function median_target(var) { temp = data[data[var].notnull()]; temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index(); return temp; } data.loc[(data['Outcome'] == 0) & (data['Insulin'].isnull()), 'Insulin'] = 102.5; data.loc[(data['Outcome'] == 1) & (data['Insulin'].isnull()), 'Insulin'] = 169.5; data.loc[(data['Outcome'] == 0) & (data['Glucose'].isnull()), 'Glucose'] = 107; data.loc[(data['Outcome'] == 1) & (data['Glucose'].isnull()), 'Glucose'] = 1; data.loc[(data['Outcome'] == 0) & (data['SkinThickness'].isnull()), 'SkinThickness'] = 27; data.loc[(data['Outcome'] == 1) & (data['SkinThickness'].isnull()), 'SkinThickness'] = 32; data.loc[(data['Outcome'] == 0) & (data['BloodPressure'].isnull()), 'BloodPressure'] = 70; data.loc[(data['Outcome'] == 1) & (data['BloodPressure'].isnull()), 'BloodPressure'] = 74.5; data.loc[(data['Outcome'] == 0) & (data['BMI'].isnull()), 'BMI'] = 30.1; data.loc[(data['Outcome'] == 1) & (data['BMI'].isnull()), 'BMI'] = 34.3; target_col = ["Outcome"]; cat_cols = data.nunique()[data.nunique() < 12].keys().tolist(); cat_cols = [x for x in cat_cols]; num_cols = [x for x in data.columns if x not in cat_cols + target_col]; bin_cols = data.nunique()[data.nunique() == 2].keys().tolist(); multi_cols = [i for i in cat_cols if i in bin_cols]; le = LabelEncoder(); for i in bin_cols: data[i] = le.fit_transform(data[i]); data = pd.get_dummies(data=data, columns=multi_cols); std = StandardScaler(); scaled = std.fit_transform(data[num_cols]); scaled = pd.DataFrame(scaled, columns=num_cols); df_data_og = data.copy(); data = data.drop(columns=num_cols, axis=1); data = data.merge(scaled, left_index=True, right_index=True, how='left'); X = data.drop('Outcome', axis=1); y = data['Outcome']; X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1); y_train = to_categorical(y_train); y_test = to_categorical(y_test);将这段代码添加注释

def median_target(var): temp = data[data[var].notnull()] temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index() return temp

函数中，首先使用 data[data[var].notnull()] 过滤掉 var 变量为空的行，然后使用 [[var, 'Outcome']] 选择 var 变量和分类变量 Outcome 两列。接着使用 groupby(['Outcome'])[[var]].median().reset_index() 对数据...

# 6、每个用户按周求和并差分（一周7天，年度分开），并求取差分结果的基本统计量，统计量同三。 res4 = pd.DataFrame() for col in data_t.columns: # 按周求和 data_week = data_t[col].resample('W').sum() # 按周差分 data_week_diff = data_week.diff(periods=1) # 去掉第一个空值 data_week_diff = data_week_diff.dropna() # 求取差分结果的基本统计量 res = pd.Series() res['最大值'] = data_week_diff.max() res['最小值'] = data_week_diff.min() res['均值'] = data_week_diff.mean() res['中位数'] = data_week_diff.median() res['和'] = data_week_diff.sum() res['方差'] = data_week_diff.var() res['偏度'] = data_week_diff.skew() res['峰度'] = data_week_diff.kurt() # 将结果存入res4中 res4[col] = res print("每个用户按周求和并差分的基本统计量") print(res4)修改运行代码

year_res['中位数'] = group.median() year_res['和'] = group.sum() year_res['方差'] = group.var() year_res['偏度'] = group.skew() year_res['峰度'] = group.kurt() res[year] = year_res # 将结果存入...

