A9 added

A9/A9.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6d803f08-aa4b-40b1-8dc5-8dac097ffd17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import zipfile\n",
+    "import io\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers, regularizers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "68a45206-e160-4bd6-ab84-f3a60ef04fb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input: 26 \tOutput:13\n",
+      "Joints: 13\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load Kinect movement data\n",
+    "\n",
+    "JOINTS = [\"head\", \"left_shoulder\", \"left_elbow\", \"right_shoulder\", \"right_elbow\",\n",
+    "          \"left_hand\", \"right_hand\", \"left_hip\", \"right_hip\", \"left_knee\", \"right_knee\",\n",
+    "          \"left_foot\", \"right_foot\",\n",
+    "]\n",
+    "N_JOINTS = len(JOINTS)          # total number of body joints\n",
+    "N_INPUT  = N_JOINTS * 2         # input for each joint(x and y)\n",
+    "N_OUTPUT = N_JOINTS * 1         # output/target z coordiante\n",
+    "print(f'Input: {N_INPUT} \\tOutput:{N_OUTPUT}')\n",
+    "print(f'Joints: {N_JOINTS}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7e950179-3350-4cd5-a9e5-98679259e049",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loads single csv file and splits into input and target array\n",
+    "def load_single_csv(filepath_or_bytes):\n",
+    "    if isinstance(filepath_or_bytes, (str, os.PathLike)):\n",
+    "        df = pd.read_csv(filepath_or_bytes)\n",
+    "    else:\n",
+    "        df = pd.read_csv(io.BytesIO(filepath_or_bytes))\n",
+    "    df.columns = df.columns.str.strip()   \n",
+    "\n",
+    "    x_cols = [f\"{j}_x\" for j in JOINTS]\n",
+    "    y_cols = [f\"{j}_y\" for j in JOINTS]\n",
+    "    z_cols = [f\"{j}_z\" for j in JOINTS]\n",
+    "\n",
+    "    xy_cols = []\n",
+    "    for j in JOINTS:\n",
+    "        xy_cols += [f\"{j}_x\", f\"{j}_y\"]\n",
+    "\n",
+    "    X = df[xy_cols].values.astype(np.float32)  # input\n",
+    "    y = df[z_cols].values.astype(np.float32)    # Target\n",
+    "\n",
+    "    return X, y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a048829e-8e40-40ef-9cf1-5b18b2c804cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load all csv file from the folder\n",
+    "def load_all_sequences(folder_path):\n",
+    "    sequences, file_names = [], []\n",
+    "\n",
+    "    # Get all CSV files in the folder\n",
+    "    csv_files = [f for f in os.listdir(folder_path) if f.endswith('.csv')]\n",
+    "    csv_files.sort()\n",
+    "    \n",
+    "    print(f\"Found {len(csv_files)} CSV files in folder.\")\n",
+    "\n",
+    "    for name in csv_files:\n",
+    "        file_path = os.path.join(folder_path, name)\n",
+    "        \n",
+    "        with open(file_path, 'rb') as f:\n",
+    "            raw = f.read()\n",
+    "        \n",
+    "        X, y = load_single_csv(raw)\n",
+    "        \n",
+    "        sequences.append((X, y))   # stores as (input,target) \n",
+    "        file_names.append(name)\n",
+    "\n",
+    "    return sequences, file_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "adfcb71f-e762-4440-b0d4-85669201957a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For Dense MLP model, which treats each frame independently\n",
+    "def flatten_sequences(sequences):\n",
+    "    X_flat = np.concatenate([s[0] for s in sequences], axis=0)\n",
+    "    y_flat = np.concatenate([s[1] for s in sequences], axis=0)\n",
+    "    return X_flat, y_flat\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "686a1d16-09bd-44c5-8c92-91f1dd89b4a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create fixed-length windows of consecutive frames from each session for conv1d, lstm and gru\n",
