我的响应对象中有一个json结果集，如下所示
select group_concat(pvid_id separator ', ') as result
from tab
where tag_id = 1 -- or tag_id = 2
result
1, 2
以及以下课程
[{
"id": "13",
"category_id": "[\"2\"]",
"store_id": "[\"3\"]",
"name": "Sp juise",
"price": "1",
"description": "<p><b><\/b><br><\/p>",
"image": "<p>You did not select a file to upload.<\/p>",
"active": "1"
}, {
"id": "12",
"category_id": "[\"2\"]",
"store_id": "[\"3\"]",
"name": "Sp juise",
"price": "1",
"description": "<p><b><\/b><br><\/p>",
"image": "<p>You did not select a file to upload.<\/p>",
"active": "1"
}, {
"id": "11",
"category_id": "[\"3\"]",
"store_id": "[\"2\"]",
"name": "Berger (Chicken)",
"price": "80",
"description": "<p>Chicken Berger<\/p>",
"image": "assets\/images\/product_image\/5b374e99c53ce.jpg",
"active": "1"
}, {
"id": "10",
"category_id": "[\"2\"]",
"store_id": "[\"2\"]",
"name": "Juce",
"price": "2",
"description": "<p>mixed<\/p>",
"image": "<p>You did not select a file to upload.<\/p>",
"active": "1"
}, {
"id": "9",
"category_id": "null",
"store_id": "null",
"name": "Orenge juse",
"price": "2.5",
"description": "",
"image": "assets\/images\/product_image\/5b374ece3d909.jpg",
"active": "1"
}]
以下用于从json获取产品列表的代码
public class Product
{
public string id;
public string category_id;
public string store_id;
public string name;
public string price;
public string description;
public string image;
public string active;
}
产品具有可以枚举的对象，但是这些字段未映射到类属性，并且它们对于列表中的每个对象都返回空值。
答案 0 :(得分：0)
这是由于缺少获取和设置 下面解决了这个问题
/***************************************************************************
* Copyright (C) 2018 by Paul Lutus *
* lutusp@arachnoid.com *
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
* This program is distributed in the hope that it will be useful, *
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
* GNU General Public License for more details. *
* *
* You should have received a copy of the GNU General Public License *
* along with this program; if not, write to the *
* Free Software Foundation, Inc., *
* 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. *
***************************************************************************/
// classic Gauss-Jordan matrix manipulation functions
var gj = gj || {}
gj.divide = function(A, i, j, m) {
for (var q = j + 1; q < m; q++) {
A[i][q] /= A[i][j];
}
A[i][j] = 1;
}
gj.eliminate = function(A, i, j, n, m) {
for (var k = 0; k < n; k++) {
if (k != i && A[k][j] != 0) {
for (var q = j + 1; q < m; q++) {
A[k][q] -= A[k][j] * A[i][q];
}
A[k][j] = 0;
}
}
}
gj.echelonize = function(A) {
var n = A.length;
var m = A[0].length;
var i = 0;
var j = 0;
var k;
var swap;
while (i < n && j < m) {
//look for non-zero entries in col j at or below row i
k = i;
while (k < n && A[k][j] == 0) {
k++;
}
// if an entry is found at row k
if (k < n) {
// if k is not i, then swap row i with row k
if (k != i) {
swap = A[i];
A[i] = A[k];
A[k] = swap;
}
// if A[i][j] is != 1, divide row i by A[i][j]
if (A[i][j] != 1) {
gj.divide(A, i, j, m);
}
// eliminate all other non-zero entries
gj.eliminate(A, i, j, n, m);
i++;
}
j++;
}
}
// a simple data class
function Pair(x,y) {
this.x = x;
this.y = y;
};
Pair.prototype.toString = function() {return x + ',' + y};
// matrix functions
var matf = matf || {}
// a weak substitue for printf()
matf.number_format = function(n,p,w) {
s = n.toExponential(p);
while(s.length < w) {
s = ' ' + s;
