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< li > < a href = "quapy.method.html#quapy.method.aggregative.AdjustedClassifyAndCount" > AdjustedClassifyAndCount (in module quapy.method.aggregative)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ACC.aggregate" > aggregate() (quapy.method.aggregative.ACC method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate" > (quapy.method.aggregative.AggregativeQuantifier method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.DistributionMatching.aggregate" > (quapy.method.aggregative.DistributionMatching method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ELM.aggregate" > (quapy.method.aggregative.ELM method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll.aggregate" > (quapy.method.aggregative.OneVsAll method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.PCC.aggregate" > (quapy.method.aggregative.PCC method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.aggregative" > aggregative (quapy.method.meta.Ensemble property)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier" > AggregativeQuantifier (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader" > asDataloader() (quapy.classification.neural.TorchDataset method)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.calibration.BCTSCalibration" > BCTSCalibration (class in quapy.classification.calibration)< / a >
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< li > < a href = "quapy.data.html#quapy.data.reader.binarize" > binarize() (in module quapy.data.reader)< / a >
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< li > < a href = "quapy.data.html#quapy.data.base.Dataset.binary" > binary (quapy.data.base.Dataset property)< / a >
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< li > < a href = "quapy.html#quapy.plot.binary_bias_bins" > binary_bias_bins() (in module quapy.plot)< / a >
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< li > < a href = "quapy.html#quapy.plot.binary_bias_global" > binary_bias_global() (in module quapy.plot)< / a >
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< li > < a href = "quapy.html#quapy.plot.binary_diagonal" > binary_diagonal() (in module quapy.plot)< / a >
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< li > < a href = "quapy.method.html#quapy.method.base.BinaryQuantifier" > BinaryQuantifier (class in quapy.method.base)< / a >
2021-11-22 18:10:48 +01:00
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< li > < a href = "quapy.html#quapy.plot.brokenbar_supremacy_by_drift" > brokenbar_supremacy_by_drift() (in module quapy.plot)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.CC" > CC (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.html#quapy.functional.check_prevalence_vector" > check_prevalence_vector() (in module quapy.functional)< / a >
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< li > < a href = "quapy.method.html#quapy.method.base.OneVsAllGeneric.classes" > classes (quapy.method.base.OneVsAllGeneric property)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_" > classes_ (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase property)< / a >
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< ul >
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< li > < a href = "quapy.data.html#quapy.data.base.Dataset.classes_" > (quapy.data.base.Dataset property)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classes_" > (quapy.method.aggregative.AggregativeQuantifier property)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll.classes_" > (quapy.method.aggregative.OneVsAll property)< / a >
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< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.classes_" > (quapy.method.neural.QuaNetTrainer property)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classifier" > classifier (quapy.method.aggregative.AggregativeQuantifier property)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ACC.classify" > classify() (quapy.method.aggregative.ACC method)< / a >
< ul >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.classify" > (quapy.method.aggregative.AggregativeProbabilisticQuantifier method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classify" > (quapy.method.aggregative.AggregativeQuantifier method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ELM.classify" > (quapy.method.aggregative.ELM method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll.classify" > (quapy.method.aggregative.OneVsAll method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.PACC.classify" > (quapy.method.aggregative.PACC method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ClassifyAndCount" > ClassifyAndCount (in module quapy.method.aggregative)< / a >
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< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.clean_checkpoint" > clean_checkpoint() (quapy.method.neural.QuaNetTrainer method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.clean_checkpoint_dir" > clean_checkpoint_dir() (quapy.method.neural.QuaNetTrainer method)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.CNNnet" > CNNnet (class in quapy.classification.neural)< / a >
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< li > < a href = "quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator" > collator() (quapy.protocol.AbstractStochasticSeededProtocol method)< / a >
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< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.counts" > counts() (quapy.data.base.LabelledCollection method)< / a >
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< li > < a href = "quapy.html#quapy.util.create_if_not_exist" > create_if_not_exist() (in module quapy.util)< / a >
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< li > < a href = "quapy.html#quapy.util.create_parent_dir" > create_parent_dir() (in module quapy.util)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.cross_generate_predictions" > cross_generate_predictions() (in module quapy.method.aggregative)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.cross_generate_predictions_depr" > cross_generate_predictions_depr() (in module quapy.method.aggregative)< / a >