+    "def make_windowed_sequences(sequences, window_size=30, stride=1):\n",
+    "    X_list, y_list = [], []\n",
+    "    for X, y in sequences:\n",
+    "        n = len(X)\n",
+    "        for start in range(0, n - window_size + 1, stride):\n",
+    "            X_list.append(X[start : start + window_size])\n",
+    "            y_list.append(y[start : start + window_size])\n",
+    "\n",
+    "    X_seq = np.array(X_list, dtype=np.float32)   # (N, window, 26)\n",
+    "    y_seq = np.array(y_list, dtype=np.float32)   # (N, window, 13)\n",
+    "    return X_seq, y_seq\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "8305831b-5824-436e-bf22-e78c775963eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 179 CSV files in folder.\n",
+      "\n",
+      "Flat dataset:      X=(24005, 26)  y=(24005, 13)\n",
+      "Windowed dataset:  X=(3831, 30, 26)  y_last=(3831, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "REPO_ROOT    = os.path.abspath(os.path.join(os.getcwd(), '..'))\n",
+    "DATA_DIR     = os.path.join(REPO_ROOT, 'Datasets_all')   \n",
+    "KINECT_DATA_PATH = os.path.join(DATA_DIR, 'kinect_good_preprocessed')\n",
+    "\n",
+    "# sequences contain list of tuples (X,y)\n",
+    "sequences, file_names = load_all_sequences(KINECT_DATA_PATH)\n",
+    "\n",
+    "# Frame-level flat data  (for Dense models)\n",
+    "X_flat, y_flat = flatten_sequences(sequences)\n",
+    "print(f\"\\nFlat dataset:      X={X_flat.shape}  y={y_flat.shape}\")\n",
+    "\n",
+    "# Windowed sequences  (for Conv1D / LSTM / GRU models)\n",
+    "WINDOW_SIZE = 30\n",
+    "X_seq, y_seq = make_windowed_sequences(sequences, window_size=WINDOW_SIZE, stride=5)\n",
+    "y_seq_last = y_seq[:, -1, :]                         # (N, 13)\n",
+    "print(f\"Windowed dataset:  X={X_seq.shape}  y_last={y_seq_last.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "90dd98c3-e87c-47e5-aee4-5b453ab291a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Deep Learning network architectures\n",
+    "\n",
+    "# DEnse MLP\n",
+    "\n",
+    "def build_dense_model( hidden_units=(128, 64), activation=\"relu\", dropout_rate=0.2,\n",
+    "                       l2_reg=1e-4,optimizer=\"adam\", loss=\"mse\",\n",
+    "):\n",
+    "    inputs = keras.Input(shape=(N_INPUT,), name=\"xy_input\")\n",
+    "    x = inputs\n",
+    "    for i, units in enumerate(hidden_units):\n",
+    "        x = layers.Dense(\n",
+    "            units,\n",
+    "            activation=activation,\n",
+    "            kernel_regularizer=regularizers.l2(l2_reg) if l2_reg else None,\n",
+    "            name=f\"dense_{i+1}\",\n",
+    "        )(x)\n",
+    "        if dropout_rate > 0:\n",
+    "            x = layers.Dropout(dropout_rate, name=f\"dropout_{i+1}\")(x)\n",
+    "\n",
+    "    outputs = layers.Dense(N_OUTPUT, activation=\"linear\", name=\"z_output\")(x)\n",
+    "    model = keras.Model(inputs, outputs, name=\"DenseModel\")\n",
+    "    return model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2f525503-f9ef-42e6-bdeb-1df33d168410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Conv1D CNN\n",
+    "\n",
+    "def build_conv1d_model(filters=(64, 128), kernel_size=3, pool_size=2, dense_units=(64,),\n",
+    "                       activation=\"relu\", dropout_rate=0.2,optimizer=\"adam\", loss=\"mse\",\n",
+    "):\n",
+    "    inputs = keras.Input(shape=(WINDOW_SIZE, N_INPUT), name=\"xy_seq_input\")\n",
+    "    x = inputs\n",
+    "    for i, f in enumerate(filters):\n",
