supportvectormachine1.ipynb

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    "### What is the mathematical formula for a linear SVM?\n",
    " \n",
    "The mathematical formula for a linear Support Vector Machine (SVM) is:\n",
    "\n",
    "Given a set of training data with feature vectors X and corresponding binary class labels y (where y can be either -1 or 1 for a binary classification problem), the goal of a linear SVM is to find a hyperplane that maximizes the margin between the two classes. This hyperplane can be represented by the equation:\n",
    "\n",
    "                       w^T.x + b = 0\n",
    "\n",
    "Here:\n",
    "- w is the weight vector (coefficients) perpendicular to the hyperplane.\n",
    "- x is the feature vector of a data point.\n",
    "- b is the bias or intercept term.\n",
    "\n",
    "The margin between the two classes is inversely proportional to the norm (length) of the weight vector w. To maximize the margin, we have to find the values of w and b that satisfy the following conditions:\n",
    "\n",
    "1. The margin is maximized: Minimize ||w|| subject to the constraint that for all data points (x_i, y_i):\n",
    "\n",
    "                      y_i(w^T.x_i + b) >= 1\n",
    "                      \n",
    "\n",
    "2. The data points are correctly classified, meaning that the hyperplane should separate the data points with a margin equal to or greater than 1.\n",
    "\n",
    "These conditions are typically expressed in the context of a convex optimization problem, which can be solved using techniques like quadratic programming.\n",
    "\n",
    "The SVM classifier then makes predictions based on the sign of the expression (w^T.x + b):\n",
    "- If (w^T.x + b) is positive, the data point is classified as class 1.\n",
    "- If (w^T.x + b) is negative, the data point is classified as class -1."
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    "###  What is the objective function of a linear SVM?\n",
    "\n",
    "The objective function of a linear Support Vector Machine (SVM) is to find the hyperplane that maximizes the margin between two classes while correctly classifying the training data. This is typically formulated as an optimization problem, specifically a constrained quadratic optimization problem.\n",
    "\n",
    "The objective function for a linear SVM can be expressed as follows:\n",
    "\n",
    "**Minimize**:\n",
    "\n",
    "\n",
    "                           (1/2) * ||w||^2\n",
    "\n",
    "\n",
    "This term represents the desire to maximize the margin by minimizing the norm (length) of the weight vector w.\n",
    "\n",
    "**Subject to the constraints**:\n",
    "\n",
    "For each training data point (x_i, y_i), where x_i is a feature vector and y_i is the corresponding binary class label (either -1 or 1), the constraints are:\n",
    "\n",
    "\n",
    "                               y_i(w^T.x_i + b) >= 1\n",
    "\n",
    "These constraints ensure that the hyperplane correctly classifies the data points and that each data point is separated from the hyperplane by a margin of at least 1.\n",
    "\n",
    "Overall, the objective function of a linear SVM is to find the values of the weight vector w and the bias term b that minimize the norm of w (which maximizes the margin) while satisfying the constraints that ensure correct classification and margin separation. This problem can be solved using various optimization techniques, such as quadratic programming, to find the optimal hyperplane that best separates the data."
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    "### What is the kernel trick in SVM?\n",
    "\n",
    "The kernel trick is a fundamental concept in Support Vector Machines (SVMs) and is used to handle nonlinear data by implicitly mapping it to a higher-dimensional space where it becomes linearly separable. It allows SVMs to classify data that cannot be effectively separated by a linear hyperplane in the original feature space.\n",
    "\n",
    "In a traditional (linear) SVM, you find a hyperplane in the original feature space that best separates the data into different classes. However, in many real-world scenarios, the data is not linearly separable, meaning there is no single straight line or hyperplane that can effectively separate the classes.\n",
    "\n",
    "The kernel trick addresses this limitation by mapping the original data into a higher-dimensional feature space where it becomes linearly separable. This mapping is achieved by applying a mathematical function called a kernel function. The kernel function takes pairs of data points in the original feature space and computes a similarity measure between them in the higher-dimensional space.\n",
    "\n",
    "The most commonly used kernel functions in SVMs include:\n",
    "\n",
    "1. **Linear Kernel**: This is the default kernel for linearly separable data, and it corresponds to the inner product of the feature vectors in the original space.\n",
    "\n",
    "2. **Polynomial Kernel**: This kernel applies a polynomial function to the inner product of feature vectors, which allows the SVM to capture nonlinear patterns in the data.\n",
    "\n",
    "3. **Radial Basis Function (RBF) Kernel**: Also known as the Gaussian kernel, it maps data into an infinite-dimensional space. It is particularly effective at capturing complex, nonlinear relationships in the data.\n",
    "\n",
    "4. **Sigmoid Kernel**: This kernel function is based on the sigmoid (logistic) function and can be used to handle data that exhibits sigmoid-like behavior.\n",
    "\n",
    "The key advantage of the kernel trick is that it allows us to work in a higher-dimensional space without explicitly computing the transformation of data into that space, which could be computationally expensive or even impossible for certain transformations. Instead, the kernel function computes the inner product or similarity measure in the higher-dimensional space directly in the original feature space. This makes SVMs capable of handling complex, nonlinear decision boundaries while still benefiting from the mathematical properties of linear classifiers."
