All Classes and Interfaces
Class
Description
Class for creating and handling constraint navigator timeseries data.
Class for creating and handling constraint navigator hub.
Class for creating and handling constraint navigator inference.
Class for creating and handling model context.
Class for creating and handling constraint navigator setup.
Interprets the column values and splits them into different categories
Base error type, generated by the engine.
Information about a feature.
Parts of the formula describing the calculation while inference.
Specification for a specific signal needed at the inference.
Information on a specific signal in the data.
Interprets the signal values and derives aspects from them.
Information about one aspect of the target.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Returns the id of the function with the largest result.
Failed to validate argument: {error}
Category-probability pair with boolean-valued category.
Single cell with row id and boolean value.
Single data point with timestamp and boolean value.
Single data point with timestamp, availability and boolean value.
Enhanced data point with timestamp and list of boolean-valued category-probability pairs.
Interprets the different column values as categories, which have no order.
Interprets the different signal values as categories, which have no order.
A CategoricalEqualsFunction applied to a categorical function yields a 0/1-function for one of the function's categories.
Handling of a specific column.
Column with ID {id} is expected to be any data type of {expected} but was of data type {actual}
Column ID {id} is already in use
Column with ID {id} has ambiguous interpreters
Failed to find column with ID {id}
Returns the constant value provided.
Model with model ID {id} is not differentiable.
The cos function.
Interprets the signal as a cyclic variable, based on their residue after division.
Failed to modify data with handle '{handle}' due to concurrent usage
Limits the use of the data based on certain criteria.
Time range of a specific signal.
Data with handle '{handle}' does not contain any training signals after data removal
Data with handle '{handle}' does not contain any training signals
Information about the data.
Interprets float columns via DtoNumericalColumnInterpreter and string and boolean columns via DtoCategoricalColumnInterpreter.
Interprets float signals via DtoNumericalSignalInterpreter and string and boolean signals via DtoCategoricalSignalInterpreter.
Lower and upper threshold signals being used to obtain lower and upper threshold when evaluating these signals at timestamps of interest.
Named parameters used in error messages.
Exponential function.
Dependency information of synthesized signal.
Result of expression {expression} is expected to be of data type {expected} but was of type {actual} at timestamp '{timestamp}'
Failed to evaluate expression {expression} at timestamp '{timestamp}': {error}
Failed to compile expression {expression}: {error}
Limits the use of features based on certain criteria.
Feature value with featureId and its value.
Quadruple with id of feature, observed value of feature, next normal value of feature, rating.
Category-probability pair with float-valued category.
Single cell with row id and floating point value.
Tuple with id of model and evaluation of model.
Enhanced data point with timestamp and list of DtoFloatConstraintValue.
Enhanced data point with timestamp, DtoFloatCostValueWithNextNormal, list of DtoFloatConstraintValueWithNextNormal and list of DtoFeatureValueWithNextNormal.
Triple with id of model, observed value of model, next normal value of model.
Tuple with cost_type and value of the cost function.
Triplet with cost_type, value of the cost function before and after optimization.
Single data point with timestamp and floating point value.
Single data point with timestamp, availability and floating point value.
Enhanced data point with timestamp and list of float-valued category-probability pairs.
Enhanced data point with timestamp, floating point value, list of DtoFeatureValueWithNextNormal.
Input configuration needed to make constraint navigator inference with next normal.
Deprecated.
Experimental: Might change in future releases.
Gaussian cummulative distribution function.
Failed to dereference handle '{handle}'
Handle '{handle}' is expected to be of type '{expected}' but was of type '{actual}'
Necessary input configuration to build a model hub.
Information from model hub creation relevant for report generation.
Assigns each input value a result based on a dictionary.
Assigns each input value a result based on a dictionary and its probability.
Identity function.
Insufficient data quality of signals at label timestamps
DtoIncremental data does not match remaining model data: {error}
Necessary input configuration to make inferences using a hub model.
