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NAGPythonExamples

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Examples and demos showing how to call functions from the nAG Library for Python

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최종 버전 다운로드 (.zip)

- quantile_linreg_easy.ipynb
- dim2_spline_ts_sctr.ipynb
- brexit_trump_urls.txt
- nmf.png
- web_site_classification_using_nnmf.ipynb
- bnd_mcs_solve.ipynb
- PSO_demo.ipynb
- SQP_multistart.ipynb
- Readme.md
- real_toeplitz_solve.ipynb
- triangular_matrix_multiply.ipynb
- - Readme.md
- - animation.gif
  - animation.mp4
  - DFO_noisy.ipynb
  - Readme.md
- - README.md
  - rosenbrock2d.ipynb
- - dfo_calib.png
  - nlls.png
  - screenshot.png
  - xmas_tree.png
- - BESS_MILP.ipynb
  - Grid_Model.png
  - portfolio_optimization_using_milp.ipynb
- - christmas_demo.ipynb
  - dense_lp_solve_ex.py
  - dense_qp_solve_ex.py
  - dynamic_pricing_lp.ipynb
  - handle_disable_ex.ipynb
  - LP_demo.ipynb
  - nlnlsq_data.pck
  - production_planning.ipynb
  - Readme.md
- - data_fitting_robustness.ipynb
  - dose_resp.dat
  - dose_resp.ipynb
  - elastic_net.ipynb
  - nldf_contour.png
  - possum.csv
  - Readme.md
- - bounds_quasi_func_easy.ipynb
  - Readme.md
- - matrix_completion.ipynb
  - NCM_SDP.ipynb
  - Readme.md
  - theta_optimization.ipynb
- - biblio.bib
  - cvxpy_classification.ipynb
  - portfolio_optimization_qcqp.ipynb
  - portfolio_optimization_using_cvxpy.ipynb
  - portfolio_optimization_using_socp.ipynb
  - Readme.md
  - robust_lp.ipynb
  - simple_SOCP.ipynb
- python-nag-opt-solvers-links.md
- Readme.md
- k-means.ipynb
- alt_proj.png
- data_sp600_06yr.csv
- ncm_nag.ipynb
- ncm_poster.ipynb
- ncm_whitepaper_2019.pdf
- Readme.md
- my-rugby-bike-stadiums-dist-dur.tsv
- nag_rugby_tsm.ipynb
- rand_corr_mat.ipynb
- Anderson_Acceleration_Poisson.ipynb
- - graphs.PNG
  - imp_vol_demo.ipynb
  - impvolsurf.png
- mathieu_functions.ipynb
- cp_pelt.ipynb
- inhom_iema.ipynb
.gitignore
_config.yml
LICENSE
Mark_27_examples.md
nag_logo.png
README.md

// repository documentation

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![nAG Logo](https://raw.githubusercontent.com/numericalalgorithmsgroup/NAGPythonExamples/master/nag_logo.png)<a name=top></a>

# Content<a name=content></a>

* [Examples using the *n*AG Library for Python](#examples)
* [How to install the *n*AG Library for Python](#install)
* [How to run the Jupyter notebook examples](#jupyter)
* [List of Chapters in the *n*AG Library for Python](#chapters)
* [Useful links](#links)

# Examples using the *n*AG Library for Python <a name=examples></a>

This repository contains examples and demonstrations using the [*n*AG Library for Python](https://nag.com/nag-library/).  The *n*AG Library for Python contains 1900+ functions spanning many areas of numerical computing and data science.

Designed to work alongside the open source Python packages, [Numpy](http://www.numpy.org/) and [Scipy](https://www.scipy.org/), The *n*AG Library for Python can augment your computational workflow in many areas.

