def main():
data = datasets.load_digits()
X = data.data
y = data.target
pca = PCA()
X = pca.transform(X, n_components=5) # Reduce to 5 dimensions
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
clf = RandomForest(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
pca.plot_in_2d(X_test, y_pred)
digit2 = 8
idx = np.append(
np.where(data.target == digit1)[0],
np.where(data.target == digit2)[0])
y = data.target[idx]
# Change labels to {0, 1}
y[y == digit1] = 0
y[y == digit2] = 1
X = data.data[idx]
X = normalize(X)
# ..........................
# DIMENSIONALITY REDUCTION
# ..........................
pca = PCA()
X = pca.transform(X, n_components=5) # Reduce to 5 dimensions
# ..........................
# TRAIN / TEST SPLIT
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescale label for Adaboost to {-1, 1}
rescaled_y_train = 2 * y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2 * y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf=8)
naive_bayes = NaiveBayes()
knn = KNN(k=4)
data = datasets.load_digits()
digit1 = 1
digit2 = 8
idx = np.append(np.where(data.target == digit1)[0], np.where(data.target == digit2)[0])
y = data.target[idx]
# Change labels to {0, 1}
y[y == digit1] = 0
y[y == digit2] = 1
X = data.data[idx]
X = normalize(X)
# ..........................
# DIMENSIONALITY REDUCTION
# ..........................
pca = PCA()
X = pca.transform(X, n_components=5) # Reduce to 5 dimensions
# ..........................
# TRAIN / TEST SPLIT
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescale label for Adaboost to {-1, 1}
rescaled_y_train = 2*y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2*y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf = 8)
naive_bayes = NaiveBayes()
