AnomalyDetector

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. def apply[T](featureShape: Shape, hiddenLayers: Array[Int] = Array(8, 32, 15), dropouts: Array[Double] = Array(0.2, 0.2, 0.2))(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AnomalyDetector[T]

   

   The factory method to create an anomaly detector for single time series

   The factory method to create an anomaly detector for single time series

   T

   Numeric type of parameter(e.g. weight, bias). Only support float/double now.

   featureShape

   The input shape of features.

   hiddenLayers

   Units of hidden layers of LSTM.

   dropouts

   Fraction of the input units to drop out. Float between 0 and 1.

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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7. def detectAnomalies[T](yTruth: RDD[T], yPredict: RDD[T], threshold: Float)(implicit arg0: ClassTag[T]): RDD[(T, T, Any)]

   

   Compare predictions and truth to detect anomalies by ranking the absolute differences.

   Compare predictions and truth to detect anomalies by ranking the absolute differences. Most distant values are considered as anomalies.

   yTruth

   Truth to be compared

   yPredict

   Predictions

   threshold

   Float. The threshold of absolute difference, data points with a difference above the threshold is considered as anomalies.

   returns

   RDD[(yTruth, yPredict, anomaly)], anomaly is null or yTruth

8. def detectAnomalies[T](yTruth: RDD[T], yPredict: RDD[T], anomalySize: Int = 5)(implicit arg0: ClassTag[T]): RDD[(T, T, Any)]

   

   Compare predictions and truth to detect anomalies by ranking the absolute differences。 Most distant values are considered as anomalies.

   Compare predictions and truth to detect anomalies by ranking the absolute differences。 Most distant values are considered as anomalies.

   yTruth

   RDD[T]. Truth to be compared

   yPredict

   RDD[T]. Predictions

   anomalySize

   Int. The size to be considered as anomalies.

9. final def eq(arg0: AnyRef): Boolean

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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12. final def getClass(): Class[_]

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. def loadModel[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AnomalyDetector[T]

    

    Load an existing AnomalyDetector model (with weights).

    Load an existing AnomalyDetector model (with weights).

    T

    Numeric type of parameter(e.g. weight, bias). Only support float/double now.

    path

    The path for the pre-defined model. Local file system, HDFS and Amazon S3 are supported. HDFS path should be like "hdfs://[host]:[port]/xxx". Amazon S3 path should be like "s3a://bucket/xxx".

    weightPath

    The path for pre-trained weights if any. Default is null.

16. final def ne(arg0: AnyRef): Boolean

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. final def synchronized[T0](arg0: ⇒ T0): T0

    

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20. def toSampleRdd[T](rdd: RDD[FeatureLabelIndex[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): RDD[Sample[T]]

    

21. def toString(): String

    

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22. def unroll[T](dataRdd: RDD[Array[T]], unrollLength: Int, predictStep: Int = 1)(implicit arg0: ClassTag[T]): RDD[FeatureLabelIndex[T]]

    

    Unroll a rdd of arrays to prepare features and labels.

    Unroll a rdd of arrays to prepare features and labels.

    dataRdd

    RDD[Array[T]]. Features to be unrolled.

    unrollLength

    Int. The length of precious values to predict future value.

    predictStep

    Int. how many time steps to predict future value, default is 1. a simple example data: (1,2,3,4,5,6); unrollLength: 2, predictStep: 1 features, label, index (1,2), 3, 0 (2,3), 4, 1 (3,4), 5, 2 (4,5), 6, 3

23. final def wait(): Unit

    

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24. final def wait(arg0: Long, arg1: Int): Unit

    

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25. final def wait(arg0: Long): Unit

    

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