解释一下这段代码 string s = GUIUtility.systemCopyBuffer; if (flag_trans == 0 && s[0] != '0' && Input.GetKeyUp(KeyCode.G)) { print("get transform martix"); transform_449 = corresponding[1].transform; transform_339 = corresponding[0].transform; tmp_T1 = Matrix4x4.TRS(transform_449.localPosition, transform_449.localRotation, transform_449.localScale); tmp_T2 = Matrix4x4.TRS(transform_339.localPosition, transform_339.localRotation, transform_339.localScale); M = tmp_T1.inverse * tmp_T2; flag_trans = 1; }

给下列程序添加注释： void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) { if (setup_ && config.restore_defaults) { config = default_config_; config.restore_defaults = false; } if ( ! setup_) { default_config_ = config; setup_ = true; } // update generic local planner params base_local_planner::LocalPlannerLimits limits; limits.max_vel_trans = config.max_vel_trans; limits.min_vel_trans = config.min_vel_trans; limits.max_vel_x = config.max_vel_x; limits.min_vel_x = config.min_vel_x; limits.max_vel_y = config.max_vel_y; limits.min_vel_y = config.min_vel_y; limits.max_vel_theta = config.max_vel_theta; limits.min_vel_theta = config.min_vel_theta; limits.acc_lim_x = config.acc_lim_x; limits.acc_lim_y = config.acc_lim_y; limits.acc_lim_theta = config.acc_lim_theta; limits.acc_lim_trans = config.acc_lim_trans; limits.xy_goal_tolerance = config.xy_goal_tolerance; limits.yaw_goal_tolerance = config.yaw_goal_tolerance; limits.prune_plan = config.prune_plan; limits.trans_stopped_vel = config.trans_stopped_vel; limits.theta_stopped_vel = config.theta_stopped_vel; planner_util_.reconfigureCB(limits, config.restore_defaults); // update dwa specific configuration dp_->reconfigure(config); }

// If the setup has been completed and restore_defaults flag is set, restore default configuration if (setup_ && config.restore_defaults) { config = default_config_; config.restore_defaults = ...

帮我给每一行代码添加注释 class DeepKalmanFilter(nn.Module): def init(self, config): super(DeepKalmanFilter, self).init() self.emitter = Emitter(config.z_dim, config.emit_hidden_dim, config.obs_dim) self.transition = Transition(config.z_dim, config.trans_hidden_dim) self.posterior = Posterior( config.z_dim, config.post_hidden_dim, config.obs_dim ) self.z_q_0 = nn.Parameter(torch.zeros(config.z_dim)) self.emit_log_sigma = nn.Parameter(config.emit_log_sigma * torch.ones(config.obs_dim)) self.config = config @staticmethod def reparametrization(mu, sig): return mu + torch.randn_like(sig) * sig @staticmethod def kl_div(mu0, sig0, mu1, sig1): return -0.5 * torch.sum(1 - 2 * sig1.log() + 2 * sig0.log() - (mu1 - mu0).pow(2) / sig1.pow(2) - (sig0 / sig1).pow(2)) def loss(self, obs): time_step = obs.size(1) batch_size = obs.size(0) overshoot_len = self.config.overshooting kl = torch.Tensor([0]).to(self.config.device) reconstruction = torch.Tensor([0]).to(self.config.device) emit_sig = self.emit_log_sigma.exp() for s in range(self.config.sampling_num): z_q_t = self.z_q_0.expand((batch_size, self.config.z_dim)) for t in range(time_step): trans_loc, trans_sig = self.transition(z_q_t) post_loc, post_sig = self.posterior(trans_loc, trans_sig, obs[:, t]) z_q_t = self.reparametrization(post_loc, post_sig) emit_loc = self.emitter(z_q_t) reconstruction += ((emit_loc - obs[:, t]).pow(2).sum(dim=0) / 2 / emit_sig + self.emit_log_sigma * batch_size / 2).sum() if t > 0: over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) else: over_loc = trans_loc.unsqueeze(0) over_sig = trans_sig.unsqueeze(0) overshooting = self.reparametrization(over_loc, over_sig) kl = kl + self.kl_div(post_loc.expand_as(over_loc), post_sig.expand_as(over_sig), over_loc, over_sig) / min(t + 1, self.config.overshooting) reconstruction = reconstruction / self.config.sampling_num kl = kl / self.config.sampling_num return reconstruction, kl

if t > 0: # 如果不是第一步 over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) # 计算超调量 over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) # 转换器的位置 over_...

