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#!/bin/bash | |
# Install CUDA Toolkit 10 | |
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb | |
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo apt update | |
sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb | |
sudo apt update | |
sudo apt install -y cuda |
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''' | |
Non-parametric computation of entropy and mutual-information | |
Adapted by G Varoquaux for code created by R Brette, itself | |
from several papers (see in the code). | |
These computations rely on nearest-neighbor statistics | |
''' | |
import numpy as np |
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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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# Time series forecasting based on multiple time series, including the original one | |
# This script is based on the following examples and discussions: | |
# https://gist.github.com/lukovkin/1aefa4509e066690b892 | |
# https://groups.google.com/forum/#!topic/keras-users/9GsDwkSdqBg | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import random | |
import theano |