All Classes and Interfaces
Class
Description
Class for creating and handling dependency analysis.
Class for creating and handling dependency analysis timeseries data.
Class for creating and handling dependency analysis setup.
Information on a specific column in the data.
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.
Necessary input configuration for a dependency analysis.
Information about the process and the result of a dependency analysis.
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.
Different values of the target correspond to different categories.
A CategoricalEqualsFunction applied to a categorical function yields a 0/1-function for one of the function's categories.
Indicates the causal role of given signals.
Carrier of relationship between a features and an indices.
Configures how signals are grouped into clusters in (different) zoom levels.
Information about a given cluster.
Handling of a specific column.
Information about the column data.
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}
In column with ID {id} there is ambiguity in the values for rows {row_ids}
Returns the constant value provided.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Model with model ID {id} is not differentiable.
Model constraints must be provided in the inference_config in order to call the endpoint infer_float_with_next_normal.
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.
Information about the removed signals
Interprets float columns via DtoNumericalColumnInterpreter and string and boolean columns via DtoCategoricalColumnInterpreter.
Interprets float signals via DtoNumericalSignalInterpreter and string and boolean signals via DtoCategoricalSignalInterpreter.
A float target signal is interpreted as Numerical, and a string or boolean target signal is interpreted as Categorical.
At least one of inner_linear_constraint_config or outer_linear_constraint_config should be present
Report of a given pair of signals, providing their mutual correlation.
Named parameters used in error messages.
Necessary input configuration for explaining next normal points from history.
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 id to index pair.
Information about signals, aspects and features.
Store statistics of the feature, sampled from its recorded timestamps within the operative periods.
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.
Deprecated.
Experimental: Might change in future releases.
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}'
Output object that contains information about the relation between the next normal point (the recommended action) and its closest historical record.
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.
Necessary data to incrementally update a model.
Engine internal.
Necessary input configuration to incrementally update the model.
Insufficient data quality of signals at label timestamps
DtoIncremental data does not match remaining model data: {error}
Configuration used for one update step.
Necessary input configuration on how to make an inference using a 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}
Specification of inference output type in model inference.
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).
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
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.
Feature created from source aspect by delaying it by some lag.
Lags the values of the source function by some fixed delay.
Consideration of signal values in the past while inference.
Failed to find license api key
License terms insufficient
Failed to communicate with license server: {error}
Weighted sum over all input functions plus intercept.
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.
Output of a signal prediction training.
For model constraint {id} the lower threshold is above the upper threshold at timestamp {timestamp}
Model ID {id} not part of the hub model
Deprecated.
Experimental: Might change in future releases.
Configuration of a kernel model.
Model {id} is expected to be '{expected}' model but it is '{actual} model
A mollifier function.
Interprets the column as being numerical.
Interprets the signal as being numerical.
Values of a target interpreted as numerical are directly used.
Configures which time periods to be regarded as operative.
Failed to identify operative periods: the operative signal must contain at least one 'true' value.
Returns true if any of the source functions returns true.
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.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Smooth max function.
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 column in the data, whose values were added directly.
Information on a specific signal in the data, whose values were added directly.
The rectified linear unit.
Adjusts the time points where the data is evaluated, called the sample time points.
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.
Handling of a specific signal.
In the constraint for signal/feature {id} the lower threshold is above the upper threshold at timestamp {timestamp}
Constraint on signal with not supported interpreter
The user set thresholds [{min_user}, {max_user}] lie outside of the data range seen in training [{min}, {max}] of signal/feature {id} at timestamp {timestamp}
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
Signal specific confguration how to consider past signal values.
Failed to find signal with ID {id}
Information about the removal of an specific signal.
Wrapper for signal and its aspects.
The sin function.
Upper and Lower thesholds for linear constraints.
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}
SubModelIncrementalInfo for model {id} cannot be found from IncrementalInfo
Sum over all input functions.
Information on a specific column in the data, with values calculated by an expression (synthesized).
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.
Dependency information of synthesized column.
Result of expression {expression} is expected to be of data type {expected} but was of type {actual} at row '{row_id}'
Failed to evaluate expression {expression} at row '{row_id}': {error}
Information on the target signal.
Configuration of how to consider past target values for inference.
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}.
Necessary input configuration for a signal prediction training.
Information from signal prediction training relevant for report generation.
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}.
