bquast

set.seed(1)

# define some functions

## convert integer to binary
i2b <- function(integer, length=8)
  as.numeric(intToBits(integer))[1:length]

## apply 
int2bin <- function(integer, length=8)

bquast

#### OLS by hand
solve(qr.R(qr(freeny.x)))%*%t(qr.Q(qr(freeny.x)))%*%freeny.y

QR1<- qr(freeny.x)
solve(qr.R(QR1))%*%t(qr.Q(QR1))%*%freeny.y
solve(qr.R(QR1))%*%crossprod(qr.Q(QR1),freeny.y)
backsolve(qr.R(QR1),crossprod(qr.Q(QR1),freeny.y))

coef(lm(freeny.y~freeny.x -1))

bquast

library(ggplot2)
library(scales)
library(gridExtra)



darkRed <- rgb(199,35,28, maxColorValue = 255)
darkGray <- rgb(128,128,128, maxColorValue = 255)
darkOLDblue <- rgb(0,15,118, maxColorValue = 255)
lightgray <- rgb(200,200,200, maxColorValue = 255)

bquast

els <- function(formula, data, ...){
  first_stage <- lm(formula,data=data,  ...)
  lm(update(formula, fitted(first_stage)~.), data, ...)
}

bquast

# https://designdatadecisions.wordpress.com/2016/05/05/manipulated-regression/
# Jyothi Subramanian, Ph. D.

library(manipulate)
 
## First define a custom function that fits a linear regression line 
## to (x,y) points and overlays the regression line in a scatterplot.
## The plot is then 'manipulated' to change as y values change.
 
linregIllustrate <- function(x, y, e, h.max, h.med){

bquast

# create explore function
# to be applied to data.frames created by importing using the haven package
# https://cran.r-project.org/package=haven

# also available in the explore package:
# https://github.com/bquast/explore

explore <- function(data) {
  description <- sapply(data,  attr, "label")
  variables    <- names(description)

bquast

import numpy as np

alpha = 0.1
hidden_dim = 4
dropout_percent = 0.05
do_dropout = True

# compute sigmoid nonlinearity
def sigmoid(x):
    output = 1/(1+np.exp(-x))