json配置：
{
"moduleconfigs": {
"create": [
{
"key": "Committed",
"type": "horizontalInput",
"templateOptions": {
"label": "Committed"
}
},
{
"key": "Uncommitted",
"type": "horizontalInput",
"templateOptions": {
"label": "Uncommitted"
}
},
{
"key": "Line",
"type": "horizontalInput",
"templateOptions": {
"label": "Line"
}
},{
"key": "Total",
"type": "horizontalInput",
"templateOptions": {
"label": "Total"
},
"expressionProperties":{
"value": function($viewValue, $modelValue, scope){
return scope.model.lineFill+scope.model.uncommitedBPD+scope.model.commitedBPD;
}
}
}
]
}
}
HTML：
<form>
<formly-for model="vm.myModel" fields="vm.myFields"></formly-form> </form>
我是个新角色。我正在使用棱角分明的json创建表单。总字段应显示Committed + Uncommitted + Line字段中提供的值的总和。我正在使用expressionProperties但无法正常工作。
答案 0 :(得分：2)
我猜你已经从这个问题上移开了......但是......
你做错了两件事。
第一个（1）：它们在形式字段配置对象中的键值是在模型上按该名称设置值。
所以你的第一个字段配置对象是：
{
"key": "Committed",
"type": "horizontalInput",
"templateOptions": {
"label": "Committed"
}
},
之后您尝试使用密钥commitedBPD访问该值，以便始终未定义。
基本上形式是在模型对象的该字段中设置值输入，使用Committed键，您需要更改要匹配的键。
第二个（2）：我可能错了，但我认为你不能使用表达式属性来设置这样的值。 Formly将自动尊重模型上的值更改，以便您更好地对其他形式上的字段配置对象进行onChange，这些对象执行解析并添加如下内容：
{
"key": "Committed",
"type": "horizontalInput",
"templateOptions": {
"label": "Committed"
"onChange": addTotal
}
}...
function addTotal() {
//You have access to the model here because it's in your controller
// NOTE: the parseInput function you'll have to write yourself
vm.model.Total = parseInput(vm.model.Committed) + ...
}
总而言之，您最大的问题是尝试使用错误的密钥
访问模型对象中的值答案 1 :(得分：0)
是的，更新模型不会更改erfcinvf()
我发现的唯一方法是这样的：
#include <math.h>
#include <stdint.h>
#define PORTABLE (1)
float my_logf (float a);
#if !PORTABLE
#include "immintrin.h"
float sse_recipf (float a);
float sse_rsqrtf (float a);
#endif // !PORTABLE
/* Compute inverse of the complementary error function. For the central region,
re-use the polynomial approximation for erfinv. For the tail regions, use an
approximation based on the observation that erfcinv(x) is very approximately
sqrt(-log(x)).
PORTABLE=1 max. ulp err. = 3.12017
PORTABLE=0 max. ulp err. = 3.13523
*/
float my_erfcinvf (float a)
{
float r;
if ((a >= 2.1875e-3f) && (a <= 1.998125f)) { // max. ulp err. = 2.77667
float p, t;
t = fmaf (-a, a, a + a);
t = my_logf (t);
p = 5.43877832e-9f; // 0x1.75c000p-28
p = fmaf (p, t, 1.43286059e-7f); // 0x1.33b458p-23
p = fmaf (p, t, 1.22775396e-6f); // 0x1.49929cp-20
p = fmaf (p, t, 1.12962631e-7f); // 0x1.e52bbap-24
p = fmaf (p, t, -5.61531961e-5f); // -0x1.d70c12p-15
p = fmaf (p, t, -1.47697705e-4f); // -0x1.35be9ap-13
p = fmaf (p, t, 2.31468701e-3f); // 0x1.2f6402p-9