Report of the clusters for a specific zoom level.
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.
Information on a specific column in the data.
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.
Necessary input configuration for a dependency analysis.
Information about the process and the result of a dependency analysis.
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.
Different values of the target correspond to different categories.
A CategoricalEqualsFunction applied to a categorical function yields a 0/1-function for one of the function's categories.
Indicates the causal role of given signals.
Carrier of relationship between a features and an indices.
Configures how signals are grouped into clusters in (different) zoom levels.
Information about a given cluster.
Handling of a specific column.
Information about the column data.
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}
In column with ID {id} there is ambiguity in the values for rows {row_ids}
Returns the constant value provided.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Model with model ID {id} is not differentiable.
Model constraints must be provided in the inference_config in order to call the endpoint infer_float_with_next_normal.
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.
Information about the removed signals
Interprets float columns via DtoNumericalColumnInterpreter and string and boolean columns via DtoCategoricalColumnInterpreter.
Interprets float signals via DtoNumericalSignalInterpreter and string and boolean signals via DtoCategoricalSignalInterpreter.
A float target signal is interpreted as Numerical, and a string or boolean target signal is interpreted as Categorical.
At least one of inner_linear_constraint_config or outer_linear_constraint_config should be present
Report of a given pair of signals, providing their mutual correlation.
Named parameters used in error messages.
Necessary input configuration for explaining next normal points from history.
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 id to index pair.
Information about signals, aspects and features.
Store statistics of the feature, sampled from its recorded timestamps within the operative periods.
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.
Deprecated.
Experimental: Might change in future releases.
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}'
Output object that contains information about the relation between the next normal point (the recommended action) and its closest historical record.
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.
Necessary data to incrementally update a model.
Engine internal.
Necessary input configuration to incrementally update the model.
Insufficient data quality of signals at label timestamps
DtoIncremental data does not match remaining model data: {error}
Configuration used for one update step.
Necessary input configuration on how to make an inference using a 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}
Specification of inference output type in model inference.
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).
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
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.
Feature created from source aspect by delaying it by some lag.
Lags the values of the source function by some fixed delay.
Consideration of signal values in the past while inference.
Failed to find license api key
License terms insufficient
Failed to communicate with license server: {error}
Weighted sum over all input functions plus intercept.
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.
Output of a signal prediction training.
For model constraint {id} the lower threshold is above the upper threshold at timestamp {timestamp}
Model ID {id} not part of the hub model
Deprecated.
Experimental: Might change in future releases.
Configuration of a kernel model.
Model {id} is expected to be '{expected}' model but it is '{actual} model
A mollifier function.
Interprets the column as being numerical.
Interprets the signal as being numerical.
Values of a target interpreted as numerical are directly used.
Configures which time periods to be regarded as operative.
Failed to identify operative periods: the operative signal must contain at least one 'true' value.
Returns true if any of the source functions returns true.
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.
Deprecated.
Experimental: Might change in future releases.
Deprecated.
Experimental: Might change in future releases.
Smooth max function.
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 column in the data, whose values were added directly.
Information on a specific signal in the data, whose values were added directly.
The rectified linear unit.
Adjusts the time points where the data is evaluated, called the sample time points.
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.
Handling of a specific signal.
In the constraint for signal/feature {id} the lower threshold is above the upper threshold at timestamp {timestamp}
Constraint on signal with not supported interpreter
The user set thresholds [{min_user}, {max_user}] lie outside of the data range seen in training [{min}, {max}] of signal/feature {id} at timestamp {timestamp}
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
Signal specific confguration how to consider past signal values.
Failed to find signal with ID {id}
Information about the removal of an specific signal.
Wrapper for signal and its aspects.
The sin function.
Upper and Lower thesholds for linear constraints.
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}
SubModelIncrementalInfo for model {id} cannot be found from IncrementalInfo
Sum over all input functions.
Information on a specific column in the data, with values calculated by an expression (synthesized).
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.
Dependency information of synthesized column.
Result of expression {expression} is expected to be of data type {expected} but was of type {actual} at row '{row_id}'
Failed to evaluate expression {expression} at row '{row_id}': {error}
Information on the target signal.
Configuration of how to consider past target values for inference.
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}.
Necessary input configuration for a signal prediction training.
Information from signal prediction training relevant for report generation.
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}.
Report of the clusters for a specific zoom level.
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.