Inference data is expected to include information on signal {id} at or before timestamp {timestamp}
Specification for the data needed at the inference.
Inference data specification for signal with ID {id} is expected to be of data type {expected} but was of data type {actual}
Inference output specification is expected to be of target interpreter type {expected} but was {actual}
Inference output specification is expected to be of data type {expected} but was of data type {actual}
Specification for a specific signal needed at the inference, whose values were added directly.
Data type of signal {id} is ambiguous in inference data specification
Specification for a specific signal needed at the inference, whose values were calculated by an expression (synthesized).
Internal engine failure: {error}
Interval used to exclude/include data.
Interval used to exclude/include data with an annotation denoting an estimated origin for an incident.
Interval used to exclude/include data with an annotation denoting an estimated origin for an incident.
Failed to parse json: {error}
Failed to validate json: {error}
Engine internal.
A KernelModel is a kernel regressor fine-tuned with the training data.
L1 distance function.
L2 distance function, i.e.
Feature created from source aspect by delaying it by some lag.
Lags the values of the source function by some fixed delay.
Failed to find license api key
License terms insufficient
Failed to communicate with license server: {error}
Natural logarithm function.
The logistic function.
Smooth max function.
Feature created from source aspect by applying a Lti filter to it.
An LtiFilterFunction is the convolution of a moving time window of the input with some fixed function.
Returns the largest function value among the source functions at a given timestamp.
Model constraints must be provided in the inference_config in order to call the endpoint infer_float_with_next_normal.
Model ID {id} not part of the hub model
Cost function which stems from a model within the hub model.
Specification of model
A mollifier function.
Interprets the column as being numerical.
Interprets the signal as being numerical.
Failed to identify operative periods: the operative signal must contain at least one 'true' value.
Unable to resolve oscillatory interpretation of signal {id} at time t {timestamp} due to too sparse data
Interprets the signal as an oscillatory wave.
Deprecated.
Experimental: Might change in future releases.
Pointer was not created by this engine instance
Pointer has unexpected type
Deprecated.
Experimental: Might change in future releases.
Engine internal.
A DtoPrincipalDirectionFunction is a linear combination of other functions.
Identifying information on the engine.
Product over all input functions.
Information on a specific signal in the data, whose values were added directly.
The rectified linear unit.
Returns the input multiplied by the factor.
Tuple containing an ID and a float value.
Triple containing an ID, a float value, and a list of DtoFeatureValueWithNextNormal.
Failed to find segment with ID {id}
Single data point consisting in a timestamp and a list of DtoSegmentFloatValue.
Single data point consisting of a timestamp and a list of DtoSegmentFloatValueWithNextNormal.
Constraint on signal with not supported interpreter
Signal with ID {id} is expected to be any data type of {expected} but was of data type {actual}
Wraps a signal into a function.
Signal ID {id} is already in use
Signal with ID {id} has ambiguous interpreters
Failed to find signal with ID {id}
Wrapper for signal and its aspects.
The sin function.
Lower and upper threshold.
Step function.
Category-probability pair with string-valued category.
Single cell with row id and string value.
Single data point with timestamp and string value.
Single data point with timestamp, availability and string value.
Enhanced data point with timestamp and list of string-valued category-probability pairs.
Pointer failed to be decoded as string: {error}
Sum over all input functions.
Information on a specific signal in the data, whose values were calculated by an expression (synthesized).
Limits the use of the data based on certain criteria.
Target signal with ID {id} is expected to be any data type of {expected} but was of data type {actual}
Signal with ID {id} cannot be used as target due to its low information quality.
Failed to find target signal with ID {id}
Target signal with ID {id} of data type Float contains too many unique values: {numb_of_cats}.
A TreeModel is a tree regressor fine-tuned with the training data.
Engine internal.
Weight column with ID {id} has negative value in row with ID {row_id}.
Engine exceptions are thrown whenever errors within the engine occur.
Interface to handle logging callbacks from the engine.
