVo6DqlNScbC2VSapyaVU2q0hZVS6ryrma5Gpyzsm5dPopqzxbCECbS1STNP15fkHuCjh48KCLosh9/etfdz/84Q/d7t27XVdXl3vttdcu+r3Dw8NOEgsLCwvLIl+Gh4cv+pl/RZ6s+ju/8zt644039MUvflGvv/66Nm3apO9+97u65pprLvq9/f39Gh4eVnd3t4wxGhsb0/r16zU8PKyenp4rsblLAvvp0rCfLg376dKwn2bnnNP4+Lj6+/sv+l7jXHvP74yNjalUKml0dJR/yBfAfro07KdLw366NOyn+ePoNwDAGyIEAPCm7SMUx7EefvjhGWfQ4Xzsp0vDfro07KdLw36av7Y/JgQAWLrafiQEAFi6iBAAwBsiBADwhggBALxp+wi182PCfXj++ef1kY98RP39/TLG6Nvf/vaM151zeuSRR9Tf36+Ojg5t375dJ06c8LOxngwMDGjLli3q7u7WmjVr9NGPflSvvvrqjPewn6THH39cmzdvVk9Pj3p6erR161Z973vfa77OPprdwMCAjDHavXt3cx376vK1dYSWy2PC52JiYkLXX3+9Dhw4MOvr+/bt0/79+3XgwAEdPnxYfX192rFjh8bHx9/mLfVnaGhIu3bt0osvvqjBwUElSaKdO3dqYmKi+R72k7Ru3To9+uijOnLkiI4cOaJbbrlFt99+e/PDk310vsOHD+trX/uaNm/ePGM9+2oe5nGf0ivu137t19wnP/nJGet+5Vd+xX32s5/1tEXtRZJ76qmnml9nWeb6+vrco48+2lxXLpddqVRyf/mXf+lhC9vD6dOnnSQ3NDTknGM/XciqVavcX//1X7OPZjE+Pu42btzoBgcH3bZt29x9993nnOPfp/lq25HQcnxM+HydPHlSIyMjM/ZZHMfatm3bst5no6OjkqTVq1dLYj/NJk1THTx4UBMTE9q6dSv7aBa7du3Shz/8YX3oQx+asZ59NT9X5C7aC2GujwmHmvtltn126tQpH5vknXNO999/v97//vdr06ZNkthPrY4fP66tW7eqXC5rxYoVeuqpp3Tdddc1PzzZR7mDBw/q5Zdf1uHDh897jX+f5qdtI9RwqY8JxzT22bR7771Xr7zyin7wgx+c9xr7SXrPe96jY8eO6c0339Q//MM/6K677tLQ0FDzdfaRNDw8rPvuu0+HDh1SsVh8y/exry5P207HzfUx4ZD6+vokiX1W9+lPf1pPP/20nn32Wa1bt665nv00rVAo6Nprr9WNN96ogYEBXX/99frqV7/KPmrx0ksv6fTp07rhhhsUhqHCMNTQ0JD+7M/+TGEYNvcH++rytG2EWh8T3mpwcFA333yzp61qbxs2bFBfX9+MfVatVjU0NLSs9plzTvfee6+efPJJPfPMM9qwYcOM19lPb805p0qlwj5qceutt+r48eM6duxYc7nxxht155136tixY3rnO9/JvpoPf+dEXNx8HhO+VI2Pj7ujR4+6o0ePOklu//797ujRo+7UqVPOOeceffRRVyqV3JNPPumOHz/ufu/3fs+tXbvWjY2Ned7yt8+nPvUpVyqV3HPPPedef/315jI5Odl8D/vJuT179rjnn3/enTx50r3yyivuc5/7nLPWukOHDjnn2EcX0np2nHPsq/lo6wg559yf//mfu2uuucYVCgX3vve9r3ma7XL17LPPzvos97vuuss5l58u+vDDD7u+vj4Xx7H7wAc+4I4fP+53o99ms+0fSe4b3/hG8z3sJ+c+8YlPNP+3dfXVV7tbb721GSDn2EcXcm6E2FeXj0c5AAC8adtjQgCApY8IAQC8IUIAAG+IEADAGyIEAPCGCAEAvCFCAABviBAAwBsiBADwhggBALwhQgAAb4gQAMCb/w89oIZdSjvIlgAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -536,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -552,56 +543,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sampling_strategy = 'none'\n",
-    "\n",
-    "Counter(df_design['Class'])\n",
-    "\n",
-    "if sampling_strategy == 'smote':\n",
-    "    print(\"Using SMOTE strategy\")\n",
-    "    over = SMOTE(sampling_strategy=0.1)\n",
-    "    design_resampled, design_classes_resampled = over.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n",
-    "    target_resampled, target_classes_resampled = over.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n",
+    "def balancer(design, target, strategy, sample_fraction=0.5):\n",
+    "    counter = Counter(design.iloc[:,-1])\n",
+    "    print(\"Amount class 0 before:\", counter[0] / (counter[0] + counter[1]) )\n",
+    "    print(\"Amount class 1 before:\", counter[1] / (counter[0] + counter[1]) )\n",
     "    \n",
-    "elif sampling_strategy == 'over':\n",
-    "    print(\"Using Oversampling\")\n",
-    "    over = RandomOverSampler()\n",
-    "    design_resampled, design_classes_resampled = over.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n",
-    "    target_resampled, target_classes_resampled = over.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n",
-    "    \n",
-    "elif sampling_strategy == 'over/under':\n",
-    "    pass\n",
-    "elif sampling_strategy == 'under':\n",
-    "    pass\n",
-    "elif sampling_strategy == 'none':\n",
-    "    pass\n",
+    "    number_features = (df_design.columns != \"Class\").sum()\n",
+    "    if(\"Class\" not in design.columns):\n",
+    "        if(\"Class\" in target.columns):\n",
+    "            classes = target['Class']\n",
+    "        else:\n",
+    "            raise(\"No class column found\")\n",
+    "    else:\n",
+    "        classes = design['Class']\n",
+    "        df = pd.concat([design.loc[:,design.columns != \"Class\"], target.loc[:, design.columns != \"Class\"], classes], axis=1)\n",
+    "        \n",
+    "    if strategy == 'smote':\n",
+    "        print(\"Using SMOTE strategy\")\n",
+    "        smote = SMOTE(sampling_strategy=sample_fraction)\n",
+    "        df_resampled, classes_resampled = smote.fit_resample(df.loc[:, df.columns != \"Class\"], df.loc[:, df.columns == \"Class\"])\n",
+    "        \n",
+    "    elif strategy == 'over':\n",
+    "        print(\"Using Oversampling\")\n",
+    "        over = RandomOverSampler()\n",
+    "        df_resampled, classes_resampled = over.fit_resample(df.loc[:, df.columns != \"Class\"], df.loc[:, df.columns == \"Class\"])\n",
+    "        \n",
+    "    elif strategy == 'under':\n",
+    "        print(\"Using Undersampling\")\n",
+    "        under = RandomUnderSampler()\n",
+    "        df_resampled, classes_resampled = under.fit_resample(df.loc[:, df.columns != \"Class\"], df.loc[:, df.columns == \"Class\"])\n",
     "\n",
-    "if(sampling_strategy != 'none'):\n",
-    "    X_train = pd.concat([design_resampled, design_classes_resampled], axis=1)\n",
-    "    y_train = pd.concat([target_resampled, target_classes_resampled], axis=1)"
+    "    counter = Counter(classes_resampled[\"Class\"])\n",
+    "    print(\"Amount class 0 after:\", counter[0] / (counter[0] + counter[1]) )\n",
+    "    print(\"Amount class 1 after:\", counter[1] / (counter[0] + counter[1]) )\n",
+    "    \n",
+    "    design_resampled = pd.concat([df_resampled.iloc[:,0:number_features], classes_resampled], axis=1)\n",
+    "    target_resampled = pd.concat([df_resampled.iloc[:,number_features:], classes_resampled], axis=1)\n",
+    "    \n",
+    "    return design_resampled, target_resampled            "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Amount class 0: 0.9879670329670329\n",
-      "Amount class 1: 0.012032967032967032\n"
+      "Amount class 0 before: 0.9878911088911089\n",
+      "Amount class 1 before: 0.012108891108891108\n",
+      "Using Oversampling\n",
+      "Amount class 0 after: 0.5\n",
+      "Amount class 1 after: 0.5\n"
      ]
     }
    ],
    "source": [
-    "counter = Counter(X_train.iloc[:,-1])\n",
-    "print(\"Amount class 0:\", counter[0] / (counter[0] + counter[1]) )\n",
-    "print(\"Amount class 1:\", counter[1] / (counter[0] + counter[1]) )"
+    "X_train, y_train = balancer(X_train, y_train, 'over')"
    ]
   },
   {
@@ -613,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -647,9 +651,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 88,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[88], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_design_log, df_results_log \u001b[38;5;241m=\u001b[39m log_scale(df_design, df_results, func_dict_in)\n\u001b[1;32m      2\u001b[0m data_min_log, data_max_log \u001b[38;5;241m=\u001b[39m get_min_max(df_design_log, df_results_log)\n",
+      "Cell \u001b[0;32mIn[87], line 8\u001b[0m, in \u001b[0;36mlog_scale\u001b[0;34m(df_design, df_result, func_dict)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m df_design\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m----> 8\u001b[0m         df_design[key] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(func_dict[key])(df_design[key])\n\u001b[1;32m      9\u001b[0m         df_result[key] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(func_dict[key])(df_result[key])\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_design, df_result\n",
+      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/numpy/lib/function_base.py:2372\u001b[0m, in \u001b[0;36mvectorize.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2369\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_stage_2(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2370\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m-> 2372\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_as_normal(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/numpy/lib/function_base.py:2365\u001b[0m, in \u001b[0;36mvectorize._call_as_normal\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2362\u001b[0m     vargs \u001b[38;5;241m=\u001b[39m [args[_i] \u001b[38;5;28;01mfor\u001b[39;00m _i \u001b[38;5;129;01min\u001b[39;00m inds]\n\u001b[1;32m   2363\u001b[0m     vargs\u001b[38;5;241m.\u001b[39mextend([kwargs[_n] \u001b[38;5;28;01mfor\u001b[39;00m _n \u001b[38;5;129;01min\u001b[39;00m names])\n\u001b[0;32m-> 2365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_vectorize_call(func\u001b[38;5;241m=\u001b[39mfunc, args\u001b[38;5;241m=\u001b[39mvargs)\n",
+      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/numpy/lib/function_base.py:2455\u001b[0m, in \u001b[0;36mvectorize._vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2452\u001b[0m \u001b[38;5;66;03m# Convert args to object arrays first\u001b[39;00m\n\u001b[1;32m   2453\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [asanyarray(a, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mobject\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m args]\n\u001b[0;32m-> 2455\u001b[0m outputs \u001b[38;5;241m=\u001b[39m ufunc(\u001b[38;5;241m*\u001b[39minputs)\n\u001b[1;32m   2457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mnout \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   2458\u001b[0m     res \u001b[38;5;241m=\u001b[39m asanyarray(outputs, dtype\u001b[38;5;241m=\u001b[39motypes[\u001b[38;5;241m0\u001b[39m])\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "df_design_log, df_results_log = log_scale(df_design, df_results, func_dict_in)\n",
     "data_min_log, data_max_log = get_min_max(df_design_log, df_results_log)"
@@ -657,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -667,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -717,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -737,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -776,175 +796,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/50\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "I0000 00:00:1736946361.112525    5608 service.cc:148] XLA service 0x7f55280081d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
-      "I0000 00:00:1736946361.112832    5608 service.cc:156]   StreamExecutor device (0): NVIDIA A2, Compute Capability 8.6\n",
-      "2025-01-15 14:06:01.159888: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
-      "I0000 00:00:1736946361.348701    5608 cuda_dnn.cc:529] Loaded cuDNN version 90300\n",
-      "2025-01-15 14:06:01.387004: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 182 configs for 6 fusions on a single thread.\n",
