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- 来自专栏潇涧技术专栏
Numerical Methods using Matlab
内容包括：基本幂法，逆幂法和移位幂法，QR分解，Householder变换，实用QR分解技术，奇异值分解SVD
88820发布于 2018-08-01 - 来自专栏机器学习与统计学
Duke@coursera 数据分析与统计推断 unit4 inference for numerical variables
inference for numerical variables 一、hypothesis testing for paired data hypotheses for paired means： ?
71630发布于 2019-04-10 - 来自专栏WD学习记录
Leetcode String to Integer (atoi)
Then, starting from this character, takes an optional initial plus or minus sign followed by as many numerical digits as possible, and interprets them as a numerical value. If the numerical value is out of the range of representable values, INT_MAX (231 − 1) or INT_MIN (−231 Then take as many numerical digits as possible, which gets 42. with words" Output: 4193 Explanation: Conversion stops at digit '3' as the next character is not a numerical
58920发布于 2018-09-04 - 来自专栏书山有路勤为径
Day and Night Image data Standardization
encode("day") should return: 1 # encode("night") should return: 0 def encode(label): numerical_val = 0 ## TODO: complete the code to produce a numerical label if(label == 'day'): numerical_val = 1 return numerical_val def standardize(image_list): # Empty image data array standard_list # Standardize the image standardized_im = standardize_input(image) # Create a numerical STANDARDIZED_LIST[image_num][1] # Display image and data about it ## TODO: Make sure the images have numerical
55020发布于 2018-08-28 - 来自专栏机器学习/数据可视化
机器学习算法竞赛实战-特征工程
if numerical_df[c].isna().sum() > 0] # 填充中位数 for c in missing_cols: numerical_df[c] = numerical_df # 异常值处理 numerical_df.loc[numerical_df['YrSold'] > numerical_df['YearBuilt'], 'YrSold'] = 2009 # 构造特征： 房屋年龄 numerical_df["Age_House"] = numerical_df["YrSold"] - numerical_df["YearBuilt"] numerical_df["TotalBsmtBath "] = numerical_df["BsmtFullBath"] + numerical_df["BsmtHalfBath"] * 0.5 # 浴池 + 半浴池 numerical_df["TotalBath "] = numerical_df["FullBath"] + numerical_df["HalfBath"] * 0.5 # 全浴 + 半浴 numerical_df["TotalSA"] = numerical_df
93830编辑于 2023-08-25 - 来自专栏生物信息学、python、R、linux
基于sklearn建立机器学习的pipeline
X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == "object"] # 找到数值变量 numerical_cols X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']] # 缺失值填补 numerical_transformer most_frequent')), ('onehot', OneHotEncoder(handle_unknown = 'ignore'))]) # Bundle preprocessing for numerical and categorical data preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer , numerical_cols), ('cat', categorical_transformer, categorical_cols) ]) # Define model
83510发布于 2020-10-26 - 来自专栏ZNing·腾创库
技术角 | 深度学习之《深度学习入门》学习笔记（四）神经网络的学习（下）
# 求点(3,4) (0,2) (3,0)处的梯度 numerical_gradient(function_2, np.array([3.0, 4.0])) array([6., 8.]) numerical_gradient numerical_gradient(f,x)会求函数的梯度，用该梯度乘以学习率得到的值进行更新操作，由step_num指定重复的次数。 grads['W2'] = numerical_gradient(loss_W, self.params['W2']) grads['b2'] = numerical_gradient( numerical_gradient(self, x, t)是计算各个参数的梯度，而gradient(self, x, t)是使用误差反向传播法高效计算梯度的方法。 numerical_gradient(self, x, t)是基于数值微分计算参数的梯度。
1.3K20发布于 2020-05-13 - 来自专栏吉吉的机器学习乐园
Python二手车价格预测（一）—— 数据处理
