add golang demo for calling so lib

docs/api_userguide.md

@@ -531,4 +531,10 @@ prob
 |---|---|---|
 | ret | int | 返回码。0:成功；非0:失败 |
 | result | c_int | 检测结果是否异常。0:异常；1:正常 |
-| prob | c_float | 概率值，值越小，判定为异常的置信度越高 |
+| prob | c_float | 概率值，值越小，判定为异常的置信度越高 |
+
+
+#### Go代码中调用:
+
+在Go中调用检测函数，需要先加载cgo文件，编写helper函数供go调用；
+详细demo见 `time_series_detector/demo/golang`目录

time_series_detector/demo/golang/handler.go

@@ -0,0 +1,95 @@
+package detector
+
+/*
+#include <dlfcn.h>
+#include <wrapper.h>
+#cgo CFLAGS: -I.
+#cgo LDFLAGS: -ldl
+*/
+import "C"
+
+import (
+	"unsafe"
+)
+
+const LibPath = "../../lib/libdetect.so"
+const xgbModelPath = "../../model/xgb_default_model"
+const xgbThreshold = 0.15
+
+var mPtr unsafe.Pointer
+
+func init() {
+	hd := C.dlopen(C.CString(LibPath), C.RTLD_LAZY)
+	if hd == nil {
+		panic("dlopen failure")
+	}
+	loadModel := C.dlsym(hd, C.CString("load_model"))
+	valuePredict := C.dlsym(hd, C.CString("value_predict"))
+	ratePredict := C.dlsym(hd, C.CString("rate_predict"))
+	if loadModel == nil || valuePredict == nil || ratePredict == nil {
+		panic("dlsym failure")
+	}
+	C.setLoadModel(loadModel)
+	C.setValuePredict(valuePredict)
+	C.setRatePredict(ratePredict)
+
+	ptr := C.callLoadModel(C.CString(xgbModelPath))
+	if ptr == nil {
+		panic("load_model return NULL")
+	}
+	mPtr = ptr
+}
+
+func ValueDetect(sample Sample) (bool, float64) {
+	dataC := make([]C.int, len(sample.WeekAgo6h))
+	for index, value := range sample.WeekAgo6h {
+		dataC[index] = C.int(value)
+	}
+	dataB := make([]C.int, len(sample.Yesterday6h))
+	for index, value := range sample.Yesterday6h {
+		dataB[index] = C.int(value)
+	}
+	dataA := make([]C.int, len(sample.Last3h))
+	for index, value := range sample.Last3h {
+		dataA[index] = C.int(value)
+	}
+	prob := float64(C.callValuePredict(mPtr,
+		(*C.int)(unsafe.Pointer(&dataA[0])),
+		(*C.int)(unsafe.Pointer(&dataB[0])),
+		(*C.int)(unsafe.Pointer(&dataC[0])),
+		(C.int)(len(dataA)),
+		(C.int)(len(dataB)),
+		(C.int)(len(dataC)),
+	))
+	return prob >= xgbThreshold, prob
+}
+
+func RateDetect(sample Sample) (bool, float64) {
+	dataC := make([]C.double, len(sample.WeekAgo6h))
+	for index, value := range sample.WeekAgo6h {
+		dataC[index] = C.double(value)
+	}
+	dataB := make([]C.double, len(sample.Yesterday6h))
+	for index, value := range sample.Yesterday6h {
+		dataB[index] = C.double(value)
+	}
+	dataA := make([]C.double, len(sample.Last3h))
+	for index, value := range sample.Last3h {
+		dataA[index] = C.double(value)
+	}
+	prob := float64(C.callRatePredict(
+		(*C.double)(unsafe.Pointer(&dataA[0])),
+		(*C.double)(unsafe.Pointer(&dataB[0])),
+		(*C.double)(unsafe.Pointer(&dataC[0])),
+		(C.int)(len(dataA)),
+		(C.int)(len(dataB)),
+		(C.int)(len(dataC)),
+	))
+	return prob > 0, prob
+}
+
+type Sample struct {
+	Last3h      []float64
+	Yesterday6h []float64
+	WeekAgo6h   []float64
+}

