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// This example assumes the Chilkat API to have been previously unlocked.
// See Global Unlock Sample for sample code.
loHttp = createobject("CkHttp")
// Use this online tool to generate code from sample JSON: Generate Code to Create JSON
// The following JSON is sent in the request body.
// {
// "predictionDefinition": "<PREDICTION_DEFINITION_ID>",
// "type": "RawData",
// "columnNames": [
// "Quantity",
// "Category",
// "Sub_Category",
// "Sales",
// "Profit_per_Order"
// ],
// "rows": [
// [
// "2",
// "Furniture",
// "Chairs",
// "300",
// "10"
// ]
// ]
// }
loJson = createobject("CkJsonObject")
loJson.UpdateString("predictionDefinition","<PREDICTION_DEFINITION_ID>")
loJson.UpdateString("type","RawData")
loJson.UpdateString("columnNames[0]","Quantity")
loJson.UpdateString("columnNames[1]","Category")
loJson.UpdateString("columnNames[2]","Sub_Category")
loJson.UpdateString("columnNames[3]","Sales")
loJson.UpdateString("columnNames[4]","Profit_per_Order")
loJson.UpdateString("rows[0][0]","2")
loJson.UpdateString("rows[0][1]","Furniture")
loJson.UpdateString("rows[0][2]","Chairs")
loJson.UpdateString("rows[0][3]","300")
loJson.UpdateString("rows[0][4]","10")
// Adds the "Authorization: Bearer <access_token>" header.
loHttp.AuthToken = "<access_token>"
loResp = loHttp.PostJson3("https://domain.com/services/data/v{{version}}/smartdatadiscovery/predict","application/json",loJson)
if (loHttp.LastMethodSuccess = .F.) then
? loHttp.LastErrorText
release loHttp
release loJson
return
endif
loSbResponseBody = createobject("CkStringBuilder")
loResp.GetBodySb(loSbResponseBody)
loJResp = createobject("CkJsonObject")
loJResp.LoadSb(loSbResponseBody)
loJResp.EmitCompact = .F.
? "Response Body:"
? loJResp.Emit()
lnRespStatusCode = loResp.StatusCode
? "Response Status Code = " + str(lnRespStatusCode)
if (lnRespStatusCode >= 400) then
? "Response Header:"
? loResp.Header
? "Failed."
release loResp
release loHttp
release loJson
release loSbResponseBody
release loJResp
return
endif
release loResp
// Sample JSON response:
// (Sample code for parsing the JSON response is shown below)
// {
// "predictionDefinition": "1OR5Y0000010ws8",
// "predictions": [
// {
// "model": {
// "id": "1Ot5Y0000010wzNSAQ"
// },
// "prediction": {
// "middleValues": [
// ],
// "total": 88.52494255547592
// },
// "prescriptions": [
// ],
// "status": "Success"
// }
// ],
// "settings": {
// "maxMiddleValues": 0,
// "maxPrescriptions": 0,
// "prescriptionImpactPercentage": 0
// }
// }
// Sample code for parsing the JSON response...
