-unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)
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// make a copy of the training data so that we can get the class // column to append to the transformed data (if necessary) m_trainHeader = new Instances(m_trainInstances, 0); m_replaceMissingFilter = new ReplaceMissingValues(); m_replaceMissingFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_replaceMissingFilter); /*if (m_normalize) {
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m_onlyNumeric = false; break; } } } m_Missing = new ReplaceMissingValues(); m_Missing.setInputFormat(instances); instances = Filter.useFilter(instances, m_Missing); if (!m_onlyNumeric) { m_NominalToBinary = new NominalToBinary();
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} insts = data; } if (!m_checksTurnedOff) { m_Missing = new ReplaceMissingValues(); m_Missing.setInputFormat(insts); insts = Filter.useFilter(insts, m_Missing); } else { m_Missing = null; }
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// remove instances with missing class train = new Instances(train); train.deleteWithMissingClass(); // Replace missing values m_ReplaceMissingValues = new ReplaceMissingValues(); m_ReplaceMissingValues.setInputFormat(train); train = Filter.useFilter(train, m_ReplaceMissingValues); // Remove useless attributes m_AttFilter = new RemoveUseless();
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insts = new Instances(insts); insts.deleteWithMissingClass(); } if (!m_checksTurnedOff) { m_Missing = new ReplaceMissingValues(); m_Missing.setInputFormat(insts); insts = Filter.useFilter(insts, m_Missing); } else { m_Missing = null; }
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insts = new Instances(insts); insts.deleteWithMissingClass(); // Filter data m_Train = new Instances(insts); m_ReplaceMissingValues = new ReplaceMissingValues(); m_ReplaceMissingValues.setInputFormat(m_Train); m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues); m_NominalToBinary = new NominalToBinary(); m_NominalToBinary.setInputFormat(m_Train);
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// remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); //replace missing values m_ReplaceMissingValues = new ReplaceMissingValues(); m_ReplaceMissingValues.setInputFormat(data); data = Filter.useFilter(data, m_ReplaceMissingValues); //convert nominal attributes m_NominalToBinary = new NominalToBinary();
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data = new Instances(data); data.deleteWithMissingClass(); m_instances = new Instances(data); m_replaceMissing = new ReplaceMissingValues(); m_replaceMissing.setInputFormat(m_instances); m_instances = Filter.useFilter(m_instances, m_replaceMissing); m_nominalToBinary = new NominalToBinary(); m_nominalToBinary.setInputFormat(m_instances);
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// remove instances with missing class Instances filteredData = new Instances(data); filteredData.deleteWithMissingClass(); //replace missing values m_replaceMissing = new ReplaceMissingValues(); m_replaceMissing.setInputFormat(filteredData); filteredData = Filter.useFilter(filteredData, m_replaceMissing); //possibly convert nominal attributes globally if (m_convertNominal) {
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*/ public void buildClusterer(Instances data) throws Exception { // can clusterer handle the data? getCapabilities().testWithFail(data); m_replaceMissing = new ReplaceMissingValues(); m_replaceMissing.setInputFormat(data); data = weka.filters.Filter.useFilter(data, m_replaceMissing); m_theInstances = new Instances(data, 0); if (m_wrappedClusterer == null) {