/**
* Method checks if Column is a existing column in R.
*
* @param col the column to check
* @return true if column exists else false
*/
protected Boolean checkRColumn(String col) {
REXP rCols = eval("colnames(" + RVAR + ")", false);
String[] cols = rCols.asStringArray();
List<String> collist = Arrays.asList(cols);
if (collist.contains(col)) {
return true;
}
return false;
}
/**
* Sends a single command to R.
*
* @param sz The R command
* @param szDescription The Description showing up before the output
*/
private String[] sendCommand2R(String sz) {
// set command
if (sz.length() == 0) {
return null;
}
// parse
long lParsed = re.rniParse(sz, 1);
// eval
long lEvaluated = re.rniEval(lParsed, 0);
// generate REXP
if (lEvaluated > 0) {
REXP exp = new REXP(re, lEvaluated);
String[] szArr = exp.asStringArray();
if (szArr != null) {
return szArr;
}
double[] dArr = exp.asDoubleArray();
if (dArr != null) {
String[] szDArr = new String[dArr.length];
for (int cnt = 0; cnt < dArr.length; ++cnt) {
szDArr[cnt] = String.valueOf(dArr[cnt]);
}
return szDArr;
}
int[] iArr = exp.asIntArray();
if (iArr != null) {
String[] szIArr = new String[iArr.length];
for (int cnt = 0; cnt < iArr.length; ++cnt) {
szIArr[cnt] = String.valueOf(iArr[cnt]);
}
return szIArr;
}
}
return null;
}
@Override
public String[] asStrings() {
return r.asStringArray();
}
// Run prediction method on data based on file name
public StockInfo[] runPrediction(Map<String, StockInfo[]> dataToProcess) {
double[] resultArrayOpen = null;
double[] resultArrayHigh = null;
double[] resultArrayLow = null;
double[] resultArrayClose = null;
double[] resultArrayVolume = null;
StockInfo[] predictedDataArray = null;
try {
logger.log("prediction will start");
// Flat the data to perform prediction on it
StockInfo[] flatStockInfo = this.flatMapOfData(dataToProcess);
Collections.reverse(Arrays.asList(flatStockInfo));
REXP x;
RVector v;
// Check for installed packages
x = re.eval("installed.packages()");
v = x.asVector();
String[] packages = x.asStringArray();
boolean isForecastInstalled = false;
logger.log("<R> getting installed packages");
for (int index = 0; index < packages.length && isForecastInstalled == false; index++) {
logger.log("<R> has installed " + packages[index]);
if (packages[index] != null && packages[index].compareTo("forecast") == 0) {
isForecastInstalled = true;
}
}
// If forecast needs to be installed
if (isForecastInstalled == false) {
logger.log("<R> will set repos");
// Set CRAN
re.eval("r <- getOption(\"repos\")");
re.eval("r[\"CRAN\"] <- \"http://cran.us.r-project.org\"");
re.eval("options(repos = r)");
re.eval("rm(r)");
// Install forecast
re.eval("install.packages(\"forecast\")");
logger.log("<R> will install forecast package");
}
// Load forecast library
re.eval("library(\"forecast\")");
logger.log("<R> loaded forecast");
// Make prediction for Open value -----------------------
// Load data into R
logger.log("<R> loading data into R");
StringBuilder builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].open);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
String stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting open values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1); // instead of 3
resultArrayOpen = x.asDoubleArray();
// Make prediction for High value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].high);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting high values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayHigh = x.asDoubleArray();
// Make prediction for Low value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].low);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting low values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayLow = x.asDoubleArray();
// Make prediction for Close value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].close);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
logger.log("<R> forecasting close values BestFit");
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayClose = x.asDoubleArray();
// Make prediction for Close value ------------------------
builder = new StringBuilder("inputData <- c(");
for (int index = 0; index < flatStockInfo.length; index++) {
builder.append(flatStockInfo[index].volume);
if (index != flatStockInfo.length - 1) {
builder.append(",");
} else {
builder.append(")");
}
}
stringFromBuilder = builder.toString();
re.eval(stringFromBuilder);
// Create time series from data
re.eval("temporalData <- ts(inputData, frequency=365)");
// Forecast data
re.eval("forecastData <- forecast(temporalData, h=30)");
// re.eval("arimaModel <- auto.arima(temporalData, max.p=5, max.q=5, max.P=5, max.Q=5)");
// re.eval("forecastData <- forecast(arimaModel, h=30)");
x = re.eval("forecastData");
v = x.asVector();
x = (REXP) v.elementAt(1);
resultArrayVolume = x.asDoubleArray();
// Create a single StockInfo[] for all data
StockInfo predictedData;
predictedDataArray = new StockInfo[30];
Date lastDate = flatStockInfo[flatStockInfo.length - 1].date;
Calendar c = Calendar.getInstance();
c.setTime(lastDate);
c.add(Calendar.DATE, 1);
logger.log("<R> values for forecasted data");
SimpleDateFormat dateFormat = new SimpleDateFormat();
dateFormat.applyPattern("dd/MM/YYYY");
// For all days that were predicted
for (int index = 0; index < 30; index++) {
predictedData = new StockInfo();
predictedData.open = (float) resultArrayOpen[index];
float maxHigh =
(float)
StrictMath.max(
resultArrayClose[index],
StrictMath.max(resultArrayHigh[index], resultArrayOpen[index]));
predictedData.high = maxHigh;
float minLow =
(float)
StrictMath.min(
resultArrayClose[index],
StrictMath.min(resultArrayLow[index], resultArrayOpen[index]));
predictedData.low = minLow;
predictedData.close = (float) resultArrayClose[index];
predictedData.volume = (int) resultArrayVolume[index];
while (c.get(Calendar.DAY_OF_WEEK) == Calendar.SUNDAY
|| c.get(Calendar.DAY_OF_WEEK) == Calendar.SATURDAY) {
c.add(Calendar.DATE, 1);
}
predictedData.date = c.getTime();
predictedDataArray[index] = predictedData;
logger.log(
"<R> stock prediction "
+ dateFormat.format(predictedData.date.getTime())
+ " open: "
+ predictedData.open
+ " high: "
+ predictedData.high
+ " low: "
+ predictedData.low
+ " close: "
+ predictedData.close);
c.add(Calendar.DATE, 1);
}
} catch (Exception e) {
logger.logException(e);
}
return predictedDataArray;
}
public void retrieveNextDesign() {
REXP expr = runCommand("colnames(inputConfig$alg.currentDesign)", true);
paramIds = expr.asStringArray();
}
