def test_original(): data_mat = file_utils.load_all_dataset(constants.FILE_TREE_EX00_PATH) tree_regres.plot_ex00_dataset(data_mat) data_mat = file_utils.load_all_dataset(constants.FILE_TREE_EX0_PATH) tree_regres.plot_ex0_dataset(data_mat) data_mat = file_utils.load_all_dataset(constants.FILE_TREE_EX2_PATH) tree_regres.plot_ex00_dataset(data_mat)
def test_diff_tree(): test_arr = file_utils.load_all_dataset(constants.FILE_TREE_BIKE_TEST_PATH) train_arr = file_utils.load_all_dataset( constants.FILE_TREE_BIKE_TRAIN_PATH) tree = tree_regres.create_tree(np.mat(train_arr), ops=(1, 20)) yhat = tree_regres.create_forecast(tree, np.mat(test_arr)[:, 0]) ret = np.corrcoef(yhat, np.mat(test_arr)[:, 1], rowvar=0)[0, 1] print("树回归的预测结果:", ret) ws, X, Y = tree_regres.linear_solve(np.mat(train_arr)) for i in range(np.shape(np.mat(test_arr))[0]): yhat[i] = np.mat(test_arr)[i, 0] * ws[1, 0] + ws[0, 0] ret = np.corrcoef(yhat, np.mat(test_arr)[:, 1], rowvar=0)[0, 1] print("线性回归的预测结果:", ret)
def test_tree(): data_mat = file_utils.load_all_dataset(constants.FILE_TREE_EX0_PATH) data_mat = np.mat(data_mat) result = tree_regres.create_tree(data_mat) print(result) data_mat = file_utils.load_all_dataset(constants.FILE_TREE_EX2_PATH) data_mat = np.mat(data_mat) tree = tree_regres.create_tree(data_mat) print(tree) test_data = file_utils.load_all_dataset(constants.FILE_TREE_EX2TEST_PATH) test_data = np.mat(test_data) new_tree = tree_regres.prune(tree, test_data) print("new_tree: \n", new_tree)
def __init__(self,root): self.f = Figure(figsize=(5, 4), dpi=100) # create canvas self.canvas = FigureCanvasTkAgg(self.f, master=root) self.canvas.draw() self.canvas.get_tk_widget().grid(row=0, columnspan=3) self.raw_data = np.mat(file_utils.load_all_dataset('assets/sine.txt')) self.test_data = np.arange(np.min(self.raw_data[:, 0]), np.max(self.raw_data[:, 0]), 0.01)
def test_model_tree(): data_arr = file_utils.load_all_dataset(constants.FILE_TREE_EXP2_PATH) data_mat = np.mat(data_arr) tree = tree_regres.create_tree(data_mat, tree_regres.model_leaf, tree_regres.model_error, (1, 10)) print(tree) tree_regres.plot_model_tree(data_arr, tree)
def test_data2(): data_mat = file_utils.load_all_dataset(constants.FILE_KMEAN_TEST2_PATH) centroids = mkmean.random_center(data_mat, 2) print(centroids) dis = mkmean.distance_eclud(data_mat[0], data_mat[1]) print(dis) print("=============") centroids, clust_assing = mkmean.kmeans(data_mat, 3) print(centroids) mkmean.plot_kmeans(data_mat, centroids, clust_assing)
def test_binary_kmeans(): data_mat = file_utils.load_all_dataset(constants.FILE_KMEAN_TEST2_PATH) cent_list, cluster_assment = mkmean.binary_kmeans(data_mat, 3) print(cent_list)
def test_data1(): data_mat = file_utils.load_all_dataset(constants.FILE_KMEAN_TEST1_PATH) mkmean.plot_orginal_dataset(data_mat)