/** This is (mostly) copied from CRF4.java */
public boolean[][] labelConnectionsIn(
Alphabet outputAlphabet, InstanceList trainingSet, String start) {
int numLabels = outputAlphabet.size();
boolean[][] connections = new boolean[numLabels][numLabels];
for (int i = 0; i < trainingSet.size(); i++) {
Instance instance = trainingSet.getInstance(i);
FeatureSequence output = (FeatureSequence) instance.getTarget();
for (int j = 1; j < output.size(); j++) {
int sourceIndex = outputAlphabet.lookupIndex(output.get(j - 1));
int destIndex = outputAlphabet.lookupIndex(output.get(j));
assert (sourceIndex >= 0 && destIndex >= 0);
connections[sourceIndex][destIndex] = true;
}
}
// Handle start state
if (start != null) {
int startIndex = outputAlphabet.lookupIndex(start);
for (int j = 0; j < outputAlphabet.size(); j++) {
connections[startIndex][j] = true;
}
}
return connections;
}
public int accept(int ins_num, int income_seq, String content) throws RemoteException {
if (!work) return -1;
Instance res = instances.get(ins_num);
if (res == null) return -1;
else {
if (income_seq >= res.n_p) {
res.n_p = income_seq;
res.n_a = income_seq;
res.content = content;
res.decided = true;
INS_NUM++;
return income_seq;
}
}
return -1;
}
public void count() {
TIntIntHashMap docCounts = new TIntIntHashMap();
int index = 0;
if (instances.size() == 0) {
logger.info("Instance list is empty");
return;
}
if (instances.get(0).getData() instanceof FeatureSequence) {
for (Instance instance : instances) {
FeatureSequence features = (FeatureSequence) instance.getData();
for (int i = 0; i < features.getLength(); i++) {
docCounts.adjustOrPutValue(features.getIndexAtPosition(i), 1, 1);
}
int[] keys = docCounts.keys();
for (int i = 0; i < keys.length - 1; i++) {
int feature = keys[i];
featureCounts[feature] += docCounts.get(feature);
documentFrequencies[feature]++;
}
docCounts = new TIntIntHashMap();
index++;
if (index % 1000 == 0) {
System.err.println(index);
}
}
} else if (instances.get(0).getData() instanceof FeatureVector) {
for (Instance instance : instances) {
FeatureVector features = (FeatureVector) instance.getData();
for (int location = 0; location < features.numLocations(); location++) {
int feature = features.indexAtLocation(location);
double value = features.valueAtLocation(location);
documentFrequencies[feature]++;
featureCounts[feature] += value;
}
index++;
if (index % 1000 == 0) {
System.err.println(index);
}
}
} else {
logger.info("Unsupported data class: " + instances.get(0).getData().getClass().getName());
}
}
public String toString() {
StringBuffer buf = new StringBuffer();
for (int i = 0; i < numSources(); i++) {
String src = getSource(i);
for (int j = 0; j < numInstances(src); j++) {
Instance inst = getInstance(src, j);
buf.append(inst.toString() + "\n");
}
}
return buf.toString();
}
public void dump() throws RemoteException {
int num_ins = instances.size();
System.out.println(getServerName() + ":");
for (int i = 1; i <= num_ins; i++) {
Instance tmp = instances.get(i);
System.out.println(i + ": " + tmp.toString());
}
return;
}
/**
* Process a dataset for classification
*
* @return Nothing
*/
public void processClassifierDataset() throws IOException {
try {
ndata = IS.getNumInstances();
ninputs = Attributes.getInputNumAttributes();
nvariables = ninputs + Attributes.getOutputNumAttributes();
// Check that there is only one output variable and
// it is nominal
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println("This algorithm can not process MIMO datasets");
System.out.println("All outputs but the first one will be removed");
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println("This algorithm cannot process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
}
// Initialice and fill our own tables
X = new double[ndata][ninputs];
missing = new boolean[ndata][ninputs];
C = new int[ndata];
// Maximum and minimum of inputs
imax = new double[ninputs];
imin = new double[ninputs];
// Maximum and minimum for output data
omax = 0;
omin = 0;
// All values are casted into double/integer
nclasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < ninputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > imax[j] || i == 0) imax[j] = X[i][j];
if (X[i][j] < imin[j] || i == 0) imin[j] = X[i][j];
}
if (noOutputs) C[i] = 0;
else C[i] = (int) IS.getOutputNumericValue(i, 0);
if (C[i] > nclasses) nclasses = C[i];
}
nclasses++;
System.out.println("Number of classes=" + nclasses);
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/**
* Process a dataset file for a clustering problem.
*
* @param nfexamples Name of the dataset file
* @param train The dataset file is for training or for test
* @throws java.io.IOException if there is any semantical, lexical or sintactical error in the
* input file.
