diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index cae4e27d0..a23efe86c 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -21,13 +21,7 @@
 				"ms-python.python"
 			]
 		}
-	},
-	// device args
-	"runArgs": ["-e DISPLAY=$DISPLAY --net=host -v /tmp/.X11-unix:/tmp/.X11-unix:rw -v /dev/video0:/dev/video0 "],
-//--device=/dev/video0:/dev/video0 
-	"privileged": true,
-	"containerUser": "root"
-
+	}
 	// Configure tool-specific properties.
 	// "customizations": {},
 
diff --git a/.gitignore b/.gitignore
index 68bc17f9f..1045afa79 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,8 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+data/
+
 # C extensions
 *.so
 
diff --git a/DataScience_trust.ipynb b/DataScience_trust.ipynb
new file mode 100644
index 000000000..283d960f9
--- /dev/null
+++ b/DataScience_trust.ipynb
@@ -0,0 +1,800 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "13cdf50f-c32a-4c99-b8bb-dedcf774bed2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cv2 as cv\n",
+    "from PIL import Image\n",
+    "import os\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "csv_path = 'data/model/keypoints_from_data.csv'\n",
+    "baseData = 'data/archive/asl_alphabet_train/'\n",
+    "\n",
+    "sign = 'A'\n",
+    "first = baseData + sign + '/A1.jpg'\n",
+    "imgstmp = []\n",
+    "\n",
+    "class SignCategory:\n",
+    "    def __init__(self, data, label):\n",
+    "        self.data = data\n",
+    "        self.label = label\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return \"<Label: \" + self.label + \"\\n\" + str(self.data) + \">\"\n",
+    "\n",
+    "categories = []\n",
+    "imgsAndLabels = []\n",
+    "\n",
+    "for subdir, dir,files in os.walk(baseData):\n",
+    "    if subdir == baseData:\n",
+    "        continue\n",
+    "    \n",
+    "    label = subdir.replace(baseData, '')\n",
+    "    data = []\n",
+    "    \n",
+    "    for idx, file_name in enumerate(files):\n",
+    "        if idx > 50:\n",
+    "            continue\n",
+    "            \n",
+    "        imgPath = subdir + '/' + file_name\n",
+    "        img = np.asarray(Image.open(imgPath))\n",
+    "        data.append(img)\n",
+    "\n",
+    "    imgsAndLabels.append((data, label))\n",
+    "    #categories.append(SignCategory(np.array(data), label))\n",
+    "\n",
+    "for i in range(1,101):\n",
+    "    imgPath = baseData + sign + '/A' + str(i) + '.jpg'\n",
+    "    img = np.asarray(Image.open(imgPath))\n",
+    "    imgstmp.append(img)\n",
+    "\n",
+    "imgs = np.array(imgstmp)\n",
+    "\n",
+    "imgplot = plt.imshow(imgs[4])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ed926554-474d-4479-9473-883d917229a1",
+   "metadata": {},
+   "source": [
+    "Something something, preprocess the thingies like from Video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "1f931e17-f941-42da-8c65-d7e1d10f1ae0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "import itertools\n",
+    "\n",
+    "def pre_process_landmark(landmark_list):\n",
+    "    temp_landmark_list = copy.deepcopy(landmark_list)\n",
+    "\n",
+    "    # Convert to relative coordinates\n",
+    "    base_x, base_y = 0, 0\n",
+    "    for index, landmark_point in enumerate(temp_landmark_list):\n",
+    "        if index == 0:\n",
+    "            base_x, base_y = landmark_point[0], landmark_point[1]\n",
+    "\n",
+    "        temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x\n",
+    "        temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y\n",
+    "\n",
+    "    # Convert to a one-dimensional list\n",
+    "    temp_landmark_list = list(\n",
+    "        itertools.chain.from_iterable(temp_landmark_list))\n",
+    "\n",
+    "    # Normalization\n",
+    "    max_value = max(list(map(abs, temp_landmark_list)))\n",
+    "\n",
+    "    def normalize_(n):\n",
+    "        return n / max_value\n",
+    "\n",
+    "    temp_landmark_list = list(map(normalize_, temp_landmark_list))\n",
+    "\n",
+    "    return temp_landmark_list\n",
+    "\n",
+    "def calc_landmark_list(image, landmarks):\n",
+    "    image_width, image_height = image.shape[1], image.shape[0]\n",
+    "\n",
+    "    landmark_point = []\n",
+    "\n",
+    "    # Keypoint\n",
+    "    for _, landmark in enumerate(landmarks.landmark):\n",
+    "        landmark_x = min(int(landmark.x * image_width), image_width - 1)\n",
+    "        landmark_y = min(int(landmark.y * image_height), image_height - 1)\n",
+    "        # landmark_z = landmark.z\n",
