Acute Lymphoblastic Leukemia data (Yeoh et. al., 2002) for GALGO package
Acute Lymphoblastic Leukemia data (Yeoh et. al., 2002) for GALGO package
A list-like Object
Represents the ensemble of the results of evolving several Galgo objects
The representation of a set of genes for genetic algorithms
The representation of a Genetic Algorithm
The representation of a gene in a chromosome for genetic algorithms
The representation of a set of chromosomes for genetic algorithms
The representation of a set of niches with migration for genetic algorithms
Focus the analysis to different sets of chromosomes
Add a chromosome to rank and frequency stability counting
Adds random pre-existed solutions
Converts the chromosome values (genes) to its numerical representation
Converts the gene parameters (shape1, shape2) to its numerical representation
Converts the chromosome values (genes) to a vector
Convert a variable of class Object to a list
Prints the representation of the BigBang object
Converts the gene parameters (shape1, shape2) to matrix
Converts the chromosome values (genes) to a matrix
Assigns a different saveFile value for parallelization
Returns the best chromosome
Returns the best chromosome of the niche
Returns the best chromosome
Returns the fitness of the best chromosome
Returns the fitness of the best chromosome in the niche
Returns the fitness of the best chromosome
Evolves Galgo objects saving the results for further analysis
Builds the rank and frequency stability counting
Function used to predict class evaluating a fitness function in many train-test sets
Predicts class for samples from chromosomes
Clones itself and its genes
Clones itself and all its objects
Clones itself and its chromosomes
Clones itself and its niches
Compute the counts for every gene from a set of chromosomes.
Creates and configure all objects needed for a "variable selection for classificacion" problem
Creates and configure all objects needed for a "variable selection" problem
Computes the class confusion matrix from a class prediction matrix
Performs crossover between chromosomes of the niche
Converts the gene values to user-readable values
Converts geneImportanceNetwork matrix to distance matrix
Evaluates all chromosomes with a fitness function
Evaluates the chromosome using a fitness function
Evaluate all niches with a fitness function
Evolves the chromosomes populations of a Galgo (Genetic Algorithm)
Filters solutions
Computes the fitness function from chromosomes for different splits
Converts chromosome for storage in BigBang object
Gets the "best" models using top-ranked genes and a forward-selection strategy
Computes the distance in GALGO for KNN based methods
Searches for shorter or better models using backward elimination strategy
Computes the fraction of genes present in the top-rank from the total genes present in chromosomes
Computes the frequency of genes based on chromosomes
Computes the number of times a couple of top-ranked-genes are present in models
Computes the rank history for top-ranked genes
Generates random values for all genes in the chromosome
Generates random values for all populations in the Galgo object
Generates a random value from the defined function
Generates random values for all genes contained in all chromosomes in the niche
Generates random values for all niches in the world
Generates Random shorter models
Converts the genes values to a numeric vector
Returns the fitness vector related to chromosomes
Computes gene freqencies
Plots models using heatmap plot
Class prediction using KNN method calling the C code
Class prediction using KNN method calling the R code
Gets the length of the object as its list version
Gets the number of genes defined in the chromosome
Gets the number of populations defined in the Galgo object
Gets the number of chromosomes defined in the niche
Gets the number of niches defined in the world
Load saved data of class Object and use reObject as necessary
Load all files saved during the parallelization
Returns the chromosome whose current fitness is maximum
Returns the chromosome in the niche whose current fitness is maximum
Returns the chromosome whose current fitness is maximum
Returns the fitness of the maximum chromosome
Returns the fitness of the maximum chromosome in the niche
Returns the fitness of the maximum chromosome
Computes the "mean" fitness from several solutions
Computes the mean number of generations requiered to reach a given fitness value
Merges the information from other BigBang objects
Class prediction using Maximum Likelihood Discriminant Functions method calling the C code
Class prediction using Maximum Likelihood Discriminant Functions method calling the R code
Function used to evaluate a fitness function in many train-test sets
Mutates a chromosome in specific positions
Mutates a gene
Mutates a niche calling mutate method for all chromosomes
Class prediction using the nearest centroid method calling the C code
Class prediction using the nearest centroid method calling the R code
Generates a list of chromosomes cloning the original chromosome object
Generates a list of cloned objects
Generates a list of cloned niches
Generates a list cloning an object
Creates a list of cloned chromosomes object with its internal values generated by random
Generates a list of cloned objects and random values
Creates a list of cloned niches with its internal values generated by random
Creates a list of cloned object with its internal values generated by random
Class prediction using the neural networks method calling the R code
Overwrites the new niche selecting a new population from the best chromosomes
Plots models in principal components space
Plots about the collected information in a BigBang object
Plots information about the Galgo object
Plots information about niche object
Plots information about world object
Predicts the class or fitting of new set of samples
Prints the representation of the Bag object
Prints the representation of a BigBang object
Prints the representation of the chromosome object
Prints the representation of a Galgo object
Prints the representation of a gene object
Prints the representation of a niche object
Prints the representation of a world object
Performs offspring, crossover, mutation, and elitism mechanism to generate the "evolved" niche
Calls progeny method to all niches in the world object
Erases all internal values in order to re-use the object
Erases all internal values in order to re-use the object
Erases all internal values in order to re-use the object
Erases all internal values in order to re-use the object
Erases all internal values in order to re-use the world object
Creates proper extended Object from a list obtained by unObject
Updates the internal values from the current populations
Updates the internal values from the current population
Updates the internal statistics from the current population
Searches for shorter or better models using backward elimination strategy
Class prediction using the recursive tree partiotions method calling the R code
Generation of random uniform integer values
Saves the BigBang object into a file in a suitable format
Assigns a weight for every chromosome to be selected for the next generation
Computes the sensitivity of class prediction
Computes the specificity of class prediction
Prints the representation of the Bag object
Prints the representation of the BigBang object
Prints the representation of the chromosome object and all its genes
Prints the representation and statistics of the galgo object
Prints the representation of a gene object
Prints the representation and statistics of the niche object
Prints the representation and statistics of the world object
Class prediction using support vector machines method calling the C/R code
Class prediction using support vector machines method calling the R code
Converts variables from class Object (and derived classes) to list