// Use this online tool to generate parsing code from sample JSON: Generate JSON Parsing Code
lcPredictionDefinition = loJResp.StringOf("predictionDefinition")
lnMaxMiddleValues = loJResp.IntOf("settings.maxMiddleValues")
lnMaxPrescriptions = loJResp.IntOf("settings.maxPrescriptions")
lnPrescriptionImpactPercentage = loJResp.IntOf("settings.prescriptionImpactPercentage")
i = 0
lnCount_i = loJResp.SizeOfArray("predictions")
do while i < lnCount_i
loJResp.I = i
lcId = loJResp.StringOf("predictions[i].model.id")
lcTotal = loJResp.StringOf("predictions[i].prediction.total")
lcStatus = loJResp.StringOf("predictions[i].status")
j = 0
lnCount_j = loJResp.SizeOfArray("predictions[i].prediction.middleValues")
do while j < lnCount_j
loJResp.J = j
j = j + 1
enddo
j = 0
lnCount_j = loJResp.SizeOfArray("predictions[i].prescriptions")
do while j < lnCount_j
loJResp.J = j
j = j + 1
enddo
i = i + 1
enddo
release loHttp
release loJson
release loSbResponseBody
release loJResp
Curl Command
curl -X POST
-H "Authorization: Bearer <access_token>"
-d '{
"predictionDefinition": "<PREDICTION_DEFINITION_ID>",
"type": "RawData",
"columnNames": [
"Quantity",
"Category",
"Sub_Category",
"Sales",
"Profit_per_Order"
],
"rows": [
[
"2",
"Furniture",
"Chairs",
"300",
"10"
]
]
}'
https://domain.com/services/data/v{{version}}/smartdatadiscovery/predict
Postman Collection Item JSON
{
"name": "Predict",
"request": {
"method": "POST",
"header": [
],
"body": {
"mode": "raw",
"raw": "{\n \"predictionDefinition\": \"<PREDICTION_DEFINITION_ID>\",\n \"type\": \"RawData\",\n \"columnNames\": [\n \"Quantity\",\n \"Category\",\n \"Sub_Category\",\n \"Sales\",\n \"Profit_per_Order\"\n ],\n \"rows\": [\n [\n \"2\",\n \"Furniture\",\n \"Chairs\",\n \"300\",\n \"10\"\n ]\n ]\n}",
"options": {
"raw": {
"language": "json"
}
}
},
"url": {
"raw": "{{_endpoint}}/services/data/v{{version}}/smartdatadiscovery/predict",
"host": [
"{{_endpoint}}"
],
"path": [
"services",
"data",
"v{{version}}",
"smartdatadiscovery",
"predict"
]
},
"description": "Get available prediction definitions."
},
"response": [
{
"name": "Predict",
"originalRequest": {
"method": "POST",
"header": [
],
"body": {
"mode": "raw",
"raw": "{\n \"predictionDefinition\": \"1OR5Y0000010ws8WAA\",\n \"type\": \"RawData\",\n \"columnNames\": [\n \"Quantity\",\n \"Category\",\n \"Sub_Category\",\n \"Sales\",\n \"Profit_per_Order\"\n ],\n \"rows\": [\n [\n \"2\",\n \"Furniture\",\n \"Chairs\",\n \"300\",\n \"10\"\n ]\n ]\n}",
"options": {
"raw": {
"language": "json"
}
}
},
"url": {
"raw": "{{_endpoint}}/services/data/v{{version}}/smartdatadiscovery/predict",
"host": [
"{{_endpoint}}"
],
"path": [
"services",
"data",
"v{{version}}",
"smartdatadiscovery",
"predict"
]
}
},
"status": "Created",
"code": 201,
"_postman_previewlanguage": "json",
"header": [
{
"key": "Date",
"value": "Thu, 04 Mar 2021 13:53:39 GMT"
},
{
"key": "Strict-Transport-Security",
"value": "max-age=31536002; includeSubDomains"
},
{
"key": "Expect-CT",
"value": "max-age=86400, report-uri=\"https://a.forcesslreports.com/Expect-CT-report/00D5Y000001crJvm\""
},
{
"key": "X-Content-Type-Options",
"value": "nosniff"
},
{
"key": "X-XSS-Protection",
"value": "1; mode=block"
},
{
"key": "X-Robots-Tag",
"value": "none"
},
{
"key": "Cache-Control",
"value": "no-cache,must-revalidate,max-age=0,no-store,private"
},
{
"key": "Content-Type",
"value": "application/json;charset=UTF-8"
},
{
"key": "Vary",
"value": "Accept-Encoding"
},
{
"key": "Content-Encoding",
"value": "gzip"
},
{
"key": "Transfer-Encoding",
"value": "chunked"
}
],
"cookie": [
],
"body": "{\n \"predictionDefinition\": \"1OR5Y0000010ws8\",\n \"predictions\": [\n {\n \"model\": {\n \"id\": \"1Ot5Y0000010wzNSAQ\"\n },\n \"prediction\": {\n \"middleValues\": [],\n \"total\": 88.52494255547592\n },\n \"prescriptions\": [],\n \"status\": \"Success\"\n }\n ],\n \"settings\": {\n \"maxMiddleValues\": 0,\n \"maxPrescriptions\": 0,\n \"prescriptionImpactPercentage\": 0\n }\n}"
}
]
}