完全背包。 http://train.usaco.org/usacoprob2?a=3Srffjlf4QI&S=inflate /* TASK:inflate ...
, // we need to apply inflation_layer only to inside of given bounds if (static_cast...unsigned int r = cell_inflation_radius_ + 2; // push the cell data onto the inflation list and...mark inflation_cells_[distance_matrix_[mx - src_x + r][my - src_y + r]].emplace_back( index..., mx, my, src_x, src_y);//推到相应的膨胀层的vector内 } } 其中inflation_cells_中将按圈层存储栅格。...inflation_layer"一般放在最后面。因为它最终将前面几个层的障碍物信息一起膨胀。如果不想膨胀某个插件层,则可以将其放在"inflation_layer"之后。
小型圆形机器人 (c) 大型圆形机器人 代价地图配置 Costmap Configuration 每一层的细则("static_layer", "obstacle_layer", "voxel_layer", "inflation_layer...障碍物层Obstacle Layer:在读取时跟踪障碍物 通过传感器数据(2D、LaserScan) - 体素层Voxel Layer:使用跟踪障碍物 点云2(3D) - 膨胀层Inflation...3 resolution: 0.05 robot_radius: 0.1 plugins: ["obstacle_layer", "voxel_layer", "inflation_layer..."] inflation_layer: plugin: "nav2_costmap_2d::InflationLayer" cost_scaling_factor...: plugin: "nav2_costmap_2d::InflationLayer" cost_scaling_factor: 1.0 inflation_radius
存在较强的多重共线性,当VIF>=100,存在严重多重共线性 # 导入计算膨胀因子的库 from statsmodels.stats.outliers_influence import variance_inflation_factor...# get_loc(i) 返回对应列名所在的索引 vif=[variance_inflation_factor(x.values,x.columns.get_loc(i)) for i in x.columns...sklearn.datasets import make_blobs # VIF膨胀因子 from statsmodels.stats.outliers_influence import variance_inflation_factor...'股票客户流失'.xlsx) # 提取特征矩阵和标签 x=data.drop(columns=['是否流失']) y=data['是否流失'] 4.3 ✌ 计算膨胀因子 vif=[variance_inflation_factor...x_test)[:,1]) 4.8.2 ✌ 删除 累计交易佣金 x=x.drop(columns=['累计交易佣金(元)']) x=pd.DataFrame(x) y=y vif=[variance_inflation_factor
方差膨胀系数(variance inflation factor,VIF)是衡量多元线性回归模型中复 (多重)共线性严重程度的一种度量。...检验方法主要有:容忍度(Tolerance)和方差膨胀系数(Variance inflation factor,VIF)。其中最常用的是VIF,计算公式为: VIF的取值大于1。...sklearn.preprocessing import MinMaxScaler from statsmodels.stats.outliers_influence import variance_inflation_factor...pd.DataFrame(iris_scaler) iris_scaler['target'] = iris.target X = np.matrix(iris_scaler) VIF_list = [variance_inflation_factor
主要有static层,obstacle层,voxel_layer层和inflation层。每个层的数据类型是以插件的形式提供的。...如下图所示: [在这里插入图片描述] 其中inflation层使用InflationLayer类型数据,static层使用StaticLayer类型数据,而obstacle层可以选择VoxelLayer...对于inflationLayer类,在ObstacleLayer层的基础上增加膨胀半径(inflation_radius)的范围。...而inflation层没有维护costmap,它直接将cost值更新到了LayeredCostmap的costmap里。 2.膨胀半径为什么还要另外设置而不是通过footprint自动计算得到?...膨胀半径使用inflation_radius参数来设定。 3.footprint_padding参数有什么作用?
