% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autoenc_conv_e.R
\name{autoenc_conv_e}
\alias{autoenc_conv_e}
\title{Convolutional Autoencoder - Encode}
\usage{
autoenc_conv_e(
  input_size,
  encoding_size,
  batch_size = 32,
  epochs = 100L,
  num_epochs = NULL,
  learning_rate = 0.001,
  validation_strategy = c("static", "dynamic"),
  stopping_rule = c("none", "patience", "sma", "ema", "h"),
  val_ratio = 0.3,
  patience = 100L,
  min_delta = 1e-04,
  sma_window = 5L,
  ema_alpha = 0.2,
  test_window = 30L,
  p_value = 0.05
)
}
\arguments{
\item{input_size}{Integer. Number of input features per observation.}

\item{encoding_size}{Integer. Size of the latent (bottleneck) representation.}

\item{batch_size}{Integer. Mini-batch size used during training. Default is 32.}

\item{epochs}{Integer. Maximum number of training epochs. Default is 100.}

\item{num_epochs}{Deprecated compatibility alias for \code{epochs}. If informed, it overrides \code{epochs}.}

\item{learning_rate}{Numeric. Optimizer learning rate. Default is 0.001.}

\item{validation_strategy}{Character. One of \code{static} or \code{dynamic}.}

\item{stopping_rule}{Character. One of \code{none}, \code{patience}, \code{sma}, \code{ema}, or \code{h}.}

\item{val_ratio}{Numeric. Validation fraction used when validation is enabled. Default is 0.3.}

\item{patience}{Integer. Early stopping patience. Default is 100.}

\item{min_delta}{Numeric. Minimum improvement to reset early stopping. Default is 1e-4.}

\item{sma_window}{Integer. Window size used by \code{sma}. Default is 5.}

\item{ema_alpha}{Numeric. Smoothing factor used by \code{ema}. Default is 0.2.}

\item{test_window}{Integer. Window size used by \code{h}. Default is 30.}

\item{p_value}{Numeric. Significance threshold used by \code{h}. Default is 0.05.}
}
\value{
A \code{autoenc_conv_e} object.
}
\description{
Creates a deep learning convolutional autoencoder (ConvAE) to encode sequences
of observations. Wraps a PyTorch implementation.
}
\examples{
\dontrun{
# Conv1D-based encoder expects data reshaped internally to (n, input_size, 1)
X <- matrix(rnorm(1000), nrow = 50, ncol = 20)
ae <- autoenc_conv_e(input_size = 20, encoding_size = 5, epochs = 100)
ae <- daltoolbox::fit(ae, X)
Z  <- daltoolbox::transform(ae, X)   # 50 x 5 encodings
}

# See:
# https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_conv_e.md
}
\references{
Masci, J., Meier, U., Ciresan, D., & Schmidhuber, J. (2011). Stacked Convolutional Auto-Encoders.
}