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< li > < a href = "quapy.html#quapy.model_selection.cross_val_predict" > cross_val_predict() (in module quapy.model_selection)< / a >
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< h2 id = "D" > D< / h2 >
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< li > < a href = "quapy.data.html#quapy.data.base.Dataset" > Dataset (class in quapy.data.base)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function" > decision_function() (quapy.classification.svmperf.SVMperf method)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.device" > device (quapy.classification.neural.NeuralClassifierTrainer property)< / a >
< ul >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetModule.device" > (quapy.method.neural.QuaNetModule property)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions" > dimensions() (quapy.classification.neural.TextClassifierNet method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.DistributionMatching" > DistributionMatching (class in quapy.method.aggregative)< / a >
2021-11-09 15:50:53 +01:00
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2021-12-15 15:27:43 +01:00
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< td style = "width: 33%; vertical-align: top;" > < ul >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.neural.CNNnet.document_embedding" > document_embedding() (quapy.classification.neural.CNNnet method)< / a >
< ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.LSTMnet.document_embedding" > (quapy.classification.neural.LSTMnet method)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding" > (quapy.classification.neural.TextClassifierNet method)< / a >
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2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.DomainMixer" > DomainMixer (class in quapy.protocol)< / a >
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2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.html#quapy.util.download_file" > download_file() (in module quapy.util)< / a >
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< li > < a href = "quapy.html#quapy.util.download_file_if_not_exists" > download_file_if_not_exists() (in module quapy.util)< / a >
2023-02-08 19:06:53 +01:00
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< li > < a href = "quapy.method.html#quapy.method.aggregative.DyS" > DyS (class in quapy.method.aggregative)< / a >
2021-11-09 15:50:53 +01:00
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< / ul > < / td >
< / tr > < / table >
< h2 id = "E" > E< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.meta.EACC" > EACC() (in module quapy.method.meta)< / a >
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< li > < a href = "quapy.html#quapy.util.EarlyStop" > EarlyStop (class in quapy.util)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.ECC" > ECC() (in module quapy.method.meta)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.EEMQ" > EEMQ() (in module quapy.method.meta)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.EHDy" > EHDy() (in module quapy.method.meta)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ELM" > ELM (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.EMQ.EM" > EM() (quapy.method.aggregative.EMQ class method)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.EMQ" > EMQ (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble" > Ensemble (class in quapy.method.meta)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.ensembleFactory" > ensembleFactory() (in module quapy.method.meta)< / a >
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< li > < a href = "quapy.method.html#quapy.method.meta.EPACC" > EPACC() (in module quapy.method.meta)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.EMQ.EPSILON" > EPSILON (quapy.method.aggregative.EMQ attribute)< / a >
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< li > < a href = "quapy.html#quapy.plot.error_by_drift" > error_by_drift() (in module quapy.plot)< / a >
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< li > < a href = "quapy.html#quapy.evaluation.evaluate" > evaluate() (in module quapy.evaluation)< / a >
2023-02-08 19:06:53 +01:00
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< li > < a href = "quapy.html#quapy.evaluation.evaluation_report" > evaluation_report() (in module quapy.evaluation)< / a >
2021-11-09 15:50:53 +01:00
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ExpectationMaximizationQuantifier" > ExpectationMaximizationQuantifier (in module quapy.method.aggregative)< / a >
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< li > < a href = "quapy.method.html#quapy.method.aggregative.ExplicitLossMinimisation" > ExplicitLossMinimisation (in module quapy.method.aggregative)< / a >
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< / tr > < / table >
< h2 id = "F" > F< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.error.f1_error" > f1_error() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.f1e" > f1e() (in module quapy.error)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.data.html#quapy.data.datasets.fetch_lequa2022" > fetch_lequa2022() (in module quapy.data.datasets)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.data.html#quapy.data.datasets.fetch_reviews" > fetch_reviews() (in module quapy.data.datasets)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.datasets.fetch_twitter" > fetch_twitter() (in module quapy.data.datasets)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.datasets.fetch_UCIDataset" > fetch_UCIDataset() (in module quapy.data.datasets)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.datasets.fetch_UCILabelledCollection" > fetch_UCILabelledCollection() (in module quapy.data.datasets)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit" > fit() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)< / a >
2021-11-09 15:50:53 +01:00
< ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit" > (quapy.classification.methods.LowRankLogisticRegression method)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.fit" > (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.svmperf.SVMperf.fit" > (quapy.classification.svmperf.SVMperf method)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit" > (quapy.data.preprocessing.IndexTransformer method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.ACC.fit" > (quapy.method.aggregative.ACC method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.fit" > (quapy.method.aggregative.AggregativeQuantifier method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.CC.fit" > (quapy.method.aggregative.CC method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.DistributionMatching.fit" > (quapy.method.aggregative.DistributionMatching method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.DyS.fit" > (quapy.method.aggregative.DyS method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.ELM.fit" > (quapy.method.aggregative.ELM method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.EMQ.fit" > (quapy.method.aggregative.EMQ method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.HDy.fit" > (quapy.method.aggregative.HDy method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll.fit" > (quapy.method.aggregative.OneVsAll method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.PACC.fit" > (quapy.method.aggregative.PACC method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.PCC.fit" > (quapy.method.aggregative.PCC method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SMM.fit" > (quapy.method.aggregative.SMM method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.ThresholdOptimization.fit" > (quapy.method.aggregative.ThresholdOptimization method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.base.BaseQuantifier.fit" > (quapy.method.base.BaseQuantifier method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.base.OneVsAllGeneric.fit" > (quapy.method.base.OneVsAllGeneric method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.fit" > (quapy.method.meta.Ensemble method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.fit" > (quapy.method.neural.QuaNetTrainer method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit" > (quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.model_selection.GridSearchQ.fit" > (quapy.model_selection.GridSearchQ method)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv" > fit_cv() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val" > fit_tr_val() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform" > fit_transform() (quapy.data.preprocessing.IndexTransformer method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward" > forward() (quapy.classification.neural.TextClassifierNet method)< / a >
< ul >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetModule.forward" > (quapy.method.neural.QuaNetModule method)< / a >
< / li >
< / ul > < / li >
< li > < a href = "quapy.data.html#quapy.data.reader.from_csv" > from_csv() (in module quapy.data.reader)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.from_name" > from_name() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.reader.from_sparse" > from_sparse() (in module quapy.data.reader)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.reader.from_text" > from_text() (in module quapy.data.reader)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "G" > G< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator" > get_collator() (quapy.protocol.OnLabelledCollectionProtocol class method)< / a >
2021-12-07 17:16:39 +01:00
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection" > get_labelled_collection() (quapy.protocol.OnLabelledCollectionProtocol method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.html#quapy.functional.get_nprevpoints_approximation" > get_nprevpoints_approximation() (in module quapy.functional)< / a >
< / li >
2021-11-12 14:30:02 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.get_params" > get_params() (quapy.classification.methods.LowRankLogisticRegression method)< / a >
2021-11-09 15:50:53 +01:00
< ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.CNNnet.get_params" > (quapy.classification.neural.CNNnet method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.LSTMnet.get_params" > (quapy.classification.neural.LSTMnet method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params" > (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params" > (quapy.classification.neural.TextClassifierNet method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll.get_params" > (quapy.method.aggregative.OneVsAll method)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.base.OneVsAllGeneric.get_params" > (quapy.method.base.OneVsAllGeneric method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.get_params" > (quapy.method.meta.Ensemble method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.get_params" > (quapy.method.neural.QuaNetTrainer method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.model_selection.GridSearchQ.get_params" > (quapy.model_selection.GridSearchQ method)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.meta.get_probability_distribution" > get_probability_distribution() (in module quapy.method.meta)< / a >
< / li >
< li > < a href = "quapy.html#quapy.util.get_quapy_home" > get_quapy_home() (in module quapy.util)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.aggregative.ACC.getPteCondEstim" > getPteCondEstim() (quapy.method.aggregative.ACC class method)< / a >
< ul >
< li > < a href = "quapy.method.html#quapy.method.aggregative.PACC.getPteCondEstim" > (quapy.method.aggregative.PACC class method)< / a >
< / li >
< / ul > < / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.html#quapy.model_selection.GridSearchQ" > GridSearchQ (class in quapy.model_selection)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "H" > H< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.aggregative.HDy" > HDy (class in quapy.method.aggregative)< / a >
< / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.functional.HellingerDistance" > HellingerDistance() (in module quapy.functional)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.HellingerDistanceY" > HellingerDistanceY (in module quapy.method.aggregative)< / a >
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< / ul > < / td >
< / tr > < / table >
< h2 id = "I" > I< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.preprocessing.index" > index() (in module quapy.data.preprocessing)< / a >
< / li >
2021-12-06 18:25:47 +01:00
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.preprocessing.IndexTransformer" > IndexTransformer (class in quapy.data.preprocessing)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "K" > K< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.kFCV" > kFCV() (quapy.data.base.Dataset class method)< / a >
< ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.kFCV" > (quapy.data.base.LabelledCollection method)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.error.kld" > kld() (in module quapy.error)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "L" > L< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection" > LabelledCollection (class in quapy.data.base)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.load" > load() (quapy.data.base.Dataset class method)< / a >
< ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.load" > (quapy.data.base.LabelledCollection class method)< / a >
< / li >
< / ul > < / li >
2021-11-12 14:30:02 +01:00
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression" > LowRankLogisticRegression (class in quapy.classification.methods)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.neural.LSTMnet" > LSTMnet (class in quapy.classification.neural)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "M" > M< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.error.mae" > mae() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.mae_loss" > mae_loss() (in module quapy.method.neural)< / a >
< / li >
< li > < a href = "quapy.html#quapy.util.map_parallel" > map_parallel() (in module quapy.util)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.MAX" > MAX (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.EMQ.MAX_ITER" > MAX_ITER (quapy.method.aggregative.EMQ attribute)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation" > MaximumLikelihoodPrevalenceEstimation (class in quapy.method.non_aggregative)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.mean_absolute_error" > mean_absolute_error() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.mean_relative_absolute_error" > mean_relative_absolute_error() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.MedianSweep" > MedianSweep (in module quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.MedianSweep2" > MedianSweep2 (in module quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.mkld" > mkld() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.mnkld" > mnkld() (in module quapy.error)< / a >
< / li >
< li >
module
< ul >
< li > < a href = "quapy.html#module-quapy" > quapy< / a >
< / li >
< li > < a href = "quapy.classification.html#module-quapy.classification" > quapy.classification< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.classification.html#module-quapy.classification.calibration" > quapy.classification.calibration< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.classification.html#module-quapy.classification.methods" > quapy.classification.methods< / a >
< / li >
< li > < a href = "quapy.classification.html#module-quapy.classification.neural" > quapy.classification.neural< / a >
< / li >
< li > < a href = "quapy.classification.html#module-quapy.classification.svmperf" > quapy.classification.svmperf< / a >
< / li >
< li > < a href = "quapy.data.html#module-quapy.data" > quapy.data< / a >
< / li >
< li > < a href = "quapy.data.html#module-quapy.data.base" > quapy.data.base< / a >
< / li >
< li > < a href = "quapy.data.html#module-quapy.data.datasets" > quapy.data.datasets< / a >
< / li >
< li > < a href = "quapy.data.html#module-quapy.data.preprocessing" > quapy.data.preprocessing< / a >
< / li >
< li > < a href = "quapy.data.html#module-quapy.data.reader" > quapy.data.reader< / a >
< / li >
< li > < a href = "quapy.html#module-quapy.error" > quapy.error< / a >
< / li >
< li > < a href = "quapy.html#module-quapy.evaluation" > quapy.evaluation< / a >
< / li >
< li > < a href = "quapy.html#module-quapy.functional" > quapy.functional< / a >
< / li >
< li > < a href = "quapy.method.html#module-quapy.method" > quapy.method< / a >
< / li >
< li > < a href = "quapy.method.html#module-quapy.method.aggregative" > quapy.method.aggregative< / a >
< / li >
< li > < a href = "quapy.method.html#module-quapy.method.base" > quapy.method.base< / a >
< / li >
< li > < a href = "quapy.method.html#module-quapy.method.meta" > quapy.method.meta< / a >
< / li >
< li > < a href = "quapy.method.html#module-quapy.method.neural" > quapy.method.neural< / a >
< / li >
< li > < a href = "quapy.method.html#module-quapy.method.non_aggregative" > quapy.method.non_aggregative< / a >
< / li >
< li > < a href = "quapy.html#module-quapy.model_selection" > quapy.model_selection< / a >
< / li >
< li > < a href = "quapy.html#module-quapy.plot" > quapy.plot< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.html#module-quapy.protocol" > quapy.protocol< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.html#module-quapy.util" > quapy.util< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.error.mrae" > mrae() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.MS" > MS (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.MS2" > MS2 (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.mse" > mse() (in module quapy.error)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "N" > N< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.n_classes" > n_classes (quapy.data.base.Dataset property)< / a >
< ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.n_classes" > (quapy.data.base.LabelledCollection property)< / a >
