code: def main():
      # Load the dataset
      data = datasets.load_iris()
      X = data.data
      y = data.target
  
      # Three -> two classes
      X = X[y != 2]
      y = y[y != 2]
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.33
      )
  
      # Fit and predict using LDA
      lda = LDA()
      lda.fit(X_train, y_train)
      y_pred = lda.predict(X_test)
  
      accuracy = accuracy_score(y_test, y_pred)
  
      print("Accuracy:", accuracy)
  
      Plot().plot_in_2d(X_test, y_pred, title="LDA", accuracy=accuracy)
  
file_path: ML-From-Scratch/mlfromscratch/examples/linear_discriminant_analysis.py
imports: from __future__ import print_function
  from sklearn import datasets
  import matplotlib.pyplot
  import numpy
  from mlfromscratch.supervised_learning import LDA
  from mlfromscratch.utils import calculate_covariance_matrix, accuracy_score
  from mlfromscratch.utils import normalize, standardize, train_test_split, Plot
  from mlfromscratch.unsupervised_learning import PCA
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types