# Define DGP parameters np.random.seed(123) n_panels = 5000 # number of panels n_periods = 3 # number of time periods in each panel n_treatments = 2 # number of treatments in each period

时间: 2023-06-20 16:03:23 浏览: 40
DGP参数的定义如下: ```python import numpy as np np.random.seed(123) # 设置随机种子,以确保结果可复现 n_panels = 5000 # 面板数 n_periods = 3 # 每个面板中的时间段数 n_treatments = 2 # 每个时间段中的处理数 ``` 其中,`np.random.seed(123)`是为了确保结果的可重复性,`n_panels`是面板的数量,`n_periods`是每个面板中的时间段数,`n_treatments`是每个时间段中的处理数。
相关问题

signal_text = "#define " + \sComGroupSignal_list[Signal_number] + signalname

如果你想将一个字符串 `signalname` 插入到 `#define` 后面,并且 `signalname` 前后都有空格,你可以使用正则表达式进行替换。以下是一个示例代码片段,展示了如何使用正则表达式在Python中完成这个任务: ```python import re signal_text = "#define ComGroupSignal_list[Signal_number] signalname" signalname = "example" pattern = r"(#define\s+ComGroupSignal_list\[Signal_number\]\s+)" replacement = r"\1" + signalname result = re.sub(pattern, replacement, signal_text) print(result) ``` 这段代码中,我们使用 `re.sub()` 函数来查找并替换匹配正则表达式的部分。正则表达式 `(#define\s+ComGroupSignal_list\[Signal_number\]\s+)` 匹配 `#define` 后面紧跟着的空格,`ComGroupSignal_list[Signal_number]`,以及其后面的空格。然后,我们在替换时使用 `\1` 来保留原始匹配的部分,并追加上 `signalname`。 注意:为了在正则表达式中正确匹配方括号 `[` 和 `]`,我们需要使用 `\` 进行转义,因此它们被写成 `\[` 和 `\]`。

#define(...)

在C语言中,#define是一个预处理指令,用于定义宏。它允许我们在代码中使用符号常量或者带参数的宏表达式来代替具体的数值或代码片段。 例如,我们可以使用#define来定义一个简单的宏常量,如#define PI 3.14,这样在代码中使用PI时就会被替换为3.14。 除了宏常量,#define还可以定义带参数的宏表达式。这可以通过在宏定义中使用括号(...)来实现。比如#define SQUARE(x) ((x) * (x)),这个宏定义可以用来计算一个数的平方。我们可以在代码中使用SQUARE(x)来代替具体的平方计算代码。 需要注意的是,在宏定义中不能出现递归,即不能在宏定义中引用它自己。此外,在预处理器搜索宏定义时,字符串常量的内容并不被搜索。 总之,#define是C语言中用于定义宏的预处理指令,可以定义宏常量和带参数的宏表达式,方便我们在代码中使用符号常量或者替代代码片段。<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* *2* [#define详解](https://blog.csdn.net/m0_62518756/article/details/125952371)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] - *3* [C语言之#define用法入门详解](https://blog.csdn.net/sunnyoldman001/article/details/127895225)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]

相关推荐

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt ## Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuarcy')# ax.set_ylabel('Categorical Crossentropy Loss') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) ## We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() ## We first vectorize the image (28*28) into a vector (784) train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]*train_data.shape[2]) # 60000*784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]*test_data.shape[2]) # 10000*784 ## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0] train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) ## start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 # ## we build a three layer model, 784 -> 64 -> 10 MLP_3 = keras.models.Sequential([ keras.layers.Dense(64, input_shape=(784,),activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_3.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history['val_accuracy']模仿此段代码,写一个双隐层感知器(输入层784,第一隐层128,第二隐层64,输出层10)

