earino

joke_map <- function(states_to_highlight, title="", highlight_colors=c("#aaaaaa", "#ee0000")) {
  library(dplyr)
  library(ggplot2)
  library(RColorBrewer)
  library(maps)
  
  highlighting = data.frame(region=tolower(state.name[match(states_to_highlight, state.abb)]), 
                             highlight=TRUE)
  
  d = map_data("state") %>% left_join(highlighting, by="region")

earino

earino

  #that title doesn't get me on a list...
  use Parallel::ForkManager;
  use Data::Dumper;

  my @sleep = (
    10,
    60,
    60,
    60,
    60,

earino

print("start.207")
for (i in 1:5) {
  t <- get(paste0("d", i));
  y <- factor(t[,c("start.207")], levels=c("1", "0"))
  x <- t[,c("stop.165","stop.166","stop.167","stop.168","stop.169","stop.185",
            "stop.186","stop.187","stop.188","stop.189","stop.205","stop.206",
            "stop.207","stop.208","stop.209","stop.225","stop.226","stop.227",
            "stop.228","stop.229","stop.245","stop.246","stop.247","stop.248","stop.249")]

  md <- train(x, y, method="rf", ntree=ntrees, trControl=trainControl(method="cv"))

earino

package LedgerSMB::Currency;
use Moose;
with 'LedgerSMB::PGOSimple::Role', 'LedgerSMB::MooseTypes';

use PGObject::Util::DBMethod;
    
sub _set_prefix { 'currency__' }

has id                => (is => 'rw', isa => 'Int', required => '0');
has symbol            => (is => 'ro', isa => 'Str', required => '1');

earino

/* from http://madebyevan.com/webgl-water/water.js
   used in the awesome WebGL water demo
   http://madebyevan.com/webgl-water/*/
   
  this.dropShader = new GL.Shader(vertexShader, '\
    const float PI = 3.141592653589793;\
    uniform sampler2D texture;\
    uniform vec2 center;\
    uniform float radius;\
    uniform float strength;\

earino

from pyspark.mllib.classification import LogisticRegressionWithSGD
from numpy import array

# Load and parse the data
data = sc.textFile("mllib/data/sample_svm_data.txt")
parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
model = LogisticRegressionWithSGD.train(parsedData)

# Build the model
labelsAndPreds = parsedData.map(lambda point: (int(point.item(0)),

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points = spark.textFile(...).map(parsePoint).cache()
w = numpy.random.ranf(size = D) # current separating plane
for i in range(ITERATIONS):
    gradient = points.map(
        lambda p: (1 / (1 + exp(-p.y*(w.dot(p.x)))) - 1) * p.y * p.x
    ).reduce(lambda a, b: a + b)
    w -= gradient
print "Final separating plane: %s" % w

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#!/usr/bin/env perl

use warnings;
use strict;

use Data::Dumper;

my @records = ();
my $now = time();
my $weeks = 604800 * 52;

earino

sourceCpp('../src/string_slicer.cpp')
sliced_string <- string_slicer(s, cutpoints$start, cutpoints$end)