for j=1:p0*N for i=1;a init_sample=x(i,:) n_samples= (1-p0)/p0; prop_dist = proposal(mu, sigma); [samples, acceptance_rate] = mcmc(@target, prop_dist, init_sample, n_samples); %生成MCMC链 for i=1:n_samples values = cell(1, N); for i = 1:N values{i} = @(i) target(samples(i,:)); end failures(i) = sum(values(i) <= 0); end end value = sort(values) ; F=value(end); end for l=1:m A=cell(1,N); A(m)=F; end end，，'cell' 类型的操作数不支持运算符 '<='。，如何改正

function y = target3(x) if numel(size(x)) ~= 2 || size(x, 2) ~= 3 error('x must be an Nx3 matrix'); end if size(x, 1) == 1 && size(x, 2) > 1 x = x.'; end y = x(:,1).^3 + 2.x(:,2) - 5.x(:,3); end function [samples, acceptance_rate] = mcmc(target3, prop_dist, init_sample, n_samples) samples = zeros(n_samples, 3); samples(1, :) = init_sample; acceptance_rate = 0; for i = 2:n_samples prop_sample = prop_dist.sample(); if size(prop_sample, 2) ~= 3 || size(samples(i-1,:), 2) ~= 3 error('prop_sample and samples must be Nx3 matrices'); end target_ratio = target3(prop_sample)/target3(samples(i-1, :)); prop_ratio = mvnpdf(samples(i-1, :), prop_sample, prop_dist.sigma)/mvnpdf(prop_sample, samples(i-1, :), prop_dist.sigma); alpha = min(1, target_ratioprop_ratio); if rand() < alpha samples(i, :) = prop_sample; acceptance_rate = acceptance_rate + 1; else samples(i, :) = samples(i-1, :); end prop_dist.update(samples(1:i, :)); end acceptance_rate = acceptance_rate/n_samples; end %% 出错 target3 (第 8 行) y = x(:,1).^3 + 2.x(:,2) - 5.*x(:,3); 出错 mcmc (第 12 行) target_ratio = target3(prop_sample)/target3(samples(i-1, :)); %% 如何改正

在计算 target_ratio 时，需要将 target3 函数的输出值转化为标量，而不是一个向量。可以使用 sum 函数将向量... prop_dist.update(samples(1:i, :)); end acceptance_rate = acceptance_rate/n_samples; end

function [samples,accept] = MH_Sampling(target_pdf,proposal_pdf,init_sample,num_samples) % target_pdf: 目标分布的概率密度函数 % proposal_pdf: 建议分布的概率密度函数 % init_sample: 初始样本值 % num_samples: 采样次数 % 参数设置 samples = zeros(num_samples,1); samples(1) = init_sample;accept = zeros(num_samples-1,1); current = samples(1); % 迭代采样 for i = 2:num_samples % 从建议分布中采样 candidate = proposal_pdf(current); % 计算接受率 accept_rate = target_pdf(candidate)/target_pdf(current)*... proposal_pdf(current,candidate)/proposal_pdf(candidate,current); % 判断是否接受 if rand < accept_rate samples(i) = candidate; current = candidate; accept(i-1) = 1; else samples(i) = current; end end end，用这个程序举一个例子

[samples, accept] = MH_Sampling(target_pdf, proposal_pdf, init_sample, num_samples); % 绘制采样结果的直方图 histogram(samples, 'Normalization', 'pdf'); hold on; % 绘制目标分布的概率密度函数 x = ...

huart2.Init.OverSampling = UART_OVERSAMPLING_16;

Oversampling is a technique used to sample the received data at a higher rate than the baud rate in order to improve the reliability of data transmission. The oversampling rate determines the number ...