-      "2025-01-15 14:06:03.621770: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 760 bytes spill stores, 708 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:10.257275: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 188 bytes spill stores, 188 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:12.491063: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170_0', 776 bytes spill stores, 724 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:14.767081: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 220 bytes spill stores, 220 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:19.678342: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 172 bytes spill stores, 384 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:28.467581: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 664 bytes spill stores, 636 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:35.654758: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 28 bytes spill stores, 28 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:37.946914: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 664 bytes spill stores, 636 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:06:45.130159: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 28 bytes spill stores, 28 bytes spill loads\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m  59/3520\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - loss: 0.0151   "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I0000 00:00:1736946413.659915    5608 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m3510/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.7823e-04"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-15 14:07:02.606589: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 170 configs for 6 fusions on a single thread.\n",
-      "2025-01-15 14:07:04.595726: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163_0', 756 bytes spill stores, 444 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:11.174922: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163_0', 56 bytes spill stores, 68 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:11.599286: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 12 bytes spill stores, 12 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:12.141013: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 204 bytes spill stores, 204 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:14.292978: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170_0', 756 bytes spill stores, 444 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:15.902316: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 4 bytes spill stores, 4 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:16.549503: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 236 bytes spill stores, 236 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:21.435252: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 12 bytes spill stores, 12 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:22.296584: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 428 bytes spill stores, 704 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:30.646925: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 776 bytes spill stores, 792 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:34.111635: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 92 bytes spill stores, 92 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:34.831349: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 88 bytes spill stores, 88 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:36.594341: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 20 bytes spill stores, 28 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:37.378454: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 32 bytes spill stores, 56 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:39.469100: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 52 bytes spill stores, 72 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:42.877171: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 776 bytes spill stores, 792 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:46.407965: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 92 bytes spill stores, 92 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:47.130611: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 88 bytes spill stores, 88 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:48.883431: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 20 bytes spill stores, 28 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:49.652582: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 32 bytes spill stores, 56 bytes spill loads\n",
-      "\n",
-      "2025-01-15 14:07:51.729864: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 52 bytes spill stores, 72 bytes spill loads\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 5.7683e-04"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-15 14:08:00.788132: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 72 configs for 3 fusions on a single thread.\n",
-      "2025-01-15 14:08:07.801469: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_31', 220 bytes spill stores, 220 bytes spill loads\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 25ms/step - loss: 5.7669e-04 - val_loss: 2.6423e-06\n",
-      "Epoch 2/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - loss: 4.9671e-06 - val_loss: 2.3180e-06\n",
-      "Epoch 3/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 3.2911e-06 - val_loss: 4.5144e-06\n",
-      "Epoch 4/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 2.4528e-06 - val_loss: 1.0435e-06\n",
-      "Epoch 5/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.7198e-06 - val_loss: 8.9310e-07\n",
-      "Epoch 6/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.5762e-06 - val_loss: 6.4216e-07\n",
-      "Epoch 7/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.1924e-06 - val_loss: 6.5215e-07\n",
-      "Epoch 8/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.0509e-06 - val_loss: 7.6646e-07\n",
-      "Epoch 9/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 9.9888e-07 - val_loss: 6.1071e-07\n",
-      "Epoch 10/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.0026e-06 - val_loss: 5.3742e-07\n",
-      "Epoch 11/50\n",
-      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 8.9634e-07 - val_loss: 6.5116e-07\n",
-      "Epoch 12/50\n",
-      "\u001b[1m2968/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 9.6697e-07"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[21], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# measure time\u001b[39;00m\n\u001b[1;32m      2\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 4\u001b[0m history \u001b[38;5;241m=\u001b[39m model_large\u001b[38;5;241m.\u001b[39mfit(X_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m      5\u001b[0m             y_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m      6\u001b[0m             batch_size \u001b[38;5;241m=\u001b[39m batch_size, \n\u001b[1;32m      7\u001b[0m             epochs \u001b[38;5;241m=\u001b[39m epochs, \n\u001b[1;32m      8\u001b[0m             validation_data \u001b[38;5;241m=\u001b[39m (X_val\u001b[38;5;241m.\u001b[39miloc[:,:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], y_val\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      9\u001b[0m )\n\u001b[1;32m     11\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end \u001b[38;5;241m-\u001b[39m start))\n",
-      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:368\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[1;32m    367\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 368\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m    369\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    370\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
-      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:217\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function..function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m    214\u001b[0m     iterator, (tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mIterator, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedIterator)\n\u001b[1;32m    215\u001b[0m ):\n\u001b[1;32m    216\u001b[0m     opt_outputs \u001b[38;5;241m=\u001b[39m multi_step_on_iterator(iterator)\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[1;32m    218\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mget_value()\n",
-      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/tensorflow/python/data/ops/optional_ops.py:176\u001b[0m, in \u001b[0;36m_OptionalImpl.has_value\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhas_value\u001b[39m(\u001b[38;5;28mself\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    175\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcolocate_with(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor):\n\u001b[0;32m--> 176\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m gen_optional_ops\u001b[38;5;241m.\u001b[39moptional_has_value(\n\u001b[1;32m    177\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor, name\u001b[38;5;241m=\u001b[39mname\n\u001b[1;32m    178\u001b[0m     )\n",
-      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/tensorflow/python/ops/gen_optional_ops.py:172\u001b[0m, in \u001b[0;36moptional_has_value\u001b[0;34m(optional, name)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tld\u001b[38;5;241m.\u001b[39mis_eager:\n\u001b[1;32m    171\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m     _result \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_FastPathExecute(\n\u001b[1;32m    173\u001b[0m       _ctx, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOptionalHasValue\u001b[39m\u001b[38;5;124m\"\u001b[39m, name, optional)\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m    175\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "Epoch 1/5\n",
+      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 0.0014 - val_loss: 1.9722e-05\n",
+      "Epoch 2/5\n",
+      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2ms/step - loss: 1.8605e-05 - val_loss: 1.6460e-05\n",
+      "Epoch 3/5\n",
+      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2ms/step - loss: 1.7344e-05 - val_loss: 1.8609e-05\n",
+      "Epoch 4/5\n",
+      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 1.6938e-05 - val_loss: 1.6669e-05\n",
+      "Epoch 5/5\n",
+      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2ms/step - loss: 1.6373e-05 - val_loss: 1.5985e-05\n",
+      "Training took 63.22352385520935 seconds\n"
      ]
     }
    ],
@@ -952,10 +821,10 @@
     "# measure time\n",
     "start = time.time()\n",
     "\n",
-    "history = model_large.fit(X_train.iloc[:, :-1], \n",
+    "history = model_simple.fit(X_train.iloc[:, :-1], \n",
     "            y_train.iloc[:, :-1], \n",
     "            batch_size = batch_size, \n",
