UserErrorMessages.properties

# file UserErrorMessages.properties
#
# For instructions on how to use this file, please see the documentation of
# com.rapidminer.operator.UserError
#

#
# Group 100: wrong process setup
#
error.101.name  = Non-nominal label
error.101.short = The label attribute ({1}) must be nominal for {0}.
error.101.long  = Certain learning schemes and algorithms require the label to be nominal.

error.102.name  = Non-numerical label
error.102.short = The label attribute ({1}) must be numerical for {0}.
error.102.long  = Certain learning schemes and algorithms require the label to be numerical.

error.103.name  = Non-nominal attribute
error.103.short = The ExampleSet contains non-nominal attribute ''{1}'' which is not allowed for {0}.
error.103.long  = Some learning schemes and algorithms can only handle nominal attributes. Consider using 'Numerical to Nominal', 'Text to Nominal', or one of the 'Discretize' operators. 

error.104.name  = Non-numerical attribute
error.104.short = The ExampleSet contains non-numerical attribute ''{1}'' which is not allowed for {0}.
error.104.long  = Some learning schemes and algorithms can only handle numerical attributes. Consider using the 'Nominal to Numerical' or 'Parse Numbers' operators. 

error.105.name  = Missing label
error.105.short = Input ExampleSet does not have a label attribute.
error.105.long  = Many operators, in particular classifications and regressions, require a target attribute to be declared as label. To do so, you can use the 'Set Role' operator.

error.106.name  = Missing attributes
error.106.short = Input ExampleSet has no attributes
error.106.long  = Learning schemes cannot be applied without at least one valid attribute.

error.107.name  = Missing predicted label
error.107.short = Input ExampleSet does not have a predicted label attribute
error.107.long  = Many operators, in particular classification and regression, require a target attribute to be declared as predicted label. To do so, you can use the 'Apply Model' operator.

error.108.name  = Generation exception
error.108.short = Generation exception: ''{0}''
error.108.long  = Could not generate a new attribute, macro, or other object which is calculated on the fly.

error.109.name  = No such operator
error.109.short = Unknown operator: ''{0}''
error.109.long  = The operator referenced by a parameter, e.g. in a ParameterOptimization is not defined in this process.

error.110.name  = Too few examples.
error.110.short = The input example set has less than {0} examples.
error.110.long  = Some operators need a minimum number of examples. Please check the input files or the process setup if this minimum number should be provided by your dataset.

error.111.name  = Attribute Selection empty
error.111.short = No attribute selected.
error.111.long  = Select an attribute by clicking on the 'attribute name' drop-down box.

error.112.name  = Illegal valuetype
error.112.short = ''{0}'' is an unknown valuetype.
error.112.long  = An illegal valuetype has been specified. The most important legal values are numerical and nominal. For details, please refer to the RapidMiner tutorial.

error.113.name  = No such special attribute
error.113.short = The special attribute ''{0}'' does not exist.
error.113.long  = The example set does not contain a special attribute of the given type.

error.114.name  = Non-binominal label
error.114.short = The label attribute ({1}) must be binominal for {0}, i.e. it is only allowed to have two values.
error.114.long  = Some operators only work for binominal labels, e.g. most SVM implementations. If you have more than two different values you can merge them during preprocessing. You can also use the meta learner Binary2MultiClass If the label is numerical you might want to discretize it first.

error.115.name  = Cannot create predicted label
error.115.short = A predicted label cannot be created since no label is defined
error.115.long  = Predicted labels are created with value type equal to the value type of the true label. Hence, this is only possible if the label is defined. If you want to label unlabelled data, use a <label> tag in the attribute description file with sourcecol=none!

error.116.name  = Illegal parameter value
error.116.short = Illegal value for parameter {0}: {1}.
error.116.long  = The parameter values do not fulfill the constraints for this parameters. Please ensure that a correct parameter value is specified in the configuration file. 

error.117.name  = Example set is empty
error.117.short = The example set has no examples
error.117.long  = The example set contains no examples. Please check the attribute description and the data files. Maybe a non existing data column was specified.

error.118.name  = Wrong number of attribute values
error.118.short = The attribute {0} has {1} different values, must be {2}.
error.118.long  = The nominal attribute has the wrong number of different values. Some operators work only for nominal attributes with a specific number of values. 

error.119.name  = Attribute must be nominal
error.119.short = The attribute {0} must be nominal to be usable for the operator {1}.
error.119.long  = Some operators can only be applied on nominal attributes, e.g. preprocessing operators which merge several categories into a new nominal value.

error.120.name  = Wrong value type
error.120.short = The attribute {0} has value type {1}, should be {2}.
error.120.long  = Some operators can only be applied on attributes with specific value types.

error.121.name  = Wrong block type
error.121.short = The attribute {0} has block type {1}, should be {2}.
error.121.long  = Some operators can only be applied on attributes with specific block types.

error.122.name  = Wrong process setup
error.122.short = The operator needs some input of type {0} which is not provided.
error.122.long  = Each operator defines which input is desired for applying this operator (these input objects are shown in operator info screen (F1)). Previous operators must load or produce the desired input objects. You can check the correct process setup by validating the process (via the icon or the menu item).

error.123.name  = Wrong process setup
error.123.short = The operator needs at least {1} input objects of type {0} which are not provided.
error.123.long  = Each operator defines which input is desired for applying this operator (these input objects are shown in operator info screen (F1)). Previous operators must load or produce the desired input objects. You can check the correct process setup by validating the process (via the icon or the menu item).

error.124.name  = Wrong process setup
error.124.short = The operator cannot handle more than {1} input objects of type {0}.
error.124.long  = Each operator defines which input is desired for applying this operator (these input objects are shown in operator info screen (F1)). Previous operators must load or produce the desired input objects. You can check the correct process setup by validating the process (via the icon or the menu item).

error.125.name  = Too few attributes.
error.125.short = The input example set has less than {1} attributes (was: {0}).
error.125.long  = Some operators need a minimum number of attributes. Please check the input files or the process setup if this minimum number is provided by your dataset.

error.126.name  = Non-equal valuetype
error.126.short = Not all attributes have the same value type.
error.126.long  = Some operators require that all attributes have the same value type, i.e. that all attributes must be real values or all must be nominal.

error.127.name  = Wrong process setup
error.127.short = Error in process setup: {0}
error.127.long  = Some operators require a specific process setup. Please refer to the documentation of the used operators for further details.

error.128.name  = Missing value type
error.128.short = The input example set does not contain any attributes with value type {0}.
error.128.long  = Some operators require example sets with attributes of a specific value type. Please refer to the documentation of the used operators for further details.

error.129.name  = Missing ID
error.129.short = Input example set does not have an id attribute
error.129.long  = Many operators like clustering methods require the input example sets to have an id attribute. If this not the case, applying these operators will not work. If you read the data using an ExampleSource, you can specify the id attribute by using a 'id' tag in the attribute description file. Alternatively you can simply use the operator Generate ID.

error.130.name  = Missing example for ID
error.130.short = The input example set does not contain an example with id {0}
error.130.long  = The input example set was expected to contain an example with the specified id value. Please check your process setup or your data in order to ensure that the missing example is provided. 

error.131.name  = Inner operator has wrong type
error.131.short = The inner operator of this operator chain has to be a {0} operator.
error.131.long  = Some operator chains ask for specific inner operators. Although in most cases the dependency between parents and their children operators can completely resolved by requirements of the in- and output, in some special cases only specific child operators are allowed. Please change the inner operator to one of the desired types. 

error.132.name  = Attribute must be binary
error.132.short = The attribute {0} must be binary to be usable for the operator {1}.
error.132.long  = Some operators can only be applied on binary attributes, e.g. binary classification learners can only handle binary label attributes. Please make sure that your data set fulfills the specified condition.

error.133.name  = Wrong number of attributes
error.133.short = The operator expects a fixed number of attributes ({0}), the actual number was {1}.
error.133.long  = Some operators need a specific number of attributes and will not work for other numbers. Please make sure that the input ExampleSet provide the correct number of attributes.

error.134.name  = Wrong number of examples
error.134.short = The operator expects a fixed number of examples ({0}), the actual number was {1}.
error.134.long  = Some operators need a specific number of examples and will not work for other numbers. Please make sure that the input ExampleSet provide the correct number of examples.

error.135.name  = Cannot update model
error.135.short = The operator can only be used on updatable models, the given model is not updatable (was: {0}).
error.135.long  = The operator ModelUpdater can only be applied to updatable models, i.e. models which can be updated according to a new example set. Please ensure that the input model is of this type. By the way: most of the Weka models are updatable, too.

error.136.name  = Incompatible attribute
error.136.short = The operator is not able to work on the attribute {0}.
error.136.long  = The operator tries to work on a user specified attribute which is not suitable for this purpose, e.g. the operator tries to calculate the sum of all values for a nominal attribute - which is of course not possible. 

error.137.name  = Wrong process setup
error.137.short = The operator needs a specific number of inner operators: {0}
error.137.long  = The operator needs a specific number of inner operators in order to work properly. Please change the process setup accordingly and restart the process.  

error.138.name  = Wrong value range
error.138.short = The attribute {0} must provide {1}, but was {2}
error.138.long  = Some operators depend on a specific value range for one or several attributes, e.g. all values of the example weights (special attribute: weight) must be positive. You could normalize the values of numerical attributes into the desired range with a Normalization operator.   

error.139.name  = Missing values
error.139.short = The data contains missing values which is not allowed for {0}
error.139.long  = Some operators cannot work on data sets with missing values. You should use one of the preprocessing operators like Replace Missing Values before applying this operator in order to replace the missing values by some valid values.   

error.140.name  = View not supported
error.140.short = The example set seems to be a view, which is not supported by this operator.
error.140.long  = Preprocessing operators or its models may create a view. This operator does not support views for performance reasons, please uncheck the create view parameter in the preprocessing operators or the model applier applying their models. You can use the operator MaterializeDataInMemory in order to remove the views or change the preprocessing operator settings so that they do no longer create views. 