## Directory of GitHub examples

* [Correlation and regression analysis](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/correlation_and_regression_analysis)
* [Curve and surface fitting](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/curve_and_surface_fitting)
* [Dimensionality Reduction](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/dimension_reduction)
* [Global Optimization](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/global_optimization)
* [Linear Algebra](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/linear_algebra)
* [Local optimization](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization)
* [Multivariate methods](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/multivariate_methods)
* [Nearest Correlation Matrices](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/nearest_correlation_matrices)
* [Operations Research](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/operations_research)
* [Random number generation](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/random_number_generation)
* [Roots](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/roots)
* [Time series analysis](https://github.com/numericalalgorithmsgroup/NAGPythonExamples/tree/master/time_series_analysis)

## Examples that ship with the product

In addition to those presented here, The *n*AG Library for Python ships with a set of usage examples.  To see them all, run the following command

```
python -m naginterfaces.library.examples --locate
```

# How to install the *n*AG Library for Python<a name=install></a>

In this section we illustrate how to install the *n*AG Library for Python, request a Trial Licence and make sure the Library is working. Details and further information regarding the installation can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation).

**Note** Before starting make sure you have access to a host that has Python 3 (3.4 or more recent).

### Step 1. Downloading and installing
Installing the *n*AG Library is done using the `pip` package manager, fire-up a terminal and create a Python 3 virtual environment where to install and test the *n*AG Library
```{bash}
guest@nag-37:~$ python3 -m venv nag3
guest@nag-37:~$ . nag3/bin/activate
(nag3) guest@nag-37:~$
```
Now use `pip` to install the *n*AG Library for Python
```{bash}
(nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_nag naginterfaces
```
or if you prefer the version of the package that relies on Intel MKL for optimized linear algebra routines, then use
```{bash}
(nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_mkl naginterfaces
```

The output should be similar to
```{bash}
Collecting naginterfaces
  Downloading https://www.nag.com/downloads/py/naginterfaces_nag/naginterfaces/naginterfaces-27.1.0.0-py2.py3-none-linux_x86_64.whl (55.8MB)
    100% |████████████████████████████████| 55.8MB 21kB/s 
Collecting numpy>=1.15 (from naginterfaces)
  Downloading https://files.pythonhosted.org/packages/45/b2/6c7545bb7a38754d63048c7696804a0d947328125d81bf12beaa692c3ae3/numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl (13.4MB)
    100% |████████████████████████████████| 13.4MB 70kB/s 
Installing collected packages: numpy, naginterfaces
Successfully installed naginterfaces-27.1.0.0 numpy-1.19.5
```
The output indicates that the installation was successful.

### Step 2. Getting a trial licence
The next step is to get the licensing info (**product code** and **KUSARI ID**) and use it to request a licence. From the same virtual terminal, try
```{bash}
(nag3) guest@nag-37:~$ python -m naginterfaces.kusari
```
The output should be similar to
```
The *n*AG Library for Python on this platform uses
underlying Library NLL6I271VL.
This Library has been installed as part of the package
and it requires a valid licence key.
No such key could be validated:
the key may not have been installed correctly or
it may have expired.
The Kusari licence-check utility reports the following:
User: guest
Directory: /home/guest
NAG_KUSARI_FILE=""
File /home/guest/nag.key does not exist
-------------------------------------------------------------------------------
Error: Licence not found; this product requires a key for NLL6I271VL
The above information has been generated on machine nag-37
For information on how to obtain a licence, please see
https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html
KUSARI ID = "ADLXt-adEclJLmvnxlrU2sseteZoo,RopA-Ld"
```
The **two** important bits are the

1. **product code** shown as **`underlying Library NLL6I271VL.`** which identifies the licence to request, and
 
 2. **KUSARI ID** shown as **`KUSARI ID = "ADLXt-adEclJLmvnxlrU2sseteZoo,RopA-Ld"`** which identifies the host you are running the library on.
 
 **Note** that the **product code** and **KUSARI ID** can be different from the previous example.
 
 With these, you are set to [contact *n*AG and request a trial licence](https://nag.com/contact-us/).
 
