{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Perceptron Algorithm for Classification of Iris Dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "# load the iris dataset\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Preprocess the data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# ToDo: split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = None, None, None, None"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define the perceptron algorithm"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# Define the perceptron algorithm\n",
    "class Perceptron:\n",
    "    def __init__(self, learning_rate=0.01, n_iters=1000):\n",
    "        pass\n",
    "\n",
    "    # define the fit function to train the model\n",
    "\n",
    "    # define the predict function to predict labels\n",
    "\n",
    "    def _unit_step_func(self, x):\n",
    "        pass"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Train the model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "# Train the model\n",
    "p = Perceptron(learning_rate=0.01, n_iters=1000)\n",
    "p.fit(X_train, y_train)\n",
    "predictions_train = p.predict(X_train)\n",
    "predictions = p.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Evaluate the model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron classification train accuracy 0.3416666666666667\n",
      "Perceptron classification accuracy 0.3\n"
     ]
    }
   ],
   "source": [
    "# evaluate train accuracy\n",
    "print(\"Perceptron classification train accuracy\", accuracy_score(y_train, predictions_train))\n",
    "print(\"Perceptron classification accuracy\", accuracy_score(y_test, predictions))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Non-linear feature transformation on the concrete compressive strength dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "from itertools import combinations_with_replacement\n",
    "\n",
    "# ToDO: implement the polynomial_features() function\n",
    "def polynomial_features(X, degree):\n",
    "    pass"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "# Non-linear feature transformation\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# load the concrete compressive strength dataset\n",
    "df = pd.read_excel('Concrete_Data.xls')\n",
    "\n",
    "# ToDo: split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = None, None, None, None\n",
    "\n",
    "# transform the features into second degree polynomial features\n",
    "X_train_poly_custom = polynomial_features(X_train.values, degree=2)\n",
    "X_test_poly_custom = polynomial_features(X_test.values, degree=2)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Train the linear regression model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error (train poly custom): 64.55\n",
      "Mean squared error (test poly custom): 58.28\n",
      "Mean squared error (train): 110.66\n",
      "Mean squared error (test): 95.98\n",
      "R^2 (train poly custom): 0.77\n",
      "R^2 (test poly custom): 0.77\n",
      "R^2 (train): 0.61\n",
      "R^2 (test): 0.63\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "lr_poly_custom = LinearRegression()\n",
    "lr = LinearRegression()\n",
    "# fit the model\n",
    "lr_poly_custom.fit(X_train_poly_custom, y_train)\n",
    "lr.fit(X_train, y_train)\n",
    "# predict values from the polynomial transformed features\n",
    "predictions_poly_custom_train = lr_poly_custom.predict(X_train_poly_custom)\n",
    "predictions_poly_custom = lr_poly_custom.predict(X_test_poly_custom)\n",
    "# predict values from the original features\n",
    "predictions_train = lr.predict(X_train)\n",
    "predictions = lr.predict(X_test)\n",
    "\n",
    "# mean squared error\n",
    "print(\"Mean squared error (train poly custom): {:.2f}\".format(mean_squared_error(y_train, predictions_poly_custom_train)))\n",
    "print(\"Mean squared error (test poly custom): {:.2f}\".format(mean_squared_error(y_test, predictions_poly_custom)))\n",
    "print(\"Mean squared error (train): {:.2f}\".format(mean_squared_error(y_train, predictions_train)))\n",
    "print(\"Mean squared error (test): {:.2f}\".format(mean_squared_error(y_test, predictions)))\n",
    "\n",
    "# coefficient of determination (R^2)\n",
    "print(\"R^2 (train poly custom): {:.2f}\".format(r2_score(y_train, predictions_poly_custom_train)))\n",
    "print(\"R^2 (test poly custom): {:.2f}\".format(r2_score(y_test, predictions_poly_custom)))\n",
    "print(\"R^2 (train): {:.2f}\".format(r2_score(y_train, predictions_train)))\n",