error.141.name  = Attributes changed
error.141.short = The example set contains attributes in different order than on creation time.
error.141.long  = The attributes have at least a different order than on creation time. This operator needs to have the same ordering to ensure correct calculation. 

error.142.name  = ExampleSet too small
error.142.short = The example set contains not enough examples to perform this operation. Needs at least ''{0}'' examples.
error.142.long  = Some operators need at least a fixed number of examples. This number might depend on the parameters of the operator like the number of clusters in some clustering operators.   

error.143.name  = No such attribute value
error.143.short = The value ''{0}'' for attribute ''{1}'' does not exist.
error.143.long  = The example set does not contain the specified value for the specified attribute.

error.144.name  = Attribute must be numerical
error.144.short = The attribute {0} must be numerical to be usable for the operator {1}.
error.144.long  = Some operators can only be applied on numerical attributes, e.g. preprocessing operators which work on a specific numerical attribute.

error.145.name  = Not a valid cluster attribute
error.145.short = The attribute {0} must not contain missing values and each value has to provide a number indicating the cluster.
error.145.long  = This operator needs a valid cluster attribute with values of the form "cluster_<index>" or at least providing any number indicating the cluster. 

error.146.name  = Provided example set must be stored in memory
error.146.short = The provided example set must be stored in memory: {0} 
error.146.long  = This operator tries to perform operations on the example set, which are only possible if it is stored in memory because of database limitations. 

error.147.name  = Predicted label is incompatible
error.147.short = The values of the predicted label does not match the model's expectation. Value {0} unknown. 
error.147.long  = The model tries to work on different attribute values than actually are present. 

error.148.name  = Label types of all documents must be the same
error.148.short = The label type of all documents must be exactly the same. 
error.148.long  = The label type of all documents must be exactly the same. 

error.149.name  = No input
error.149.short = No input for port ''{0}''.
error.149.long  = The specified port did not receive any input. Either the port is not connected or the preceding operator did not deliver any output. 

error.150.name  = Wrong data
error.150.short = Wrong input of type ''{0}'' at port ''{1}''.
error.150.long  = The specified port received input of the wrong type. Please make sure your ports are connected correctly. 

error.151.name  = Dummy Operator
error.151.short = The dummy operator {0} (replacing {1}) cannot be executed. 
error.151.long  = A dummy operator was inserted to replace an operator provided by a plug-in that is currently not installed. In order to execute this process, you must download the plugin. 

error.152.name  = Attribute already present
error.152.short = The attribute {0} was already present in the example set.
error.152.long  = An attribute was already present in the example set, which is not allowed because attribute names must be unique.

error.153.name  = Wrong number of attributes
error.153.short = The operator needs at least attributes ({0}) to be selected, the actual number was {1}.
error.153.long  = Some operators offer the possibility to select attributes but need at least one selected. Please check the corresponding parameter settings for errors.

error.154.name  = Missing confidence attribute
error.154.short = Input example set does not have a confidence attribute for the class {0}.
error.154.long  = Some operators need to have confidences for each possible class, but at least the confidence for this class is missing.

error.155.name  = Wrong model type
error.155.short = This operator expects a model of type ''{0}''.
error.155.long  = Please connect the input port to an operator producing the correct type of model.

error.156.name  = Wrong data
error.156.short = Wrong input of type ''{0}'' at port ''{1}''. Expected type ''{2}''. 
error.156.long  = The specified port received input of the wrong type. Please make sure your ports are connected correctly. 

error.157.name  = Incompatible Label and Prediction
error.157.short = The prediction attribute's values do not match the label attribute's. 
error.157.long  = If the prediction attribute's values do not match the label attribute's, performance cannot be calculated. 

error.158.name  = Wrong SQL type
error.158.short = Cannot parse value ''{0}'' for parameter type ''{1}''.
error.158.long  = You specified an SQL type for a query. However, the value filled in at this place cannot be parsed accordingly. You must either change the type or use another value.

error.159.name  = No such element
error.159.short = Collection size is {1}, requested element index is {0}.
error.159.long  = You selected an element from a collection whose index is larger than the number of elements in the list.

error.160.name  = Attribute not existing
error.160.short = The attribute ''{0}'' does not exist.
error.160.long  = Please check the entered attribute name in the Parameters tab. It does not exist in the input example set.

error.161.name  = Missing performance value
error.161.short = The operator expects the inner process to deliver a performance value.
error.161.long  = There has to be at least one execution of the inner process which delivers a performance value.

error.162.name  = Label with missing values
error.162.short = The label attribute ({0}) contains missing values which is not allowed for the operator.
error.162.long  = The label attribute must not contain missing values. You should use one of the preprocessing operators like Replace Missing Values before applying this operator in order to replace the missing values by some valid values.

error.163.name  = Attributes not existing
error.163.short = The attributes {0} do not exist.
error.163.long  = Please check the entered attribute names in the Parameters tab. They do not exist in the input example set.

error.164.name  = Attribute not existing
error.164.short = Regular attribute ''{0}'' does not exist.
error.164.long  = Please check the specified attribute name. If the attribute is not a regular attribute, ensure that special attributes are included in the operator parameters.

error.165.name  = Attributes not existing
error.165.short = Regular attributes {0} do not exist.
error.165.long  = Please check the specified attribute names. If the attributes are not regular attributes, ensure that special attributes are included in the operator parameters.


error.aggregate_group_by_not_found.name = Group by attributes not found
error.aggregate_group_by_not_found.short = The example set does not contain the following attributes: {0}
error.aggregate_group_by_not_found.long= Please check the entered attribute names for the ''group by attributes'' parameter and make sure the  attribute names do not contain a ''|''.

error.aggregate_group_by_not_found_single.name = Group by attribute not found
error.aggregate_group_by_not_found_single.short = The example set does not contain the following attribute: {0}
error.aggregate_group_by_not_found_single.long= Please check the entered attribute name for the ''group by attributes'' parameter and make sure the  attribute names do not contain a ''|''.

error.infinite_values.name  = Infinite values
error.infinite_values.short = The data contains infinite values which is not allowed for {0}
error.infinite_values.long  = Some operators cannot work on data sets with infinite values. You should use one of the preprocessing operators like Replace Infinite Values before applying this operator in order to replace the infinite values by some valid values.

error.similarity_example_set_not_extendable.name = Similarity example set
error.similarity_example_set_not_extendable.short= There is no underlying data table holding the similarities
error.similarity_example_set_not_extendable.long = Please materialize the example set if you want to add attributes or change values. Keep in mind that this could take a lot of RAM (O(n*n), where n is the number of examples in the original data set). 

#
# Group 200: parameters
#

error.201.name  = Parameter dependency
error.201.short = If parameter ''{1}'' is set to ''{2}'', the parameter ''{0}'' must be defined.
error.201.long  = Usually, parameters are either mandatory or optional. However, some operators have parameters which are mandatory or optional depending on the value of another parameter. Unfortunately, there a parameter dependency test is not yet implemented and hence, this cannot be checked beforehand and will cause an error at process time.

error.202.name  = Parameter dependency
error.202.short = Either parameter ''{0}'', ''{1}'', or ''{2}'' must be set!;
error.202.long  = Exactly one of the three parameters must be set.

error.203.name  = Parameter dependency
error.203.short = If system property ''{0}'' is not set, parameter ''{1}'' must be set!
error.203.long  = If a property is not set in one of the rapidminer-studio-settings files, an process parameter must be set.

error.204.name  = Model does not support parameter 
error.204.short = The learned model ''{0}'' does not support the parameter ''{1}''!
error.204.long  = Some models support parameters for the prediction of values. This model does not support the given parameter.

error.205.name  = Missing mandatory parameter
error.205.short = A value for the parameter ''{0}'' must be specified! {1}
error.205.long  = A non-optional parameter without any default value was not specified. Please define a proper parameter value.

error.206.name  = Regular expression error
error.206.short = The regular expression  ''{0}'' is not well defined: {1}.
error.206.long  = Regular expressions can be used to describe a pattern which should be found in other strings. The given expression was not well defined, please refer to the documentation of regular expressions available in Java. 

error.207.name  = Impossible parameter value
error.207.short = The value ''{0}'' for the parameter ''{1}'' cannot be used: {2}.
error.207.long  = The specified value is not possible for this parameter. Please set a proper parameter value. 

error.208.name  = Parameter dependency
error.208.short = Either parameter ''{0}'' or ''{1}'' must be set!";
error.208.long  = Exactly one of both parameters must be set.

error.209.name  = Parameter dependency
error.209.short = It is not allowed to set both parameter ''{0}'' and parameter ''{1}''.
error.209.long  = Only one of both parameter definitions is allowed, please set the other parameter to its default value or clear it.

error.210.name  = Parameter dependency
error.210.short = The value of parameter ''{0}'' must not be smaller than the value of parameter ''{1}''.
error.210.long  = A parameter must be at least as large as another. This often happens for ranges. Please ensure that this parameter dependency is fulfilled. 

error.211.name  = Non-numerical parameter value
error.211.short = The value of parameter ''{0}'' must be numerical, was ''{1}''.
error.211.long  = Sometimes a parameter value must be of a certain type which cannot be assured during the process setup (as usual). Please make sure that you set an appropriate parameter value. 

error.212.name  = Non-nominal parameter value
error.212.short = The value of parameter ''{0}'' must be nominal, was ''{1}''.
error.212.long  = Sometimes a parameter value must be of a certain type which cannot be assured during the process setup (as usual). Please make sure that you set an appropriate parameter value. 

error.213.name  = No such parameter
error.213.short = Parameter: ''{0}'' in operator ''{1}'' was tried to read by operator ''{2}'' but does not exist.
error.213.long  = The specified parameter was tried to read but does not exist. Some operators like ParameterCloner try to read parameters of other operators and depend on their existence.

error.214.name  = No valid XPath query
error.214.short = The specified XPath query is not valid: {0}
error.214.long  = You have specified an invalid XPath query.