+    "        x = layers.Conv1D(f, kernel_size, activation=activation, padding=\"same\",\n",
+    "                          name=f\"conv_{i+1}\")(x)\n",
+    "        x = layers.MaxPooling1D(pool_size, padding=\"same\", name=f\"pool_{i+1}\")(x)\n",
+    "        if dropout_rate > 0:\n",
+    "            x = layers.Dropout(dropout_rate, name=f\"drop_conv_{i+1}\")(x)\n",
+    "\n",
+    "    x = layers.GlobalAveragePooling1D(name=\"gap\")(x)\n",
+    "\n",
+    "    for i, units in enumerate(dense_units):\n",
+    "        x = layers.Dense(units, activation=activation, name=f\"fc_{i+1}\")(x)\n",
+    "        if dropout_rate > 0:\n",
+    "            x = layers.Dropout(dropout_rate, name=f\"drop_fc_{i+1}\")(x)\n",
+    "\n",
+    "    outputs = layers.Dense(N_OUTPUT, activation=\"linear\", name=\"z_output\")(x)\n",
+    "    model = keras.Model(inputs, outputs, name=\"Conv1DModel\")\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7d8b4b93-bd1a-4443-aa87-c4f5f39b22b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# layers.LSTM\n",
+    "\n",
+    "def build_lstm_model(lstm_units=(64, 32), dense_units=(32,), activation=\"tanh\",\n",
+    "                     dropout_rate=0.2, recurrent_dropout=0.0, optimizer=\"adam\", loss=\"mse\",\n",
+    "):\n",
+    "    inputs = keras.Input(shape=(WINDOW_SIZE, N_INPUT), name=\"xy_seq_input\")\n",
+    "    x = inputs\n",
+    "    for i, units in enumerate(lstm_units):\n",
+    "        return_sequences = (i < len(lstm_units) - 1)  \n",
+    "        x = layers.LSTM(\n",
+    "            units,\n",
+    "            return_sequences=return_sequences,\n",
+    "            dropout=dropout_rate,\n",
+    "            recurrent_dropout=recurrent_dropout,\n",
+    "            name=f\"lstm_{i+1}\",\n",
+    "        )(x)\n",
+    "\n",
+    "    for i, units in enumerate(dense_units):\n",
+    "        x = layers.Dense(units, activation=\"relu\", name=f\"fc_{i+1}\")(x)\n",
+    "        if dropout_rate > 0:\n",
+    "            x = layers.Dropout(dropout_rate, name=f\"drop_fc_{i+1}\")(x)\n",
+    "\n",
+    "    outputs = layers.Dense(N_OUTPUT, activation=\"linear\", name=\"z_output\")(x)\n",
+    "    model = keras.Model(inputs, outputs, name=\"LSTMModel\")\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1841d0b4-4805-4667-91a2-00c8d3696e77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# layers.GRU \n",
+    "\n",
+    "def build_gru_model(gru_units=(64, 32), dense_units=(32,), dropout_rate=0.2, \n",
+    "                    recurrent_dropout=0.0, optimizer=\"adam\", loss=\"mse\",):\n",
+    "    inputs = keras.Input(shape=(WINDOW_SIZE, N_INPUT), name=\"xy_seq_input\")\n",
+    "    x = inputs\n",
+    "    for i, units in enumerate(gru_units):\n",
+    "        return_sequences = (i < len(gru_units) - 1)\n",
+    "        x = layers.GRU(\n",
+    "            units,\n",
+    "            return_sequences=return_sequences,\n",
+    "            dropout=dropout_rate,\n",
+    "            recurrent_dropout=recurrent_dropout,\n",
+    "            name=f\"gru_{i+1}\",\n",
+    "        )(x)\n",
+    "\n",
+    "    for i, units in enumerate(dense_units):\n",
+    "        x = layers.Dense(units, activation=\"relu\", name=f\"fc_{i+1}\")(x)\n",
+    "        if dropout_rate > 0:\n",
+    "            x = layers.Dropout(dropout_rate, name=f\"drop_fc_{i+1}\")(x)\n",
+    "\n",
+    "    outputs = layers.Dense(N_OUTPUT, activation=\"linear\", name=\"z_output\")(x)\n",
+    "    model = keras.Model(inputs, outputs, name=\"GRUModel\")\n",
+    "    return model"
+   ]
+  }
+ ],
+ "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}