将下列代码变为伪代码def median_target（var）： temp = data[data[var].notnull（）] temp = temp[[var， 'Outcome']].groupby（['Outcome']）[[var]].median（）.reset_index（） return temp data.loc[（data['Outcome'] == 0 ） & （data['Insulin'].isnull（））， 'Insulin'] = 102.5 data.loc[（data['Result'] == 1 ） & （data['Insulin'].isnull（））， 'Insulin'] = 169.5 data.loc[（data['Result'] == 0 ） & （data['Glucose'].isnull（））， 'Glucose'] = 107 data.loc[（data['Result'] == 1 ） & （data['Glucose'].isnull（））， 'Glucose'] = 1 data.loc[（data['Result'] == 0 ） & （data['SkinThickness'].isnull（））， 'SkinThickness'] = 27 data.loc[（data['Result'] == 1 ） & （data['SkinThickness'].isnull（））， 'SkinThickness'] = 32 data.loc[（data['Result'] == 0 ） & （data['BloodPressure'].isnull（））， 'BloodPressure'] = 70 data.loc[（data['Result'] == 1 ） & （data['BloodPressure'].isnull（））， 'BloodPressure'] = 74.5 data.loc[（data['Result'] == 0 ） & （data['BMI'].isnull（））， 'BMI'] = 30.1 data.loc[（data['Result'] == 1 ） & （data['BMI'].isnull（））， 'BMI'] = 34.3 target_col = [“Outcome”] cat_cols = data.nunique（）[data.nunique（） < 12].keys（）.tolist（） cat_cols = [x for x in cat_cols ] #numerical列 num_cols = [x for x in data.columns if x 不在 cat_cols + target_col] #Binary列有 2 个值 bin_cols = data.nunique（）[data.nunique（） == 2].keys（）.tolist（） #Columns 2 个以上的值 multi_cols = [i 表示 i in cat_cols if i in bin_cols] #Label编码二进制列 le = LabelEncoder（） for i in bin_cols ： data[i] = le.fit_transform（data[i]） #Duplicating列用于多值列 data = pd.get_dummies（data = data，columns = multi_cols ） #Scaling 数字列 std = StandardScaler（）缩放 = std.fit_transform（数据[num_cols]）缩放 = pd。数据帧（缩放，列=num_cols） #dropping原始值合并数字列的缩放值 df_data_og = 数据.copy（）数据 = 数据.drop（列 = num_cols，轴 = 1）数据 = 数据.合并（缩放，left_index=真，right_index=真，如何 = “左”） # 定义 X 和 Y X = 数据.drop（'结果'，轴=1） y = 数据['结果'] X_train， X_test， y_train， y_test = train_test_split（X， y， train_size=0.8， shuffle=True， random_state=1） y_train = to_categorical（y_train） y_test = to_categorical（y_test）

temp = data[data[var].notnull()]; temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index(); return temp; } data.loc[(data['Outcome'] == 0) & (data['Insulin'].isnull()), '...

p_boxplot_exp_mt.gene = boxplot(sample_exp_mt.gene) > p_boxplot_exp_mt.gene = p_boxplot_exp_mt.gene + + geom_boxplot(color = "black", size = 0.25, outlier.shape = NA) + + xlab(NULL) + ylab("log2(CPM+1)") + + scale_color_manual(values = "black") + + stat_summary(fun = max, geom = "crossbar", color = "black", width = 0.1) + + stat_summary(fun = min, geom = "crossbar", color = "black", width = 0.1) + + theme(axis.line = element_line(color = "black", size = 0.25), + axis.ticks = element_line(color = "black", size = 0.25), + axis.text = element_text(color = "black", angle = 90, size = 18), + axis.title = element_text(color = "black", size = 18)) + + geom_text(data = sample_exp_mt.gene, aes(x = sample, y = median_expression, + label = paste0("Median: ", round(median_expression, 2))), + vjust = -1, hjust = -0.5, size = 4, color = "black")+ + theme(panel.background = element_blank()) Error in p_boxplot_exp_mt.gene + geom_boxplot(color = "black", size = 0.25, : non-numeric argument to binary operator

geom_text(data = df_boxplot, aes(x = 1, y = median, label = paste0("Median: ", round(median, 2))), vjust = -1, hjust = -0.5, size = 4, color = "black") + theme(panel.background = element_blank()...

ggplot(median_T, aes(x = Tissue, y = aov_cpm_median_T_means)) + + geom_boxplot(width = 2, outlier.shape = NA) + + scale_x_discrete(linetype = "solid", color = "black") + + scale_y_continuous(linetype = "solid", color = "black") + + theme_classic() + + theme(panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + panel.border = element_blank(), + axis.line = element_line(color = "black", size = 0.5), + axis.text = element_text(size = 12), + axis.title = element_text(size = 14), + plot.title = element_text(size = 16, face = "bold"), + plot.subtitle = element_text(size = 14), + plot.caption = element_text(size = 12), + legend.position = "none") Error in discrete_scale(c("x", "xmin", "xmax", "xend"), "position_d", : unused arguments (linetype = "solid", color = "black")

首先，你指定了数据框 median_T，然后使用 aes() 函数来设置 x 轴为 Tissue，y 轴为 aov_cpm_median_T_means。接着，你使用 geom_boxplot() 函数来绘制箱线图，并设置参数 width = 2 来控制箱体的宽度，outlier....