error.215.name  = Regular expression error
error.215.short = The capturing group specified in ''{0}'' was not defined in parameter ''{1}''.
error.215.long  = Regular expressions can be used to describe a pattern which should be found in other strings. The given expression was not well defined, please refer to the documentation of regular expressions available in Java. 

error.216.name  = Parameter must be single character
error.216.short = Parameter: Parameter ''{0}'' in operator ''{1}'' must be a single character.
error.216.long  = The specified parameter must be a string of length exactly one.

error.217.name  = Missing mandatory parameter
error.217.short = A value for the parameter ''{0}'' of operator ''{1}'' must be specified! {2}
error.217.long  = A non-optional parameter without any default value was not specified. Please define a proper parameter value.

error.218.name  = Non-date parameter value
error.218.short = The value of parameter ''{0}'' must be a date, was ''{1}''.
error.218.long  = Valid parameter values depend on the type of the data attribute and might be either "MM/dd/yyyy", "hh.mm a" or "MM/dd/yyyy hh.mm a"  

error.219.name  = Hierarchy invalid
error.219.short = The entry ''{0}'' could not be found.
error.219.long  = The defined hierarchy must contain each possible label value on the right hand side. Each other value must be a left hand side value.  

error.220.name  = Hierarchy invalid
error.220.short = Two root values have been found: ''{0}'' and ''{1}''.
error.220.long  = The defined hierarchy must contain each left hand side value as right hand side value, too, except the single root value.  

error.221.name  = Hierarchy invalid
error.221.short = No root value has been found.
error.221.long  = The defined hierarchy must contain one root value, that is not part of the left hand side. Please check if you have any circles in the hierarchy.  

error.222.name  = Hierarchy invalid
error.222.short = Node ''{0}''needs at least two children but has only ''{1}''. 
error.222.long  = A non leaf node with less than 2 child nodes does not contribute any information and hence should be avoided.  

error.223.name  = Invalid Excel Range
error.223.short = The Range ''{0}'' is not a valid Excel range.
error.223.long = The format for valid Excel ranges is for example B3:C6 or B3 for the open interval starting at B3. The first cell must be smaller than the second.

error.224.name  = Invalid Excel Cell Address
error.224.short = The String ''{0}'' is not a valid Excel cell address.
error.224.long = The format for valid Excel addresses is for example B3, characters for the column, numbers for the row. 

error.225.name  = Non-integer parameter value
error.225.short = The value of parameter ''{0}'' must be integer, was ''{1}''.
error.225.long  = Sometimes a parameter value must be of a certain type which cannot be assured during the process setup (as usual). Please make sure that you set an appropriate parameter value.

error.226.name =  Upper bound smaller than lower bound
error.226.short = Setting the lower bound to ''{0}'' and the upper bound to ''{1}'' is not possible.
error.226.long = The upper bound must not be smaller than the lower bound.

error.227.name  = Undefined Macro
error.227.short = Undefined macro: {0}
error.227.long  = The macro is accessed in the operator parameters but has neither been defined in a preceding operator nor in the process context. You can define macros in the context view of the process or by using one of the macro operators.


#
# Group 300: resources / os / external tool errors
#

error.301.name  = File not found
error.301.short = The file ''{0}'' does not exist.
error.301.long  = A file does not exist. For a given filename, RapidMiner resolves the filename against the directory the process file is stored in. If there is no process file (which is possible in GUI mode for a new, but not saved process) the current working directory is used. If the file name denotes an absolute path, the file is not resolved.

error.302.name  = Cannot read file
error.302.short = Could not read file ''{0}'': {1}.
error.302.long  = The given file could not be read. Please make sure that the file exists and that the RapidMiner process has sufficient privileges.

error.303.name  = Cannot write to file
error.303.short = Could not write to file ''{0}'': {1}.
error.303.long  = The given file could not be written. Please make sure, that the RapidMiner process has sufficient privileges to create the file in the file's directory and that there is sufficient disc space. Disc space may be critical if many temporary files are created.

error.304.name  = Database error
error.304.short = Database error occurred: {0}
error.304.long  = The JDBC driver has thrown an SQLException. This may because of a lack of privileges, wrong table name or url and similar problems. Please note that some databases are case sensitive. Details are given in the message.

error.305.name  = Query file empty
error.305.short = The query file ''{0}'' is empty.
error.305.long  = The query file used in a DatabaseExampleSource was empty.

error.306.name  = External tool error
error.306.short = Process ''{0}'' exited with error code {1}.
error.306.long  = An external program exited with an error code that indicates an error. Please refer to the documentation of this tool and your operating system for further details.

error.307.name  = External Error
error.307.short = Process ''{0}'' did not produce output.
error.307.long  = An external program exited silently.

error.308.name  = Process IO Error
error.308.short = Cannot read output of process ''{0}''.
error.308.long  = RapidMiner failed to read the output of an external program.

error.309.name  = Process IO Error
error.309.short = Cannot write to process ''{0}''.
error.309.long  = RapidMiner failed to write to the standard input of an external program.

error.310.name  = Process Error
error.310.short = Cannot execute ''{0}'': {1}
error.310.long  = An exception occurred while executing an external tool.

error.311.name  = Cannot create directory
error.311.short = Could not create directory ''{0}''.
error.311.long  = The given directory could not be created. Please make sure that the RapidMiner process has sufficient privileges to create the directory in its parent's directory.

error.312.name  = Cannot retrieve repository data
error.312.short = Cannot retrieve repository data from entry ''{0}''. Reason: {1}.
error.312.long  = The repository did not deliver the requested data. This can be caused by wrong file names, network errors, file system errors or broken entries in the repository.

error.313.name  = Malformed URL
error.313.short = The URL you specified is invalid: {0}
error.313.long  = Please correct the URL you specified. For further details see RFC 2396.

error.314.name  = Could not access URL
error.314.short = Cannot retrieve data from the specified URL ''{0}''.
error.314.long  = Could not connect to the specified URL. Please check your network connections.

error.315.name  = Cannot save repository data
error.315.short = Cannot store data in repository at entry ''{0}''. Reason: {1}.
error.315.long  = The repository was not able to store the delivered data. This can be caused by wrong file names, network errors, file system errors or broken entries in the repository.

error.316.name  = Cannot read input
error.316.short = Cannot read input from ''{0}'': ''{1}''
error.316.long  = The specified input resource cannot be read. Please make sure that the RapidMiner process has sufficient privileges for reading this resource and that the resource format fulfills the requested standard format.  

error.317.name  = Cannot resolve relative repository location.
error.317.short = Cannot resolve relative repository location ''{0}''. Process is not associated with a repository.
error.317.long  = Relative repository locations can be resolved only if the process is associated with a repository. Store your process in a repository to avoid this error.

error.318.name  = Database connection failed
error.318.short = Database connection failed: connection {0} is unknown. Please define the connection properly.
error.318.long  = The database connection has failed. Please check that the connection you have entered is properly defined. You can define and test database connections by choosing the Manage Database Connections menu item in the Tools menu.

error.319.name  = Malformed repository location
error.319.short = The repository location ''{0}'' is malformed.
error.319.long  = Please correct the repository location you specified. Correct repository locations look for example like this: ''//Repository/path/to/object''.

error.320.name  = Unresolvable repository location
error.320.short = Cannot resolve relative repository location {0} since process is not saved in repository.
error.320.long  = Relative repository locations cannot be resolved if the process is not saved in a repository. Either use absolute repository locations or save the process in the repository.

error.321.name  = I/O Error
error.321.short = Error reading {0}: {1}
error.321.long  = The given resource could not be read and parsed. Please make sure the file is well-formed and parsing parameters are specified correctly.

error.322.name  = I/O Error
error.322.short = Error writing to {0}: {1}
error.322.long  = RapidMiner was unable to write to the given destination. Please make sure you have sufficient privileges and the destination does not have spelling errors.

error.323.name	= Repository folder not found
error.323.short	= The repository folder ''{0}'' does not exist.
error.323.long	= The repository folder does not exist. Please enter a correct repository folder location.

error.324.name  = Directory not found
error.324.short = The directory ''{0}'' was not found.
error.324.long  = The given directory could not be opened. Please make sure that the directory exists and that the RapidMiner process has sufficient access privileges.

error.325.name = Cannot retrieve repository data
error.325.short = {0}
error.325.long = The repository did not deliver the requested data. This can be caused by wrong file names, network errors, file system errors or broken entries in the repository.
#
# Group 400: File format
#
error.401.name  = XML Error
error.401.short = XML Error: {0}
error.401.long  = An XML file could not be parsed. Specific information about the error is provided in the error message above.

error.402.name  = Attribute Error
error.402.short = Attribute description error: {0}
error.402.long  = An XML file could not be parsed. Specific information about the error is provided in the error message.

error.403.name  = Malformed data file
error.403.short = Malformed data file: {0}
error.403.long  = A data file could not be read. Please check file format, permissions, and settings to read this file.

error.404.name  = Empty Excel Sheet
error.404.short = Cannot import range of excel sheet, because it was empty.
error.404.long  = The range that has been selected in the sheet seems to be empty.

#
# Group 500: learning errors
#
error.501.name  = Insufficient capability
error.501.short = The operator {0} does not have sufficient capabilities for the given data set: {1} not supported
error.501.long  = Each operator has particular capabilities for data set handling. For example, some learners can only handle numerical attributes and can not learn from nominal attributes. Please perform a preprocessing step to transform your data set or use an alternative learning scheme. In case of a polynominal label attribute, i.e. a classification task with more than two classes, you can use a learning scheme capable only for binominal classes by wrapping a Binary2MultiClassLearner around the learning operator. 

# used for nominal mappings with size 1
error.502.name  = Only one label
error.502.short = The learning scheme {0} does not have sufficient capabilities for handling an example set with only one label.
error.502.long  = There are existing special modeling operators if only examples for one class are known. They support the ''one class label'' capability.  

