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Hands-On Markov Models with Python

You're reading from   Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781788625449
Length 178 pages
Edition 1st Edition
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Authors (2):
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Ankur Ankan Ankur Ankan
Author Profile Icon Ankur Ankan
Ankur Ankan
Abinash Panda Abinash Panda
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Abinash Panda
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Toc

Installing Python and packages

Installation on Windows

Miniconda can be installed on a Windows system by just double-clicking on the downloaded .exe file and following the installation instructions. After installation, we will need to create a conda environment and install all the required packages in the environment. To create a new Python 3.4 environment with the name hmm, run the following command:

conda create -n hmm python=3.4

After creating the environment, we will need to activate it and install the required packages in it. This can be done using the following commands:

activate hmm
conda install numpy scipy

Installation on Linux

On Linux, after downloading the Miniconda file, we will need to give it execution permissions and then install it. This can be done using the following commands:

chmod +x Miniconda.sh
./Miniconda.sh

After executing the file, we can simply follow the installation instructions. Once installed, we will need to create a new environment and install the required packages. We can create a new Python 3.4 environment with the name hmm using the following commands:

conda create -n hmm python=3.4

Once the environment has been created, we will need to activate it and install the packages inside it using the following:

source activate hmm
conda install numpy scipy
You have been reading a chapter from
Hands-On Markov Models with Python
Published in: Sep 2018
Publisher: Packt
ISBN-13: 9781788625449
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