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    "### What is the role of support vectors in SVM Explain with example.\n",
    "\n",
    "Support vectors play a crucial role in Support Vector Machines (SVMs). They are the data points from the training dataset that are closest to the decision boundary (hyperplane). These points have a direct impact on defining the decision boundary and maximizing the margin, which is the separation between different classes. Here's an explanation with an example:\n",
    "\n",
    "Let's consider a simple binary classification problem where we want to separate two classes, Class A and Class B, in a two-dimensional feature space. The decision boundary is represented by a straight line (in this case, a hyperplane) that separates the two classes.\n",
    "\n",
    "In this example, we have 10 data points, with 5 belonging to Class A and 5 belonging to Class B. The data points are represented as follows (x1, x2) coordinates:\n",
    "\n",
    "Class A:\n",
    "1. (2, 2)\n",
    "2. (3, 3)\n",
    "3. (4, 4)\n",
    "4. (5, 5)\n",
    "5. (6, 6)\n",
    "\n",
    "Class B:\n",
    "1. (1, 1)\n",
    "2. (2, 1)\n",
    "3. (1, 2)\n",
    "4. (3, 1)\n",
    "5. (2, 3)\n",
    "\n",
    "When you train a linear SVM on this data, the goal is to find the hyperplane that best separates the two classes while maximizing the margin. The margin is the distance between the hyperplane and the closest data points from each class. These closest data points are the support vectors.\n",
    "\n",
    "In this example, the support vectors might be:\n",
    "\n",
    "For Class A:\n",
    "1. (2, 2)\n",
    "2. (3, 3)\n",
    "\n",
    "For Class B:\n",
    "1. (1, 1)\n",
    "2. (2, 1)\n",
    "\n",
    "The SVM focuses on these support vectors because they are the ones that are closest to the decision boundary. The distances from these support vectors to the hyperplane are equal and represent the margin. The other data points that are farther from the hyperplane do not influence the decision boundary.\n",
    "\n",
    "The role of support vectors in SVM can be summarized as follows:\n",
    "\n",
    "1. **Defining the Margin**: Support vectors determine the width of the margin. The margin is maximized by making sure that the hyperplane is equidistant from the closest data points of each class.\n",
    "\n",
    "2. **Determining the Decision Boundary**: The position of the support vectors influences the orientation and position of the hyperplane. The hyperplane is chosen in such a way that it minimizes classification error while maximizing the margin.\n",
    "\n",
    "3. **Efficient Representation**: SVMs are efficient because they only depend on the support vectors. Once these support vectors are identified, the other data points can be ignored for most purposes, which simplifies the computation."
