| #' Cut Into Bins | |
| #' | |
| #' No boundaries on the endpoints, and handles character \code{x}. | |
| #' A little different than normal \code{\link[base]{cut}} | |
| #' | |
| #' @param x column to be cut | |
| #' @param breaks define the bins | |
| #' @param bin_names names for the result | |
| #' @return bins factor | |
| cutbin = function(x, breaks, bin_names) |
| > lookup::lookup(rt) | |
| stats::rt [closure] | |
| function (n, df, ncp) | |
| { | |
| if (missing(ncp)) | |
| .Call(C_rt, n, df) | |
| else if (is.na(ncp)) { | |
| warning("NAs produced") | |
| rep(NaN, n) | |
| } |
Fri May 19 09:34:10 PDT 2017
Working through install problems for RSQLiteUDF.
** testing if installed package can be loaded
Error in dyn.load(file, DLLpath = DLLpath, ...) :
unable to load shared object '/usr/local/lib/R/site-library/RSQLiteUDF/libs/RSQLiteUDF.so':
/usr/local/lib/R/site-library/RSQLiteUDF/libs/RSQLiteUDF.so: undefined symbol: sqlite3_enable_load_extension
Error: loading failed
| import numpy as np | |
| import pandas as pd | |
| from numba import jit | |
| n_smpl = int(1e6) | |
| ni = 5 | |
| group_id = np.repeat(np.arange(n_smpl), ni) | |
| a = np.repeat(1, len(group_id)) | |
| b = np.repeat(1, len(group_id)) |
| """ | |
| http://stackoverflow.com/questions/41886507/data-table-faster-row-wise-recursive-update-within-group/41891693#41891693 | |
| require(data.table) # v1.10.0 | |
| n_smpl = 1e6 | |
| ni = 5 | |
| id = rep(1:n_smpl, each = ni) | |
| smpl = data.table(id) | |
| smpl[, time := 1:.N, by = id] | |
| a_init = 1; b_init = 1 |
| x = 1:3 | |
| y = 11:13 | |
| # Given vectors x, y, what's the cleanest general way to make z? | |
| # z = c(1, 11, 2, 12, 3, 13) | |
| shuffle = function(x, y) | |
| { | |
| as.vector(mapply(c, x, y)) | |
| } |
First install ripser.
Then make it locatable on your system PATH, something like:
$ ln -s /home/clark/dev/ripser/ripser /usr/local/bin/ripser
After this your system should be able to find ripser:
$ which ripser
| # A very simple parallel program | |
| # | |
| # We specify the probability that each individual | |
| # votes for a candidate, and then simulate the counts | |
| # for n such voters. | |
| # | |
| # count_votes and count_votes_slow are the functions | |
| # to parallelize. Typically each run will take some | |
| # time to complete. | |
| # |
| # Mon Jul 18 08:08:09 PDT 2016 | |
| # Goal: Store arbitrary objects in DataFrames as bytes to make dapply more | |
| # general | |
| # | |
| # Inefficient- this uses CLOB rather than BLOB | |
| # Comments throughout this question are helpful | |
| # http://stackoverflow.com/questions/5950084/how-to-handle-binary-strings-in-r | |
| library(SparkR) |
| > library(ddR) | |
| Welcome to 'ddR' (Distributed Data-structures in R)! | |
| For more information, visit: https://github.com/vertica/ddR | |
| Attaching package: ‘ddR’ | |
| The following objects are masked from ‘package:base’: | |
| cbind, rbind |