code: def main():
      # 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 = time  # fraction of the year [0, 1]
      y = temp
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.4
      )
  
      n_samples, n_features = np.shape(X)
  
      # Prior parameters
      # - Weights are assumed distr. according to a Normal distribution
      # - The variance of the weights are assumed distributed according to
      #   a scaled inverse chi-squared distribution.
      # High prior uncertainty!
      # Normal
      mu0 = np.array([0] * n_features)
      omega0 = np.diag([0.0001] * n_features)
      # Scaled inverse chi-squared
      nu0 = 1
      sigma_sq0 = 100
  
      # The credible interval
      cred_int = 10
  
      clf = BayesianRegression(
          n_draws=2000,
          poly_degree=4,
          mu0=mu0,
          omega0=omega0,
          nu0=nu0,
          sigma_sq0=sigma_sq0,
          cred_int=cred_int,
      )
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
  
      mse = mean_squared_error(y_test, y_pred)
  
      # Get prediction line
      y_pred_, y_lower_, y_upper_ = clf.predict(X=X, eti=True)
  
      # Print the mean squared error
      print("Mean Squared Error:", mse)
  
      # Color map
      cmap = plt.get_cmap("viridis")
  
      # 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)
      p1 = plt.plot(
          366 * X,
          y_pred_,
          color="black",
          linewidth=2,
          label="Prediction",
      )
      p2 = plt.plot(
          366 * X,
          y_lower_,
          color="gray",
          linewidth=2,
          label="{0}% Credible Interval".format(cred_int),
      )
      p3 = plt.plot(366 * X, y_upper_, color="gray", linewidth=2)
      plt.axis((0, 366, -20, 25))
      plt.suptitle("Bayesian Regression")
      plt.title("MSE: %.2f" % mse, fontsize=10)
      plt.xlabel("Day")
      plt.ylabel("Temperature in Celcius")
      plt.legend(loc="lower right")
      # plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
      plt.legend(loc="lower right")
  
      plt.show()
  
imports: import numpy
  import pandas
  import matplotlib.pyplot
  from mlfromscratch.utils.data_operation import mean_squared_error
  from mlfromscratch.utils.data_manipulation import train_test_split, polynomial_features
  from mlfromscratch.supervised_learning import BayesianRegression
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types