+    "\n",
+    "        landmark_point.append([landmark_x, landmark_y])\n",
+    "\n",
+    "    return landmark_point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "cc7db773-7823-42dc-8371-d49d6fa8e82b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0000 00:00:1707398582.466911   22032 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
+      "I0000 00:00:1707398582.467975   34796 gl_context.cc:344] GL version: 3.2 (OpenGL ES 3.2 Mesa 23.3.2-1pop0~1704238321~22.04~36f1d0e), renderer: Mesa Intel(R) Xe Graphics (TGL GT2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1479\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mediapipe as mp\n",
+    "\n",
+    "use_static_image_mode = 'store_true'\n",
+    "min_detection_confidence = 0.7\n",
+    "min_tracking_confidence = 0.5\n",
+    "\n",
+    "mp_hands = mp.solutions.hands\n",
+    "hands = mp_hands.Hands(\n",
+    "    static_image_mode=use_static_image_mode,\n",
+    "    max_num_hands=1,\n",
+    "    min_detection_confidence=min_detection_confidence,\n",
+    "    min_tracking_confidence=min_tracking_confidence,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Mediapipe has seen your hands :eyes:\n",
+    "#results2d = [ hands.process(img) for img in imgs ]\n",
+    "\n",
+    "results = []\n",
+    "for label, data in imgsAndLabels:\n",
+    "    for img in data:\n",
+    "        r = ( label, hands.process(img), img )\n",
+    "        results.append( r )\n",
+    "\n",
+    "\n",
+    "#results = hands.process(img)\n",
+    "#hand_landmarks = results.multi_hand_landmarks\n",
+    "#handedness = results.multi_handedness\n",
+    "\n",
+    "print(len(results))\n",
+    "#print(hand_landmarks, \"\\n-------------------\\n\", handedness)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "id": "26836ddc-879e-4cbc-9e59-174eef7c6688",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "for label, mediapipe_result, raw_img in results: \n",
+    "    \n",
+    "    if mediapipe_result.multi_hand_landmarks is not None:\n",
+    "        # Let's spit out the preprocessed landmarks to a CSV for training later.\n",
+    "        for hand_landmarks, handedness in zip(mediapipe_result.multi_hand_landmarks,\n",
+    "                                              mediapipe_result.multi_handedness):\n",
+    "            \n",
+    "            landmark_list = calc_landmark_list(raw_img, hand_landmarks)\n",
+    "            \n",
+    "            pre_processed_landmark_list = pre_process_landmark(landmark_list)\n",
+    "            with open(csv_path, 'a', newline=\"\") as f:\n",
+    "                writer = csv.writer(f)\n",
+    "                writer.writerow([label, *pre_processed_landmark_list])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c7da97b-4338-4d68-81ae-beb241edb3fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Training commences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "a4a6b7a9-f1de-48cf-9c2f-1d087f4d3003",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open(csv_path, newline='') as f:\n",
+    "    reader = csv.reader(f)\n",
+    "    data = list(reader)\n",
+    "\n",
+    "landmarks_list = []\n",
+    "labels_list = []\n",
+    "for entry in data:\n",
+    "    landmarks_list.append(np.array(entry[1:]))\n",
+    "    labels_list.append(entry[0])\n",
+    "\n",
+    "landmarks_array = np.array(landmarks_list)\n",
+    "labels_array = np.array(labels_list)\n",
+    "\n",
+    "len(labels_array) == len(landmarks_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "id": "567fb3d6-698c-4f34-967c-c2d0870cf1eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "#Zip landmarks and labels, then shuffle, then unzip\n",
+    "zipped = list(zip(landmarks_array, labels_array))\n",
+    "\n",
+    "#then shuffle\n",
+    "train_set, test_set = train_test_split(zipped, test_size=0.2, random_state=42)\n",
+    "\n",
+    "landmarks_train = []\n",
+    "labels_train = []\n",
+    "landmarks_test = []\n",
+    "labels_test = []\n",
+    "for landmark, label in train_set:\n",
+    "    landmarks_train.append(landmark)\n",
+    "    labels_train.append(label)\n",
+    "for landmark, label in test_set:\n",
+    "    landmarks_test.append(landmark)\n",
+    "    labels_test.append(label)\n",
+    "landmarks_train = np.array(landmarks_train)\n",
+    "labels_train = np.array(labels_train)\n",
+    "landmarks_test = np.array(landmarks_test)\n",
