如何从本机JS调用本机android模块

时间：2018-04-12 06:12:10

标签： react-native react-native-android

我在从JS调用我的java类方法（android本机代码）时遇到了问题。 我已经关注https://facebook.github.io/react-native/docs/native-modules-android.html，但它不起作用。请在下面找到我所面临的问题。

  1. 我们应该在哪里导出android模块，我已经在index.js
    2. 中完成了 
    setwd("C:/Users/user/Desktop")
library(forecast)
library(vars)
library(tseries)
cay <- read.csv("C:/Users/User/Desktop/cay.csv")
sp500 <- read.csv("C:/Users/User/Desktop/sp500.csv")
cay_indicator <- cay$indicator
cay <- cay$cay
sp500c <- sp500[,2]
sp500c <- data.matrix(sp500c)
sp500c.ts=ts(data = sp500c, start = c(1950,2), end = c(2015,4), frequency = 4)
plot(sp500c.ts)
sp500=ts(data = sp500c, start = c(1953, 1), end = c(2014, 3), frequency = 4)
cay=ts(data = cay, start = c(1953, 2), end = c(2014, 3), frequency = 4)
pp.test(sp500)
adf.test(sp500)
returnsp500=diff(log(sp500))
pp.test(returnsp500)
adf.test(returnsp500)
var1=ts(cbind(returnsp500, cay), start = c(1953, 2), end = c(2014, 3), frequency = 4)
var1     
var2=ts(cbind(returnsp500, cay_indicator), start = c(1953, 2), end = c(2014, 3), frequency = 4)
var2
var3=ts(cbind(returnsp500, cay, cay_indicator), start = c(1953, 2), end = c(2014, 3), frequency = 4)
var3
layout(1)
plot(var3,type="l",col="blue",main="Stock Price, Consumption Wealth Ratio, Negativity Indicator")
acf(var1, lag.max=24)
acf(var2, lag.max=24)
acf(var3,lag.max=24)
info.crit1=VARselect(var1,lag.max=5,type="const")
info.crit2=VARselect(var2,lag.max=5,type="const")
info.crit3=VARselect(var3,lag.max=5,type="const")
info.crit1
info.crit2
info.crit3
model1=VAR(var1,p=1,type="const")
model1
summary(model1)
model2=VAR(var2,p=1,type="const")
model2
summary(model2)
model3=VAR(var3,p=1,type="const")
model3
summary(model3)
causality(model1,cause="cay")$Granger
causality(model2,cause="cay_indicator")$Granger

n.end=127
t=length(returnsp500)
n=t-n.end

pred1=matrix(rep(0,4*n),n,4)
pred2=matrix(rep(0,4*n),n,4)
pred3=matrix(rep(0,4*n),n,4)
pred4=matrix(rep(0,4*n),n,4)

for(i in 1:n){
  x_var=var1[1:n.end+i-1,]
  x_var2=var2[1:n.end+i-1,]
  x_var3=var3[1:n.end+i-1,]
  x_ar=returnsp500[1:n.end+i-1]

  model.var=VAR(x_var,p=1,type="const")
  for_var=predict(model.var,n.ahead=1,se.fit=FALSE)
  pred1[i,1]=for_var$fcst$returnsp500[,1]

  model.var=VAR(x_var2,p=1,type="const")
  for_var=predict(model.var,n.ahead=1,se.fit=FALSE)
  pred2[i,1]=for_var$fcst$returnsp500[,1]

  model.var=VAR(x_var3,p=1,type="const")
  for_var=predict(model.var,n.ahead=1,se.fit=FALSE)
  pred3[i,1]=for_var$fcst$returnsp500[,1]

  model.ar=arima(x_ar,order=c(1,0,0),method="ML")
  pred4[i,1]=predict(model.ar,n.ahead=1,se.fit=FALSE)[1]
}