}
return s;
}
// produce a single y result for a given x
matf.regress = function(x, terms) {
var y = 0;
var m = 1;
for (var i = 0; i < terms.length;i++) {
y += terms[i] * m;
m *= x;
}
return y;
}
// compute correlation coefficient
matf.corr_coeff = function(data, terms) {
var r = 0;
var n = data.length;
var sx = 0;
var sx2 = 0, sy = 0, sy2 = 0, sxy = 0;
var x, y;
for (var i = 0;i < data.length;i++) {
pr = data[i];
var x = matf.regress(pr.x, terms);
var y = pr.y;
sx += x;
sy += y;
sxy += x * y;
sx2 += x * x;
sy2 += y * y;
}
var div = Math.sqrt((sx2 - (sx * sx) / n) * (sy2 - (sy * sy) / n));
if (div != 0) {
r = Math.pow((sxy - (sx * sy) / n) / div, 2);
}
return r;
}
// compute standard error
matf.std_error = function(data, terms) {
var r = 0;
var n = data.length;
if (n > 2) {
var a = 0;
for (var i = 0;i < data.length;i++) {
pr = data[i];
a += Math.pow((matf.regress(pr.x, terms) - pr.y), 2);
}
r = Math.sqrt(a / (n - 2));
}
return r;
}
// create regression coefficients
// for provided data set
// data = pair array
// p = polynomial degree
matf.compute_coefficients = function(data, p) {
p += 1;
var n = data.length;
var r, c;
var rs = 2 * p - 1;
//
// by request: read each datum only once
// not the most efficient processing method
// but required if the data set is huge
//
// create square matrix with added RH column
m = Array();
for (var i = 0; i < p; i++) {
mm = Array();
for (var j = 0; j <= p; j++) {
mm[j] = 0;
}
m[i] = mm;
}
//double[][] m = new double[p][p + 1];
// create array of precalculated matrix data
mpc = Array();
for(var i = 0;i < rs;i++) {
mpc[i] = 0;
}
mpc[0] = n;
for (var i = 0;i < data.length;i++) {
pr = data[i];
// process precalculation array
for (r = 1; r < rs; r++) {
mpc[r] += Math.pow(pr.x, r);
}
// process RH column cells
m[0][p] += pr.y;
for (r = 1; r < p; r++) {
m[r][p] += Math.pow(pr.x, r) * pr.y;
}
}
// populate square matrix section
for (r = 0; r < p; r++) {
for (c = 0; c < p; c++) {
m[r][c] = mpc[r + c];
}
}
// reduce matrix
gj.echelonize(m);
// extract result column
terms = Array();
for (var i = 0;i < m.length;i++) {
mc = m[i];
terms[i] = mc[p];
}
return terms;
}
// test the system using known data
matf.test = function() {
var xd = [-1,0,1,2,3,5,7,9];
var yd = [-1,3,2.5,5,4,2,5,4];
data = Array();
for(var i = 0;i < xd.length;i++) {
data[i] = new Pair(xd[i],yd[i]);
}
terms = compute_coefficients(data,6);
var prec = 16;
var width = 24;
for(var i = 0;i < terms.length;i++) {
print(number_format(terms[i],prec,width) + ' * x^' + i);
}
cc = corr_coeff(data,terms);
print ('cc = ' + number_format(cc,prec,width));
se = std_error(data,terms);
print('se = ' + number_format(se,prec,width));
}
//test();
// "data" is an array of Pair(x,y) data
// p = polynomial degree
matf.process_data = function(data,p) {
var terms = matf.compute_coefficients(data,p);
var cc = matf.corr_coeff(data,terms);
var se = matf.std_error(data,terms);
return [terms,cc,se];
}
/**** END Paul Lutus' code ****/
function f(cs, x){
let n = cs.length - 1;
let result = 0;
for (let i=0; i<cs.length; i++)
result += cs[i] * Math.pow(x, i);
return result;
}
var data = [[1,1], [5,2], [20,3], [50,4], [100,5]];
var xy_data = []
for (let i of data)
xy_data.push(new Pair(i[0], i[1]));
var result = matf.process_data(xy_data, xy_data.length - 1);
for (let i=0; i<data.length; i++)
console.log(data[i][0], f(result[0], data[i][0]));