我有一个带有重复项的对象数组。对象根级别的数据是相同的，但嵌套的对象数组则不相同。这是我需要在运行lodash uniqBy之前进行合并或者删除重复项的任何函数。
这是具有重复项的对象数组。
[
{
"id": "66E175A2-A29F-4F1A-AD81-2422B1EB00F6",
"name": "College Park / Brookhaven",
"mktId": 0,
"status": "Unknown",
"code": "197D6",
"ownershipType": null,
"series": [
{
"id": "80004F2E-E3C8-4B6A-BCCC-81259AEAF22D",
"name": "01",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
},
{
"id": "E053E656-4D14-4F2A-AD70-A37F65195CD1",
"name": "College Park / Hampshire",
"mktId": 0,
"status": "Unknown",
"code": "316D6",
"ownershipType": null,
"series": [
{
"id": "46830FBD-CD68-4D4C-A095-FB9C3D93D01A",
"name": "02,03",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
},
{
"id": "E053E656-4D14-4F2A-AD70-A37F65195CD1",
"name": "College Park / Hampshire",
"mktId": 0,
"status": "Unknown",
"code": "316D6",
"ownershipType": null,
"series": [
{
"id": "1BC31692-AAB8-4A00-9D8D-9B8CF7E426E0",
"name": "01",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
},
{
"id": "34F7C7AF-3D1B-4EE7-8271-C99294169C01",
"name": "College Park / Hillsdale",
"mktId": 0,
"status": "Unknown",
"code": "295D6",
"ownershipType": null,
"series": [
{
"id": "807144A1-26ED-4657-9775-7DF7563107D3",
"name": "02",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
}
]
大学公园/汉普郡在这个例子中是重复的。我需要找到2个重复项并返回此预期结果..
[
{
"id": "66E175A2-A29F-4F1A-AD81-2422B1EB00F6",
"name": "College Park / Brookhaven",
"mktId": 0,
"status": "Unknown",
"code": "197D6",
"ownershipType": null,
"series": [
{
"id": "80004F2E-E3C8-4B6A-BCCC-81259AEAF22D",
"name": "01",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
},
{
"id": "E053E656-4D14-4F2A-AD70-A37F65195CD1",
"name": "College Park / Hampshire",
"mktId": 0,
"status": "Unknown",
"code": "316D6",
"ownershipType": null,
"series": [
{
"id": "46830FBD-CD68-4D4C-A095-FB9C3D93D01A",
"name": "02,03",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
},
{
"id": "1BC31692-AAB8-4A00-9D8D-9B8CF7E426E0",
"name": "01",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
},
{
"id": "34F7C7AF-3D1B-4EE7-8271-C99294169C01",
"name": "College Park / Hillsdale",
"mktId": 0,
"status": "Unknown",
"code": "295D6",
"ownershipType": null,
"series": [
{
"id": "807144A1-26ED-4657-9775-7DF7563107D3",
"name": "02",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}
]
}
]
vanilla javascript或lodash很好。
答案 0 :(得分：1)
我使用HashMap创建了reduce，在该hashMap中，键是项的名称，值是项本身。
const data = [{
"id": "66E175A2-A29F-4F1A-AD81-2422B1EB00F6",
"name": "College Park / Brookhaven",
"mktId": 0,
"status": "Unknown",
"code": "197D6",
"ownershipType": null,
"series": [{
"id": "80004F2E-E3C8-4B6A-BCCC-81259AEAF22D",
"name": "01",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}]
},
{
"id": "E053E656-4D14-4F2A-AD70-A37F65195CD1",
"name": "College Park / Hampshire",
"mktId": 0,
"status": "Unknown",
"code": "316D6",
"ownershipType": null,
"series": [{
"id": "46830FBD-CD68-4D4C-A095-FB9C3D93D01A",
"name": "02,03",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}]
},
{
"id": "E053E656-4D14-4F2A-AD70-A37F65195CD1",
"name": "College Park / Hampshire",
"mktId": 0,
"status": "Unknown",
"code": "316D6",
"ownershipType": null,
"series": [{
"id": "1BC31692-AAB8-4A00-9D8D-9B8CF7E426E0",
"name": "01",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}]
},
{
"id": "34F7C7AF-3D1B-4EE7-8271-C99294169C01",
"name": "College Park / Hillsdale",
"mktId": 0,
"status": "Unknown",
"code": "295D6",
"ownershipType": null,
"series": [{
"id": "807144A1-26ED-4657-9775-7DF7563107D3",
"name": "02",
"productType": "Detached",
"productClass": "Single Family",
"salesStartDate": null,
"modelOpenDate": null
}]
}
];
const hashMap = data.reduce((hash, item) => {
const key = item.name;
if (hash.hasOwnProperty(key)) {
hash[key].series = hash[key].series.concat(item.series);
} else {
hash[key] = item;
}
return hash;
}, {});
const result = Object.keys(hashMap).map((key) => hashMap[key]);
console.log(result);
答案 1 :(得分：0)
只需为id和结果数组设置一个hashmap：
set.seed(100)
library(caTools)
library(caret)
library(e1071)
folds<-createFolds(wpdc$outcome, k=10)
CV <- lapply(folds, function(x){
traing_folds=wpdc[-x,]
test_folds=wpdc[x,]
dataset_model_nb<-naiveBayes(outcome ~ ., data = traing_folds)
dataset_predict_nB<-predict(dataset_model_nb, test_folds[-1])
dataset_table_nB<-table(test_folds[,1],dataset_predict_nB)
accuracy<-confusionMatrix(dataset_table_nB, positive ="R")
return(accuracy)
})
outcome radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave_points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se
1 N 18.02 27.60 117.50 1013.0 0.09489 0.1036 0.1086 0.07055 0.1865 0.06333 0.6249 1.8900 3.972 71.55 0.004433
2 N 17.99 10.38 122.80 1001.0 0.11840 0.2776 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399
3 N 21.37 17.44 137.50 1373.0 0.08836 0.1189 0.1255 0.08180 0.2333 0.06010 0.5854 0.6105 3.928 82.15 0.006167
然后迭代数组：
const hash = {}, result = [];
现在我们可以检查id是否已经出现在哈希中，如果是，只需添加系列：
for(const el of array){
const { id, series } = el;
如果id尚未出现，我们需要将对象添加到结果和哈希表中：
if(hash[id]){
hash[id].series.push(...series);
} else {
那已经是它了:)）