*/
public void processClusterDataset(String nfexamples, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(nfexamples, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVariables = nInputs + Attributes.getOutputNumAttributes();
if (Attributes.getOutputNumAttributes() != 0) {
System.out.println("This algorithm can not process datasets with outputs");
System.out.println("All outputs will be removed");
}
// Initialize and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
// Maximum and minimum of inputs
iMaximum = new double[nInputs];
iMinimum = new double[nInputs];
// Maximum and minimum for output data
oMaximum = 0;
oMinimum = 0;
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > iMaximum[j] || i == 0) {
iMaximum[j] = X[i][j];
}
if (X[i][j] < iMinimum[j] || i == 0) {
iMinimum[j] = X[i][j];
}
}
}
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/**
* Process a dataset for modelling
*
* @return Nothing
*/
public void processModelDataset() throws IOException {
try {
// Load in memory a dataset that contains a classification problem
// IS.readSet(nfejemplos,train);
ndata = IS.getNumInstances();
ninputs = Attributes.getInputNumAttributes();
nvariables = ninputs + Attributes.getOutputNumAttributes();
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println("This algorithm can not process MIMO datasets");
System.out.println("All outputs but the first one will be removed");
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println("This algorithm can not process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
}
// Initialice and fill our own tables
X = new double[ndata][ninputs];
missing = new boolean[ndata][ninputs];
Y = new double[ndata];
// Maximum and minimum of inputs
imax = new double[ninputs];
imin = new double[ninputs];
// Maximum and minimum for output data
omax = 0;
omin = 0;
// All values are casted into double/integer
nclasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < ninputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > imax[j] || i == 0) imax[j] = X[i][j];
if (X[i][j] < imin[j] || i == 0) imin[j] = X[i][j];
}
if (noOutputs) Y[i] = 0;
else Y[i] = IS.getOutputNumericValue(i, 0);
if (Y[i] > omax || i == 0) omax = Y[i];
if (Y[i] < omin || i == 0) omin = Y[i];
}
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
public Instance prepare(int ins_num, int income_seq) throws RemoteException {
if (!work) return null;
Instance res = instances.get(ins_num);
if (res == null) return null;
else {
if (income_seq > res.n_p) {
res.n_p = income_seq;
return res;
} else return null;
}
}
/**
* Calculates the distance between two instances
*
* @param test the first instance
* @param train the second instance
* @return the distance between the two given instances, between 0 and 1
*/
protected double distance(Instance first, Instance second) {
double distance = 0;
int firstI, secondI;
for (int p1 = 0, p2 = 0; p1 < first.numValues() || p2 < second.numValues(); ) {
if (p1 >= first.numValues()) {
firstI = m_instances.numAttributes();
} else {
firstI = first.index(p1);
}
if (p2 >= second.numValues()) {
secondI = m_instances.numAttributes();
} else {
secondI = second.index(p2);
}
if (firstI == m_instances.classIndex()) {
p1++;
continue;
}
if (secondI == m_instances.classIndex()) {
p2++;
continue;
}
double diff;
if (firstI == secondI) {
diff = difference(firstI, first.valueSparse(p1), second.valueSparse(p2));
p1++;
p2++;
} else if (firstI > secondI) {
diff = difference(secondI, 0, second.valueSparse(p2));
p2++;
} else {
diff = difference(firstI, first.valueSparse(p1), 0);
p1++;
}
distance += diff * diff;
}
return Math.sqrt(distance / m_instances.numAttributes());
}
/**
* Input an instance for filtering. Ordinarily the instance is processed and made available for
* output immediately. Some filters require all instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be collected with output().
* @exception IllegalStateException if no input format has been defined.
