code: def main():
      data = datasets.load_iris()
      X = normalize(data.data[data.target != 0])
      y = data.target[data.target != 0]
      y[y == 1] = -1
      y[y == 2] = 1
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.33
      )
  
      clf = SupportVectorMachine(
          kernel=polynomial_kernel, power=4, coef=1
      )
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
  
      accuracy = accuracy_score(y_test, y_pred)
  
      print("Accuracy:", accuracy)
  
      # Reduce dimension to two using PCA and plot the results
      Plot().plot_in_2d(
          X_test,
          y_pred,
          title="Support Vector Machine",
          accuracy=accuracy,
      )
  
imports: from __future__ import division, print_function
  import numpy
  from sklearn import datasets
  from mlfromscratch.utils import train_test_split, normalize, accuracy_score, Plot
  from mlfromscratch.utils.kernels import *
  from mlfromscratch.supervised_learning import SupportVectorMachine
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types