import jieba from collections import Counter def read_dataset(path): labels = [] inputs = [] with open(path, 'r', encoding='utf-8') as file: for i, line in enumerate(file): line = line.strip() sample = line.split('\t') inputs.append(sample[0]) labels.append(sample[1]) return inputs, labels class MyDataset(): def init(self) -> None: self.vocab = {} self.stop_words = [] def set_stopword(self, path='data/scu_stopwords'): with open(path, 'r', encoding='utf-8') as fr: self.stop_words = [line.strip() for line in fr.readline()] def build_vocab(self, inputs, max_size='5000', min_freg=1): cnt = {} # 临时词典存储词频 for data in inputs: data = jieba.lcut(data) for word in data: if word not in cnt: cnt[word] = 1 else: cnt[word] += 1 cnt = sorted([_ for _ in cnt.items() if _[1]>=min_freg and _[0] not in self.stop_words], key=lambda t:t[1], reverse=True) self.vocab[''] = 0 if len(cnt) > max_size: i = 1 for w, _ in cnt: if len(self.vocab)>max_size: break self.vocab[w] = i i += 1 else: i = 1 for w, _ in cnt: self.vocab[w] = i i += 1 def transform(self, inputs, flag = 0): samples = [] iter = 0 for doc in inputs: if iter % 1000 == 0: print('-------%d------' % iter) doc = jieba.cut(doc) if flag==0: wordset = set(doc) # print(wordset) sample = [] for word in self.vocab.keys(): if word in wordset: sample.append(1) else: sample.append(0) elif flag == 1: sample = [0 for i in range(len(self.vocab.items()))] word_count = Counter(doc) for word in word_count.items(): if word[0] in self.vocab.keys(): id = self.vocab[word[0]] sample[id] = word[1] iter +=1 samples.append(sample) return samples def buid_tfidf_vocab(self, inputs, max_size): pass试着调参重构，提升精确率

很抱歉，您的代码片段缺少模型和评估指标等关键信息，我无法直接提供调参建议。不过，一些常见的提升精确率的方法包括： - 调整模型结构，可以尝试更深的网络层、增加隐藏单元、添加正则化等等；...

class PointnetSAModuleMSG(_PointnetSAModuleBase): """Pointnet set abstraction layer with multiscale grouping""" def init(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().init() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method我想将以上模块输出的张量的shape在模块尾部打印出来方便检查，你可以提供详细的代码吗？

nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if ...

def init(self, json_dir, n_src=2, sample_rate=8000, segment=4.0): super().init() # Task setting self.json_dir = json_dir self.sample_rate = sample_rate if segment is None: self.seg_len = None else: self.seg_len = int(segment * sample_rate) self.n_src = n_src self.like_test = self.seg_len is None # Load json files mix_json = os.path.join(json_dir, "mix.json") sources_json = [ os.path.join(json_dir, source + ".json") for source in [f"s{n+1}" for n in range(n_src)] ] with open(mix_json, "r") as f: mix_infos = json.load(f) sources_infos = [] for src_json in sources_json: with open(src_json, "r") as f: sources_infos.append(json.load(f)) # Filter out short utterances only when segment is specified orig_len = len(mix_infos) drop_utt, drop_len = 0, 0 if not self.like_test: for i in range(len(mix_infos) - 1, -1, -1): # Go backward if mix_infos[i][1] < self.seg_len: drop_utt += 1 drop_len += mix_infos[i][1] del mix_infos[i] for src_inf in sources_infos: del src_inf[i] print( "Drop {} utts({:.2f} h) from {} (shorter than {} samples)".format( drop_utt, drop_len / sample_rate / 36000, orig_len, self.seg_len ) ) self.mix = mix_infos self.sources = sources_infos

这是一个 Python 类的初始化函数，看起来是用于处理音频混合数据和其源数据的。具体来说，它的输入参数包括一个 JSON 目录、音频源的数量、采样率和段长度等。它会从指定的 JSON 文件中读取混合音频和其源音频的信息...