*/
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
push((Instance) instance.copy());
return true;
}
public Instance pipe(Instance carrier) {
TokenSequence ts = (TokenSequence) carrier.getData();
TokenSequence targets =
carrier.getTarget() instanceof TokenSequence ? (TokenSequence) carrier.getTarget() : null;
TokenSequence source =
carrier.getSource() instanceof TokenSequence ? (TokenSequence) carrier.getSource() : null;
StringBuffer sb = new StringBuffer();
if (prefix != null) sb.append(prefix);
sb.append("name: " + carrier.getName() + "\n");
for (int i = 0; i < ts.size(); i++) {
if (source != null) {
sb.append(source.get(i).getText());
sb.append(' ');
}
if (carrier.getTarget() instanceof TokenSequence) {
sb.append(((TokenSequence) carrier.getTarget()).get(i).getText());
sb.append(' ');
}
if (carrier.getTarget() instanceof FeatureSequence) {
sb.append(((FeatureSequence) carrier.getTarget()).getObjectAtPosition(i).toString());
sb.append(' ');
}
PropertyList pl = ts.get(i).getFeatures();
if (pl != null) {
PropertyList.Iterator iter = pl.iterator();
while (iter.hasNext()) {
iter.next();
double v = iter.getNumericValue();
if (v == 1.0) sb.append(iter.getKey());
else sb.append(iter.getKey() + '=' + v);
sb.append(' ');
}
}
sb.append('\n');
}
System.out.print(sb.toString());
return carrier;
}
public void decide(int income_seq, Operation op) throws RemoteException {
// set corresponding Op decided, assume no collision happens
if (!work) return;
if (op == null) return;
Instance ins = instances.get(op.op_num);
if (!ins.decided) {
ins.decided = true;
INS_NUM++;
ins.n_a = income_seq;
ins.content = op.content;
}
return;
}
/* The main tree traversal function; if node is a leaf,
* return classification; otherwise, determine whether
* instance is higher or lower than this attribute-value
* pair and returns testInstance of proper child
*/
public boolean testInstance(Instance in) {
if (majority) {
return majClass;
}
if (leaf) {
return classification;
} else {
if (in.get((int) attribute) <= value) {
return leftChild.testInstance(in);
} else {
return rightChild.testInstance(in);
}
}
}
public Instance pipe(Instance carrier) {
TokenSequence ts = (TokenSequence) carrier.getData();
for (int i = 0; i < ts.size(); i++) {
Token t = ts.get(i);
String s = t.getText();
if (distinguishBorders) s = startBorderChar + s + endBorderChar;
int slen = s.length();
for (int j = 0; j < gramSizes.length; j++) {
int size = gramSizes[j];
for (int k = 0; k < (slen - size) + 1; k++)
t.setFeatureValue((prefix + s.substring(k, k + size)).intern(), 1.0);
}
}
return carrier;
}
public Instance pipe(Instance carrier) {
TokenSequence ts = (TokenSequence) carrier.getData();
for (int i = 0; i < ts.size(); i++) {
Token t = ts.get(i);
String s = t.getText();
if (distinguishBorders) s = startBorderChar + s + endBorderChar;
int slen = s.length();
for (int j = 0; j < gramSizes.length; j++) {
int size = gramSizes[j];
for (int k = 0; k < slen - size; k++)
t.setFeatureValue(
s.substring(k, k + size), 1.0); // original was substring(k, size), changed by Fuchun
}
}
return carrier;
}
/**
* Updates the minimum and maximum values for all the attributes based on a new instance.
*
* @param instance the new instance
*/
private void updateMinMax(Instance instance) {
for (int j = 0; j < instance.numAttributes(); j++) {
if (Double.isNaN(m_Min[j])) {
m_Min[j] = instance.value(j);
m_Max[j] = instance.value(j);
} else {
if (instance.value(j) < m_Min[j]) {
m_Min[j] = instance.value(j);
} else {
if (instance.value(j) > m_Max[j]) {
m_Max[j] = instance.value(j);
}
}
}
}
}
/** Computes the difference between two given attribute values. */
protected double difference(int index, double val1, double val2) {
switch (m_instances.attribute(index).type()) {
case Attribute.NOMINAL:
// If attribute is nominal
if (Instance.isMissingValue(val1)
|| Instance.isMissingValue(val2)
|| ((int) val1 != (int) val2)) {
return 1;
} else {
return 0;
}
case Attribute.NUMERIC:
// If attribute is numeric
if (Instance.isMissingValue(val1) || Instance.isMissingValue(val2)) {
if (Instance.isMissingValue(val1) && Instance.isMissingValue(val2)) {
return 1;
} else {
double diff;
if (Instance.isMissingValue(val2)) {
diff = norm(val1, index);
} else {
diff = norm(val2, index);
}
if (diff < 0.5) {
diff = 1.0 - diff;
}
return diff;
}
} else {
return norm(val1, index) - norm(val2, index);
}
default:
return 0;
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables]; // matrix with transformed data
boolean[] isMissed =
new boolean[ndatos]; // vector which points out instances with missed data
try {
FileWriter file_write = new FileWriter(output_train_name);
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
// file_write.close();
PrintWriter pw = new PrintWriter(file_write);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (!inst.existsAnyMissingValue()) {
inst.printAsOriginal(pw);
// file_write.write(inst.toString()); // DOES NOT WRITE BACK NON-DEF DIRECTION
// ATTRIBUTES!!!!
file_write.write("\n");
}
}
pw.close();
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
System.exit(-1);
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
// does a test file associated exist?