pred1.ts=ts(data=pred1,start=c(1985,1),frequency=4)
pred2.ts=ts(data=pred2,start=c(1985,1),frequency=4)
pred3.ts=ts(data=pred3,start=c(1985,1),frequency=4)
pred4.ts=ts(data=pred4,start=c(1985,1),frequency=4)
logsp500returns.ts=ts(data=returnsp500[(n.end+1):(t)],start=c(1985,1),frequency=4)
plot(logsp500returns.ts,col="blue",ylim=c(-.1,.1))
lines(pred1.ts[,1],col="green")
lines(pred2.ts[,1],col="red")
lines(pred3.ts[,1],col="black")
lines(pred4.ts[,1],col="yellow")

pred1.ts[,1]

e1_var=returnsp500[(n.end+1):(t)]-pred1.ts[,1] #1-step ahead bivariate cay & sp500
e2_var=returnsp500[(n.end+1):(t)]-pred2.ts[,1] #1-step ahead bivariate cay_indicator & sp500
e3_var=returnsp500[(n.end+1):(t)]-pred3.ts[,1] #1-step ahead bivariate cay and cay_indicator & sp500
e4_var=returnsp500[(n.end+1):(t)]-pred4.ts[,1] #1-step ahead univariate model just a lagged sp500 

n.end=127
t=length(returnsp500) 
n=t-n.end-1

pred1=matrix(rep(0,4*n),n,4)
pred2=matrix(rep(0,4*n),n,4)
pred3=matrix(rep(0,4*n),n,4)
pred4=matrix(rep(0,4*n),n,4)

for(i in 1:n){
  x_var=var1[1:n.end+i-1,]
  x_var2=var2[1:n.end+i-1,]
  x_var3=var3[1:n.end+i-1,]
  x_ar=returnsp500[1:n.end+i-1]

  model.var=VAR(x_var,p=1,type="const")
  for_var=predict(model.var,n.ahead=2,se.fit=FALSE)
  pred1[i,1:2]=for_var$fcst$returnsp500[1:2]

  model.var=VAR(x_var2,p=1,type="const")
  for_var=predict(model.var,n.ahead=2,se.fit=FALSE)
  pred2[i,1:2]=for_var$fcst$returnsp500[1:2]

  model.var=VAR(x_var3,p=1,type="const")
  for_var=predict(model.var,n.ahead=2,se.fit=FALSE)
  pred3[i,1:2]=for_var$fcst$returnsp500[1:2]

  model.ar=arima(x_ar,order=c(1,0,0),method="ML")
  pred4[i,1:2]=predict(model.ar,n.ahead=2,se.fit=FALSE)[1]
}

pred1.ts=ts(data=pred1,start=c(1985,2),frequency=4)
pred2.ts=ts(data=pred2,start=c(1985,2),frequency=4)
pred3.ts=ts(data=pred3,start=c(1985,2),frequency=4)
pred4.ts=ts(data=pred4,start=c(1985,2),frequency=4)
logsp500returns.ts=ts(data=returnsp500[(n.end+2):(t)],start=c(1985,2),frequency=4)
plot(logsp500returns.ts,col="blue",ylim=c(-.1,.1))
lines(pred1.ts[,2],col="green")
lines(pred2.ts[,2],col="red")
lines(pred3.ts[,2],col="black")
lines(pred4.ts[,2],col="yellow")

pred1.ts[,2]

e1_var2=returnsp500[(n.end+2):(t)]-pred1.ts[,2] #2-step ahead bivariate cay & sp500
e2_var2=returnsp500[(n.end+2):(t)]-pred2.ts[,2] #2-step ahead bivariate cay_indicator & sp500
e3_var2=returnsp500[(n.end+2):(t)]-pred3.ts[,2] #2-step ahead bivariate cay and cay_indicator & sp500
e4_var2=returnsp500[(n.end+2):(t)]-pred4.ts[,2] #2-step ahead univariate model just a lagged sp500 


n.end=127
t=length(returnsp500) 
n=t-n.end-2

pred1=matrix(rep(0,4*n),n,4)
pred2=matrix(rep(0,4*n),n,4)
pred3=matrix(rep(0,4*n),n,4)
pred4=matrix(rep(0,4*n),n,4)