class PointnetSAModuleMSG(_PointnetSAModuleBase): """Pointnet set abstraction layer with multiscale grouping""" def init(self, , npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().init() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method这是PointnetSAModuleMSG的代码，而这是selfattention的代码：class SelfAttention(nn.Module): def init(self, in_channels, reduction=4): super(SelfAttention, self).init() self.avg_pool = nn.AdaptiveAvgPool1d(1) self.fc1 = nn.Conv1d(in_channels, in_channels // reduction, 1, bias=False) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv1d(in_channels // reduction, in_channels, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, n = x.size() y = self.avg_pool(x) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x y.expand_as(x)；我想将SelfAttention作为PointnetSAModuleMSG的子模块，我是为了加入SA注意力机制，所以需要对PointnetSAModuleMSG进行修改。我想在每个SA模块中添加一个注意力机制，以使得网络可以更好地聚焦于重要的点。具体实现方式是在每个SA模块的最后一层MLP后加入一个Self-Attention层，（如SelfAttention类所示）用于计算每个点的注意力分数。你可以给我写出详细的修改代码吗？

nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if ...

import time import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import MiniBatchKMeans, KMeans from sklearn.metrics.pairwise import pairwise_distances_argmin from sklearn.datasets import make_blobs # Generate sample data np.random.seed(0) batch_size = 45 centers = [[1, 1], [-1, -1], [1, -1]] n_clusters = len(centers) X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7) # Compute clustering with Means k_means = KMeans(init='k-means++', n_clusters=3, n_init=10) t0 = time.time() k_means.fit(X) t_batch = time.time() - t0 # Compute clustering with MiniBatchKMeans mbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size, n_init=10, max_no_improvement=10, verbose=0) t0 = time.time() mbk.fit(X) t_mini_batch = time.time() - t0 # Plot result fig = plt.figure(figsize=(8, 3)) fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9) colors = ['#4EACC5', '#FF9C34', '#4E9A06'] # We want to have the same colors for the same cluster from the # MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per # closest one. k_means_cluster_centers = k_means.cluster_centers_ order = pairwise_distances_argmin(k_means.cluster_centers_, mbk.cluster_centers_) mbk_means_cluster_centers = mbk.cluster_centers_[order] k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers) mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers) # KMeans for k, col in zip(range(n_clusters), colors): my_members = k_means_labels == k cluster_center = k_means_cluster_centers[k] plt.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6) plt.title('KMeans') plt.xticks(()) plt.yticks(()) plt.show() 这段代码每一句在干什么

X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7) 3. 使用KMeans算法进行聚类 # 初始化KMeans模型 k_means = KMeans(init='k-means++', n_clusters=3, n_init=10) # 训练...

lame_set_num_samples

lame_set_num_samples 是 LAME 编码器的一个函数，它用于设置输入音频的采样数，表示要编码的音频文件的采样数。这个函数的原型如下： void lame_set_num_samples(lame_t gfp, unsigned long num_samples); ...

class PointnetSAModuleMSG(_PointnetSAModuleBase): """Pointnet set abstraction layer with multiscale grouping""" def init(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().init() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method根据以上代码，我在每一层以上模块的后面都加入了一层注意力机制层，而注意力机制层需要接收该模块的输出，所以该模块的输出是什么呢？

该模块的输出是经过多尺度聚合后的局部特征表示，具体来...因此，该模块的输出是一个张量，其形状为 (B, C_out, npoint)，其中 B 为 batch size，C_out 为输出通道数，npoint 为该模块输出的点数，即聚合后的中心点数。