< / li >
< / ul > < / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.calibration.NBVSCalibration" > NBVSCalibration (class in quapy.classification.calibration)< / a >
2021-11-09 15:50:53 +01:00
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer" > NeuralClassifierTrainer (class in quapy.classification.neural)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.error.nkld" > nkld() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.html#quapy.functional.normalize_prevalence" > normalize_prevalence() (in module quapy.functional)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.html#quapy.protocol.NPP" > NPP (class in quapy.protocol)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.html#quapy.functional.num_prevalence_combinations" > num_prevalence_combinations() (in module quapy.functional)< / a >
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< / ul > < / td >
< / tr > < / table >
< h2 id = "O" > O< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances" > on_preclassified_instances() (quapy.protocol.OnLabelledCollectionProtocol method)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll" > OneVsAll (class in quapy.method.aggregative)< / a >
2023-02-08 19:06:53 +01:00
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< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.base.OneVsAllGeneric" > OneVsAllGeneric (class in quapy.method.base)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.OnLabelledCollectionProtocol" > OnLabelledCollectionProtocol (class in quapy.protocol)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "P" > P< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.p" > p (quapy.data.base.LabelledCollection property)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.aggregative.PACC" > PACC (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.html#quapy.util.parallel" > parallel() (in module quapy.util)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.PCC" > PCC (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.html#quapy.util.pickled_resource" > pickled_resource() (in module quapy.util)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict" > predict() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)< / a >
2021-11-09 15:50:53 +01:00
< ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict" > (quapy.classification.methods.LowRankLogisticRegression method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict" > (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.svmperf.SVMperf.predict" > (quapy.classification.svmperf.SVMperf method)< / a >
< / li >
< / ul > < / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba" > predict_proba() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)< / a >
2021-11-09 15:50:53 +01:00
< ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba" > (quapy.classification.methods.LowRankLogisticRegression method)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict_proba" > (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba" > (quapy.classification.neural.TextClassifierNet method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.EMQ.predict_proba" > (quapy.method.aggregative.EMQ method)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.evaluation.prediction" > prediction() (in module quapy.evaluation)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.prevalence" > prevalence() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.functional.prevalence_from_labels" > prevalence_from_labels() (in module quapy.functional)< / a >
< / li >
< li > < a href = "quapy.html#quapy.functional.prevalence_from_probabilities" > prevalence_from_probabilities() (in module quapy.functional)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.APP.prevalence_grid" > prevalence_grid() (quapy.protocol.APP method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.functional.prevalence_linspace" > prevalence_linspace() (in module quapy.functional)< / a >
2021-11-09 15:50:53 +01:00
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.probabilistic" > probabilistic (quapy.method.meta.Ensemble property)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount" > ProbabilisticAdjustedClassifyAndCount (in module quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount" > ProbabilisticClassifyAndCount (in module quapy.method.aggregative)< / a >
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< / ul > < / td >
< / tr > < / table >
< h2 id = "Q" > Q< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetModule" > QuaNetModule (class in quapy.method.neural)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer" > QuaNetTrainer (class in quapy.method.neural)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.quantify" > quantify() (quapy.method.aggregative.AggregativeQuantifier method)< / a >
2021-11-09 15:50:53 +01:00
< ul >
< li > < a href = "quapy.method.html#quapy.method.base.BaseQuantifier.quantify" > (quapy.method.base.BaseQuantifier method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify" > (quapy.method.base.OneVsAllGeneric method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.quantify" > (quapy.method.meta.Ensemble method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.quantify" > (quapy.method.neural.QuaNetTrainer method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify" > (quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.model_selection.GridSearchQ.quantify" > (quapy.model_selection.GridSearchQ method)< / a >
< / li >
< / ul > < / li >
< li >
quapy
< ul >
< li > < a href = "quapy.html#module-quapy" > module< / a >
< / li >
< / ul > < / li >
< li >
quapy.classification
< ul >
< li > < a href = "quapy.classification.html#module-quapy.classification" > module< / a >
2023-02-08 19:06:53 +01:00
< / li >
< / ul > < / li >
< li >
quapy.classification.calibration
< ul >
< li > < a href = "quapy.classification.html#module-quapy.classification.calibration" > module< / a >
2021-11-09 15:50:53 +01:00
< / li >
< / ul > < / li >
< li >
quapy.classification.methods
