labels = X.columns importances = classifier.feature_importances_ # 获取特征权重值 indices = np.argsort(importances)[::-1]# 打印特征等级 features = [labels[i] for i in indices] weights = [importances[i] for i in indices] print("Feature ranking:") for f in range(len(features)): print("%d. %s (%f)" % (f + 1, features[f], weights[f]))# 绘制随机森林的特征重要性 plt.figure() plt.title("Feature importances") plt.bar(features, np.array(weights), color='r') plt.xticks(rotation=90) plt.title('Feature Weights') plt.show() Feature ranking: 1. adult_male (0.313632) 2. fare (0.251215) 3. age (0.237226) 4. pclass (0.117736) 5. parch (0.029511) 6. embarked (0.025722) 7. sibsp (0.017953) 8. sex (0.003675) 9. alone (0.003331)

labels = np.array(features['actual']) features= features.drop('actual', axis = 1) feature_list = list(features.columns) features = np.array(features)features.shape

这段代码将原始的特征数据features中的标签（即需要预测的目标变量）保存到labels变量中，其中np.array()函数用于将Pandas数据框转换为NumPy数组。接着，它使用drop()函数将features中的'actual'列删除，因为它已经...

优化 import numpy as np import open3d as o3d from sklearn.cluster import DBSCAN # 读取点云数据 pcd = o3d.io.read_point_cloud("laser.pcd") points = np.asarray(pcd.points) # DBSCAN聚类 dbscan = DBSCAN(eps=0.2, min_samples=10) dbscan.fit(points) labels = dbscan.labels_ # 获取可行驶区域点云数据 drivable_mask = labels != -1 drivable_points = points[drivable_mask] # 获取路沿点云数据 curb_mask = np.logical_and(labels != -1, points[:, 1] < 0) curb_points = points[curb_mask] # 获取车道线点云数据 line_mask = np.logical_and(labels != -1, points[:, 1] >= 0) line_points = points[line_mask] # 可视化结果 drivable_pcd = o3d.geometry.PointCloud() drivable_pcd.points = o3d.utility.Vector3dVector(drivable_points) o3d.visualization.draw_geometries([drivable_pcd]) curb_pcd = o3d.geometry.PointCloud() curb_pcd.points = o3d.utility.Vector3dVector(curb_points) o3d.visualization.draw_geometries([curb_pcd]) line_pcd = o3d.geometry.PointCloud() line_pcd.points = o3d.utility.Vector3dVector(line_points) o3d.visualization.draw_geometries([line_pcd]) 加上预处理

import numpy as np import open3d as o3d from sklearn.cluster import DBSCAN # 读取点云数据 pcd = o3d.io.read_point_cloud("laser.pcd") points = np.asarray(pcd.points) # 预处理：去除离群点 mean = np....

importance_scores = one_vs_rest.estimators_[class_label].feature_importances_ AttributeError: '_ConstantPredictor' object has no attribute 'feature_importances_'

这个错误可能是由于OneVsRestClassifier中的基础分类器不支持feature_importances_属性导致的。为了解决这个问题，您可以尝试使用其他具有特征重要性属性的基础分类器，例如RandomForestClassifier。下面是修改后...

def predict(self, X_test): y_pred = [] for test_sample in X_test: distances = [self.euclidean_distance(test_sample, x) for x in self.X] nearest_indices = np.argsort(distances)[:self.n_neighbors] nearest_labels = self.y[nearest_indices] unique_labels, counts = np.unique(nearest_labels, return_counts=True) predicted_label = unique_labels[np.argmax(counts)] y_pred.append(predicted_label) return np.array(y_pred)

接着，使用np.unique()函数获取最近邻居标签数组中的唯一值和对应的计数值，并分别存储在unique_labels和counts中。最后，根据计数值最大的标签作为预测结果，并将其添加到y_pred列表中。循环结束后，将...

