vankesteren

# Meuse universal kriging for a large grid 
# The grid is enriched with osm data
# CC-BY @vankesteren

# load packages ----
library(tidyverse)
library(sf)
library(ggspatial)
library(osmdata)
library(gstat)

vankesteren

# Gierzwaluw (common swift) analysis using osmenrich
# Last edited 2021-02-09 by @vankesteren
# CC-BY ODISSEI SoDa team

# Packages
# Data
library(tidyverse)
library(sf)
library(osmenrich)

vankesteren

rtrunc <- function(rdist, min, max) {
  # deal with pipe and also with normal evaluation
  # https://github.com/tidyverse/magrittr/issues/115#issuecomment-173894787
  parents <- lapply(sys.frames(), parent.env)
  is_magrittr_env <- vapply(parents, identical, logical(1), y = environment(`%>%`))
  if (any(is_magrittr_env)) {
    distcall <- get("lhs", sys.frames()[[base::max(which(is_magrittr_env))]])
  } else {
    distcall <- substitute(rdist)
  }

vankesteren

library(tidyverse)
library(sf)
library(osmdata)
library(osmenrich)
library(patchwork)

# conditional IDW functions

conditional_idw <- function(formula, data, p = 1, max.iter = 100, tol = 1e-10) {
  

vankesteren

library(sf)
library(httr) 
library(tidyverse) 


url <- parse_url("https://geodata.nationaalgeoregister.nl/bag/wfs/v1_1")

postcode_filter <- function(postcode, gebruiksdoel = "woonfunctie") {
  sprintf(
  "<Filter>

vankesteren

library(deldir)
library(ggplot2)
library(firatheme)

N <- 40
dat <- matrix(rnorm(N*2), N)
dat <- dat  %*% chol(matrix(c(1, 0, 0, 1.5), 2)) %*% chol(solve(cov(dat)))
dat2 <- deldir(dat[,1], dat[,2])
dat1 <- data.frame(dat)
colnames(dat1) <- c("x1", "y1")

vankesteren

# script that outputs a graph
library(tidyverse)
library(firatheme)
wine <- read_delim("https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data", 
  delim = ",",
  col_names = c(
    "Cultivar", "Alcohol", "Malic acid", "Ash", "Alcalinity of ash", "Magnesium",
    "Total phenols", "Flavanoids", "Nonflavanoid phenols", "Proanthocyanins",
    "Color intensity", "Hue", "OD280/OD315", "Proline"
  )

vankesteren

vankesteren

vankesteren

module Adamopt

# This is a module implementing vanilla Adam (https://arxiv.org/abs/1412.6980).
export Adam, step!

# Struct containing all necessary info
mutable struct Adam
  theta::AbstractArray{Float64} # Parameter array
  loss::Function                # Loss function
  grad::Function                # Gradient function