print("sklearn implementations") print(" Decision tree classifier info gain") evaluate_model(DecisionTreeClassifier(criterion="entropy")) print(" Random forest info gain") evaluate_model(RandomForestClassifier(criterion="entropy")) print(" Random forest info classifier gain, more trees") evaluate_model( RandomForestClassifier(criterion="entropy", n_estimators=50)) elif question == '3': X = load_dataset('clusterData.pkl')['X'] model = Kmeans(k=4) model.fit(X) print(model.predict(X)) model.error(X) #print(X) plot_2dclustering(X, model.predict(X)) fname = os.path.join("..", "figs", "kmeans_basic.png") plt.savefig(fname) print("\nFigure saved as '%s'" % fname) elif question == '3.1': X = load_dataset('clusterData.pkl')['X'] N, D = X.shape print('N =', N) print('D =', D) for n in range(50): model = Kmeans(k=4)
print("sklearn implementations") print(" Decision tree info gain") evaluate_model(DecisionTreeClassifier(criterion="entropy")) print(" Random forest info gain") evaluate_model(RandomForestClassifier(criterion="entropy")) print(" Random forest info gain, more trees") evaluate_model(RandomForestClassifier(criterion="entropy", n_estimators=50)) elif question == '3': X = load_dataset('clusterData.pkl')['X'] model = Kmeans(k=4) model.fit(X) error = model.error(X) plot_2dclustering(X, model.predict(X)) fname = os.path.join("..", "figs", "kmeans_basic.png") plt.savefig(fname) print("\nFigure saved as '%s'" % fname) elif question == '3.1': # part 1: implement quantize_image.py # part 2: make figure X = load_dataset('clusterData.pkl')['X'] min_model = None min_error = np.inf for i in range(50): model = Kmeans(4)
print("sklearn implementations") print(" Decision tree info gain") evaluate_model(DecisionTreeClassifier(criterion="entropy")) print(" Random forest info gain") evaluate_model(RandomForestClassifier(criterion="entropy")) print(" Random forest info gain, more trees") evaluate_model( RandomForestClassifier(criterion="entropy", n_estimators=50)) elif question == '3': X = load_dataset('clusterData.pkl')['X'] model = Kmeans(k=4) model.fit(X) print("Error:", model.error(X)) plot_2dclustering(X, model.predict(X)) fname = os.path.join("..", "figs", "kmeans_basic.png") plt.savefig(fname) print("\nFigure saved as '%s'" % fname) elif question == '3.1': X = load_dataset('clusterData.pkl')['X'] N = 50 lowestError = np.inf model = Kmeans(k=4) model.fit(X) # for n in range(N):