svm.py

class SVM:
  def __init__(self, learning_rate = 0.001, lambda_param=0.01, n_iters=1000):
    self.lr = learning_rate
    self.lambda_param = lambda_param
    self.n_iters = n_iters
    self.w = None
    self.b = None

  def fit(self, X, y):
    y_ = np.where(y<=0,-1,1)
    n_samples, n_features = X.shape

    self.w = np.zeros(n_features)
    self.b = 0

    for _ in range(self.n_iters):
      for idx, x_i in enumerate(X):
        condition = y_[idx] * (np.dot(x_i, self.w) - self.b) >=1
        if condition:
          self.w -= self.lr * (2*self.lambda_param * self.w)
        else:
          self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_[idx]))
          self.b -= self.lr * y_[idx]
  
  def predict(self, X):
    linear_output = np.dot(X, self.w) - self.b
    return(np.sign(linear_output))

X, y =  datasets.make_blobs(n_samples=50, n_features=3, centers=3, cluster_std=1.05, random_state=40)
y = np.where(y == 0, -1, 1)

clf = SVM()
clf.fit(X, y)
predictions = clf.predict(X)
 
print(clf.w, clf.b)

def visualize_svm():
     def get_hyperplane_value(x, w, b, offset):
          return (-w[0] * x + b + offset) / w[1]

     fig = plt.figure()
     ax = fig.add_subplot(1,1,1)
     plt.scatter(X[:,0], X[:,1], marker='o',c=y)

     x0_1 = np.amin(X[:,0])
     x0_2 = np.amax(X[:,0])

     x1_1 = get_hyperplane_value(x0_1, clf.w, clf.b, 0)
     x1_2 = get_hyperplane_value(x0_2, clf.w, clf.b, 0)

     x1_1_m = get_hyperplane_value(x0_1, clf.w, clf.b, -1)
     x1_2_m = get_hyperplane_value(x0_2, clf.w, clf.b, -1)

     x1_1_p = get_hyperplane_value(x0_1, clf.w, clf.b, 1)
     x1_2_p = get_hyperplane_value(x0_2, clf.w, clf.b, 1)

     ax.plot([x0_1, x0_2],[x1_1, x1_2], 'y--')
     ax.plot([x0_1, x0_2],[x1_1_m, x1_2_m], 'k')
     ax.plot([x0_1, x0_2],[x1_1_p, x1_2_p], 'k')

     x1_min = np.amin(X[:,1])
     x1_max = np.amax(X[:,1])
     ax.set_ylim([x1_min-3,x1_max+3])

     plt.show()

visualize_svm()