ramhiser

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I hereby claim:

• I am ramhiser on github.
• I am ramhiser (https://keybase.io/ramhiser) on keybase.
• I have a public key ASBotXs-LlQCC_m4Y3nVJlvF-fOMjq9idZtoXkYd-jekzQo

To claim this, I am signing this object:

ramhiser

# Based on this blog post: http://nowave.it/pages/bayesian-changepoint-detection-with-r-and-stan.html
library(rstan)
rstan_options(auto_write = TRUE)
set.seed(42)

beta0 <- 3
beta1 <- 9
beta2 <- 15

set.seed(42)

ramhiser

import json

def fullname(o):
  return o.__module__ + "." + o.__class__.__name__

def export_pipeline(scikit_pipeline):
  """JSON export of a scikit-learn pipeline.
  
  Especially useful when paired with GridSearchCV, TPOT, etc.
    

ramhiser

from scipy import stats
import numpy as np

def mean_confidence_interval(x, alpha=0.05):
    """Computes two-sided confidence interval for a Normal mean
    
    Assumes population variance is unknown.
    
    x is assumed to be a list or a 1-d Numpy array
    """

ramhiser

# The data set and model are described in the *brms* vignette
library(brms)

url <- paste0("https://raw.githubusercontent.com/mages/diesunddas/master/Data/ClarkTriangle.csv")
loss <- read.csv(url)

set.seed(42)

# Generated a random continuous feature
loss$ramey <- runif(nrow(loss))

ramhiser

library(tidyverse)
library(randomForest)
library(rpart)

set.seed(42)
num_points <- 20
x <- sort(runif(num_points, min=-5, max=6))
y <- x^2/5 + sin(3*x) # + rnorm(num_points, sd=0.1)

df <- data_frame(x=x, y=y)

ramhiser

# The Dogs data set was analyzed by D.V. Lindley using a loglinear model for binary data
# For details about the Dogs data set and model, see: http://www.openbugs.net/Examples/Dogs.html

library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

num_dogs <- 30
num_trials <- 25
Y <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

ramhiser

library(dplyr)
iris_char <- iris %>%
  mutate(Species=as.character(Species),
         char_column=sample(letters[1:5], nrow(iris), replace=TRUE))
sum(sapply(iris_char, is.character)) # 2

iris_factor <- iris_char %>%
  mutate_if(sapply(iris_char, is.character), as.factor)
# Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species  char_column 
# "numeric"    "numeric"    "numeric"    "numeric"  "character"  "character" 

ramhiser

# Uses a subset of the Iris data set with different proportions of the Species factor
set.seed(42)
iris_subset <- iris[c(1:50, 51:80, 101:120), ]

stratified_sample <- iris_subset %>%
  group_by(Species) %>%
  mutate(num_rows=n()) %>%
  sample_frac(0.4, weight=num_rows) %>%
  ungroup

ramhiser

library(dplyr)
library(ggplot2)

A <- seq(0, 10, length=100)
F <- seq(0, 10, length=100)

symmetric_mape <- function(A, F) {
  abs(F - A) / (abs(A) + abs(F))
}