model_selection import statsmodels.api as sn from statsmodels.stats.outliers_influence import variance_inflation_factor...RD_Spend', 'Marketing_Spend']]) vif = pd.DataFrame() vif["Ficture"] = X.columns vif["Fctor"] = [variance_inflation_factor...model_selection import statsmodels.api as sn from statsmodels.stats.outliers_influence import variance_inflation_factor
pandas as pd import statsmodels.api as sm from statsmodels.stats.outliers_influence import variance_inflation_factor...VIF全称是Variance Inflation Factor,即方差扩大因子,我们对自变量X作中心标准化,则X变为Xs,然后可以得到Xs’ Xs = (rij),这个就是自变量的相关阵。...sm.add_constant(X) def process(data, col): data = data.loc[:, col] #读取对应列标数据 vif = [variance_inflation_factor...在process函数中,data = data.loc[:, col]就是读取只含有col列标的那些数据, vif = [variance_inflation_factor(data.values, i...) for i in range(data.shape[1])][1:]这行代码就是计算vif的过程,variance_inflation_factor函数需要输入两个参数,分别是数据和每列数据的标号,
inflation 机制 因为很多情况下,反射只会调用一次,因此 JVM 想了一招,设置了 15 这个 sun.reflect.inflationThreshold 阈值,反射方法调用超过 15 次时(...这种方式被称为 「inflation 机制」。inflation 这个单词也比较有意思,它的字面意思是「膨胀;通货膨胀」。...JVM 与 inflation 相关的属性有两个,一个是刚提到的阈值 sun.reflect.inflationThreshold,还有一个是是否禁用 inflation的属性 sun.reflect.noInflation
在Python中,我们可以使用statmodels库中的variance_inflation_factor函数来计算VIF。...下面是这样做的代码和结果: import statsmodels.api as sm from statsmodels.stats.outliers_influence import variance_inflation_factor...X = df[list(df.columns[:-2])] vif_info = pd.DataFrame() vif_info['VIF'] = [variance_inflation_factor...现在让我们看看数据的VIF值是怎样的: vif_info = pd.DataFrame() vif_info['VIF'] = [variance_inflation_factor(X.values,...'MinTemp', 'TempDiff', 'Sunshine'], axis=1) vif_info = pd.DataFrame() vif_info['VIF'] = [variance_inflation_factor
相关人气论文 1、Quantitative Credit(Man Group) 2、A Dynamic Multi-Asset Approach to Inflation Hedging(S&P Dow...Cost of Capital: 2021 Summary Edition(CFA Institute Research Foundation) 2、Stocks, Bonds, Bills, and Inflation...(State Street) 6、A Source-based Approach to Managing Inflation Risk(Wellington Management) 全部论文下载 在后台输入
2、Cosmic Inflation and Genetic Algorithms https://arxiv.org/pdf/2208.13804 Steven Abel, Andrei Constantin...bounds on the spectral index of scalar perturbations, the tensor-to-scalar ratio, and the scale of inflation...semi-comprehensive search for sextic polynomial potentials results in roughly O(300,000) viable models for inflation
VIF(variance inflation factors)VIF =1/(1-R^2) 式中,R^2是以xj为因变量时对其它自变量回归的复测定系数。...删除导致高共线性的变量 import numpy as np import pandas as pd from statsmodels.stats.outliers_influence import variance_inflation_factor...(X.shape[1])) dropped = True while dropped: dropped = False vif = [variance_inflation_factor
False) VIF = np.linalg.inv(cc) VIF.diagonal() from statsmodels.stats.outliers_influence import variance_inflation_factor...'c': [4, 6, 7, 8, 9], 'd': [4, 3, 4, 5, 4]} ) X = add_constant(df) >>> pd.Series([variance_inflation_factor
an arm is mounted on top of the robot # Obstacle Cost Shaping (http://wiki.ros.org/costmap_2d/hydro/inflation...were now moved to the inflation_layer ns inflation_layer: enabled: true cost_scaling_factor...: 5.0 # exponential rate at which the obstacle cost drops off (default: 10) inflation_radius:...inflation_radius参数则给定了机器人与障碍物之间必须要保持的最小距离。 cost_scaling_factor参数修改机器人绕过障碍物的行为,可以通过修改参数设计一个激进或保守的行为。...:StaticLayer"} - {name: obstacle_layer, type: "costmap_2d::VoxelLayer"} - {name: inflation_layer
在近期 ICLR 2024 工作中,北大王奕森团队针对这一「数据扩充」(Data Inflation)问题展开了深入研究。...为了区分,本文将生成数据视为数据扩充(Data Inflation),二者的区别是,数据扩充是扩大原始数据集的大小,而数据增广是对每个原始样本,在训练过程中进行随机增强。...图 5 数据扩充和数据增广对 labeling error 和图 的连通性的影响 基于以上的理解,论文提出自适应的数据扩充 Adaptive Inflation(AdaInf),根据生成数据的质量、大小...表 1 不同模型和不同数据集下的对比学习线性探测性能 本文在图像识别任务上表 1 表明,AdaInf 在不同的对比学习模型和不同数据集上的性能显著好于没有数据扩充(No Inflation)或者直接进行数据扩充...(Vanilla Inflation)。
independence 独立性,“ 0”表示“无独立性”,“ 1”表示“独立性” currency_crises 货币危机,“ 0”表示当年未发生“货币危机”,“ 1”表示当年有发生“货币危机” inflation_crises...______(data[data.country == country]['year'],data[data.country == country]['inflation_annual_cpi'],color...']),np.max(data[data.country == country]['inflation_annual_cpi'])], color = 'black',linestyle...['systemic_crisis','domestic_debt_in_default','sovereign_external_debt_default','currency_crises','inflation_crises...', 'independence', 'currency_crises','inflation_crises','banking_crisis'] corr = data[selected_features
df['Constant Value']=1 #添加常数项 df.head() from statsmodels.stats.outliers_influence import variance_inflation_factor...vif=[] for i in range(df_tezheng.shape[1]-1): #计算第i+1个变量的(第i+1列)的方差膨胀因子 vif.append(variance_inflation_factor
wb.define_name('inflation',"=输入!..., variables['收入'],'income_increase') annual_increase(ws_2, 10, 3, variables['年数'],variables['支出'], 'inflation...wb.define_name('inflation',"=输入!..., variables['收入'],'income_increase') annual_increase(ws_2, 10, 3, variables['年数'],variables['支出'], 'inflation
1.训练模型 import statsmodels.api as sm from statsmodels.stats.outliers_influence import variance_inflation_factor...直接输出到excel不方便 summary = LR.summary() #查看VIF X_m = np.matrix(X) VIF_list = [variance_inflation_factor
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