brnn (version 0.8)

# brnn: brnn

## Description

The brnn function fits a two layer neural network as described in MacKay (1992) and Foresee and Hagan (1997). It uses the Nguyen and Widrow algorithm (1990) to assign initial weights and the Gauss-Newton algorithm to perform the optimization. This function implements the functionality of the function trainbr in Matlab 2010b.

## Usage

brnn(x, …)

# S3 method for formula
brnn(formula, data, contrasts=NULL,…)  # S3 method for default
brnn(x,y,neurons=2,normalize=TRUE,epochs=1000,mu=0.005,mu_dec=0.1,
verbose=FALSE,Monte_Carlo = FALSE,tol = 1e-06, samples = 40,…)

## Arguments

formula

A formula of the form y ~ x1 + x2 + …

data

Data frame from which variables specified in formula are preferentially to be taken.

x

(numeric, $$n \times p$$) incidence matrix.

y

(numeric, $$n$$) the response data-vector (NAs not allowed).

neurons

positive integer that indicates the number of neurons.

normalize

logical, if TRUE will normalize inputs and output, the default value is TRUE.

epochs

positive integer, maximum number of epochs(iterations) to train, default 1000.

mu

positive number that controls the behaviour of the Gauss-Newton optimization algorithm, default value 0.005.

mu_dec

positive number, is the mu decrease ratio, default value 0.1.

mu_inc

positive number, is the mu increase ratio, default value 10.

mu_max

maximum mu before training is stopped, strict positive number, default value $$1\times 10^{10}$$.

change

The program will stop if the maximum (in absolute value) of the differences of the F function in 3 consecutive iterations is less than this quantity.

cores

Number of cpu cores to use for calculations (only available in UNIX-like operating systems). The function detectCores in the R package parallel can be used to attempt to detect the number of CPUs in the machine that R is running, but not necessarily all the cores are available for the current user, because for example in multi-user systems it will depend on system policies. Further details can be found in the documentation for the parallel package.

verbose

logical, if TRUE will print iteration history.

Monte_Carlo

If TRUE it will estimate the trace of the inverse of the hessian using Monte Carlo procedures, see Bai et al. (1996) for more details. This routine calls the function estimate.trace() to perform the computations.

tol

numeric tolerance, a tiny number useful for checking convergenge in the Bai's algorithm.

samples

positive integer, number of Monte Carlo replicates to estimate the trace of the inverse, see Bai et al. (1996) for more details.

contrasts

an optional list of contrasts to be used for some or all of the factors appearing as variables in the model formula.

arguments passed to or from other methods.

## Value

object of class "brnn" or "brnn.formula". Mostly internal structure, but it is a list containing:

$theta A list containing weights and biases. The first $$s$$ components of the list contains vectors with the estimated parameters for the $$k$$-th neuron, i.e. $$(w_k, b_k, \beta_1^{[k]},...,\beta_p^{[k]})'$$.$message

String that indicates the stopping criteria for the training process.

$alpha $$\alpha$$ parameter.$beta

$$\beta$$ parameter.

$gamma effective number of parameters.$Ew

The sum of the squares of the bias and weights.