    "print(\"R^2 (test): {:.2f}\".format(r2_score(y_test, predictions)))\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "RBFs on the California Housing Prices dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "# ToDO: implement the rbf_kernel() function\n",
    "def rbf_kernel(X, centers, gamma):\n",
    "    pass"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression on original data:\n",
      "MSE: 0.5558915986952443\n",
      "R^2: 0.5757877060324508\n",
      "\n",
      "Linear regression on RBF-transformed data:\n",
      "MSE: 0.37106446913117447\n",
      "R^2: 0.7168330839511696\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Load the California Housing Prices dataset\n",
    "data = fetch_california_housing()\n",
    "X = data['data']\n",
    "y = data['target']\n",
    "\n",
    "# ToDo: split the data into training and testing sets\n",
    "\n",
    "# ToDo: standardize the data\n",
    "X_train_std = None\n",
    "X_test_std = None\n",
    "\n",
    "# Choose the number of centroids and the RBF kernel width\n",
    "num_centroids = 100\n",
    "gamma = 0.1\n",
    "\n",
    "# Randomly select the centroids from the training set\n",
    "np.random.seed(42)\n",
    "idx = np.random.choice(X_train_std.shape[0], num_centroids, replace=False)\n",
    "centroids = X_train_std[idx]\n",
    "\n",
    "# Compute the RBF features for the training and testing sets\n",
    "rbf_train = rbf_kernel(X_train_std, centroids, gamma)\n",
    "rbf_test = rbf_kernel(X_test_std, centroids, gamma)\n",
    "\n",
    "# Fit a linear regression model on the original and RBF-transformed data\n",
    "linreg_orig = LinearRegression().fit(X_train_std, y_train)\n",
    "linreg_rbf = LinearRegression().fit(rbf_train, y_train)\n",
    "\n",
    "# Evaluate the models on the testing set\n",
    "y_pred_orig = linreg_orig.predict(X_test_std)\n",
    "mse_orig = mean_squared_error(y_test, y_pred_orig)\n",
    "r2_orig = r2_score(y_test, y_pred_orig)\n",
    "\n",
    "y_pred_rbf = linreg_rbf.predict(rbf_test)\n",
    "mse_rbf = mean_squared_error(y_test, y_pred_rbf)\n",
    "r2_rbf = r2_score(y_test, y_pred_rbf)\n",
    "\n",
    "# Print the results\n",
    "print(\"Linear regression on original data:\")\n",
    "print(\"MSE:\", mse_orig)\n",
    "print(\"R^2:\", r2_orig)\n",
    "\n",
    "print(\"\\nLinear regression on RBF-transformed data:\")\n",
    "print(\"MSE:\", mse_rbf)\n",
    "print(\"R^2:\", r2_rbf)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# **(Bonus)** Multilayer Perceptron Algorithm for Regression of Concrete Compressive Strength Dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Download the Concrete Compressive Strength Dataset from the UCI Machine Learning Repository."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1030, 9)\n"
     ]
    }
   ],
   "source": [
    "# Download the Concrete Compressive Strength Dataset from the UCI Machine Learning Repository.\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# ToDo: load the dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Preprocess the data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "\n",
    "# ToDo: normalize the features\n",
    "\n",
    "# ToDo: split the data into training and testing sets"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define the multilayer perceptron algorithm"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "# ToDo: Implement the functions in the MLP class\n",
    "class MLP:\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, lr=0.01, epochs=1000):\n",
    "        pass\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Train the model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "# Create an instance of the MLP class\n",
    "mlp = MLP(input_dim=X_train.shape[1], hidden_dim=10, output_dim=1, lr=0.01, epochs=1000)\n",
    "# Train the model\n",
    "mlp.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Evaluate the model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 36.8911071801165\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = mlp.predict(X_test)\n",
    "print(\"Mean Squared Error:\", mean_squared_error(y_test, y_pred))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Compare the results with the linear regression model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 95.97548435337708\n"
     ]
    }
   ],
   "source": [
    "# ToDo: fit a linear regression model on the training data"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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