if (input_train_name.compareTo(input_test_name) != 0) {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables]; // matrix with transformed data
boolean[] isMissed =
new boolean[ndatos]; // vector which points out instances with missed data
try {
FileWriter file_write = new FileWriter(output_test_name);
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
PrintWriter pw = new PrintWriter(file_write);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (!inst.existsAnyMissingValue()) {
inst.printAsOriginal(pw);
file_write.write("\n");
}
}
pw.close();
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
System.exit(-1);
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
}
// write_results(); / since there ins't any data transformation, is not needed
}
/**
* Computes the distance between two instances (without previous normalization)
*
* @param i First instance
* @param j Second instance
* @return The Euclidean distance between i and j
*/
private double distance(Instance i, Instance j) {
double dist = 0;
int in = 0;
int out = 0;
for (int l = 0; l < nvariables; l++) {
Attribute a = Attributes.getAttribute(l);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !i.getInputMissingValues(in)) {
// real value, apply euclidean distance
dist +=
(i.getInputRealValues(in) - j.getInputRealValues(in))
* (i.getInputRealValues(in) - j.getInputRealValues(in));
} else {
if (!i.getInputMissingValues(in)
&& i.getInputNominalValues(in) != j.getInputNominalValues(in)) dist += 1;
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !i.getOutputMissingValues(out)) {
dist +=
(i.getOutputRealValues(out) - j.getOutputRealValues(out))
* (i.getOutputRealValues(out) - j.getOutputRealValues(out));
} else {
if (!i.getOutputMissingValues(out)
&& i.getOutputNominalValues(out) != j.getOutputNominalValues(out)) dist += 1;
}
out++;
}
}
}
return dist;
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
// declarations
double[] outputs;
double[] outputs2;
Instance neighbor;
double dist, mean;
int actual;
Randomize rnd = new Randomize();
Instance ex;
gCenter kmeans = null;
int iterations = 0;
double E;
double prevE;
int totalMissing = 0;
boolean allMissing = true;
rnd.setSeed(semilla);
// PROCESS
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables]; // matrix with transformed data
kmeans = new gCenter(K, ndatos, nvariables);
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
// first, we choose k 'means' randomly from all
// instances
totalMissing = 0;
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (inst.existsAnyMissingValue()) totalMissing++;
}
if (totalMissing == ndatos) allMissing = true;
else allMissing = false;
for (int numMeans = 0; numMeans < K; numMeans++) {
do {
actual = (int) (ndatos * rnd.Rand());
ex = IS.getInstance(actual);
} while (ex.existsAnyMissingValue() && !allMissing);
kmeans.copyCenter(ex, numMeans);
}
// now, iterate adjusting clusters' centers and
// instances to them
prevE = 0;
iterations = 0;
do {
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
kmeans.setClusterOf(inst, i);
}
// set new centers
kmeans.recalculateCenters(IS);
// compute RMSE
E = 0;
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
E += kmeans.distance(inst, kmeans.getClusterOf(i));
}
iterations++;
// System.out.println(iterations+"\t"+E);
if (Math.abs(prevE - E) == 0) iterations = maxIter;
else prevE = E;
} while (E > minError && iterations < maxIter);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
out++;
}
}
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/** ************************************************************************************ */
// does a test file associated exist?
if (input_train_name.compareTo(input_test_name) != 0) {
try {
// Load in memory a dataset that contains a classification problem
IStest.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IStest.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
for (int i = 0; i < ndatos; i++) {
Instance inst = IStest.getInstance(i);
in = 0;
out = 0;
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
out++;
}
}
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_test_name);
}
}
public Instance pipe(Instance carrier) {
carrier.setData(new TokenIterator((TokenSequence) carrier.getData()));
return carrier;
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
double[] outputs;
double[] outputs2;
Instance neighbor;
double dist, mean;
int actual;
int[] N = new int[nneigh];
double[] Ndist = new double[nneigh];
boolean allNull;
svm_problem SVMp = null;
svm_parameter SVMparam = new svm_parameter();
svm_model svr = null;
svm_node SVMn[];
double[] outputsCandidate = null;
boolean same = true;
Vector instancesSelected = new Vector();
Vector instancesSelected2 = new Vector();
// SVM PARAMETERS
SVMparam.C = C;
SVMparam.cache_size = 10; // 10MB of cache
SVMparam.degree = degree;
SVMparam.eps = eps;
SVMparam.gamma = gamma;
SVMparam.nr_weight = 0;
SVMparam.nu = nu;
SVMparam.p = p;
SVMparam.shrinking = shrinking;
SVMparam.probability = 0;
if (kernelType.compareTo("LINEAR") == 0) {
SVMparam.kernel_type = svm_parameter.LINEAR;
} else if (kernelType.compareTo("POLY") == 0) {
SVMparam.kernel_type = svm_parameter.POLY;
} else if (kernelType.compareTo("RBF") == 0) {
SVMparam.kernel_type = svm_parameter.RBF;
} else if (kernelType.compareTo("SIGMOID") == 0) {
SVMparam.kernel_type = svm_parameter.SIGMOID;
}
SVMparam.svm_type = svm_parameter.EPSILON_SVR;
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][2]; // matrix with transformed data
mostCommon = new String[nvariables];