 The trial licence is a plain text chunk similar to
 ```
 NLL6I271V TRIAL 2021/01/27 "RverXn0Pc-Ib?ctdgF=Wpis2j7I"
 ```
 Save or copy the text into the file `/home/guest/nag.key`.
 
 The final step is to make sure the licence is valid and the library is working as expected.
 
### Step 3. Testing the *n*AG Library
The last step is to make sure the licence was correctly stored and that the *n*AG Library is working correctly. From the same virtual terminal re-run the Kusari licence module
```{bash}
(nag3) guest@nag-37:~$ python -m naginterfaces.kusari
``` 
This time the output should be similar to
```
Licence available; the required NLL6I271VL licence key for this product is valid
TRIAL licence, 27 days remaining (licence from file)
```
Now let's try a more interesting example ([list of optimization examples](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html#examples))

This command runs the example for the [FOAS (First-Order Active set method) solver and minimizes the Rosenbrock 2D function](./FOAS).
```
(nag3) guest@nag-37:~$ python -m naginterfaces.library.examples.opt.handle_solve_bounds_foas_ex
```
Should generate an outputsimilar to
```{bash}
Trying:
    main()
Expecting:
    naginterfaces.library.opt.handle_solve_bounds_foas Python Example Results.
    Minimizing a bound-constrained Rosenbrock problem.
     E04KF, First order method for bound-constrained problems
...
     Status: converged, an optimal solution was found
     Value of the objective             4.00000E-02
    ...
ok
```
indicating that the example was successfully executed. The source code can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/_modules/naginterfaces/library/examples/opt/handle_solve_bounds_foas_ex.html#main).

### Running more examples

To display the full list of example source files on disk, but not run them, execute
```
python -m naginterfaces.library.examples --locate
```
All examples may be executed sequentially by running
```
python -m naginterfaces.library.examples
```
Run `python -m naginterfaces.library.examples --help` to see any additional usage.

# How to run the Jupyter notebook examples<a name=jupyter></a>

This section briefly illustrates how to setup a host in order to open and run the [Jupyter notebooks](https://jupyter.org/) provided in this repository.
Before running the notebooks make sure the [*n*AG Library is installed and working](#install). Before starting, it is advised to read [Jupyter's installation page](https://jupyter.org/install.html).

### Installing Jupyter notebook
To install Jupyter, launch a terminal and activate the virtual environment used to install the *n*AG Library for Python
```{bash}
guest@nag-37:~$ . nag3/bin/activate
(nag3) guest@nag-37:~$ pip install notebook matplotlib
Collecting notebook
  Downloading https://files.pythonhosted.org/packages/74/19/50cd38acf22e33370d01fef764355f1e3517f6e12b4fceb8d434ece4f8fd/notebook-6.2.0-py3-none-any.whl (9.5MB)
    100% |████████████████████████████████| 9.5MB 115kB/s 
Collecting argon2-cffi (from notebook)
...
Successfully installed jupyter-client-6.1.11 jupyterlab-pygments-0.1.2 ... wcwidth-0.2.5
```
This indicates that Jupyter and matplotlib were successfully installed. The next section shows how to start the notebok interface and open an example.

### Running the notebook examples
To run an example, grab a copy of the notebook of interest and start up the notebook interface.
For example, download the [Rosenbrock 2D optimization example](./FOAS/rosenbrock2d.ipynb) notebook `rosenbrock2d.ipynb` into the current directory
```{bash}
(nag3) guest@nag-37:~$ curl -O https://raw.githubusercontent.com/numericalalgorithmsgroup/NAGPythonExamples/master/local_optimization/FOAS/rosenbrock2d.ipynb
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 61961  100 61961    0     0   382k      0 --:--:-- --:--:-- --:--:--  382k
```
and now open it using `jupyter-notebook`
```{bash}
(nag3) guest@nag-37:~$ jupyter-notebook rosenbrock2d.ipynb
[I 12:24:07.336 NotebookApp] Serving notebooks from local directory: /home/guest
[I 12:24:07.336 NotebookApp] Jupyter Notebook 6.2.0 is running at:
[I 12:24:07.336 NotebookApp] http://localhost:8888/?token=f1836a06799a92f25ef9966439bf3491b2f0960dcb51806d
...
```
This command will fire-up your web browser and open the `rosenbrock2d.ipynb` notebook, the window should be similar to

