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dataset | Methods

Creator of the dataset class

Syntax

    data

    data(d)

    data(data,'attr1',val1,'attr2',val2,...)

    data(data,symbols,vartypes,labels,variables)

    data(classes,data,symbols,vartypes,labels,variables)

    data(datafile,variables,symbols,vartypes,labels)

Description

`data` creates an empty data object.

`data(d)` clone the data object or fix a broken data object from its struct form.

`data(data,'attr1',val1,'attr2',val2,...)` create a data object. `data` are the data (each piece of data is on a different line). The couples of `attr1`, `val1`, ... are used to set the attributes.

`data(data,symbols,vartypes,labels,variables)` create a data object. `data` are the data (each piece of data is on a different line). `labels` is a column vector of the labels (optional) corresponding to each piece of data. `variables` [cell], are the names of the columns of the data. `symbols` [cell] are the symbolic correspondance for the symbolic data. `vartypes` is a cell array containing the type of the variable for each column ('continuous','discrete','symbolic','class'). There can only be one 'class' variable at a time. If vartypes is not specified, the types are deduced from the `data` and the symbol field

`data(classes,data,symbols,vartypes,labels,variables)` create a data object. `data` are the data (each piece of data is on a different line). `labels` is a column vector of the labels (optional) corresponding to each piece of data. `classes` contains the class associated with each piece of data. `variables` [cell], are the names of the columns of the data. `symbols` [cell] are the symbolic correspondance for the symbolic data.

`data(datafile,variables,symbols,vartypes,labels)` create a data object from a mat datafile. `datafile` is the name of the mat file where the data are saved. The file must contain a series of variables referenced by `variables`[cell]. Each of this variable must be a column vector([double] or [cell]) of length equal to the number of data points.

Methods

- addclass add a class to the data set (obsolete)
- addfeature Adds a new feature to the data set
- addpoint Add a point to the dataset
- addsfeature Adds a new symbolic feature to the data set
- addvariable Add a new variable(feature) to the data set
- bootstrap BOOTSTRAP creates n new datasets ready for Bagging purpose or cross validation
- btstrap2 BTSTRAP2 creates n new datasets ready for Bagging purpose or cross validation
- classgroup Groups a few classes into more general classes
- classpart selects a part (of the classes) of the data set
- csplit Cut the dataset into n pieces for x validation purposes
- cut Cut the dataset into n pieces for x validation purposes
- normalise performs a denormalisation of the continuous data field of a dataset
- display displays the dataset object
- double converts the dataset object to double
- export Export a data set to a comma delimited text file
- fs FS performs a features selection on the dataset d
- fuzzyknn performs fuzzy nearest neightbours algorithm
- fuzzyknn2 performs fuzzy nearest neightbours algorithm (matlab release)
- get Access/query dataset property values
- getmiss get missing values from a dataset
- gfkclus performs Gustaffson Kessel clustering
- horzcat Horizontal concatenation operator
- join joins two data sets
- kmeans performs kMeans clustering
- leave1out LEAVE1OUT performs the leave-one-out validation and computes the confusion matrix, error rates and Kappa inside a report structure
- mkmeans performs multi prototypes clustering
- normalise NORMALISE performs the linear normalisation of a learning dataset and transforms the corresponding testing dataset if it is provided
- normstd NORMSTD performs a normalisation of the continuous features of a learning set and a testing set
- pick picks randomly chosen examples out of a data set
- placekernels Place kernels to be used with smoothknn
- plot plot a dataset
- postmining compute the confusion matrix and error rates between supervision and prediction
- randfs1 RANDFS1 : a features subsets builder by sampling with replacement
- randfs2 RANDFS2 : a features subsets builder by sampling without replacement
- remPoint remove a point from a data set
- set Set object properties
- simple_knn classifies a testing set using kNN
- simple_knn2 classifies the query using kNN (matlab release)
- simple_MLP SIMPLE_MLP performs a one-hidden-layer neural network classification
- simpleknn classifies the query using kNN
- simpleknn classifies the query using kNN
- size get the size of the data set
- smoothknn classifies the query using smoothknn
- subsref overload the subscript reference operator
- svm Support Vectors Machine classification
- svminit Initialise the Support Vectors for the svm classification
- vertcat vertical concatenation operator

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