SVMp = new svm_problem();
SVMp.l = ndatos;
SVMp.y = new double[SVMp.l];
SVMp.x = new svm_node[SVMp.l][nentradas + 1];
for (int l = 0; l < SVMp.l; l++) {
for (int n = 0; n < Attributes.getInputNumAttributes() + 1; n++) {
SVMp.x[l][n] = new svm_node();
}
}
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
SVMp.y[i] = inst.getAllOutputValues()[0];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMp.x[i][n].index = n;
SVMp.x[i][n].value = inst.getAllInputValues()[n];
SVMp.y[i] = inst.getAllOutputValues()[0];
}
// end of instance
SVMp.x[i][nentradas].index = -1;
}
if (svm.svm_check_parameter(SVMp, SVMparam) != null) {
System.out.println("SVM parameter error in training:");
System.out.println(svm.svm_check_parameter(SVMp, SVMparam));
System.exit(-1);
}
// train the SVM
if (ndatos > 0) {
svr = svm.svm_train(SVMp, SVMparam);
}
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
// the values used for regression
SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMn[n] = new svm_node();
SVMn[n].index = n;
SVMn[n].value = inst.getAllInputValues()[n];
}
SVMn[nentradas] = new svm_node();
SVMn[nentradas].index = -1;
// pedict the class
X[i][1] = new String(String.valueOf((svm.svm_predict(svr, SVMn))));
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/** ************************************************************************************ */
try {
// Load in memory a dataset that contains a classification
// problem
IS.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][2]; // matrix with transformed data
// data
mostCommon = new String[nvariables];
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMn[n] = new svm_node();
SVMn[n].index = n;
SVMn[n].value = inst.getAllInputValues()[n];
}
SVMn[nentradas] = new svm_node();
SVMn[nentradas].index = -1;
// pedict the class
X[i][1] = new String(String.valueOf(svm.svm_predict(svr, SVMn)));
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
System.out.println("escribiendo test");
write_results(output_test_name);
}
/**
* If we know in advance that the table is numeric, can optimize a lot... For example, on 9803 x
* 294 table, TableFileLoader.readNumeric takes 6s compared to 12s for WekaMine readFromTable.
*/
public static Instances readNumeric(String fileName, String relationName, String delimiter)
throws Exception {
int numAttributes = FileUtils.fastCountLines(fileName) - 1; // -1 exclude heading.
String[] attrNames = new String[numAttributes];
// Read the col headings and figure out the number of columns in the table..
BufferedReader reader = new BufferedReader(new FileReader(fileName), 4194304);
String line = reader.readLine();
String[] instanceNames = parseColNames(line, delimiter);
int numInstances = instanceNames.length;
System.err.print("reading " + numAttributes + " x " + numInstances + " table..");
// Create an array to hold the data as we read it in...
double dataArray[][] = new double[numAttributes][numInstances];
// Populate the matrix with values...
String valToken = "";
try {
int rowIdx = 0;
while ((line = reader.readLine()) != null) {
String[] tokens = line.split(delimiter, -1);
attrNames[rowIdx] = tokens[0].trim();
for (int colIdx = 0; colIdx < (tokens.length - 1); colIdx++) {
valToken = tokens[colIdx + 1];
double value;
if (valToken.equals("null")) {
value = Instance.missingValue();
} else if (valToken.equals("?")) {
value = Instance.missingValue();
} else if (valToken.equals("NA")) {
value = Instance.missingValue();
} else if (valToken.equals("")) {
value = Instance.missingValue();
// }else value = DoubleParser.lightningParse(valToken); // faster double parser with
// MANY assumptions
} else value = Double.parseDouble(valToken);
dataArray[rowIdx][colIdx] = value;
}
rowIdx++;
}
} catch (NumberFormatException e) {
System.err.println(e.toString());
System.err.println("Parsing line: " + line);
System.err.println("Parsing token: " + valToken);
}
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
for (int a = 0; a < numAttributes; a++) {
atts.addElement(new Attribute(attrNames[a]));
}
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
System.err.print("creating instances..");
// System.err.println("DEBUG: numAttributes "+numAttributes);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < numInstances; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
for (int r = 0; r < numAttributes; r++) {
double val = dataArray[r][c];
vals[r] = val;
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
// System.err.println("DEBUG: data.relationNAme"+data.relationName());
System.err.print("add feature names..");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
for (int c = 0; c < numInstances; c++) {
newData.instance(c).setValue(attrIdx, instanceNames[c]);
}
data = newData;
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
return (data);
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>rows</b> representing individual </b>instances</b> and <b>columns</b> representing
* <b>attributes</b>
*/
public Instances tableRowsToNominalInstances(Table t, String relationName) {
System.err.print("Converting table rows to instances...");
// Set up attributes, which for rowInstances will be the colNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int c = 0; c < t.numCols; c++) {
// It's nominal... determine the range of values
isNominal.add(true);
FastVector attVals = getColValues(t, c);
atts.addElement(new Attribute(t.colNames[c], attVals));
// Save it for later
allAttVals.add(attVals);
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
// Fill the instances with data...
