diff --git a/CNN+TCN+LSTM+ATTENTION NEW.ipynb b/CNN+TCN+LSTM+ATTENTION NEW.ipynb new file mode 100644 index 0000000..b49caf3 --- /dev/null +++ b/CNN+TCN+LSTM+ATTENTION NEW.ipynb @@ -0,0 +1,811 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMBDiuw1IDIhMQ8URm6LC/0", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FkAotuRzefyl" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", + "from keras.models import Model\n", + "from keras.layers import Input, Conv1D, MaxPooling1D, Flatten, Dense, Dropout, concatenate\n", + "from keras.layers import TimeDistributed, LSTM\n", + "from keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "# Replace 'your_dataset.csv' with the\n", + "data = pd.read_csv('PhiUSIIL_Phishing_URL_Dataset.csv')" + ], + "metadata": { + "id": "vo7b7h0kjTSO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X = data.drop(columns=['label'])\n", + "y = data['label']" + ], + "metadata": { + "id": "Q_c0I6iRjg8k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "8PQ0YCv0oxHm" + } + }, + { + "cell_type": "code", + "source": [ + "label_encoders = {}\n", + "for column in X.select_dtypes(include='object'):\n", + " label_encoders[column] = LabelEncoder()\n", + " X[column] = label_encoders[column].fit_transform(X[column])\n", + "\n" + ], + "metadata": { + "id": "Qxfqg4UQjvDs" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Split the dataset into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" + ], + "metadata": { + "id": "tq5v4kLkjz5u" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Standardize numerical features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train.select_dtypes(include=['int64', 'float64']))\n", + "X_test_scaled = scaler.transform(X_test.select_dtypes(include=['int64', 'float64']))\n" + ], + "metadata": { + "id": "ownfWliMpGKi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "input_shape = (X_train_scaled.shape[1],) # Number of features" + ], + "metadata": { + "id": "9Qs7dx1Yerm-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install tensorflow" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4NzfcllsiwXD", + "outputId": "7c84da09-5736-4a3c-8e71-775b1b8b10fa" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from 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{ + "base_uri": "https://localhost:8080/" + }, + "id": "S3OmZL1HyvWb", + "outputId": "80951bb5-52b0-4ed5-d605-2c1f1f532927" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement 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/usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# CNN layer\n", + "cnn_input = Input(shape=(None, 10))\n", + "cnn_layer = Conv1D(filters=64, kernel_size=3, activation='relu')(cnn_input)\n", + "cnn_layer = MaxPooling1D(pool_size=2)(cnn_layer)" + ], + "metadata": { + "id": "z1G51_JLykx7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "from tensorflow.keras.layers import Input, TimeDistributed, Dense\n", + "# TCN layer\n", + "tcn_input = Input(shape=(1, 55))\n", + "tcn_layer = TimeDistributed(Dense(64, activation='relu'))(tcn_input)" + ], + "metadata": { + "id": "uo9K23NEy8Yw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras.layers import Bidirectional, LSTM, Input\n", + "lstm_input = Input(shape=(1, 55))\n", + "lstm_layer = Bidirectional(LSTM(64, return_sequences=True))(lstm_input)" + ], + "metadata": { + "id": "ss2VAByzzC0s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Import the Reshape layer\n", + "from keras.layers import Reshape\n", + "\n", + "# Reshape LSTM output to match dimensions\n", + "lstm_reshaped = Reshape((-1, 64))(lstm_layer)" + ], + "metadata": { + "id": "53YavPPGzEop" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"cnn_layer shape:\", cnn_layer.shape)\n", + "print(\"tcn_layer shape:\", tcn_layer.shape)\n", + "print(\"lstm_reshaped shape:\", lstm_reshaped.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GMY6gUwMzUSR", + "outputId": "21a13c09-8857-4415-f8b1-c50deb00c03f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cnn_layer shape: (None, None, 64)\n", + "tcn_layer shape: (None, 1, 64)\n", + "lstm_reshaped shape: (None, 2, 64)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -q tensorflow\n", + "import tensorflow as tf" + ], + "metadata": { + "id": "6Xcgu9Ci3nfC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "cnn_layer = tf.keras.layers.Lambda(lambda x: tf.keras.backend.reshape(x, (-1, 1, x.shape[-1])))(cnn_layer)" + ], + "metadata": { + "id": "fP_1i1en39BW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Reshape\n", + "print(\"CNN Layer Shape:\", cnn_layer.shape)\n", + "print(\"TCN Layer Shape:\", tcn_layer.shape)\n", + "print(\"LSTM Reshaped Layer Shape:\", lstm_reshaped.shape)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0BMKz9PRzmVz", + "outputId": "bd617b27-4823-4530-df5c-2c852765ac53" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CNN Layer Shape: (None, 1, 64)\n", + "TCN Layer Shape: (None, 1, 64)\n", + "LSTM Reshaped Layer Shape: (None, 2, 64)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "cnn_layer = tf.keras.layers.Reshape((1, cnn_layer.shape[-1]))(cnn_layer)\n", + "tcn_layer = tf.keras.layers.Reshape((1, tcn_layer.shape[-1]))(tcn_layer)" + ], + "metadata": { + "id": "bVBgS-tNDnz2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "\n", + "# Assuming lstm_reshaped has shape (None, 2, 64)\n", + "lstm_reshaped = tf.keras.layers.Reshape((1, -1))(lstm_reshaped)\n" + ], + "metadata": { + "id": "W1Y0qLQ453IC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras.layers import Input, Dense, Concatenate, Conv1D, MaxPooling1D, Flatten, Dropout, LSTM, Bidirectional, Attention" + ], + "metadata": { + "id": "vjKnZxCzF7ou" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "cnn_layer_reshaped = Dense(128, activation=\"relu\")(cnn_layer)\n", + "attention = Attention()([cnn_layer_reshaped, tcn_layer, lstm_reshaped])" + ], + "metadata": { + "id": "gox1whorGHDb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Merge layers\n", + "merged_layer = concatenate([cnn_layer, tcn_layer, lstm_reshaped, attention])\n", + "merged_layer = Flatten()(merged_layer)\n" + ], + "metadata": { + "id": "4xNtjw72GJ9n" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Fully connected layer\n", + "fc_layer = Dense(64, activation='relu')(merged_layer)" + ], + "metadata": { + "id": "b3fYJzTkGQoy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Output layer\n", + "output_layer = Dense(1, activation='sigmoid')(fc_layer)" + ], + "metadata": { + "id": "RQru8f--GUJw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create model\n", + "model = Model(inputs=[cnn_input, tcn_input, lstm_input], outputs=output_layer)\n" + ], + "metadata": { + "id": "0z2fan52GYIK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])" + ], + "metadata": { + "id": "2akcY7QBGbnH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(model.input_shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "chenQTMwGsbI", + "outputId": "56a85a66-56c8-4266-fea0-93948a8f2560" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[(None, None, 10), (None, 1, 55), (None, 1, 55)]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(X_train_scaled.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eriUP9ViGxQR", + "outputId": "4b93a168-0bb9-4f9c-8d70-554eeabd44e6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(188636, 55)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "X_train_scaled = X_train_scaled.reshape((-1, 1, 55))" + ], + "metadata": { + "id": "jxPxxmC0G1d1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = keras.Sequential([\n", + " keras.layers.InputLayer(input_shape=(None, 55)),\n", + " # ...\n", + "])" + ], + "metadata": { + "id": "etjXDHUIHHvf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model.compile(\n", + " optimizer=\"adam\",\n", + " loss=\"binary_crossentropy\",metrics=['accuracy']\n", + ")" + ], + "metadata": { + "id": "EOo97jvHHc4D" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model.fit(X_train_scaled, y_train, epochs=10, batch_size=64, validation_split=0.2)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QUaFmjXiG5iP", + "outputId": "2e762f13-93fb-462f-de52-fe3e071d66bf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "2358/2358 [==============================] - 15s 6ms/step - loss: 7.0652 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 2/10\n", + "2358/2358 [==============================] - 18s 7ms/step - loss: 7.0660 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 3/10\n", + "2358/2358 [==============================] - 12s 5ms/step - loss: 7.0656 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 4/10\n", + "2358/2358 [==============================] - 9s 4ms/step - loss: 7.0649 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 5/10\n", + "2358/2358 [==============================] - 10s 4ms/step - loss: 7.0645 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 6/10\n", + "2358/2358 [==============================] - 9s 4ms/step - loss: 7.0653 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 7/10\n", + "2358/2358 [==============================] - 10s 4ms/step - loss: 7.0653 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 8/10\n", + "2358/2358 [==============================] - 10s 4ms/step - loss: 7.0651 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 9/10\n", + "2358/2358 [==============================] - 9s 4ms/step - loss: 7.0659 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n", + "Epoch 10/10\n", + "2358/2358 [==============================] - 11s 5ms/step - loss: 7.0651 - accuracy: 0.0871 - val_loss: 7.0660 - val_accuracy: 0.0854\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 190 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Step 4: Evaluate the model\n", + "loss, accuracy = model.evaluate([X_test_scaled], y_test)\n", + "print(f'Test Accuracy: {accuracy}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1nyxfj59M1pN", + "outputId": "1cf7a942-b1c1-495b-9eb0-a2033346ad27" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1474/1474 [==============================] - 2s 1ms/step - loss: 6.0942 - accuracy: 0.0837\n", + "Test Accuracy: 0.08365317434072495\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.save('url_malicious_model.h5')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H_v7zsBd5lbg", + "outputId": "d4fa9812-7137-4a34-fe58-8f0225149839" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + " #Step 5: Make predictions\n", + "def predict_malicious(url_features):\n", + " scaled_features = scaler.transform(url_features)\n", + " prediction = model.predict([scaled_features, scaled_features, scaled_features])\n", + " if prediction >= 0.5:\n", + " return \"Malicious\"\n", + " else:\n", + " return \"Not Malicious\"" + ], + "metadata": { + "id": "OcnhoY_ANIur" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "HnRRxWbdPujY" + } + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "tokenizer = Tokenizer()\n", + "max_len = 100\n", + "def preprocess_url(url):\n", + " # Tokenize and pad the URL\n", + " url_seq = tokenizer.texts_to_sequences([url])\n", + " url_padded = pad_sequences(url_seq, maxlen=max_len)\n", + " return url_padded\n", + "\n", + "def predict_url_maliciousness(url):\n", + " preprocessed_url = preprocess_url(url)\n", + " prediction = model.predict(preprocessed_url)[0][0]\n", + " if prediction >= 0.5:\n", + " return \"Malicious\"\n", + " else:\n", + " return \"Not Malicious\"\n", + "\n", + "# Example usage:\n", + "user_url = input(\"Enter a URL to check if it's malicious: \")\n", + "result = predict_url_maliciousness(user_url)\n", + "print(f\"The URL '{user_url}' is predicted to be: {result}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kjz1IjfXzEsm", + "outputId": "68753e04-48c6-4694-f82f-69dcf0a46b10" + }, + "execution_count": null, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter a URL to check if it's malicious: wwww.google.com\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:6 out of the last 11 calls to .predict_function at 0x7f6afa551fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1/1 [==============================] - 0s 83ms/step\n", + "The URL 'wwww.google.com' is predicted to be: Not Malicious\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "B_9ZiYQ62kaf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model.save('url_malicious_model.h5')\n" + ], + "metadata": { + "id": "P4b-zYdy1d9k" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Queue b/Queue index 569c984..ce7de35 100644 --- a/Queue +++ b/Queue @@ -1,73 +1,118 @@ -public class QueuesArray { - private int data[]; - private int front; - private int rear; - private int size; - - public QueuesArray(int n) { - data = new int[n]; - front = 0; - rear = 0; - size = 0; - } - - public int length() { - return size; - } - - public boolean isEmpty() { - return size == 0; - } - - public boolean isFull() { - return size == data.length; - } - - public void enqueue(int e) { - if(isFull()) { - System.out.println("Queue is Full"); - return; - } - else { - data[rear] = e; - rear = rear + 1; - size = size + 1; - } - } - - public int dequeue() { - if(isEmpty()) { - System.out.println("Queue is Empty"); - return -1; - } - else { - int e = data[front]; - front = front + 1; - size = size - 1; - return e; - } - } - - public void display() { - for(int i=front;i +#include + +#define MAX 6 + +int intArray[MAX]; +int front = 0; +int rear = -1; +int itemCount = 0; + +int peek() { + return intArray[front]; +} + +bool isEmpty() { + return itemCount == 0; +} + +bool isFull() { + return itemCount == MAX; +} + +int size() { + return itemCount; +} + +void insert(int data) { + + if(!isFull()) { + + if(rear == MAX-1) { + rear = -1; + } + + intArray[++rear] = data; + itemCount++; + } +} + +int removeData() { + int data = intArray[front++]; + + if(front == MAX) { + front = 0; + } + + itemCount--; + return data; +} + +int main() { + /* insert 5 items */ + insert(3); + insert(5); + insert(9); + insert(1); + insert(12); + + // front : 0 + // rear : 4 + // ------------------ + // index : 0 1 2 3 4 + // ------------------ + // queue : 3 5 9 1 12 + insert(15); + + // front : 0 + // rear : 5 + // --------------------- + // index : 0 1 2 3 4 5 + // --------------------- + // queue : 3 5 9 1 12 15 + + if(isFull()) { + printf("Queue is full!\n"); + } + + // remove one item + int num = removeData(); + + printf("Element removed: %d\n",num); + // front : 1 + // rear : 5 + // ------------------- + // index : 1 2 3 4 5 + // ------------------- + // queue : 5 9 1 12 15 + + // insert more items + insert(16); + + // front : 1 + // rear : -1 + // ---------------------- + // index : 0 1 2 3 4 5 + // ---------------------- + // queue : 16 5 9 1 12 15 + + // As queue is full, elements will not be inserted. + insert(17); + insert(18); + + // ---------------------- + // index : 0 1 2 3 4 5 + // ---------------------- + // queue : 16 5 9 1 12 15 + printf("Element at front: %d\n",peek()); + + printf("----------------------\n"); + printf("index : 5 4 3 2 1 0\n"); + printf("----------------------\n"); + printf("Queue: "); + + while(!isEmpty()) { + int n = removeData(); + printf("%d ",n); + } }