{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f49724a-a1cc-4973-8919-68d31c6186f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8a233e36-5810-4cfb-b6d1-452acf432793",
   "metadata": {},
   "outputs": [],
   "source": [
    "# three types of fake data for testing\n",
    "\n",
    "def signal1(A, t):\n",
    "    return A * np.sin(30*t) * np.exp(-t) + np.random.randn(t.shape[0])\n",
    "\n",
    "def signal2(A, t):\n",
    "    return A * np.sin(30.5*t) * np.exp(-t)+ np.random.randn(t.shape[0])\n",
    "\n",
    "def signal3(A, t):\n",
    "    return A * np.sin(31*t) * np.exp(-t) + np.random.randn(t.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f9d9086a-538e-4410-afd3-333093722ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tests\n",
    "A = 2\n",
    "t = np.linspace(0.0001, 5, 40000)\n",
    "s1 = signal1(A, t)\n",
    "s2 = signal2(A, t)\n",
    "s3 = signal3(A, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d7510935-dd2c-4484-96b5-eefec9837f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot fn for convenience\n",
    "def plot(x, y, title):\n",
    "    plt.figure()\n",
    "    plt.plot(x, y, linewidth=0.7, c='blue')\n",
    "    plt.title(title)\n",
    "    plt.savefig(title + '.png')\n",
    "    plt.show()\n",
    "\n",
    "plot(t, s1, 'Example1')\n",
    "plot(t, s2, 'Example2')\n",
    "plot(t, s3, 'Example3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3cfb129b-0ce8-4790-94ae-cb31fa3e0ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of samples and length of each signal\n",
    "n_samples = 200\n",
    "signal_length = 40000\n",
    "\n",
    "# Initialize arrays\n",
    "X_data = np.zeros((n_samples, signal_length))\n",
    "y_labels = np.zeros(n_samples)\n",
    "\n",
    "# time array\n",
    "t = np.linspace(0, 1, signal_length)\n",
    "\n",
    "# Generate samples\n",
    "for i in range(n_samples):\n",
    "    if i < n_samples // 3:\n",
    "        X_data[i] = signal1(A, t)\n",
    "        y_labels[i] = 0\n",
    "    elif i < 2 * n_samples // 3:\n",
    "        X_data[i] = signal2(A, t)\n",
    "        y_labels[i] = 1\n",
    "    else:\n",
    "        X_data[i] = signal3(A, t)\n",
    "        y_labels[i] = 2\n",
    "\n",
    "# One-hot encode the labels\n",
    "y_labels_one_hot = to_categorical(y_labels, num_classes=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "092a184e-a79f-462a-8017-741471a05ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_14\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_14\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40000</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_56 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">10,240,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_57 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_58 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_59 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
       "│ input_layer_14 (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40000\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_56 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │    \u001b[38;5;34m10,240,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_57 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_58 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_59 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m195\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,281,603</span> (39.22 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,281,603\u001b[0m (39.22 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,281,603</span> (39.22 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,281,603\u001b[0m (39.22 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_shape = (40000,)\n",
    "\n",
    "# Build the classifier model\n",
    "def build_classifier(input_shape):\n",
    "    inputs = layers.Input(shape=input_shape)\n",
    "    x = layers.Dense(256, activation='relu')(inputs)\n",
    "    x = layers.Dense(128, activation='relu')(x)\n",
    "    x = layers.Dense(64, activation='relu')(x)\n",
    "    \n",
    "    # Output layer (one hot encoding)\n",
    "    outputs = layers.Dense(3, activation='softmax')(x)\n",
    "    \n",
    "    model = models.Model(inputs, outputs)\n",
    "    return model\n",
    "\n",
    "classifier = build_classifier(input_shape)\n",
    "\n",
    "# Compile the model\n",
    "classifier.compile(optimizer='adam', \n",
    "                   loss='categorical_crossentropy', \n",
    "                   metrics=['CategoricalAccuracy'])\n",
    "\n",
    "# Display model\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ec34ffe0-d4ed-484d-b82a-c3270083f082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 103ms/step - CategoricalAccuracy: 0.4166 - loss: 9.3746 - val_CategoricalAccuracy: 0.0000e+00 - val_loss: 62.7976\n",
      "Epoch 2/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 0.5618 - loss: 10.2041 - val_CategoricalAccuracy: 0.6750 - val_loss: 0.6425\n",
      "Epoch 3/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 0.6952 - loss: 1.6685 - val_CategoricalAccuracy: 1.0000 - val_loss: 0.0000e+00\n",
      "Epoch 4/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - CategoricalAccuracy: 0.9716 - loss: 0.0921 - val_CategoricalAccuracy: 1.0000 - val_loss: 1.3485e-05\n",
      "Epoch 5/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - CategoricalAccuracy: 0.9812 - loss: 0.0438 - val_CategoricalAccuracy: 0.3000 - val_loss: 1.4833\n",
      "Epoch 6/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 0.9966 - loss: 0.0041 - val_CategoricalAccuracy: 0.2500 - val_loss: 1.5841\n",
      "Epoch 7/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - CategoricalAccuracy: 1.0000 - loss: 1.0457e-06 - val_CategoricalAccuracy: 1.0000 - val_loss: 8.7625e-04\n",
      "Epoch 8/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 1.0000 - loss: 1.6579e-05 - val_CategoricalAccuracy: 1.0000 - val_loss: 2.3305e-06\n",
      "Epoch 9/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - CategoricalAccuracy: 1.0000 - loss: 9.9684e-04 - val_CategoricalAccuracy: 1.0000 - val_loss: 5.0664e-08\n",
      "Epoch 10/10\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - CategoricalAccuracy: 1.0000 - loss: 0.0012 - val_CategoricalAccuracy: 1.0000 - val_loss: 2.9802e-09\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x28680244e60>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training\n",
    "classifier.fit(X_data, y_labels_one_hot, epochs=10, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2bd36515-2777-4d49-82ec-b50f1e0904a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 1.0902163e-08, 1.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test on newly generated data\n",
    "test = signal3(A, t)\n",
    "test = np.reshape(test, (1, 40000))\n",
    "\n",
    "classifier.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "931bb66b-7171-4630-9753-d9ff7fa3cb78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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