p = fmaf (p, t, 1.15392562e-2f); // 0x1.7a1e4cp-7
p = fmaf (p, t, -2.32015476e-1f); // -0x1.db2aeep-3
t = fmaf (p, t, 8.86226892e-1f); // 0x1.c5bf88p-1
r = fmaf (t, -a, t);
} else {
float p, q, s, t;
t = (a >= 1.0f) ? (2.0f - a) : a;
t = 0.0f - my_logf (t);
#if PORTABLE
s = sqrtf (1.0f / t);
#else // PORTABLE
s = sse_rsqrtf (t);
#endif // PORTABLE
p = 2.23100796e+1f; // 0x1.64f616p+4
p = fmaf (p, s, -5.23008537e+1f); // -0x1.a26826p+5
p = fmaf (p, s, 5.44409714e+1f); // 0x1.b3871cp+5
p = fmaf (p, s, -3.35030403e+1f); // -0x1.0c063ap+5
p = fmaf (p, s, 1.38580027e+1f); // 0x1.bb74c2p+3
p = fmaf (p, s, -4.37277269e+0f); // -0x1.17db82p+2
p = fmaf (p, s, 1.53075826e+0f); // 0x1.87dfc6p+0
p = fmaf (p, s, 2.97993328e-2f); // 0x1.e83b76p-6
p = fmaf (p, s, -3.71997419e-4f); // -0x1.86114cp-12
p = fmaf (p, s, s);
#if PORTABLE
r = 1.0f / p;
#else // PORTABLE
r = sse_recipf (p);
if (t == INFINITY) r = t;
#endif // PORTABLE
if (a >= 1.0f) r = 0.0f - r;
}
return r;
}
/* Compute inverse of the CDF of the standard normal distribution.
max ulp err = 4.08385
*/
float my_normcdfinvf (float a)
{
return fmaf (-1.41421356f, my_erfcinvf (a + a), 0.0f);
}
/* natural logarithm. max ulp err = 0.85089 */
float my_logf (float a)
{
float m, r, s, t, i, f;
int32_t e;
const float cutoff_f = 0.666666667f;
if ((a > 0.0f) && (a <= 0x1.fffffep+127f)) { // 3.40282347e+38
m = frexpf (a, &e);
if (m < cutoff_f) {
m = m + m;
e = e - 1;
}
i = (float)e;
f = m - 1.0f;
s = f * f;
/* Compute log1p(f) for f in [-1/3, 1/3] */
r = -0x1.0ae000p-3f; // -0.130310059
t = 0x1.208000p-3f; // 0.140869141
r = fmaf (r, s, -0x1.f1988ap-4f); // -0.121483363
t = fmaf (t, s, 0x1.1e5740p-3f); // 0.139814854
r = fmaf (r, s, -0x1.55b36ep-3f); // -0.166846141
t = fmaf (t, s, 0x1.99d8b2p-3f); // 0.200120345
r = fmaf (r, s, -0x1.fffe02p-3f); // -0.249996200
r = fmaf (t, f, r);
r = fmaf (r, f, 0x1.5554fap-2f); // 0.333331972
r = fmaf (r, f, -0x1.000000p-1f); // -0.500000000
r = fmaf (r, s, f);
r = fmaf (i, 0x1.62e430p-01f, r); // 0.693147182 // log(2)
} else {
r = a + a; // silence NaNs if necessary
if (a < 0.0f) r = 0.0f/0.0f; // QNaN INDEFINITE
if (a == 0.0f) r = -INFINITY; // -INF
}
return r;
}
float sse_recipf (float a)
{
__m128 t;
float e, r;
t = _mm_set_ss (a);
t = _mm_rcp_ss (t);
_mm_store_ss (&r, t);
e = fmaf (0.0f - a, r, 1.0f);
e = fmaf (e, e, e);
r = fmaf (e, r, r);
return r;
}
float sse_rsqrtf (float a)
{
__m128 t;
float e, r;
t = _mm_set_ss (a);
t = _mm_rsqrt_ss (t);
_mm_store_ss (&r, t);
e = fmaf (0.0f - a, r * r, 1.0f);
r = fmaf (fmaf (0.375f, e, 0.5f), e * r, r);
return r;
}