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REF

https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/ https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data

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#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

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### Lending Club example using cleaned up dataset & h2o.ensemble ###

library(h2o)
h2o.init(nthreads = -1, max_mem_size = "8G")

loan_csv <- "https://raw.githubusercontent.com/h2oai/app-consumer-loan/master/data/loan.csv"

data <- h2o.importFile(loan_csv)  # 163994 x 15
data$bad_loan <- as.factor(data$bad_loan)
rand  <- h2o.runif(data, seed = 1)

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# install.packages("h2o", type="source", repos=(c("http://h2o-release.s3.amazonaws.com/h2o/rel-slater/5/R")))

library(h2o)
localH2O <- h2o.init(ip = "XX.XX.XX.XX", port = 54321)

#higgs <- h2o.importFile("/home/0xdiag/datasets/higgs/HIGGS.csv", destination_frame = "higgs")  #Local copy
higgs <- h2o.importFile("http://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz", destination_frame = "higgs")
dim(higgs)  #11M x 29

higgs$C1 <- as.factor(higgs$C1)  #Encode response as categorical

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# Quiz 4

# Question 1.
library(ElemStatLearn)
library(randomForest)
library(caret)

data(vowel.train)
data(vowel.test)

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import pandas as pd
import numpy as np

'''
Some functions for loading a dataset and performing simple data preparation
'''

def load_dataset(filename, filetype='csv', header=True):

    '''

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# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

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{
  "lines.linewidth": 2.0,
  "examples.download": true,
  "patch.linewidth": 0.5,
  "legend.fancybox": true,
  "axes.color_cycle": [
    "#30a2da",
    "#fc4f30",
    "#e5ae38",
    "#6d904f",

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import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np