dataset = dataset.map(trans_fn)

As an AI language model, I do not have access to the specific context or information about the dataset and trans_fn mentioned in the code snippet. However, in general, the code is using the map() ...

virtual function void write_apb_master(lvc_apb_transfer tr); uvm_reg r; if(enable) begin r = cfg.rgm.default_map.get_reg_by_offset(tr.addr); if(r.get_name() == "IC_DATA_CMD" && ( (tr.trans_kind == lvc_apb_pkg::WRITE && cfg.rgm.IC_DATA_CMD_CMD.get() == RGM_WRITE && cfg.rgm.IC_STATUS_TFNF.get()) || (tr.trans_kind == lvc_apb_pkg::READ && cfg.rgm.IC_DATA_CMD_CMD.get() == RGM_READ && cfg.rgm.IC_STATUS_RFNE.get()) ) ) apb_trans_observed.push_back(tr); end endfunction: write_apb_master

这是一个虚拟函数，用于写入APB主机端口的数据。...这段代码通常用于在UVM测试中模拟APB总线的读写操作，并记录实际观测到的传输信息，以便后续的比较和验证。需要注意的是，该函数的执行前提是enable为真。

逐行解释代码： def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

逐行解释这段代码： 1. def forward_Boosting(self, x, weight_mat=None):：这是一个方法定义，接受输入张量x和权重矩阵weight_mat（可选参数）作为输入。 2. out = self.gru_features(x)：将输入张量x...

task lvc_apb_master_monitor::collect_transfer(); // Advance clock @(vif.cb_mon iff (vif.cb_mon.psel === 1'b1 && vif.cb_mon.penable === 1'b0)); trans_collected = lvc_apb_transfer::type_id::create("trans_collected"); case(vif.cb_mon.pwrite) 1'b1 : begin @(vif.cb_mon iff vif.cb_mon.pready === 1'b1); trans_collected.addr = vif.cb_mon.paddr; trans_collected.data = vif.cb_mon.pwdata; trans_collected.trans_kind = WRITE; trans_collected.trans_status = vif.cb_mon.pslverr === 1'b0 ? OK : ERROR; end 1'b0 : begin @(vif.cb_mon iff vif.cb_mon.pready === 1'b1); trans_collected.addr = vif.cb_mon.paddr; trans_collected.data = vif.cb_mon.prdata; trans_collected.trans_kind = READ; trans_collected.trans_status = vif.cb_mon.pslverr === 1'b0 ? OK : ERROR; end default : uvm_error(get_type_name(), "ERROR pwrite signal value") endcase endtask: collect_transfer

这段代码是关于一个名为 lvc_apb_master_monitor 的任务，它的作用是收集 APB 总线上的传输信息。在这个任务中，先是通过 lvc_apb_transfer::type_id::create("trans_collected") 创建了一个名为 trans_...

UPDATE FILE_BANK_TRANS_HELP T1 JOIN FILE_BANK_TRANS T2 ON T2.REFER_NO_FULL = T1.TR_XSERNO AND T1.TR_DATE = '#{BATCH_WORK_DATE}' SET T1.TERM_ID = T2.TERM_ID, T1.CARD_NO = T2.CARD_NO;转换为Oracle语句

MERGE INTO FILE_BANK_TRANS_HELP T1 USING ( SELECT T2.TERM_ID, T2.CARD_NO, T2.REFER_NO_FULL FROM FILE_BANK_TRANS T2 WHERE T2.REFER_NO_FULL IN ( SELECT TR_XSERNO FROM FILE_BANK_TRANS_HELP WHERE TR_...

