Want Genuine Suggestion To Build Support Vector Machine In Python Without Using Scikit-learn

As i know how to build a Support Vector Machine using Scikit-Learn but now i want to make it from scratch in python without using Scikit-Learn. As i am confused and having lack of

Solution 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
    defmy_svm(dataset, label):
        rate = 1# rate for gradient descent
        epochs = 10000# no of iterations
        weights = np.zeros(dataset.shape[1]) # Create an array for storing the weights# Min. the objective function(Hinge loss) by gradient descentfor epoch inrange(1,epochs):
            for n, data inenumerate(dataset):
                if (label[n] * np.dot(dataset[n], weights)) < 1:
                    weights = weights + rate * ( (dataset[n] * label[n]) + (-2  *(1/epoch)* weights) )
                else:
                    weights = weights + rate * (-2  * (1/epoch) * weights)

        return weights

    defpredict(test_data,weights):
        results = []
        for data in test_data:
            result = np.dot(data,weights)
            results.append(-1if result < 0else1)
        return results

Generate dataset for training and testing

dataset = np.array([
        [-2, 4,-1], #x_cood,y_cood,bias
        [4, 1, -1],
        [0, 2, -1],
        [1, 6, -1],
        [2, 5, -1],
        [6, 2, -1]
        ])
    label = np.array([-1,-1,-1,1,1,1])

    weights = my_svm(dataset,label)

Test it

    test_data = np.array([
                [0,3,-1], #Should belong to -1
                [4,5,-1]  #Should belong to 1
                ])
    predict(test_data, weights)
    >Out[10]: [-1, 1]