# project states onto the main directions of the gradient updates using n samples over all steps starting from sample x # the directions are calculated using the last sample as a reference directions, state_ids, loss_coordinates = get_state_directions(states, n_states=10, start_from=0, reference_id=-1) # compute the losses over the main directions of the gradient updates x, y, Z, _ = get_loss_grid(net, data_loader, loss_fn, directions=directions, resolution=(20, 20), scale=loss_coordinates.abs().max().item()) # plot the landscape as a contour plot fig = plot_contour(np.copy(x), np.copy(y), np.copy(Z), scale=True) fig.add_traces(go.Scatter(x=np.copy(loss_coordinates[0].cpu().numpy()), y=np.copy(loss_coordinates[1].cpu().numpy()))) print('loss samples:', np.array(losses)[state_ids]) conf_pltly() init_notebook_mode(connected=False) iplot(fig)帮我完善这段代码

# project states onto the main directions of the gradient updates using n samples over all steps starting from sample x # the directions are calculated using the last sample as a reference directions,...

class PointnetSAModuleMSG(_PointnetSAModuleBase): """ Pointnet set abstraction layer with multiscale grouping and attention mechanism """ def init(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().init() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() # Add attention module self.attentions = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 # Add attention module for each scale self.attentions.append(Attention(mlp_spec[-1])) self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method def forward(self, xyz, features): """ :param xyz: (B, N, 3) xyz coordinates of the points :param features: (B, N, C) input features :return: (B, npoint, mlp[-1]) tensor """ new_features_list = [] for i in range(len(self.groupers)): grouper = self.groupers[i] mlp = self.mlps[i] attention = self.attentions[i] # Group points and features grouped_xyz, grouped_features = grouper(xyz, features) # Apply MLP to each group grouped_features = mlp(grouped_features) # Apply attention mechanism to the features of each group grouped_features = attention(grouped_features) # Perform pooling over each group if self.pool_method == 'max_pool': pooled_features = torch.max(grouped_features, dim=2)[0] else: pooled_features = torch.mean(grouped_features, dim=2) new_features_list.append(pooled_features) # Concatenate features from different scales new_features = torch.cat(new_features_list, dim=1) return new_features在该类中使用的QueryAndGroup类会主动将该类所继承的父类的返回值传入QueryAndGroup类中的forward函数吗

是的，该类中使用的QueryAndGroup类会调用其继承的父类_PointnetSAModuleBase的forward函数，并将其返回值传入QueryAndGroup类中的forward函数中。具体来说，QueryAndGroup类的forward函数接收点云数据和特征数据...

我需要你使用手动实现的BO算法实现y=x**2+108在10-15上的最优值寻找，并添加详细的中文注释

### 回答1：好的，我来为你演示如何使用手动实现的BO算法寻找函数y=x**2+108在10-15上的最优值。首先，我们需要引入一些必要的库和函数： python import numpy as np from scipy.stats import norm import ...

def __clear_samples(): if not os.path.exists("./samples"): os.mkdir("./samples") for file_name in os.listdir('./samples'): if file_name.startswith('sample-') and file_name.endswith('.mp3'): os.remove('./samples/' + file_name) def __clear_songs(): if not os.path.exists("./songs"): os.mkdir("./songs") for file_name in os.listdir('./songs'): if file_name.endswith('.mp3'): os.remove('./songs/' + file_name) if name == 'main': __clear_samples() __clear_songs() config_util.load_config() dbstatus = os.path.exists("fay.db") if(dbstatus == False): contentdb = Content_Db() contentdb.init_db() ws_server = wsa_server.new_instance(port=10002) ws_server.start_server() web_ws_server = wsa_server.new_web_instance(port=10003) web_ws_server.start_server() ali_nls.start() flask_server.start() app = QApplication(sys.argv) app.setWindowIcon(QtGui.QIcon('icon.png')) win = MainWindow() win.show() app.exit(app.exec_())如何把这个部署在网页上

这个代码似乎是一个 Python 项目，如果要将其部署在网页上，你需要先将其转换为 Web 应用程序。这可以通过将其转换为一个 Web 框架（例如 Flask、Django 等）中的应用程序来实现。一旦你将其转换为 Web 应用程序，...

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huart2.Init.OverSampling = UART_OVERSAMPLING_16;

lame_set_num_samples

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