for(i in 1:n){
  x_var=var1[1:n.end+i-1,]
  x_var2=var2[1:n.end+i-1,]
  x_var3=var3[1:n.end+i-1,]
  x_ar=returnsp500[1:n.end+i-1]

  model.var=VAR(x_var,p=1,type="const")
  for_var=predict(model.var,n.ahead=3,se.fit=FALSE)
  pred1[i,1:3]=for_var$fcst$returnsp500[1:3]

  model.var=VAR(x_var2,p=1,type="const")
  for_var=predict(model.var,n.ahead=3,se.fit=FALSE)
  pred2[i,1:3]=for_var$fcst$returnsp500[1:3]

  model.var=VAR(x_var3,p=1,type="const")
  for_var=predict(model.var,n.ahead=3,se.fit=FALSE)
  pred3[i,1:3]=for_var$fcst$returnsp500[1:3]

  model.ar=arima(x_ar,order=c(1,0,0),method="ML")
  pred4[i,1:3]=predict(model.ar,n.ahead=3,se.fit=FALSE)[1]
}

pred1.ts=ts(data=pred1,start=c(1985,3),frequency=4)
pred2.ts=ts(data=pred2,start=c(1985,3),frequency=4)
pred3.ts=ts(data=pred3,start=c(1985,3),frequency=4)
pred4.ts=ts(data=pred4,start=c(1985,3),frequency=4)
logsp500returns.ts=ts(data=returnsp500[(n.end+1):(t)],start=c(1985,3), frequency=4)
plot(logsp500returns.ts,col="blue",ylim=c(-.1,.1))
lines(pred1.ts[,3],col="green")
lines(pred2.ts[,3],col="red")
lines(pred3.ts[,3],col="black")
lines(pred4.ts[,3],col="yellow")

e1_var3=returnsp500[(n.end+3):(t)]-pred1.ts[,3] #3-step ahead bivariate cay & sp500
e2_var3=returnsp500[(n.end+3):(t)]-pred2.ts[,3] #3-step ahead bivariate cay_indicator & sp500
e3_var3=returnsp500[(n.end+3):(t)]-pred3.ts[,3] #3-step ahead bivariate cay and cay_indicator & sp500
e4_var3=returnsp500[(n.end+3):(t)]-pred4.ts[,3] #3-step ahead univariate model just a lagged sp500 


n.end=127
t=length(returnsp500) 
n=t-n.end-3

pred1=matrix(rep(0,4*n),n,4)
pred2=matrix(rep(0,4*n),n,4)
pred3=matrix(rep(0,4*n),n,4)
pred4=matrix(rep(0,4*n),n,4)

for(i in 1:n){
  x_var=var1[1:n.end+i-1,]
  x_var2=var2[1:n.end+i-1,]
  x_var3=var3[1:n.end+i-1,]
  x_ar=returnsp500[1:n.end+i-1]

  model.var=VAR(x_var,p=1,type="const")
  for_var=predict(model.var,n.ahead=4,se.fit=FALSE)
  pred1[i,1:4]=for_var$fcst$returnsp500[1:4]

  model.var=VAR(x_var2,p=1,type="const")
  for_var=predict(model.var,n.ahead=4,se.fit=FALSE)
  pred2[i,1:4]=for_var$fcst$returnsp500[1:4]

  model.var=VAR(x_var3,p=1,type="const")
  for_var=predict(model.var,n.ahead=4,se.fit=FALSE)
  pred3[i,1:4]=for_var$fcst$returnsp500[1:4]

  model.ar=arima(x_ar,order=c(1,0,0),method="ML")
  pred4[i,1:4]=predict(model.ar,n.ahead=4,se.fit=FALSE)[1]
}

pred1.ts=ts(data=pred1,start=c(1985,4),frequency=4)
pred2.ts=ts(data=pred2,start=c(1985,4),frequency=4)
pred3.ts=ts(data=pred3,start=c(1985,4),frequency=4)
pred4.ts=ts(data=pred4,start=c(1985,4),frequency=4)
logsp500returns.ts=ts(data=returnsp500[(n.end+4):t],start=c(1985,4),frequency=4)
plot(logsp500returns.ts,col="blue",ylim=c(-.1,.1))
lines(pred1.ts[,4],col="green")
lines(pred2.ts[,4],col="red")
lines(pred3.ts[,4],col="black")
lines(pred4.ts[,4],col="yellow")