time_series_detector/demo/golang/handler_test.go

@@ -0,0 +1,54 @@
+package detector
+
+import (
+	_ "net/http/pprof"
+	"testing"
+)
+
+func TestValueDetect(t *testing.T) {
+	weekAgoData := make([]float64, 361)
+	for index := range weekAgoData {
+		weekAgoData[index] = 1
+	}
+	yesterdayData := weekAgoData
+
+	last3hData := make([]float64, 180)
+	for index := range last3hData {
+		last3hData[index] = 1
+	}
+	last3hData = append(last3hData, 10)
+
+	isOk, prob := ValueDetect(Sample{
+		WeekAgo6h:   weekAgoData,
+		Last3h:      last3hData,
+		Yesterday6h: yesterdayData,
+	})
+	t.Logf("value detect prob:%f", prob)
+	if isOk || prob == 1 || prob < 0 {
+		t.Fail()
+	}
+}
+
+func TestRateDetect(t *testing.T) {
+	weekAgoData := make([]float64, 361)
+	for index := range weekAgoData {
+		weekAgoData[index] = 1
+	}
+	yesterdayData := weekAgoData
+
+	last3hData := make([]float64, 180)
+	for index := range last3hData {
+		last3hData[index] = 1
+	}
+	last3hData = append(last3hData, 10)
+
+	isOk, prob := RateDetect(Sample{
+		WeekAgo6h:   weekAgoData,
+		Last3h:      last3hData,
+		Yesterday6h: yesterdayData,
+	})
+	t.Logf("rate detect prob:%f", prob)
+	if isOk || prob != 0 {
+		t.Fail()
+	}
+}

time_series_detector/demo/golang/wrapper.h

@@ -0,0 +1,85 @@
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+
+
+typedef struct {
+    int* data_a;
+    int* data_b;
+    int* data_c;
+    int len_a;
+    int len_b;
+    int len_c;
+} ValueData;
+
+typedef struct {
+    double* data_a;
+    double* data_b;
+    double* data_c;
+    int len_a;
+    int len_b;
+    int len_c;
+} RateData;
+
+void * (*load_model)(const char*);
+void setLoadModel(void* fn) {
+    load_model = fn;
+}
+void * callLoadModel(const char *fname){
+    return load_model(fname);
+};
+
+int (*value_predict)(void *, ValueData*, int*, float*);
+void setValuePredict(void* fn){
+	value_predict = fn;
+}
+float callValuePredict(void * mhandle, int* data_a, int* data_b, int* data_c, int len_a, int len_b, int len_c){
+	int sample_result=0;
+	float prob=0;
+
+    ValueData *dt=malloc(sizeof(ValueData));
+    dt->data_a = (int*)malloc(sizeof(int)*len_a);
+    dt->data_b = (int*)malloc(sizeof(int)*len_b);
+    dt->data_c = (int*)malloc(sizeof(int)*len_c);
+    memcpy(dt->data_a, data_a, sizeof(int)*len_a);
+    memcpy(dt->data_b, data_b, sizeof(int)*len_b);
+    memcpy(dt->data_c, data_c, sizeof(int)*len_c);
+	dt->len_a = len_a;
+    dt->len_b = len_b;
+    dt->len_c = len_c;
+
+	value_predict(mhandle, dt, &sample_result,&prob);
+	free(dt->data_a);
+	free(dt->data_b);
+	free(dt->data_c);
+	free(dt);
+	return prob;
+}
+
+int (*rate_predict)(RateData* data, int* sample_result, float* prob);
+void setRatePredict(void* fn){
+	rate_predict = fn;
+}
+float callRatePredict(double *data_a, double* data_b, double* data_c, int len_a, int len_b, int len_c){
+	int sample_result=0;
+	float prob=0;
+
+	RateData *dt= malloc(sizeof(RateData));
+
+    dt->data_a = (double*)malloc(sizeof(double)*len_a);
+    dt->data_b = (double*)malloc(sizeof(double)*len_b);
+    dt->data_c = (double*)malloc(sizeof(double)*len_c);
+    memcpy(dt->data_a, data_a, sizeof(double)*len_a);
+    memcpy(dt->data_b, data_b, sizeof(double)*len_b);
+    memcpy(dt->data_c, data_c, sizeof(double)*len_c);
+	dt->len_a = len_a;
+    dt->len_b = len_b;
+    dt->len_c = len_c;
+
+	rate_predict(dt, &sample_result, &prob);
+	free(dt->data_a);
+	free(dt->data_b);
+	free(dt->data_c);
+	free(dt);
+	return prob;
+}