// For each instance...
for (int r = 0; r < t.numRows; r++) {
double[] vals = new double[data.numAttributes()];
// for each attribute
for (int c = 0; c < t.numCols; c++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[c] = Instance.missingValue();
else if (isNominal.get(c)) {
vals[c] = allAttVals.get(c).indexOf(val);
} else {
vals[c] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int r = 0; r < t.rowNames.length; r++) {
instanceNames.add(t.rowNames[r]);
newData.instance(r).setValue(attrIdx, t.rowNames[r]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/** @param args */
public static void main(final String[] args) throws Exception {
final BufferedReader br =
new BufferedReader(
new InputStreamReader(
(new GZIPInputStream(
new FileInputStream(
new File(
new File(System.getProperty("user.dir")), "testdata/mobo1.txt.gz"))))));
final List<Instance> instances = Lists.newLinkedList();
int count = 0;
while (true) {
count++;
final String line = br.readLine();
if (line == null) {
break;
}
final JSONObject jo = (JSONObject) JSONValue.parse(line);
final HashMapAttributes a = new HashMapAttributes();
a.putAll((JSONObject) jo.get("attributes"));
Instance instance = new Instance(a, (String) jo.get("output"));
instances.add(instance);
}
final List<Instance> train = instances.subList(0, instances.size() / 2);
final List<Instance> test = instances.subList(instances.size() / 2 + 1, instances.size() - 1);
System.out.println("Read " + instances.size() + " instances");
System.out.println("Testing scorers with single decision node");
for (final Scorer scorer : Sets.newHashSet(new Scorer1())) {
final TreeBuilder tb = new TreeBuilder(scorer);
final long startTime = System.currentTimeMillis();
final Node tree = tb.buildPredictiveModel(train).node;
System.out.println(
scorer.getClass().getSimpleName()
+ " build time "
+ (System.currentTimeMillis() - startTime)
+ ", size: "
+ tree.size()
+ " mean depth: "
+ tree.meanDepth());
int correctlyClassified = 0;
for (Instance testInstance : test) {
String result = (String) tree.getLeaf(testInstance.getAttributes()).getBestClassification();
if (result.equals(testInstance.getClassification())) {
correctlyClassified++;
}
}
System.out.println(", accuracy: " + (double) correctlyClassified / test.size());
System.out.println("Testing random forest");
for (int i = 2; i <= 20; i++) {
RandomForestBuilder rfBuilder = new RandomForestBuilder(new TreeBuilder());
RandomForest randomForest = rfBuilder.buildPredictiveModel(train);
correctlyClassified = 0;
for (Instance testInstance : test) {
Serializable result =
randomForest.getClassificationByMaxProb(testInstance.getAttributes());
if (result.equals(testInstance.getClassification())) {
correctlyClassified++;
}
}
System.out.println(
"accuracy with " + i + " trees: " + (double) correctlyClassified / test.size());
// ObjectOutputStream out = new ObjectOutputStream(new FileOutputStream(new
// File("baggedTree.ser")));
// out.writeObject(baggedTree);
}
}
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>columns</b> representing individual </b>instances</b> and <b>rows</b> representing
* <b>attributes</b> (e.g. as is common with microarray data)
*/
public Instances tableColsToInstances(Table t, String relationName) {
System.err.print("Converting table cols to instances...");
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int r = 0; r < t.numRows; r++) {
if (rowIsNumeric(t, r)) {
isNominal.add(false);
atts.addElement(new Attribute(t.rowNames[r]));
allAttVals.add(null); // No enumeration of attribute values.
} else {
// It's nominal... determine the range of values and create a nominal attribute...
isNominal.add(true);
FastVector attVals = getRowValues(t, r);
atts.addElement(new Attribute(t.rowNames[r], attVals));
// Save it for later
allAttVals.add(attVals);
}
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < t.numCols; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
// For each attribute fill in the numeric or attributeValue index...