e1_var4=returnsp500[(n.end+4):(t)]-pred1.ts[,4] #4-step ahead bivariate cay & sp500
e2_var4=returnsp500[(n.end+4):(t)]-pred2.ts[,4] #4-step ahead bivariate cay_indicator & sp500
e3_var4=returnsp500[(n.end+4):(t)]-pred3.ts[,4] #4-step ahead bivariate cay and cay_indicator & sp500
e4_var4=returnsp500[(n.end+4):(t)]-pred4.ts[,4] #4-step ahead univariate model just a lagged sp500 

rmse1_var1=sqrt(mean(e1_var^2)) #16. RMSE's in order, 1-step, 2-step, 3-step, 4-step
rmse2_var1=sqrt(mean(e2_var^2))
rmse3_var1=sqrt(mean(e3_var^2))
rmse4_var1=sqrt(mean(e4_var^2))

rmse1_var2=sqrt(mean(e1_var2^2)) 
rmse2_var2=sqrt(mean(e2_var2^2))
rmse3_var2=sqrt(mean(e3_var2^2))
rmse4_var2=sqrt(mean(e4_var2^2))

rmse1_var3=sqrt(mean(e1_var3^2)) 
rmse2_var3=sqrt(mean(e2_var3^2))
rmse3_var3=sqrt(mean(e3_var3^2))
rmse4_var3=sqrt(mean(e4_var3^2))

rmse1_var4=sqrt(mean(e1_var4^2)) 
rmse2_var4=sqrt(mean(e2_var4^2))
rmse3_var4=sqrt(mean(e3_var4^2))
rmse4_var4=sqrt(mean(e4_var4^2))

rmse1_var1
rmse2_var1                                    
rmse3_var1
rmse4_var1

rmse1_var2
rmse2_var2
rmse3_var2
rmse4_var2

rmse1_var3
rmse2_var3
rmse3_var3
rmse4_var3

rmse1_var4
rmse2_var4
rmse3_var4
rmse4_var4

c=rep(1,116)
cw=e4_var^2-e1_var^2+(e4_var-e1_var)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var2^2-e1_var2^2+(e4_var2-e1_var2)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var3^2-e1_var3^2+(e4_var3-e1_var3)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var4^2-e1_var4^2+(e4_var4-e1_var4)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var^2-e2_var^2+(e4_var-e2_var)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var2^2-e2_var2^2+(e4_var2-e2_var2)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var3^2-e2_var3^2+(e4_var3-e2_var3)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e4_var4^2-e2_var4^2+(e4_var4-e2_var4)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

dmw=(e2_var^2-e1_var^2)
c=rep(1,116)
reg=lm(dmw~c-1)
avar=NeweyWest(reg,lag=NULL,prewhite=FALSE)
dmw.q=reg$coef/sqrt(avar)
dmw.q
pnorm(dmw.q)
p.value=1-pnorm(dmw.q)
pstr.value=p.value/2 
pstr.value

dmw=(e2_var2^2-e1_var2^2)
c=rep(1,116)
reg=lm(dmw~c-1)
avar=NeweyWest(reg,lag=NULL,prewhite=FALSE)
dmw.q=reg$coef/sqrt(avar)
dmw.q
pnorm(dmw.q)
p.value=1-pnorm(dmw.q)
pstr.value=p.value/2 
pstr.value

dmw=(e2_var3^2-e1_var3^2)
c=rep(1,116)
reg=lm(dmw~c-1)
avar=NeweyWest(reg,lag=NULL,prewhite=FALSE)
dmw.q=reg$coef/sqrt(avar)
dmw.q
pnorm(dmw.q)
p.value=1-pnorm(dmw.q)
pstr.value=p.value/2 
pstr.value