< ul >
< li > < a href = "quapy.classification.html#module-quapy.classification.methods" > module< / a >
< / li >
< / ul > < / li >
< li >
quapy.classification.neural
< ul >
< li > < a href = "quapy.classification.html#module-quapy.classification.neural" > module< / a >
< / li >
< / ul > < / li >
< li >
quapy.classification.svmperf
< ul >
< li > < a href = "quapy.classification.html#module-quapy.classification.svmperf" > module< / a >
< / li >
< / ul > < / li >
< li >
quapy.data
< ul >
< li > < a href = "quapy.data.html#module-quapy.data" > module< / a >
< / li >
< / ul > < / li >
< li >
quapy.data.base
< ul >
< li > < a href = "quapy.data.html#module-quapy.data.base" > module< / a >
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< / ul > < / li >
< li >
quapy.data.datasets
< ul >
< li > < a href = "quapy.data.html#module-quapy.data.datasets" > module< / a >
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< / ul > < / li >
< li >
quapy.data.preprocessing
< ul >
< li > < a href = "quapy.data.html#module-quapy.data.preprocessing" > module< / a >
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< td style = "width: 33%; vertical-align: top;" > < ul >
< li >
quapy.data.reader
< ul >
< li > < a href = "quapy.data.html#module-quapy.data.reader" > module< / a >
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< / ul > < / li >
< li >
quapy.error
< ul >
< li > < a href = "quapy.html#module-quapy.error" > module< / a >
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< / ul > < / li >
< li >
quapy.evaluation
< ul >
< li > < a href = "quapy.html#module-quapy.evaluation" > module< / a >
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< / ul > < / li >
< li >
quapy.functional
< ul >
< li > < a href = "quapy.html#module-quapy.functional" > module< / a >
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< / ul > < / li >
< li >
quapy.method
< ul >
< li > < a href = "quapy.method.html#module-quapy.method" > module< / a >
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< / ul > < / li >
< li >
quapy.method.aggregative
< ul >
< li > < a href = "quapy.method.html#module-quapy.method.aggregative" > module< / a >
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< / ul > < / li >
< li >
quapy.method.base
< ul >
< li > < a href = "quapy.method.html#module-quapy.method.base" > module< / a >
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< / ul > < / li >
< li >
quapy.method.meta
< ul >
< li > < a href = "quapy.method.html#module-quapy.method.meta" > module< / a >
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< / ul > < / li >
< li >
quapy.method.neural
< ul >
< li > < a href = "quapy.method.html#module-quapy.method.neural" > module< / a >
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< / ul > < / li >
< li >
quapy.method.non_aggregative
< ul >
< li > < a href = "quapy.method.html#module-quapy.method.non_aggregative" > module< / a >
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< / ul > < / li >
< li >
quapy.model_selection
< ul >
< li > < a href = "quapy.html#module-quapy.model_selection" > module< / a >
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< / ul > < / li >
< li >
quapy.plot
< ul >
< li > < a href = "quapy.html#module-quapy.plot" > module< / a >
2023-02-08 19:06:53 +01:00
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< / ul > < / li >
< li >
quapy.protocol
< ul >
< li > < a href = "quapy.html#module-quapy.protocol" > module< / a >
2021-11-09 15:50:53 +01:00
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< / ul > < / li >
< li >
quapy.util
< ul >
< li > < a href = "quapy.html#module-quapy.util" > module< / a >
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< / tr > < / table >
< h2 id = "R" > R< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.error.rae" > rae() (in module quapy.error)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.random_state" > random_state (quapy.protocol.AbstractStochasticSeededProtocol property)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier" > RecalibratedProbabilisticClassifier (class in quapy.classification.calibration)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" > RecalibratedProbabilisticClassifierBase (class in quapy.classification.calibration)< / a >
2021-11-09 15:50:53 +01:00
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< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.preprocessing.reduce_columns" > reduce_columns() (in module quapy.data.preprocessing)< / a >
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.reader.reindex_labels" > reindex_labels() (in module quapy.data.reader)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.relative_absolute_error" > relative_absolute_error() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params" > reset_net_params() (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.html#quapy.protocol.OnLabelledCollectionProtocol.RETURN_TYPES" > RETURN_TYPES (quapy.protocol.OnLabelledCollectionProtocol attribute)< / a >
2021-11-09 15:50:53 +01:00
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< / ul > < / td >
< / tr > < / table >
< h2 id = "S" > S< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample" > sample() (quapy.protocol.AbstractStochasticSeededProtocol method)< / a >
< ul >
< li > < a href = "quapy.html#quapy.protocol.APP.sample" > (quapy.protocol.APP method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.DomainMixer.sample" > (quapy.protocol.DomainMixer method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.NPP.sample" > (quapy.protocol.NPP method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.USimplexPP.sample" > (quapy.protocol.USimplexPP method)< / a >
< / li >
< / ul > < / li >
< li > < a href = "quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters" > samples_parameters() (quapy.protocol.AbstractStochasticSeededProtocol method)< / a >
< ul >