# 筛选出可以转化为数值型数据的列 numerical_col = ['售价', '新车售价', '行驶里程', '过户记录', '载客/人', '排量(L)', ' = data[numerical_col] # 将非数值型数据替换为np.nan for c in numerical_col[5:]: numerical_df[c] = numerical_df = numerical_df.astype(float) # 进行填充 for c in mean_fill_col: numerical_df[c].fillna(numerical_df [c].mean(), inplace=True) for c in many_fill_col: numerical_df[c].fillna(4, inplace=True) # 将处理完的数据更新至data中 data[ numerical_col ] = numerical_df # 处理 ['座位数', '行李厢容积(L)', '最大功率转速(rpm)', '最大扭矩转速
1.9K30编辑于 2022-07-13 - 来自专栏图像处理与模式识别研究所
以单元为中心的方案对一维对流扩散方程的数值解。
u(j+1)*pe*(dx(j)+dx(j+1))/2; end s = hybrid(p,type); % Switch factors for hybrid scheme % Numerical = spdiags(B, [-1 0 1], n,n)\f; % Computation of error norms error = numerical_solution - exact_solution (spdiags(B, [-1 0 1], n,n) - spdiags(C, [-1 0 1], n,n))*numerical_solution; numerical_solution = spdiags(B, [-1 0 1], n,n)\g; end plot(y,numerical_solution,'o ','markersize',10) % Computation of error norms error = numerical_solution - exact_solution(y,a,alpha,pe); norm1 = norm(error,1)/
38030编辑于 2022-05-28 - 来自专栏ZNing·腾创库
【玩转腾讯云】深度学习之《深度学习入门》学习笔记（四）神经网络的学习
return (f(x+h) - f(x-h)) / (2*h) 利用微小的差分求导数的过程称为数值微分（numerical differentiation）。 # 求点(3,4) (0,2) (3,0)处的梯度 numerical_gradient(function_2, np.array([3.0, 4.0])) array([6., 8.]) numerical_gradient numerical_gradient(f,x)会求函数的梯度，用该梯度乘以学习率得到的值进行更新操作，由step_num指定重复的次数。 grads['W2'] = numerical_gradient(loss_W, self.params['W2']) grads['b2'] = numerical_gradient( numerical_gradient(self, x, t)是基于数值微分计算参数的梯度。
1.7K30发布于 2020-05-05 - 来自专栏皮皮星球
String - 8. String to Integer (atoi)
Then, starting from this character, takes an optional initial plus or minus sign followed by as many numerical digits as possible, and interprets them as a numerical value. If the numerical value is out of the range of representable values, INT_MAX (231 − 1) or INT_MIN (−231 Then take as many numerical digits as possible, which gets 42. with words" Output: 4193 Explanation: Conversion stops at digit '3' as the next character is not a numerical
68610发布于 2020-09-23 - 来自专栏红色石头的机器学习之路
斯坦福CS231n项目实战（三）：Softmax线性分类
error: 8.029571e-09 numerical: -1.840196 analytic: -1.840196, relative error: 1.781980e-09 numerical relative error: 1.643225e-08 numerical: 1.122692 analytic: 1.122692, relative error: 1.600617e-08 numerical error: 1.452262e-08 numerical: 1.976238 analytic: 1.976238, relative error: 1.619212e-08 numerical: error: 2.672068e-08 numerical: 1.991475 analytic: 1.991475, relative error: 3.035301e-08 numerical: error: 1.916174e-08 numerical: 1.688600 analytic: 1.688600, relative error: 6.298778e-10 numerical:
1K10发布于 2019-05-25 - 来自专栏iRF射频前端产业观察
射频前端模组：自屏蔽工艺的量化建模和测量结果
Abstract A reliable numerical modelling for shielding evaluation of on-packageconformal shields based As a result, an accurate andreliable numerical modelling of a conformal shielding structure including (GND) pads and the thickness of a conformalshield on the shielding performance are investigated by numerical For numerical analysis, a 3D electromagnetic field simulation using CST Microwave Studio is employed 致谢和引用 全文引用自Shielding evaluation of on‐package conformal shields by numerical modelling and experimental
91030编辑于 2022-05-16 - 来自专栏气象杂货铺
Science Bulletin | 气溶胶是导致全球数值天气预报模型中气温预报偏差的关键因素
Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction Current numerical weather prediction models such as the Global Forecast System (GFS) are still subject X., and Ding, A.: Aerosol as a critical factor causing forecast biases of air temperature in global numerical