# used when example set has examples for only one label
error.503.name  = Only one label is present
error.503.short = The learning scheme {0} does not have sufficient capabilities for handling an example set where examples are known for only one label.
error.503.long  = There are existing special modeling operators if only examples for one class are known. They support the ''one class label'' capability.  

#
# Group 900: special errors (for special operators)
#
error.901.name  = Example pattern expression syntax
error.901.short = Compile error in expression ''{0}'': {1}.
error.901.long  = The expression used to specify the output format of an ExampleSetWriter could not be parsed. Please see the documentation of ExampleSetWriter for details about the expression syntax.

error.902.name  = No data file specified
error.902.short = Attribute data file neither specified by parameter ''attribute_file'' nor by attribute description file!
error.902.long  = For a SparseFormatExampleSource, either the parameter 'attribute_file' must be set, or the attribute description file specified by the parameter 'attributes' must reference the data file.

error.903.name  = Non measured performance criterion
error.903.short = All criteria must be of subclasses of MeasuredPerformance
error.903.long  = PerformanceEvaluator can evaluate the performance only for performance criteria that are subclasses of MeasuredPerformance. This error can only occur for PerformanceCriteria implemented by the user.

error.904.name  = Instantiation error
error.904.short = Cannot instantiate ''{0}'': {1}
error.904.long  = Some operators instantiate classes specified by the user. This may cause errors for mainly two reasons: The class cannot be found because it is not in the classpath or misspelled or the classes constructor or initializer throws an exception. Always use the fully qualified classnames.

error.905.name  = External Error
error.905.short = {0} caused an error: {1}
error.905.long  = An external program or library has reported an error. Please see the documentation of this program or library for further information.

error.906.name  = Unknown key for optimization
error.906.short = Unknown key for optimization: ''{0}''
error.906.long  = The specified parameter referenced by a ParameterOptimization does not exist.

error.907.name  = Parameter key syntax error
error.907.short = Illegal key for optimization: ''{0}''
error.907.long  = The keys in the parameter list must have the form 'operator_name.parameter_name'.

error.908.name  = Illegal optimization parameter
error.908.short = Parameter optimization not supported for non-single parameter type ''{0}''.
error.908.long  = Currently, all ParameterOptimization operators can only work on single parameters, i.e. strings, integers, real values or boolean values. The list parameter type is not supported for parameter opimization.

error.909.name  = Illegal optimization parameter
error.909.short = Parameter optimization not supported for non-number parameter type ''{0}''.
error.909.long  = The used parameter optimization operator can only work on numerical parameters, i.e. integers and real numbers.

error.910.name  = No performance criteria selected
error.910.short = There is no performance criterion selected.
error.910.long  = Please activate at least one performance criterion for a PerformanceEvaluator. Be sure to select classification or regression criteria with respect to your learning task.

error.911.name  = Parse error
error.911.short = Cannot parse {0}: {1}
error.911.long  = The output of an external tool could not be parsed. Please make sure that a compatible version is installed.

error.912.name  = Learner cannot estimate performance
error.912.short = The learner {0} is not able to estimate performances: {1}
error.912.long  = The enclosed learner must be able to estimate performances based on the training set. Make sure all settings are properly made and the learner suits the learning task.

error.913.name  = Illegal stream name
error.913.short = Illegal stream name {0} read in data_stream_relevance.
error.913.long  = An illegal stream was read in data_stream_relevance which does not occur in data_stream_names.

error.914.name  = Illegal class
error.914.short = Class {0} does not inherit from {1}.
error.914.long  = Some operators allow to specify implementations of interfaces or subclasses. This error occurs if the specified class does not inherit from the superclass or interface.

error.915.name  = Cannot set PLAF
error.915.short = Cannot set pluggable look and feel ''{0}'': {1}
error.915.long  = The specified pluggable look and feel could not be set. Make sure that the implementation of the PLAF is contained in the classpath and supports your operating system. Legal values are also system and cross_platform. Edit your local config files if this occurs during startup.

error.916.name  = Learner is not able to calculate weights
error.916.short = The learner {0} is not able to calculate weights: {1}
error.916.long  = The enclosed learner must be able to calculate weights based on the training set. This may depend on the parameter settings of the learner and the learning task. 

error.917.name  = No label defined
error.917.short = The label attribute is not defined.
error.917.long  = If the parameter format of a SparseFormatExampleSource is set to anything different from no_label and an attribute description file is specified by the parameter attributes, the label attribute must be defined.

error.918.name  = Cannot create function
error.918.short = Cannot create the function {0}: {1}
error.918.long  = Some target functions desire a specific number of attributes or ranges.

error.919.name  = Cannot calculate performance
error.919.short = Cannot calculate the value of the performance criterion {0}: {1}
error.919.long  = Some performance criteria can only be applied to specific example sets. For example, classification criteria needs a nominal label.

error.920.name  = Significance test error
error.920.short = Cannot calculate the the significance values: {0}
error.920.long  = The calculation of significance test like ANOVA (analysis of variances) or paired t-tests needs specific parameters like a minimum number of groups. Please ensure that the operation can be properly performed.

error.921.name  = No dimension specified
error.921.short = The data set dimension was not specified.
error.921.long  = If the the SparseFormatExampleSource should read data without an attribute description file the total dimension must be specified.

error.922.name  = No optimization parameters specified
error.922.short = No parameters were specified which should be optimized
error.922.long  = You need to specify parameters of inner operators in order to allow the ParameterOptimization operator for optimization.

error.923.name  = Process setup change not allowed
error.923.short = A change in the process setup was tried during process runtime which is not allowed. 
error.923.long  = Certain operations are not allowed during the running of an process. These operations include the adding or the removal of an operator. Please do not perform these setup changes during runtime but stop the process, change the setup, and start it again.

error.924.name  = RVMLearner cannot be applied
error.924.short = It was not possible to decompose the matrix induced by the input example set. 
error.924.long  = The RVMLearner was not able to create a model since the input data set cannot be decomposed. This often happens for small data sets or data sets with a high skew of class probabilities.

error.925.name  = Example sets are not compatible
error.925.short = The given example sets are not compatible: {0}
error.925.long  = The operator expected one or more example sets which are compatible to other ones. This usually means that they must have the same number of attributes and attribute names.

error.926.name  = Wrong iteration parameters
error.926.short = The number of parameter values must be the same for all parameters in the synchronized case.
error.926.long  = For the parameter iteration, the number of parameter possibilities must be the same for all parameters (this applies only for the synchronized iteration mode).

error.927.name  = Thresholds are not sorted
error.927.short = The thresholds given as upper limits must be sorted in ascending order in the parameter list.
error.927.long  = The operator expects the thresholds that mark the upper limits of each nominal/ordinal class to be sorted in ascending order. Each upper limit of a given class is also the lower limit of the class which is given subsequently.

error.928.name  = Cluster model is empty
error.928.short = The given cluster model is empty, i.e. it does not contain any objects.
error.928.long  = The given cluster model does not contain any objects. Please check if it was properly created.

error.929.name  = Cluster model is empty
error.929.short = The given cluster model is empty, i.e. it does not contain any clusters.
error.929.long  = The given cluster model does not contain any clusters. Please check if it was properly created.

error.930.name  = Wrong similarity type
error.930.short = Only example based similarity measures are allowed
error.930.long  = Only using a similarity measure which calculated the values from the attribute values of the examples can be used.

error.931.name  = Cannot parse date
error.931.short = Cannot parse the data in line {2} for attribute {1} with the date format {0}: {3}
error.931.long  = Please check the specified date format and if it conforms to the data in the data set. Please refer to the documentation for supported date formats and how to specify them.

error.932.name  = No input texts specified
error.932.short = No input texts were specified for this text input operator.
error.932.long  = No input texts were specified for this text input operator, please specify at least one text or directory in order to process any text at all.

error.933.name  = No report specified
error.933.short = No report was specified for this reporting operator.
error.933.long  = Before objects can be reported, the operator ReportGenerator has to be applied which defines the format of the report and a name. In cases where several different reports are created in a single process, the reports can be distinguished with this name which then has to be set.

error.934.name  = No report settings specified
error.934.short = No report settings were specified for this reporting operator.
error.934.long  = Before objects can be reported, the operator Reporter has to be configured with information about which object should be reported and which settings should be used (if available).

error.935.name  = Parallel execution failure
error.935.short = The operator {0} has caused an error within the parallel execution: {1}
error.935.long  = An operator within parallel execution has caused an error.

error.936.name  = Reporting caused an error
error.936.short = The reporting caused an error: {0}
error.936.long  = The reporting caused an error preventing the report generation to complete.

error.937.name  = No process log statistics available
error.937.short = The operator needs a statistics data table like those generated by the Log operator 
error.937.long  = Some operators needs one or more statistics data tables in order to properly work. Please add a ProcessLog operator before this operator is applied and collect the necessary statistics data.

error.938.name  = Impossible number of ranges
error.938.short = The selected number of ranges is not applicable in combination with the specified range name since the range names will not differ. 
error.938.long  = If numerical values should be discretized and the range names should be derived from the data (e.g. with range name type 'interval'), it can happen that the value differences are so small that two ranges will get the same name. This will often break the following process and hence the number of ranges should be reduced or a different range name type should be selected.

error.939.name  = No log data table available
error.939.short = There is no log data table (or none with the specified name) available. 
error.939.long  = Some operators work on the statistics which are aggregated by the ProcessLog operator. So the reason for this behavior might be that the process lacks of this operator and no statistics are generated. If you use more than one ProcessLog operator, please note that the corresponding data tables can be distinguished by the generating operator's name.

error.940.name  = Retrieved object does not match type
error.940.short = The object retrieved under the name {0} does not match the specified object type {1}. 
error.940.long  = The IORetriever has found an object which does not match the object type specified by the corresponding parameter. The main reason for this most often is that the type was not correctly set.

error.941.name  = Object retrieval not possible
error.941.short = No object with name {0} was found during retrieval from the object store. 
error.941.long  = The IORetriever was not able to find object with the specified name in the object storage. The main reasons for this most often are that the name is not correct or the object was not previously stored.