解释一下这段代码，并每一句给出注释：def db_scan_new(mkpts, min_samples=5, max_dst=40): # min_samples = 6 # round(len(mkpt1) * 0.8) # max_dst = 40 # maximum distance between two samples db = DBSCAN(eps=max_dst, min_samples=min_samples).fit(mkpts) labels = db.labels_ # Number of clusters in labels, ignoring noise if present. n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1) if n_clusters_ < 1: return None filtered_labels = [x for x in labels if x != -1] unique, counts = np.unique(filtered_labels, return_counts=True) T = 0.2 all_idxs = [] for lbl_idx in np.argsort(counts)[::-1]: if counts[lbl_idx] / counts.max() >= T: idxs = np.argwhere(filtered_labels == lbl_idx).flatten() all_idxs.extend(idxs) all_idxs = np.array(sorted(all_idxs)) dense_mkpts = mkpts[all_idxs] return dense_mkpts

filtered_labels = [x for x in labels if x != -1] # 过滤掉噪声点，得到所有非噪声点的聚类标签。 unique, counts = np.unique(filtered_labels, return_counts=True) # 统计每个聚类中的点数。 T = 0.2 #...

path = 'data/cora/' data_name = 'cora' print('Loading from raw data file...') idx_features_labels = np.genfromtxt("{}{}.content".format(path, data_name), dtype=np.dtype(str)) features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32) _, _, labels = np.unique(idx_features_labels[:, -1], return_index=True, return_inverse=True)

然后，代码使用切片操作idx_features_labels[:, 1:-1]提取特征值，并使用np.float32将其转换为浮点数类型，创建一个稀疏矩阵。接下来，代码使用np.unique函数确定Cora数据集中的唯一标签，并将它们转换为整数索引...

为以下代码写注释：class TransformerClassifier(torch.nn.Module): def init(self, num_labels): super().init() self.bert = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=num_labels) # print(self.bert.config.hidden_size) #768 self.dropout = torch.nn.Dropout(0.1) self.classifier1 = torch.nn.Linear(640, 256) self.classifier2 = torch.nn.Linear(256, num_labels) self.regress1 = torch.nn.Linear(640, 256) self.regress2 = torch.nn.Linear(256, 2) self.regress3 = torch.nn.Linear(640, 256) self.regress4 = torch.nn.Linear(256, 2) # self.regress3 = torch.nn.Linear(64, 1) # self.regress3 = torch.nn.Linear(640, 256) # self.regress4 = torch.nn.Linear(256, 1) # self.soft1 = torch.nn.Softmax(dim=1) def forward(self, input_ids, attention_mask, token_type_ids): # outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) # pooled_output = outputs.logits # # pooled_output = self.dropout(pooled_output) # # logits = self.classifier(pooled_output) outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) logits = outputs.logits clas = F.relu(self.classifier1(logits)) clas = self.classifier2(clas) death = F.relu(self.regress1(logits)) # xingqi = F.relu(self.regress2(xingqi)) death = self.regress2(death) life = F.relu(self.regress3(logits)) # xingqi = F.relu(self.regress2(xingqi)) life = self.regress4(life) # fakuan = F.relu(self.regress3(logits)) # fakuan = self.regress4(fakuan) # print(logits.shape) # logits = self.soft1(logits) # print(logits) # print(logits.shape) return clas,death,life

self.bert = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=num_labels) # 用预训练的中文Bert模型初始化self.bert, 输出维度为num_labels # print(self.bert.config.hidden_...

解释下列代码：def knn(test_data, train_datas, train_labels, k): nums = train_datas.shape[0] # 获取已知样本的数量 test_datas = np.tile(test_data, (nums, 1)) d_1 = test_datas - train_datas # 相应位置相减 d_2 = np.square(d_1) # 求平方 d_3 = np.sum(d_2, axis=1) # 按行求和 d_4 = np.sqrt(d_3) # 开平方，得到距离 index = np.argsort(d_4) # 排序获取排序后元素的索引 count = Counter(train_labels[index[:k]]) # 统计最邻近的k个邻居的标签 print(count) return count.most_common()[0][0] # 返回出现次数最多的标签

7. 使用 np.argsort 方法对 d_4 中的元素进行排序，获取排序后元素的索引，存储在变量 index 中。 8. 使用 Counter 方法统计最邻近的k个邻居的标签，将它们存储在一个计数器对象 count 中。 9. 打印 ...

# 获取路沿点云数据 curb_mask = np.logical_and(labels != -1, points[:, 1] < 0) curb_points = points[curb_mask]

这段代码的作用是获取点云中所有标签不为-1且高度小于0的点，也就是路边的点。具体解释如下： - labels != -1 表示标签不为-1，即排除掉所有未被标记的点。 - points[:, 1] 表示点云中所有高度小于0的点，即获取...

cnn = torch.load('../CNN-LSTM-ATT/model/Densenet+bl+att.pkl') cnn = cnn.to(device) with torch.no_grad(): class_accuary_List=[] true_labels = [] predicted_labels = [] for data in testloader: images, labels = data images = images.view(len(images), 1, 121).float() inputs, labels = images.to(device), labels.to(device) outputs = cnn(inputs) _, predicted = torch.max(outputs, 1) true_labels.append(labels.cpu().numpy()) predicted_labels.append(predicted.cpu().numpy()) true_labels = np.concatenate(true_labels) predicted_labels = np.concatenate(predicted_labels) cm = confusion_matrix(true_labels, predicted_labels, labels=[0, 1, 2, 3, 4]) print(cm)修改代码

predicted_labels = np.concatenate(predicted_labels) cm = confusion_matrix(true_labels, predicted_labels, labels=[0, 1, 2, 3, 4]) print(cm) 主要的修改包括： 1. 将变量 class_accuary_List 改为...

优化这段代码：import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectKBest, f_classif from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score # 读取Excel文件 data = pd.read_excel("output.xlsx") # 提取特征和标签 features = data.iloc[:, 1:].values labels = np.where(data.iloc[:, 0] > 59, 1, 0) # 特征选择 selector = SelectKBest(score_func=f_classif, k=11) selected_features = selector.fit_transform(features, labels) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(selected_features, labels, test_size=0.2, random_state=42) # 创建随机森林分类器 rf_classifier = RandomForestClassifier() # 定义要调优的参数范围 param_grid = { 'n_estimators': [50, 100, 200], # 决策树的数量 'max_depth': [None, 5, 10], # 决策树的最大深度 'min_samples_split': [2, 5, 10], # 拆分内部节点所需的最小样本数 'min_samples_leaf': [1, 2, 4] # 叶节点上所需的最小样本数 } # 使用网格搜索进行调优 grid_search = GridSearchCV(rf_classifier, param_grid, cv=5) grid_search.fit(X_train, y_train) # 输出最佳参数组合和对应的准确率 print("最佳参数组合：", grid_search.best_params_) print("最佳准确率：", grid_search.best_score_) # 使用最佳参数组合训练模型 best_rf_classifier = grid_search.best_estimator_ best_rf_classifier.fit(X_train, y_train) # 预测 y_pred = best_rf_classifier.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) # 打印最高准确率分类结果 print("最高准确率分类结果：", accuracy)

labels = np.where(data.iloc[:, 0] > 59, 1, 0) # 特征选择 selector = SelectKBest(score_func=f_classif, k=11) selected_features = selector.fit_transform(features, labels) # 划分训练集和测试集 X_...

def get_cluster_labels_from_indices(indices): n_clusters = len(indices) cluster_labels = np.zeros(n_clusters) for i in range(n_clusters): cluster_labels[i] = indices[i][1] return cluster_labels

这个函数的作用是从索引列表中获取聚类标签。让我来解释一下这个函数的算法流程： 1. 根据索引列表 indices 的长度，确定聚类的数量 n_clusters。 2. 创建一个长度为 n_clusters 的全零数组 cluster_labels...

# 获取车道线点云数据 line_mask = np.logical_and(labels != -1, points[:, 1] >= 0) line_points = points[line_mask]

这段代码是用来获取车道线点云数据的。在点云数据中，车道线的点云被标记为“-1”，因此首先使用 labels != -1 来筛选出非车道线的点云，然后再通过 points[:, 1] >= 0 来选择点云中 y 坐标大于等于 0 的部分，...

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knn=np.argsort(dists[i,:])[0:k] labels=self.y_train[knn] closest_y=labels

labels = np.array(features['actual']) features= features.drop('actual', axis = 1) feature_list = list(features.columns) features = np.array(features)features.shape

importance_scores = one_vs_rest.estimators_[class_label].feature_importances_ AttributeError: '_ConstantPredictor' object has no attribute 'feature_importances_'

# 获取路沿点云数据 curb_mask = np.logical_and(labels != -1, points[:, 1] < 0) curb_points = points[curb_mask]

def get_cluster_labels_from_indices(indices): n_clusters = len(indices) cluster_labels = np.zeros(n_clusters) for i in range(n_clusters): cluster_labels[i] = indices[i][1] return cluster_labels

# 获取车道线点云数据 line_mask = np.logical_and(labels != -1, points[:, 1] >= 0) line_points = points[line_mask]

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