-    "            epochs = epochs, \n",
+    "            epochs = 5, \n",
     "            validation_data = (X_val.iloc[:,:-1], y_val.iloc[:, :-1])\n",
     ")\n",
     "\n",
@@ -966,12 +835,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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Xd+nSpXr9FixYcDcfm9UaNcAD7s4ylF+vw4ECLlUSERGZQtRibOXKlXjhhRfw4osvYsiQIUhJSYGfnx/WrVtntP/69evRp08fpKSkYMiQIXjxxRfx/PPP46233tL1SUlJQUREBJKTkzF48GAkJydj/PjxSElJMWtelUqFf/zjH3jkkUeMxiKVSqFUKvVeW7duxfTp09GtWze9vi4uLnr97txuL2RSB0QHcqmSiIjIHI5iTVxXV4fDhw8bLNlFRkZi3759RvdRq9WIjIzUa4uKikJqaio0Gg1kMhnUajUSExMN+miLsbbMa4rDhw8jPz8fa9euNdi2YsUKvPHGG/Dz88MTTzyB119/HU5OTs2OVVtbi9raWt37qqoqAIBGo4FGo2lzjHfSjmXJMaMDvPDpwQv4+lgJFk4cBJlU3JXw9sjRmjA/22fvOdp7foD958j87n7s1ohWjJWVlaGhoQHe3t567d7e3igpKTG6T0lJidH+9fX1KCsrg4+PT7N9tGO2ZV5TpKamYsiQIRg1apRe+6xZszBixAi4u7vj4MGDSE5ORkFBAT744INmx1q+fDmWLFli0J6VlQVnZ+c2x9ic7Oxsi43VIADdZFJcvaHB6rRdGNLdOh5zYckcrRHzs332nqO95wfYf47Mz3w1NTUm9ROtGNOSSCR67wVBMGhrrf+d7aaMae68Lblx4wY++eQTLFy40GDb7WfpgoKC4O7ujtjYWKxYsQIeHh5Gx0tOTkZSUpLufVVVFfz8/BAZGQlXV9c2xWiMRqNBdnY2IiIiIJPJLDbuj40n8cnB33C5ix9emxhosXHbor1ytBbMz/bZe472nh9g/zkyv7bTrmy1RrRizNPTE1Kp1OBsVGlpqcFZKy2lUmm0v6Ojo66waa6Pdsy2zNuazz//HDU1Nfjzn//cat+RI0cCAM6ePdtsMSaXyyGXyw3aZTJZu/wgWHrcyX/ojU8O/obsk6VY/rgUTo6i37Tbbp+dtWB+ts/ec7T3/AD7z5H5tW1MU4j2W9LJyQnBwcEGpwWzs7MNlvq0wsLCDPpnZWUhJCREl3BzfbRjtmXe1qSmpmLy5Mno2bNnq33z8vIAAD4+Pm2ayxbc368HerrIUXWzHnvPXhY7HCIiIqsm6jJlUlISVCoVQkJCEBYWhg0bNqCwsBDx8fEAmpbrLl68iI8//hgAEB8fjzVr1iApKQlxcXFQq9VITU3Fp59+qhtz1qxZGDNmDFasWIEpU6Zg27Zt2L17N/bu3WvyvABQUVGBwsJCFBU13RV45swZANDdEal19uxZfP/998jIyDDIT61WY//+/XjooYfg5uaGQ4cOITExEZMnT0afPn0s+ElaF6mDBJOG+mDjvnPYcaQYDw9u2xlHIiKizkDUYmz69OkoLy/XPYcrMDAQGRkZ6Nu3LwCguLhY79lf/v7+yMjIQGJiItauXQtfX1+sXr0aU6dO1fUZNWoUtmzZggULFmDhwoUYMGAA0tLSEBoaavK8ALB9+3Y899xzuvdPPvkkAGDRokVYvHixrv3DDz9Er169DO7yBJqWG9PS0rBkyRLU1taib9++iIuLw5w5c+7+w7NyMUFNxVj2yUu4qWmAQiYVOyQiIiKrJPoF/AkJCUhISDC6bePGjQZtY8eORW5ubotjxsbGIjY2ts3zAsCzzz6LZ599tsUxAGDZsmVYtmyZ0W0jRozA/v37Wx3DHo3o4w6lqwIlVTfx/U+XEXmfsvWdiIiIOiHxr6wmu+TgIMGkoKbr4nYcLRY5GiIiIuvFYozaTcytYmz3qaalSiIiIjLEYozazR/8uqNX9y6oqWvAt6dLxQ6HiIjIKrEYo3YjkUh0Z8e4VElERGQcizFqVzFBvgCAb05fQk1dvcjREBERWR8WY9SuAnu5ok8PZ9zUNOKbU1yqJCIiuhOLMWpXty9V7uRSJRERkQEWY9TutEuV354pxbVaLlUSERHdjsUYtbshPi7o79kVtfWN2H3yktjhEBERWRUWY9TueFclERFR81iMUYeYdGup8vufLqPyhkbkaIiIiKwHizHqEIOULhjo1Q11DY3I5lIlERGRDosx6jDaC/l3Hi0SORIiIiLrwWKMOoz2i8P3/FyGqzV1IkdDRERkHViMUYe5x6sbBitdUN8oYNeJErHDISIisgosxqhDPTqsaamSd1USERE1YTFGHWrS0Kalyn2/lKP8Wq3I0RAREYmPxRh1qH6eXRHYyxUNjQJ2neBdlURERCzGqMNp76rcwbsqiYiIWIxRx9MuVe7/tRyXq7lUSUREnRuLMepwfj2cMcyvOxoFIPM4L+QnIqLOjcUYiSLm1tmxr3hXJRERdXIsxkgU2gfAHjpXgUtVN0WOhoiISDwsxkgUvt27ILivOwQByDjGs2NERNR5sRgj0Wgv5OcDYImIqDNjMUaimRTkA4kEOHz+Coqu3hA7HCIiIlGwGCPReLsqcH+/HgC4VElERJ0XizESVUwQ76okIqLOjcUYiWpCoA8cJMCRC1dxoaJG7HCIiIg6HIsxElVPFzlG9vcAAOzkUiUREXVCLMZIdNpnjvG7KomIqDNiMUaii75PCamDBMcvVuFc2XWxwyEiIupQohdj7777Lvz9/aFQKBAcHIw9e/a02D8nJwfBwcFQKBTo378/1q9fb9AnPT0dAQEBkMvlCAgIwNatW82e94svvkBUVBQ8PT0hkUiQn59vMMa4ceMgkUj0Xk8++aRenytXrkClUsHNzQ1ubm5QqVS4evVq6x9MJ+LRTY5RA7hUSUREnZOoxVhaWhpmz56N+fPnIy8vD+Hh4ZgwYQIKCwuN9i8oKMDEiRMRHh6OvLw8zJs3DzNnzkR6erquj1qtxvTp06FSqXDkyBGoVCpMmzYNBw4cMGve69evY/To0XjzzTdbzCEuLg7FxcW613vvvae3fcaMGcjPz0dmZiYyMzORn58PlUrVlo/LrunuqjzCpUoiIupcRC3GVq5ciRdeeAEvvvgihgwZgpSUFPj5+WHdunVG+69fvx59+vRBSkoKhgwZghdffBHPP/883nrrLV2flJQUREREIDk5GYMHD0ZycjLGjx+PlJQUs+ZVqVT4xz/+gUceeaTFHJydnaFUKnUvNzc33bZTp04hMzMTH3zwAcLCwhAWFob3338fO3bswJkzZ9r4qdmnqPuUcHSQ4HRJNc6WXhM7HCIiog7jKNbEdXV1OHz4MObOnavXHhkZiX379hndR61WIzIyUq8tKioKqamp0Gg0kMlkUKvVSExMNOijLcbaMm9LNm/ejE2bNsHb2xsTJkzAokWL4OLioovXzc0NoaGhuv4jR46Em5sb9u3bh0GDBhkds7a2FrW1tbr3VVVVAACNRgONRmN2jM3RjmXJMduqq0yC0QM8kPNzGbbn/4ZXHxpgkXGtKcf2wPxsn73naO/5AfafI/O7+7FbI1oxVlZWhoaGBnh7e+u1e3t7o6SkxOg+JSUlRvvX19ejrKwMPj4+zfbRjtmWeZvz1FNPwd/fH0qlEsePH0dycjKOHDmC7OxsXbxeXl4G+3l5ebU41/Lly7FkyRKD9qysLDg7O5sVoym08Yqtd6MEgBT/UZ/FgBuWPXNoLTm2F+Zn++w9R3vPD7D/HJmf+WpqTHt+pmjFmJZEItF7LwiCQVtr/e9sN2VMc+c1Ji4uTvf3wMBADBw4ECEhIcjNzcWIESOMzmPKXMnJyUhKStK9r6qqgp+fHyIjI+Hq6mpWjC3RaDTIzs5GREQEZDKZxcZtqwdvaPCfFd+h5AYwMHgMBnp3u+sxrS1HS2N+ts/ec7T3/AD7z5H5tZ12Zas1ohVjnp6ekEqlBmeISktLDc5aaSmVSqP9HR0d4eHh0WIf7ZhtmddUI0aMgEwmw88//4wRI0ZAqVTi0qVLBv0uX77c4lxyuRxyudygXSaTtcsPQnuNay4PmQxj7+2J3adKkXmyFAG93S02trXk2F6Yn+2z9xztPT/A/nNkfm0b0xSiXcDv5OSE4OBgg9OC2dnZGDVqlNF9wsLCDPpnZWUhJCREl3BzfbRjtmVeU504cQIajQY+Pj66WCorK3Hw4EFdnwMHDqCysvKu57JXMUG+AIAdx4p1Zz2JiIjsmajLlElJSVCpVAgJCUFYWBg2bNiAwsJCxMfHA2harrt48SI+/vhjAEB8fDzWrFmDpKQkxMXFQa1WIzU1FZ9++qluzFmzZmHMmDFYsWIFpkyZgm3btmH37t3Yu3evyfMCQEVFBQoLC1FU1PSoBe3dj9q7Jn/55Rds3rwZEydOhKenJ06ePInXXnsNw4cPx+jRowEAQ4YMQXR0NOLi4nSPvHjppZcQExPT7MX7nd0jAd5wcnTAr5ev41RxNQJ8LbcsS0REZI1ELcamT5+O8vJyLF26FMXFxQgMDERGRgb69u0LACguLtZ79pe/vz8yMjKQmJiItWvXwtfXF6tXr8bUqVN1fUaNGoUtW7ZgwYIFWLhwIQYMGIC0tDS9OxpbmxcAtm/fjueee073Xvsw10WLFmHx4sVwcnLCN998g7fffhvXrl2Dn58fJk2ahEWLFkEqler227x5M2bOnKm7C3Ty5MlYs2aNhT9J+9FN7oiHBvXErhOXsONoEYsxIiKye6JfwJ+QkICEhASj2zZu3GjQNnbsWOTm5rY4ZmxsLGJjY9s8LwA8++yzePbZZ5vd7ufnh5ycnBbnAIAePXpg06ZNrfaj38UE+WLXiUvYeawYr0cNMvvGCiIiIlsi+tchEd3p4cFeUMgccL68BscvmnYnChERka1iMUZWp6vcEeMHN91tuuMovx6JiIjsG4sxskra76rccZR3VRIRkX1jMUZWadwgLzg7SXHx6g3kX7gqdjhERETthsUYWaUuTlI8MkS7VFkscjRERETth8UYWS3tUmXGsWI0NnKpkoiI7BOLMbJaY+7tCRe5I4orbyK38IrY4RAREbULFmNktRQyKSICuFRJRET2jcUYWbWYYb8vVTZwqZKIiOwQizGyag/e0xOuCkeUVtfi0LkKscMhIiKyOBZjZNWcHB0QdZ8SALCTS5VERGSHWIyR1Zt0667Kr48Xo76hUeRoiIiILIvFGFm90fd4wt1ZhrJrdThQwKVKIiKyL47m7nDu3Dns2bMH586dQ01NDXr27Inhw4cjLCwMCoWiPWKkTk4mdUB0oBKfHryAHUeLMfoeT7FDIiIishiTi7FPPvkEq1evxsGDB+Hl5YVevXqhS5cuqKiowC+//AKFQoGnnnoKf//739G3b9/2jJk6oUlDffHpwQvIPF6MpVPug0zKk7pERGQfTPqNNmLECKxcuRJPP/00zp07h5KSEhw+fBh79+7FyZMnUVVVhW3btqGxsREhISH47LPP2jtu6mRG9u8Bj65OuFKjwb5fysUOh4iIyGJMKsbeeOMN/Pjjj/jrX/+KPn36GGyXy+UYN24c1q9fj1OnTqFfv36WjpM6OUepAyYM1d5VWSRyNERERJZjUjE2adIkAEB9fT0++ugjlJSUNNvX09MT999/v2WiI7rNpKG+AIDM4yWoq+ddlUREZB/MuvDG0dERr7zyCmpra9srHqJmPeDfAz1d5Ki6WY+9Zy+LHQ4REZFFmH0VdGhoKPLz89shFKKWSR0kmDS06Zlj/K5KIiKyF2Y/2iIhIQFJSUm4cOECgoOD0bVrV73tQUFBFguO6E6Tgnywcd85ZJ+4hJuaBihkUrFDIiIiuitmF2PTp08HAMycOVPXJpFIIAgCJBIJGhoaLBcd0R2C+7hD6apASdVN7Pm5DBEB3mKHREREdFfMLsYKCgraIw4ikzg4SDBxqA8+/KEAO44WsRgjIiKbZ3Yxxge6kthihjUVY7tPcqmSiIhsX5seY/5///d/GD16NHx9fXH+/HkAQEpKCrZt22bR4IiMGe7XHb26d8H1ugZ8d6ZU7HCIiIjuitnF2Lp165CUlISJEyfi6tWrumvEunfvjpSUFEvHR2RAIpFgUlDTXZVf8a5KIiKycWYXY++88w7ef/99zJ8/H1Lp78tDISEhOHbsmEWDI2pOzK1i7L+nSlFTVy9yNERERG1ndjFWUFCA4cOHG7TL5XJcv37dIkERtWZoLzf06eGMG5oG/Pc0lyqJiMh2mV2M+fv7G33o69dff42AgABLxETUqtuXKncc4VIlERHZLrPvpnz99dfxl7/8BTdv3oQgCDh48CA+/fRTLF++HB988EF7xEhkVEyQD9Z99wu+PVOKa7X16CY3+//OREREojP7t9dzzz2H+vp6zJkzBzU1NZgxYwZ69eqFt99+G08++WR7xEhkVICPK/p7dsWvZdfxzalLmPKHXmKHREREZLY2PdoiLi4O58+fR2lpKUpKSnDhwgW88MILlo6NqEV6d1VyqZKIiGxUm4oxACgtLcWpU6fw008/4fLly20O4N1334W/vz8UCgWCg4OxZ8+eFvvn5OQgODgYCoUC/fv3x/r16w36pKenIyAgAHK5HAEBAdi6davZ837xxReIioqCp6cnJBKJwXVyFRUVePXVVzFo0CA4OzujT58+mDlzJiorK/X69evXDxKJRO81d+5cEz8dak1MkC8A4PufLqPqpkbkaIiIiMxndjFWVVUFlUoFX19fjB07FmPGjIGvry+efvppg0KkNWlpaZg9ezbmz5+PvLw8hIeHY8KECSgsLDTav6CgABMnTkR4eDjy8vIwb948zJw5E+np6bo+arUa06dPh0qlwpEjR6BSqTBt2jQcOHDArHmvX7+O0aNH48033zQaS1FREYqKivDWW2/h2LFj2LhxIzIzM42eIVy6dCmKi4t1rwULFpj1OVHz7vXuhnu8uqGuoRHZJy6JHQ4REZH5BDM98cQTwsCBA4XMzEyhsrJSqKqqEjIzM4VBgwYJTzzxhFljPfDAA0J8fLxe2+DBg4W5c+ca7T9nzhxh8ODBem0vv/yyMHLkSN37adOmCdHR0Xp9oqKihCeffLJN8xYUFAgAhLy8vFbz+c9//iM4OTkJGo1G19a3b19h1apVre7bksrKSgGAUFlZeVfj3Kmurk748ssvhbq6OouO29FWZZ8R+v59h/DshwcMttlLjs1hfrbP3nO09/wEwf5zZH5tZ+rvb7Mv4N+5cyd27dqFBx98UNcWFRWF999/H9HR0SaPU1dXh8OHDxss2UVGRmLfvn1G91Gr1YiMjNRri4qKQmpqKjQaDWQyGdRqNRITEw36aL8doC3zmqqyshKurq5wdNT/WFesWIE33ngDfn5+eOKJJ/D666/Dycmp2XFqa2tRW1ure19VVQUA0Gg00GgstxSnHcuSY4ohakhPpOz+GXt+LkNZVQ3cush02+wlx+YwP9tn7znae36A/efI/O5+7NaYXYx5eHjAzc3NoN3NzQ3u7u4mj1NWVoaGhgZ4e3vrtXt7e6OkpMToPiUlJUb719fXo6ysDD4+Ps320Y7ZlnlNUV5ejjfeeAMvv/yyXvusWbMwYsQIuLu74+DBg0hOTkZBQUGLjwFZvnw5lixZYtCelZUFZ2fnNsfYnOzsbIuP2dF8nKUorgH+X9pujPQSDLbbQ44tYX62z95ztPf8APvPkfmZr6amxqR+ZhdjCxYsQFJSEj7++GP4+DTdyVZSUoLXX38dCxcuNHc4SCQSvfeCIBi0tdb/znZTxjR33pZUVVVh0qRJCAgIwKJFi/S23X6WLigoCO7u7oiNjcWKFSvg4eFhdLzk5GQkJSXpje/n54fIyEi4urq2KUZjNBoNsrOzERERAZlM1voOVuyc869Y9c1ZXJB4YenEYF27PeVoDPOzffaeo73nB9h/jsyv7bQrW60xqRgbPny4XqHy888/o2/fvujTpw8AoLCwEHK5HJcvXzY4M9QcT09PSKVSg7NRpaWlBmettJRKpdH+jo6OusKmuT7aMdsyb0uqq6sRHR2Nbt26YevWra0eyJEjRwIAzp4922wxJpfLIZfLDdplMlm7/CC017gdafLw3lj1zVmof61AdZ2AHl31l4HtIceWMD/bZ+852nt+gP3nyPzaNqYpTCrG/vjHP95NLEY5OTkhODgY2dnZeOyxx3Tt2dnZmDJlitF9wsLC8NVXX+m1ZWVlISQkRJdwWFgYsrOz9c5IZWVlYdSoUW2etzlVVVWIioqCXC7H9u3boVAoWt0nLy8PAHRnFcky/D274j5fV5woqkLm8RLMCO0jdkhEREQmMakYu3PpzVKSkpKgUqkQEhKCsLAwbNiwAYWFhYiPjwfQtFx38eJFfPzxxwCA+Ph4rFmzBklJSYiLi4NarUZqaio+/fRT3ZizZs3CmDFjsGLFCkyZMgXbtm3D7t27sXfvXpPnBZqeI1ZYWIiioiIAwJkzZwA0nXlTKpWorq5GZGQkampqsGnTJlRVVelOR/bs2RNSqRRqtRr79+/HQw89BDc3Nxw6dAiJiYmYPHmy7qwiWU5MkC9OFFVhx9EiFmNERGQzRP0yv+nTp6O8vFz3HK7AwEBkZGSgb9++AIDi4mK9Z3/5+/sjIyMDiYmJWLt2LXx9fbF69WpMnTpV12fUqFHYsmULFixYgIULF2LAgAFIS0tDaGioyfMCwPbt2/Hcc8/p3mu/6mnRokVYvHgxDh8+rHt22T333KOXV0FBAfr16we5XI60tDQsWbIEtbW16Nu3L+Li4jBnzhwLfoqkFRPkgxWZp7H/13Jcrq5FTxfDpV4iIiJrY3Yx5uDg0OKF7g0NDWaNl5CQgISEBKPbNm7caNA2duxY5ObmtjhmbGwsYmNj2zwvADz77LN49tlnm90+btw43c0DzRkxYgT279/fYh+yHL8ezhjW2w1HfqtE5vFiqML6iR0SERFRq8wuxu78aiGNRoO8vDx89NFHRh/HQNSRYoJ8ceS3Suw4ymKMiIhsg9nFmLGL3GNjY3HfffchLS2NXxhOopoY5IN/ZpzCwXMVuFR1Ez26SMUOiYiIqEVt/qLwO4WGhmL37t2WGo6oTXp174IRfbpDEICMY8Vih0NERNQqixRjN27cwDvvvIPevXtbYjiiuxIT5AsA2HmUxRgREVk/s5cp3d3d9S7gFwQB1dXVcHZ2xqZNmywaHFFbTBzqgzd2nsSP56+guPKm2OEQERG1yOxibNWqVXrFmIODA3r27InQ0FCzvpuSqL0o3RS4v28PHDxXga+Pl0ApdkBEREQtMLsYa+lxD0TWImaYDw6eq0DG8Ut43k/saIiIiJpncjF29OhRk/oFBQW1ORgiS4kOVGLx9hM48lslynuKHQ0REVHzTC7G/vCHP0AikegedKpdqrz9wacSicTsh74StQcvFwVC/T2g/rUc+eUSqMQOiIiIqBkmF2MFBQW6vwuCYPQrhIisScwwH6h/LUdeucWe4EJERGRxJhdjdxZdEokEvXv3ZjFGViv6PiX+se0ELlwHzpfX4B6lm9ghERERGeApA7JbHt3kGOnfAwCQcbxE5GiIiIiMYzFGdm3SUG8AQMbxSyJHQkREZNxdFWO3P2+MyBpFDPGGg0TA6ZJq/HL5mtjhEBERGTD5mrHhw4frFV83btzAo48+CicnJ71+ubm5louO6C51d5ZhkJuAU1cl2Hm0GDPHDxQ7JCIiIj0mF2N//OMf9d5PmTLF0rEQtYvhHgJOXQV2HC1iMUZERFbH5GJs0aJF7RkHUbsZ2kOA7JwEP126hp8uVeNebxexQyIiItLhBfxk95wdgfB7PAEAO44WixwNERGRPpOKsejoaOzbt6/VftXV1VixYgXWrl1714ERWdLEwKa7KnccLdL71ggiIiKxmbRM+cQTT2DatGlwcXHB5MmTERISAl9fXygUCly5cgUnT57E3r17kZGRgZiYGPzv//5ve8dNZJaHB3vBydEBv16+jlPF1QjwdRU7JCIiIgAmFmMvvPACVCoVPv/8c6SlpeH999/H1atXATQ93iIgIABRUVE4fPgwBg0a1J7xErWJi8IRDw3qiV0nLmHnsSIWY0REZDVMvoDfyckJM2bMwIwZMwAAlZWVuHHjBjw8PCCTydotQCJLmRTki10nLmHH0WL8LXIQn5NHRERWoc0X8Lu5uUGpVLIQI5sxfrAXFDIHnC+vwfGLVWKHQ0REBIB3U1In0lXuiPGDb13If6xI5GiIiIiasBijTmVSkA8AYOfRYt5VSUREVoHFGHUqDw3ygrOTFL9duYEjv1WKHQ4RERGLMepcujhJMX7IraXKI1yqJCIi8ZldjF24cAG//fab7v3Bgwcxe/ZsbNiwwaKBEbWXGO1S5bFiNDZyqZKIiMRldjE2Y8YMfPvttwCAkpISRERE4ODBg5g3bx6WLl1q8QCJLG3svT3RTe6I4sqbyLtwRexwiIiokzO7GDt+/DgeeOABAMB//vMfBAYGYt++ffjkk0+wceNGS8dHZHEKmRQRAU1LlV8d4XdVEhGRuMwuxjQaDeRyOQBg9+7dmDx5MgBg8ODBKC7mLzayDdqlyoxjxWjgUiUREYnI7GLsvvvuw/r167Fnzx5kZ2cjOjoaAFBUVAQPDw+LB0jUHsIH9oSLwhGl1bX48VyF2OEQEVEnZnYxtmLFCrz33nsYN24c/vSnP2HYsGEAgO3bt+uWL83x7rvvwt/fHwqFAsHBwdizZ0+L/XNychAcHAyFQoH+/ftj/fr1Bn3S09MREBAAuVyOgIAAbN261ex5v/jiC0RFRcHT0xMSiQT5+fkGY9TW1uLVV1+Fp6cnunbtismTJ+vd3AAAV65cgUqlgpubG9zc3KBSqXTf60nicXJ0QNR9SgDAjqM8o0tEROIxuxgbN24cysrKUFZWhg8//FDX/tJLLxktjFqSlpaG2bNnY/78+cjLy0N4eDgmTJiAwsJCo/0LCgowceJEhIeHIy8vD/PmzcPMmTORnp6u66NWqzF9+nSoVCocOXIEKpUK06ZNw4EDB8ya9/r16xg9ejTefPPNZuOfPXs2tm7dii1btmDv3r24du0aYmJi0NDQoOszY8YM5OfnIzMzE5mZmcjPz4dKpTLrc6L2oV2q/Pp4MeobGkWOhoiIOi3BTDU1NcL169d178+dOyesWrVKyMzMNHco4YEHHhDi4+P12gYPHizMnTvXaP85c+YIgwcP1mt7+eWXhZEjR+reT5s2TYiOjtbrExUVJTz55JNtmregoEAAIOTl5em1X716VZDJZMKWLVt0bRcvXhQcHBx0n8XJkycFAML+/ft1fdRqtQBAOH36tNEcjamsrBQACJWVlSbvY4q6ujrhyy+/FOrq6iw6rjVpKce6+gZh2JJdQt+/7xB++PmyCNHdPXs/hvaenyDYf472np8g2H+OzK/tTP397Whu8TZlyhQ8/vjjiI+Px9WrVxEaGgqZTIaysjKsXLkSr7zyiknj1NXV4fDhw5g7d65ee2RkJPbt22d0H7VajcjISL22qKgopKamQqPRQCaTQa1WIzEx0aBPSkpKm+c15vDhw9BoNHrx+Pr66u4ujYqKglqthpubG0JDQ3V9Ro4cCTc3N+zbtw+DBg0yOnZtbS1qa2t176uqmr7UWqPRQKPRmBxja7RjWXJMa9NajpFDvPCfwxexLf8i7u/r1pGhWYS9H0N7zw+w/xztPT/A/nNkfnc/dmvMLsZyc3OxatUqAMDnn38Ob29v5OXlIT09Hf/4xz9MLsbKysrQ0NAAb29vvXZvb2+UlJQY3aekpMRo//r6epSVlcHHx6fZPtox2zJvc7E4OTnB3d292XFKSkrg5eVlsK+Xl1eLcy1fvhxLliwxaM/KyoKzs7PJMZoqOzvb4mNam+Zy9KyRAJBiR/4FhDqeg1TSsXFZir0fQ3vPD7D/HO09P8D+c2R+5qupqTGpn9nFWE1NDVxcXAA0FQePP/44HBwcMHLkSJw/f97c4SCR6P/2EwTBoK21/ne2mzKmufOa6s5xjI3Z2lzJyclISkrSva+qqoKfnx8iIyPh6up61zFqaTQaZGdnIyIiAjKZzGLjWpPWcoxsaMSW/81BxXUNug96AOH3eIoQZdvZ+zG09/wA+8/R3vMD7D9H5td22pWt1phdjN1zzz348ssv8dhjj2HXrl26JcHS0lKzCgVPT09IpVKDM0SlpaUGZ620lEql0f6Ojo66x2o010c7ZlvmbS6Wuro6XLlyRe/sWGlpKUaNGqXrc+nSJYN9L1++3OJccrlc9yy328lksnb5QWivca1JcznKZMCEQB9sPlCIzBOleHiIjwjR3T17P4b2nh9g/znae36A/efI/No2pinMvpvyH//4B/72t7+hX79+eOCBBxAWFgag6SzZ8OHDTR7HyckJwcHBBqcFs7OzdcXMncLCwgz6Z2VlISQkRJdwc320Y7ZlXmOCg4Mhk8n0xikuLsbx48d144SFhaGyshIHDx7U9Tlw4AAqKyvNmovaV0yQLwBg14lLqKvnXZVERNSxzD4zFhsbiwcffBDFxcW6Z4wBwPjx4/HYY4+ZNVZSUhJUKhVCQkIQFhaGDRs2oLCwEPHx8QCalusuXryIjz/+GAAQHx+PNWvWICkpCXFxcVCr1UhNTcWnn36qG3PWrFkYM2YMVqxYgSlTpmDbtm3YvXs39u7da/K8AFBRUYHCwkIUFRUBAM6cOQOg6WyXUqmEm5sbXnjhBbz22mvw8PBAjx498Le//Q1Dhw7FI488AgAYMmQIoqOjERcXh/feew9A0yNAYmJimr14nzreA/490NNFjsvVtfjhbBkeGmx4nR8REVF7MbsYA34vSH777TdIJBL06tWrTQ98nT59OsrLy7F06VIUFxcjMDAQGRkZ6Nu3L4CmM023P/vL398fGRkZSExMxNq1a+Hr64vVq1dj6tSpuj6jRo3Cli1bsGDBAixcuBADBgxAWlqa3h2Nrc0LND3E9rnnntO9f/LJJwEAixYtwuLFiwEAq1atgqOjI6ZNm4YbN25g/Pjx2LhxI6RSqW6/zZs3Y+bMmbq7LidPnow1a9aY/VlR+5E6SDAxUImP1Ofx1dEiFmNERNShzC7GGhsb8T//8z/4f//v/+HatWsAABcXF7z22muYP38+HBzMW/lMSEhAQkKC0W3Gvnh87NixyM3NbXHM2NhYxMbGtnleAHj22Wfx7LPPtjiGQqHAO++8g3feeafZPj169MCmTZtaHIfEFzPMFx+pzyP7xCXU1jdA7ihtfSciIiILMLsYmz9/PlJTU/Hmm29i9OjREAQBP/zwAxYvXoybN2/in//8Z3vESdSugvu4Q+mqQEnVTXz/UxkiAky/mYOIiOhumF2MffTRR/jggw8wefJkXduwYcPQq1cvJCQksBgjm+TgIMHEoT748IcC7DhaxGKMiIg6jNl3U1ZUVGDw4MEG7YMHD0ZFRYVFgiISQ8ywpsda7D55CTc1Da30JiIisgyzi7Fhw4YZvQB9zZo1endXEtma4X7d0at7F1yva8B3Z0rFDoeIiDoJs5cp//Wvf2HSpEnYvXs3wsLCIJFIsG/fPly4cAEZGRntESNRh5BIJJgU5IMN3/+KHUeLER1omw+AJSIi22L2mbGxY8fip59+wmOPPYarV6+ioqICjz/+OM6cOYPw8PD2iJGow0wa2lSAfXOqFDV19SJHQ0REnUGbnjPm6+trcKH+hQsX8Pzzz+PDDz+0SGBEYgjq7YY+PZxRWFGD/54u1T2dn4iIqL2YfWasORUVFfjoo48sNRyRKLRLlQCw82ixyNEQEVFnYLFijMheaJcq/3u6FNdquVRJRETti8UY0R3u83WFv2dX1NY34ptTl8QOh4iI7ByLMaI7SCQSxNxaqtzBpUoiImpnJl/A//jjj7e4/erVq3cbC5HVmBTkg3f+exY5Zy6j6qYGrgqZ2CEREZGdMrkYc3Nza3X7n//857sOiMgaDPJ2wT1e3XC29BqyT1zC1ODeYodERER2yuRi7N///nd7xkFkVbRLlSm7f8bOY8UsxoiIqN3wmjGiZmivG9vz82VU1mhEjoaIiOwVizGiZtzj5YLBShdoGgTsOlEidjhERGSnWIwRtUD7zLEdx3hXJRERtQ8WY0QtiBnW9HVIP5wtQ8X1OpGjISIie8RijKgF/p5dcZ+vKxoauVRJRETtg8UYUSsm6R4AWyRyJEREZI9YjBG1ImZo01Kl+pdyXK6uFTkaIiKyNyzGiFrRx8MZw3q7oVEAMrlUSUREFsZijMgEuqXKI1yqJCIiy2IxRmSCSUFNS5UHz1XgUtVNkaMhIiJ7wmKMyAS9unfBiD7dIQjA13zmGBERWRCLMSITac+O7TjKYoyIiCyHxRiRibRP4//x/BUUXb0hcjRERGQvWIwRmUjppsD9/dwBABlcqiQiIgthMUZkhhguVRIRkYWxGCMyw4ShSkgkQP6Fq7hQUSN2OEREZAdYjBGZwctFgVD/HgC4VElERJbBYozITFyqJCIiSxK9GHv33Xfh7+8PhUKB4OBg7Nmzp8X+OTk5CA4OhkKhQP/+/bF+/XqDPunp6QgICIBcLkdAQAC2bt1q9ryCIGDx4sXw9fVFly5dMG7cOJw4cUK3/dy5c5BIJEZfn332ma5fv379DLbPnTvX3I+JrMiEQCUcJMCxi5U4X35d7HCIiMjGiVqMpaWlYfbs2Zg/fz7y8vIQHh6OCRMmoLCw0Gj/goICTJw4EeHh4cjLy8O8efMwc+ZMpKen6/qo1WpMnz4dKpUKR44cgUqlwrRp03DgwAGz5v3Xv/6FlStXYs2aNTh06BCUSiUiIiJQXV0NAPDz80NxcbHea8mSJejatSsmTJigF/fSpUv1+i1YsMCSHyN1MI9ucowa4AmAZ8eIiOjuOYo5+cqVK/HCCy/gxRdfBACkpKRg165dWLduHZYvX27Qf/369ejTpw9SUlIAAEOGDMGPP/6It956C1OnTtWNERERgeTkZABAcnIycnJykJKSgk8//dSkeQVBQEpKCubPn4/HH38cAPDRRx/B29sbn3zyCV5++WVIpVIolUq9+LZu3Yrp06ejW7dueu0uLi4GfVtSW1uL2tpa3fuqqioAgEajgUajMXmc1mjHsuSY1qa9cpxwnxf2ni3DV0eK8NKDfS06tjns/Rjae36A/edo7/kB9p8j87v7sVsjEQRBsPjsJqirq4OzszM+++wzPPbYY7r2WbNmIT8/Hzk5OQb7jBkzBsOHD8fbb7+ta9u6dSumTZuGmpoayGQy9OnTB4mJiUhMTNT1WbVqFVJSUnD+/HmT5v31118xYMAA5ObmYvjw4bo+U6ZMQffu3fHRRx8ZxHb48GGEhITghx9+wKhRo3Tt/fr1Q21tLerq6uDn54cnnngCr7/+OpycnJr9bBYvXowlS5YYtH/yySdwdnZudj/qONc1wILDUjQKEsz7Qz28u4gdERERWZuamhrMmDEDlZWVcHV1bbafaGfGysrK0NDQAG9vb712b29vlJSUGN2npKTEaP/6+nqUlZXBx8en2T7aMU2ZV/unsT7nz583GltqaiqGDBmiV4gBTUXeiBEj4O7ujoMHDyI5ORkFBQX44IMPjI4DNJ3NS0pK0r2vqqqCn58fIiMjWzyY5tJoNMjOzkZERARkMpnFxrUm7ZljZuVhfP9zOa73GISJDw2w6NimsvdjaO/5Afafo73nB9h/jsyv7bQrW60RdZkSACQSid57QRAM2lrrf2e7KWNaqg8A3LhxA5988gkWLlxosO32M3RBQUFwd3dHbGwsVqxYAQ8PD4P+ACCXyyGXyw3aZTJZu/wgtNe41qQ9cnx0WC98/3M5vj5xCYmRgy06trns/Rjae36A/edo7/kB9p8j82vbmKYQ7QJ+T09PSKVSg7NgpaWlBmektJRKpdH+jo6OusKmuT7aMU2ZV3t9l6mxff7556ipqcGf//znFnMGgJEjRwIAzp4922pfsm6RAUrIpBL8dOkafrpULXY4RERko0QrxpycnBAcHIzs7Gy99uzsbIOlPq2wsDCD/llZWQgJCdFVn8310Y5pyrz+/v5QKpV6ferq6pCTk2M0ttTUVEyePBk9e/ZsNe+8vDwAgI+PT6t9ybq5OcswZmDTMeddlURE1FaiLlMmJSVBpVIhJCQEYWFh2LBhAwoLCxEfHw+g6dqpixcv4uOPPwYAxMfHY82aNUhKSkJcXBzUajVSU1N1d0kCTddojRkzBitWrMCUKVOwbds27N69G3v37jV5XolEgtmzZ2PZsmUYOHAgBg4ciGXLlsHZ2RkzZszQy+Hs2bP4/vvvkZGRYZCfWq3G/v378dBDD8HNzQ2HDh1CYmIiJk+ejD59+lj886SOFzPMB9+cLsXOo0VIfGRgi0vsRERExohajE2fPh3l5eW653AFBgYiIyMDffs2PSqguLhY79lf/v7+yMjIQGJiItauXQtfX1+sXr1a91gLABg1ahS2bNmCBQsWYOHChRgwYADS0tIQGhpq8rwAMGfOHNy4cQMJCQm4cuUKQkNDkZWVBRcXF70cPvzwQ/Tq1QuRkZEG+cnlcqSlpWHJkiWora1F3759ERcXhzlz5ljsMyRxPTLEG06ODvjl8nWcLqnGEB/L3WBBRESdg+gX8CckJCAhIcHoto0bNxq0jR07Frm5uS2OGRsbi9jY2DbPCzSdHVu8eDEWL17c4jjLli3DsmXLjG4bMWIE9u/f3+L+ZNtcFDKMu7cnsk5ewo6jRSzGiIjIbKJ/HRKRrYsZ1vRdlTuPFkOkx/YREZENYzFGdJfGD/aCQuaAc+U1OFFk2jNliIiItFiMEd2lrnJHPDzYCwDw1dEikaMhIiJbw2KMyAJigrhUSUREbcNijMgCHhrkBWcnKX67cgNHfqsUOxwiIrIhLMaILKCLkxTjhzR9O8OOI1yqJCIi07EYI7KQmKCmb1XIOFaMxkYuVRIRkWlYjBFZyNh7e6Kb3BFFlTeRd+GK2OEQEZGNYDFGZCEKmRQRAbeWKvldlUREZCIWY0QWNGkolyqJiMg8LMaILCj8Xk+4KBxxqaoWh85ViB0OERHZABZjRBYkd5Qi6j4lAGDnMS5VEhFR61iMEVnYJN1dlSVo4FIlERG1gsUYkYU9eI8nujvLUHatFgd+LRc7HCIisnIsxogsTCZ1QPStpcodXKokIqJWsBgjagfapcrM4yWob2gUORoiIrJmLMaI2kFYfw94dHVCxfU67PuFS5VERNQ8FmNE7cBR6oDowFt3VfIBsERE1AIWY0TtRLdUeaIEdfVcqiQiIuNYjBG1k1B/D3h2k6PyhgY//FImdjhERGSlWIwRtROpgwQTh966q/IIlyqJiMg4FmNE7SgmyBcAkHWyBLX1DSJHQ0RE1ojFGFE7CunrDm9XOapv1mPPT1yqJCIiQyzGiNqRg4MEE4c2Xci/42iRyNEQEZE1YjFG1M60S5XZJy/hpoZLlUREpI/FGFE7G9GnO3p174LrdQ347sxlscMhIiIrw2KMqJ1JJLfdVcmlSiIiugOLMaIOoF2q/OZUKWrq6kWOhoiIrAmLMaIOENTbDX49uuCGpgHfnuZSJRER/Y7FGFEHkEgkmDS06ewYlyqJiOh2LMaIOkjMre+q/O/pUlyr5VIlERE1YTFG1EHu83VFPw9n1NY34ptTl8QOh4iIrIToxdi7774Lf39/KBQKBAcHY8+ePS32z8nJQXBwMBQKBfr374/169cb9ElPT0dAQADkcjkCAgKwdetWs+cVBAGLFy+Gr68vunTpgnHjxuHEiRN6fcaNGweJRKL3evLJJ/X6XLlyBSqVCm5ubnBzc4NKpcLVq1dN/HTInkgkEt2F/DuO8rsqiYioiajFWFpaGmbPno358+cjLy8P4eHhmDBhAgoLC432LygowMSJExEeHo68vDzMmzcPM2fORHp6uq6PWq3G9OnToVKpcOTIEahUKkybNg0HDhwwa95//etfWLlyJdasWYNDhw5BqVQiIiIC1dXVejHFxcWhuLhY93rvvff0ts+YMQP5+fnIzMxEZmYm8vPzoVKpLPHxkQ2KGda0VJlz5jKqb2pEjoaIiKyBo5iTr1y5Ei+88AJefPFFAEBKSgp27dqFdevWYfny5Qb9169fjz59+iAlJQUAMGTIEPz444946623MHXqVN0YERERSE5OBgAkJycjJycHKSkp+PTTT02aVxAEpKSkYP78+Xj88ccBAB999BG8vb3xySef4OWXX9bF5OzsDKVSaTS/U6dOITMzE/v370doaCgA4P3330dYWBjOnDmDQYMGGd2vtrYWtbW1uvdVVVUAAI1GA43Gcr/AtWNZckxrY2059u+hQH/Prvi17DoyjxXhj3/wvavxrC0/S7P3/AD7z9He8wPsP0fmd/djt0a0Yqyurg6HDx/G3Llz9dojIyOxb98+o/uo1WpERkbqtUVFRSE1NRUajQYymQxqtRqJiYkGfbQFnCnzFhQUoKSkRG8uuVyOsWPHYt++fXrF2ObNm7Fp0yZ4e3tjwoQJWLRoEVxcXHTxurm56QoxABg5ciTc3Nywb9++Zoux5cuXY8mSJQbtWVlZcHZ2NrrP3cjOzrb4mNbGmnK8V+GAX+GAf39zFE5F+RYZ05ryaw/2nh9g/znae36A/efI/MxXU1NjUj/RirGysjI0NDTA29tbr93b2xslJSVG9ykpKTHav76+HmVlZfDx8Wm2j3ZMU+bV/mmsz/nz53Xvn3rqKfj7+0OpVOL48eNITk7GkSNHdAe0pKQEXl5eBnl4eXk1myPQdDYvKSlJ976qqgp+fn6IjIyEq6trs/uZS6PRIDs7GxEREZDJZBYb15pYY44DS68h8519+LlaitEPjYdbl7bHZY35WZK95wfYf472nh9g/zkyv7bTrmy1RtRlSqDpoubbCYJg0NZa/zvbTRnTEn3i4uJ0fw8MDMTAgQMREhKC3NxcjBgxwugYzc11O7lcDrlcbtAuk8na5Qehvca1JtaUY0AvdwzydsGZS9X470/lmBbid9djWlN+7cHe8wPsP0d7zw+w/xyZX9vGNIVoF/B7enpCKpUanCEqLS01OCOlpVQqjfZ3dHSEh4dHi320Y5oyr/YaMHNiA4ARI0ZAJpPh559/1o1z6ZLhIwwuX77c4jhk/7TPHONdlUREJFox5uTkhODgYIM12uzsbIwaNcroPmFhYQb9s7KyEBISoqs+m+ujHdOUebVLj7f3qaurQ05OTrOxAcCJEyeg0Wjg4+Oji6WyshIHDx7U9Tlw4AAqKytbHIfs36RbxdgPZ8tw5XqdyNEQEZGYRF2mTEpKgkqlQkhICMLCwrBhwwYUFhYiPj4eQNO1UxcvXsTHH38MAIiPj8eaNWuQlJSEuLg4qNVqpKam6u6SBIBZs2ZhzJgxWLFiBaZMmYJt27Zh9+7d2Lt3r8nzSiQSzJ49G8uWLcPAgQMxcOBALFu2DM7OzpgxYwYA4JdffsHmzZsxceJEeHp64uTJk3jttdcwfPhwjB49GkDT3Z7R0dGIi4vTPfLipZdeQkxMTLMX71Pn0L9nNwT4uOJkcRUyT5TgTw/0ETskIiISiajF2PTp01FeXo6lS5eiuLgYgYGByMjIQN++fQEAxcXFes/+8vf3R0ZGBhITE7F27Vr4+vpi9erVusdaAMCoUaOwZcsWLFiwAAsXLsSAAQOQlpamd0dja/MCwJw5c3Djxg0kJCTgypUrCA0NRVZWlu5OSScnJ3zzzTd4++23ce3aNfj5+WHSpElYtGgRpFKpbpzNmzdj5syZujszJ0+ejDVr1rTPB0o2JWaYD04WV2HH0SIWY0REnZjoF/AnJCQgISHB6LaNGzcatI0dOxa5ubktjhkbG4vY2Ng2zws0nR1bvHgxFi9ebHS7n58fcnJyWpwDAHr06IFNmza12o86n5ihvvhX5hmofylH2bVaeHYzvGmDiIjsn+hfh0TUWfXxcEZQbzc0CsDXx5t/1AkREdk3FmNEItLeVbnzaJHIkRARkVhYjBGJaOLQpmLsQEEFSqtuihwNERGJgcUYkYh6uztjeJ/uEAQg4xifOUZE1BmxGCMSWUxQ05eF72QxRkTUKbEYIxLZxKFN3/hw6NwVFFfeEDkaIiLqaCzGiETm49YF9/dzBwDs5NcjERF1OizGiKwAlyqJiDovFmNEVmBCoBISCZBXeBW/XakROxwiIupALMaIrICXqwKh/j0AcKmSiKizYTFGZCUmcamSiKhTYjFGZCUmBCrhIAGO/laJ8+XXxQ6HiIg6CIsxIivh2U2OUQM8AfDsGBFRZ8JijMiKTLr1XZU7jrAYIyLqLFiMEVmR6PuUcHSQ4GRxFX69fE3scIiIqAOwGCOyIu5dnTD6nltLlbyrkoioU2AxRmRldEuVLMaIiDoFFmNEViYqQAmZVIIzl6rx86VqscMhIqJ2xmKMyMq4OcswZmBPADw7RkTUGbAYI7JCvy9VFkEQBJGjISKi9sRijMgKRQR4w8nRAb9cvo7TJVyqJCKyZyzGiKyQi0KGsfc2LVXyrkoiIvvGYozISsVwqZKIqFNgMUZkpR4Z4g25owPOldfgRFGV2OEQEVE7YTFGZKW6yh3x8GAvALyrkojInrEYI7JiMUG+ALhUSURkz1iMEVmxhwd7oYtMit+u3MDR3yrFDoeIiNoBizEiK9bFSYrxQ7RLlUUiR0NERO2BxRiRldMuVe48WozGRi5VEhHZGxZjRFZu3KCe6OokRVHlTeRduCp2OEREZGEsxoisnEImRUSANwAuVRIR2SMWY0Q2QLtUmXGMS5VERPZG9GLs3Xffhb+/PxQKBYKDg7Fnz54W++fk5CA4OBgKhQL9+/fH+vXrDfqkp6cjICAAcrkcAQEB2Lp1q9nzCoKAxYsXw9fXF126dMG4ceNw4sQJ3faKigq8+uqrGDRoEJydndGnTx/MnDkTlZX6d7z169cPEolE7zV37lxzPiIihN/rCReFIy5V1eLH81fEDoeIiCxI1GIsLS0Ns2fPxvz585GXl4fw8HBMmDABhYWFRvsXFBRg4sSJCA8PR15eHubNm4eZM2ciPT1d10etVmP69OlQqVQ4cuQIVCoVpk2bhgMHDpg177/+9S+sXLkSa9aswaFDh6BUKhEREYHq6qYvbS4qKkJRURHeeustHDt2DBs3bkRmZiZeeOEFg7iXLl2K4uJi3WvBggWW+gipk5A7ShEZoATApUoiInvjKObkK1euxAsvvIAXX3wRAJCSkoJdu3Zh3bp1WL58uUH/9evXo0+fPkhJSQEADBkyBD/++CPeeustTJ06VTdGREQEkpOTAQDJycnIyclBSkoKPv30U5PmFQQBKSkpmD9/Ph