/**
* Method for fitting a Baysian Network.
*
* @param network a Baysian Network to fit
* @param fitted a REXP fitted object
*/
@SuppressWarnings({"unused", "rawtypes"})
public static void fit(Network network, REXP fitted) {
if (fitted == null) {
return;
}
network.setFitted();
ArrayList<Node> nodes;
if (network != null) {
nodes = (ArrayList<Node>) network.getNodes();
} else {
nodes = new ArrayList<Node>();
Vector size = fitted.asVector().getNames();
//// add code
}
// Step 5 add cpts to nodes
for (int i = 0; i < nodes.size(); i++) {
Node node = nodes.get(i); // the node to edit
double[] da;
da = fitted.asVector().at(nodes.get(i).getNodeName()).asVector().at("prob").asDoubleArray();
node.setCpt(da);
}
}
/**
* Method for converting a REXP object into a BN Network.
*
* @param data the REXP object containing the dataset.
* @param network the REXP object to convert
* @return a BNNetwork object
*/
public static Network toBnNetwork(REXP data, REXP network) {
List<Node> nodelist = new ArrayList<Node>(); // List of Nodes
// Error Check
if (data == null || network == null) {
return new Network("null", nodelist);
}
// Temporary Hashmap for storing nodes
HashMap<String, Node> nodemap = new HashMap<String, Node>();
REXP rexp1 = network.asVector().at("nodes"); // Nodes
@SuppressWarnings("unchecked") // Suppressing Type check
Vector<String> nodeNames = rexp1.asVector().getNames(); // Node Names
// Step 1 create nodes and put in HashMap
for (int i = 0; i < nodeNames.size(); i++) {
Node node = new Node();
String nodeName = nodeNames.get(i);
node.setNodeName(nodeName);
nodemap.put(nodeName, node); // add node to HashMap
nodelist.add(node); // add node to List
}
// Step 2 add parents to nodes
for (int i = 0; i < nodeNames.size(); i++) {
Node node = nodemap.get(nodeNames.get(i)); // node to edit
String[] parents = rexp1.asVector().at(i).asVector().at("parents").asStringArray();
// comment to previous line: array of parentnames
for (int j = 0; j < parents.length; j++) {
Node pnode = nodemap.get(parents[j]); // parent node
node.addParentnode(pnode); // add parent node to node
}
}
// Step 3 add children to nodes
for (int i = 0; i < nodeNames.size(); i++) {
Node node = nodemap.get(nodeNames.get(i)); // node to edit
String[] children = rexp1.asVector().at(i).asVector().at("children").asStringArray();
// comment to previous line: array of childrennames
for (int j = 0; j < children.length; j++) {
Node cnode = nodemap.get(children[j]); // child node
node.addChildnode(cnode); // add child node to node
}
}
// Step 4 get levels
for (int i = 0; i < nodeNames.size(); i++) {
String rfactor = data.asVector().at(i).asFactor().toString(); // the RFactor as a String
Node node = nodemap.get(nodeNames.get(i)); // the node to edit
int end = rfactor.indexOf(')'); // getting level String
String temp = rfactor.substring(POSITION, end); // getting level String
String[] temp2 = temp.split(","); // splitting String
for (int j = 0; j < temp2.length; j++) {
String str = temp2[j].substring(1, temp2[j].length() - 1);
node.addNodeLevel(str); // add level to node
}
}
// Step 5 get algorithm on BN Network
String algorithm = network.asVector().at("learning").asVector().at("algo").asString();
// Step 6 add Arcs to BN Network
ArrayList<Association> arcs = new ArrayList<Association>();
String[] temp = network.asVector().at("arcs").asStringArray();
for (int i = 0; i < temp.length / 2; i++) {
arcs.add(new Association(nodemap.get(temp[temp.length / 2 + i]), nodemap.get(temp[i])));
}
// Return BNNetwork
return new Network(algorithm, nodelist, arcs);
}
// 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 SpotDES initializeDesign() throws InPUTException {
REXP designs = runCommand("inputConfig$alg.currentDesign", true);
return new SpotDES(designs.asVector(), paramIds, inputROI);
}