jamesthomson

%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np
plays = pd.read_table("usersha1-artmbid-artname-plays-sample.tsv", usecols=[0, 2, 3], names=['user', 'artist', 'plays'])
users = pd.read_table("usersha1-profile-sample.tsv", usecols=[0, 1], names=['user', 'gender'])

users=users.dropna()

jamesthomson

#import data
import pandas as pd
plays = pd.read_table("usersha1-artmbid-artname-plays-sample.tsv", usecols=[0, 2, 3], names=['user', 'artist', 'plays'])
users = pd.read_table("usersha1-profile-sample.tsv", usecols=[0, 1], names=['user', 'gender'])
#print plays.head()
#print users.head()

#clear people who don't know gender for
users=users.dropna()
#dummy code up gender

jamesthomson

import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
print X

#scale the data
from sklearn.preprocessing import StandardScaler
SS=StandardScaler()
XS=SS.fit_transform(X)

jamesthomson

import pandas as pd

#open and split file then convert to df
lines = [line.strip().split("\t") for line in open("P:\\A.tsv.a.txt", "r")]
df=pd.DataFrame(lines)

#pull out columns for further split
cols=range(18,22)+range(33,42)
arrays=df.loc[1:5,cols].values

jamesthomson

#import all data add column headers and run checks

dist <- read.delim("~/Documents/my blog/million song database/7plus songs/output1.txt", header=FALSE)
colnames(dist)<-c('length', 'freq')

  
  
dist

dist_time <- read.csv("~/Documents/my blog/million song database/7plus songs/output2.txt", header=FALSE)

jamesthomson

DROP TABLE IF EXISTS msd_data;
CREATE EXTERNAL TABLE msd_data
( 
ref						string,       
analysis_sample_rate	float	,
artist_7digitalid	int	,
artist_familiarity	float	,

jamesthomson

library(XML)

#read url
search<-readLines('http://tbmmsd.s3.amazonaws.com/')
#convert to data.frame
df<-xmlToDataFrame(search)
#pull out files list
Files<-df$Key
#clean up NAs
Files2<-Files[!is.na(Files)]

jamesthomson

#server.r

library(shiny)

shinyServer(function(input, output) {
  
  
  library(rjson)
  library(ggplot2)
  library(grid)

jamesthomson

# ui.R

library(shiny)

shinyUI(fluidPage(
  titlePanel("Lastfm Dashboard"),

  sidebarLayout(position ="left",
    sidebarPanel(
      img(src = "logo.png", height = 80, width = 80),

jamesthomson

library(nnet)
library(ggplot2)
 
 
neuralNetScoreTime<-function(var, score){
 
modelset<-NULL
  for (i in 1:var) {
    eval(parse(text=paste0("temp.", i, "<-rnorm(150, mean=1, sd=1)")))
    eval(parse(text=paste0("modelset<-cbind(modelset, temp.", i, ")")))