解释下面代码含义def show_batch(display_transform=None): if display_transform is None: display_transform = transforms.ToTensor() display_set = torchvision.datasets.CIFAR10( root='./' + data_folder, train=True, download=True, transform=display_transform ) display_loader = torch.utils.data.DataLoader(display_set, batch_size=32) topil = transforms.ToPILImage() for batch_img, batch_label in display_loader: grid = make_grid(batch_img, nrow=8) grid_img = topil(grid) plt.figure(figsize=(10, 10)) plt.imshow(grid_img) grid_img.save('./img/trans_cifar10.png') plt.show() break

这段代码的作用是展示 CIFAR10 数据集中的一批图片。具体来说，它实例化了一个 torchvision.datasets.CIFAR10 数据集对象，对该数据集进行了预处理（可选参数 display_transform），然后使用 torch.utils.data....

def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

这个方法是用于模型的前向传播过程，实现了Boosting机制。它接收输入张量x和权重矩阵weight_mat（可选参数）作为输入。首先，通过self.gru_features对输入张量x进行特征提取，得到一个输出张量out。其中...

if name == "main": init_mat = {'B': 0, 'M': 0, 'E': 0, 'S': 0} trans_mat = {'B': {'B': 0, 'M': 0, 'E': 0, 'S': 0}, 'M': {'B': 0, 'M': 0, 'E': 0, 'S': 0}, 'E': {'B': 0, 'M': 0, 'E': 0, 'S': 0}, 'S': {'B': 0, 'M': 0, 'E': 0, 'S': 0}} BMES = [] emit_mat = pd.DataFrame(index=['B', 'M', 'E', 'S'])#Dataframe有行和列的索引；它可以被看作是一个Series的字典 while (1): print("请在下面输入你要分词的句子（用空格将词分开，输入0结束输入），例如：项目的研究") a = input("情输入：") if a == '0': print("输入结束！正在存储模型矩阵...") with open('./01/init_mat.pkl',"wb") as f0: pickle.dump(init_mat,f0) with open('./01/trans_mat.pkl',"wb") as f1: pickle.dump(trans_mat,f1) with open('./01/emit_mat.pkl',"wb") as f2: pickle.dump(emit_mat,f2) print("存储模型成功！") break markov(a,init_mat,trans_mat,emit_mat) init_mat_compute = copy.deepcopy(init_mat) trans_mat_compute = copy.deepcopy(trans_mat) emit_mat_compute = copy.deepcopy(emit_mat) print(init_mat) print(trans_mat) print(emit_mat) compute(init_mat_compute,trans_mat_compute,emit_mat_compute) print("当前初始状态向量", init_mat_compute) print("当前转移矩阵", trans_mat_compute) print("当前发射矩阵:",emit_mat_compute)请给这段代码每行代码加上详细注释

trans_mat = {'B': {'B': 0, 'M': 0, 'E': 0, 'S': 0}, 'M': {'B': 0, 'M': 0, 'E': 0, 'S': 0}, 'E': {'B': 0, 'M': 0, 'E': 0, 'S': 0}, 'S': {'B': 0, 'M': 0, 'E': 0, 'S': 0}} # 初始化转移矩阵 BMES = [] #...

y_trans = paddle.transpose(y, perm=[0, 2, 3, 1]) 解释这行代码的含义

这行代码用于转置一个4维的张量y，将其第1维和第4维交换位置，即将batch维和channel维交换位置，并保持其他维度不变。具体来说，将y的形状从(batch_size, channel, height, width)转变为(batch_size, height, width,...

