elm | R Documentation |
Formula interface for elm_train
, transforms a data frame and formula
into the necessary input for elm_train, automatically calls onehot_encode
for classification.
elm( formula, data, nhid, actfun, init_weights = "normal_gaussian", bias = FALSE, moorep_pseudoinv_tol = 0.01, leaky_relu_alpha = 0, seed = 1, verbose = FALSE )
formula |
formula used to specify the regression or classification. |
data |
data.frame with the data |
nhid |
a numeric value specifying the hidden neurons. Must be >= 1 |
actfun |
a character string specifying the type of activation function. It should be one of the following : 'sig' ( sigmoid ), 'sin' ( sine ), 'radbas' ( radial basis ), 'hardlim' ( hard-limit ), 'hardlims' ( symmetric hard-limit ), 'satlins' ( satlins ), 'tansig' ( tan-sigmoid ), 'tribas' ( triangular basis ), 'relu' ( rectifier linear unit ) or 'purelin' ( linear ) |
init_weights |
a character string spcecifying the distribution from which the input-weights and the bias should be initialized. It should be one of the following : 'normal_gaussian' (normal / Gaussian distribution with zero mean and unit variance), 'uniform_positive' ( in the range [0,1] ) or 'uniform_negative' ( in the range [-1,1] ) |
bias |
either TRUE or FALSE. If TRUE then bias weights will be added to the hidden layer |
moorep_pseudoinv_tol |
a numeric value. See the references web-link for more details on Moore-Penrose pseudo-inverse and specifically on the pseudo inverse tolerance value |
leaky_relu_alpha |
a numeric value between 0.0 and 1.0. If 0.0 then a simple relu ( f(x) = 0.0 for x < 0, f(x) = x for x >= 0 ) activation function will be used, otherwise a leaky-relu ( f(x) = alpha * x for x < 0, f(x) = x for x >= 0 ). It is applicable only if actfun equals to 'relu' |
seed |
a numeric value specifying the random seed. Defaults to 1 |
verbose |
a boolean. If TRUE then information will be printed in the console |
elm object which can be used with predict, residuals and fitted.
elm(Species ~ ., data = iris, nhid = 20, actfun="sig") mod_elm <- elm(Species ~ ., data = iris, nhid = 20, actfun="sig") # predict classes predict(mod_elm, newdata = iris[1:3,-5]) # predict probabilities predict(mod_elm, newdata = iris[1:3,-5], type="prob") # predict elm output predict(mod_elm, newdata = iris[1:3,-5], type="raw") data("Boston") elm(medv ~ ., data = Boston, nhid = 40, actfun="relu") data("ionosphere") elm(class ~ ., data = ionosphere, nhid=20, actfun="relu")
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