text: from __future__ import division, print_function
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  from mlfromscratch.utils import train_test_split, standardize, accuracy_score
  from mlfromscratch.utils import mean_squared_error, calculate_variance, Plot
  from mlfromscratch.supervised_learning import RegressionTree
  
  def main():
  
      print ("-- Regression Tree --")
  
      # Load temperature data
      data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
  
      time = np.atleast_2d(data["time"].values).T
      temp = np.atleast_2d(data["temp"].values).T
  
      X = standardize(time)        # Time. Fraction of the year [0, 1]
      y = temp[:, 0]  # Temperature. Reduce to one-dim
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
  
      model = RegressionTree()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      y_pred_line = model.predict(X)
  
      # Color map
      cmap = plt.get_cmap('viridis')
  
      mse = mean_squared_error(y_test, y_pred)
  
      print ("Mean Squared Error:", mse)
  
      # Plot the results
      # Plot the results
      m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
      m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
      m3 = plt.scatter(366 * X_test, y_pred, color='black', s=10)
      plt.suptitle("Regression Tree")
      plt.title("MSE: %.2f" % mse, fontsize=10)
      plt.xlabel('Day')
      plt.ylabel('Temperature in Celcius')
      plt.legend((m1, m2, m3), ("Training data", "Test data", "Prediction"), loc='lower right')
      plt.show()
  
  
  if __name__ == "__main__":
      main()