< li > < a href = "quapy.html#quapy.protocol.APP.samples_parameters" > (quapy.protocol.APP method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.DomainMixer.samples_parameters" > (quapy.protocol.DomainMixer method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.NPP.samples_parameters" > (quapy.protocol.NPP method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.USimplexPP.samples_parameters" > (quapy.protocol.USimplexPP method)< / a >
< / li >
< / ul > < / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.sampling" > sampling() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index" > sampling_from_index() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.sampling_index" > sampling_index() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.util.save_text_file" > save_text_file() (in module quapy.util)< / a >
< / li >
< li > < a href = "quapy.html#quapy.error.se" > se() (in module quapy.error)< / a >
< / li >
2021-11-12 14:30:02 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.set_params" > set_params() (quapy.classification.methods.LowRankLogisticRegression method)< / a >
2021-11-09 15:50:53 +01:00
< ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.set_params" > (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.svmperf.SVMperf.set_params" > (quapy.classification.svmperf.SVMperf method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.OneVsAll.set_params" > (quapy.method.aggregative.OneVsAll method)< / a >
< / li >
2023-02-08 19:06:53 +01:00
< li > < a href = "quapy.method.html#quapy.method.base.OneVsAllGeneric.set_params" > (quapy.method.base.OneVsAllGeneric method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.set_params" > (quapy.method.meta.Ensemble method)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetTrainer.set_params" > (quapy.method.neural.QuaNetTrainer method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.model_selection.GridSearchQ.set_params" > (quapy.model_selection.GridSearchQ method)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
2021-12-15 15:27:43 +01:00
< li > < a href = "quapy.method.html#quapy.method.aggregative.SLD" > SLD (in module quapy.method.aggregative)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SMM" > SMM (class in quapy.method.aggregative)< / a >
2021-12-15 15:27:43 +01:00
< / li >
2021-11-09 15:50:53 +01:00
< li > < a href = "quapy.html#quapy.error.smooth" > smooth() (in module quapy.error)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.ACC.solve_adjustment" > solve_adjustment() (quapy.method.aggregative.ACC class method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.split_random" > split_random() (quapy.data.base.LabelledCollection method)< / a >
2021-11-09 15:50:53 +01:00
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.split_stratified" > split_stratified() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.SplitStratified" > SplitStratified() (quapy.data.base.Dataset class method)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.preprocessing.standardize" > standardize() (in module quapy.data.preprocessing)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.stats" > stats() (quapy.data.base.Dataset method)< / a >
< ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.stats" > (quapy.data.base.LabelledCollection method)< / a >
< / li >
< / ul > < / li >
< li > < a href = "quapy.html#quapy.functional.strprev" > strprev() (in module quapy.functional)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SVMAE" > SVMAE (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SVMKLD" > SVMKLD (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SVMNKLD" > SVMNKLD (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.svmperf.SVMperf" > SVMperf (class in quapy.classification.svmperf)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SVMQ" > SVMQ (class in quapy.method.aggregative)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.aggregative.SVMRAE" > SVMRAE (class in quapy.method.aggregative)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
< h2 id = "T" > T< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.aggregative.T50" > T50 (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.html#quapy.util.temp_seed" > temp_seed() (in module quapy.util)< / a >
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< li > < a href = "quapy.data.html#quapy.data.preprocessing.text2tfidf" > text2tfidf() (in module quapy.data.preprocessing)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet" > TextClassifierNet (class in quapy.classification.neural)< / a >
< / li >
2021-12-15 15:27:43 +01:00
< li > < a href = "quapy.method.html#quapy.method.aggregative.ThresholdOptimization" > ThresholdOptimization (class in quapy.method.aggregative)< / a >
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< li > < a href = "quapy.html#quapy.functional.TopsoeDistance" > TopsoeDistance() (in module quapy.functional)< / a >
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< / li >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.TorchDataset" > TorchDataset (class in quapy.classification.neural)< / a >
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< / li >
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< li > < a href = "quapy.html#quapy.protocol.AbstractProtocol.total" > total() (quapy.protocol.AbstractProtocol method)< / a >
< ul >
< li > < a href = "quapy.html#quapy.protocol.APP.total" > (quapy.protocol.APP method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.DomainMixer.total" > (quapy.protocol.DomainMixer method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.protocol.NPP.total" > (quapy.protocol.NPP method)< / a >
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< li > < a href = "quapy.html#quapy.protocol.USimplexPP.total" > (quapy.protocol.USimplexPP method)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.train_test" > train_test (quapy.data.base.Dataset property)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.CNNnet.training" > training (quapy.classification.neural.CNNnet attribute)< / a >
< ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.LSTMnet.training" > (quapy.classification.neural.LSTMnet attribute)< / a >
