基于决策树判断是否降雪，机器学习课程设计.zip

import pandas as pd import numpy as np import random from config import all_data_road, after_clean_saving_data, after_clean_saving_rate # 主成分分析 def pca(dataset, k, x): n, m = np.shape(dataset) # cov为协方差矩阵 cov = np.cov(dataset.astype(float)) # a为特征值， b为特征向量 a, b = np.linalg.eig(cov*(m-1)/m) c = np.array(list(range(1, 13))) a = np.vstack((a, c)) a = a.T a = a[np.argsort(a[:, 0]), :] res = x[int(a[-1, 1])] for i in range(1, k): res = np.vstack([res, x[int(a[-1-i, 1])]]) return a, res def normalize(x): n, m = np.shape(x) max_arr = np.max(x, axis=1) min_arr = np.min(x, axis=1) for i in range(n): for j in range(m): if max_arr[i] == min_arr[i]: x[i, j] = 0 else: x[i, j] = (x[i, j]-min_arr[i])/(max_arr[i]-min_arr[i]) return x def load_data(): data = np.loadtxt(after_clean_saving_data, dtype=np.float, delimiter=',') return data def data_load_clean(k): df = pd.read_csv(all_data_road) dataset = np.array(df) n, m = np.shape(dataset) dataset = dataset[0:n, 3:m] n, m = np.shape(dataset) for i in range(n): dataset[i, m-1] = int(dataset[i, m-1] == "YES") pos_example = dataset[1, :] neg_example = dataset[1, :] pos = 0 neg = 0 for i in range(n): if dataset[i, m - 1] == 1: # 由于正样本太少，把正样本额外存起来 pos_example = np.vstack([pos_example, dataset[i, :]]) pos = pos + 1 else: neg_example = np.vstack([neg_example, dataset[i, :]]) neg = neg + 1 tnum = int(neg / pos / 4) dataset = dataset[1, :] for i in range(tnum): pos_example_1 = pos_example * 1.1 pos_example_9 = pos_example * 0.9 for t in pos_example_1: t[-1] = round(t[-1]) dataset = np.vstack([dataset, pos_example_1]) dataset = np.vstack([dataset, pos_example]) dataset = np.vstack([dataset, neg_example]) n, m = np.shape(dataset) # 防止nan的出现 for i in range(0, n): for j in range(0, m): if np.isnan(dataset[i, j]): dataset[i, j] = (dataset[i - 1, j] + dataset[i - 2, j]) / 2 dataset = dataset.T data = dataset.copy() n, m = np.shape(data) # 拆分标签和属性 x = data[0:n - 1, :] y = data[n - 1, :] x = normalize(x) # 主成分分析，保留k个属性 feature_sort, res = pca(x, k, dataset) res = np.vstack([res, y]) res = res.T np.savetxt(after_clean_saving_data, res, delimiter=',', fmt='%s') np.savetxt(after_clean_saving_rate, feature_sort, delimiter=',', fmt='%s') n, m = np.shape(res) pos = n - neg return res, pos, neg def split_tt(data, pos, neg): n, m = np.shape(data) ls_test = [] ls_vail = [] ls_train = [] test_num = int(n / 32) * 4 for i in range(int(test_num/2)): x = -1 y = -1 while (x < 0) | ((x in ls_test) & (y in ls_test)): x = random.randint(0, pos) y = random.randint(pos, n - 1) ls_test.append(x) ls_test.append(y) vail_num = int(n / 20) * 2 for i in range(int(vail_num/2)): x = -1 y = -1 while (x < 0) | ((x in ls_test) & (y in ls_test)) | ((x in ls_vail) & (y in ls_vail)): x = random.randint(0, pos) y = random.randint(pos, n - 1) ls_vail.append(x) ls_vail.append(y) train_num = n - vail_num - test_num for i in range(n): if (i not in ls_test) & (i not in ls_vail): ls_train.append(i) train = data[ls_train[0]] test = data[ls_test[0]] vail = data[ls_vail[0]] for i in range(1, train_num): train = np.vstack([train, data[ls_train[i]]]) for i in range(1, vail_num): vail = np.vstack([vail, data[ls_vail[i]]]) for i in range(1, test_num): test = np.vstack([test, data[ls_test[i]]]) n, m = np.shape(test) np.savetxt("test_data.csv", test[:, 0:m-1], delimiter=',') return train, vail, test