Valid options are:
-unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)
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else { m_NominalToBinary = null; } if (m_filterType == FILTER_STANDARDIZE) { m_Filter = new Standardize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); m_Filter.setInputFormat(insts);
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} double y1 = instances.instance(index).classValue(); // apply filters if (m_filterType == FILTER_STANDARDIZE) { m_Filter = new Standardize(); ((Standardize)m_Filter).setIgnoreClass(true); m_Filter.setInputFormat(instances); instances = Filter.useFilter(instances, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize();
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((Center) m_Filter).setIgnoreClass(true); break; case PREPROCESSING_STANDARDIZE: m_ClassMean = instances.meanOrMode(instances.classIndex()); m_ClassStdDev = StrictMath.sqrt(instances.variance(instances.classIndex())); m_Filter = new Standardize(); ((Standardize) m_Filter).setIgnoreClass(true); break; default: m_ClassMean = 0; m_ClassStdDev = 1;
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else { m_NominalToBinary = null; } if (m_filterType == FILTER_STANDARDIZE) m_Filter = new Standardize(); else if (m_filterType == FILTER_NORMALIZE) m_Filter = new Normalize(); else m_Filter = null;
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m_ConvertToProp.setInputFormat(train); train = Filter.useFilter( train, m_ConvertToProp); train.deleteAttributeAt(0); //remove the bagIndex attribute; if (m_filterType == FILTER_STANDARDIZE) m_Filter = new Standardize(); else if (m_filterType == FILTER_NORMALIZE) m_Filter = new Normalize(); else m_Filter = null;
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} } } // now standardize the input data m_standardizeFilter = new Standardize(); m_standardizeFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_standardizeFilter); }
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} } /* filter the training data */ if (m_filterType == FILTER_STANDARDIZE) m_Filter = new Standardize(); else if (m_filterType == FILTER_NORMALIZE) m_Filter = new Normalize(); else m_Filter = null;
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} /* filter the training data */ if (m_filterType == FILTER_STANDARDIZE) m_Filter = new Standardize(); else if (m_filterType == FILTER_NORMALIZE) m_Filter = new Normalize(); else m_Filter = null;
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// convert the training dataset into single-instance dataset m_ConvertToSI.setInputFormat(train); train = Filter.useFilter( train, m_ConvertToSI); if (m_filterType == FILTER_STANDARDIZE) m_Filter = new Standardize(); else if (m_filterType == FILTER_NORMALIZE) m_Filter = new Normalize(); else m_Filter = null;
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