+    "labels_test = np.array(labels_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "275577ce-92e7-409f-993b-6a627e34ab75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_train_A = (labels_train == 'A')\n",
+    "labels_test_A = (labels_test == 'A')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "id": "04a43cfa-05e2-4dfb-affb-e78c65ed3c5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SGDClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SGDClassifier(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SGDClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import SGDClassifier \n",
+    "sgd_clf = SGDClassifier(random_state=42) \n",
+    "sgd_clf.fit(landmarks_train, labels_train_A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "id": "af89ba85-2ea4-41b1-a5af-c32299bc0452",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "dtype='numeric' is not compatible with arrays of bytes/strings.Convert your data to numeric values explicitly instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[276], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m some_lm\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m#landmarks_train[0]\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[43msgd_clf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[43msome_lm\u001b[49m\u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:351\u001b[0m, in \u001b[0;36mLinearClassifierMixin.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;124;03mPredict class labels for samples in X.\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;124;03m    Vector containing the class labels for each sample.\u001b[39;00m\n\u001b[1;32m    349\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    350\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[0;32m--> 351\u001b[0m scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(scores\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    353\u001b[0m     indices \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(scores \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mint\u001b[39m)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:332\u001b[0m, in \u001b[0;36mLinearClassifierMixin.decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    329\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m    330\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[0;32m--> 332\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    333\u001b[0m scores \u001b[38;5;241m=\u001b[39m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n\u001b[1;32m    334\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39mreshape(scores, (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,)) \u001b[38;5;28;01mif\u001b[39;00m scores\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m scores\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/base.py:633\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    631\u001b[0m         out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 633\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[1;32m    635\u001b[0m     out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:992\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m    991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(array\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 992\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    993\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    994\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    995\u001b[0m     )\n\u001b[1;32m    996\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nd \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[1;32m    997\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    998\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    999\u001b[0m         \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m   1000\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: dtype='numeric' is not compatible with arrays of bytes/strings.Convert your data to numeric values explicitly instead."
+     ]
+    }
+   ],
+   "source": [
+    "#some_label, some_imgs = imgsAndLabels[0]\n",
+    "#some_img = some_imgs[0]\n",
+    "\n",
+    "#imgplot = plt.imshow(some_img)\n",
+    "#print(\"should be label: \" + some_label)\n",
+    "\n",
+    "#print(some_img)\n",
+    "some_lm = landmarks_array[0]\n",
+    "\n",
+    "some_lm\n",
+    "\n",
+    "#landmarks_train[0]\n",
+    "sgd_clf.predict( [ some_lm ])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1adc838b-415a-47d1-b818-eb667f2eb251",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}