import mindspore.nn as nn import mindspore.ops.operations as P from mindspore import Model from mindspore import Tensor from mindspore import context from mindspore import dataset as ds from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.nn.metrics import Accuracy # Define the ResNet50 model class ResNet50(nn.Cell): def __init__(self, num_classes=10): super(ResNet50, self).__init__() self.resnet50 = nn.ResNet50(num_classes=num_classes) def construct(self, x): x = self.resnet50(x) return x # Load the CIFAR-10 dataset data_home = "/path/to/cifar-10/" train_data = ds.Cifar10Dataset(data_home, num_parallel_workers=8, shuffle=True) test_data = ds.Cifar10Dataset(data_home, num_parallel_workers=8, shuffle=False) # Define the hyperparameters learning_rate = 0.1 momentum = 0.9 epoch_size = 200 batch_size = 32 # Define the optimizer optimizer = nn.Momentum(filter(lambda x: x.requires_grad, resnet50.get_parameters()), learning_rate, momentum) # Define the loss function loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') # Define the model net = ResNet50() # Define the model checkpoint config_ck = CheckpointConfig(save_checkpoint_steps=1000, keep_checkpoint_max=10) ckpt_cb = ModelCheckpoint(prefix="resnet50", directory="./checkpoints/", config=config_ck) # Define the training dataset train_data = train_data.batch(batch_size, drop_remainder=True) # Define the testing dataset test_data = test_data.batch(batch_size, drop_remainder=True) # Define the model and train it model = Model(net, loss_fn=loss_fn, optimizer=optimizer, metrics={"Accuracy": Accuracy()}) model.train(epoch_size, train_data, callbacks=[ckpt_cb, LossMonitor()], dataset_sink_mode=True) # Load the trained model and test it param_dict = load_checkpoint("./checkpoints/resnet50-200_1000.ckpt") load_param_into_net(net, param_dict) model = Model(net, loss_fn=loss_fn, metrics={"Accuracy": Accuracy()}) result = model.eval(test_data) print("Accuracy: ", result["Accuracy"])这段代码有错误

帮我为下面的代码加上注释:class SimpleDeepForest: def __init__(self, n_layers): self.n_layers = n_layers self.forest_layers = [] def fit(self, X, y): X_train = X for _ in range(self.n_layers): clf = RandomForestClassifier() clf.fit(X_train, y) self.forest_layers.append(clf) X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1) return self def predict(self, X): X_test = X for i in range(self.n_layers): X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1) return self.forest_layers[-1].predict(X_test[:, :-2]) # 1. 提取序列特征(如:GC-content、序列长度等) def extract_features(fasta_file): features = [] for record in SeqIO.parse(fasta_file, "fasta"): seq = record.seq gc_content = (seq.count("G") + seq.count("C")) / len(seq) seq_len = len(seq) features.append([gc_content, seq_len]) return np.array(features) # 2. 读取相互作用数据并创建数据集 def create_dataset(rna_features, protein_features, label_file): labels = pd.read_csv(label_file, index_col=0) X = [] y = [] for i in range(labels.shape[0]): for j in range(labels.shape[1]): X.append(np.concatenate([rna_features[i], protein_features[j]])) y.append(labels.iloc[i, j]) return np.array(X), np.array(y) # 3. 调用SimpleDeepForest分类器 def optimize_deepforest(X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = SimpleDeepForest(n_layers=3) model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) # 4. 主函数 def main(): rna_fasta = "RNA.fasta" protein_fasta = "pro.fasta" label_file = "label.csv" rna_features = extract_features(rna_fasta) protein_features = extract_features(protein_fasta) X, y = create_dataset(rna_features, protein_features, label_file) optimize_deepforest(X, y) if __name__ == "__main__": main()

解释:target = self.survey.source.target collection = self.survey.source.collection '''Mesh''' # Conductivity in S/m (or resistivity in Ohm m) background_conductivity = 1e-6 air_conductivity = 1e-8 # Permeability in H/m background_permeability = mu_0 air_permeability = mu_0 dh = 0.1 # base cell width dom_width = 20.0 # domain width # num. base cells nbc = 2 ** int(np.round(np.log(dom_width / dh) / np.log(2.0))) # Define the base mesh h = [(dh, nbc)] mesh = TreeMesh([h, h, h], x0="CCC") # Mesh refinement near transmitters and receivers mesh = refine_tree_xyz( mesh, collection.receiver_location, octree_levels=[2, 4], method="radial", finalize=False ) # Refine core mesh region xp, yp, zp = np.meshgrid([-1.5, 1.5], [-1.5, 1.5], [-6, -4]) xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] mesh = refine_tree_xyz(mesh, xyz, octree_levels=[0, 6], method="box", finalize=False) mesh.finalize() '''Maps''' # Find cells that are active in the forward modeling (cells below surface) ind_active = mesh.gridCC[:, 2] < 0 # Define mapping from model to active cells active_sigma_map = maps.InjectActiveCells(mesh, ind_active, air_conductivity) active_mu_map = maps.InjectActiveCells(mesh, ind_active, air_permeability) # Define model. Models in SimPEG are vector arrays N = int(ind_active.sum()) model = np.kron(np.ones((N, 1)), np.c_[background_conductivity, background_permeability]) ind_cylinder = self.getIndicesCylinder( [target.position[0], target.position[1], target.position[2]], target.radius, target.length, [target.pitch, target.roll], mesh.gridCC ) ind_cylinder = ind_cylinder[ind_active] model[ind_cylinder, :] = np.c_[target.conductivity, target.permeability] # Create model vector and wires model = mkvc(model) wire_map = maps.Wires(("sigma", N), ("mu", N)) # Use combo maps to map from model to mesh sigma_map = active_sigma_map * wire_map.sigma mu_map = active_mu_map * wire_map.mu '''Simulation''' simulation = fdem.simulation.Simulation3DMagneticFluxDensity( mesh, survey=self.survey.survey, sigmaMap=sigma_map, muMap=mu_map, Solver=Solver ) '''Predict''' # Compute predicted data for your model. dpred = simulation.dpred(model) dpred = dpred * 1e9 # Data are organized by frequency, transmitter location, then by receiver. # We had nFreq transmitters and each transmitter had 2 receivers (real and # imaginary component). So first we will pick out the real and imaginary # data bx_real = dpred[0: len(dpred): 6] bx_imag = dpred[1: len(dpred): 6] bx_total = np.sqrt(np.square(bx_real) + np.square(bx_imag)) by_real = dpred[2: len(dpred): 6] by_imag = dpred[3: len(dpred): 6] by_total = np.sqrt(np.square(by_real) + np.square(by_imag)) bz_real = dpred[4: len(dpred): 6] bz_imag = dpred[5: len(dpred): 6] bz_total = np.sqrt(np.square(bz_real) + np.square(bz_imag)) mag_data = np.c_[mkvc(bx_total), mkvc(by_total), mkvc(bz_total)] if collection.SNR is not None: mag_data = self.mag_data_add_noise(mag_data, collection.SNR) data = np.c_[collection.receiver_location, mag_data] # data = (data, ) return data