改进这段代码import cv2 import numpy as np img = cv2.imread('E:\数字图像处理实验\数字图像处理1.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) f = np.fft.fft2(gray) fshift = np.fft.fftshift(f) rows, cols = gray.shape crow, ccol = int(rows/2), int(cols/2) tx, ty = 50, 50 M = np.float32([[1, 0, tx], [0, 1, ty]]) fshift_trans = cv2.warpAffine(fshift, M, (cols, rows)) angle = 30 M = cv2.getRotationMatrix2D((ccol, crow), angle, 1) fshift_rot = cv2.warpAffine(fshift, M, (cols, rows)) fshift_center = fshift_trans[crow-100:crow+100, ccol-100:ccol+100] fshift_rot_center = fshift_rot[crow-100:crow+100, ccol-100:ccol+100] img_trans = np.fft.ifft2(np.fft.ifftshift(fshift_center)).real img_trans = np.uint8(img_trans) img_rot = np.fft.ifft2(np.fft.ifftshift(fshift_rot_center)).real img_rot = np.uint8(img_rot) cv2.imshow('gray', gray) cv2.imshow('img_trans', img_trans) cv2.imshow('img_rot', img_rot) cv2.waitKey(0) cv2.destroyAllWindows()

这段代码主要是对灰度图像进行图像平移和旋转，然后对平移和旋转后的图像进行傅里叶变换和傅里叶反变换，最后显示出平移和旋转后的图像。以下是代码改进的建议： 1. 路径中的反斜杠需要进行转义，或者使用正斜杠来...

def readTrajectory(path): columns_names = ["V_ID","LNG","TIMESTAMP", "agl","mlg","spd","hgt","LAT","tt","T_ID"] #df = pd.read_csv(path, names = columns_names, header = None) df = pd.read_csv(path,header=0) df["TIMESTAMP"] = pd.to_datetime(df["TIMESTAMP"], format ="%Y-%m-%d %H:%M:%S") df.sort_values(["TIMESTAMP"], inplace = True) df.reset_index(drop = True) df = df.round({'LNG': 8, 'LAT': 8}) df = df.reset_index(drop = True) df["Index"] = df.index return df[["V_ID","LNG","TIMESTAMP", "agl","mlg","spd","hgt","LAT","tt","T_ID","Index"]] def main(): prior_decay_coff = 10 # 先验概率衰减系数 trans_prob_decay_coff = 100 # 转移概率衰减系数 buffer_dis = 50 # 缓冲区半径 GPS误差 network_path = "data/network/111.shp" network_path_wgs84 = "data/network/84打断.shp" network_feature_layer = ArcpyUtil.toFeatureLayer(network_path, "network_feature_layer") network_wgs84_feature_layer = ArcpyUtil.toFeatureLayer(network_path_wgs84, "network_wgs84_feature_layer") road_segment_points_lengths = ArcpyUtil.getRoadSegmentInfo(network_path) # 提取路段信息 connected_graph = ArcpyUtil.getNetworkGraph(network_path, road_segment_points_lengths) # 构建图 LL=[]解释一下这段代码

这段代码定义了一个函数readTrajectory(path)，它的作用是读取一个轨迹数据文件，将数据存储为一个DataFrame对象，并对...最后，LL=[]语句定义了一个空列表LL，但没有被使用，可能是该代码段被精简过程中留下的无用行。

if name == "main": BMES = [] print("正在读取本地模型矩阵...") with open(r'01/init_mat.pkl', "rb") as f0: init_mat = np.array(list(pickle.load(f0).values())) with open(r'01/trans_mat.pkl', "rb") as f1: init_trans_mat = np.array(list(pickle.load(f1).values())) with open(r'01/emit_mat.pkl', "rb") as f2: init_emit_mat = pickle.load(f2) catalog = list(init_emit_mat) trans_mat = [] emit_mat = [] hidden_state = ["B", "M", "E", "S"] for item in init_trans_mat: trans_mat.append(np.array(list(item.values()))) for i in hidden_state: emit_mat.append(np.array(list(init_emit_mat.loc[i]))) emit_mat = np.array(emit_mat).reshape(4,-1) print("读取模型矩阵成功！") print("目前模型的汉字库",catalog)请给这段代码的每行代码加上详细注释

这段代码的作用是读取本地保存的隐马尔可夫模型（HMM）的参数矩阵，并将其转化为程序中可用的格式。首先，定义了一个 BMES 列表。接下来，通过打开三个文件，分别读取了初始概率矩阵 init_mat、状态转移矩阵 init_...

