Package edu.columbia.tjw.item.spark
Class ItemClassifierSettings
- java.lang.Object
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- edu.columbia.tjw.item.spark.ItemClassifierSettings
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- All Implemented Interfaces:
Serializable
public final class ItemClassifierSettings extends Object implements Serializable
All the collected settings needed in order to run the ItemClassifier.- Author:
- tyler
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description edu.columbia.tjw.item.ItemSettings_settings
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Constructor Summary
Constructors Constructor Description ItemClassifierSettings(edu.columbia.tjw.item.ItemSettings settings_, String intercept_, edu.columbia.tjw.item.base.SimpleStatus status_, int maxParamCount_, List<String> regressors_, Set<String> curveRegressors_)Use this to construct settings unless you really know what you're doing.ItemClassifierSettings(ItemClassifierSettings base_, edu.columbia.tjw.item.ItemSettings settings_)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description SortedSet<edu.columbia.tjw.item.base.SimpleRegressor>getCurveRegressors()edu.columbia.tjw.item.ItemCurveFactory<edu.columbia.tjw.item.base.SimpleRegressor,edu.columbia.tjw.item.base.StandardCurveType>getFactory()edu.columbia.tjw.item.base.SimpleStatusgetFromStatus()edu.columbia.tjw.item.base.SimpleRegressorgetIntercept()intgetMaxParamCount()Set<edu.columbia.tjw.item.base.SimpleRegressor>getNonCurveRegressors()List<edu.columbia.tjw.item.base.SimpleRegressor>getRegressors()edu.columbia.tjw.item.ItemSettingsgetSettings()static ItemClassifierSettingsload(String filename_)voidsave(String fileName_)
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Constructor Detail
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ItemClassifierSettings
public ItemClassifierSettings(ItemClassifierSettings base_, edu.columbia.tjw.item.ItemSettings settings_)
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ItemClassifierSettings
public ItemClassifierSettings(edu.columbia.tjw.item.ItemSettings settings_, String intercept_, edu.columbia.tjw.item.base.SimpleStatus status_, int maxParamCount_, List<String> regressors_, Set<String> curveRegressors_)Use this to construct settings unless you really know what you're doing.- Parameters:
settings_- Item settings to use, can be null.intercept_- Which regressor will be the intercept. You don't need to specify this in the data, it will be assumed to be 1.0 always.status_- Which status are we projecting from (only interesting for markov chains, just make a simple 2 status family if in doubt).maxParamCount_- How many parameters is the model allowed to use. Each curve will use approximately 3 params.regressors_- The list (in order) of the regressors to use. This must be in the same order that they appear in the features column.curveRegressors_- The regressors that can support curves (i.e. they are not binary flags), order doesn't matter for these.
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Method Detail
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getSettings
public edu.columbia.tjw.item.ItemSettings getSettings()
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getFromStatus
public edu.columbia.tjw.item.base.SimpleStatus getFromStatus()
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getIntercept
public edu.columbia.tjw.item.base.SimpleRegressor getIntercept()
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getFactory
public edu.columbia.tjw.item.ItemCurveFactory<edu.columbia.tjw.item.base.SimpleRegressor,edu.columbia.tjw.item.base.StandardCurveType> getFactory()
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getMaxParamCount
public int getMaxParamCount()
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getRegressors
public List<edu.columbia.tjw.item.base.SimpleRegressor> getRegressors()
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getCurveRegressors
public SortedSet<edu.columbia.tjw.item.base.SimpleRegressor> getCurveRegressors()
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getNonCurveRegressors
public Set<edu.columbia.tjw.item.base.SimpleRegressor> getNonCurveRegressors()
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save
public void save(String fileName_) throws IOException
- Throws:
IOException
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load
public static ItemClassifierSettings load(String filename_) throws IOException
- Throws:
IOException
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