R/test.R

In jewelltaylor/fdcvi:

source("R/main.R")
source("R/generateData.R")
library(fda.usc)
library(weathercan)
library(tidyverse)
set.seed(1)

test <- function() {
  sequence = seq(from=0,to=pi/3, length = 100)
  number_of_clusters = 2
  nbasis = 6
  curves_per_cluster = 10
  B = get_B(sequence, nbasis)

  function_data = get_function_data(sequence, number_of_clusters)
  cluster_data = generate_cluster_data_2(function_data, curves_per_cluster)


  p = fit(cluster_data, number_of_clusters, nbasis=6, function_data = NULL, sequence = sequence, use_phi=FALSE)

  print(p)
}

validation_test <- function() {
  number_of_clusters = 2
  nbasis = 6

  boys = growth[[1]]

  girls  = growth[[2]]
  age = growth$age
  cluster_data = matrix(0, NCOL(boys) + NCOL(girls), length(age))
  for (i in 1:NCOL(boys)) {
    cluster_data[i, ] = boys[, i]
  }

  for (i in 1:NCOL(girls)) {
    cluster_data[NCOL(boys) + i, ] = girls[, i]
  }

  p = fit(cluster_data, number_of_clusters, nbasis=6, function_data = NULL, sequence = age, use_phi=FALSE)

  print(p)

}

# validation_test()

test_viz <- function() {
  sequence = seq(from=0,to=pi/3, length = 100)
  number_of_clusters = 2
  nbasis = 6
  curves_per_cluster = 10
  B = get_B(sequence, nbasis)

  function_data = get_function_data(sequence, number_of_clusters)
  cluster_data = generate_cluster_data_2(function_data, curves_per_cluster)


  m_list = fit(cluster_data, number_of_clusters, nbasis=6, function_data = NULL, sequence = sequence, use_phi=FALSE)

  dev.new(width=5, height=4)
  matplot(sequence, t(cluster_data), type="l",col="gray",lty=3, xlab="x", ylab="f(x)", main="Simulation Results (Medium)")

  lines(sequence, function_data[1, ],col="red", xlab="x", ylab="f(x)")


  lines(sequence, function_data[2, ], col="green")

  lines(sequence, B %*% t(m_list[[1]]), col="blue")

  lines(sequence, B %*% t(m_list[[2]]), col="black")

  legend(x="topright", legend=c("True Function 1", "True Function 2", "Estimated Function 1", "Estimated Function 2", "Functional Data"), col=c("red", "green", "blue", "black", "grey"), lty=1, lwd=1)

}

validation_test2 <- function() {
  cluster_data = matrix(0, 4, 365)
  sequence = seq(1, 365)
  number_of_clusters = 4
  nbasis = 6
  cluster_data[1, ] = CanadianWeather$dailyAv[,,1][,26]
  cluster_data[2, ] = CanadianWeather$dailyAv[,,1][,35]

  p = fit(cluster_data, number_of_clusters, nbasis=6, function_data = NULL, sequence = sequence, use_phi=FALSE)

  print(p)
}

#validation_test2()


validation_test3 <- function() {
  #print((stations_search("VANCOUVER", interval = "day")))

  res_temp = as.vector(weather_dl(station_ids = 1776, start = "1994-01-01", end = "2003-12-31", interval = "day")$max_temp)
  tor_temp = as.vector(weather_dl(station_ids = 5051, start = "1994-01-01", end = "2003-12-31", interval = "day")$max_temp)
  #print(van_temp[1:365])

  # print(van_temp[1:10])
  cl = matrix(0, 18, 31)
  count = 1
  for (i in seq(1, 3285, 365)) {
    upperbound1 = i + 364
    res_temp_curr = replace_na(res_temp[i:upperbound1], 0)[187:217]
    print(res_temp_curr)
    cl[count, ] = res_temp_curr

    count = count + 1
  }

  for (j in seq(1, 3285, 365)) {
    upperbound2 = j + 364
    tor_temp_curr = replace_na(tor_temp[j:upperbound2], 0)[187:217]
    cl[count, ] = tor_temp_curr
    count = count + 1
  }

  sequence = seq(1, 31)
  par(mar=c(1,1,1,1))

  matplot(sequence, t(cl), type="l",col="gray",lty=3)
  B = get_B(sequence, 10)

  view(cl)
  m = fit(cl, 2, nbasis=10, function_data = NULL, sequence = sequence, use_phi=FALSE)
  dev.new(width=5, height=4)
  matplot(sequence, t(cl), type="l",col="gray",lty=3, xlab="Day", ylab="Temperature", main="Validation Results")
  lines(sequence, B %*% t(m[[1]]), col="red")
  lines(sequence, B %*% t(m[[2]]), col="blue")
  #title(main="Valid")
  legend(x="center", legend=c("Estimated Function Vancouver", "Estimated Function Resolute", "Functional Data"), col=c("blue", "red", "grey"), lty=1, lwd=1)

}

test_viz()