error.942.name  = Retrieved object does not match type
error.942.short = The object located at {0} does not match the expected object type {1} (but is: {2}). 
error.942.long  = The reader has found an object which does not match the expected object type. The main reason for this most often is that you selected an incorrect entry in the repository.

error.943.name  = Number of clusters doesn't match number of labels
error.943.short = The example sets label only contain {0} values while there are existing {1} clusters. Mapping impossible. 
error.943.long  = The mapping between label and cluster can only be performed if there are the same number of values.
       
error.944.name  = Impossible number of ranges
error.944.short = The selected number of ranges is not applicable for the attribute {0}, because it has too many equal values. 
error.944.long  = If there are to many same values, a bin might grow over specified size, because values cannot be distinguished. If it grows more than twice it's size some bins would vanish completely, causing this error.

error.945.name  = Script error
error.945.short = The scripting engine {0} reported an error in the script: {1}. 
error.945.long  = Stacktrace: {2}

error.946.name  = No number
error.946.short = According to the specified format, {0} cannot be parsed as a number. 
error.946.long  = This indicates that a nominal value does not represent a number. Numbers are parsed in a localized format that is specified by the user. Please make sure that decimal character etc. are correctly specified.  

error.947.name  = Unknown PMML export type
error.947.short = The class {0} cannot be exported to PMML.
error.947.long  = For some classes are no PMML writer available due to restrictions of the standard or other technical reasons.       

error.948.name  = Error on PMML export
error.948.short = Could not initialize the PMML writer for class {0}.
error.948.long  = This error might result from a wrong class path setup, security restrictions or other restrictions limiting the access to the constructor of the writer.

error.949.name  = Error on PMML export
error.949.short = Some properties of an object of class {0} are not compatible with PMML.
error.949.long  = In some cases it might happen that features are not covered by PMML. These objects cannot be exported because the standard does not support their functionality.

error.950.name  = Instantiation error
error.950.short = Cannot instantiate ''{0}'': {1}
error.950.long  = Some operators instantiate other classes. This may cause errors only if your setup is corrupt.
       
error.951.name  = Example set has wrong size 
error.951.short = All ExampleSets must be of the same size
error.951.long  = All ExampleSets delivered must be of the same size.

error.952.name  = Missing attribute
error.952.short = The training example set must contain all attributes of all other example sets, but {0} is missing at port "unrelated example sets {1}". 
error.952.long  = The training example set must contain the union of all attributes of all other example sets delivered.

error.953.name  = Specified Excel Sheet does not exists
error.953.short = The specified excel sheet number {0} does not exist.
error.953.long  = The specified excel sheet number does not exist. Please specify an existing sheet number.  

error.954.name  = Incompatible Label Attribute
error.954.short = The label attribute contains more than 2 classes.
error.954.long  = Cannot calculate ROC data for non-classification labels or for labels with more than 2 classes.

error.955.name  = Weight class not found
error.955.short = The class name ''{0}'' specified in the class weights parameter does not exist.
error.955.long  = Please remove the entry in question in the class weights parameter or change the class name to an existing one.

error.956.name  = Index attribute name already exists
error.956.short = The index attribute name ''{0}'' already exists in the input example set.
error.956.long  = The attribute specified in the index attribute parameter already exists in the input example set. Please rename it so its name does not exist in the input example set.

error.957.name  = Cannot read SAS file
error.957.short	= The given SAS file cannot be read.
error.957.long	= The internal SAS file structure specifications are not available to the public, therefore reading SAS files may fail for some files.

error.958.name  = No combination of parameters specified
error.958.short = No possible combination of parameters were specified.
error.958.long  = You need to specify a possible combination of parameters of inner operators.

error.959.name  = Wrong attributes
error.959.short = The operator expects an ExampleSet with the same attributes as the ExampleSet used for training of the {0}.
error.959.long  = The operator needs an ExampleSet where the regular attributes have the same names and order as the in the ExampleSet used for training of the {0} operator to calculate useful results. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.960.name  = Attributes do not match
error.960.short = The input ExampleSet does not match the training ExampleSet. Misfitting Attribute: ''{0}''.
error.960.long  = The operator expects the input ExampleSet to have the same set of attributes as the ExampleSet used for training of the input model. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.961.name  = Attributes do not match
error.961.short = The input ExampleSet does not match the training ExampleSet. Wrong Attribute: ''{0}''.
error.961.long  = The operator expects the input ExampleSet to have a set of Attributes which is equal or a subset of the ExampleSet used for training of the input model. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.962.name  = Attributes do not match
error.962.short = The input ExampleSet does not match the training ExampleSet. Missing Attribute: ''{0}''.
error.962.long  = The operator expects the input ExampleSet to have a set of Attributes which is equal or a superset of the ExampleSet used for training of the input model. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.963.name  = Attribute types do not match
error.963.short = The input ExampleSet does not match the training ExampleSet. Attribute ''{0}'' is of value type ''{2}'' but should be ''{1}''. 
error.963.long  = The operator needs an input ExampleSet where the attributes have the same names and types as the ExampleSet used for training of the input model. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.964.name  = Attribute types do not match
error.964.short = The input ExampleSet does not match the training ExampleSet. Attribute ''{0}'' is of value type ''{1}'' but it should be ''{2}'' or a sub-type.
error.964.long  = The operator needs an input ExampleSet where the attributes have the same names and value (sub)types as the ExampleSet used for training of the input model. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.965.name  = Attribute types do not match
error.965.short = The input ExampleSet does not match the training ExampleSet. Attribute ''{0}'' is of value type ''{1}'' but it should be ''{2}'' or a super-type.
error.965.long  = The operator needs an input ExampleSet where the attributes have the same names and value (super)types as the ExampleSet used for training of the input model. Please make sure that the attributes of the two ExampleSets satisfy this condition.

error.966.name	= Unsupported operation
error.966.short	= 'Add generated primary keys' is not supported for MSSQL.
error.966.long	= Please deselect the option 'Add generated primary keys'.

error.967.name = Class name does not exist
error.967.short = A class named ''{0}'' does not exist. 
error.967.long = You specified a class weight for a class which is not present in the input example set. Please check if you misspelled the class name.

error.968.name  = Wrong process setup
error.968.short = Error in process setup
error.968.long  = The Hyper Hyper model has to be build with only one positive and one negative example.

error.969.name = Too deeply nested process executions
error.969.short = The "Execute Process" nesting depth exceeded the limit of {0}.
error.969.long = To prevent serious problems, the maximum nesting process execution depth is limited. Please make sure that you have not accidentally built an endless loop via "Execute Process". The maximum depth can be increased in the preferences.

error.970.name  = Non-matching values
error.970.short = The example sets label contains other values than the selected attribute.
error.970.long  = It should be made sure that the selected attribute has the same set of possible values as the label.

error.971.name = Error in executed process
error.971.short = The ''{0}'' operator in the process executed by ''{1}'' failed with: {2}
error.971.long = This error needs to be fixed before the current process can run.

error.972.name = Error in executed process
error.972.short = The process executed by ''{0}'' failed with: {1}
error.972.long = This error needs to be fixed before the current process can run.


error.io.dir_creation_fail.name  = Cannot create directory
error.io.dir_creation_fail.short = Failed to create directory ''{0}''.
error.io.dir_creation_fail.long	= Please check if you have permissions to create a directory in the specified location.

error.io.delete_file.name  = Cannot delete file
error.io.delete_file.short = Could not delete the file ''{0}''.
error.io.delete_file.long  = The given file could not be deleted. Please make sure, that the RapidMiner process has sufficient privileges to delete this file.

error.io.directory_not_found.name  = Directory not found
error.io.directory_not_found.short = The directory ''{0}'' does not exist.
error.io.directory_not_found.long  = A directory does not exist or cannot be accessed with the current user rights.

error.io.retrieve.dashboard.not_possible.name=Object retrieval not possible
error.io.retrieve.dashboard.not_possible.short=No app object with the name ''{0}'' exists.
error.io.retrieve.dashboard.not_possible.long=Objects can be retrieved only after they have been added with a Publish to App operator executed within the same app or within the same RapidMiner Studio session.

error.io.retrieve.dashboard.empty_object_name.name=No App object selected
error.io.retrieve.dashboard.empty_object_name.short=App object selection is empty! 
error.io.retrieve.dashboard.empty_object_name.long=To subscribe to an App object enter the name into the subscription text field.

error.io.retrieve.dashboard.not_possible_server.name=Object retrieval not possible
error.io.retrieve.dashboard.not_possible_server.short=An object with the name ''{0}'' does not exist in this app.
error.io.retrieve.dashboard.not_possible_server.long=Objects can be retrieved only after they have been added with a Publish to App operator executed within the same app.

error.aggregation.no_groups_defined.name = No Groups Defined
error.aggregation.no_groups_defined.short = To aggregate an example set, you have to select at least one attribute for grouping.
error.aggregation.no_groups_defined.long = During the aggregation, the example set will be distributed into groups that are defined by the values of all group-by attributes. Every example having the same values in the group-by attributes will be assigned to that group. Over these groups the aggregation functions will be evaluated, hence you need to select at least on attribute for grouping.

error.aggregation.incompatible_attribute_type.name = Attribute is not compatible
error.aggregation.incompatible_attribute_type.short = Attribute {0} is not compatible with the aggregation function {1}.
error.aggregation.incompatible_attribute_type.long = Certain aggregation functions can only be evaluated on attributes that have a certain value type. For example it is impossible to calculate a mean value or the product of nominal values.

error.aggregation.aggregation_attribute_not_present.name = Attribute not present
error.aggregation.aggregation_attribute_not_present.short = The given example set does not contain an attribute {0}.
error.aggregation.aggregation_attribute_not_present.long = Please check whether it's missing in the input example set erroneously or correct the settings in parameter 'aggregation attributes'.