   ]
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XHQXoRuwXlDZ37lycY7EiTmtYEZ2Gnke34FyKfeHNTUREtwqfMUFENI5wYV1eXo5rX1wo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\nG0FVgUqsrq4+dOjQv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RqNBj9xHJx6/MLGjIlqbOQRDkiuHAfEKXB32Moyp8AI4woZTKbg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FhoAL2NnZee3atYiIiAceeIDP5zc2NnZ1dUHDBwcHw2fq6+vr7++Hw22970XjHnEXuDUbngB/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Zw5E8dQV+++VlJScubMGZlMhsaQk5Pj7OyMg+Dq6jpu3DiJRLJ161Z9s7G2tkazQahQU1MT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\nRERERERERGQwT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    }
   },
   "cell_type": "markdown",
   "id": "f3edebef-bdce-47c7-b7b4-232ce4ea443f",
   "metadata": {},
   "source": [
    "###  Illustrate with examples and graphs of Hyperplane, Marginal plane, Soft margin and Hard margin in SVM?\n",
    "\n",
    "Concepts of a hyperplane, marginal plane, soft margin, and hard margin in Support Vector Machines (SVM) with examples and accompanying graphs.\n",
    "\n",
    "1. **Hyperplane**:\n",
    "   - A hyperplane is the decision boundary that separates two classes in an SVM.\n",
    "   - In a two-dimensional feature space, a hyperplane is a straight line.\n",
    "   - In a three-dimensional space, it is a plane, and in higher-dimensional spaces, it's a hyperplane.\n",
    "\n",
    "   **Example**: In a simple 2D space with two classes, the hyperplane is a line that separates the data points.\n",
    "\n",
    "   ![Hyperplane.png](attachment:572c9a30-c4d0-47a6-9d90-3d68e368d5e5.png)\n",
    "\n",
    "2. **Marginal Plane**:\n",
    "   - The marginal plane is the decision boundary that maximizes the margin between two classes in a hard margin SVM.\n",
    "   - It is the hyperplane that is equidistant from the nearest data points of each class.\n",
    "\n",
    "   **Example**: In a 2D space with a hard margin SVM, the marginal plane is the line that maximizes the gap between the classes.\n",
    "\n",
    "3. **Soft Margin**:\n",
    "   - In some cases, data might not be perfectly separable by a single hyperplane (hard margin).\n",
    "   - Soft margin SVMs allow for some misclassification and find a hyperplane that balances margin maximization with a tolerance for misclassification.\n",
    "   - The margin is still maximized, but a few data points may be inside the margin or even on the wrong side of the hyperplane.\n",
    "\n",
    "   **Example**: In a 2D space with a soft margin SVM, some data points can be inside the margin or on the wrong side, but the margin is maximized while allowing for these errors.\n",
    "\n",
    "4. **Hard Margin**:\n",
    "   - In a hard margin SVM, no misclassification is allowed. The SVM aims to find a hyperplane that perfectly separates the classes, with no data points inside the margin or on the wrong side.\n",
    "\n",
    "   **Example**: In a 2D space with a hard margin SVM, the hyperplane perfectly separates the classes, and there are no data points inside the margin or on the wrong side.\n",
    "\n",
    "   ![Hard-margin-and-soft-margin.png](attachment:23641a38-39d9-4993-8961-15d01c3eeb66.png)\n",
    "\n",
    "In practice, the choice between soft margin and hard margin depends on the data's nature. Soft margin SVMs are more robust to noisy or overlapping data, while hard margin SVMs are suitable for well-separated and noise-free data. The balance between maximizing the margin and tolerating misclassification is controlled by a parameter often denoted as \"C\" in soft margin SVMs. Higher values of C make the SVM more sensitive to misclassification, while lower values prioritize larger margins."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a267b373-e003-4a80-8bca-0a8f6cf4cc38",
   "metadata": {},
   "source": [
    "### SVM Implementation through Iris dataset.\n",
    "\n",
    "~ Load the iris dataset from the scikit-learn library and split it into a training set and a testing setl\n",
    "\n",
    "~ Train a linear SVM classifier on the training set and predict the labels for the testing setl\n",
    "\n",
    "~ Compute the accuracy of the model on the testing setl\n",
    "\n",
    "~ Plot the decision boundaries of the trained model using two of the featuresl\n",
    "\n",
    "~ Try different values of the regularisation parameter C and see how it affects the performance of\n",
    "the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ca190c15-0ff3-4195-b8b5-a5b895407b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d87de34a-1bee-472c-a0be-4e2794ae6abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data[:, :2]     # first two feature for visualization\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b8b666f3-84ca-4142-91dc-f9dfd0bae695",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "96215129-b44e-4c03-82ed-39a26085cf60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_classifier = SVC(kernel='linear')\n",