解释一下from sklearn.preprocessing import LabelEncoder from collections import defaultdict d = defaultdict(LabelEncoder) X_trans = x.apply(lambda x: d[x.name].fit_transform(x)) X_trans.head()

这段代码是对数据进行标签编码的处理。其中，sklearn.preprocessing中的LabelEncoder类可以将分类变量转换为数字标签，而collections中的defaultdict类则是一个字典，它可以为字典中的每个键提供一个默认值。在这段...

def create_dataset(dataset_path, batch_size=8, train_image_size=224, do_train=False): """ create a train or evaluate flowers dataset for resnet50 Args: dataset_path(string): the path of dataset. do_train(bool): whether dataset is used for train or eval. batch_size(int): the batch size of dataset. Default: 8 Returns: dataset """ if do_train: trans = [RandomCropDecodeResize(size=(224, 224)), RandomHorizontalFlip(prob=0.5), Rescale(1.0 / 255.0, 0.0), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), HWC2CHW()] else: trans = [RandomCropDecodeResize(size=(256, 256)), CenterCrop(224), Rescale(1.0 / 255.0, 0.0), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), HWC2CHW()] type_cast_op = ts.c_transforms.TypeCast(ms.int32) dataset_loader = ds.ImageFolderDataset(dataset_dir=dataset_path) dataset_trans = dataset_loader.map(operations=trans, input_columns="image", num_parallel_workers=get_num_parallel_workers(8)) dataset_trans = dataset_trans.map(operations=type_cast_op, input_columns="label", num_parallel_workers=get_num_parallel_workers(8)) data_set = dataset_trans.batch(batch_size=batch_size, drop_remainder=True) return data_set

这是一个用于创建训练或评估数据集的函数。它接受以下参数：dataset_path（数据集路径）、batch_size（批处理大小，默认为8）、train_image_size（训练图像大小，默认为224）、do_train（是否用于训练，默认为False...

相关推荐

paper.zip_CRLB_IEEE Trans_TDOA_TDOA FDOA CRLB_无源定位

FILE_TRANS_SOCKET.rar_FILE_TRANS_SOCKET_PowerBuilder_socket_传输

trans_table.rar_Table_trans_table

dataset = dataset.map(trans_fn)

UPDATE FILE_BANK_TRANS_HELP T1 JOIN FILE_BANK_TRANS T2 ON T2.REFER_NO_FULL = T1.TR_XSERNO AND T1.TR_DATE = '#{BATCH_WORK_DATE}' SET T1.TERM_ID = T2.TERM_ID, T1.CARD_NO = T2.CARD_NO;转换为Oracle语句

y_trans = paddle.transpose(y, perm=[0, 2, 3, 1]) 解释这行代码的含义

解释一下from sklearn.preprocessing import LabelEncoder from collections import defaultdict d = defaultdict(LabelEncoder) X_trans = x.apply(lambda x: d[x.name].fit_transform(x)) X_trans.head()

最新推荐

基于SpringBoot框架的中小企业完全开源的ERP.zip

zigbee-cluster-library-specification

管理建模和仿真的文件

实现实时数据湖架构：Kafka与Hive集成

SQL怎么实现 数据透视表

JSBSim Reference Manual

"互动学习：行动中的多样性与论文攻读经历"

实现实时监控告警系统：Kafka与Grafana整合

使用vue3+elementsplus封装一个提示确认框的组件，要求将请求地址和确认框展示信息作为参数暴露出去

c++校园超市商品信息管理系统课程设计说明书(含源代码) (2).pdf

SQL怎么实现数据透视表