error.aggregation.illegal_function_name.name = Unknown aggregation function
error.aggregation.illegal_function_name.short = You tried to create an aggregation function with the name {0} that does not exist.
error.aggregation.illegal_function_name.long = The aggregation operator offers two parameters, the default aggregation function and the list of aggregation attributes with the respective function. You have to select an existing function to avoid this problem.

error.nominal_to_numerical.illegal_comparison_group.name  = Illegal comparison group
error.nominal_to_numerical.illegal_comparison_group.short = No comparison group has been set for attribute {0}, or it has an invalid value. Value is "{1}"
error.nominal_to_numerical.illegal_comparison_group.long  = If dummy coding or effect coding is used, the comparison group must be defined for each transformed attribute. In addition, the value must be present in the example set.

error.join.illegal_key_attribute.name  = Illegal key attribute
error.join.illegal_key_attribute.short = The attribute {0} could not be found in the {1} example set, or it has a different type than attribute {2} in the {3} example set.
error.join.illegal_key_attribute.long  = All key attributes must be present, and each pair that should be matched must be of the same type.

error.join.unsupported_key_attribute.name  = Unsupported key attribute
error.join.unsupported_key_attribute.short = The key attribute {0} has a type that is not supported by the join operator.
error.join.unsupported_key_attribute.long  = Date-time and time are the only supported object column types.
        
error.join.id_key_attribute.name  = Wrong number of id attributes
error.join.id_key_attribute.short = There must be exactly one id attribute in both inputs.
error.join.id_key_attribute.long  = For a join with id attribute as key, both inputs must contain exactly one id attribute.

error.nominal_to_numerical.duplicate_comparison_group.name  = Duplicate comparison group
error.nominal_to_numerical.duplicate_comparison_group.short = There has been set more than one comparison group for attribute {0}.
error.nominal_to_numerical.duplicate_comparison_group.long  = If dummy coding or effect coding is used, the comparison group must be defined exactly once for each transformed attribute.

error.performance_costs.class_order_definition_misses_value.name = Missing value in class order definition
error.performance_costs.class_order_definition_misses_value.short = You defined an explicit ordering of the class values, but the value {0} that occurs in the label attribute is not mentioned.
error.performance_costs.class_order_definition_misses_value.long = In order to make the explicit ordering work, you have to define all possible values.

error.performance_costs.cost_matrix_with_wrong_dimension.name = Wrong size of cost matrix
error.performance_costs.cost_matrix_with_wrong_dimension.short = Your cost matrix has a size of {0} but must be a quadratic matrix with a size of {1}.
error.performance_costs.cost_matrix_with_wrong_dimension.long = You entered a cost matrix that does not have an entry for each class value as defined in the explicit ordering of the class order definition parameter.

error.superset.incompatible_roles.name  = Incompatible roles
error.superset.incompatible_roles.short = Incompatible roles: the attribute named {0} has different roles in the input sets ({1} vs. {2}).
error.superset.incompatible_roles.long  = The roles of common attributes must be the same for both example sets.

error.superset.special_not_found.name = Special attribute not found
error.superset.special_not_found.short = The special attribute named {0} does not exist in both example sets. 
error.superset.special_not_found.long = Please make sure that both example sets contain the same special attributes.

error.file_consumer.no_file_defined.name  = No input file defined
error.file_consumer.no_file_defined.short = No input file was defined.
error.file_consumer.no_file_defined.long  = No file input defined: depending on the source type configuration of your operator, you have to either connect the file input port or define the parameter which specifies the file to be read. 

error.file_consumer.error_loading_file.name  = Failed to load file
error.file_consumer.error_loading_file.short = The file could not be loaded.
error.file_consumer.error_loading_file.long  = The file could not be loaded. Make sure the format is valid and the file exists! 

error.failed_to_instantiate_operator.name = Failed copyto instantiate operator
error.failed_to_instantiate_operator.short = The operator {0} could not be instantiated: {1}
error.failed_to_instantiate_operator.long  = An operator has tried to instantiate a utility operator to execute a subtask. This operator could not be created. A common reason for this are dependency issues.

error.data_import.specified_more_columns_than_exist.name = Reading data set failed
error.data_import.specified_more_columns_than_exist.short = An attribute {0} was specified for column {1}, but this column does not exist in input data.
error.data_import.specified_more_columns_than_exist.long = You specified an attribute in the data set meta data information parameter, that does not exist in the input data. You need to correct the settings, re-run the Import Configuration Wizard or select another input source.

error.data_import.non_unique_column_name.name = Reading data set failed
error.data_import.non_unique_column_name.short = The column name ''{0}'' is not unique.
error.data_import.non_unique_column_name.long = You used a certain name for more than one column. Column names must be unique.

error.aggregation_operator.unsupported_value_type.name  = Unsupported value type
error.aggregation_operator.unsupported_value_type.short = The grouping attribute {0} is of type {1}, which is not supported by the operator.
error.aggregation_operator.unsupported_value_type.long  = The Aggregation operator supports only grouping attributes of type numeric, nominal and date_time (and their sub-types). 

error.repository_management.relocate_repository_entry.name  = Could not relocate repository entry
error.repository_management.relocate_repository_entry.short = Relocating the repository entry "{0}" has failed. Reason: "{1}".
error.repository_management.relocate_repository_entry.long  = Relocating the repository entry has failed. Maybe there is an equally named entry at the destination folder whereas overwriting is not allowed.

error.repository_management.rename_repository_entry.name  = Could not rename repository entry
error.repository_management.rename_repository_entry.short = Renaming the repository entry "{0}" to "{1}" has failed. Reason: "{2}".
error.repository_management.rename_repository_entry.long  = Renaming the repository entry has failed. Maybe the repository entry does not exist or there is an equally named entry in the same folder whereas overwriting is not allowed.

error.repository_management.dest_not_in_folder.name  = Destination not inside a folder
error.repository_management.dest_not_in_folder.short = The parent "{1}" of destination "{0}" is not a folder.
error.repository_management.dest_not_in_folder.long  = It is not possible to copy/move an entry if the parent is not a folder.

error.repository_management.copy_repository_entry.name = Error copying repository entry 
error.repository_management.copy_repository_entry.short = Error copying repository entry {0}: {1}
error.repository_management.copy_repository_entry.long = The repository entry could not be copied due to an underlying repository error, such as access permission violation, an invalid entry name, etc.

error.repository_management.move_repository_entry.name = Error moving repository entry 
error.repository_management.move_repository_entry.short = Error moving repository entry {0}: {1}
error.repository_management.move_repository_entry.long = The repository entry could not be moved due to an underlying repository error, such as access permission violation, an invalid entry name, etc.

error.illegal_send_mail_method.name  = Illegal send mail method
error.illegal_send_mail_method.short = The illegal mail sending method {0} was set 
error.illegal_send_mail_method.long  = It is not possible to send mails with the above set method

error.sending_mail_to_address_error.name  = Sending mail failed 
error.sending_mail_to_address_error.short = Cannot send mail to ''{0}''. {1}
error.sending_mail_to_address_error.long  = An error occurred while trying to send a mail. See log for more details.

error.excel_sheet_name_too_long.name  = Sheet name too long
error.excel_sheet_name_too_long.short = Sheet name ''{0}'' is too long. Only 31 characters allowed but {1} used.
error.excel_sheet_name_too_long.long  = Sheet names in Excel must not exceed 31 characters.

error.excel_sheet_name_duplicate.name  = Duplicate sheet name
error.excel_sheet_name_duplicate.short = Duplicate excel sheet name ''{0}''.
error.excel_sheet_name_duplicate.long  = Duplicate excel sheet name ''{0}''. Sheet names must be unique.

error.cannot_parse_expression.name  = Invalid expression
error.cannot_parse_expression.short = The expression "{0}" cannot be parsed. Error was: {1}
error.cannot_parse_expression.long  = The expression is not valid. Please have a look at the operator help for syntax information.

error.attribute_subset_preprocessing.role_conflict.name  = Incompatible role
error.attribute_subset_preprocessing.role_conflict.short = Incompatible role: the role {0} of the attribute "{1}" exists already.
error.attribute_subset_preprocessing.role_conflict.long  = Please set another role or choose a way to handle it in the parameter role conflict handling.

error.attribute_subset_preprocessing.name_conflict.name  = Incompatible attribute name
error.attribute_subset_preprocessing.name_conflict.short = Incompatible attribute name: the attribute "{0}" exists already in the original Table.
error.attribute_subset_preprocessing.name_conflict.long  = Please rename the attribute or choose a way to handle it in the parameter name conflict handling.

error.attribute_subset_preprocessing.role_name_conflict.name  = Incompatible attribute names and roles
error.attribute_subset_preprocessing.role_name_conflict.short = Incompatible attribute names: the name "{0}" is duplicated with different roles
error.attribute_subset_preprocessing.role_name_conflict.long  = The name attribute exists in input table and in the sub-table. Please rename one of these attributes.

error.copy_file.ioerror.name 	= I/O error
error.copy_file.ioerror.short 	= Cannot copy from {0} to {1}. {2}
error.copy_file.ioerror.long 	= An I/O error occurred while copying file.

error.copy_file.exists.name 	= File already exists.
error.copy_file.exists.short 	= Cannot copy. File {0} already exists.
error.copy_file.exists.long		= The file could not be copied, because the target file already exists and overwriting is disabled.

error.move_file.exists.name		= File already exists.
error.move_file.exists.short	= Cannot move. File {0} already exists.
error.move_file.exists.long		= The file could not be moved, because the target file already exists and overwriting is disabled.

error.move_file.failure.name	= Moving failure
error.move_file.failure.short	= The file {0} could not be moved to {1}.
error.move_file.failure.long	= Unable to move the file to the given destination. 

error.rename_file.exists.name	= File already exists.
error.rename_file.exists.short	= The file ''{0}'' cannot be renamed to ''{1}''.
error.rename_file.exists.long 	= The file could not be renamed, because a file with this name already exists.

error.delete_file.failure.name	= Deleting failure
error.delete_file.failure.short	= Deleting of ''{0}'' not successful
error.delete_file.failure.long	= The file or the directory could not be deleted.