50810编辑于 2022-09-23 - 来自专栏图像处理与模式识别研究所
以单元格为中心的网格对二维定常奇异摄动问题的数值解。
A(m-J,m) = A(m-J,m) - a; A(m,m-J) = A(m,m-J) - a; A(m,m) = A(m,m) + a; end end numerical_solution for j = 1:J, exsol(j + (k-1)*J) = exact_solution(x(j),y(k),D); end end % Text output error = numerical_solution plot(exact,yy) % Solution profile at y = y(kkk) vv = zeros(J,1); kkk = 1; for j = 1:J, vv(j) = numerical_solution end hold on, plot(yy,exact) % Solution profile at x = x(J) hh = zeros(K,1); for k = 1:K, hh(k) = numerical_solution ; end plot(exact,yy) % Contour plot consol = zeros(K,J); for k = 1:K,for j = 1:J, consol(k,j) = numerical_solution
40920编辑于 2022-05-28 - 来自专栏红色石头的机器学习之路
斯坦福CS231n项目实战（二）：线性支持向量机SVM
numerical: -7.522645 analytic: -7.522645, relative error: 3.601909e-11 numerical: 14.561062 analytic -11 numerical: 9.577850 analytic: 9.577850, relative error: 6.228243e-11 numerical: -5.397272 analytic -11 numerical: 14.054682 analytic: 14.054682, relative error: 2.879899e-12 numerical: 0.444995 analytic -10 numerical: -1.160105 analytic: -1.160105, relative error: 5.096445e-10 numerical: -3.007970 analytic -10 numerical: -16.032463 analytic: -16.032463, relative error: 1.920198e-11 numerical: 5.949340 analytic
1K10发布于 2019-05-25 - 来自专栏图像处理与模式识别研究所
一维对流扩散方程的Dirichlet精确解。
exact_solution(x,pe,a,b) res = a + (b-a)*(exp((x-1)*pe) - exp(-pe))/(1 - exp(-pe)); convection_diffusion.m % Numerical contains coordinates of cell centers for j = 1:n y(j) = 0.5*(x(j)+x(j+1)); dx(j) = x(j+1)-x(j); end % Numerical 2) = -0.5 + (2/pe)*( 1/(dx(j)+dx(j-1)) +1/dx(j)); f(j) = -b +(2*b)/(dx(j)*pe); numerical_solution = spdiags(B, [-1 0 1], n,n)\f; % Computation of error norms error = numerical_solution - exact_solution (pe),', ',num2str(n),' cells, ',num2str(m2),... ' cells in boundary layer'],'fontsize',18) plot(y,numerical_solution
77430编辑于 2022-05-28 - 来自专栏Michael阿明学习之路
【Kaggle】Intermediate Machine Learning（管道+交叉验证）
sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder # Preprocessing for numerical data 数字数据插值 numerical_transformer = SimpleImputer(strategy='constant') # Preprocessing for categorical most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) # Bundle preprocessing for numerical categorical data # 上面两者合并起来，形成完整的数据处理流程 preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer , numerical_cols), ('cat', categorical_transformer, categorical_cols) ]) 步骤2： 定义模型 from sklearn.ensemble
83420发布于 2020-07-13 - 来自专栏刷题笔记
【LeetCode】8. 字符串转换整数 (atoi)
Then, starting from this character, takes an optional initial plus or minus sign followed by as many numerical digits as possible, and interprets them as a numerical value. If the numerical value is out of the range of representable values, INT_MAX (231 − 1) or INT_MIN (−231 Then take as many numerical digits as possible, which gets 42. "words and 987" Output: 0 Explanation: The first non-whitespace character is 'w', which is not a numerical
65730发布于 2020-06-23 - 来自专栏R语言
R4DS-学习记录-ggplot2_2-CG
A numerical variableA variable is numerical (or quantitative) if it can take on a wide range of numerical
26500编辑于 2024-04-01
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