reg_data: Function to compute the regularized version of COSNet (Frasca...

Description Usage Arguments Value References Examples

Description

This function modifies the weights and the thresholds of the network to realized the COSNet regularization.

Usage

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reg_data(W, theta, eta, M, m, pos_num)

Arguments

W

square symmetric named matrix of the network weights. The components of W are in the [0,1] interval. The i,j-th component is the weight between neuron i and neuron j. The components of the diagonal of W are 0

theta

vector of the neuron activation thresholds

eta

real value corresponding to the eta regularization coefficient in the energy function (Frasca et al. 2013). If eta = 0 no regularization is applied. The higher the value of eta, the more the influence of the regularization term

M

positive neuron activation value

m

negative neuron activation value

pos_num

number of expected positive neurons in the equilibrium state of the network

Value

list of two element:

W

the regularized connection matrix

theta

regularized threshold vector

References

Frasca M., Bertoni A., Re M., Valentini G.: A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Networks, Volume 43, July, 2013 Pages 84-98.

Examples

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library(bionetdata);
data(Yeast.STRING.data);
n <- nrow(Yeast.STRING.data);
dim(Yeast.STRING.data);
range(Yeast.STRING.data);
## setting values for parameter alpha, for the rate of positive examples,
## for neuron thresholds and for eta parameter
alpha <- 1;
pos.rate <- 0.01;
thresholds <- runif(n);
range(thresholds);
eta <- 0.001;
a <- reg_data(Yeast.STRING.data, thresholds, eta, sin(alpha),
    -cos(alpha), ceiling(pos.rate*n));
## new connection matrix
dim(a$W);
range(a$W);
## new thresholds
range(a$theta);


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