    "svm_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1899c638-9a1b-4f5e-abd1-e994c3b14600",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0cedc373-9cfb-4cb2-a69a-c97b8a1fa028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f278a259-c57b-4fdf-bac3-33e0b0806aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))\n",
    "\n",
    "# predict class labels for each point in the mesh grid\n",
    "Z = svm_classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# decision boundries\n",
    "plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "\n",
    "# original data \n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1])\n",
    "plt.title(\"SVM Decision Boundaries (Two Features)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "900c5a4c-5f1d-437a-8d0f-f78c4c5d1b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (C=0.1): 0.8333333333333334\n",
      "Accuracy (C=1): 0.9\n",
      "Accuracy (C=5): 0.9\n",
      "Accuracy (C=10): 0.9\n"
     ]
    }
   ],
   "source": [
    "C_values = [0.1, 1, 5, 10]  \n",
    "for C in C_values:\n",
    "    svm_classifier = SVC(kernel='linear', C=C)\n",
    "    svm_classifier.fit(X_train, y_train)\n",
    "    y_pred = svm_classifier.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(f\"Accuracy (C={C}): {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007af2ed-dd11-430b-8d50-f8099bc6a91f",
   "metadata": {},
   "source": [
    "### Implement a linear SVM classifier from scratch using Python and compare its performance with the scikit-learn implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5fe2da65-526f-4265-ae6c-b31c6cf75495",
   "metadata": {},
   "outputs": [],
   "source": [
    "## custom linear SVM classifier\n",
    "import numpy as np\n",
    "\n",
    "class LinearSVM:\n",
    "    def __init__(self, learning_rate=0.01, num_epochs=1000, C=1.0):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.num_epochs = num_epochs\n",
    "        self.C = C\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "\n",
    "    def hinge_loss(self, X, y):\n",
    "        loss = 1 - y * (np.dot(X, self.weights) - self.bias)\n",
    "        loss[loss < 0] = 0 \n",
    "        return loss.mean()\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        num_samples, num_features = X.shape\n",
    "        self.weights = np.zeros(num_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        for _ in range(self.num_epochs):\n",
    "            for i, x in enumerate(X):\n",
    "                condition = y[i] * (np.dot(x, self.weights) - self.bias) >= 1\n",
    "                if condition:\n",
    "                    self.weights -= self.learning_rate * (2 * self.C * self.weights)\n",
    "                else:\n",
    "                    self.weights -= self.learning_rate * (2 * self.C * self.weights - np.dot(x, y[i]))\n",
    "                    self.bias -= self.learning_rate * y[i]\n",
    "\n",
    "    def predict(self, X):\n",
    "        linear_output = np.dot(X, self.weights) - self.bias\n",
    "        return np.sign(linear_output)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5f86ff8f-36de-4042-93e1-5d724bbe8c9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data     \n",
    "y = iris.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "svm = LinearSVM(learning_rate=0.01, num_epochs=1000, C=1.0)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "predictions = svm.predict(X_test)\n",
    "print(\"Predictions:\", predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "727711be-ed37-4674-b339-f7f898318e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-learn SVM Accuracy: 1.0\n",
      "Custom Linear SVM Accuracy: 0.3\n"
     ]
    }
   ],
   "source": [
    "# comparison \n",
    "# Scikit-learn SVM classifier\n",
    "sklearn_svm = SVC(kernel='linear')\n",
    "sklearn_svm.fit(X_train, y_train)\n",
    "y_pred_sklearn = sklearn_svm.predict(X_test)\n",
    "accuracy_sklearn = accuracy_score(y_test, y_pred_sklearn)\n",
    "print(\"Scikit-learn SVM Accuracy:\", accuracy_sklearn)\n",
    "\n",
    "# custom linear SVM classifier\n",
    "custom_svm = LinearSVM(learning_rate=0.01, num_epochs=1000, C=1.0)\n",
    "custom_svm.fit(X_train, y_train)\n",
    "y_pred_custom = custom_svm.predict(X_test)\n",
    "accuracy_custom = accuracy_score(y_test, y_pred_custom)\n",
    "print(\"Custom Linear SVM Accuracy:\", accuracy_custom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d10e196-4f67-4a6a-98f9-517d9c999d55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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