class_docstr: The Support Vector Machine classifier.
  Uses cvxopt to solve the quadratic optimization problem.
  
  Parameters:
  -----------
  C: float
      Penalty term.
  kernel: function
      Kernel function. Can be either polynomial, rbf or linear.
  power: int
      The degree of the polynomial kernel. Will be ignored by the other
      kernel functions.
  gamma: float
      Used in the rbf kernel function.
  coef: float
      Bias term used in the polynomial kernel function.
class_docstr_tok: ["The","Support","Vector","Machine","classifier",".","Uses","cvxopt","to","solve","the","quadratic","optimization","problem",".","Parameters",":","--","--","--","--","--","-","C",":","float","Penalty","term",".","kernel",":","function","Kernel","function",".","Can","be","either","polynomial",",","rbf","or","linear",".","power",":","int","The","degree","of","the","polynomial","kernel",".","Will","be","ignored","by","the","other","kernel","functions",".","gamma",":","float","Used","in","the","rbf","kernel","function",".","coef",":","float","Bias","term","used","in","the","polynomial","kernel","function","."]
code: def predict(self, X):
      y_pred = []
      # Iterate through list of samples and make predictions
      for sample in X:
          prediction = 0
          # Determine the label of the sample by the support vectors
          for i in range(len(self.lagr_multipliers)):
              prediction += (
                  self.lagr_multipliers[i]
                  * self.support_vector_labels[i]
                  * self.kernel(self.support_vectors[i], sample)
              )
          prediction += self.intercept
          y_pred.append(np.sign(prediction))
      return np.array(y_pred)
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/support_vector_machine.py
imports: from __future__ import division, print_function
  import numpy
  import cvxopt
  from mlfromscratch.utils import train_test_split, normalize, accuracy_score
  from mlfromscratch.utils.kernels import *
  from mlfromscratch.utils import Plot
label_desc: Use image node_id 3 for calling the SupportVectorMachine obj's underlying member method code with example usage: obj.predict(X) and returns: np