rinze

package jclustering;

import ij.ImagePlus;

/**
 * This class extends {@link ImagePlus} in order to add a handy {@link #getTac} 
 * method that allows to easily grab time-activity curves.
 * 
 * @author <a href="mailto:jmmateos@mce.hggm.es">José María Mateos</a>.
 *

rinze

rinze

# Eustat data analysis
library(ggplot2)

eustat <- read.csv(file = "/tmp/ilc_di01_1_Data.csv", na.string = ":")
eustat$Value <- gsub(" ", "", eustat$Value)
eustat$Value <- as.numeric(eustat$Value)

spain <- eustat[eustat$GEO == "Spain" & 
                eustat$CURRENCY == "Purchasing Power Standard" & 
                eustat$INDIC_IL == "Top cut-off point", 

rinze

open("C:\\temp\\barcenas.tif");
run("32-bit");
run("Mean...", "radius=30");
open("C:\\temp\\mariano.tif");
run("32-bit");
run("Duplicate...", "title=mariano-1.tif");
run("Mean...", "radius=20");
imageCalculator("Subtract create 32-bit", "mariano.tif","mariano-1.tif");
imageCalculator("Add create 32-bit", "Result of mariano.tif","barcenas.tif");
selectWindow("barcenas.tif");

rinze

# Timing measurement in R. Vectorized operation on matrix / data.frame
# Author: José María Mateos - jmmateos@ieee.org
#
# For certain vectorized operations, it makes sense to convert your data.frame
# into a matrix. Even if you are using apply, the data frame iteration can be
# real slow.
#
# In this example, I will compute the Euclidean distance for a random vector
# and a random matrix / data.frame of thousands of elements. Operations will be
# done in two different ways: using the apply function over the columns and 

rinze

localpath <- "/home/chema/tmp/kaggle/digits/"
if (Sys.info()["sysname"] == "Windows")
    localpath <- "C:/temp/kaggle/digits"

testFile <- file.path(localpath, "test.Rda")
trainFile <- file.path(localpath, "train.Rda")

if(!file.exists(testFile) && !file.exists(trainFile)) {
    testCSV <- file.path(localpath, "test.csv")
    trainCSV <- file.path(localpath, "train.csv")

rinze

Keybase proof

I hereby claim:

• I am rinze on github.
• I am rinzewind (https://keybase.io/rinzewind) on keybase.
• I have a public key whose fingerprint is C7F0 E413 1FFF 47C6 A2A7 F76B F4FE 866D 2948 FA19

To claim this, I am signing this object:

rinze

### Keybase proof

I hereby claim:

  * I am rinze on github.
  * I am rinzewind (https://keybase.io/rinzewind) on keybase.
  * I have a public key whose fingerprint is 67AF C4B2 E15C 4F4C 67BD  5F58 5ADF 9021 2630 80EC

To claim this, I am signing this object:

rinze

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import csv
import codecs
import cStringIO
import os
from collections import namedtuple

def getParties(parties_file):

rinze

library(C50)

# Test data
group <- c(rep('groupA', 10), rep('groupB', 10))
data <- data.frame(group = group, var = c(rep(0, 10), rep(0, 10)))

# Leave-one-out
probs <-  lapply(1:nrow(data), function(i) {
    train <- data[-i, ]
    test <- data[i, ]