![Notebook screenshot](https://raw.githubusercontent.com/numericalalgorithmsgroup/NAGPythonExamples/master/local_optimization/images/screenshot.png)

# List of Chapters in the *n*AG Library for Python<a name=chapters></a>

The following links take you to the relevant section in the official documentation

* [library.anova](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.anova.html) - Analysis of Variance
* [library.blas](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.blas.html) - Linear Algebra Support Routines
* [library.blast](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.blast.html) - Further Linear Algebra Support Routines
* [library.blgm](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.blgm.html) - Linear Model Specification
* [library.complex](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.complex.html) - Complex Arithmetic
* [library.contab](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.contab.html) - Contingency Table Analysis
* [library.correg](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.correg.html) - Correlation and Regression Analysis
* [library.det](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.det.html) - Determinants
* [library.dot](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.dot.html) - Inner Products
* [library.eigen](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.eigen.html) - Eigenvalues and Eigenvectors
* [library.fit](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.fit.html) - Curve and Surface Fitting
* [library.glopt](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.glopt.html) - Global Optimization of a Function
* [library.ieee](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.ieee.html) - IEEE Arithmetic
* [library.info](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.info.html) - Library Identification
* [library.inteq](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.inteq.html) - Integral Equations
* [library.interp](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.interp.html) - Interpolation
* [library.lapackeig](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapackeig.html) - Least Squares and Eigenvalue Problems
* [library.lapacklin](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.lapacklin.html) - Linear Equations
* [library.linsys](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.linsys.html) - Simultaneous Linear Equations
* [library.machine](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.machine.html) - Machine Constants
* [library.math](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.math.html) - Mathematical Constants
* [library.matop](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.matop.html) - Matrix Operations, Including Inversion
* [library.mesh](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.mesh.html) - Mesh Generation
* [library.mip](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.mip.html) - Operations Research
* [library.mv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.mv.html) - Multivariate Methods
* [library.nonpar](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.nonpar.html) - Nonparametric Statistics
* [library.numdiff](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.numdiff.html) - Numerical Differentiation
* [library.ode](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.ode.html) - Ordinary Differential Equations
* [library.omp](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.omp.html) - OpenMP Utilities
* [library.opt](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html) - Minimizing or Maximizing a Function
* [library.orthog](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.orthog.html) - Orthogonalization
* [library.pde](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.pde.html) - Partial Differential Equations
* [library.quad](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.quad.html) - Quadrature
* [library.rand](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.rand.html) - Random Number Generators
* [library.rnla](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.rnla.html) - Randomized Numerical Linear Algebra
* [library.roots](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.roots.html) - Roots of One or More Transcendental Equations
* [library.smooth](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.smooth.html) - Smoothing in Statistics
* [library.sort](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.sort.html) - Sorting and Searching
* [library.sparse](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.sparse.html) - Large Scale Linear Systems
* [library.sparseig](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.sparseig.html) - Large Scale Eigenproblems
* [library.specfun](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.specfun.html) - Approximations of Special Functions
* [library.stat](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.stat.html) - Simple Calculations on Statistical Data
* [library.sum](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.sum.html) - Summation of Series
* [library.surviv](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.surviv.html) - Survival Analysis
* [library.tsa](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.tsa.html) - Time Series Analysis
* [library.univar](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.univar.html) - Univariate Estimation
* [library.wav](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.wav.html) - Wavelet Transforms
* [library.zeros](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.zeros.html) - Zeros of Polynomials

# Useful links<a name=links></a>

* [*n*AG Library for Python Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html)
* [Kusari licence module Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html#kusari)

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