/**
* 从字节流快速加载
*
* @param byteArray
* @return
*/
public static MaxEntModel create(ByteArray byteArray) {
MaxEntModel m = new MaxEntModel();
m.correctionConstant = byteArray.nextInt(); // correctionConstant
m.correctionParam = byteArray.nextDouble(); // getCorrectionParameter
// label
int numOutcomes = byteArray.nextInt();
String[] outcomeLabels = new String[numOutcomes];
m.outcomeNames = outcomeLabels;
for (int i = 0; i < numOutcomes; i++) outcomeLabels[i] = byteArray.nextString();
// pattern
int numOCTypes = byteArray.nextInt();
int[][] outcomePatterns = new int[numOCTypes][];
for (int i = 0; i < numOCTypes; i++) {
int length = byteArray.nextInt();
int[] infoInts = new int[length];
for (int j = 0; j < length; j++) {
infoInts[j] = byteArray.nextInt();
}
outcomePatterns[i] = infoInts;
}
// feature
int NUM_PREDS = byteArray.nextInt();
String[] predLabels = new String[NUM_PREDS];
m.pmap = new DoubleArrayTrie<Integer>();
for (int i = 0; i < NUM_PREDS; i++) {
predLabels[i] = byteArray.nextString();
}
Integer[] v = new Integer[NUM_PREDS];
for (int i = 0; i < v.length; i++) {
v[i] = byteArray.nextInt();
}
m.pmap.load(byteArray, v);
// params
Context[] params = new Context[NUM_PREDS];
int pid = 0;
for (int i = 0; i < outcomePatterns.length; i++) {
int[] outcomePattern = new int[outcomePatterns[i].length - 1];
for (int k = 1; k < outcomePatterns[i].length; k++) {
outcomePattern[k - 1] = outcomePatterns[i][k];
}
for (int j = 0; j < outcomePatterns[i][0]; j++) {
double[] contextParameters = new double[outcomePatterns[i].length - 1];
for (int k = 1; k < outcomePatterns[i].length; k++) {
contextParameters[k - 1] = byteArray.nextDouble();
}
params[pid] = new Context(outcomePattern, contextParameters);
pid++;
}
}
// prior
m.prior = new UniformPrior();
m.prior.setLabels(outcomeLabels);
// eval
m.evalParams =
new EvalParameters(params, m.correctionParam, m.correctionConstant, outcomeLabels.length);
return m;
}
/**
* 从文件加载，同时缓存为二进制文件
*
* @param path
* @return
*/
public static MaxEntModel create(String path) {
MaxEntModel m = new MaxEntModel();
try {
BufferedReader br =
new BufferedReader(new InputStreamReader(new FileInputStream(path), "UTF-8"));
DataOutputStream out = new DataOutputStream(new FileOutputStream(path + Predefine.BIN_EXT));
br.readLine(); // type
m.correctionConstant = Integer.parseInt(br.readLine()); // correctionConstant
out.writeInt(m.correctionConstant);
m.correctionParam = Double.parseDouble(br.readLine()); // getCorrectionParameter
out.writeDouble(m.correctionParam);
// label
int numOutcomes = Integer.parseInt(br.readLine());
out.writeInt(numOutcomes);
String[] outcomeLabels = new String[numOutcomes];
m.outcomeNames = outcomeLabels;
for (int i = 0; i < numOutcomes; i++) {
outcomeLabels[i] = br.readLine();
TextUtility.writeString(outcomeLabels[i], out);
}
// pattern
int numOCTypes = Integer.parseInt(br.readLine());
out.writeInt(numOCTypes);
int[][] outcomePatterns = new int[numOCTypes][];
for (int i = 0; i < numOCTypes; i++) {
StringTokenizer tok = new StringTokenizer(br.readLine(), " ");
int[] infoInts = new int[tok.countTokens()];
out.writeInt(infoInts.length);
for (int j = 0; tok.hasMoreTokens(); j++) {
infoInts[j] = Integer.parseInt(tok.nextToken());
out.writeInt(infoInts[j]);
}
outcomePatterns[i] = infoInts;
}
// feature
int NUM_PREDS = Integer.parseInt(br.readLine());
out.writeInt(NUM_PREDS);
String[] predLabels = new String[NUM_PREDS];
m.pmap = new DoubleArrayTrie<Integer>();
TreeMap<String, Integer> tmpMap = new TreeMap<String, Integer>();
for (int i = 0; i < NUM_PREDS; i++) {
predLabels[i] = br.readLine();
TextUtility.writeString(predLabels[i], out);
tmpMap.put(predLabels[i], i);
}
m.pmap.build(tmpMap);
for (Map.Entry<String, Integer> entry : tmpMap.entrySet()) {
out.writeInt(entry.getValue());
}
m.pmap.save(out);
// params
Context[] params = new Context[NUM_PREDS];
int pid = 0;
for (int i = 0; i < outcomePatterns.length; i++) {
int[] outcomePattern = new int[outcomePatterns[i].length - 1];
for (int k = 1; k < outcomePatterns[i].length; k++) {
outcomePattern[k - 1] = outcomePatterns[i][k];
}
for (int j = 0; j < outcomePatterns[i][0]; j++) {
double[] contextParameters = new double[outcomePatterns[i].length - 1];
for (int k = 1; k < outcomePatterns[i].length; k++) {
contextParameters[k - 1] = Double.parseDouble(br.readLine());
out.writeDouble(contextParameters[k - 1]);
}
params[pid] = new Context(outcomePattern, contextParameters);
pid++;
}
}
// prior
m.prior = new UniformPrior();
m.prior.setLabels(outcomeLabels);
// eval
m.evalParams =
new EvalParameters(params, m.correctionParam, m.correctionConstant, outcomeLabels.length);
} catch (Exception e) {
logger.severe("从" + path + "加载最大熵模型失败！" + TextUtility.exceptionToString(e));
return null;
}
return m;
}