6、每个用户按周求和并差分（一周7天，年度分开），并求取差分结果的基本统计量，统计量同三。 res4 = pd.DataFrame() for col in data_t.columns: # 按周求和 data_weekly = data_t[col].resample('W').sum() # 差分 data_diff = data_weekly.diff(periods=1) # 去除第一个NaN值 data_diff = data_diff[1:] # 求取差分结果的基本统计量 res_temp = pd.DataFrame() res_temp['最大值'] = data_diff.max() res_temp['最小值'] = data_diff.min() res_temp['均值'] = data_diff.mean() res_temp['中位数'] = data_diff.median() res_temp['和'] = data_diff.sum() res_temp['方差'] = data_diff.var() res_temp['偏度'] = data_diff.skew() res_temp['峰度'] = data_diff.kurt() res4 = pd.concat([res4, res_temp.T], axis=1) res4.columns = data_t.columns print("每个用户按周求和并差分的基本统计量") print(res4)修改运行代码

res_temp['中位数'] = data_diff.median() res_temp['和'] = data_diff.sum() res_temp['方差'] = data_diff.var() res_temp['偏度'] = data_diff.skew() res_temp['峰度'] = data_diff.kurt() res4 = pd....

解析 def explore_city_data(self,city_data): housing_prices = city_data.target housing_features = city_data.data num_houses = np.shape(city_data.data) num_features = np.shape(city_data.data) min_price = np.min(city_data.target) max_price = np.max(city_data.target) mean_price = np.mean(city_data.target) median_price = np.median(city_data.target) stand_dev = np.std(city_data.target)

这段代码定义了一个名为"explore_city_data"的函数，该函数有一个参数"city_data"。该函数的主要目的是从给定的城市数据中提取一些基本信息，包括房屋价格、特征数量、房屋数量、最小价格、最大价格、平均价格、中位...

mport numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] # 处理首尾值 smoothed_data[:half_window] = data[:half_window] smoothed_data[data_length - half_window:] = data[data_length - half_window:] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 3 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)这段代码如果输入数据有很多维度怎么改

smoothed_data[i] = median else: smoothed_data[i] = x[i] smoothed_data[:half_window] = x[:half_window] smoothed_data[data_length - half_window:] = x[data_length - half_window:] return smoothed_...

将这段代码变为伪代码形式target_col = ["Outcome"] cat_cols = data.nunique()[data.nunique() < 12].keys().tolist() cat_cols = [x for x in cat_cols ] #numerical columns num_cols = [x for x in data.columns if x not in cat_cols + target_col] #Binary columns with 2 values bin_cols = data.nunique()[data.nunique() == 2].keys().tolist() #Columns more than 2 values multi_cols = [i for i in cat_cols if i not in bin_cols] #Label encoding Binary columns le = LabelEncoder() for i in bin_cols :median_target('BMI') data.loc[(data['Outcome'] == 0 ) & (data['BMI'].isnull()), 'BMI'] = 30.1 data.loc[(data['Outcome'] == 1 ) & (data['BMI'].isnull()), 'BMI'] = 34.3 data[i] = le.fit_transform(data[i]) #Duplicating columns for multi value columns data = pd.get_dummies(data = data,columns = multi_cols ) #Scaling Numerical columns std = StandardScaler() scaled = std.fit_transform(data[num_cols]) scaled = pd.DataFrame(scaled,columns=num_cols) #dropping original values merging scaled values for numerical columns df_data_og = data.copy() data = data.drop(columns = num_cols,axis = 1) data = data.merge(scaled,left_index=True,right_index=True,how = "left")

设置目标列为"Outcome" 将唯一值少于12个的列作为分类变量列，并存储在cat_cols列表中从数据集中选择数值列，不包括分类变量和目标列，存储在num_cols列表中 ...将数据集的副本存储在df_data_og中

median_y_pre= xy_df.groupby('city')['y_pre'].transform('median') median_aqi=xy_df.groupby('city')['AQI'].transform('median') df_median = pd.DataFrame({ 'city':median_y_pre.index, 'median_y_pre':median_y_pre.values, 'median_aqi':median_aqi.values }) sorted_df=df_median.sort_values('median_y_pre') sorted_df.head(10)，请根据以上代码进行修改，以满足以下请求Pandas中如何对指定的定类数据的各类进行指定的两列定量都进行平均值处理，接着根据每个类别和对应的中位数建立新的pandas

median_y_pre = xy_df.groupby('city')['y_pre'].median() median_aqi = xy_df.groupby('city')['AQI'].median() # 将中位数结果合并到df_mean中 df_mean['median_y_pre'] = median_y_pre.values df_mean['median_...

import numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 5 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)怎么修改不影响首尾值

smoothed_data[i] = median else: smoothed_data[i] = data[i] # 处理首尾值 smoothed_data[:half_window] = data[:half_window] smoothed_data[data_length - half_window:] = data[data_length - half_...

