glamp

info:    Creating snapshot 0.0.0-12
info	 Uploading: [============================ ] 98%
info:    Updating app movie-match
info:    Activating snapshot 0.0.0-12 for movie-match
info:    Starting app movie-match
error:   Error running command deploy
error:   Nodejitsu Error (500): Internal Server Error
error:   There was an error while attempting to deploy the app
error:   
error:   Rackspace Error (404): Item not found

glamp

info:    Creating snapshot 0.0.0-15
info	 Uploading: [=============================] 99%
info:    Updating app movie-match
info:    Activating snapshot 0.0.0-15 for movie-match
info:    Starting app movie-match
error:   Error running command deploy
error:   socket hang up
info:    
jitsu's client request timed out before the server could respond
info:    This error may be due to network connection problems

glamp

import numpy as np
import pylab as pl
import pandas as pd

from sklearn import svm
from sklearn import linear_model
from sklearn import tree

from sklearn.metrics import confusion_matrix

glamp

  o		o		o	
	o		 	x		
o			x	x	x	
			x			o
			x	x	x	o
		x				
				o		o
		o				
						

glamp

from sklearn.cluster import AffinityPropagation, KMeans, MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
import odcdata
import pprint as pp

q = "select UPPER(description) as description  from odc_bank_transaction limit 1000"
data = odcdata.read_gp_as_dict(q)
descriptions = [row['description'] for row in data]

tfidf = TfidfVectorizer()

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library(sqldf)

sqldf("SELECT
        day
        , avg(temp) as avg_temp
      FROM beaver2
      GROUP BY
        day;")

#   day avg_temp

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library(plyr)

# split a data frame by Species, summarize it, then convert the results
# into a data frame
ddply(iris, .(Species), summarise,
      mean_petal_length=mean(Petal.Length)
)
#     Species mean_petal_length
#1     setosa             1.462
#2 versicolor             4.260

glamp

library(stringr)

names(iris)
#[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
names(iris) <- str_replace_all(names(iris), "[.]", "_")
names(iris)
#[1] "Sepal_Length" "Sepal_Width"  "Petal_Length" "Petal_Width"  "Species"

s <- c("Go to Heaven for the climate, Hell for the company.")
str_extract_all(s, "[H][a-z]+ ")

glamp

library(RPostgreSQL)

drv <- dbDriver("PostgreSQL")
db <- dbConnect(drv, dbname="ncaa",
                 user="YOUR USER NAME", password="YOUR PASSWORD")

q <- "SELECT
        *
      FROM
        game_scores;"

glamp

library(lubridate)

year("2012-12-12")
#[1] 2012
day("2012-12-12")
#[1] 12
ymd("2012-12-12")
#1 parsed with %Y-%m-%d
#[1] "2012-12-12 UTC"