5//HEAwEcffQRvb2988sknePnllxEYGKhXBA4YMAD//Oc/8fTTT6O+vh6Ojr9/tC4uLlAqlSZ/LrW1taitrdW9r6pq+iocjUYDjUZj8jit0Y5lyTGtjT3lOOG+nkjP/Q0Zx4oxL/peSB0kdpWfMfaeH2D/Odp7foD958j87n7s1kgEkR7rXVdXB2dnZ3z22Wd47LHHdO2zZs1Cfn4+cnJyDPYZM2YMhg8fjrffflvXtnXrVkybNg01NTWQyWTo06cPEhMTkZiYqOuzatUqpKSk4Pz58ybN++uvv2LAgAHIzc3F8OHDdX2mTJmC7t2746OPPjKa0wcffIDk5GRcvnxZ19avXz/U1tairq4Ofn5+eOKJJ/D666/Dycmp2c9m8eLFWLJkiUH7J598Amdn52b3I/tW3wgs/FGKmgYJ/hrQgIFuvHaMiMia1dTUYMaMGaisrISrq2uz/UQ7M1ZWVoaGhgZ4e3vrtXt7e6OkpMToPiUlJUb719fXo6ysDD4+Ps320Y5pyrzaP431OX/+vNHYysvL8cYbb+Dll1/Wa581axZGjBgBd3d3HDx4EMnJySgoKMAHH3xgdByg6WxeUlKS7n1VVRX8/PwQGRnZ4sE0l0ajQXZ2NiIiIiCTySw2rjWxtxz315/AZ4cvoqxrX8yaGGB3+d3J3vMD7D9He88PsP8cmV/baVe2WiPqMiUASCQSvfeCIBi0tdb/znZTxrRUH6Dpw540aRICAgKwaNEivW23n6ELCgqCu7s7YmNjsWLFCnh4eBjNUS6XQy6XG7TLZLJ2+UFor3Gtib3kOPkPvfDZ4YvIOlmK//njUGhTspf8mmPv+QH2n6O95wfYf47Mr21jmkK0C/g9PT0hlUoNzoKVlpYanJHSUiqVRvs7OjrqCpvm+mjHNGVe7fVdpsRWXV2N6OhodOvWDVu3bm31gx85ciQA4OzZsy32IzImrL8HenR1QsX1Oqh/LRc7HCIisgDRijEnJycEBwcjOztbrz07OxujRo0yuk9YWJhB/6ysLISEhOiKoOb6aMc0ZV5/f38olUq9PnV1dcjJydGLraqqCpGRkXBycsL27duhUChazTsvLw8A4OPj02pfojs5Sh0QHXjrrsojxSJHQ0REliDqMmVSUhJUKhVCQkIQFhaGDRs2oLCwEPHx8QCarp26ePEiPv74YwBAfHw81qxZg6SkJMTFxUGtViM1NVV3lyTQdI3WmDFjsGLFCkyZMgXbtm3D7t27sXfvXpPnlUgkmD17NpYtW4aBAwdi4MCBWLZsGZydnTFjxgwATWfEIiMjUVNTg02bNqGqqkq3NtyzZ09IpVKo1Wrs378fDz30ENzc3HDo0CEkJiZi8uTJ6NOnT4d8xmR/YoJ88MmBQmSeKMGimEFih0NERHdJ1GJs+vTpKC8v1z2HKzAwEBkZGejbty8AoLi4WO/ZX/7+/sjIyEBiYiLWrl0LX19frF69WvdYCwAYNWoUtmzZggULFmDhwoUYMGAA0tLSEBoaavK8ADBnzhzcuHEDCQkJuHLlCkJDQ5GVlQUXFxcAwOHDh3XPLrvnnnv08iooKEC/fv0gl8uRlpaGJUuWoLa2Fn379kVcXBzmzJlj+Q+TOo1Qfw94dpOj7Fot9v3CpUoiIlsn+gX8CQkJSEhIMLpt48aNBm1jx45Fbm5ui2PGxsYiNja2zfMCTWfHFi9ejMWLFxvdPm7cOLT2VJARI0Zg//79LfYhMpfUQYKJQ5X4WH0eG9WF6C+RwKOgAmH3eEHq0PzNL0REZJ1EL8aIyHw9uzXdbbv3bDn2QoqPf/4RPm4KLHo0ANGBvB6RiMiWiP7dlERknszjxViZ/ZNBe0nlTbyyKReZx3lhv61oaBRwoKACh8skOFBQgQZ+CbzN4TEkS+CZMSIb0tAoYMlXJ2HsP/cCAAmAJV+dRESAkkuWVi7zeDGWfHUSxZU3AZ7dtEk8hrbv9mJazMs9WIwR2ZCDBRW3/sNvnACguPImBi/8GnJHKaQOkt9fkqY/HaW//137cnSQwOHWn7+3OzS1S/Tbjfe9Nab0Vrvk1v7S3/c3to/BuBLJbfs4AEIDfqkC8gqvQu4k05/vjn20MTg6OMDBAXB0cND1c5AYPsRZTJnHi/HKplyDolp7dnPd0yP4y9zK8RjaPmsqplmMEdmQ0urmC7HbaRoEaBrq2zmajuKI1ScO3v0otxeEtxWOumLToEh1MCwi9YrF1gtaRwcHg2JRIgH+/cO5Zs9uAsDf04+h7FqdLj6JBEb/bHo1FZq/txn2kUhgdJ87+1pyXO2fDfUNqG8ENA2NcJAKVlcYtwXPUNs+ayumWYwR2RAvl9YfLAwAq5/8A4b5dUd9o4DGRgH1jQIabr1u/3vT+0Y0CgLqG4SmP1vs+/t42n0aGhvRoN2vQUCDYKSv0Rga0SAADY2NRuduaBRQ39CIquprkHdxRqOA38cVmrY1NP4+X0OjgJYu16lvFIBGAXUWOhbtqfKGBgu+PC52GBbkiNcO7NZrub3Iu70wdJBIINFudzBS5OG29w76haF2X72i0UH7XrvNcB8Hh6ZxDQtP4wVrxfU6k85Qv/R/P0LpqmgqWm8bX5u/tk2br7b4NWjXvr/ts7m7fW/v39To0My+kABCYwOOXpagLr8Ijo5SXT9A/7jcvi9uy/f3GH8fU9LMvrjjszI25u3HWD8W0/YVBGDhlyesqphmMUZkQx7w7wEfNwVKKm8a/Q+JBIDSTYFJQb528S9yjUaDjIwMTJwYbtJ3vDXeUZwZFILC7QVjo0GBeGfRaFCUavdrpnhstoC9rXA9e/kafjjb+vPhAnu5QumqQFMN2VRoCoIAQff+9zZtH+GO979vN7b/7/s03tF++z6NggA0s0+jIEBA0y83cwkCmj7PpnfmD2ADvjlVKnYIFiTF5rP29A+E5mmL6YMFFQgbYPw7pC2NxRiRDZE6SLDo0QC8sim36V94t23Tll6LHg2wi0KsLRwcJHCABDKp2JE0T/1LuUnF2PyJAR32i+ButVTk1Wk02LUrC49EREAqddQvDHHbPo3GirzbCsvGO4pHaN//vr/RcbV9GmF83NuKz6ax7xjXSCH76+Xr+L/951v9XKaO6IXe7s5NP6e3Fa4CtGM2/f3W/3Rz/J7bbZ/vbftq4wS0Bfjv7bp+t+X6+753tDezL3DHZ9nYiNLSy/Ds2RMSiUSvgL99X+jFpz+vQf6Nv8cDGMah/UeAYQ63xW2svaX8b/1dU9+Im/WNrR4/Uy8LsQQWY0Q2JjrQB+ueHnHbhadNlLyLyyaYenbzAf8eHR1am+muLYPhPwKcHAR0cQTcushMOrtpCxoaBew+danVY/iv2GF28Q+j389QB9vFMVT/Uo4/vd/6A9lNvSzEEvicMSIbFB3og71/fxibng/Bnwc2YNPzIdj794dZiNkA7dlNAAalC89u2gYeQ9um/QdRc0dHAsCng/9BxGKMyEZJHSQI9e+BYE8Bof49+B9+G6I9u6l00/+Xt9JNwUci2AgeQ9tljcU0lymJiEQQHeiDiAAl1GdLkbXnACLDQ/n9ojaGx9B2WdvlHizGiIhEoj27WX6KZzdtFY+h7bKmYprFGBEREXVK1lJM85oxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhHxCfw2QBAEAEBVVZVFx9VoNKipqUFVVRVkMplFx7YW9p4j87N99p6jvecH2H+OzK/ttL+3tb/Hm8NizAZUV1cDAPz8/ESOhIiIiMxVXV0NNze3ZrdLhNbKNRJdY2MjioqK4OLiAonEct+bVVVVBT8/P1y4cAGurq4WG9ea2HuOzM/22XuO9p4fYP85Mr+2EwQB1dXV8PX1hYND81eG8cyYDXBwcEDv3r3bbXxXV1e7/AG7nb3nyPxsn73naO/5AfafI/Nrm5bOiGnxAn4iIiIiEbEYIyIiIhIRi7FOTC6XY9GiRZDL5WKH0m7sPUfmZ/vsPUd7zw+w/xyZX/vjBfxEREREIuKZMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMTv37rvvwt/fHwqFAsHBwdizZ0+L/XNychAcHAyFQoH+/ftj/fr1HRRp25iT33fffQeJRGLwOn36dAdGbJ7vv/8ejz76KHx9fSGRSPDll1+2uo8tHUNz87O1Y7h8+XLcf//9cHFxgZeXF/74xz/izJkzre5nK8ewLfnZ2jFct24dgoKCdA8EDQsLw9dff93iPrZy/ADz87O143en5cuXQyKRYPbs2S326+hjyGLMjqWlpWH27NmYP38+8vLyEB4ejgkTJqCwsNBo/4KCAkycOBHh4eHIy8vDvHnzMHPmTKSnp3dw5KYxNz+tM2fOoLi4WPcaOHBgB0VsvuvXr2PYsGFYs2aNSf1t7Riam5+WrRzDnJwc/OUvf8H+/fuRnZ2N+vp6REZG4vr1683uY0vHsC35adnKMezduzfefPNN/Pjjj/jxxx/x8MMPY8qUKThx4oTR/rZ0/ADz89OyleN3u0OHDmHDhg0ICgpqsZ8ox1Agu/XAAw8I8fHxem2DBw8W5s6da7T/nDlzhMGDB+u1vfzyy8LIkSPbLca7YW5+3377rQBAuHLlSgdEZ3kAhK1bt7bYx9aO4e1Myc/Wj2FpaakAQMjJyWm2jy0fQ1Pys/VjKAiC4O7uLnzwwQdGt9ny8dNqKT9bPX7V1dXCwIEDhezsbGHs2LHCrFmzmu0rxjHkmTE7VVdXh8OHDyMyMlKvPTIyEvv27TO6j1qtNugfFRWFH3/8ERqNpt1ibYu25Kc1fPhw+Pj4YPz48fj222/bM8wOZ0vH8G7Y6jGsrKwEAPTo0aPZPrZ8DE3JT8sWj2FDQwO2bNmC69evIywszGgfWz5+puSnZWvH7y9/+QsmTZqERx55pNW+YhxDFmN2qqysDA0NDfD29tZr9/b2RklJidF9SkpKjPavr69HWVlZu8XaFm3Jz8fHBxs2bEB6ejq++OILDBo0COPHj8f333/fESF3CFs6hm1hy8dQEAQkJSXhwQcfRGBgYLP9bPUYmpqfLR7DY8eOoVu3bpDL5YiPj8fWrVsREBBgtK8tHj9z8rPF47dlyxbk5uZi+fLlJvUX4xg6tsuoZDUkEonee0EQDNpa62+s3VqYk9+gQYMwaNAg3fuwsDBcuHABb731FsaMGdOucXYkWzuG5rDlY/jXv/4VR48exd69e1vta4vH0NT8bPEYDho0CPn5+bh69SrS09PxzDPPICcnp9mCxdaOnzn52drxu3DhAmbNmoWsrCwoFAqT9+voY8gzY3bK09MTUqnU4CxRaWmpQcWvpVQqjfZ3dHSEh4dHu8XaFm3Jz5iRI0fi559/tnR4orGlY2gptnAMX331VWzfvh3ffvstevfu3WJfWzyG5uRnjLUfQycnJ9xzzz0ICQnB8uXLMWzYMLz99ttG+9ri8TMnP2Os+fgdPnwYpaWlCA4OhqOjIxwdHZGTk4PVq1fD0dERDQ0NBvuIcQxZjNkpJycnBAcHIzs7W689Ozsbo0aNMrpPWFiYQf+srCyEhIRAJpO1W6xt0Zb8jMnLy4OPj4+lwxONLR1DS7HmYygIAv7617/iiy++wH//+1/4+/u3uo8tHcO25GeMNR9DYwRBQG1trdFttnT8mtNSfsZY8/EbP348jh07hvz8fN0rJCQETz31FPLz8yGVSg32EeUYttutASS6LVu2CDKZTEhNTRVOnjwpzJ49W+jatatw7tw5QRAEYe7cuYJKpdL1//XXXwVnZ2chMTFROHnypJCamirIZDLh888/FyuFFpmb36pVq4StW7cKP/30k3D8+HFh7ty5AgAhPT1drBRaVV1dLeTl5Ql5eXkCAGHlypVCXl6ecP78eUEQbP8YmpufrR3DV155RXBzcxO+++47obi4WPeqqanR9bHlY9iW/GztGCYnJwvff/+9UFBQIBw9elSYN2+e4ODgIGRlZQmCYNvHTxDMz8/Wjp8xd95NaQ3HkMWYnVu7dq3Qt29fwcnJSRgxYoTeLefPPPOMMHbsWL3+3333nTB8+HDByclJ6Nevn7Bu3boOjtg85uS3YsUKYcCAAYJCoRDc3d2FBx98UNi5c6cIUZtOexv5na9nnnlGEATbP4bm5mdrx9BYbgCEf//737o+tnwM25KfrR3D559/XvffmJ49ewrjx4/XFSqCYNvHTxDMz8/Wjp8xdxZj1nAMJYJw66o0IiIiIupwvGaMiIiISEQsxoiIiIhExGKMiIiISEQsxoiIiIhExGKMiIiISEQsxoiIiIhExGKMiIiISEQsxoiIiIhExGKMiMgGSSQSfPnll2KHQUQWwGKMiMhMzz77LCQSicErOjpa7NCIyAY5ih0AEZEtio6Oxr///W+9NrlcLlI0RGTLeGaMiKgN5HI5lEql3svd3R1A0xLiunXrMGHCBHTp0gX+/v747LPP9PY/duwYHn74YXTp0gUeHh546aWXcO3aNb0+H374Ie677z7I5XL4+Pjgr3/9q972srIyPPbYY3B2dsbAgQOxffv29k2aiNoFizEionawcOFCTJ06FUeOHMHTTz+NP/3pTzh16hQAoKamBtHR0XB3d8ehQ4fw2WefYffu3XrF1rp16/CXv/wFL730Eo4dO4bt27fjnnvu0ZtjyZIlmDZtGo4ePYqJEyfiqaeeQkVFRYfmSUQWIBARkVmeeeYZQSqVCl27dtV7LV26VBAEQQAgxMfH6+0TGhoqvPLKK4IgCMKGDRsEd3d34dq1a7rtO3fuFBwcHISSkhJBEATB19dXmD9/frMxABAWLFige3/t2jVBIpEIX3/9tcXyJKKOwWvGiIja4KGHHsK6dev02nr06KH7e1hYmN62sLAw5OfnAwBOnTqFYcOGoWvXrrrto0ePRmNjI86cOQOJRIKioiKMHz++xRiCgoJ0f+/atStcXFxQWlra1pSISCQsxoiI2qBr164Gy4atkUgkAABBEHR/N9anS5cuJo0nk8kM9m1sbDQrJiISH68ZIyJqB/v37zd4P3jwYABAQEAA8vPzcf36dd32H374AQ4ODrj33nvh4uKCfv364ZtvvunQmIlIHDwzRkTUBrW1tSgpKdFrc3R0hKenJwDgs88+Q0hICB588EFs3rwZBw8eRGpqKgDgqaeewqJFi/DMM89g8eLFuHz5Ml599VWoVCp4e3sDABYvXoz4+Hh4eXlhwoQJqK6uxg8//IBXX321YxMlonbHYoyIqA0yMzPh4+Oj1zZo0CCcPn0aQNOdjlu2bEFCQgKUSiU2b96MgIAAAICzszN27dqFWbNm4f7774ezszOmTp2KlStX6sZ65plncPPmTaxatQp/+9vf4OnpidjY2I5LkIg6jEQQBEHsIIiI7IlEIsHWrVvxxz/+UexQiMgG8JoxIiIiIhGxGCMiIiISEa8ZIyKyMF79QUTm4JkxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiIS0f8HDwNwoe9MYnEAAAAASUVORK5CYII=",
       "text/plain": [
        "
"
       ]
@@ -992,12 +861,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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5xXjxqyMI61ACM706MmqvV4aAvdIce6U59kpz7JV2dN2vgoICjcfq1V+Fly9fxqxZs/Dee+9h4MCBUCgUeOuttzBt2jSEh4erxwmCUGY/URTLbdNkzN/Nnz8fYWFh6udKpRKurq4ICAiAlZXVv/lY5ahUKkRGRmLAgAEwMTHR6dyV6da7CMO/PIE7yiIcUDpi3bhOkMkq70ddIUWv9BV7pTn2SnPslebYK+3UVL+efBOkCb0KSUuXLkXPnj3x1ltvAQA6duwICwsL9OrVC0uWLIGjoyMcHBzKnRHKzMxUnzmys7ODkZFRlWMqIpfLIZfLy203MTGpsYO9Juf+J2cbE3wd4oeRXx3H71fvYvXhJLw10KNW3lsXarNX+o690hx7pTn2SnPslXZ03S9t5tKrdZIKCgogk5Ut2cjICADUP2Hv3r17uVNzBw8eRI8ePQAApqam8PX1LTcmMjJSPaa+8nZthGUjOgIAvvjjBn45ly5xRURERNKR9ExSXl4erl+/rn6enJyM+Ph42NjYoFmzZpg/fz5u376NjRs3AgCCgoLwyiuvYN26deqv22bPno2uXbvCyckJAPD666/j2Wefxccff4yhQ4fi559/xqFDhxATE6N+n7CwMISEhMDPzw/du3fH119/jZSUFEybNq12G1AHDfNxRoJCia+ik/DW9nNwt7NAB2drqcsiIiKqdZKGpNjYWPTt21f9/Mk1P6GhoYiIiIBCoSizdtHEiRORm5uLNWvW4M0330SjRo3w3HPP4eOPP1aP6dGjB7Zs2YJ33nkH7777Llq2bImtW7fC399fPWb06NG4d+8e3n//fSgUCnTo0AH79u2Dm5tbLXzqum/uIA9cychF1LW7mLIxFntmPgO7huW/aiQiIjJkkoakPn36VLnSc0RERLltM2fOxMyZM6ucNzg4GMHBwVWOmT59OqZPn65RnfWNkUzA6rE+eOGLo0jKyserP8Rh08vdYGqsV9/OEhER/Sv8W48qZN3ABOtD/WApN8bpm/excM8l3rqEiIjqFYYkqlTLJg2xeqwPBAH48VQKfjhxS+qSiIiIag1DElWpr0dT/N+gx0sBLP7lMo7fuFfNHkRERIaBIYmqNfXZFhjWyQnFpSKmb4pDarbmq5USERHpK4YkqpYgCPhoREd0dLHG/QIVXtkYi/yiYqnLIiIiqlEMSaQRMxMjfBXiC7uGclzJyMWcbedQWsoLuYmIyHAxJJHGHK0b4KuQzjA1kmH/xQx8/vv16nciIiLSUwxJpBVfNxssGdYBALDi0DX8ejGjmj2IiIj0E0MSaW1UF1dM7NEcABD2UzyuZGh+R2UiIiJ9wZBET+Wdwe3Qs5UtCh6V4JWNscjOfyR1SURERDrFkERPxdhIhjVjO6OZjTlSsx/itU1noCoplbosIiIinWFIoqfW2MIU34T6wcLUCMeT7mHJ/y5LXRIREZHOMCTRv9LG3hIrRncCAHx3/Ba2nEqRtiAiIiIdYUiify3A0wFvDmgDAHj354uIvZktcUVERET/HkMS6cSM51rheS8HqEpETPshDukPHkpdEhER0b/CkEQ6IQgCPh3pjXaOVsjKe4Qp38fi4aMSqcsiIiJ6agxJpDPmpsZYP8EXNhamuHhbibk7zkMUeesSIiLSTwxJpFMujc2x7sXOMJYJ+OVcOtZF3ZC6JCIioqfCkEQ659/CFouGeAIAPjlwFb8l3JG4IiIiIu0xJFGNGN/NDS/6N4MoAq9vicf1zFypSyIiItIKQxLVmIVBnujqboO8omK8sjEOOQUqqUsiIiLSGEMS1RhTYxnWvdgZzo0aIDkrHzO3nEVJKS/kJiIi/cCQRDXKtqEcX0/wRQMTI0Rfu4uP9idIXRIREZFGGJKoxnk6WePTkd4AgPVHkrEjLk3iioiIiKrHkES1YnBHR8x8rhUAYP6uC4hPfSBtQURERNVgSKJa80b/NhjQ3h6PiksxZWMs7igLpS6JiIioUgxJVGtkMgErRndCG/uGyMwtwpTv41Co4q1LiIiobmJIolrVUG6M9RP80MjcBOdSH+DtXRd46xIiIqqTGJKo1rnZWuCLcZ1hJBOw88xthMckS10SERFROQxJJImerezwzuB2AIAP9yUg+tpdiSsiIiIqiyGJJDOxR3OM9HVBqQjM2HwGyVn5UpdERESkxpBEkhEEAUte6IDOzRpBWViMVzbGIreQty4hIqK6gSGJJCU3NsKXIb5wsDLD9cw8zN4Sz1uXEBFRncCQRJJrammGryf4Qm4sw29XMvHZwatSl0RERMSQRHVDR5dGWBbcEQCw9vAN7DmXLnFFRERU3zEkUZ0xtJMzpvZuAQCYu/0cLt7OkbgiIiKqzxiSqE6ZO9ADfdo2QaHq8a1L7uYWSV0SERHVUwxJVKcYyQSsGuODFk0skJ5TiFd/iMOj4lKpyyIionpI0pAUHR2NoKAgODk5QRAE7N69u8rxEydOhCAI5R6enp7qMX369KlwzODBg9VjFi1aVO51BweHmvqYpCXrBiZYP8EPlmbGiL11Hwv3XOStS4iIqNZJGpLy8/Ph7e2NNWvWaDR+1apVUCgU6kdqaipsbGwwcuRI9ZidO3eWGXPx4kUYGRmVGQMAnp6eZcZduHBBp5+N/p2WTRpi9VgfCALw46lUfH/iltQlERFRPWMs5ZsHBgYiMDBQ4/HW1tawtrZWP9+9ezfu37+PSZMmqbfZ2NiU2WfLli0wNzcvF5KMjY159qiO69u2KeYN8sDS/Vew+JfLaNW0IXq0tJO6LCIiqif0+pqk8PBw9O/fH25ublWOGTNmDCwsLMpsT0xMhJOTE9zd3TFmzBgkJSXVdLn0FKY82wIv+DijpFTEa5vOIDW7QOqSiIionpD0TNK/oVAosH//fmzevLnSMadOncLFixcRHh5eZru/vz82btyINm3a4M6dO1iyZAl69OiBS5cuwdbWtsK5ioqKUFT01y+tlEolAEClUkGl0u2tNJ7Mp+t59dX7QR64npmLC7eVePm709j6SldYyB8fuuyV5tgrzbFXmmOvNMdeaaem+qXNfIJYR66IFQQBu3btwrBhwzQav3TpUnz22WdIT0+HqalphWOmTp2KY8eOVXu9UX5+Plq2bIm5c+ciLCyswjGLFi3C4sWLy23fvHkzzM3NNaqZnt6DIuCzC0ZQqgR0tCnFpDalkAlSV0VERPqmoKAA48aNQ05ODqysrKocq5dnkkRRxIYNGxASElJpQCooKMCWLVvw/vvvVzufhYUFvLy8kJiYWOmY+fPnlwlQSqUSrq6uCAgIqLbJ2lKpVIiMjMSAAQNgYmKi07n1WTvfB3hxw2mcz5YhuUFrzHyuJXulBfZKc+yV5tgrzbFX2qmpfj35JkgTehmSoqKicP36dUyePLnSMT/99BOKioowfvz4aucrKipCQkICevXqVekYuVwOuVxebruJiUmNHew1Obc+6tqyCf77ghfmbj+P1X/cQHtna/Rr+/hCbvZKc+yV5tgrzbFXmmOvtKPrfmkzl6QXbufl5SE+Ph7x8fEAgOTkZMTHxyMlJQXA47M3EyZMKLdfeHg4/P390aFDh0rnDg8Px7Bhwyq8xmjOnDmIiopCcnIyTp48ieDgYCiVSoSGhurmg1GNGeXnikk9mwMAwn46h6sZudIWREREBkvSM0mxsbHo27ev+vmTr7NCQ0MREREBhUKhDkxP5OTkYMeOHVi1alWl8167dg0xMTE4ePBgha+npaVh7NixyMrKQpMmTdCtWzecOHGiyl/JUd2x4Pl2SLyTh5jrWZi26SxebSV1RUREZIgkDUl9+vSpciXliIiIctusra1RUFD1z8DbtGlT5bxbtmzRuEaqe4yNZFgzzgdDvziKW/cK8O01GV4oKQXPXhMRkS7p9TpJVH81MjfF+gl+sDA1wnWlDB/uvyp1SUREZGAYkkhvtbG3xGfBXhAg4oeTqfjxVEr1OxEREWmIIYn0Wr92TfG8aykA4L2fL+L0zWyJKyIiIkPBkER6b4CziEBPe6hKRLz6QxxuP3godUlERGQAGJJI7wkC8NFwT7R3tEJW3iNM2RiLh49KpC6LiIj0HEMSGQRzU2N8PcEXthamuJSuxFvbz1X5C0ciIqLqMCSRwXBpbI51431hLBPwv/MKrD18Q+qSiIhIjzEkkUHp6m6DxUM9AQCfHryKQ5fvSFwRERHpK4YkMjgv+rthfLdmEEVg9tZ4JN7hrUuIiEh7DElkkBYGecLf3QZ5RcV4ZWMscgpUUpdERER6hiGJDJKJkQxrX+wM50YNcPNeAWb8eAbFJaVSl0VERHqEIYkMlm1DOdZP8EMDEyMcSczCR/uvSF0SERHpEYYkMmjtnazw2ShvAMA3McnYEZcmcUVERKQvGJLI4D3v5YhZz7UCAMzfdQFnU+5LXBEREekDhiSqF2b3b4OA9vZ4VFyKqd/H4Y6yUOqSiIiojmNIonpBJhOwfHQntLFviMzcIkz5Pg6FKt66hIiIKseQRPVGQ7kxvpnQBY3MTXAu9QHe3nmBty4hIqJKMSRRvdLM1hxrx3WGkUzAzrO3ER6TLHVJRERURzEkUb3To5Ud3h3cDgDw4b4ERF27K3FFRERUFzEkUb0U2qM5Rvm5oFQEZm4+g+SsfKlLIiKiOoYhieolQRDwwbAO8HVrDGVhMV7+7jSUhbx1CRER/YUhieotubER1o3vDEdrM9y4m4/ZW+JRUsoLuYmI6DGGJKrXmlqa4esQP8iNZfj9SiY+PXhV6pKIiKiOYEiies/LxRrLgjsCANYdvoGf429LXBEREdUFDElEAIZ2csa03i0BAHO3n8eFtByJKyIiIqkxJBH96a2BbfGcR1MUFZdiyvexuJtbJHVJREQkIYYkoj8ZyQSsHNMJLZpYQJFTiFd/iENRMW9dQkRUXzEkEf2NlZkJvpngB0szY8Teuo+FP1/irUuIiOophiSif2jRpCE+H+sDmQBsOZ2KjcdvSV0SERFJgCGJqAJ92jbFvEAPAMD7/7uMY9ezJK6IiIhqG0MSUSVe6dUCL/g4o6RUxPTNZ5Byr0DqkoiIqBYxJBFVQhAELB3uBW8XazwoUOGVjbHIKyqWuiwiIqolDElEVTAzMcJXIX5oainH1Tu5ePOneJTy1iVERPUCQxJRNRyszfBliC9MjWQ4cOkOVv2WKHVJRERUC4y13eHmzZs4cuQIbt68iYKCAjRp0gQ+Pj7o3r07zMzMaqJGIsl1btYYHw73wpxt57Dqt0R4OFgi0MtR6rKIiKgGaRySNm/ejNWrV+PUqVNo2rQpnJ2d0aBBA2RnZ+PGjRswMzPDiy++iP/7v/+Dm5tbTdZMJIlgXxdcTldiw9FkhP10Ds3tLNDO0UrqsoiIqIZo9HVb586dsXz5cowfPx43b95ERkYG4uLiEBMTg8uXL0OpVOLnn39GaWkp/Pz8sG3btpqum0gSbz/vgV6t7fBQVYJXNsYiO/+R1CUREVEN0SgkffDBB4iNjcWMGTPQrFmzcq/L5XL06dMHX375JRISEtC8eXNd10lUJxgbyfD5WB+42Zoj7f5DTN8UB1VJqdRlERFRDdAoJA0ePBgAUFxcjO+++w4ZGRmVjrWzs0OXLl10Ux1RHdTI3BTfTPBDQ7kxTiRl4/1fLktdEhER1QCtft1mbGyMV199FUVFurk7enR0NIKCguDk5ARBELB79+4qx0+cOBGCIJR7eHp6qsdERERUOKawsLDMXGvXroW7uzvMzMzg6+uLI0eO6OQzUf3Q2t4SK0d3giAA35+4hc0nU6QuiYiIdEzrJQD8/f0RHx+vkzfPz8+Ht7c31qxZo9H4VatWQaFQqB+pqamwsbHByJEjy4yzsrIqM06hUJT55d3WrVsxe/ZsLFiwAGfPnkWvXr0QGBiIlBT+RUea69/eHnMC2gIA3vv5Ik4lZ0tcERER6ZLWSwBMnz4dYWFhSE1Nha+vLywsLMq83rFjR43nCgwMRGBgoMbjra2tYW1trX6+e/du3L9/H5MmTSozThAEODg4VDrP8uXLMXnyZLz88ssAgJUrV+LAgQNYt24dli5dqnE9RNP7tESCQon/nVfg1R/isGfmM3Bu1EDqsoiISAe0DkmjR48GAMyaNUu9TRAEiKIIQRBQUlKiu+qqER4ejv79+5dbciAvLw9ubm4oKSlBp06d8MEHH8DHxwcA8OjRI8TFxWHevHll9gkICMCxY8cqfa+ioqIyXzMqlUoAgEqlgkql0tVHUs/59/+lytWFXn04tD2S7ubhsiIXr3x3Glte7ooGpkaS1VOZutArfcFeaY690hx7pZ2a6pc282kdkpKTk7XdpUYoFArs378fmzdvLrPdw8MDERER8PLyglKpxKpVq9CzZ0+cO3cOrVu3RlZWFkpKSmBvb19mP3t7+yovSF+6dCkWL15cbvvBgwdhbm6umw/1D5GRkTUyryGSulcjHYHP7hrhsiIXoWsjEdq6FIIgaUmVkrpX+oS90hx7pTn2Sju67ldBgeY3K9c6JNWVhSIjIiLQqFEjDBs2rMz2bt26oVu3burnPXv2ROfOnfH5559j9erV6u3CP/4Ge3ImrDLz589HWFiY+rlSqYSrqysCAgJgZaXbBQVVKhUiIyMxYMAAmJiY6HRuQ1OXetXW5z5CI2Jx9p4MfX3a4NXeLSSt55/qUq/qOvZKc+yV5tgr7dRUv558E6QJrUMSAHz//ff48ssvkZycjOPHj8PNzQ0rV66Eu7s7hg4d+jRTakUURWzYsAEhISEwNTWtcqxMJkOXLl2QmPj4flt2dnYwMjIqd9YoMzOz3Nmlv5PL5ZDL5eW2m5iY1NjBXpNzG5q60KserZti8ZAOeHvXBaz47TraOzVC//aVH1NSqQu90hfslebYK82xV9rRdb+0mUvrX7etW7cOYWFheP755/HgwQP1NUiNGjXCypUrtZ3uqURFReH69euYPHlytWNFUUR8fDwcHR/fZ8vU1BS+vr7lTt9FRkaiR48eNVIv1R/j/JshpJsbRBGYvTUeiXdypS6JiIiektYh6fPPP8f69euxYMECGBn9dXGqn58fLly4oNVceXl5iI+PVy8pkJycjPj4ePVP8efPn48JEyaU2y88PBz+/v7o0KFDudcWL16MAwcOICkpCfHx8Zg8eTLi4+Mxbdo09ZiwsDB888032LBhAxISEvDGG28gJSWlzBiip/VeUHt0a2GDvKJivLwxFg8KeOsSIiJ99FQXbj/5pdjfyeVy5OfnazVXbGws+vbtq37+5Jqf0NBQREREQKFQlFu7KCcnBzt27MCqVasqnPPBgweYMmUKMjIyYG1tDR8fH0RHR6Nr167qMaNHj8a9e/fw/vvvQ6FQoEOHDti3b1+dud6K9JuJkQxrX/TFkDUxuHWvADN/PItvJ3aBsZHW/01CREQS0jokubu7Iz4+vlyg2L9/P9q3b6/VXH369IEoipW+HhERUW6btbV1lVemr1ixAitWrKj2vadPn47p06drVCeRtmwsTLF+gh+Grz2GI4lZWLr/Ct79j3Z/PoiISFpah6S33noLr732GgoLCyGKIk6dOoUff/wRS5cuxTfffFMTNRLppXaOVlg+yhuvbjqD8JhktHO0QrCvi9RlERGRhrQOSZMmTUJxcTHmzp2LgoICjBs3Ds7Ozli1ahXGjBlTEzUS6a1AL0fM6tcaq39LxNs7L6BFEwt0btZY6rKIiEgDT3WRxCuvvIJbt24hMzMTGRkZSE1N1eiXZkT10ex+rTHQ0x6PSkox9fs4ZOQUVr8TERFJ7qmvJM3MzERCQgKuXbuGu3fv6rImIoMikwlYPqoT2tpb4m5uEaZ+H4tCVe3dvoeIiJ6O1iFJqVQiJCQETk5O6N27N5599lk4OTlh/PjxyMnJqYkaifSehdwY6yf4oZG5Cc6l5WD+zgtV/miBiIikp3VIevnll3Hy5Ens3bsXDx48QE5ODv73v/8hNjYWr7zySk3USGQQmtmaY+24zjCSCdh19ja+OVI37oNIREQV0zok7d27Fxs2bMDAgQNhZWUFS0tLDBw4EOvXr8fevXtrokYig9GjlR3e+3MpgKX7ExB1jV9VExHVVVqHJFtbW1hbW5fbbm1tjcaN+asdoupM6O6GMV1cUSoCMzafQdLdPKlLIiKiCmgdkt555x2EhYVBoVCot2VkZOCtt97Cu+++q9PiiAyRIAhYPNQTvm6NkVv4+NYlykKV1GUREdE/aLROko+PDwRBUD9PTEyEm5sbmjVrBgBISUmBXC7H3bt3MXXq1JqplMiAyI2N8OX4x7cuSbqbj9d/PItvQrvASCZUvzMREdUKjULSsGHDargMovqniaUcX4f4IfjLY/jj6l18cuAq5gV6SF0WERH9SaOQtHDhwpqug6he8nKxxrLgjnh9Szy+jLqBdo6WGNrJWeqyiIgI/2IxSSLSjaGdnPFqn5YAgLnbz+N82gNpCyIiIgBPEZJkMhmMjIwqfRCR9uYEtMVzHk1RVPz41iWZubx1CRGR1LS+we2uXbvKPFepVDh79iy+++47LF68WGeFEdUnRjIBK8d0wgtfHMWNu/l49Ycz2PyKP+TG/A8PIiKpaB2Shg4dWm5bcHAwPD09sXXrVt7olugpWZmZ4JvQLhi6JgZxt+7jvd2X8NEIrzK/LCUiotqjs2uS/P39cejQIV1NR1QvudtZ4PNxnSETgK2xqfju2E2pSyIiqrd0EpIePnyIzz//HC4uLrqYjqhe692mCeYHtgMAfLA3AUevZ0lcERFR/aT1122NGzcuc/pfFEXk5ubC3NwcP/zwg06LI6qvXu7ljgSFEjvP3sZrm89gz2vPoJmtudRlERHVK1qHpBUrVpQJSTKZDE2aNIG/vz/v3UakI4Ig4MPhXriRlY9zqQ/w8sbT2Dm9JxrKtf4jS0RET0nrf+NOnDixBsogon8yMzHC1yG+CPo8Btfu5CFsazy+HO8LGW9dQkRUKzQOSefPn9doXMeOHZ+6GCIqy97KDF+F+GL01ydw8PIdrPwtEWED2khdFhFRvaBxSOrUqRMEQYAoigCg/srtyfMn20pKSnRcIlH95tOsMZa+4IU3t53D6t8S0c7BEoFejlKXRURk8DQOScnJyep/FkURHTp0wL59++Dm5lYjhRHRX0b4uiBBocQ3MckI++kc3Gwt0N7JSuqyiIgMmsYh6Z9hSBAEuLi4MCQR1ZJ5gR64eicXRxKz8MrGWOyZ0RO2DeVSl0VEZLB4g1siPWFsJMOasZ3R3NYctx88xPRNZ6AqKZW6LCIig8WQRKRHrM1N8E2oHxrKjXEyORuLf7kkdUlERAbrX4Uk3lOKqPa1amqJVWM6QRCAH06kYNPJW1KXRERkkDS+JsnHx6dMKHr48CGCgoJgampaZtyZM2d0Vx0RVahfO3vMCWiLTw5cxcKfL6F1U0t0dbeRuiwiIoOicUgaNmxYmedDhw7VdS1EpIXpfVriSkYufjmXjld/iMPPM3rCpTFvXUJEpCsah6SFCxfWZB1EpCVBELBsREck3c3DpXQlpmyMw/ZXu8PclLcuISLSBV64TaTHGpga4esJfrBraIrLCiXe2na+zAKvRET09DQKSYMGDcKxY8eqHZebm4uPP/4YX3zxxb8ujIg049yoAdaN94WJkYC9FxT44o/rUpdERGQQNDovP3LkSIwaNQqWlpYYMmQI/Pz84OTkBDMzM9y/fx+XL19GTEwM9u3bh//85z/45JNParpuIvqbLs1t8P7QDpi/8wI+PXgNbR2sMKC9vdRlERHpNY1C0uTJkxESEoLt27dj69atWL9+PR48eADg8XUR7du3x8CBAxEXF4e2bdvWZL1EVImxXZshQaHExuO3MHvLWex6rSfa2FtKXRYRkd7S+ApPU1NTjBs3DuPGjQMA5OTk4OHDh7C1tYWJiUmNFUhEmnv3P+2ReCcPx5Pu4ZWNsfj5tZ5oZG5a/Y5ERFTOU1+4bW1tDQcHBwYkojrExEiGL17sDJfGDXDrXgFmbD6LYt66hIjoqfDXbUQGxsbCFN+E+sHc1Agx17Pw4b4rKCkVcTI5G3FZAk4mZ6OklL+AIyKqjqQhKTo6GkFBQXBycoIgCNi9e3eV4ydOnAhBEMo9PD091WPWr1+PXr16oXHjxmjcuDH69++PU6dOlZln0aJF5eZwcHCoiY9IJAkPByssH+UNANhwNBmdP4jE+A2x2JhohPEbYvHMx7/j14sKiaskIqrbJA1J+fn58Pb2xpo1azQav2rVKigUCvUjNTUVNjY2GDlypHrM4cOHMXbsWPzxxx84fvw4mjVrhoCAANy+fbvMXJ6enmXmunDhgk4/G5HUBnVwxGAvRwBAzkNVmdcycgrx6g9nGJSIiKog6dK8gYGBCAwM1Hi8tbU1rK2t1c93796N+/fvY9KkSeptmzZtKrPP+vXrsX37dvz222+YMGGCeruxsTHPHpFBKykVEXfrfoWviQAEAIt/uYwB7R1gJOPNqomI/knrM0mpqalIS0tTPz916hRmz56Nr7/+WqeFaSI8PBz9+/eHm5tbpWMKCgqgUqlgY1P25p+JiYlwcnKCu7s7xowZg6SkpJoul6hWnUrORoaysNLXRQCKnEKcSs6uvaKIiPSI1meSxo0bhylTpiAkJAQZGRkYMGAAPD098cMPPyAjIwPvvfdeTdRZjkKhwP79+7F58+Yqx82bNw/Ozs7o37+/epu/vz82btyINm3a4M6dO1iyZAl69OiBS5cuwdbWtsJ5ioqKUFRUpH6uVCoBACqVCiqVqsJ9ntaT+XQ9ryFiryqneJCv8TiVyqqGq9EvPK40x15pjr3STk31S5v5BFHLGz01btwYJ06cQNu2bbF69Wps3boVR48excGDBzFt2rSnPiMjCAJ27dqFYcOGaTR+6dKl+Oyzz5Ceng5T04rXgVm2bBk++ugjHD58GB07dqx0rvz8fLRs2RJz585FWFhYhWMWLVqExYsXl9u+efNmmJvzzutU9yTmCFhz2ajacTPal6C1NX/tRkT1Q0FBAcaNG4ecnBxYWVX9H4han0lSqVSQy+UAgEOHDmHIkCEAAA8PDygUtXMRqCiK2LBhA0JCQioNSJ9++ik+/PBDHDp0qMqABAAWFhbw8vJCYmJipWPmz59fJkAplUq4uroiICCg2iZrS6VSITIyEgMGDOA6VNVgrypXUipi+2fRuKMsQlURKMXYGeN7e8DWgotOPsHjSnPslebYK+3UVL+efBOkCa1DkqenJ7788ksMHjwYkZGR+OCDDwAA6enplX5VpWtRUVG4fv06Jk+eXOHrn3zyCZYsWYIDBw7Az8+v2vmKioqQkJCAXr16VTpGLperw+HfmZiY1NjBXpNzGxr2qjwTAIuGeOLVH85AAMoEpb8/33M+A9HX7+Ht59thpK8LBIEXcT/B40pz7JXm2Cvt6Lpf2syl9YXbH3/8Mb766iv06dMHY8eOhbf347VY9uzZg65du2o1V15eHuLj4xEfHw8ASE5ORnx8PFJSUgA8Pnvz91+kPREeHg5/f3906NCh3GvLli3DO++8gw0bNqB58+bIyMhARkYG8vLy1GPmzJmDqKgoJCcn4+TJkwgODoZSqURoaKhW9RPVdYM6OGLd+M5wsDYrs93B2gxfju+M3a/1RDtHKzwoUGHu9vMYu/4Eku7mVTIbEVH9ovWZpD59+iArKwtKpRKNGzdWb58yZYrW1+bExsaib9++6udPvs4KDQ1FREQEFAqFOjA9kZOTgx07dmDVqlUVzrl27Vo8evQIwcHBZbYvXLgQixYtAgCkpaVh7NixyMrKQpMmTdCtWzecOHGiyl/JEemrQR0cMaC9A45fz8TBIycR0Msf3Vs1Vf/sf8+MntgQk4wVh67hRFI2Bq08gtf6tsK0Pi0gN67+miYiIkOldUh6+PAhRFFUB6Rbt25h165daNeuHQYOHKjVXH369EFV141HRESU22ZtbY2CgoJK97l582a177tlyxZNyiMyGEYyAf7uNriXIMLf3abMukgmRjJM7d0Sz3s5YsHui4i+dhcrDl3DL+fT8eELXujqblPFzEREhkvrr9uGDh2KjRs3AgAePHgAf39/fPbZZxg2bBjWrVun8wKJqHa42pjju0ldsGpMJ9g1NMX1zDyM+uo45u88j5wC/mSZiOofrUPSmTNn1Bc4b9++Hfb29rh16xY2btyI1atX67xAIqo9giBgaCdnHArrjTFdXAEAP55KRb/lUdhzLr3KM79ERIZG65BUUFAAS0tLAMDBgwcxfPhwyGQydOvWDbdu3dJ5gURU+xqZm+KjER3x09TuaNnEAll5RZj141lM/PY0UrMr/7qbiMiQaB2SWrVqhd27dyM1NRUHDhxAQEAAACAzM1Pn6wURkbS6uttg3+u98Eb/NjA1kiHq2l0MWBGFr6JuQFVSKnV5REQ1SuuQ9N5772HOnDlo3rw5unbtiu7duwN4fFbJx8dH5wUSkbTkxkZ4vX9r7J/dC91a2KBQVYql+69gyJqjiE99IHV5REQ1RuuQFBwcjJSUFMTGxuLAgQPq7f369cOKFSt0WhwR1R0tmzTEj690w7LgjmhkboIEhRIvrD2KRXsuIbeQF3YTkeHROiQBgIODA3x8fJCeno7bt28DALp27QoPDw+dFkdEdYsgCBjl54rfwnrjBR9niCIQcewmBiyPxoFLGVKXR0SkU1qHpNLSUrz//vuwtraGm5sbmjVrhkaNGuGDDz5AaSmvUSCqD2wbyrFidCd8P7kr3GzNkaEsxNTv4zBlYywUOQ+lLo+ISCe0DkkLFizAmjVr8NFHH+Hs2bM4c+YMPvzwQ3z++ed49913a6JGIqqjerVuggOzn8X0Pi1hLBNw8PIdDFgejYijySgp5XIBRKTftF5x+7vvvsM333yDIUOGqLd5e3vD2dkZ06dPx3//+1+dFkhEdZuZiRHmDvLAkE5OeHvnBZxJeYBFv1zGrvh0LH3BC+2d+KtXItJPWp9Jys7OrvDaIw8PD2RnZ+ukKCLSPx4OVtg+rQc+GNYBlnJjnEt9gKA1MVi6LwEFj4qlLo+ISGtahyRvb2+sWbOm3PY1a9bA29tbJ0URkX6SyQSEdHPDoTd743kvB5SUivgqOgkBK6Jx+Gqm1OUREWlF66/bli1bhsGDB+PQoUPo3r07BEHAsWPHkJqain379tVEjUSkZ+ytzLD2RV/8lnAH7+6+iLT7DzHx29MI8nbCe/9pjyaWcqlLJCKqltZnknr37o1r167hhRdewIMHD5CdnY3hw4fj6tWr6nu6EREBQL929ogM643Jz7hDJgC/nEtHv88O48dTKSjlhd1EVMdpfSYJAJycnMpdoJ2amoqXXnoJGzZs0ElhRGQYLOTGePc/7TGskzPm7zqPi7eVmL/zAnaeScPS4V5o1dRS6hKJiCr0VItJViQ7OxvfffedrqYjIgPj5WKN3dN74p3B7WBuaoTTN+8jcNURLD94FYWqEqnLIyIqR2chiYioOsZGMrzcqwUOvvEs+nk0hapExOrfr+P5VUdw7EaW1OUREZXBkEREtc6lsTm+CfXD2hc7o6mlHElZ+Ri3/iTmbDuH+/mPpC6PiAgAQxIRSUQQBDzv5YhDb/bG+G7NIAjA9rg09FsehZ1n0iCKvLCbiKSl8YXbw4cPr/L1Bw8e/NtaiKgesjIzwZJhXnjBxwVv77yAq3dyEfbTOew8cxtLhnVAczsLqUskonpK4zNJ1tbWVT7c3NwwYcKEmqyViAyYr1tj/DLzGbw1sC3kxjLEXM/CwJXR+OKP