dmw=(e2_var4^2-e1_var4^2)
c=rep(1,116)
reg=lm(dmw~c-1)
avar=NeweyWest(reg,lag=NULL,prewhite=FALSE)
dmw.q=reg$coef/sqrt(avar)
dmw.q
pnorm(dmw.q)
p.value=1-pnorm(dmw.q)
pstr.value=p.value/2 
pstr.value

c=rep(1,116)
cw=e1_var^2-e3_var^2+(e1_var-e3_var)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e1_var2^2-e3_var2^2+(e1_var2-e3_var2)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e1_var3^2-e3_var3^2+(e1_var3-e3_var3)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

c=rep(1,116)
cw=e1_var4^2-e3_var4^2+(e1_var4-e3_var4)^2
reg.cw=lm(cw~c-1)
avar.cw=NeweyWest(reg.cw,lag=NULL,prewhite=FALSE)
cw.test=reg.cw$coef/sqrt(avar.cw)
cw.test
pnorm(cw.test)
(1-pnorm(cw.test))/2

    或

    import {NativeModules} from 'react-native';

const { ScannerInteractor } = NativeModules;

export default ScannerInteractor;
    1. 在我要调用此方法的javascript类中，我应该如何导入我的类，我编写了以下代码。如果index.js与我的javascript类处于同一级别是否重要？
      2.   

      从“./ScannerInteractor”

      导入ScannerInteractor
      1. 我创建了成员方法然后如何调用它们？例如，它需要作为静态方法调用调用。
        2. import {NativeModules} from 'react-native';
module.exports = NativeModules.ScannerInteractor;
        1. 如何从JS传递上下文变量，传递'this'参数会起作用吗？

          2. 我没有在logcat中收到任何错误或日志消息，但方法也没有被触发。

          NativeModule类

             ScannerInteractor.startScan(this, null, null);

          ApplicationClass 

          import com.facebook.react.ReactPackage; 
import com.facebook.react.bridge.NativeModule; 
import com.facebook.react.bridge.ReactApplicationContext; 
import com.facebook.react.uimanager.ViewManager;

import java.util.ArrayList; 
import java.util.Collections; 
import java.util.List; 
import com.kohl.scan.common.ScannerInteractor;

public class ModuleInjector implements ReactPackage {

@Override   
public List<ViewManager> createViewManagers(ReactApplicationContext reactContext) {
        return Collections.emptyList();
   }

      @Override   public List<NativeModule> createNativeModules(
                                  ReactApplicationContext reactContext) {
        List<NativeModule> modules = new ArrayList<>();

        modules.add(new ScannerInteractor(reactContext));

        return modules;
   }

 }

          我从JS脚本中调用它，如下所述：

          import com.facebook.react.ReactPackage;

import java.util.Arrays;
import java.util.List;

import com.lugg.ReactNativeConfig.ReactNativeConfigPackage;
import com.reactnativenavigation.NavigationApplication;
import com.oblador.vectoricons.VectorIconsPackage;
import com.rfpproject.ModuleInjector;

public class MainApplication extends NavigationApplication {

  @Override
  public boolean isDebug() {
    // Make sure you are using BuildConfig from your own application
    return BuildConfig.DEBUG;
  }

  protected List<ReactPackage> getPackages() {
    // Add additional packages you require here
    // No need to add RnnPackage and MainReactPackage
    return Arrays.<ReactPackage>asList(
    // eg. new VectorIconsPackage()
            new ReactNativeConfigPackage(),
            new VectorIconsPackage(),
            new ModuleInjector()
    );
  }

  @Override
  public String getJSMainModuleName() {
    return "index";
  }

  @Override
  public List<ReactPackage> createAdditionalReactPackages() {
    return getPackages();
  }
}

1 个答案:

答案 0 :(得分：1)

使用后有效 从'react-native'导入{NativeModules}; module.exports = NativeModules.ScannerInteractor;

和 从'react-native'导入{NativeModules};在JS类中并使用NativeModules.ScannerInteractor.check（）

进行调用

仍然不清楚如何将上下文变量作为参数传递。

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