error.create_directory.failure.name		= Couldn''t create directory
error.create_directory.failure.short	= Unable to create directory ''{0}''.
error.create_directory.failure.long		= The directories needed couldn''t be created.

error.create_directory.exists.name	= Directory already exists
error.create_directory.exists.short	= Directory ''{0}'' could not be created.
error.create_directory.exists.long	= Unable to create directory, because it already exists.

error.wrong_date_format.name  = Wrong Date Format
error.wrong_date_format.short = Could not parse date ''{0}'' for parameter ''{1}''.
error.wrong_date_format.long  = The date string has to be of format 'yyyy.MM.dd HH:mm:ss Z' where Z is the RFC 822 time zone. For example 2001.07.04 14:08:56 -0700.

error.invalid_date_format.name  = Invalid Date Format
error.invalid_date_format.short = Date format ''{0}'' is invalid: ''{1}''.
error.invalid_date_format.long  = Only certain characters are allowed in a date or time format pattern: GyMdkHmsSEDFwWahKzZYuXL. For predefined formats, please use the parameter dropdown list. 

error.scheduling_failed.name  = Scheduling failed
error.scheduling_failed.short = Scheduling process failed. Reason: {0}
error.scheduling_failed.long  = There was an error scheduling the process.

error.not_on_ra_repository.name  = No RapidMiner AI Hub Repository
error.not_on_ra_repository.short = The selected entry''s repository ''{0}'' is no RapidMiner AI Hub repository. 
error.not_on_ra_repository.long  = Only processes on a RapidMiner AI Hub repository can be scheduled.


error.archive_file.stream_error.name  = Error creating archive stream
error.archive_file.stream_error.short = Error creating a stream for reading or writing the archived data.
error.archive_file.stream_error.long  = Before an archive file can be created, the compressed data must be read from a stream. This stream could not be created.

error.zip_file_object.cannot_add_entry_after_finish.name  = Error adding entry to zip file
error.zip_file_object.cannot_add_entry_after_finish.short = No entries can be added to a zip file after the file has been closed.
error.zip_file_object.cannot_add_entry_after_finish.long  = A zip file is closed when it is accessed for the first time other than adding entries, e.g. when it is written to disk or when it's entries are looped. 

error.zip_file_object.zip_error_while_adding.name  = Error adding entry to zip file
error.zip_file_object.zip_error_while_adding.short = Error adding entry to zip file. Reason: {0}
error.zip_file_object.zip_error_while_adding.long  = 

error.archive_file.undefined_filename.name  = Undefined filename
error.archive_file.undefined_filename.short = One of the input files does not have a filename.
error.archive_file.undefined_filename.long  = When adding file objects to an archive file, the filename is extracted from an annotation called ''Filename''. If that does not exist, the operator falls back to the ''Source'' annotation. If that still fails, this error message appears. You can define annotations with the ''Annotate'' operator.


error.annotate.duplicate_annotation.name  = Duplicate annotation
error.annotate.duplicate_annotation.short = The annotation with the name ''{0}'' already exists.
error.annotate.duplicate_annotation.long  = Annotation names must be unique. If you want to overwrite existing annotations, activate the corresponding parameter.

error.annotations.annotation_not_exist.name  = Annotation does not exist
error.annotations.annotation_not_exist.short = The annotation ''{0}'' does not exist.
error.annotations.annotation_not_exist.long  = The data object does not contain the annotation with the given annotation.

error.log.duplicate_column.name  = Duplicate log column
error.log.duplicate_column.short = The log column with the name ''{0}'' already exists.
error.log.duplicate_column.long  = Log column names must be unique. Please rename the columns so that every one has a unique name.

error.som_dimension_reduction.too_high_parameter_values.name = Too high parameter values
error.som_dimension_reduction.too_high_parameter_values.short = The SOM Operator is not able to run with these parameter values.
error.som_dimension_reduction.too_high_parameter_values.long = The SOM Operator cannot run with these parameter values. Please reduce the net size and/or the number of dimensions.



error.averagable_input_missing.name  = Average operator missing an input
error.averagable_input_missing.short = The operator needs at least one input for which an average can be computed.
error.averagable_input_missing.long  = The operator was not supplied with an input, or the supplied input is not averagable. Please supply an input for which average can be computed for instance attribute weights or performances.

error.pca_attribute_names.name = Attribute name is not allowed
error.pca_attribute_names.short = The attribute name ''{0}'' is not allowed in the input example set.
error.pca_attribute_names.long  = It is not allowed to use attribute names which start with 'pc_' in the input example set for the PCA operator. Please rename the attribute so its name does not start with 'pc_' in the input example set.

error.processembeddingoperator.not_in_subprocess.name = Undefined macro
error.processembeddingoperator.not_in_subprocess.short = The macro ''{0}'' is not defined in the context of the included process, but "fail for undefined macros" is checked.
error.processembeddingoperator.not_in_subprocess.long = Please remove the undefined macro or select a process which uses the macro. If you do not want the operator to check for undefined macros, deselect the parameter "fail_for_undefined_macros".

error.predefinedMacro_shiftedExecutionCounter_format.name = Wrong Macro Format
error.predefinedMacro_shiftedExecutionCounter_format.short = The predefined macro ''p[]'' was in wrong format.
error.predefinedMacro_shiftedExecutionCounter_frmat.long = Please make sure to use the p[] macro as follows : %{p[offset]}. Where %{} is the indicator for a macro.

error.predefinedMacro_OperatorValue_format.name = Wrong Macro Format
error.predefinedMacro_OperatorValue_format.short = The predefined macro ''v[]'' was in wrong format.
error.predefinedMacro_OperatorValue_format.long = Please make sure to use the v[] macro as follows : %{v[OperatorName.ValueName]}. Where %{} is the indicator for a macro, OperatorName the displayed name of your Operator and ValueName the name of the value you want to address.

error.predefinedMacro_OperatorValue_wrongOperator.name = Wrong Operator Name
error.predefinedMacro_OperatorValue_wrongOperator.short = The specified operator with name ''{0}'' does not exist.
error.predefinedMacro_OperatorValue_wrongOperator.long = Please make sure that the operator specified in the v[] macro exists and can be found inside the current process or its subprocesses.

error.predefinedMacro_OperatorValue_noValue.name = Wrong Value Name
error.predefinedMacro_OperatorValue_noValue.short = The predefined value with name ''{0}'' does not exist.
error.predefinedMacro_OperatorValue_noValue.long = Please make sure that the operator specified in the v[] macro contains the selected value.

error.malformed_regexp_replacement.name =  Regular Expression Replacement Invalid
error.malformed_regexp_replacement.short = The replacement ''{0}'' for the regular expression ''{1}'' is invalid.
error.malformed_regexp_replacement.long = Dollar signs may be treated as references to captured subsequences, and backslashes are used to escape literal characters in the replacement string.

error.filter_wrong_type.name = Incompatible Filter Comparator
error.filter_wrong_type.short = Comparator and attribute have incompatible value types: ''{0}''
error.filter_wrong_type.long = Please make sure that each filter contains a comparator which matches the value type of the attribute.

error.xlsx_file_missing_entry.name = XLSX file is malformed
error.xlsx_file_missing_entry.short= The selected XLSX file seems to be malformed as it is missing a required file entry: ''{0}'' 
error.xlsx_file_missing_entry.long = Try to repair it by opening it in Excel and saving it again.

error.xlsx_content_malformed.name = XLSX file content is malformed
error.xlsx_content_malformed.short = The selected XLSX file seems to be malformed as it's content is in an invalid state.
error.xlsx_content_malformed.long = Try to repair it by opening it in Excel and saving it again.

error.expression_evaluation_failed.name  = Expression evaluation error
error.expression_evaluation_failed.short = Error evaluating expression: {0}
error.expression_evaluation_failed.long  = Please check the erroneous expression ''{1}''.

error.scriptingOperator_security.name = Security violation
error.scriptingOperator_security.short = The given script violated security constraints of RapidMiner Studio.
error.scriptingOperator_security.long = The error was: {0}

error.scriptingOperator.advanced_columns.name = Cannot handle advanced columns
error.scriptingOperator.advanced_columns.short = The input contains advanced columns which this operator cannot handle.
error.scriptingOperator.advanced_columns.long = Please remove the advanced column ''{0}'' of type ''{1}'' from the input.

error.license_data_row_limit.name = License limit reached
error.license_data_row_limit.short = Your license only permits up to {0} rows of data in a process, however the input data contained {1} rows.
error.license_data_row_limit.long = Please consider upgrading your license in case you want to use data with more than {0} rows.

error.cartesian_product_too_big.name = Cartesian product too big
error.cartesian_product_too_big.short = The cartesian product of the two input example sets exceeds the maximum supported rows.
error.cartesian_product_too_big.long = Please provide smaller input example sets, for example by using one of the sampling operators beforehand.

error.append.result_too_big.name = Append result too big
error.append.result_too_big.short = The appended example sets together exceeds the maximum supported rows.
error.append.result_too_big.long = Please provide smaller input example sets, for example by using one of the sampling operators beforehand.

error.join_too_big.name = Join too big
error.join_too_big.short = The joined example set exceeds the maximum supported rows.
error.join_too_big.long = To reduce the size of the joined example set consider using another join type or applying a filter to the inputs.

error.context_input_other.name  = Problem with context data
error.context_input_other.short = Problem occurred while loading context data
error.context_input_other.long  = The error was: {0}

error.optimize.parameters.no_results.name  = No results from optimization
error.optimize.parameters.no_results.short = The operator {0} did not produce a result.
error.optimize.parameters.no_results.long  = Check the process setup for errors in operators generating a{1} object.

error.tree_criterion.nominal_chi.name  = Wrong criterion
error.tree_criterion.nominal_chi.short = The {0} criterion cannot be used  for nominal labels.
error.tree_criterion.nominal_chi.long  = Please use one of the other criteria for data with a nominal label.