#include "bflb_adc.h" #include "bflb_mtimer.h" #include "board.h" struct bflb_device_s adc; #define TEST_ADC_CHANNELS 2 #define TEST_COUNT 10 struct bflb_adc_channel_s chan[] = { { .pos_chan = ADC_CHANNEL_2, .neg_chan = ADC_CHANNEL_GND }, { .pos_chan = ADC_CHANNEL_GND, .neg_chan = ADC_CHANNEL_3 }, }; int main(void) { board_init(); board_adc_gpio_init(); adc = bflb_device_get_by_name("adc"); / adc clock = XCLK / 2 / 32 */ struct bflb_adc_config_s cfg; cfg.clk_div = ADC_CLK_DIV_32; cfg.scan_conv_mode = true; cfg.continuous_conv_mode = false; cfg.differential_mode = true; cfg.resolution = ADC_RESOLUTION_16B; cfg.vref = ADC_VREF_3P2V; bflb_adc_init(adc, &cfg); bflb_adc_channel_config(adc, chan, TEST_ADC_CHANNELS); for (uint32_t i = 0; i < TEST_COUNT; i++) { bflb_adc_start_conversion(adc); while (bflb_adc_get_count(adc) < TEST_ADC_CHANNELS) { bflb_mtimer_delay_ms(1); } for (size_t j = 0; j < TEST_ADC_CHANNELS; j++) { struct bflb_adc_result_s result; uint32_t raw_data = bflb_adc_read_raw(adc); printf("raw data:%08x\r\n", raw_data); bflb_adc_parse_result(adc, &raw_data, &result, 1); printf("pos chan %d,neg chan %d,%d mv \r\n", result.pos_chan, result.neg_chan, result.millivolt); } bflb_adc_stop_conversion(adc); bflb_mtimer_delay_ms(100); } while (1) { } }根据以上代码对bl618程序的编写对以下stm32中代码#include "stm32f10x.h" #include "delay.h" #include "FSR.h" #include "usart.h" #include "adc.h" #define PRESS_MIN 20 #define PRESS_MAX 6000 #define VOLTAGE_MIN 150 #define VOLTAGE_MAX 3300 u8 state = 0; u16 val = 0; u16 value_AD = 0; long PRESS_AO = 0; int VOLTAGE_AO = 0; long map(long x, long in_min, long in_max, long out_min, long out_max); int main(void) { delay_init(); NVIC_Configuration(); uart_init(9600); Adc_Init(); delay_ms(1000); printf("Test start\r\n"); while(1) { value_AD = Get_Adc_Average(1,10); VOLTAGE_AO = map(value_AD, 0, 4095, 0, 3300); if(VOLTAGE_AO < VOLTAGE_MIN) { PRESS_AO = 0; } else if(VOLTAGE_AO > VOLTAGE_MAX) { PRESS_AO = PRESS_MAX; } else { PRESS_AO = map(VOLTAGE_AO, VOLTAGE_MIN, VOLTAGE_MAX, PRESS_MIN, PRESS_MAX); } printf("ADÖµ = %d,µçѹ = %d mv,ѹÁ¦ = %ld g\r\n",value_AD,VOLTAGE_AO,PRESS_AO); delay_ms(500); } } long map(long x, long in_min, long in_max, long out_min, long out_max) { return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min; }移植到bl618进行改写