Interface for handling engine tabular data.
Interface for handling engine timeseries data.
Flavours define different sets of library features.
This annotation defines a list of library flavours being allowed to call the annotated method.
Interprets the column values and splits them into different categories
Base error type, generated by the engine.
Information about a feature.
Parts of the formula describing the calculation while inference.
Specification for a specific signal needed at the inference.
Information on a specific signal in the data.
Interprets the signal values and derives aspects from them.
Information about one aspect of the target.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Returns the id of the function with the largest result.
Failed to validate argument: {error}
Category-probability pair with boolean-valued category.
Single cell with row id and boolean value.
Single data point with timestamp and boolean value.
Single data point with timestamp, availability and boolean value.
Enhanced data point with timestamp and list of boolean-valued category-probability pairs.
Interprets the different column values as categories, which have no order.
Interprets the different signal values as categories, which have no order.
A CategoricalEqualsFunction applied to a categorical function yields a 0/1-function for one of the function's categories.
Handling of a specific column.
Column with ID {id} is expected to be any data type of {expected} but was of data type {actual}
Column ID {id} is already in use
Column with ID {id} has ambiguous interpreters
Failed to find column with ID {id}
Returns the constant value provided.
Model with model ID {id} is not differentiable.
The cos function.
Interprets the signal as a cyclic variable, based on their residue after division.
Failed to modify data with handle '{handle}' due to concurrent usage
Limits the use of the data based on certain criteria.
Time range of a specific signal.
Data with handle '{handle}' does not contain any training signals after data removal
Data with handle '{handle}' does not contain any training signals
Information about the data.
Interprets float columns via DtoNumericalColumnInterpreter and string and boolean columns via DtoCategoricalColumnInterpreter.
Interprets float signals via DtoNumericalSignalInterpreter and string and boolean signals via DtoCategoricalSignalInterpreter.
Lower and upper threshold signals being used to obtain lower and upper threshold when evaluating these signals at timestamps of interest.
Named parameters used in error messages.
Exponential function.
Dependency information of synthesized signal.
Result of expression {expression} is expected to be of data type {expected} but was of type {actual} at timestamp '{timestamp}'
Failed to evaluate expression {expression} at timestamp '{timestamp}': {error}
Failed to compile expression {expression}: {error}
Limits the use of features based on certain criteria.
Feature value with featureId and its value.
Quadruple with id of feature, observed value of feature, next normal value of feature, rating.
Category-probability pair with float-valued category.
Single cell with row id and floating point value.
Tuple with id of model and evaluation of model.
Enhanced data point with timestamp and list of DtoFloatConstraintValue.
Enhanced data point with timestamp, DtoFloatCostValueWithNextNormal, list of DtoFloatConstraintValueWithNextNormal and list of DtoFeatureValueWithNextNormal.
Triple with id of model, observed value of model, next normal value of model.
Tuple with cost_type and value of the cost function.
Triplet with cost_type, value of the cost function before and after optimization.
Single data point with timestamp and floating point value.
Single data point with timestamp, availability and floating point value.
Enhanced data point with timestamp and list of float-valued category-probability pairs.
Enhanced data point with timestamp, floating point value, list of DtoFeatureValueWithNextNormal.
Input configuration needed to make constraint navigator inference with next normal.
Deprecated.
Experimental: Might change in future releases.
Gaussian cummulative distribution function.
Failed to dereference handle '{handle}'
Handle '{handle}' is expected to be of type '{expected}' but was of type '{actual}'
Necessary input configuration to build a model hub.
Information from model hub creation relevant for report generation.
Assigns each input value a result based on a dictionary.
Assigns each input value a result based on a dictionary and its probability.
Identity function.
Insufficient data quality of signals at label timestamps
DtoIncremental data does not match remaining model data: {error}
Necessary input configuration to make inferences using a hub model.
Inference data is expected to include information on signal {id} at or before timestamp {timestamp}
Specification for the data needed at the inference.
