Package | Description |
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com.bayesserver.analysis | |
com.bayesserver.data.discovery |
Modifier and Type | Method and Description |
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Interval<Double> |
AutoInsightOutput.getContinuousTargetInterval()
Gets the target interval (if any).
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Interval<Double> |
ParameterTuningOneWay.getInterval()
Gets the interval for the parameter which satisfies the constraint used in parameter tuning.
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Modifier and Type | Method and Description |
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static ParameterTuningOneWay |
ParameterTuning.oneWayDifference(SensitivityFunctionOneWay f1,
SensitivityFunctionOneWay f2,
Interval<Double> constraint)
Given a pair of sensitivity functions (evaluated on the same parameter and evidence but different hypotheses), determines how the parameter under consideration can be altered so that the difference between the hypothesis probabilities P(h1|e) - P(h2|e) is within a given range.
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static ParameterTuningOneWay |
ParameterTuning.oneWayRatio(SensitivityFunctionOneWay f1,
SensitivityFunctionOneWay f2,
Interval<Double> constraint)
Given a pair of sensitivity functions (evaluated on the same parameter and evidence but different hypotheses), determines how the parameter under consideration can be altered so that the ratio between the hypothesis probabilities P(h1|e) / P(h2|e) is within a given range.
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static ParameterTuningOneWay |
ParameterTuning.oneWaySimple(SensitivityFunctionOneWay f,
Interval<Double> constraint)
Given a sensitivity function, determines how the parameter under consideration can be altered so that the resulting value of the hypothesis is within a given range.
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void |
AutoInsightOutput.setContinuousTargetInterval(Interval<Double> value)
Gets the target interval (if any).
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Modifier and Type | Method and Description |
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static AutoInsightOutput[] |
AutoInsight.calculate(Variable continuousTarget,
List<Interval<Double>> targetIntervals,
List<Variable> testVariables,
Evidence evidence,
AutoInsightOptions options)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
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Constructor and Description |
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HistogramDensity(List<Interval<Double>> intervals,
List<Double> intervalCounts)
Constructs an empirical density function.
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Modifier and Type | Method and Description |
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List<Interval<Double>> |
Clustering.discretize(Iterable<Double> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
Discretize.discretize(Iterable<Double> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
EqualFrequencies.discretize(Iterable<Double> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
EqualIntervals.discretize(Iterable<Double> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
Clustering.discretizeWeighted(Iterable<WeightedValue> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted weighted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
Discretize.discretizeWeighted(Iterable<WeightedValue> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted weighted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
EqualFrequencies.discretizeWeighted(Iterable<WeightedValue> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted weighted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
EqualIntervals.discretizeWeighted(Iterable<WeightedValue> unsortedData,
DiscretizationOptions options,
String dataColumn)
Discretizes unsorted weighted continuous data that may contain missing (null) values.
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List<Interval<Double>> |
DiscretizationInfo.getIntervals()
Gets the intervals generated by a discretization algorithm for a column of data.
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