63hUzJtnE1Ht0/hM0rfffluTdRARwdRYhtf6tsJgL0e8s/siYq5n4ZMDV7EnPh0fDu8AXzcbqUskonqE1yQRUZ3T3M4C30/uihWjvWFjYYqrd3IR/OVxLNh1ATkPVVKXR0T1BEMSEdVJgiDgBR8X/BbWGyN9XSCKwKaTKei/PAp7zyt4YTcR1TiGJCKq0xpbmOKTkd7Y/Io/3O0scDe3CK9tPoPJ38Ui7X6B1OURkQFjSCIivdCjpR32v94Ls55rBRMjAb9fyUTAimh8cyQJxSW8sJuIdI8hiYj0hpmJEcIC2mLfrF7o0rwxCh6VYMneBAxbexQX0nKkLo+IDAxDEhHpndb2ltg6pTuWDveClZkxLt5WYugXMXj/l8vILyqWujwiMhAMSUSkl2QyAWO7NsOhN3sjyNsJpSKw4WgyAlZE47eEO1KXR0QGgCGJiPRaU0szfD7WBxGTusClcQPcfvAQk7+LxfRNcbijLJS6PCLSYwxJRGQQ+rRtioNvPIupz7aAkUzAvgsZ6P9ZFL4/cQulpVwugIi0x5BERAbD3NQY859vhz0zesLbxRq5RcV4d/dFBH95DFczcqUuj4j0DEMSERkcTydr7JzeE4uC2sPC1AhnUh5g8OojWPbrFRSqSqQuj4j0BEMSERkkI5mAiT3dcejN3ghob4/iUhFrD9/AwJXRiEnMkro8ItIDDElEZNAcrRvg6wl++CrEFw5WZrh1rwDjw0/ija3xuJdXJHV5RFSHMSQRUb0w0NMBkWHPYmKP5hAEYNfZ2+i3PAo/xabyPnBEVCGGJCKqNyzNTLBoiCd2Te+Jdo5WeFCgwtzt5zF2/Qkk3c2TujwiqmMYkoio3unk2gh7ZvTE/EAPmJnIcCIpG4NWHsGqQ4koKuaF3UT0GEMSEdVLJkYyTO3dEpFv9EbvNk3wqKQUKw5dw+DVMTh9877U5RFRHSBpSIqOjkZQUBCcnJwgCAJ2795d5fiJEydCEIRyD09PzzLjduzYgfbt20Mul6N9+/bYtWtXubnWrl0Ld3d3mJmZwdfXF0eOHNHlRyMiPeFqY46ISV2weqwP7Bqa4npmHsaFn8aWGzLkPFRJXR4RSUjSkJSfnw9vb2+sWbNGo/GrVq2CQqFQP1JTU2FjY4ORI0eqxxw/fhyjR49GSEgIzp07h5CQEIwaNQonT55Uj9m6dStmz56NBQsW4OzZs+jVqxcCAwORkpKi889IRHWfIAgY4u2E38L6YGxXVwDA8UwZBq0+ij3n0nlhN1E9JWlICgwMxJIlSzB8+HCNxltbW8PBwUH9iI2Nxf379zFp0iT1mJUrV2LAgAGYP38+PDw8MH/+fPTr1w8rV65Uj1m+fDkmT56Ml19+Ge3atcPKlSvh6uqKdevW6fojEpEesTY3wdLhHbF5chfYNxCRlfcIs348i4nfnkZqdoHU5RFRLTOWuoB/Izw8HP3794ebm5t62/Hjx/HGG2+UGTdw4EB1SHr06BHi4uIwb968MmMCAgJw7NixSt+rqKgIRUV/ramiVCoBACqVCiqVbk/JP5lP1/MaIvZKc+yV5jo5N8TcjiW42aA1vjxyE1HX7mLAiijMeq4lJnZ3g4kRL+d8gseV5tgr7dRUv7SZT29DkkKhwP79+7F58+Yy2zMyMmBvb19mm729PTIyMgAAWVlZKCkpqXJMRZYuXYrFixeX237w4EGYm5s/7ceoUmRkZI3Ma4jYK82xV5oxlgGtihIx1wvYmiTDdSWw7EAiNh25htEtSuBmKXWFdQuPK82xV9rRdb8KCjQ/K6y3ISkiIgKNGjXCsGHDyr0mCEKZ56IoltumyZi/mz9/PsLCwtTPlUolXF1dERAQACsrq6f4BJVTqVSIjIzEgAEDYGJiotO5DQ17pTn2SnP/7FWoKGLn2XR89Os13C5QYcUlY4z3b4Y3+rWCpZne/mtUJ3hcaY690k5N9evJN0Ga0Ms/3aIoYsOGDQgJCYGpqWmZ1xwcHMqdEcrMzFSfObKzs4ORkVGVYyoil8shl8vLbTcxMamxg70m5zY07JXm2CvN/b1XY/ybY4CnI/67NwE7z97G9ydSEHk5E4uHemKgp4PElUqPx5Xm2Cvt6Lpf2syll1+sR0VF4fr165g8eXK517p3717u1NzBgwfRo0cPAICpqSl8fX3LjYmMjFSPISKqiG1DOZaP7oQfJvvDzdYcGcpCTP0+DlM2xkKR81Dq8ohIxyQ9k5SXl4fr16+rnycnJyM+Ph42NjZo1qwZ5s+fj9u3b2Pjxo1l9gsPD4e/vz86dOhQbs7XX38dzz77LD7++GMMHToUP//8Mw4dOoSYmBj1mLCwMISEhMDPzw/du3fH119/jZSUFEybNq3mPiwRGYxnWtvhwOxn8fnvifgqKgkHL9/BsRv3MCegDUK6N4eRrPKv7olIf0h6Jik2NhY+Pj7w8fEB8Di8+Pj44L333gPw+OLsf65dlJOTgx07dlR4FgkAevTogS1btuDbb79Fx44dERERga1bt8Lf3189ZvTo0Vi5ciXef/99dOrUCdHR0di3b1+ZX8kREVXFzMQIbw30wN5ZvdC5WSPkFRVj0S+XMXzdMVxO1/yaByKquyQ9k9SnT58qF2mLiIgot83a2rraK9ODg4MRHBxc5Zjp06dj+vTpGtVJRFSZtg6W2D6tBzafSsHH+6/gXOoDBK2JwcvPuOP1/q1hbqqXl34SEfT0miQiorpEJhMwvpsbfnuzNwZ7OaKkVMRX0UkIWBGNw1czpS6PiJ4SQxIRkY40tTLDFy92RnioH5wbNUDa/YeY+O1pzPzxLO7mFlU/ARHVKQxJREQ61q+dPQ6+8SwmP+MOmQD8ci4d/T47jB9PpaC0lPeBI9IXDElERDXAQm6Md//THj+/9gw6OFtBWViM+TsvYPTXx3E9M1fq8ohIAwxJREQ1yMvFGrun98Q7g9vB3NQIp2/eR+CqI1h+8CoKVSVSl0dEVWBIIiKqYcZGMrzcqwUiw3qjn0dTqEpErP79Op5fdQTHbmRJXR4RVYIhiYioljg3aoBvQv2w9sXOaGopR1JWPsatP4k5287hfv4jqcsjon9gSCIiqkWCIOB5L0ccerM3xndrBkEAtselod/yKOw8k1bl2nFEVLsYkoiIJGBlZoIlw7ywfVoPtLW3RHb+I4T9dA4h4adwMytf6vKICAxJRESS8nVrjP/NegZvDWwLubEMMdezMHBlNL744zoeFZdKXR5RvcaQREQkMRMjGV7r2woH33gWz7SyQ1FxKT45cBVBn8cg7la21OUR1VsMSUREdYSbrQW+n9wVK0Z7w8bCFFfv5CL4y+NYsOsCch6qpC6PqN5hSCIiqkMEQcALPi74Law3Rvq6QBSBTSdT0H95FPaeV/DCbqJaxJBERFQHNbYwxScjvfHjK93Qws4Cd3OL8NrmM5j8XSzS7hdIXR5RvcCQRERUh3VvaYt9r/fCrH6tYWIk4PcrmQhYEY1vjiShuIQXdhPVJIYkIqI6zszECGED2mD/673QtbkNCh6VYMneBAxbexQX0nKkLo/IYDEkERHpiVZNLbFlSjd8NNwLVmbGuHhbiaFfxOD9Xy4jv6hY6vKIDA5DEhGRHpHJBIzp2gy/vdkHQ7ydUCoCG44mI2BFNH5LuCN1eUQGhSGJiEgPNbGUY/VYH0RM6gJXmwa4/eAhJn8Xi+mb4nBHWSh1eUQGgSGJiEiP9WnbFAdn98bU3i1gJBOw70IG+n8Whe9P3EJpKZcLIPo3GJKIiPRcA1MjzA9sh19mPANv10bILSrGu7svIvjLY7iakSt1eUR6iyGJiMhAtHeyws5Xe2DxEE80lBvjTMoDDF59BMt+vYJCVYnU5RHpHYYkIiIDYiQTENqjOSLDnsVAT3sUl4pYe/gGBq6MRkxiltTlEekVhiQiIgPkaN0AX4X44esQXzhYmeHWvQKMDz+JN7bG415ekdTlEekFhiQiIgMW4OmAQ2/2xsQezSEIwK6zt9FveRR+ik3lfeCIqsGQRERk4BrKjbFoiCd2T++Jdo5WeFCgwtzt5zF2/QncuJsndXlEdRZDEhFRPeHt2gi/zOiJt5/3QAMTI5xIykbgyiNYdSgRRcW8sJvonxiSiIjqEWMjGaY82xIH33gWfdo2waOSUqw4dA3PrzqCU8nZUpdHVKcwJBER1UOuNub4dmIXfD7WB3YN5bhxNx+jvjqOeTvOI6dAJXV5RHUCQxIRUT0lCAKCvJ3wW1hvjO3aDACw5XQq+i0/jJ/jb/PCbqr3GJKIiOo5a3MTLB3uhW3TuqNV04bIynuE17fEI/Tb00jNLpC6PCLJMCQREREAoEtzG+yd9QzCBrSBqbEM0dfuYsCKKHwZdQOqklKpyyOqdQxJRESkJjc2wqx+rfHr673QvYUtClWl+Gj/FQR9HoP41AdSl0dUqxiSiIionBZNGmLzK/74JLgjGpmb4EpGLl5YexQLf76I3MLHF3aXlIo4mZyNuCwBJ5OzUVLKa5jIsBhLXQAREdVNgiBgpJ8rnvNoiv/uTcDOs7fx3fFbOHDpDoZ2csKec+lQ5BQCMMLGxFg4WpthYVB7DOrgKHXpRDrBM0lERFQl24ZyLB/dCT9M9oebrTkylIX4Kjrpz4D0l4ycQrz6wxn8elEhUaVEusWQREREGnmmtR32zeoFC7lRha8/+bJt8S+X+dUbGQSGJCIi0tj5tBzkF1V+CxMRgCKnkKt3k0GQNCRFR0cjKCgITk5OEAQBu3fvrnafoqIiLFiwAG5ubpDL5WjZsiU2bNigfr1Pnz4QBKHcY/DgweoxixYtKve6g4NDTXxEIiKDkplbWP0gAD+eStF4LFFdJemF2/n5+fD29sakSZMwYsQIjfYZNWoU7ty5g/DwcLRq1QqZmZkoLi5Wv75z5048evRI/fzevXvw9vbGyJEjy8zj6emJQ4cOqZ8bGVV8+piIiP7S1NJMo3F7zqVj7wUF+rZtor7428SIX16QfpE0JAUGBiIwMFDj8b/++iuioqKQlJQEGxsbAEDz5s3LjHmy/YktW7bA3Ny8XEgyNjbm2SMiIi11dbeBo7UZMnIKUdFVRwIAqwYmcLczR3xqDg4lZOJQQiZsLUzxgo8zRvq5oq2DZW2XTfRU9CrW79mzB35+fli2bBmcnZ3Rpk0bzJkzBw8fPqx0n/DwcIwZMwYWFhZlticmJsLJyQnu7u4YM2YMkpKSarp8IiK9ZyQTsDCoPYDHgejvnjz/eIQXdr/2DA6FPYupz7aAXUM57uU/wjcxyRi4MhpD18Tg+xO3kPOQN9Kluk2v1klKSkpCTEwMzMzMsGvXLmRlZWH69OnIzs4uc13SE6dOncLFixcRHh5eZru/vz82btyINm3a4M6dO1iyZAl69OiBS5cuwdbWtsL3LioqQlFRkfq5UqkEAKhUKqhUuv2D/mQ+Xc9riNgrzbFXmmOvqtavrR0+H+ONJfuuIEP5178XHazlWBDogX5t7aBSqeDW2AxzBrTC68+1QHRiFnacSccfV+/iXFoOzqXlYMn/LmNAu6YI9nVGd3cbyGT/jF2GhceVdmqqX9rMJ4h15DbPgiBg165dGDZsWKVjAgICcOTIEWRkZMDa2hrA42uQgoODkZ+fjwYNGpQZP3XqVBw7dgwXLlyo8r3z8/PRsmVLzJ07F2FhYRWOWbRoERYvXlxu++bNm2Fubl7NpyMiMjylInBDKUCpAqxMgJZWIqrLObkqIPaugJOZMige/jW4samIrk1F+Dcpha1mlz0RPZWCggKMGzcOOTk5sLKyqnKsXp1JcnR0hLOzszogAUC7du0giiLS0tLQunVr9faCggJs2bIF77//frXzWlhYwMvLC4mJiZWOmT9/fpkApVQq4erqioCAgGqbrC2VSoXIyEgMGDAAJiYmOp3b0LBXmmOvNMdeae5pejUagCiKuJiuxI4z6fjlvAL3C4txIE3AgTQZurk3xojOzhjY3h4NTA3nRzU8rrRTU/168k2QJvQqJPXs2RPbtm1DXl4eGjZsCAC4du0aZDIZXFxcyoz96aefUFRUhPHjx1c7b1FRERISEtCrV69Kx8jlcsjl8nLbTUxMauxgr8m5DQ17pTn2SnPsleaepledm9uhc3M7vBvkiQOXMrA9Lg0x17NwIvk+TiTfx+L/XUGQtyNG+rnCx7URBMEwvo7jcaUdXfdLm7kkvXA7Ly8P8fHxiI+PBwAkJycjPj4eKSkpAB6fvZkwYYJ6/Lhx42Bra4tJkybh8uXLiI6OxltvvYWXXnqp3Fdt4eHhGDZsWIXXGM2ZMwdRUVFITk7GyZMnERwcDKVSidDQ0Jr7sEREVCEzEyMM7eSM7yf7I+b/nkPYgDZwtWmAvKJi/HgqFcPXHkP/5VH4KuoG116iWiXpmaTY2Fj07dtX/fzJ11mhoaGIiIiAQqFQByYAaNiwISIjIzFz5kz4+fnB1tYWo0aNwpIlS8rMe+3aNcTExODgwYMVvm9aWhrGjh2LrKwsNGnSBN26dcOJEyfg5uZWA5+SiIg05dyoAWb1a40ZfVvhZHI2tsWlYt8FBW7czcfS/Vew7MBV9G3bBMG+j9deMjXWqx9pk56RNCT16dMHVV03HhERUW6bh4cHIiMjq5y3TZs2Vc67ZcsWjWskIqLaJ5MJ6N7SFt1b2mLxEE/sPa/AT7GpOJPyoMzaS8N8nDHSzwUeDrq9NpQI0LNrkoiIqP6xNDPBmK7NMKZrM1zPzMO2uFTsPHMbd3OLEB6TjPCYZHR0scZIP1cM6egEa3Ne70O6wZBERER6o1XThpgf2A5vBbRF1LW72BabhkMJd3A+LQfn03Lwwf8uY5CnA0b6uaBnSzuDX3uJahZDEhER6R1jIxn6tbNHv3b2uJdXhN3x6dgWm4orGbnYcy4de86lw7lRA4zo7IxgX1c0s+V6dqQ9hiQiItJrtg3lmPyMO17q2RwXbyvxU2wqfo6/jdsPHmL179ex+vfr6NbCBiN9XRHo5QBzU/7VR5rhkUJERAZBEAR4uVjDy8UaCwa3w8HLd7AtNvXx2ktJ2TiRlI2Fey7hPx0fr73UuZnhrL1ENYMhiYiIDI6ZiRGGeDthiLcTbj94iJ1xadgWl4aU7AJsOZ2KLadT0bKJBUb6uWK4jzOaWvFeKFQeQxIRERk050YNMLNfa7zWtxVO3czGT7Gp2H8hAzfu5uOj/VfwyYGr6NOmCUb6ce0lKoshiYiI6gWZTEC3Frbo1sIWi4eosPe8Atvi0hB36z5+u5KJ365kwsbCFC9w7SX6E0MSERHVO/9ce2l7XBp2nEkrs/aSl7M1Rvm5YIi3M9deqqcYkoiIqF5r1bQh5gV6YE5AG0Qn3sVPp9Pw25U7uHA7Bxdu5+CDvQkY6OmAkb4u6NnKDkZce6neYEgiIiLC47WXnvOwx3Me5dde+uVcOn45lw4nazOM8HVBsK8L3GwtpC6ZahhDEhER0T/8fe2lS+lP1l5KR3pOIT7//To+//06/N1tMMqPay8ZMv6/SkREVAlBENDB2RodnK3x9vPtEHn5Dn76c+2lk8nZOJnMtZcMGUMSERGRBsxMjBDk7YQgbyekP3iIHRWsvdSiiQVG+rpiRGeuvWQIGJKIiIi05PSPtZe2xaZh3wUFku7m4+Nfr+DTg1fRu00TjPJzwXMe9lx7SU8xJBERET2lMmsvDfXE3vPp2Babhthb9/H7lUz8/ufaS8M6OeOFTg5Sl0taYkgiIiLSgYZyY4zu0gyjuzTDjbt/rr0Ul4bM3CJsOJqMDUeT4WphhGzbFAzv3IxrL+kBnv8jIiLSsZZNGuL/Bnng2Lzn8O3ELgjs4AATIwGp+QIW/+8Kunx4CDM2n0H0tbsoKRWlLpcqwTNJRERENcTYSIa+Hk3R16Mp7jzIx0dbfkPCQ2tcuZOH/51X4H/nFXC0NkMw116qkxiSiIiIaoGNhSn6OIr4OLA7rt19iG2xqdgdnw7FP9ZeGunniue59lKdwP8HiIiIatHf116a/3w7HEq4g59i03Ak8e5fay/9fBH/6eiEUV1c0LlZY669JBGGJCIiIomYmRjhPx2d8J+Oj9de2nnm8dpLt+4VYGtsKrbG/rX20vDOzrDn2ku1iiGJiIioDnBq1AAznvtz7aXkbGyLS8Pe83+tvfTJgSvo07YpRvq6oF87rr1UGxiSiIiI6hBBEODfwhb+LWyxaIgn9p1X4KfY1HJrLw3t5ISRvq5o72QldckGiyGJiIiojmooN8aoLq4Y1cUVSXfzsC0uDTvPpOGOsgjfHr2Jb4/eRAdnK4zyc8UQbyc0MjeVumSDwpBERESkB1r8ufbSmwPa4EhiFrbFpSLy8h1cvK3ExduXsOR/CQjwtMdIP1c808oORjJe7P1vMSQRERHpkb+vvZSd/wg/x9/GT7FpSFAoy6y9NKLz47WXmttx7aWnxZBERESkp2wsTDGppzsm9XTHxds5ZdZeWvPHdaz54zq6uttgpK8LnvdyhIWcf+1rg90iIiIyAP9ce2lbbBqiE+/iVHI2TiVnY9GeSxjc0RGj/Fzh68a1lzTBkERERGRA/r72kiLnIXaeuY1tsam4ea8AP8Wm4afYNLSws0CwnwtGdHbh2ktVYEgiIiIyUI7WDfBa31aY3qclTt+8j59iU7HvggJJWflY9utVfHrgKnq3aYJRfq5ce6kCDElEREQGThAEdHW3QVd3G/XaS9viUnH65n38cfUu/rh6F43NTTDMx5lrL/0NQxIREVE98s+1l7bHpWFHBWsvjfR1xdBO9XvtJYYkIiKieqpFk4aYO8gDbwa0RXTiXWyPTcPByxnqtZf+uzcBAzztMdLXBb1aN6l3ay8xJBEREdVzRjIBfds2Rd+2TXH/b2svXVYosfe8Anv/XHtpeOfHX8fVl7WXGJKIiIhIrbGFKSb2dMfEP9de2h6Xht3xt6HIKcQXf9zAF3/cqDdrLxnuJyMiIqJ/5a+1lzxw6HImtsWlIvpa/Vl7iSGJiIiIqiQ3NsLgjo4Y3NERGTmF2HEmrcK1l0b4Pl57ycHaMNZeYkgiIiIijTlYm6nXXoq9dR8/nU7F3j/XXvrkwFV8dvAqnlWvvdQUcmMjqUt+apKuGhUdHY2goCA4OTlBEATs3r272n2KioqwYMECuLm5QS6Xo2XLltiwYYP69YiICAiCUO5RWFhYZp61a9fC3d0dZmZm8PX1xZEjR3T98YiIiAyWIAjo0twGn4z0xukF/bEsuCO6NrdBqQgcvnoX0zedgf+Hv2HRnku4lJ4jdblPRdIzSfn5+fD29sakSZMwYsQIjfYZNWoU7ty5g/DwcLRq1QqZmZkoLi4uM8bKygpXr14ts83M7K9Tf1u3bsXs2bOxdu1a9OzZE1999RUCAwNx+fJlNGvW7N9/MCIionrEQm6MUX6uGOXniuSsfGyPS8WOuNvIUBYi4thNRBy7CU8nK4z0dcHQTs5obKEfay9JGpICAwMRGBio8fhff/0VUVFRSEpKgo2NDQCgefPm5cYJggAHB4dK51m+fDkmT56Ml19+GQCwcuVKHDhwAOvWrcPSpUu1+xBERESk5m5ngbcGeiBsQFscSbyLbbFpiLx8B5fSlbiUfhkf7ruCAe3tMdKv8rWXSkpFnEzORlyWANvkbHRv1VSSNZr06pqkPXv2wM/PD8uWLcP3338PCwsLDBkyBB988AEaNGigHpeXlwc3NzeUlJSgU6dO+OCDD+Dj4wMAePToEeLi4jBv3rwycwcEBODYsWOVvndRURGKiorUz5VKJQBApVJBpVLp8mOq59P1vIaIvdIce6U59kpz7JXm6mOverZojJ4tGuN+QVv8cj4DO87cxmVFLvZeUGDvBQXsreQY3skJwzs7obnt47WXDly6gyX7riBDWQTACBsTY+FgJcc7z3tgoKf9v65Jm/7rVUhKSkpCTEwMzMzMsGvXLmRlZWH69OnIzs5WX5fk4eGBiIgIeHl5QalUYtWqVejZsyfOnTuH1q1bIysrCyUlJbC3L9toe3t7ZGRkVPreS5cuxeLFi8ttP3jwIMzNzXX7Qf8UGRlZI/MaIvZKc+yV5tgrzbFXmquvvbIDMLU5kNYEOJkpQ2yWgDvKIqyLTsa66GS0tBThaF6KmDtPLpf+68xRhrIQM7bE46U2pfC2Ff9VHQUFBRqPFURR/HfvpiOCIGDXrl0YNmxYpWMCAgJw5MgRZGRkwNraGgCwc+dOBAcHIz8/v8zZpCdKS0vRuXNnPPvss1i9ejXS09Ph7OyMY8eOoXv37upx//3vf/H999/jypUrFb53RWeSXF1dkZWVBSsr3d4IUKVSITIyEgMGDICJiYlO5zY07JXm2CvNsVeaY680x16VVVRcit+vZGLHmXQcuZ6F0mrSiADAwVqOP8Ke/VdfvSmVStjZ2SEnJ6fav7/16kySo6MjnJ2d1QEJANq1awdRFJGWlobWrVuX20cmk6FLly5ITEwEANjZ2cHIyKjcWaPMzMxyZ5f+Ti6XQy6Xl9tuYmJSYwd7Tc5taNgrzbFXmmOvNMdeaY69eszEBBji44ohPq7IyCnEikPXsPV0aqXjRQCKnCKcTctF95a2/+J9Ne+9pEsAaKtnz55IT09HXl6eetu1a9cgk8ng4uJS4T6iKCI+Ph6Ojo4AAFNTU/j6+pY73RkZGYkePXrUXPFERERUIQdrM/TQMPhk5hZWP0hHJA1JeXl5iI+PR3x8PAAgOTkZ8fHxSElJAQDMnz8fEyZMUI8fN24cbG1tMWnSJFy+fBnR0dF466238NJLL6m/alu8eDEOHDiApKQkxMfHY/LkyYiPj8e0adPU84SFheGbb77Bhg0bkJCQgDfeeAMpKSllxhAREVHtaWqp2Srdmo7TBUm/bouNjUXfvn3Vz8PCwgAAoaGhiIiIgEKhUAcmAGjYsCEiIyMxc+ZM+Pn5wdbWFqNGjcKSJUvUYx48eIApU6aor1vy8fFBdHQ0unbtqh4zevRo3Lt3D++//z4UCgU6dOiAffv2wc3NrRY+NREREf1TV3cbOFqbISOnEBVdnvT4miQzdHW3qbWaJA1Jffr0QVXXjUdERJTb5uHhUeUvA1asWIEVK1ZU+97Tp0/H9OnTNaqTiIiIapaRTMDCoPZ49YczEIAyQenJZdoLg9rX6npJenVNEhERERmuQR0csW5853I3yHWwNsO68Z0xqINjrdajV79uIyIiIsM2qIMjBrR3wPHrmTh45CQCevlzxW0iIiIi4PFXb/7uNriXIMLf3UaSgATw6zYiIiKiCjEkEREREVWAIYmIiIioAgxJRERERBVgSCIiIiKqAEMSERERUQUYkoiIiIgqwJBEREREVAGGJCIiIqIKcMXtp/TkxrxKpVLnc6tUKhQUFECpVMLExETn8xsS9kpz7JXm2CvNsVeaY6+0U1P9evL39pO/x6vCkPSUcnNzAQCurq4SV0JERETays3NhbW1dZVjBFGTKEXllJaWIj09HZaWlhAE3d5TRqlUwtXVFampqbCystLp3IaGvdIce6U59kpz7JXm2Cvt1FS/RFFEbm4unJycIJNVfdURzyQ9JZlMBhcXlxp9DysrK/5B0hB7pTn2SnPslebYK82xV9qpiX5VdwbpCV64TURERFQBhiQiIiKiCjAk1UFyuRwLFy6EXC6XupQ6j73SHHulOfZKc+yV5tgr7dSFfvHCbSIiIqIK8EwSERERUQUYkoiIiIgqwJBEREREVAGGJImsXbsW7u7uMDMzg6+vL44cOVLl+KioKPj6+sLMzAwtWrTAl19+WUuVSk+bXh0+fBiCIJR7XLlypRYrlkZ0dDSCgoLg5OQEQRCwe/fuavepr8eVtr2qr8fV0qVL0aVLF1haWqJp06YYNmwYrl69Wu1+9fG4eppe1dfjCgDWrVuHjh07qtdA6t69O/bv31/lPlIcVwxJEti6dStmz56NBQsW4OzZs+jVqxcCAwORkpJS4fjk5GQ8//zz6NWrF86ePYu3334bs2bNwo4dO2q58tqnba+euHr1KhQKhfrRunXrWqpYOvn5+fD29saaNWs0Gl+fjytte/VEfTuuoqKi8Nprr+HEiROIjIxEcXExAgICkJ+fX+k+9fW4eppePVHfjisAcHFxwUcffYTY2FjExsbiueeew9ChQ3Hp0qUKx0t2XIlU67p27SpOmzatzDYPDw9x3rx5FY6fO3eu6OHhUWbb1KlTxW7dutVYjXWFtr36448/RADi/fv3a6G6uguAuGvXrirH1Ofj6u806RWPq8cyMzNFAGJUVFSlY3hcPaZJr3hcldW4cWPxm2++qfA1qY4rnkmqZY8ePUJcXBwCAgLKbA8ICMCxY8cq3Of48ePlxg8cOBCxsbFQqVQ1VqvUnqZXT/j4+MDR0RH9+vXDH3/8UZNl6q36elz9G/X9uMrJyQEA2NjYVDqGx9VjmvTqifp+XJWUlGDLli3Iz89H9+7dKxwj1XHFkFTLsrKyUFJSAnt7+zLb7e3tkZGRUeE+GRkZFY4vLi5GVlZWjdUqtafplaOjI77++mvs2LEDO3fuRNu2bdGvXz9ER0fXRsl6pb4eV0+Dx9Xjm4KGhYXhmWeeQYcOHSodx+NK817V9+PqwoULaNiwIeRyOaZNm4Zdu3ahffv2FY6V6rjiDW4lIghCmeeiKJbbVt34irYbIm161bZtW7Rt21b9vHv37khNTcWnn36KZ599tkbr1Ef1+bjSBo8rYMaMGTh//jxiYmKqHVvfjytNe1Xfj6u2bdsiPj4eDx48wI4dOxAaGoqoqKhKg5IUxxXPJNUyOzs7GBkZlTsTkpmZWS4lP+Hg4FDheGNjY9ja2tZYrVJ7ml5VpFu3bkhMTNR1eXqvvh5XulKfjquZM2diz549+OOPP+Di4lLl2Pp+XGnTq4rUp+PK1NQUrVq1gp+fH5YuXQpvb2+sWrWqwrFSHVcMSbXM1NQUvr6+iIyMLLM9MjISPXr0qHCf7t27lxt/8OBB+Pn5wcTEpMZqldrT9KoiZ8+ehaOjo67L03v19bjSlfpwXImiiBkzZmDnzp34/fff4e7uXu0+9fW4eppeVaQ+HFeVEUURRUVFFb4m2XFVo5eFU4W2bNkimpiYiOHh4eLly5fF2bNnixYWFuLNmzdFURTFefPmiSEhIerxSUlJorm5ufjGG2+Ily9fFsPDw0UTExNx+/btUn2EWqNtr1asWCHu2rVLvHbtmnjx4kVx3rx5IgBxx44dUn2EWpObmyuePXtWPHv2rAhAXL58uXj27Fnx1q1boijyuPo7bXtVX4+rV199VbS2thYPHz4sKhQK9aOgoEA9hsfVY0/Tq/p6XImiKM6fP1+Mjo4Wk5OTxfPnz4tvv/22KJPJxIMHD4qiWHeOK4YkiXzxxReim5ubaGpqKnbu3LnMz0RDQ0PF3r17lxl/+PBh0cfHRzQ1NRWbN28urlu3rpYrlo42vfr444/Fli1bimZmZmLjxo3FZ555Rty7d68EVde+Jz8n/ucjNDRUFEUeV3+nba/q63FVUY8AiN9++616DI+rx56mV/X1uBJFUXzppZfU/15v0qSJ2K9fP3VAEsW6c1wJovjnlU9EREREpMZrkoiIiIgqwJBEREREVAGGJCIiIqIKMCQRERERVYAhiYiIiKgCDElEREREFWBIIiIiIqoAQxIRERFRBRiSiIh0RBAE7N69W+oyiEhHGJKIyCBMnDgRgiCUewwaNEjq0ohITxlLXQARka4MGjQI3377bZltcrlcomqISN/xTBIRGQy5XA4HB4cyj8aNGwN4/FXYunXrEBgYiAYNGsDd3R3btm0rs/+FCxfw3HPPoUGDBrC1tcWUKVOQl5dXZsyGDRvg6ekJuVwOR0dHzJgxo8zrWVlZeOGFF2Bubo7WrVtjz549NfuhiajGMCQRUb3x7rvvYsSIETh37hzGjx+PsWPHIiEhAQBQUFCAQYMGoXHjxjh9+jS2bduGQ4cOlQlB69atw2uvvYYpU6bgwoUL2LNnD1q1alXmPRYvXoxRo0bh/PnzeP755/Hiiy8iOzu7Vj8nEemISERkAEJDQ0UjIyPRwsKizOP9998XRVEUAYjTpk0rs4+/v7/46quviqIoil9//bXYuHFjMS8vT/363r17RZlMJmZkZIiiKIpOTk7iggULKq0BgPjOO++on+fl5YmCIIj79+/X2eckotrDa5KIyGD07dsX69atK7PNxsZG/c/du3cv81r37t0RHx8PAEhISIC3tzcsLCzUr/fs2ROlpaW4evUqBEFAeno6+vXrV2UNHTt2VP+zhYUFLC0tkZmZ+bQfiYgkxJBERAbDwsKi3Ndf1REEAQAgiqL6nysa06BBA43mMzExKbdvaWmpVjURUd3Aa5KIqN44ceJEueceHh4AgPbt2yM+Ph75+fnq148ePQqZTIY2bdrA0tISzZs3x2+//VarNRORdHgmiYgMRlFRETIyMspsMzY2hp2dHQBg27Zt8PPzwzPPPINNmzbh1KlTCA8PBwC8+OKLWLhwIUJDQ7Fo0SLcvXsXM2fOREhICOzt7QEAixYtwrRp09C0aVMEBgYiNzcXR48excyZM2v3gxJRrWBIIiKD8euvv8LR0bHMtrZt2+LKlSsAHv/ybMuWLZg+fTocHBywadMmtG/fHgBgbm6OAwcO4PXXX0eXLl1gbm6OESNGYPny5eq5QkNDUVhYiBUrVmDOnDmws7NDcHBw7X1AIqpVgiiKotRFEBHVNEEQsGvXLgwbNkzqUohIT/CaJCIiIqIKMCQRERERVYDXJBFRvcArC4hIWzyTRERERFQBhiQiIiKiCjAkEREREVWAIYmIiIioAgxJRERERBVgSCIiIiKqAEMSERERUQUYkoiIiIgqwJBEREREVIH/B9/VG6xINVbyAAAAAElFTkSuQmCC",
       "text/plain": [
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       ]
@@ -1015,281 +884,6 @@
     "plt.savefig(\"loss_1_to_end.png\", dpi=300)\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "    \n",
-       "  \n",
-       "
HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestiteClass
0111.01243455.508192-7.779554e-092.697041e-262.210590e-152.041069e-024.082138e-020.000000e+000.0004940.0004940.0011.0000000
1111.01243455.508427-4.736083e-091.446346e-262.473481e-151.094567e-022.189133e-020.000000e+000.0005530.0005530.0011.0000000
2111.01243455.508691-1.311169e-093.889826e-282.769320e-152.943745e-045.887491e-040.000000e+000.0006190.0006190.0011.0000000
3111.01243455.508698-1.220023e-091.442658e-292.777193e-151.091776e-052.183551e-050.000000e+000.0006200.0006200.0011.0000000
4111.01243455.508699-1.216643e-095.350528e-312.777485e-154.049176e-078.098352e-070.000000e+000.0006200.0006200.0011.0000000
..........................................
2502495111.01243455.5074883.573728e-095.424062e-1451.375204e-109.953520e-072.266555e-035.509534e-1490.0003180.0014500.0011.0000140
2502496111.01243455.5075013.494007e-092.011675e-1461.377139e-109.817216e-072.217997e-032.043375e-1500.0003210.0014290.0011.0000100
2502497111.01243455.5075123.429764e-097.460897e-1481.377819e-109.706451e-072.179066e-037.578467e-1520.0003240.0014120.0011.0000060
2502498111.01243455.5075203.381745e-092.767237e-1491.371144e-109.621074e-072.149820e-032.810844e-1530.0003260.0014000.0011.0000040
2502499111.01243455.5075253.348864e-095.321610e-1511.376026e-109.564401e-072.129912e-035.405468e-1550.0003270.0013910.0011.0000010
\n",
-       "
2502500 rows × 13 columns
\n",
-       "
"
-      ],
-      "text/plain": [
-       "                  H          O        Charge           H_0_          O_0_  \\\n",
-       "0        111.012434  55.508192 -7.779554e-09   2.697041e-26  2.210590e-15   \n",
-       "1        111.012434  55.508427 -4.736083e-09   1.446346e-26  2.473481e-15   \n",
-       "2        111.012434  55.508691 -1.311169e-09   3.889826e-28  2.769320e-15   \n",
-       "3        111.012434  55.508698 -1.220023e-09   1.442658e-29  2.777193e-15   \n",
-       "4        111.012434  55.508699 -1.216643e-09   5.350528e-31  2.777485e-15   \n",
-       "...             ...        ...           ...            ...           ...   \n",
-       "2502495  111.012434  55.507488  3.573728e-09  5.424062e-145  1.375204e-10   \n",
-       "2502496  111.012434  55.507501  3.494007e-09  2.011675e-146  1.377139e-10   \n",
-       "2502497  111.012434  55.507512  3.429764e-09  7.460897e-148  1.377819e-10   \n",
-       "2502498  111.012434  55.507520  3.381745e-09  2.767237e-149  1.371144e-10   \n",
-       "2502499  111.012434  55.507525  3.348864e-09  5.321610e-151  1.376026e-10   \n",
-       "\n",
-       "                   Ba            Cl           S_2_      S_6_        Sr  \\\n",
-       "0        2.041069e-02  4.082138e-02   0.000000e+00  0.000494  0.000494   \n",
-       "1        1.094567e-02  2.189133e-02   0.000000e+00  0.000553  0.000553   \n",
-       "2        2.943745e-04  5.887491e-04   0.000000e+00  0.000619  0.000619   \n",
-       "3        1.091776e-05  2.183551e-05   0.000000e+00  0.000620  0.000620   \n",
-       "4        4.049176e-07  8.098352e-07   0.000000e+00  0.000620  0.000620   \n",
-       "...               ...           ...            ...       ...       ...   \n",
-       "2502495  9.953520e-07  2.266555e-03  5.509534e-149  0.000318  0.001450   \n",
-       "2502496  9.817216e-07  2.217997e-03  2.043375e-150  0.000321  0.001429   \n",
-       "2502497  9.706451e-07  2.179066e-03  7.578467e-152  0.000324  0.001412   \n",
-       "2502498  9.621074e-07  2.149820e-03  2.810844e-153  0.000326  0.001400   \n",
-       "2502499  9.564401e-07  2.129912e-03  5.405468e-155  0.000327  0.001391   \n",
-       "\n",
-       "         Barite  Celestite  Class  \n",
-       "0         0.001   1.000000      0  \n",
-       "1         0.001   1.000000      0  \n",
-       "2         0.001   1.000000      0  \n",
-       "3         0.001   1.000000      0  \n",
-       "4         0.001   1.000000      0  \n",
-       "...         ...        ...    ...  \n",
-       "2502495   0.001   1.000014      0  \n",
-       "2502496   0.001   1.000010      0  \n",
-       "2502497   0.001   1.000006      0  \n",
-       "2502498   0.001   1.000004      0  \n",
-       "2502499   0.001   1.000001      0  \n",
-       "\n",
-       "[2502500 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_design"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1299,86 +893,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m15641/15641\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 336us/step - loss: 6.6414e-07\n"
+      "\u001b[1m15641/15641\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1ms/step - loss: 0.0904\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "8.585521982240607e-07"
+       "0.09046255797147751"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on all test data\n",
-    "model.evaluate(X_test.iloc[:,:-1], y_test)"
+    "model_large.evaluate(X_test_preprocess.iloc[:,:-1], y_test_preprocess.iloc[:, :-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m15454/15454\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 331us/step - loss: 2.7927e-07\n"
+      "\u001b[1m15455/15455\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 996us/step - loss: 0.0902\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "3.939527175589319e-07"
+       "0.0901983454823494"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on non-reactive data\n",
-    "model.evaluate(X_test[X_test['Class'] == 0].iloc[:,:-1], y_test[X_test['Class'] == 0])"
+    "model_large.evaluate(X_test_preprocess[X_test_preprocess['Class'] == 0].iloc[:,:-1], y_test_preprocess[X_test_preprocess['Class'] == 0].iloc[:,:-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 400us/step - loss: 3.3173e-05\n"
+      "\u001b[1m186/186\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1127\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "3.921399184037e-05"
+       "0.11247223615646362"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on reactive data\n",
-    "model.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1])"
+    "model_large.evaluate(X_test_preprocess[X_test_preprocess['Class'] == 1].iloc[:,:-1], y_test_preprocess[X_test_preprocess['Class'] == 1].iloc[:, :-1])"
    ]
   },
   {
@@ -1401,7 +995,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "training",
    "language": "python",
    "name": "python3"
   },