tests/testthat/test-neuronLogicalUnitInput.R

In tomaszbiegacz/yan: Yet Another Neuron

context("yan.neuronLogicalUnitInput")

test_that("propagation zero", {
  model <- as.yan.neuronLogicalUnitInput(0)
  expect_identical(c(0, 0, 0), propagation(model, c(0.5, 1, 2)))
})

test_that("propagation positive", {
  model <- as.yan.neuronLogicalUnitInput(2)
  expect_identical(c(1, 2, 4), propagation(model, c(0.5, 1, 2)))

  nv <- propagation(model, 0.25)
  nv2 <- propagation(model, 0.1)
  expect_true(nv > 0 && nv2 > 0)
  expect_true(nv < 1)
  expect_true(nv2 < nv)
})

test_that("propagation negative", {
  model <- as.yan.neuronLogicalUnitInput(-2)
  expect_identical(c(4, 2, 1), propagation(model, c(0.5, 1, 2)))

  nv <- propagation(model, 4)
  nv2 <- propagation(model, 8)
  expect_true(nv > 0 && nv2 > 0)
  expect_true(nv < 1)
  expect_true(nv2 < nv)
})

test_that("backpropagation zero", {
  model <- as.yan.neuronLogicalUnitInput(0)
  input <- c(0.5, 1, 2)
  output <- c(0, 0, 0)
  expect_identical(data.frame(
    input = input,
    output = output,
    dX = c(0, 0, 0),
    dCutOff = c(0, 0, 0)
  ), backpropagation(model, input, output))
})

test_that("backpropagation positive", {
  model <- as.yan.neuronLogicalUnitInput(2)
  input <- c(0.5, 1, 2)
  output <- c(1, 2, 4)
  expect_identical(data.frame(
    input = input,
    output = output,
    dX = c(2, 2, 2),
    dCutOff = c(0.5, 1, 2)
  ), backpropagation(model, input, output))
})

test_that("backpropagation negative", {
  model <- as.yan.neuronLogicalUnitInput(-2)
  input <- c(0.5, 1, 2)
  output <- c(4, 2, 1)
  expect_identical(data.frame(
    input = input,
    output = output,
    dX = c(-8, -2, -0.5),
    dCutOff = c(-2, -1, -0.5)
  ), backpropagation(model, input, output))
})