com.github.ozancicek.artan.ml.smoother
LinearKalmanSmoother
Related Doc:
package smoother
Param for column name to specify control vector.
Param for column name to specify control vector.
Param for column name to specify control matrix
Param for column name to specify control matrix
Param for event time column name, which marks the event time of the received measurements.
Param for event time column name, which marks the event time of the received measurements. If set, the measurements will be processed in ascending order according to event time.
Param for controlling the weight of older measurements, should be >= 1.0 .
Param for controlling the weight of older measurements, should be >= 1.0 . A factor of 1.0 will result in equal weight of all measurements. With larger factor, the weight of recent measurements will increase. Typically set around 1.01 ~ 1.5
Default is 1.0
Getter for control vector column
Getter for control vector column
Getter for control function matrix column
Getter for control function matrix column
Getter for event time column parameter
Getter for event time column parameter
Getter for fading factor param
Getter for fading factor param
Getter for the initial covariance matrix param
Getter for the initial covariance matrix param
Getter for initial state covariance matrix column
Getter for initial state covariance matrix column
Getter for the initial value of the state vector
Getter for the initial value of the state vector
Getter for initial state vector column
Getter for initial state vector column
Getter for measurement vector column.
Getter for measurement vector column.
Getter for measurement model matrix param
Getter for measurement model matrix param
Getter for measurement model column
Getter for measurement model column
Getter for measurement noise matrix param.
Getter for measurement noise matrix param.
Getter for measurement noise matrix column
Getter for measurement noise matrix column
Getter for system matrices output flag
Getter for system matrices output flag
Getter for the process model matrix param
Getter for the process model matrix param
Getter for process model matrix column.
Getter for process model matrix column.
Getter for process noise covariance matrix param
Getter for process noise covariance matrix param
Getter for process noise matrix column.
Getter for process noise matrix column.
Getter for state key column name parameter
Getter for state key column name parameter
Getter for state key column
Getter for state key column
Getter for state timeout duration parameter
Getter for state timeout duration parameter
Getter for timeout mode
Getter for timeout mode
TimeoutMode
Getter for watermark duration parameter
Getter for watermark duration parameter
Param for the initial covariance matrix
Param for the initial covariance matrix
Param for initial state covariance matrix as a dataframe column.
Param for initial state covariance matrix as a dataframe column. Overrides initialCovariance param. Can be used for initializing separate state covariance matrix for each state.
Param for initial value of the state vector
Param for initial value of the state vector
Param for initial state vector as a dataframe column.
Param for initial state vector as a dataframe column. Overrides initialState param. Can be used for initializing separate state vector for each state.
Param for measurement column containing measurement vector.
Param for measurement column containing measurement vector.
Param for measurement model matrix.
Param for measurement model matrix. Its dot-product with state should produce the measurement vector.
Param for column name to specify measurement model from input DataFrame rather than a constant measurement model for all filters.
Param for column name to specify measurement model from input DataFrame rather than a constant measurement model for all filters. Overrides measurementModel param.
Param for measurement noise covariance matrix, should be a square matrix with dimensions measurementSize x measurementSize.
Param for measurement noise covariance matrix, should be a square matrix with dimensions measurementSize x measurementSize.
Param for column name to specify measurement noise from input DataFrame rather than a constant measurement noise for all filters.
Param for column name to specify measurement noise from input DataFrame rather than a constant measurement noise for all filters. Overrides measurementNoise param
size of the measurement vector
size of the measurement vector
Param for enabling output of system matrices.
Param for enabling output of system matrices. When true, system matrices will be also added to output DataFrame. Default is false
Param for the process model matrix, transitions the state to the next state with dot product.
Param for the process model matrix, transitions the state to the next state with dot product.
Param for column name to specify process model from input DataFrame rather than a constant process model for all filters.
Param for column name to specify process model from input DataFrame rather than a constant process model for all filters. Overrides processModel param.
Param for process noise covariance matrix, should be a square matrix with dimensions stateSize x stateSize.
Param for process noise covariance matrix, should be a square matrix with dimensions stateSize x stateSize.
Default is identity matrix.
Param for column name to specify process noise matrix from input DataFrame rather than a constant process noise for all filters.
Param for column name to specify process noise matrix from input DataFrame rather than a constant process noise for all filters. Overrides processNoise param
Set the column for input control vectors.
Set the column for input control vectors.
Control vectors should have compatible size with control function (controlVectorSize). The product of control matrix & vector should produce a vector with stateSize. null values are allowed, which will result in state transition without control input.
Set the column for input control matrices.
Set the column for input control matrices.
Control matrices should have dimensions (stateSize, controlVectorSize). null values are allowed, which will result in state transition without control input
Sets the event time column in the input DataFrame for event time based state timeout.
Sets the event time column in the input DataFrame for event time based state timeout.
Fading factor for giving more weights to more recent measurements.
Fading factor for giving more weights to more recent measurements. If needed, it should be greater than one. Typically set around 1.01 ~ 1.05. Default is 1.0, which will result in equally weighted measurements.
Sets the smoother fixed lag.
Sets the smoother fixed lag.
Default is 2.
Set the initial covariance matrix with dimensions (stateSize, stateSize)
Set the initial covariance matrix with dimensions (stateSize, stateSize)
It will be applied to all states. If the state timeouts and starts receiving measurements after timeout, it will again start from this initial covariance vector. Default is identity matrix.
Set the column corresponding to initial covariance matrix.
Set the column corresponding to initial covariance matrix.