最新推荐

recommend-type

C# #define条件编译详解

主要介绍了C# #define条件编译,告诉大家#define是用来做什么?如何使用#define,具有一定的参考价值,感兴趣的小伙伴们可以参考一下
recommend-type

详解C语言中的#define宏定义命令用法

有的时候为了程序的通用性,可以使用#define预处理宏定义命令,它的具体作用就是方便程序段的定义和修改,下面就来详解C语言中的#define宏定义命令用法.
recommend-type

预编译#define_#ifdef_#endif用法

最近在看Linux底层代码,发现好多代码里有#define #ifdef #endif,找了个介绍详细的文章,供大家参考!
recommend-type

#define用法集锦.doc

The #define Directive  You can use the #define directive to give a meaningful name to a constant in your program. The two forms of the syntax are:  Syntax  #define identifier token-stringopt  #...
recommend-type

node-v0.10.13-sunos-x86.tar.gz

Node.js,简称Node,是一个开源且跨平台的JavaScript运行时环境,它允许在浏览器外运行JavaScript代码。Node.js于2009年由Ryan Dahl创立,旨在创建高性能的Web服务器和网络应用程序。它基于Google Chrome的V8 JavaScript引擎,可以在Windows、Linux、Unix、Mac OS X等操作系统上运行。 Node.js的特点之一是事件驱动和非阻塞I/O模型,这使得它非常适合处理大量并发连接,从而在构建实时应用程序如在线游戏、聊天应用以及实时通讯服务时表现卓越。此外,Node.js使用了模块化的架构,通过npm(Node package manager,Node包管理器),社区成员可以共享和复用代码,极大地促进了Node.js生态系统的发展和扩张。 Node.js不仅用于服务器端开发。随着技术的发展,它也被用于构建工具链、开发桌面应用程序、物联网设备等。Node.js能够处理文件系统、操作数据库、处理网络请求等,因此,开发者可以用JavaScript编写全栈应用程序,这一点大大提高了开发效率和便捷性。 在实践中,许多大型企业和组织已经采用Node.js作为其Web应用程序的开发平台,如Netflix、PayPal和Walmart等。它们利用Node.js提高了应用性能,简化了开发流程,并且能更快地响应市场需求。
recommend-type

zigbee-cluster-library-specification

最新的zigbee-cluster-library-specification说明文档。
recommend-type

管理建模和仿真的文件

管理Boualem Benatallah引用此版本:布阿利姆·贝纳塔拉。管理建模和仿真。约瑟夫-傅立叶大学-格勒诺布尔第一大学,1996年。法语。NNT:电话:00345357HAL ID:电话:00345357https://theses.hal.science/tel-003453572008年12月9日提交HAL是一个多学科的开放存取档案馆,用于存放和传播科学研究论文,无论它们是否被公开。论文可以来自法国或国外的教学和研究机构,也可以来自公共或私人研究中心。L’archive ouverte pluridisciplinaire
recommend-type

实现实时数据湖架构:Kafka与Hive集成

![实现实时数据湖架构:Kafka与Hive集成](https://img-blog.csdnimg.cn/img_convert/10eb2e6972b3b6086286fc64c0b3ee41.jpeg) # 1. 实时数据湖架构概述** 实时数据湖是一种现代数据管理架构,它允许企业以低延迟的方式收集、存储和处理大量数据。与传统数据仓库不同,实时数据湖不依赖于预先定义的模式,而是采用灵活的架构,可以处理各种数据类型和格式。这种架构为企业提供了以下优势: - **实时洞察:**实时数据湖允许企业访问最新的数据,从而做出更明智的决策。 - **数据民主化:**实时数据湖使各种利益相关者都可
recommend-type

SPDK_NVMF_DISCOVERY_NQN是什么 有什么作用

SPDK_NVMF_DISCOVERY_NQN 是 SPDK (Storage Performance Development Kit) 中用于查询 NVMf (Non-Volatile Memory express over Fabrics) 存储设备名称的协议。NVMf 是一种基于网络的存储协议,可用于连接远程非易失性内存存储器。 SPDK_NVMF_DISCOVERY_NQN 的作用是让存储应用程序能够通过 SPDK 查询 NVMf 存储设备的名称,以便能够访问这些存储设备。通过查询 NVMf 存储设备名称,存储应用程序可以获取必要的信息,例如存储设备的IP地址、端口号、名称等,以便能
recommend-type

JSBSim Reference Manual

JSBSim参考手册,其中包含JSBSim简介,JSBSim配置文件xml的编写语法,编程手册以及一些应用实例等。其中有部分内容还没有写完,估计有生之年很难看到完整版了,但是内容还是很有参考价值的。