for (int r = 0; r < t.numRows; r++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[r] = Instance.missingValue();
else if (isNominal.get(r)) {
vals[r] = allAttVals.get(r).indexOf(val);
} else {
vals[r] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int c = 0; c < t.colNames.length; c++) {
instanceNames.add(t.colNames[c]);
newData.instance(c).setValue(attrIdx, t.colNames[c]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
public JSONArray Cluster(String wekaFilePath, int clusterNum) throws Exception {
File inputFile = new File(wekaFilePath);
ArffLoader arf = new ArffLoader();
arf.setFile(inputFile);
Instances originIns = arf.getDataSet();
Instances insTest = new Instances(originIns);
insTest.deleteStringAttributes();
int totalNum = insTest.numInstances();
// SimpleKMeans sm = new SimpleKMeans();
EM em = new EM();
em.setNumClusters(clusterNum);
MakeDensityBasedClusterer sm = new MakeDensityBasedClusterer();
sm.setClusterer(em);
sm.buildClusterer(insTest);
System.out.println("totalNum:" + insTest.numInstances());
System.out.println("============================");
System.out.println(sm.toString());
Map<Integer, ArrayList<String>> result = new HashMap<Integer, ArrayList<String>>();
for (int i = 0; i < clusterNum; i++) {
result.put(i, new ArrayList<String>());
}
for (int i = 0; i < totalNum; i++) {
Instance ins = originIns.instance(i);
String word = ins.stringValue(0);
Instance tempIns = new Instance(ins);
tempIns.deleteAttributeAt(0);
int cluster = sm.clusterInstance(tempIns);
result.get(cluster).add(word);
}
// print the result
ArrayList<String> words = new ArrayList<String>();
JSONArray keyWords = new JSONArray();
for (int k : result.keySet()) {
words = result.get(k);
PriorityQueue<MyTerm> clusterQueue = new PriorityQueue<MyTerm>(1, MyTermCompare);
for (int i = 0; i < words.size(); i++) {
String s = words.get(i);
assert linkMap.containsKey(s);
int freq = linkMap.get(s).totalFreq;
clusterQueue.add(linkMap.get(s));
words.set(i, "(" + s + ":" + freq + ")");
}
JSONArray clusterArray = new JSONArray();
int num = clusterQueue.size() / 10 + 1; // 5%
int totalFreq = 0;
int totalLength = 0;
for (int i = 0; i < num && !clusterQueue.isEmpty(); ) {
JSONObject mem = new JSONObject();
MyTerm myTerm = clusterQueue.poll();
String word = myTerm.originTrem.text();
if (word.length() == 1) {
continue;
}
mem.put("text", word);
mem.put("freq", myTerm.totalFreq);
clusterArray.put(mem);
i++;
totalFreq += myTerm.totalFreq;
totalLength += word.length();
}
double averFreq = totalFreq * 1.0 / num;
double averLength = totalLength * 1.0 / num;
int count = 0;
while (!clusterQueue.isEmpty() && count < num) {
MyTerm myTerm = clusterQueue.poll();
String word = myTerm.originTrem.text();
int freq = myTerm.totalFreq;
int times = (int) (word.length() / averFreq) + 1;
if (freq > averFreq / times) {
JSONObject mem = new JSONObject();
mem.put("text", word);
mem.put("freq", freq);
mem.put("extra", true);
clusterArray.put(mem);
}
}
keyWords.put(clusterArray);
System.out.println(
"cluster" + k + ":" + words.size() + ":\t" + (int) (words.size() * 1.0 / totalNum * 100));
if (result.get(k).size() < 100) {
System.out.println(result.get(k));
}
}
// System.out.println("errorNum:"+errorNum);
return keyWords;
}
/**
* Process a dataset file for a classification problem.
*
* @param nfejemplos Name of the dataset file
* @param train The dataset file is for training or for test
* @throws java.io.IOException if there is any semantical, lexical or sintactical error in the
* input file.
*/
public void processClassifierDataset(String nfejemplos, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(nfejemplos, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVariables = nInputs + Attributes.getOutputNumAttributes();
// Check that there is only one output variable and
// it is nominal
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println("This algorithm can not process MIMO datasets");
System.out.println("All outputs but the first one will be removed");
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println("This algorithm can not process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
}
// Initialize and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
C = new int[nData];
// Maximum and minimum of inputs
iMaximum = new double[nInputs];
iMinimum = new double[nInputs];
// Maximum and minimum for output data
oMaximum = 0;
oMinimum = 0;
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > iMaximum[j] || i == 0) {
iMaximum[j] = X[i][j];
}
if (X[i][j] < iMinimum[j] || i == 0) {
iMinimum[j] = X[i][j];
}
}
if (noOutputs) {
C[i] = 0;
} else {
C[i] = (int) IS.getOutputNumericValue(i, 0);
}
if (C[i] > nClasses) {
nClasses = C[i];
}
}
nClasses++;
System.out.println("Number of classes=" + nClasses);
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
double[] outputs;
double[] outputs2;
try {
FileWriter file_write = new FileWriter(output_train_name);
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
int in2 = 0;
int out2 = 0;
int lastMissing = -1;
boolean fin = false;
boolean stepNext = false;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
String[] row = null;
X = new Vector[ndatos]; // matrix with transformed data
for (int i = 0; i < ndatos; i++) X[i] = new Vector();
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
// now, search for missed data, and replace them with
// the most common value
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
row = new String[nvariables];
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.existsAnyMissingValue()) {
row[j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.existsAnyMissingValue()) row[j] = inst.getInputNominalValues(in);
else {
// missing data
outputs = inst.getAllOutputValues();
in2 = 0;
out2 = 0;
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in2)) {
row[attr] = new String(String.valueOf(inst.getInputRealValues(in2)));
} else {
if (!inst.getInputMissingValues(in2))
row[attr] = inst.getInputNominalValues(in2);
}
in2++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out2)) {
row[attr] = new String(String.valueOf(inst.getOutputRealValues(out2)));
} else {
if (!inst.getOutputMissingValues(out2))
row[attr] = inst.getOutputNominalValues(out2);
}
out2++;
}
}
}
// make frecuencies for each attribute
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
lastMissing = attr;
timesSeen[attr] = new FreqList();
for (int m = 0; m < ndatos; m++) {
Instance inst2 = IS.getInstance(m);
outputs2 = inst2.getAllOutputValues();
boolean sameClass = true;
// are they same concept instances??