The matrices in the column should be of dimensions (stateSize, statesize).
Set the initial state vector with size (stateSize).
Set the initial state vector with size (stateSize).
It will be applied to all states. If the state timeouts and starts receiving measurements after timeout, it will again start from this initial state vector. Default is zero. For different initial state vector across filters or measurements, set the dataframe column with setInitialStateCol
Set the column corresponding to initial state vector.
Set the column corresponding to initial state vector.
The vectors in the column should be of size (stateSize).
Set the column corresponding to measurements.
Set the column corresponding to measurements.
The vectors in the column should be of size (measurementSize). null values are allowed, which will result in only state prediction step.
Set default value for measurement model matrix with dimensions (stateSize, measurementSize) which maps states to measurement.
Set default value for measurement model matrix with dimensions (stateSize, measurementSize) which maps states to measurement.
Note that if this parameter is set through here, it will result in same measurement model for all filters & measurements. For different measurement models across filters or measurements, set a dataframe column for measurement model from setMeasurementModelCol.
Default value maps the first state value to measurements.
Set the column for input measurement model matrices
Set the column for input measurement model matrices
Measurement model matrices should have dimensions (stateSize, measurementSize)
Set default value for measurement noise matrix with dimensions (measurementSize, measurementSize).
Set default value for measurement noise matrix with dimensions (measurementSize, measurementSize).
Note that if this parameter is set through here, it will result in same measurement noise for all filters & measurements. For different measurement noise values across filters or measurements, set a dataframe column for measurement noise from setMeasurementNoiseCol.
Default is identity matrix.
Set the column for input measurement noise matrices.
Set the column for input measurement noise matrices.
Measurement noise matrices should have dimensions (measurementSize, measurementSize)
Enable outputting system matrices
Enable outputting system matrices
Default is false
Set default value for process model matrix with dimensions (stateSize, stateSize) which governs state transition.
Set default value for process model matrix with dimensions (stateSize, stateSize) which governs state transition.
Note that if this parameter is set through here, it will result in same process model for all filters & measurements. For different process models across filters or measurements, set a dataframe column for process model from setProcessModelCol.
Default is identity matrix.
Set the column for input process model matrices.
Set the column for input process model matrices.
Process model matrices should have dimensions (stateSize, stateSize)
Set default value for process noise matrix with dimensions (stateSize, stateSize).
Set default value for process noise matrix with dimensions (stateSize, stateSize).
Note that if this parameter is set through here, it will result in same process noise for all filters & measurements. For different process noise values across filters or measurements, set a dataframe column for process noise from setProcessNoiseCol.
Default is identity matrix.
Set the column for input process noise matrices.
Set the column for input process noise matrices.
Process noise matrices should have dimensions (stateSize, stateSize)
Sets the state key column.
Sets the state key column. Each value in the column should uniquely identify a stateful transformer. Each unique value will result in a separate state.
Sets the state timeout duration for all states, only valid when state timeout mode is not 'none'.
Sets the state timeout duration for all states, only valid when state timeout mode is not 'none'. Must be a valid duration string, such as '10 minutes'.
Sets the state timeout mode.
Sets the state timeout mode. Supported values are 'none', 'process' and 'event'. Enabling state timeout will clear the state after a certain timeout duration which can be set. If a state receives measurements after it times out, the state will be initialized as if it received no measurements.
- 'none': No state timeout, state is kept indefinitely.
- 'process': Process time based state timeout, state will be cleared if no measurements are received for a duration based on processing time. Effects all states. Timeout duration must be set with setStateTimeoutDuration.
- 'event': Event time based state timeout, state will be cleared if no measurements are recieved for a duration based on event time determined by watermark. Effects all states. Timeout duration must be set with setStateTimeoutDuration. Additionally, event time column and it's watermark duration must be set with setEventTimeCol and setWatermarkDuration. Note that this will result in dropping measurements occuring later than the watermark.
Default is 'none'
Set the watermark duration for all states, only valid when state timeout mode is 'event'.
Set the watermark duration for all states, only valid when state timeout mode is 'event'. Must be a valid duration string, such as '10 minutes'.
Param for state key column.
Param for state key column. State keys uniquely identify the each state in stateful transformers, thus controlling the number of states and the degree of parallelization"
size of the state vector
size of the state vector
Param for state timeout duration.
Param for state timeout duration.
Param for timeout mode, controlling the eviction of states which receive no measurement for a certain duration
Param for timeout mode, controlling the eviction of states which receive no measurement for a certain duration
Param for watermark duration as string, measured from the eventTimeCol column.
Param for watermark duration as string, measured from the eventTimeCol column. If set, measurements will be processed in append mode with the specified watermark duration.
Fixed lag linear kalman smoother using Rauch-Tung-Striebel method. The smoother is implemented with a stateful spark transformer for running parallel smoothers /w spark dataframes. Transforms an input dataframe of noisy measurements to dataframe of state estimates using stateful spark transformations, which can be used in both streaming and batch applications.
At a time step k and a fixed lag N, the fixed lag linear kalman smoother computes the state estimates of a linear kalman filter based on all measurements made between step k and step k-t. For each time step k >= N, the smoother outputs an estimate for all the time steps between k and k-N. When k < N, the smoother doesn't output any estimates. As a result, the memory requirements of this filter is N times of a linear kalman filter. Since the smoother outputs multiple estimates for a single measurement, it is advised to set event time column of the measurements with setEventTimeCol.
Except fixedLag parameter, LinearKalmanSmoother has the same parameters with LinearKalmanFilter