/** * default constructor */ public PLSFilter() { super(); // setup pre-processing m_Missing = new ReplaceMissingValues(); m_Filter = new Center(); }
/** * Input an instance for filtering. Filter requires all * training instances be read before producing output. * * @param instance the input instance * @return true if the filtered instance may now be * collected with output(). * @throws IllegalStateException if no input format has been set. */ public boolean input(Instance instance) { if (getInputFormat() == null) throw new IllegalStateException("No input instance format defined"); if (m_NewBatch) { resetQueue(); m_NewBatch = false; } if (m_Means == null) { bufferInput(instance); return false; } else { convertInstance(instance); return true; } }
/** * Signify that this batch of input to the filter is finished. * If the filter requires all instances prior to filtering, * output() may now be called to retrieve the filtered instances. * * @return true if there are instances pending output * @throws IllegalStateException if no input structure has been defined */ public boolean batchFinished() { if (getInputFormat() == null) throw new IllegalStateException("No input instance format defined"); if (m_Means == null) { Instances input = getInputFormat(); m_Means = new double[input.numAttributes()]; for (int i = 0; i < input.numAttributes(); i++) { if (input.attribute(i).isNumeric() && (input.classIndex() != i)) { m_Means[i] = input.meanOrMode(i); } } // Convert pending input instances for (int i = 0; i < input.numInstances(); i++) convertInstance(input.instance(i)); } // Free memory flushInput(); m_NewBatch = true; return (numPendingOutput() != 0); }
protected void fillCovariance() throws Exception { // just center the data or standardize it? if (m_center) { m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_centerFilter); } else { m_standardizeFilter = new Standardize(); m_standardizeFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_standardizeFilter); } // now compute the covariance matrix m_Correlation = new UpperSymmDenseMatrix(m_NumAttribs); for (int i = 0; i < m_NumAttribs; i++) { for (int j = i; j < m_NumAttribs; j++) { double cov = 0; for (Instance inst: m_TrainInstances) { cov += inst.value(i) * inst.value(j); } cov /= m_TrainInstances.numInstances() - 1; m_Correlation.set(i, j, cov); } } }
tempInst = m_standardizeFilter.output(); } else { m_centerFilter.input(tempInst); m_centerFilter.batchFinished(); tempInst = m_centerFilter.output();
m_ClassMean = instances.meanOrMode(instances.classIndex()); m_ClassStdDev = 1; m_Filter = new Center(); ((Center) m_Filter).setIgnoreClass(true); break; case PREPROCESSING_STANDARDIZE:
if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) { } else { double[] vals = instance.toDoubleArray(); for (int j = 0; j < getInputFormat().numAttributes(); j++) { if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) { vals[j] = (vals[j] - m_Means[j]); push(inst, false); // No need to copy instance
protected void fillCovariance() throws Exception { // just center the data or standardize it? if (m_center) { m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_centerFilter); } else { m_standardizeFilter = new Standardize(); m_standardizeFilter.setInputFormat(m_TrainInstances); m_TrainInstances = Filter.useFilter(m_TrainInstances, m_standardizeFilter); } // now compute the covariance matrix m_Correlation = new UpperSymmDenseMatrix(m_NumAttribs); for (int i = 0; i < m_NumAttribs; i++) { for (int j = i; j < m_NumAttribs; j++) { double cov = 0; for (Instance inst: m_TrainInstances) { cov += inst.value(i) * inst.value(j); } cov /= m_TrainInstances.numInstances() - 1; m_Correlation.set(i, j, cov); } } }
tempInst = m_standardizeFilter.output(); } else { m_centerFilter.input(tempInst); m_centerFilter.batchFinished(); tempInst = m_centerFilter.output();
if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) { } else { double[] vals = instance.toDoubleArray(); for (int j = 0; j < getInputFormat().numAttributes(); j++) { if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) { vals[j] = (vals[j] - m_Means[j]); push(inst, false); // No need to copy instance
/** Creates a default Center */ public Filter getFilter() { return new Center(); }
m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter); } else {
tempInst = m_standardizeFilter.output(); } else { m_centerFilter.input(tempInst); m_centerFilter.batchFinished(); tempInst = m_centerFilter.output();
/** * Signify that this batch of input to the filter is finished. * If the filter requires all instances prior to filtering, * output() may now be called to retrieve the filtered instances. * * @return true if there are instances pending output * @throws IllegalStateException if no input structure has been defined */ public boolean batchFinished() { if (getInputFormat() == null) throw new IllegalStateException("No input instance format defined"); if (m_Means == null) { Instances input = getInputFormat(); m_Means = new double[input.numAttributes()]; for (int i = 0; i < input.numAttributes(); i++) { if (input.attribute(i).isNumeric() && (input.classIndex() != i)) { m_Means[i] = input.meanOrMode(i); } } // Convert pending input instances for (int i = 0; i < input.numInstances(); i++) convertInstance(input.instance(i)); } // Free memory flushInput(); m_NewBatch = true; return (numPendingOutput() != 0); }
/** * Input an instance for filtering. Filter requires all * training instances be read before producing output. * * @param instance the input instance * @return true if the filtered instance may now be * collected with output(). * @throws IllegalStateException if no input format has been set. */ public boolean input(Instance instance) { if (getInputFormat() == null) throw new IllegalStateException("No input instance format defined"); if (m_NewBatch) { resetQueue(); m_NewBatch = false; } if (m_Means == null) { bufferInput(instance); return false; } else { convertInstance(instance); return true; } }
/** Creates a default Center */ public Filter getFilter() { return new Center(); }
m_centerFilter = new Center(); m_centerFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter); } else {
tempInst = m_standardizeFilter.output(); } else { m_centerFilter.input(tempInst); m_centerFilter.batchFinished(); tempInst = m_centerFilter.output();