for (int k = 0; k < nsalidas && sameClass; k++)
if (outputs[k] != outputs2[k]) sameClass = false;
if (sameClass) {
if (tipo != Attribute.NOMINAL && !inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(
new String(String.valueOf(inst2.getInputRealValues(attr))));
} else {
if (!inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(inst2.getInputNominalValues(attr));
}
}
}
}
}
}
for (int attr = 0; attr < nvariables; attr++) {
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
timesSeen[attr].reset();
}
}
fin = false;
stepNext = false;
while (!fin) {
in2 = 0;
for (int attr = 0; attr < nvariables && !fin; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(in2)) {
if (stepNext) {
timesSeen[attr].iterate();
stepNext = false;
}
if (timesSeen[attr].outOfBounds()) {
stepNext = true;
if (attr == lastMissing) fin = true;
timesSeen[attr].reset();
}
if (!fin)
row[attr] =
((ValueFreq) timesSeen[attr].getCurrent())
.getValue(); // replace missing data
}
in2++;
}
if (!fin) {
stepNext = true;
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
// X[i].addElement(row);
// row = (String[])row.clone();
}
}
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
row[j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) row[j] = inst.getOutputNominalValues(out);
else row[j] = new String("?");
}
out++;
}
}
}
if (!inst.existsAnyMissingValue()) {
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
e.printStackTrace();
System.exit(-1);
}
/** ************************************************************************************ */
// does a test file associated exist?
if (input_train_name.compareTo(input_test_name) != 0) {
try {
FileWriter file_write = new FileWriter(output_test_name);
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_test_name, false);
int in = 0;
int out = 0;
int in2 = 0;
int out2 = 0;
int lastMissing = -1;
boolean fin = false;
boolean stepNext = false;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
String[] row = null;
X = new Vector[ndatos]; // matrix with transformed data
for (int i = 0; i < ndatos; i++) X[i] = new Vector();
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
// now, search for missed data, and replace them with
// the most common value
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
row = new String[nvariables];
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.existsAnyMissingValue()) {
row[j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.existsAnyMissingValue()) row[j] = inst.getInputNominalValues(in);
else {
// missing data
outputs = inst.getAllOutputValues();
in2 = 0;
out2 = 0;
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in2)) {
row[attr] = new String(String.valueOf(inst.getInputRealValues(in2)));
} else {
if (!inst.getInputMissingValues(in2))
row[attr] = inst.getInputNominalValues(in2);
}
in2++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out2)) {
row[attr] = new String(String.valueOf(inst.getOutputRealValues(out2)));
} else {
if (!inst.getOutputMissingValues(out2))
row[attr] = inst.getOutputNominalValues(out2);
}
out2++;
}
}
}
// make frecuencies for each attribute
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
lastMissing = attr;
timesSeen[attr] = new FreqList();
for (int m = 0; m < ndatos; m++) {
Instance inst2 = IS.getInstance(m);
outputs2 = inst2.getAllOutputValues();
boolean sameClass = true;
// are they same concept instances??
for (int k = 0; k < nsalidas && sameClass; k++)
if (outputs[k] != outputs2[k]) sameClass = false;
if (sameClass) {
if (tipo != Attribute.NOMINAL && !inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(
new String(String.valueOf(inst2.getInputRealValues(attr))));
} else {
if (!inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(inst2.getInputNominalValues(attr));
}
}
}
}
}
}
for (int attr = 0; attr < nvariables; attr++) {
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
timesSeen[attr].reset();
}
}
fin = false;
stepNext = false;
while (!fin) {
in2 = 0;
for (int attr = 0; attr < nvariables && !fin; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(in2)) {
if (stepNext) {
timesSeen[attr].iterate();
stepNext = false;
}
if (timesSeen[attr].outOfBounds()) {
stepNext = true;
if (attr == lastMissing) fin = true;
timesSeen[attr].reset();
}
if (!fin)
row[attr] =
((ValueFreq) timesSeen[attr].getCurrent())
.getValue(); // replace missing data
}
in2++;
}
if (!fin) {
stepNext = true;
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
// X[i].addElement(row);
// row = (String[])row.clone();
}
}
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
row[j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out))
row[j] = inst.getOutputNominalValues(out);
else row[j] = new String("?");
}
out++;
}
}
}
if (!inst.existsAnyMissingValue()) {
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
e.printStackTrace();
System.exit(-1);
}
}
}