请帮我完善如下代码：from skimage.filters import median median_filter = median() #Please fill this function titles = ['noisy', 'median'] imshow_all(noisy, median_filter, titles=titles) mse = np.mean((image-median_filter255)**2) psnr=20*math.log10(255/math.sqrt(mse)) print('PSNR: ',psnr)

这段代码定义了一个进行中值滤波的函数，具体实现需要在median()括号内输入相应的参数来指定滤波的大小和图像。例如median_filter = median(image, size=(3, 3))可以对原始图像进行3x3大小的中值滤波。

median_T <- read_excel("median_T.xlsx") median_T$Tissue <- factor(median_T$Tissue) aov_cpm_median_T <- aov(median_expression ~ Tissue, data = median_T) pairwise_T <- pairwise.t.test(median_expression ~ Tissue, data = median_T)，以上代码的报错信息为：Error in factor(g) : argument "g" is missing, with no default，应怎么修改该代码？

aov_cpm_median_T (median_expression ~ Tissue, data = median_T) pairwise_T <- pairwise.t.test(median_expression ~ Tissue, data = median_T) 在上述代码中，使用as.factor()函数将median_T$Tissue...

import pandas as pd data=pd.read_csv('housing.csv') total_bedrooms_mean=data['total_bedrooms'].mean() data['total_bedrooms'].fillna(total_bedrooms_mean,inplace=True) onehot=pd.get_dummies((data[['ocean_proximity']]),prefix='ocean_proximity') data.drop(columns = ['ocean_proximity'],inplace=True) X=pd.concat([data['housing_median_age'],data['total_rooms'],data['total_bedrooms'],data['population'],data['households'],data['median_income'],onehot],axis=1) y=data[["median_house_value"]] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42) from sklearn.linear_model import LinearRegression lin_reg=LinearRegression() lin_reg.fit(X_train,y_train) y_pre=lin_reg.predict(X_test) from sklearn import metrics metrics.accuracy_score(y_test,y_pre)报错import pandas as pd data=pd.read_csv('housing.csv') total_bedrooms_mean=data['total_bedrooms'].mean() data['total_bedrooms'].fillna(total_bedrooms_mean,inplace=True) onehot=pd.get_dummies((data[['ocean_proximity']]),prefix='ocean_proximity') data.drop(columns = ['ocean_proximity'],inplace=True) X=pd.concat([data['housing_median_age'],data['total_rooms'],data['total_bedrooms'],data['population'],data['households'],data['median_income'],onehot],axis=1) y=data[["median_house_value"]] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42) from sklearn.linear_model import LinearRegression lin_reg=LinearRegression() lin_reg.fit(X_train,y_train) y_pre=lin_reg.predict(X_test) from sklearn import metrics metrics.accuracy_score(y_test,y_pre)

这个代码会报错，因为metrics.accuracy_score()是用于分类问题的指标，而这里是回归问题，所以不能使用该指标。如果你想评估回归模型的性能，可以使用回归指标，如均方误差（mean squared error, MSE）、均方根...

> write.xlsx(median_T, file = "4.All_SMT/1.Gene_expression_distribution/median_T.xlsx", + sheetName = "Sheet1", + row.names = FALSE, col.names = TRUE) Error in if (is.na(value)) { : the condition has length > 1

median_T <- data %>% group_by(sample) %>% summarize(median_expression = median(expression, na.rm = TRUE)) 如果你还是遇到相同的错误，可以尝试将数据框中的所有NA值替换为其他值（例如0），然后再...

NLMS_onlynoise.rar_Modified_median_nlms matlab

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def median_target(var): temp = data[data[var].notnull()] temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index() return temp

请帮我完善如下代码：from skimage.filters import median median_filter = median() #Please fill this function titles = ['noisy', 'median'] imshow_all(noisy, median_filter, titles=titles) mse = np.mean((image-median_filter255)**2) psnr=20*math.log10(255/math.sqrt(mse)) print('PSNR: ',psnr)

> write.xlsx(median_T, file = "4.All_SMT/1.Gene_expression_distribution/median_T.xlsx", + sheetName = "Sheet1", + row.names = FALSE, col.names = TRUE) Error in if (is.na(value)) { : the condition has length > 1

NLMS_onlynoise.rar_Modified_median_nlms matlab

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