error.tree_criterion.numerical_not_chi.name  = Wrong criterion 
error.tree_criterion.numerical_not_chi.short = The {1} criterion cannot be used for numeric labels.
error.tree_criterion.numerical_not_chi.long  = Please use the {0} criterion for data with a numeric label.

error.weight_extraction.regression_forest.name  = Incompatible criterion
error.weight_extraction.regression_forest.short = Attribute Weights cannot be extracted from regression trees.
error.weight_extraction.regression_forest.long  = Use this operator only for trees calculated with the specified criterion.

error.loop_parameters.too_many_combinations.name  = Too many combinations of parameters specified
error.loop_parameters.too_many_combinations.short = Too many possible combinations of parameters were specified.
error.loop_parameters.too_many_combinations.long  = The possible number of combinations exceeds the integer range. Please specify less parameter combinations or split them up with a nested loop/optimize.

error.seemingly_unrelated_regression.too_few_inputs.name  = Too few input ExampleSets
error.seemingly_unrelated_regression.too_few_inputs.short = Too few input ExampleSets are provided.
error.seemingly_unrelated_regression.too_few_inputs.long  = This operator needs at least 2 unrelated input ExampleSets besides the main ExampleSet. For building a regression model on a single ExampleSet, use the Linear Regression operator.

error.seemingly_unrelated_regression.singular_matrix_found.name  = Similar inputs
error.seemingly_unrelated_regression.singular_matrix_found.short = Similar inputs are connected to unrelated ports.
error.seemingly_unrelated_regression.singular_matrix_found.long  = This operator cannot work when similar inputs are connected to unrelated ports. Did you connect the same ExampleSet to 2 ports?

error.regularized_discriminant_analysis.singular_covariance_matrix.name  = Covariance matrix not invertible
error.regularized_discriminant_analysis.singular_covariance_matrix.short = The covariance matrix could not inverted.
error.regularized_discriminant_analysis.singular_covariance_matrix.long  = Check the "approximate_covariance_inverse" parameter and/or make sure that the data provided is sufficient.

error.transpose_duplicate_id.name  = Duplicate ID found
error.transpose_duplicate_id.short = Example set cannot be transposed because of a duplicate ID value: <em>{0}</em>.
error.transpose_duplicate_id.long  = The ID was already present, which is not allowed because IDs must be unique.

# same as error.152
error.duplicate_attribute.name  = Attribute already present
error.duplicate_attribute.short = The attribute <em>{0}</em> was already present in the example set.
error.duplicate_attribute.long  = An attribute was already present in the example set, which is not allowed because attribute names must be unique.

# simliar to error.152
error.duplicate_attribute_role.name  = Attribute role already present
error.duplicate_attribute_role.short = The attribute role <em>{0}</em> was already present in the example set.
error.duplicate_attribute_role.long  = An attribute role was already present in the example set, which is not allowed because attribute role names must be unique.

# similar to error.aggregation.aggregation_attribute_not_present
error.no_such_attribute.name  = Attribute not present
error.no_such_attribute.short = The given example set does not contain an attribute <em>{0}</em>.
error.no_such_attribute.long  = Please check whether it's missing in the input example set erroneously or correct the settings in the parameters.

# similar to error.aggregation.aggregation_attribute_not_present
error.no_such_attribute_role.name  = Attribute role not present
error.no_such_attribute_role.short = The given example set does not contain an attribute role <em>{0}</em>.
error.no_such_attribute_role.long  = Please check whether it's missing in the input example set erroneously or correct the settings in the parameters.

error.invalid_positive_class.name  = Invalid positive class
error.invalid_positive_class.short = The specified positive class ''{0}'' does not match any of the label's values.
error.invalid_positive_class.long  = Please check whether it's missing in the input example set erroneously or correct the settings in the parameters.

error.performance_criterion_class_mismatch.name  = Mismatched criterion class
error.performance_criterion_class_mismatch.short = The criteria classes<br/>''{0}''<br/>and<br/>''{1}''<br/>are not comparable.
error.performance_criterion_class_mismatch.long  = Different performance criteria can not be easily compared. They may vastly differ in their value range, especially in regards to the positive/negative value spectrum. If you want to compare different performance criteria or combine them for one model, you can either select to use more than one criteria in a Performance Operator or combine them with the Combine Performance Operator. 

error.performance_criterion_type_mismatch.name   = Mismatched criterion type
error.performance_criterion_type_mismatch.short  = The criteria types ''{0}'' and ''{1}'' do not match.
error.performance_criterion_type_mismatch.long   = Different performance criteria can not be easily compared. They may vastly differ in their value range, especially in regards to the positive/negative value spectrum. If you want to compare different performance criteria or combine them for one model, you can either select to use more than one criteria in a Performance Operator or combine them with the Combine Performance Operator.

error.no_cluster_model_calculated.name  = No cluster model calculated
error.no_cluster_model_calculated.short = No cluster model could be calculated.
error.no_cluster_model_calculated.long  = Please check your data and try again.

error.de_pivot.type_mismatch.name = Mismatched attribute types
error.de_pivot.type_mismatch.short = Please make sure that all selected attributes of {3} have the same type.
error.de_pivot.type_mismatch.long = Attribute {0} is of type ''{1}'', expected ''{2}''.

error.de_pivot.length_mismatch.name = Mismatched attribute selections
error.de_pivot.length_mismatch.short = The number of selected attributes for {0} ({1}) and {2} ({3}) is different.
error.de_pivot.length_mismatch.long = Please check your <i>attribute name</i> regexes for spelling errors. Also be aware that attributes can only be selected once.

error.inapplicable_bregman_divergence.name = Zero or negative Attribute
error.inapplicable_bregman_divergence.short = The bregman divergence {0} requires positive nonzero attribute values.
error.inapplicable_bregman_divergence.long = Proceed with the 'Squared Euclidean distance' bregman divergence.

error.inapplicable_bregman_divergence.multiple.name = Multiple Attributes
error.inapplicable_bregman_divergence.multiple.short = The bregman divergence {0} requires a single attribute.
error.inapplicable_bregman_divergence.multiple.long = Proceed with the 'Squared Euclidean distance' bregman divergence.

error.inapplicable_bregman_divergence.range.name = Attribute out of range
error.inapplicable_bregman_divergence.range.short = The bregman divergence {0} requires values between 0 (exclusive) and 1 (exclusive).
error.inapplicable_bregman_divergence.range.long = Proceed with the 'Squared Euclidean distance' bregman divergence.

error.operator_blacklisted.name = Operator is prohibited
error.operator_blacklisted.short = The usage of this operator has been prohibited by your system administrator.
error.operator_blacklisted.long = Please contact your administrator for more information.

error.aggregation.percentile.outofbounds.name = Aggregate percentile error
error.aggregation.percentile.outofbounds.short = Percentile values must be greater than 0 and lower than or equal to 100.
error.aggregation.percentile.outofbounds.long = Input was {0}.

error.aggregation.percentile.notparsable.name = Percentile value not parsable
error.aggregation.percentile.notparsable.short = Percentile value could not be read, expected ''{0}''.
error.aggregation.percentile.notparsable.long = Input was ''{1}''.

error.aggregation.percentile.numberformat.name = Percentile number not parsable
error.aggregation.percentile.numberformat.short = Percentile value could not be read.
error.aggregation.percentile.numberformat.long = Expecting an integer or a floating point value but found ''{0}''.

error.connection.no_connection.name = No connection selected
error.connection.no_connection.short = No repository connection entry was selected
error.connection.no_connection.long = Choose a connection entry from a repository or connect the corresponding input port

error.connection.wrong_entry_type.name = Wrong repository entry 
error.connection.wrong_entry_type.short = The selected repository entry is not a connection 
error.connection.wrong_entry_type.long = Choose a connection entry from a repository or connect the corresponding input port

error.connection.wrong_entry_data.name = Wrong repository entry data
error.connection.wrong_entry_data.short = The selected repository entry does not contain a connection 
error.connection.wrong_entry_data.long = Choose a connection entry from a repository or connect the corresponding input port

error.connection.repository_error.name = Repository error
error.connection.repository_error.short = An error occurred while accessing repository entry ''{0}''
error.connection.repository_error.long =

error.connection.no_container.name = Error while resolving connection
error.connection.no_container.short = The selected/provided connection cannot be resolved
error.connection.no_container.long =

error.connection.mismatched_type.name = Mismatched connection type
error.connection.mismatched_type.short = Expected connection of type ''{0}'' but found ''{1}'' instead
error.connection.mismatched_type.long = Choose a connection of type ''{0}'' and try again

error.connection.adapter.no_handler_registered.name  = No handler registered
error.connection.adapter.no_handler_registered.short = No handler could be found for the type ''{0]''
error.connection.adapter.no_handler_registered.long  = Please check if you need to install an extension to use this connection type

error.connection.adapter.validation_error.name  = Connection not correctly configured
error.connection.adapter.validation_error.short = Some values are not set correctly for the connection {0} of type {1}.
error.connection.adapter.validation_error.long  = Please check your connection again for missing values, missing/undefined placeholders or misconfigured sources.

error.table_not_convertible.custom_column.name  = Table not convertible
error.table_not_convertible.custom_column.short = The input data table cannot be converted to the required format because it contains advanced columns.
error.table_not_convertible.custom_column.long  = Please remove the advanced column ''{0}'' of type ''{1}'' from the input.

error.connection.mail.retrieval_error.name = Error while retrieving mail connection
error.connection.mail.retrieval_error.short = Check the selected connection and it's settings.
error.connection.mail.retrieval_error.long = Could not read the specified connection in the repository.

connection.mail.no_connection_specified.name = No connection selected
connection.mail.no_connection_specified.short = Select a mail connection to send a mail from
connection.mail.no_connection_specified.long = Please specify a connection in the parameter or connect the connection input port.

error.cannot_access_repository_for_location.name = Cannot access repository for given location
error.cannot_access_repository_for_location.short = The selected repository for the given location ''{0}'' cannot be accessed.
error.cannot_access_repository_for_location.long = Please consult the log for more information.