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Seems to work with Milky Way images. But I'm not sure what to do with it | |
define sequence | |
convert sequence to FITS | |
stack | |
remove background | |
plate solve withe the belot | |
solve-field --scale-low 45 --scale-units degwidth --crpix-center --overwrite --downsample 2 r_saggita_stacked_crop.fit |
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library(purrr) | |
y_test <- sample(0:9, 2000, replace = TRUE) | |
pred1 <- sample(0:9, 2000, replace= TRUE) | |
pred2 <- sample(0:9, 2000, replace= TRUE) | |
preds <- list(pred1, pred2) | |
# want to apply the following to preds | |
# sum(diag(table(y_test, pred)))/length(y_test) |
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ls | |
pwd | |
hi | |
man hi | |
man cats | |
yes "we have no bananas" | |
yes | |
man yes | |
exit | |
git |
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// Databricks notebook source exported at Sat, 6 Aug 2016 14:28:53 UTC | |
// MAGIC %md | |
// MAGIC # Breckenridge Property Description Topic Modeling | |
// MAGIC This notebook turns the text contained in property descriptions in the Breckenridge CO US destination into topic probability distributions for subsequent math. The chief output is the LDA-determined topic distributions. These distributions are analyzed for similarity scores in an R document elsewhere. | |
// MAGIC | |
// MAGIC For getting all the topic distributions, you'll want the 'clusteredDF' object | |
// COMMAND ---------- | |
import org.apache.spark.sql.SaveMode; |