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< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.training" > (quapy.classification.neural.TextClassifierNet attribute)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.neural.QuaNetModule.training" > (quapy.method.neural.QuaNetModule attribute)< / a >
< / li >
< / ul > < / li >
2021-11-12 14:30:02 +01:00
< li > < a href = "quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform" > transform() (quapy.classification.methods.LowRankLogisticRegression method)< / a >
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< ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.transform" > (quapy.classification.neural.NeuralClassifierTrainer method)< / a >
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< li > < a href = "quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform" > (quapy.data.preprocessing.IndexTransformer method)< / a >
< / li >
< / ul > < / li >
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< li > < a href = "quapy.classification.html#quapy.classification.calibration.TSCalibration" > TSCalibration (class in quapy.classification.calibration)< / a >
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< / tr > < / table >
< h2 id = "U" > U< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.html#quapy.functional.uniform_prevalence_sampling" > uniform_prevalence_sampling() (in module quapy.functional)< / a >
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< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling" > uniform_sampling() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index" > uniform_sampling_index() (quapy.data.base.LabelledCollection method)< / a >
< / li >
< li > < a href = "quapy.html#quapy.functional.uniform_simplex_sampling" > uniform_simplex_sampling() (in module quapy.functional)< / a >
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< li > < a href = "quapy.html#quapy.protocol.USimplexPP" > USimplexPP (class in quapy.protocol)< / a >
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< / tr > < / table >
< h2 id = "V" > V< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.classification.html#quapy.classification.svmperf.SVMperf.valid_losses" > valid_losses (quapy.classification.svmperf.SVMperf attribute)< / a >
< / li >
< li > < a href = "quapy.method.html#quapy.method.meta.Ensemble.VALID_POLICIES" > VALID_POLICIES (quapy.method.meta.Ensemble attribute)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.CNNnet.vocabulary_size" > vocabulary_size (quapy.classification.neural.CNNnet property)< / a >
< ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.LSTMnet.vocabulary_size" > (quapy.classification.neural.LSTMnet property)< / a >
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.vocabulary_size" > (quapy.classification.neural.TextClassifierNet property)< / a >
< / li >
< li > < a href = "quapy.data.html#quapy.data.base.Dataset.vocabulary_size" > (quapy.data.base.Dataset property)< / a >
< / li >
< / ul > < / li >
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size" > vocabulary_size() (quapy.data.preprocessing.IndexTransformer method)< / a >
2023-02-08 19:06:53 +01:00
< / li >
< li > < a href = "quapy.classification.html#quapy.classification.calibration.VSCalibration" > VSCalibration (class in quapy.classification.calibration)< / a >
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< / ul > < / td >
< / tr > < / table >
< h2 id = "W" > W< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.datasets.warn" > warn() (in module quapy.data.datasets)< / a >
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< / ul > < / td >
< / tr > < / table >
< h2 id = "X" > X< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.method.html#quapy.method.aggregative.X" > X (class in quapy.method.aggregative)< / a >
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< ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.X" > (quapy.data.base.LabelledCollection property)< / a >
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< / li >
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< / ul > < / li >
2021-11-09 15:50:53 +01:00
< / ul > < / td >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform" > xavier_uniform() (quapy.classification.neural.TextClassifierNet method)< / a >
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< / li >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.Xp" > Xp (quapy.data.base.LabelledCollection property)< / a >
2021-11-09 15:50:53 +01:00
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< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.Xy" > Xy (quapy.data.base.LabelledCollection property)< / a >
< / li >
< / ul > < / td >
< / tr > < / table >
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< h2 id = "Y" > Y< / h2 >
< table style = "width: 100%" class = "indextable genindextable" > < tr >
< td style = "width: 33%; vertical-align: top;" > < ul >
< li > < a href = "quapy.data.html#quapy.data.base.LabelledCollection.y" > y (quapy.data.base.LabelledCollection property)< / a >
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< / ul > < / td >
< / tr > < / table >
2021-11-09 15:50:53 +01:00
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< div class = "sphinxsidebar" role = "navigation" aria-label = "main navigation" >
< div class = "sphinxsidebarwrapper" >
< div id = "searchbox" style = "display: none" role = "search" >
< h3 id = "searchlabel" > Quick search< / h3 >
< div class = "searchformwrapper" >
< form class = "search" action = "search.html" method = "get" >
< input type = "text" name = "q" aria-labelledby = "searchlabel" autocomplete = "off" autocorrect = "off" autocapitalize = "off" spellcheck = "false" / >
< input type = "submit" value = "Go" / >
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< script > document . getElementById ( 'searchbox' ) . style . display = "block" < / script >
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< li class = "right" >
< a href = "py-modindex.html" title = "Python Module Index"
>modules< / a > |< / li >
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