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#gmum.R Build Status

gmum.R is a package consisting in various models. We focus on efficiency (underlying C++ implementation) and easy of usage. gmum.r is a close collaboration between GMUM group members (http://gmum.net) and students.

Package includes three subpackages:

  • SVM with various underlying libraries and modifications
  • Cross Entropy Clustering (CEC) clustering algorithm
  • Growing Neural Gas clustering algorithm

Links

SVM

SVM Wrapper is a part of the gmum.R project which provides a popular Support Vector Machine inplementations wrapped in the R package.

SVM Iris and sample weights examples

Example usage

library('gmum.r')

# Load a dataset, here we have provided an example 
data(svm_breast_cancer_dataset)
ds <- svm.breastcancer.dataset

# Create new SVM object
svm <- SVM( formula = X1~. ,
            data = ds,
            core = "libsvm",
            kernel = "linear",
            prep = "none",
            C = 10) 

x <- ds.X(svm)
y <- ds.Y(svm)

# Classify your dataset using predict function
prediction <- predict(svm, x)

# Check models accuracy
acc <- svm.accuracy(prediction=y, target=prediction)

Growing Neural Gas

Subpackage containing efficient, online GNG algorithm. It produces topological graph, that you can easily convert to igraph, or you can dump your model to optimized binary file and load it later on.

Clustering of the UCI wine dataset

Example: cluster wine dataset

In this example we will construct a clustering of UCI wine dataset using offline GNG.

library(gmum.r)

# Load data
wine <- get.wine.dataset.X()

# Train in an offline manner
gng <- GNG(wine, labels=get.wine.dataset.y(), max.nodes=20)

# Find closest node to vector composed of 1
predict(gng, rep(1,ncol(wine)))

# Find mean error
meanError(gng)

# Plot with first 2 coordinates as position
plot(gng, vertex.color="cluster")

Cross Entropy Clustering

CEC aims to efficiently implement Cross Entropy Clustering Algorithm as R extension.

Cross-entropy clustering (shortly CEC) joins advantages of classical k-means with those of EM. Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing redundant ones.

CEC clustering

Example usage

library(gmum.r)

data(cec.mouse1.spherical)
dataset = cec.mouse1.spherical

# That is the dataset we want to cluster
plot(dataset)

# Run cec with default parameters. Set the number of clusters and the dataset.
c <- CEC(k=3, x=dataset)
plot(c)

# Since initial clusterization is random. It may be a good idea to run cec multiple times and choose the best result.
c <- CEC(k=3, x=dataset, control.nstart=10)
plot(c)

# Better than before, however, we know that clusters are spherical; let's inform cec about that.
c <- CEC(k=3, x=dataset, control.nstart=10, method.type='spherical')
plot(c)

# You can learn details of clustering like this
centers(c)
covMatrix(c)

# You can predict cluster which a point would belong to:
predict(c, c(1,1))

# You can visualise size and shape of clusters
plot(c, ellipses=TRUE)

# Try the same with random assignment
c <- CEC(k=3, x=dataset, control.nstart=10, method.type='spherical', method.init='random')
plot(c)

Citation

If you use gmum.r in your work please cite one of the papers, see citation().