$Ed The sum of the squares between observed and predicted values. ## Details The software fits a two layer network as described in MacKay (1992) and Foresee and Hagan (1997). The model is given by: $$y_i=g(\boldsymbol{x}_i)+e_i = \sum_{k=1}^s w_k g_k (b_k + \sum_{j=1}^p x_{ij} \beta_j^{[k]}) + e_i, i=1,...,n$$ where: • $$e_i \sim N(0,\sigma_e^2)$$. • $$s$$ is the number of neurons. • $$w_k$$ is the weight of the $$k$$-th neuron, $$k=1,...,s$$. • $$b_k$$ is a bias for the $$k$$-th neuron, $$k=1,...,s$$. • $$\beta_j^{[k]}$$ is the weight of the $$j$$-th input to the net, $$j=1,...,p$$. • $$g_k(\cdot)$$ is the activation function, in this implementation $$g_k(x)=\frac{\exp(2x)-1}{\exp(2x)+1}$$. The software will minimize $$F=\beta E_D + \alpha E_W$$ where • $$E_D=\sum_{i=1}^n (y_i-\hat y_i)^2$$, i.e. the error sum of squares. • $$E_W$$ is the sum of squares of network parameters (weights and biases). • $$\beta=\frac{1}{2\sigma^2_e}$$. • $$\alpha=\frac{1}{2\sigma_\theta^2}$$, $$\sigma_\theta^2$$ is a dispersion parameter for weights and biases. ## References Bai, Z. J., M. Fahey and G. Golub. 1996. "Some large-scale matrix computation problems." Journal of Computational and Applied Mathematics 74(1-2), 71-89. Foresee, F. D., and M. T. Hagan. 1997. "Gauss-Newton approximation to Bayesian regularization", Proceedings of the 1997 International Joint Conference on Neural Networks. Gianola, D. Okut, H., Weigel, K. and Rosa, G. 2011. "Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat". BMC Genetics, 12,87. MacKay, D. J. C. 1992. "Bayesian interpolation", Neural Computation, 4(3), 415-447. Nguyen, D. and Widrow, B. 1990. "Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights", Proceedings of the IJCNN, 3, 21-26. Paciorek, C. J. and Schervish, M. J. 2004. "Nonstationary Covariance Functions for Gaussian Process Regression". In Thrun, S., Saul, L., and Scholkopf, B., editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA. ## See Also predict.brnn ## Examples # NOT RUN { # } # NOT RUN { #Load the library library(brnn) ############################################################### #Example 1 #Noise triangle wave function, similar to example 1 in Foresee and Hagan (1997) #Generating the data x1=seq(0,0.23,length.out=25) y1=4*x1+rnorm(25,sd=0.1) x2=seq(0.25,0.75,length.out=50) y2=2-4*x2+rnorm(50,sd=0.1) x3=seq(0.77,1,length.out=25) y3=4*x3-4+rnorm(25,sd=0.1) x=c(x1,x2,x3) y=c(y1,y2,y3) #With the formula interface out=brnn(y~x,neurons=2) #With the default S3 method the call is #out=brnn(y=y,x=as.matrix(x),neurons=2) plot(x,y,xlim=c(0,1),ylim=c(-1.5,1.5), main="Bayesian Regularization for ANN 1-2-1") lines(x,predict(out),col="blue",lty=2) legend("topright",legend="Fitted model",col="blue",lty=2,bty="n") ############################################################### #Example 2 #sin wave function, example in the Matlab 2010b demo. x = seq(-1,0.5,length.out=100) y = sin(2*pi*x)+rnorm(length(x),sd=0.1) #With the formula interface out=brnn(y~x,neurons=3) #With the default method the call is #out=brnn(y=y,x=as.matrix(x),neurons=3) plot(x,y) lines(x,predict(out),col="blue",lty=2) legend("bottomright",legend="Fitted model",col="blue",lty=2,bty="n") ############################################################### #Example 3 #2 Inputs and 1 output #the data used in Paciorek and #Schervish (2004). The data is from a two input one output function with Gaussian noise #with mean zero and standard deviation 0.25 data(twoinput) #Formula interface out=brnn(y~x1+x2,data=twoinput,neurons=10) #With the default S3 method #out=brnn(y=as.vector(twoinput$y),x=as.matrix(cbind(twoinput$x1,twoinput$x2)),neurons=10)

f=function(x1,x2) predict(out,cbind(x1,x2))
x1=seq(min(twoinput$x1),max(twoinput$x1),length.out=50)
x2=seq(min(twoinput$x2),max(twoinput$x2),length.out=50)
z=outer(x1,x2,f) # calculating the density values

transformation_matrix=persp(x1, x2, z,
main="Fitted model",
sub=expression(y==italic(g)~(bold(x))+e),
col="lightgreen",theta=30, phi=20,r=50,
d=0.1,expand=0.5,ltheta=90, lphi=180,
points(trans3d(twoinput$x1,twoinput$x2, f(twoinput$x1,twoinput$x2),
transformation_matrix), col = "red")

###############################################################
#Example 4
#Gianola et al. (2011).
#Warning, it will take a while

data(Jersey)

#Fit the model with the FULL DATA
#Formula interface
out=brnn(pheno$yield_devMilk~G,neurons=2,verbose=TRUE) #Obtain predictions and plot them against fitted values plot(pheno$yield_devMilk,predict(out))

#Predictive power of the model using the SECOND set for 10 fold CROSS-VALIDATION
data=pheno
data$X=G data$partitions=partitions

#Fit the model for the TESTING DATA
out=brnn(yield_devMilk~X,
data=subset(data,partitions!=2),neurons=2,verbose=TRUE)

#Plot the results
#Predicted vs observed values for the training set
par(mfrow=c(2,1))
plot(out$y,predict(out),xlab=expression(hat(y)),ylab="y") cor(out$y,predict(out))

#Predicted vs observed values for the testing set
yhat_R_testing=predict(out,newdata=subset(data,partitions==2))
ytesting=pheno\$yield_devMilk[partitions==2]
plot(ytesting,yhat_R_testing,xlab=expression(hat(y)),ylab="y")
cor(ytesting,yhat_R_testing)

# }
# NOT RUN {
# }