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# Benjamin Ogorekbaogorek

Last active Oct 7, 2019
View sleepstudy.py
 import pandas as pd import numpy as np import patsy import tensorflow as tf class SleepReg(tf.Module): def __init__(self, sleepdata_path): """Initializing tensorflow variables and other necessary matrices""" # These two TensorFlow variables show up in the trainable_variables
Created Jun 25, 2019
non-invertible-ma-process.R
View non-invertible-ma-process.R
 N <- 10000 theta <- 1.4 e <- rnorm(N) e_lag <- c(NA, e[1:(N-1)]) y <- e - theta * e_lag acf(y[2:N])
Last active Sep 11, 2019
Gist version of Miscellaneous/methods/kalman-filter/kalman-derivations.ipynb
View kalman-derivations.ipynb
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Last active Apr 15, 2019
View Part-III-code-block-4.R
 library(dplyr) library(ggplot2) library(splines) days_grid <- 0:180 interior_knots <- c(2, 6, 25) #interior_knots <- c(6, 25, 80) my_spline <- ns(days_grid, knots = interior_knots)
Last active Apr 24, 2019
View part-III-code-block-3.R
 library(dplyr) library(gridExtra) library(ggplot2) exp_decay <- function(t, tau) { exp(-t / tau) } convolve_training<- function(training, n, tau) { sum(training[1:(n - 1)] * exp_decay((n - 1):1, tau))
Last active Apr 7, 2019
View Part-III-code-block-2.R
 library(ggplot2) # Files are at ... folder_path <- "c:/devl/swimming" # Update accordingly perf_params <- get_perf_params(world_record_perf = 1.8, able_bodied_perf = 0.5, limit = 2.0, type = "jumping") get_perf_in_cp <- function(perf, a, b, limit) {
Created Apr 7, 2019
View get-performance-parameters.R
 get_perf_params <- function(world_record_perf, able_bodied_perf, limit, type = "running") { # sum of squares around fixed point rss <- function(a, b, world_record_perf, able_bodied_perf, limit) { (1000 - b * log(a / (world_record_perf - limit))) ** 2 + (0 - b * log(a / (able_bodied_perf - limit))) ** 2 } stopifnot(type %in% c("running", "jumping")) a_starting <- ifelse(type == "running", 5, -5)
Last active Mar 5, 2019
View cumulative-impact-part-ii-graphics.R
 library(ggplot2) # Figure 1 plot_df <- rbind( data.frame(day = days_grid, level = phi, type = "true \u03D5(t)"), data.frame(day = days_grid, level = eta_star, type = "best fit \u03B7*(t)") ) ggplot(plot_df, aes(x = day, y = level, color = type, linetype = type)) + geom_line(size = 2) +
Last active Mar 5, 2019
Code Block 4. Closing the loop.
View cumulative-impact-part-ii-close-the-loop.R
 get_eta_hat <- function(t_seq) { spline_vars_grid <- predict(my_spline, newx = t_seq) spline_vars_grid <- cbind(1, spline_vars_grid) eta_hat <- spline_vars_grid %*% coef(spline_reg)[-1] as.numeric(eta_hat) } convolve_with_fn <- function(training, n, impulse_fn) { sum(training[1:(n - 1)] * impulse_fn((n - 1):1)) }
Last active Mar 5, 2019
Code Block 3: Introducing a spline-based method for modeling cumulative impact.
View spline-convolution-estimation.R
 library(splines) my_spline <- ns(1:259, Boundary.knots = c(1, 259), knots = c(14, 40, 100)) z_vars <- list() for (n in 1:nrow(train_df)) { spline_pred <- predict(my_spline, newx = (n - 1):1) spline_vars <- colSums(spline_pred * train_df\$w[1:(n - 1)]) # convolution spline_const <- sum(train_df\$w[1:(n - 1)]) z_vars[[n]] <- c(spline_const, spline_vars) }
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