{"id":33721,"date":"2023-02-13T00:20:22","date_gmt":"2023-02-12T18:50:22","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-want-genuine-suggestion-to-build-support-vector-machine-in-python-without-using-scikit-learn-closed\/"},"modified":"2023-02-13T00:20:22","modified_gmt":"2023-02-12T18:50:22","slug":"solved-want-genuine-suggestion-to-build-support-vector-machine-in-python-without-using-scikit-learn-closed","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-want-genuine-suggestion-to-build-support-vector-machine-in-python-without-using-scikit-learn-closed\/","title":{"rendered":"[Solved] Want genuine suggestion to build Support Vector Machine in python without using Scikit-Learn [closed]"},"content":{"rendered":"<p> [ad_1]<br \/>\n<\/p>\n<div id=\"answer-49768883\" class=\"answer js-answer accepted-answer js-accepted-answer\" data-answerid=\"49768883\" data-parentid=\"49767638\" data-score=\"1\" data-position-on-page=\"1\" data-highest-scored=\"1\" data-question-has-accepted-highest-score=\"1\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n<div class=\"post-layout\">\n<div class=\"votecell post-layout--left\"><\/div>\n<div class=\"answercell post-layout--right\">\n<div class=\"s-prose js-post-body\" itemprop=\"text\">\n<p>You can implement a simple linear SVM with <code>numpy<\/code> only like below. BTW, please google before you ask question next time. There are lots of resources and tutorial online.<\/p>\n<pre><code>    import numpy as np\n    def my_svm(dataset, label):\n        rate = 1 # rate for gradient descent\n        epochs = 10000 # no of iterations\n        weights = np.zeros(dataset.shape[1]) # Create an array for storing the weights\n\n        # Min. the objective function(Hinge loss) by gradient descent\n        for epoch in range(1,epochs):\n            for n, data in enumerate(dataset):\n                if (label[n] * np.dot(dataset[n], weights)) &lt; 1:\n                    weights = weights + rate * ( (dataset[n] * label[n]) + (-2  *(1\/epoch)* weights) )\n                else:\n                    weights = weights + rate * (-2  * (1\/epoch) * weights)\n\n        return weights\n\n    def predict(test_data,weights):\n        results = []\n        for data in test_data:\n            result = np.dot(data,weights)\n            results.append(-1 if result &lt; 0 else 1)\n        return results\n<\/code><\/pre>\n<p>Generate dataset for training and testing<\/p>\n<pre><code>    dataset = np.array([\n        [-2, 4,-1], #x_cood,y_cood,bias\n        [4, 1, -1],\n        [0, 2, -1],\n        [1, 6, -1],\n        [2, 5, -1],\n        [6, 2, -1]\n        ])\n    label = np.array([-1,-1,-1,1,1,1])\n\n    weights = my_svm(dataset,label)\n<\/code><\/pre>\n<p>Test it<\/p>\n<pre><code>    test_data = np.array([\n                [0,3,-1], #Should belong to -1\n                [4,5,-1]  #Should belong to 1\n                ])\n    predict(test_data, weights)\n    &gt;Out[10]: [-1, 1]\n<\/code><\/pre>\n<\/p><\/div>\n<div class=\"mt24\"><\/div>\n<\/div>\n<p>            <span class=\"d-none\" itemprop=\"commentCount\"><\/span> <\/p><\/div>\n<\/div>\n<p>[ad_2]<\/p>\n<p>solved Want genuine suggestion to build Support Vector Machine in python without using Scikit-Learn [closed] <\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] You can implement a simple linear SVM with numpy only like below. BTW, please google before you ask question next time. There are lots of resources and tutorial online. import numpy as np def my_svm(dataset, label): rate = 1 # rate for gradient descent epochs = 10000 # no of iterations weights = np.zeros(dataset.shape[1]) &#8230; <a title=\"[Solved] Want genuine suggestion to build Support Vector Machine in python without using Scikit-Learn [closed]\" class=\"read-more\" href=\"https:\/\/jassweb.com\/solved\/solved-want-genuine-suggestion-to-build-support-vector-machine-in-python-without-using-scikit-learn-closed\/\" aria-label=\"More on [Solved] Want genuine suggestion to build Support Vector Machine in python without using Scikit-Learn [closed]\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[1173,349,3648],"class_list":["post-33721","post","type-post","status-publish","format-standard","hentry","category-solved","tag-machine-learning","tag-python","tag-svm"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>[Solved] Want genuine suggestion to build Support Vector Machine in python without using Scikit-Learn [closed] - JassWeb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jassweb.com\/solved\/solved-want-genuine-suggestion-to-build-support-vector-machine-in-python-without-using-scikit-learn-closed\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] Want genuine suggestion to build Support Vector Machine in python without using Scikit-Learn [closed] - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] You can implement a simple linear SVM with numpy only like below. BTW, please google before you ask question next time. There are lots of resources and tutorial online. import numpy as np def my_svm(dataset, label): rate = 1 # rate for gradient descent epochs = 10000 # no of iterations weights = np.zeros(dataset.shape[1]) ... 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