Inference data specification for signal with ID {id} is expected to be of data type {expected} but was of data type {actual}
Inference output specification is expected to be of target interpreter type {expected} but was {actual}
Inference output specification is expected to be of data type {expected} but was of data type {actual}
Specification for a specific signal needed at the inference, whose values were added directly.
Data type of signal {id} is ambiguous in inference data specification
Specification for a specific signal needed at the inference, whose values were calculated by an expression (synthesized).
Internal engine failure: {error}
Interval used to exclude/include data.
Interval used to exclude/include data with an annotation denoting an estimated origin for an incident.
Interval used to exclude/include data with an annotation denoting an estimated origin for an incident.
Failed to parse json: {error}
Failed to validate json: {error}
Engine internal.
A KernelModel is a kernel regressor fine-tuned with the training data.
L1 distance function.
L2 distance function, i.e.
Feature created from source aspect by delaying it by some lag.
Lags the values of the source function by some fixed delay.
Failed to find license api key
License terms insufficient
Failed to communicate with license server: {error}
Natural logarithm function.
The logistic function.
Smooth max function.
Feature created from source aspect by applying a Lti filter to it.
An LtiFilterFunction is the convolution of a moving time window of the input with some fixed function.
Returns the largest function value among the source functions at a given timestamp.
Model constraints must be provided in the inference_config in order to call the endpoint infer_float_with_next_normal.
Model ID {id} not part of the hub model
Cost function which stems from a model within the hub model.
Specification of model
A mollifier function.
Interprets the column as being numerical.
Interprets the signal as being numerical.
Failed to identify operative periods: the operative signal must contain at least one 'true' value.
Unable to resolve oscillatory interpretation of signal {id} at time t {timestamp} due to too sparse data
Interprets the signal as an oscillatory wave.
Deprecated.
Experimental: Might change in future releases.
Pointer was not created by this engine instance
Pointer has unexpected type
Deprecated.
Experimental: Might change in future releases.
Engine internal.
A DtoPrincipalDirectionFunction is a linear combination of other functions.
Identifying information on the engine.
Product over all input functions.
Information on a specific signal in the data, whose values were added directly.
The rectified linear unit.
Returns the input multiplied by the factor.
Tuple containing an ID and a float value.
Triple containing an ID, a float value, and a list of DtoFeatureValueWithNextNormal.
Failed to find segment with ID {id}
Single data point consisting in a timestamp and a list of DtoSegmentFloatValue.
Single data point consisting of a timestamp and a list of DtoSegmentFloatValueWithNextNormal.
Constraint on signal with not supported interpreter
Signal with ID {id} is expected to be any data type of {expected} but was of data type {actual}
Wraps a signal into a function.
Signal ID {id} is already in use
Signal with ID {id} has ambiguous interpreters
Failed to find signal with ID {id}
Wrapper for signal and its aspects.
The sin function.
Lower and upper threshold.
Step function.
Category-probability pair with string-valued category.
Single cell with row id and string value.
Single data point with timestamp and string value.
Single data point with timestamp, availability and string value.
Enhanced data point with timestamp and list of string-valued category-probability pairs.
Pointer failed to be decoded as string: {error}
Sum over all input functions.
Information on a specific signal in the data, whose values were calculated by an expression (synthesized).
Limits the use of the data based on certain criteria.
Target signal with ID {id} is expected to be any data type of {expected} but was of data type {actual}
Signal with ID {id} cannot be used as target due to its low information quality.
Failed to find target signal with ID {id}
Target signal with ID {id} of data type Float contains too many unique values: {numb_of_cats}.
A TreeModel is a tree regressor fine-tuned with the training data.
Engine internal.
Weight column with ID {id} has negative value in row with ID {row_id}.
Super class for all library exceptions thrown by the engine.
Missing library exceptions are thrown when a library file for this platform cannot be found.
Wrong flavour exceptions are thrown whenever an engine function is called that is not supported by the current library flavour.