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sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io

sudo docker run hello-world # Command to verify Docker is installed.

## Adding your user to Docker group - Recommended Step

sudo groupadd docker
sudo usermod -aG docker $USER

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sudo apt-get remove docker docker-engine docker.io containerd runc # Remove any traces of older Docker installation

sudo apt-get update

sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg \
    lsb-release

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trace_high = go.Scatter(
                x=test_dates, 
                y=y_test,
                name = "Original",
                line = dict(color = '#17BECF'),
                opacity = 0.8)

trace_low = go.Scatter(
                x=test_dates,
                y=pd.DataFrame(yhat)[0].values,

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# train model
bidirectional_model = build_bidirectional_model(X, y)

# predict using the test data
yhat = bidirectional_model.predict(X_test, verbose=0)

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# choose a number of time steps
n_steps = 5

# split into sequence
X, y = split_sequence(real_price.values, n_steps)

# reshape from [samples, timesteps] into [samples, timesteps, features] to fit in LSTM model
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))

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# read bitcoin pricing data
bitcoin_data = pd.read_csv('input/bitstampUSD_1-min_data_2012-01-01_to_2018-11-11.csv')

# parse date using pandas
bitcoin_data['date'] = pd.to_datetime(bitcoin_data['Timestamp'],unit='s').dt.date

# group by date and pick 'Weighted_Price'
grouped_data = bitcoin_data.groupby('date')
real_price = grouped_data['Weighted_Price'].mean()

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def split_sequence(sequence, n_steps):
	X, y = list(), list()
	for i in range(len(sequence)):
		end_ix = i + n_steps
		if end_ix > len(sequence)-1:
			break
		seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
		X.append(seq_x)
		y.append(seq_y)
	return array(X), array(y)

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def build_bidirectional_model(X, y):
    # define model
    model = Sequential()
    model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(n_steps, n_features)))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    # fit model
    model.fit(X, y, epochs=200, verbose=0)
    return model

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library(data.table)
library(h2o)

# Load train and properties data

properties <- fread("../input/properties_2016.csv", header=TRUE, stringsAsFactors=FALSE, colClasses = list(character = 50))
train      <- fread("../input/train_2016_v2.csv")
training   <- merge(properties, train, by="parcelid",all.y=TRUE)

# Initialise h20

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#!/bin/bash

# install CUDA Toolkit v9.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda