diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..07764a7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +* text eol=lf \ No newline at end of file diff --git a/.gitignore b/.gitignore index 7f0bfa3..cc6b26f 100644 --- a/.gitignore +++ b/.gitignore @@ -98,4 +98,10 @@ ENV/ /site # mypy -.mypy_cache/ \ No newline at end of file +.mypy_cache/ + +#documentation +./docs/build/ + +#IntelliJ files. +.idea \ No newline at end of file diff --git a/AUTHORS.rst b/AUTHORS.rst new file mode 100644 index 0000000..543daa1 --- /dev/null +++ b/AUTHORS.rst @@ -0,0 +1,15 @@ +Authors +========== + + +Core Development Team +--------------------- + +- Cuesta Merino, David (`david.cuesta@shapelets.io `_) +- Ruíz-Ferrer, Justo (`justo.ruiz@shapelets.io `_) +- Torreño Tirado, Óscar (`oscar.torreno@shapelets.io `_) +- Vilches Reina, Antonio (`antonio.vilches@shapelets.io `_) + +Contributions +------------- +- Sánchez de Ybargüen, Luis (`luis.sanchez@shapelets.io `_) diff --git a/CHANGES.rst b/CHANGES.rst new file mode 100644 index 0000000..2e76590 --- /dev/null +++ b/CHANGES.rst @@ -0,0 +1,10 @@ +========= +Changelog +========= + +KHIVA uses `Semantic Versioning `_ + + +Version 0.1.0 +============== +- Initial version diff --git a/LICENSE b/LICENSE.txt similarity index 100% rename from LICENSE rename to LICENSE.txt diff --git a/README.md b/README.md index 8e926de..d126254 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,47 @@ # README # - -This is the TSA library for Python. -This project provides the Python wrapper for the original TSA library written in C++ programming language. - -### What is this repository for? ### - -* This repository provides the full installable Python Package for using the TSA library with the target of analyse -time series in a super-efficient fashion, giving a solution for time series analysis in the Big Data Ecosystem. - - - -### How do I get set up? ### - -* For installing this package, please, first it is necessary to satisfy the reuqirements indicated in the requirements.txt file. - -* Configuration -* Dependencies -* Database configuration -* How to run tests -* Deployment instructions - -### Contribution guidelines ### - -* Writing tests -* Code review -* Other guidelines - -### Who do I talk to? ### - -* Repo owner or admin -* Other community or team contact \ No newline at end of file +This is the KHIVA binding for connecting the Python programming language and the KHIVA library. + +## License +This project is licensed under [MPL-v2](https://www.mozilla.org/en-US/MPL/2.0/). + +## Quick Summary +This Python library called 'khiva' provides all the functionalities of the KHIVA library for time series analytics. + +## Set up +It is just needed to execute the next command in the root directory of the project: +```bash +python3 setup.py install +``` + +## Executing the tests: +The tests can be executed and they are located in /tests/unit_tests. + +## Documentation +This Python library follows the standard way of writing documentation of Python by using Sphinx. + +In order to generate the documentation (in html format), run the following command under the /docs folder: +```bash +make html +``` + +## Contributing + +### Branching model +Our branching model has two permanent branches, **develop** and **master**. +We aim at using `develop` as the main branch, where all features are merged. +In this sense, we use the master branch to push the release versions of the binding for the KHIVA library. + +### Contribution process +In order to contribute to the code base, we follow the next process: +1. The main branch is develop, every developer should pull the current status of the branch before stating to develop any new feature. +`git pull` +2. Create a new branch with the following pattern "feature/[name_of_the_feature]" +`git checkout -b feature/exampleFeature` +3. Develop the new feature on the the new branch. It includes testing and documentation. +`git commit -a -m "Bla, Bla, Bla"; git push` +4. Open a Pull Request to merge the feature branch in to develop. Currently, a pull request has to be reviewed at least by one person. +5. Finally, delete the feature branch. +6. Move back to develop branch. +`git checkout develop` +7. Pull the latest changes. +`git pull` \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..bbb76d6 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = khiva +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/source/authors.rst b/docs/source/authors.rst new file mode 100644 index 0000000..466dffa --- /dev/null +++ b/docs/source/authors.rst @@ -0,0 +1,2 @@ +.. _authors: +.. include:: ../../AUTHORS.rst \ No newline at end of file diff --git a/docs/source/changes.rst b/docs/source/changes.rst new file mode 100644 index 0000000..b5e2224 --- /dev/null +++ b/docs/source/changes.rst @@ -0,0 +1,2 @@ +.. _changes: +.. include:: ../../CHANGES.rst \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..e764929 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,161 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/stable/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +import subprocess + +sys.path.insert(0, os.path.abspath('../..')) + + + +# -- Project information ----------------------------------------------------- + +project = 'khiva' +copyright = '2018, Shapelets' +author = 'Shapelets.io' + +# The short X.Y version +version = '' +# The full version, including alpha/beta/rc tags +release = subprocess.check_output(["git", "describe"]).strip().decode("utf-8").split('-')[0] + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.mathjax', +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path . +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'khivadoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'khiva.tex', 'khiva Documentation', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'khiva', 'khiva Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'khiva', 'khiva Documentation', + author, 'khiva', 'One line description of project.', + 'Miscellaneous'), +] + +# -- Extension configuration ------------------------------------------------- \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..49dc562 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,27 @@ +.. khiva documentation master file, created by + sphinx-quickstart on Thu Apr 5 13:28:29 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to KHIVA's documentation! +================================= + +.. toctree:: + :maxdepth: 4 + :caption: Contents: + :numbered: + + Quick Start + Modules + FAQ + Authors + License + Changelog + How to contribute + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/source/khiva.rst b/docs/source/khiva.rst new file mode 100644 index 0000000..2e71d4c --- /dev/null +++ b/docs/source/khiva.rst @@ -0,0 +1,101 @@ +khiva +=========== + +Submodules +++++++++++ + +khiva.array +--------------------------------- + +.. automodule:: khiva.array + :members: + :undoc-members: + :show-inheritance: + +khiva.dimensionality +--------------------------------- + +.. automodule:: khiva.dimensionality + :members: + :undoc-members: + :show-inheritance: + +khiva.distances +--------------------------------- + +.. automodule:: khiva.distances + :members: + :undoc-members: + :show-inheritance: + +khiva.features +--------------------------------- + +.. automodule:: khiva.features + :members: + :undoc-members: + :show-inheritance: + +khiva.library +--------------------------------- + +.. automodule:: khiva.library + :members: + :undoc-members: + :show-inheritance: + +khiva.linalg +--------------------------------- + +.. automodule:: khiva.linalg + :members: + :undoc-members: + :show-inheritance: + +khiva.matrix +--------------------------------- + +.. automodule:: khiva.matrix + :members: + :undoc-members: + :show-inheritance: + +khiva.normalization +--------------------------------- + +.. automodule:: khiva.normalization + :members: + :undoc-members: + :show-inheritance: + +khiva.polynomial +--------------------------------- + +.. automodule:: khiva.polynomial + :members: + :undoc-members: + :show-inheritance: + +khiva.regression +--------------------------------- + +.. automodule:: khiva.regression + :members: + :undoc-members: + :show-inheritance: + +khiva.regularization +--------------------------------- + +.. automodule:: khiva.regularization + :members: + :undoc-members: + :show-inheritance: + +khiva.statistics +--------------------------------- + +.. automodule:: khiva.statistics + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/license.rst b/docs/source/license.rst new file mode 100644 index 0000000..0adc5ab --- /dev/null +++ b/docs/source/license.rst @@ -0,0 +1,7 @@ +.. _license: + +======= +License +======= + +.. literalinclude:: ../../LICENSE.txt \ No newline at end of file diff --git a/docs/source/modules.rst b/docs/source/modules.rst new file mode 100644 index 0000000..ae77ff5 --- /dev/null +++ b/docs/source/modules.rst @@ -0,0 +1,7 @@ +KHIVA +===== + +.. toctree:: + :maxdepth: 4 + + khiva diff --git a/docs/source/text/faq.rst b/docs/source/text/faq.rst new file mode 100644 index 0000000..ddd553e --- /dev/null +++ b/docs/source/text/faq.rst @@ -0,0 +1,15 @@ +FAQ +=== + + + 1. **What are the KHIVA Array restrictions?** + + KHIVA Arrays can have up to 4 dimensions, each one needs to have the same length and the same type. + + 2. **Can I use the KHIVA package without having a GPU for computation?** + + Of course. It is right that KHIVA algorithms are designed to run on GPU, but a CPU backend could be set. + + 3. **What OS are supported?** + + Nowadays, KHIVA is supported on Windows, Linux and MacOS. \ No newline at end of file diff --git a/docs/source/text/how_to_contribute.rst b/docs/source/text/how_to_contribute.rst new file mode 100644 index 0000000..1cb9bf5 --- /dev/null +++ b/docs/source/text/how_to_contribute.rst @@ -0,0 +1,46 @@ +How to contribute +================= + +We have just started! Our aim is to make the KHIVA library the reference library for time series analysis in the fastest fashion. +To achieve this target we need your help! + +All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. If you +want to add one or two interesting feature calculators, implement a new feature selection process or just fix 1-2 typos, +your help is appreciated. + +If you want to help, just create a pull request on our github page. + + +Guidelines +'''''''''' +Branching model +++++++++++++++++ +Our branching model has two permanent branches, **develop** and **master**. +We aim at using `develop` as the main branch, where all features are merged. +In this sense, we use the master branch to push the release versions of the binding for the KHIVA library. + +Contribution process ++++++++++++++++++++++ +In order to contribute to the code base, we follow the next process: + +1. The main branch is develop, every developer should pull the current status of the branch before stating to develop any new feature. +`git pull` + +2. Create a new branch with the following pattern "feature/[name_of_the_feature]" +`git checkout -b feature/example_feature` + +3. Develop the new feature on the the new branch. It includes testing and documentation. +`git commit -a -m "Bla, Bla, Bla"` + +`git push` + +4. Open a Pull Request to merge the feature branch in to develop. Currently, a pull request has to be reviewed at least by +one person. + +5. Finally, delete the feature branch. + +6. Move back to develop branch. +`git checkout develop` + +7. Pull the latest changes. +`git pull` \ No newline at end of file diff --git a/docs/source/text/quick_start.rst b/docs/source/text/quick_start.rst new file mode 100644 index 0000000..8ae4ba5 --- /dev/null +++ b/docs/source/text/quick_start.rst @@ -0,0 +1,235 @@ +.. _quick-start-label: + +Quick Start +=========== + + +Install khiva +--------------- + +First of all, the KHIVA C++ library should be installed by using the installer that we offer in wwww.shapelets.io + +Then, the compiled KHIVA package is hosted on the Python Package Index (PyPI) so it can be installed with pip: + +.. code:: shell + + pip3 install khiva + + +Dive in +------- + +In order to quickly dive into khiva, you can follow the following example: + +First step, consists in setting what backend and device you want to use (there is a backend and a device set by default): + +.. code-block:: python + + from khiva.library import * + set_backend(KHIVABackend.KHIVA_BACKEND_OPENCL) + set_device(0) + +After that, we can create an array in the device: + +.. code-block:: python + + from khiva.array import * + a = Array([1, 2, 3, 4, 5, 6, 7, 8]) + a.print() + +The lines contained above print the dimensions and the content of the created array: + ++-----------+ +| [8 1 1 1] | ++===========+ +|1.0000 | ++-----------+ +|2.0000 | ++-----------+ +|3.0000 | ++-----------+ +|4.0000 | ++-----------+ +|5.0000 | ++-----------+ +|6.0000 | ++-----------+ +|7.0000 | ++-----------+ +|8.0000 | ++-----------+ + +We have to know that this array is created on the device and now we can concatenate operations applied to this array in +an asynchronous way and we will only receive the data in the host when 'to_list()', 'to_numpy()' or 'to_pandas()' (this +one only supports bi-dimensional time series) functions are called. + +.. code-block:: python + + a = a.to_pandas() + print(a) + +The result is the next one: + ++-+-------+ +| | 0 | ++=+=======+ +|1|1.0 | ++-+-------+ +|2|2.00 | ++-+-------+ +|3|3.00 | ++-+-------+ +|4|4.00 | ++-+-------+ +|5|5.00 | ++-+-------+ +|6|6.00 | ++-+-------+ +|7|7.00 | ++-+-------+ +|8|8.00 | ++-+-------+ + +Now let's dive into the asynchronous usage of the library. + +KHIVA's library provide us several time series analysis functionalities which include features extaction, time-series +re-dimension, distance calculations, motifs and discords detection, tools for similarity study, statistical parameters +extraction or time series normalization. + +All of these functionalities can be concatenated in order to improve the performance, getting the data just in the +moment that you will not use functions of this library: + +.. code-block:: python + + from khiva.matrix import * + stomp_result = stomp(Array(np.array([11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11])), + Array(np.array([9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9])), + 3) + find_best_n_discords_result = find_best_n_discords(stomp_result[0], + stomp_result[1], 2) + a = find_best_n_discords_result[2].to_numpy() + print(a) + +The previous produces the following output: + ++-------------------------------------+ +| [1.73190141 1.73185158] [8 8] [0 9] | ++-------------------------------------+ + +The first numpy array represents the minimum distances between the subsequences of length 3 between the two time series. +The second numpy array represents the location of those subsequences in the first time series and the +third one represents the indices in the second time series. + +Another interesting thing that we want to demonstrate in this introductory section is the possibility of using the library +for computing the functions in different backends and with different devices, knowing that the operations should be executed +in the same device where the array was created. + +.. code-block:: python + + #Adding operations in the different backends and devices. + from khiva.features import * + set_backend(KHIVABackend.KHIVA_BACKEND_OPENCL) + set_device(0) + a = Array([1, 2, 3, 4, 5, 6, 7, 8]) + b = mean(a) + + set_device(1) + c = Array([1, 2, 3, 4, 5, 6, 7, 8]) + d = mean(c) + + set_backend(KHIVABackend.KHIVA_BACKEND_CPU) + set_device(0) + e = Array([1, 2, 3, 4, 5, 6, 7, 8]) + f = mean(e) + + #Retrieving the results of the previous operations + set_backend(KHIVABackend.KHIVA_BACKEND_OPENCL) + set_device(0) + print(b.to_numpy()) + + set_device(1) + print(d.to_numpy()) + + set_backend(KHIVABackend.KHIVA_BACKEND_CPU) + set_device(0) + print(f.to_numpy()) + + +The output is the next one: + ++-----+ +| 4.5 | ++-----+ +| 4.5 | ++-----+ +| 4.5 | ++-----+ + +Another important fact is that, by default, the data type used is floating point of 32 +bits in order to not have problems with the different devices, but it can be changed deliberately. + +The available data types are the next ones: + ++-----------+----------------------+ +| Data type | Explanation | ++===========+======================+ +| f32 | 32 bits Float | ++-----------+----------------------+ +| c32 | 32 bits Complex | ++-----------+----------------------+ +| f64 | 64 bits Double | ++-----------+----------------------+ +| c64 | 64 bits Complex | ++-----------+----------------------+ +| b8 | 8 bits Boolean | ++-----------+----------------------+ +| s32 | 32 bits Int | ++-----------+----------------------+ +| 32u | 32 bits Unsigned Int | ++-----------+----------------------+ +| u8 | 8 bits Unsigned Int | ++-----------+----------------------+ +| s64 | 64 bits Integer | ++-----------+----------------------+ +| u64 | 64 bits Unsigned Int | ++-----------+----------------------+ +| s16 | 16 bits Int | ++-----------+----------------------+ +| u16 | 16 bits Unsigned Int | ++-----------+----------------------+ + + +There are functions that do not support 32 bits floating point data type, so it is necessary to indicate the data type. +The following is an example function requiring a 32bit signed integer array: + +.. code:: python + + cwt_coefficients_result = cwt_coefficients(Array([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]), + Array(data=[1, 2, 3], khiva_type=dtype.s32), 2, 2).to_numpy() + print(cwt_coefficients_result) + +The output is: + ++-------------------------+ +| [0.26517162 0.26517162] | ++-------------------------+ + + +Limitations +----------- + +This open-source library provides a very good performance but it has got memory limitations. +For cases where you need to apply a time series analysis over a huge amount of data and in short-term fashion, please, +contact us. We are working on a cluster version which is coming soon. + + +Let's Rock! +----------- +Now, you have the basic concepts to start using the library. Please, follow the documentation of each function in order to +know how to use them. +Each function has its corresponding test(s). You can check there how to use the function. + +Furthermore, we provide use cases and examples that you can use to learn where and how to apply the library. + + + diff --git a/khiva/__init__.py b/khiva/__init__.py new file mode 100644 index 0000000..6f0d49d --- /dev/null +++ b/khiva/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +from khiva.array import * +from khiva.dimensionality import * +from khiva.distances import * +from khiva.features import * +from khiva.library import * +from khiva.linalg import * +from khiva.matrix import * +from khiva.normalization import * +from khiva.normalization import * +from khiva.polynomial import * +from khiva.regression import * +from khiva.regularization import * +from khiva.statistics import * \ No newline at end of file diff --git a/khiva/array.py b/khiva/array.py new file mode 100644 index 0000000..830df10 --- /dev/null +++ b/khiva/array.py @@ -0,0 +1,801 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import numpy as np +import ctypes +from collections import deque +from khiva.library import KhivaLibrary +from enum import Enum +import pandas as pd +import logging +import sys + + +######################################################################################################################## + +class dtype(Enum): + """ + KHIVA array available types. + """ + f32 = 0 + """ + Float. khiva.dtype + """ + c32 = 1 + """ + 32 bits Complex. khiva.dtype + """ + f64 = 2 + """ + 64 bits Double. khiva.dtype + """ + c64 = 3 + """ + 64 bits Complex. khiva.dtype + """ + b8 = 4 + """ + Boolean. khiva.dtype + """ + s32 = 5 + """ + 32 bits Int. khiva.dtype + """ + u32 = 6 + """ + 32 bits Unsigned Int. khiva.dtype + """ + u8 = 7 + """ + 8 bits Unsigned Int. khiva.dtype + """ + s64 = 8 + """ + 64 bits Integer. khiva.dtype + """ + u64 = 9 + """ + 64 bits Unsigned Int. khiva.dtype + """ + s16 = 10 + """ + 16 bits Int. khiva.dtype + """ + u16 = 11 + """ + 16 bits Unsigned int. khiva.dtype + """ + + +def _get_array_type(khiva_type): + """ + Transform the KHIVA type to its equivalent in ctypes. + + :param khiva_type: KHIVA type. + + :return: The ctypes equivalent. + """ + return { + dtype.f32.value: ctypes.c_float, + dtype.c32.value: ctypes.c_float, + dtype.f64.value: ctypes.c_double, + dtype.c64.value: ctypes.c_double, + dtype.b8.value: ctypes.c_bool, + dtype.u8.value: ctypes.c_uint8, + dtype.s16.value: ctypes.c_int16, + dtype.u16.value: ctypes.c_uint16, + dtype.s32.value: ctypes.c_int32, + dtype.u32.value: ctypes.c_uint32, + dtype.s64.value: ctypes.c_int64, + dtype.u64.value: ctypes.c_uint64 + }[khiva_type] + + +def _get_numpy_type(khiva_type): + """ + Transform the KHIVA type to its equivalent in Numpy. + + :param khiva_type: KHIVA type. + + :return: The Numpy type equivalent. + """ + return { + dtype.f32.value: np.float, + dtype.c32.value: np.complex64, + dtype.f64.value: np.double, + dtype.c64.value: np.complex128, + dtype.b8.value: np.bool, + dtype.u8.value: np.uint8, + dtype.s16.value: np.int8, + dtype.u16.value: np.uint16, + dtype.s32.value: np.int16, + dtype.u32.value: np.uint32, + dtype.s64.value: np.int64, + dtype.u64.value: np.uint64, + }[khiva_type] + + +class Array: + __array_priority__ = 50 + + def __init__(self, data=None, khiva_type=dtype.f32, array_reference=None, arrayfire_reference=False): + """ + Creates a KHIVA array in one of the following ways: 1) using a previously created array; or 2) with data (in + numpy, list, or pandas dataframe format) + + :param data: Numpy array, List of elements or a Pandas dataframe. + :param khiva_type: KHIVA type. + :param array_reference: Reference of the array. + """ + if array_reference is None: + self.khiva_type = khiva_type + self.arr_reference = self._create_array(data) + self.dims = self.get_dims() + self.result_l = self._get_result_length() + else: + self.arr_reference = array_reference + self.khiva_type = self.get_type() + self.dims = self.get_dims() + self.result_l = self._get_result_length() + + self.arrayfire_reference = arrayfire_reference + + @classmethod + def from_arrayfire(cls, arrayfire): + """ + Creates a KHIVA array from an array of ArrayFire. + + :param arrayfire: An ArrayFire array. + :return: a KHIVA array. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.from_arrayfire(ctypes.pointer(arrayfire.arr), ctypes.pointer(result)) + return cls(array_reference=result, arrayfire_reference=True) + + def _create_array(self, data): + """ Creates the KHIVA array in the device. + + :param data: The data used for creating the khiva array. + + :return An opaque pointer to the Array. + """ + if isinstance(data, list): + data = np.asarray(data) + if isinstance(data, pd.DataFrame): + data = data.as_matrix() + shape = np.array(data.shape) + shape = shape[shape > 1] + shape = deque(shape) + shape.rotate(1) + c_array_n = (ctypes.c_longlong * len(shape))(*(np.array(shape)).astype(np.longlong)) + c_ndims = ctypes.c_uint(len(shape)) + c_complex = np.iscomplexobj(data) + + if c_complex: + data = np.array([data.real, data.imag]) + c = deque(range(1, len(data.shape))) + c.rotate(1) + c.append(0) + array_joint = np.transpose(data, c).flatten() + else: + array_joint = data.flatten() + + c_array_joint = (_get_array_type(self.khiva_type.value) * len(array_joint))( + *array_joint) + opaque_pointer = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.create_array(ctypes.pointer(c_array_joint), + ctypes.pointer(c_ndims), + ctypes.pointer(c_array_n), + ctypes.pointer(opaque_pointer), + ctypes.pointer(ctypes.c_int(self.khiva_type.value))) + return opaque_pointer + + def _get_data(self): + """ Retrieves the data from the device to the host. + + :return A numpy array with the data. + """ + initialized_result_array = np.zeros(self.result_l).astype(_get_array_type(self.khiva_type.value)) + c_result_array = (_get_array_type(self.khiva_type.value) * self.result_l)(*initialized_result_array) + KhivaLibrary().c_khiva_library.get_data(ctypes.pointer(self.arr_reference), ctypes.pointer(c_result_array)) + + dims = self.get_dims() + dims = dims[dims > 1] + a = np.array(c_result_array) + + if self._is_complex(): + a = np.array(np.split(a, self.result_l / 2)) + a = np.apply_along_axis(lambda args: [complex(*args)], 1, a) + a = a.reshape(dims) + c = deque(range(len(a.shape))) + c.rotate(-1) + a = np.transpose(a, c) + else: + dims = deque(dims) + dims.rotate(1) + a = a.reshape(dims) + + a = a.astype(_get_numpy_type(self.khiva_type.value)) + return a + + def _get_result_length(self): + """ Gets the length of the result. + + :return: The length of the result, used in order to get the data to the host. + """ + result = 1 + for value in self.dims: + result *= value + + if self._is_complex(): + result *= 2 + + return result + + def get_dims(self): + """ Gets the dimensions of the KHIVA array. + + :return: The dimensions of the KHIVA array. + """ + c_array_n = (ctypes.c_longlong * 4)(*(np.zeros(4)).astype(np.longlong)) + KhivaLibrary().c_khiva_library.get_dims(ctypes.pointer(self.arr_reference), ctypes.pointer(c_array_n)) + return np.array(c_array_n) + + def get_type(self): + """ Gets the type of the KHIVA array. + + :return: The type of the KHIVA array. + """ + c_type = ctypes.c_int() + KhivaLibrary().c_khiva_library.get_type(ctypes.pointer(self.arr_reference), ctypes.pointer(c_type)) + return dtype(c_type.value) + + def _is_complex(self): + """ Returns True if the array contains complex numbers and False otherwise. + + :return: True if the array contains complex numbers and False otherwise. + """ + return self.khiva_type.value == dtype.c32.value or self.khiva_type.value == dtype.c64.value + + def to_arrayfire(self): + """ Creates an Arrayfire array from this KHIVA array. This need to be used carefully as the same array + reference is oging to be used by both of them. Once the Arrayfire array is created, the destructor of + the KHIVA array is not going to free the allocated array. + + :return: an Arrayfire Array + """ + try: + import arrayfire as af + except ModuleNotFoundError: + logging.error("In order to use `to_arrayfire()` function, you need to install the Arrayfire Python library") + sys.exit(1) + result = af.Array() + result.arr = self.arr_reference + self.arrayfire_reference = True + return result + + def to_list(self): + """ Converts the KHIVA array to a list. + + :return: KHIVA array converted to list. + """ + return self._get_data().tolist() + + def to_numpy(self): + """ Converts the KHIVA array to a numpy array. + + :return: KHIVA array converted to numpy.array. + """ + return self._get_data() + + def to_pandas(self): + """ Converts the KHIVA array to a pandas data frame. + + :return: KHIVA array converted to a pandas data frame. + """ + return pd.DataFrame(data=self._get_data()) + + def print(self): + """ + Prints the data stored in the KHIVA array. + """ + KhivaLibrary().c_khiva_library.print(ctypes.pointer(self.arr_reference)) + + def __len__(self): + """ + Return the length. + """ + if self._is_complex(): + return self.result_l / 2 + else: + return self.result_l + + def __del__(self): + """ + Class destructor. + """ + if not self.arrayfire_reference: + KhivaLibrary().c_khiva_library.delete_array(ctypes.pointer(self.arr_reference)) + + def __add__(self, other): + """ + Return self + other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_add(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __iadd__(self, other): + """ + Perform self += other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_add(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __radd__(self, other): + """ + Return other + self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_add(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __sub__(self, other): + """ + Return self - other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_sub(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __isub__(self, other): + """ + Perform self -= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_sub(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __rsub__(self, other): + """ + Return other - self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_sub(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __mul__(self, other): + """ + Return self * other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_mul(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __imul__(self, other): + """ + Perform self *= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_mul(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __rmul__(self, other): + """ + Return other * self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_mul(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __truediv__(self, other): + """ + Return self / other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_div(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __itruediv__(self, other): + """ + Perform self /= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_div(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __rtruediv__(self, other): + """ + Return other / self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_div(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __div__(self, other): + """ + Return self / other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_div(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __idiv__(self, other): + """ + Perform other / self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_div(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __rdiv__(self, other): + """ + Return other / self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_div(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __mod__(self, other): + """ + Return self % other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_mod(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __imod__(self, other): + """ + Perform self %= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_mod(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __rmod__(self, other): + """ + Return other % self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_mod(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __pow__(self, other): + """ + Return self ** other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_pow(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __ipow__(self, other): + """ + Perform self **= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_pow(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __rpow__(self, other): + """ + Return other ** self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_pow(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __lt__(self, other): + """ + Return self < other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_lt(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __gt__(self, other): + """ + Return self > other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_gt(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __le__(self, other): + """ + Return self <= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_le(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __ge__(self, other): + """ + Return self >= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_ge(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __eq__(self, other): + """ + Return self == other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_eq(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __ne__(self, other): + """ + Return self != other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_ne(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __and__(self, other): + """ + Return self & other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitand(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __iand__(self, other): + """ + Perform self &= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitand(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __or__(self, other): + """ + Return self | other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitor(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __ior__(self, other): + """ + Perform self |= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitor(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __xor__(self, other): + """ + Return self ^ other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitxor(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __ixor__(self, other): + """ + Perform self ^= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitxor(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def __lshift__(self, other): + """ + Return self << other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitshiftl(ctypes.pointer(self.arr_reference), + ctypes.pointer(ctypes.c_int32(other)), ctypes.pointer(result)) + return Array(array_reference=result) + + def __ilshift__(self, other): + """ + Perform self <<= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitshiftl(ctypes.pointer(self.arr_reference), + ctypes.pointer(ctypes.c_int32(other)), ctypes.pointer(result)) + return Array(array_reference=result) + + def __rshift__(self, other): + """ + Return self >> other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitshiftr(ctypes.pointer(self.arr_reference), + ctypes.pointer(ctypes.c_int32(other)), ctypes.pointer(result)) + return Array(array_reference=result) + + def __irshift__(self, other): + """ + Perform self >>= other. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_bitshiftl(ctypes.pointer(self.arr_reference), + ctypes.pointer(ctypes.c_int32(other)), ctypes.pointer(result)) + return Array(array_reference=result) + + def __neg__(self): + """ + Return -self + """ + return 0 - self + + def __pos__(self): + """ + Return +self + """ + return self + + def __invert__(self): + """ + Return ~self + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_not(ctypes.pointer(self.arr_reference), ctypes.pointer(result)) + return Array(array_reference=result) + + def _get_metadata_str(self, dims=True): + return 'khiva.Array()\nType: {}\n{}' \ + .format(self.khiva_type, 'Dims: {}'.format(str(self.dims)) if dims else '') + + def __str__(self): + """ + Converts the khiva array to string showing its meta data and contents. + """ + + return self._get_metadata_str() + + def __nonzero__(self): + """ + Returns if the Array is non-zero. + """ + return self != 0 + + def __repr__(self): + """ + Displays the meta data of the arrayfire array. + """ + + return self._get_metadata_str() + + def transpose(self, conjugate=False): + """ + Transpose the KHIVA Array. + + :param conjugate: Indicates if the transpose is conjugated or not. + :return: The transposed KHIVA Array. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_transpose(ctypes.pointer(self.arr_reference), + ctypes.pointer(ctypes.c_bool(conjugate)), ctypes.pointer(result)) + + return Array(array_reference=result) + + def get_col(self, index): + """ + Gets a desired column. + + :param index: Index of the desired column. + :return: The desired column. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_col(ctypes.pointer(self.arr_reference), ctypes.pointer(ctypes.c_int32(index)), + ctypes.pointer(result)) + return Array(array_reference=result) + + def get_cols(self, first, last): + """ + Gets a sequence of columns using the first column index and the last column index, both columns included. + + :param first: First column of the subsequence of columns. + :param last: Last column of the subsequence of columns. + :return: A subsequence of columns between 'first' and 'last'. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_cols(ctypes.pointer(self.arr_reference), ctypes.pointer(ctypes.c_int32(first)), + ctypes.pointer(ctypes.c_int32(last)), + ctypes.pointer(result)) + return Array(array_reference=result) + + def get_row(self, index): + """ + Gets a desired row. + + :param index: Index of the desired row. + :return: The desired row. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_row(ctypes.pointer(self.arr_reference), ctypes.pointer(ctypes.c_int32(index)), + ctypes.pointer(result)) + return Array(array_reference=result) + + def get_rows(self, first, last): + """ + Gets a sequence of rows using the first row index and the last row index, both rows included. + + :param first: First row of the subsequence of rows. + :param last: Last row of the subsequence of rows. + :return: A subsequence of rows between 'first' and 'last'. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_rows(ctypes.pointer(self.arr_reference), ctypes.pointer(ctypes.c_int32(first)), + ctypes.pointer(ctypes.c_int32(last)), + ctypes.pointer(result)) + return Array(array_reference=result) + + def matmul(self, other): + """ + Matrix multiplication. + + :param other: KHIVA Array + :return: The matrix multiplication between these two KHIVA Arrays. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_matmul(ctypes.pointer(self.arr_reference), ctypes.pointer(other.arr_reference), + ctypes.pointer(result)) + return Array(array_reference=result) + + def copy(self): + """ + Performs a deep copy of the array. + + return: An identical copy of self. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.copy(ctypes.pointer(self.arr_reference), ctypes.pointer(result)) + return Array(array_reference=result) + + def as_type(self, dtype): + """ + Converts the array to a desired array with a desired type. + + :param dtype: The desired KHIVA data type. + :return: An array with the desired data type. + """ + result = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.khiva_as(ctypes.pointer(self.arr_reference), + ctypes.pointer(ctypes.c_int32(dtype.value)), ctypes.pointer(result)) + self.khiva_type = self.get_type() + return Array(array_reference=result) diff --git a/khiva/dimensionality.py b/khiva/dimensionality.py new file mode 100644 index 0000000..b3ed1bf --- /dev/null +++ b/khiva/dimensionality.py @@ -0,0 +1,167 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + + +def paa(a, bins): + """ Piecewise Aggregate Approximation (PAA) approximates a time series :math:`X` of length :math:`n` into vector + :math:`\\bar{X}=(\\bar{x}_{1},…,\\bar{x}_{M})` of any arbitrary length :math:`M \\leq n` where each of + :math:`\\bar{x_{i}}` is calculated as follows: + + .. math:: + \\bar{x}_{i} = \\frac{M}{n} \\sum_{j=n/M(i-1)+1}^{(n/M)i} x_{j}. + + Which simply means that in order to reduce the dimensionality from :math:`n` to :math:`M`, we first divide the original + time series into :math:`M` equally sized frames and secondly compute the mean values for each frame. The sequence + assembled from the mean values is the PAA approximation (i.e., transform) of the original time series. + + :param a: Set of points. + :param bins: Sets the total number of divisions. + + :return: KHIVA array of points with the reduced dimensionality. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.paa(ctypes.pointer(a.arr_reference), ctypes.pointer(ctypes.c_int(bins)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def pip(a, number_ips): + """ Calculates the number of Perceptually Important Points (PIP) in the time series. + + [1] Fu TC, Chung FL, Luk R, and Ng CM. Representing financial time series based on data point importance. + Engineering Applications of Artificial Intelligence, 21(2):277-300, 2008. + + :param a: KHIVA array whose dimension zero is the length of the time series. + :param number_ips: The number of points to be returned. + + :return: KHIVA array with the most Perceptually Important number_ips. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.pip(ctypes.pointer(a.arr_reference), ctypes.pointer(ctypes.c_int(number_ips)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def pla_bottom_up(ts, max_error): + """ + Applies the Piecewise Linear Approximation (PLA BottomUP) to the time series. + + [1] Zhu Y, Wu D, Li Sh (2007). A Piecewise Linear Representation Method of Time Series Based on Feature Points. + Knowledge-Based Intelligent Information and Engineering Systems 4693:1066-1072. + + :param ts: Expects a khiva array containing the set of points to be reduced. The first component of the points in + the first column and the second component of the points in the second column. + :param max_error: The maximum approximation error allowed. + :return: The reduced number of points. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.pla_bottom_up(ctypes.pointer(ts.arr_reference), + ctypes.pointer(ctypes.c_float(max_error)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def pla_sliding_window(ts, max_error): + """ + Applies the Piecewise Linear Approximation (PLA Sliding Window) to the time series. + + [1] Zhu Y, Wu D, Li Sh (2007). A Piecewise Linear Representation Method of Time Series Based on Feature Points. + Knowledge-Based Intelligent Information and Engineering Systems 4693:1066-1072. + + :param ts: Expects a khiva array containing the set of points to be reduced. The first component of the points in + the first column and the second component of the points in the second column. + :param max_error: The maximum approximation error allowed. + :return: The reduced number of points. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.pla_sliding_window(ctypes.pointer(ts.arr_reference), + ctypes.pointer(ctypes.c_float(max_error)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def ramer_douglas_peucker(a, epsilon): + """ The Ramer–Douglas–Peucker algorithm (RDP) is an algorithm for reducing the number of points in a curve that is + approximated by a series of points. It reduces a set of points depending on the perpendicular distance of the points + and epsilon, the greater epsilon, more points are deleted. + + [1] Urs Ramer, "An iterative procedure for the polygonal approximation of plane curves", Computer Graphics and Image + Processing, 1(3), 244–256 (1972) doi:10.1016/S0146-664X(72)80017-0. + + [2] David Douglas & Thomas Peucker, "Algorithms for the reduction of the number of points required to represent a + digitized line or its caricature", The Canadian Cartographer 10(2), 112–122 (1973) doi:10.3138/FM57-6770-U75U-7727 + + :param a: KHIVA array with the x-coordinates and y-coordinates of the input points (x in column 0 and y in column 1). + :param epsilon: It acts as the threshold value to decide which points should be considered meaningful or not. + + :return: KHIVA array with the x-coordinates and y-coordinates of the selected points (x in column 0 and y in + column 1). + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.ramer_douglas_peucker(ctypes.pointer(a.arr_reference), + ctypes.pointer(ctypes.c_double(epsilon)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def sax(a, alphabet_size): + """ Symbolic Aggregate approXimation (SAX). It transforms a numeric time series into a time series of symbols with + the same size. The algorithm was proposed by Lin et al.) and extends the PAA-based approach inheriting the original + algorithm simplicity and low computational complexity while providing satisfactory sensitivity and selectivity in + range query processing. Moreover, the use of a symbolic representation opened a door to the existing wealth of + data-structures and string-manipulation algorithms in computer science such as hashing, regular expression, pattern + matching, suffix trees, and grammatical inference. + + [1] Lin, J., Keogh, E., Lonardi, S. & Chiu, B. (2003) A Symbolic Representation of Time Series, with Implications + for Streaming Algorithms. In proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and + Knowledge Discovery. San Diego, CA. June 13. + + :param a: KHIVA array with the input time series. + :param alphabet_size: Number of element within the alphabet. + + :return: KHIVA array of points with the reduced dimensionality. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.sax(ctypes.pointer(a.arr_reference), ctypes.pointer(ctypes.c_int(alphabet_size)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def visvalingam(a, num_points): + """ Reduces a set of points by applying the Visvalingam method (minimun triangle area) until the number + of points is reduced to numPoints. + + [1] M. Visvalingam and J. D. Whyatt, Line generalisation by repeated elimination of points, + The Cartographic Journal, 1993. + + :param a: KHIVA array with the x-coordinates and y-coordinates of the input points (x in column 0 and y in column 1). + :param num_points: Sets the number of points returned after the execution of the method. + + :return: KHIVA array with the x-coordinates and y-coordinates of the selected points (x in column 0 and y in + column 1). + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.visvalingam(ctypes.pointer(a.arr_reference), + ctypes.pointer(ctypes.c_int(num_points)), + ctypes.pointer(b)) + + return Array(array_reference=b) diff --git a/khiva/distances.py b/khiva/distances.py new file mode 100644 index 0000000..32bbf09 --- /dev/null +++ b/khiva/distances.py @@ -0,0 +1,91 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + + +def euclidean(tss): + """ Calculates euclidean distances between time series. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and + dimension one indicates the number of time series. + :return: Array with an upper triangular matrix where each position corresponds to the distance between two + time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance + between time series 0 and time series 1. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.euclidean(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def dtw(tss): + """ Calculates the Dynamic Time Warping Distance. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and + dimension one indicates the number of time series. + :return: Array with an upper triangular matrix where each position corresponds to the distance between + two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the + distance between time series 0 and time series 1. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.dtw(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def hamming(tss): + """ Calculates Hamming distances between time series. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and + dimension one indicates the number of time series. + :return: Array with an upper triangular matrix where each position corresponds to the distance between two + time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance + between time series 0 and time series 1. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.hamming(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def manhattan(tss): + """ Calculates Manhattan distances between time series. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and + dimension one indicates the number of time series. + :return: Array with an upper triangular matrix where each position corresponds to the distance between two + time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance + between time series 0 and time series 1. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.manhattan(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def squared_euclidean(tss): + """ Calculates the non squared version of the euclidean distance. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and + dimension one indicates the number of time series. + :return: Array with an upper triangular matrix where each position corresponds to the distance between two time series. + Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 + and time series 1. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.squared_euclidean(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + diff --git a/khiva/features.py b/khiva/features.py new file mode 100644 index 0000000..ece8a7e --- /dev/null +++ b/khiva/features.py @@ -0,0 +1,1170 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + + +def abs_energy(arr): + """ Calculates the sum over the square values of the time series. + + :param arr: KHIVA array with the time series. + :type arr: khiva.array + :return: KHIVA array with the absEnergy. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.abs_energy(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def absolute_sum_of_changes(arr): + """ Calculates the value of an aggregation function f_agg (e.g. var or mean) of the autocorrelation + (Compare to http://en.wikipedia.org/wiki/Autocorrelation#Estimation), taken over different all possible + lags (1 to length of x) + + :param arr: KHIVA array with the time series. + :type arr: khiva.array + :return: KHIVA array with the absolute sum of changes. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.absolute_sum_of_changes(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def aggregated_autocorrelation(arr, aggregation_function): + """ Calculates a linear least-squares regression for values of the time series that were aggregated + over chunks versus the sequence from 0 up to the number of chunks minus one. + + :param arr: A KHIVA array with the time series. + :param aggregation_function: Function to be used in the aggregation. It receives an integer which indicates + the function to be applied. + 0 : mean, + 1 : median + 2 : min, + 3 : max, + 4 : stdev, + 5 : var, + default : mean + + :return: KHIVA array that contains the aggregated correlation for each time series. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.aggregated_autocorrelation(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(aggregation_function)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def aggregated_linear_trend(arr, chunk_size, aggregation_function): + """ Calculates a linear least-squares regression for values of the time series that were aggregated + over chunks versus the sequence from 0 up to the number of chunks minus one. + + :param arr: A KHIVA array with the time series. + :param chunk_size: The chunk size used to aggregate the data. + :param aggregation_function: Function to be used in the aggregation. It receives an integer which indicates the + function to be applied: + 0 : mean, + 1 : median + 2 : min, + 3 : max, + 4 : stdev, + default : mean + :return: ( pvalue: KHIVA array with the pvalues for all time series. + rvalue: KHIVA array with the rvalues for all time series. + intercept: KHIVA array with the intercept values for all time series. + slope: KHIVA array with the slope for all time series. + stdrr: KHIVA array with the stderr values for all time series. ) + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + d = ctypes.c_void_p(0) + e = ctypes.c_void_p(0) + f = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.aggregated_linear_trend(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_long(chunk_size)), + ctypes.pointer(ctypes.c_int(aggregation_function)), + ctypes.pointer(b), + ctypes.pointer(c), + ctypes.pointer(d), + ctypes.pointer(e), + ctypes.pointer(f)) + + return Array(array_reference=b), Array(array_reference=c, + khiva_type=arr.khiva_type), Array( + array_reference=d), Array(array_reference=e), Array( + array_reference=f) + + +def approximate_entropy(arr, m, r): + """ Calculates a vectorized Approximate entropy algorithm. + https://en.wikipedia.org/wiki/Approximate_entropy + For short time-series this method is highly dependent on the parameters, but should be stable for N > 2000, + see: Yentes et al. (2012) - The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets + Other shortcomings and alternatives discussed in: + Richman & Moorman (2000) - Physiological time-series analysis using approximate entropy and sample entropy + + + :param arr: A KHIVA array with the time series. + :param m: Length of compared run of data. + :param r: Filtering level, must be positive. + :return: KHIVA array with the vectorized approximate entropy for all the input time series in tss. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.approximate_entropy(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(m)), + ctypes.pointer(ctypes.c_float(r)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def cross_covariance(xss, yss, unbiased): + """ Calculates the cross-covariance of the given time series. + + :param xss: A KHIVA array with time series. + :param yss: A KHIVA Array with time series. + :param unbiased: Determines whether it divides by n - lag (if true) or n (if false). + :return: KHIVA array with the cross-covariance value for the given time series. + """ + + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.cross_covariance(ctypes.pointer(xss.arr_reference), ctypes.pointer(yss.arr_reference), + ctypes.pointer(ctypes.c_bool(unbiased)), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def auto_covariance(arr, unbiased=False): + """ Calculates the auto-covariance the given time series. + + :param arr: KHIVA array with the time series. + :param unbiased: Determines whether it divides by n - lag (if true) or n (if false). + :return: KHIVA array with the auto-covariance value for the given time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.auto_covariance(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_bool(unbiased)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def cross_correlation(xss, yss, unbiased): + """ Calculates the cross-correlation of the given time series. + + :param xss: KHIVA array with the time series. + :param yss: KHIVA array with the time series. + :param unbiased: Determines whether it divides by n - lag (if true) or n (if false). + :return: KHIVA array with cross-correlation value for the given time series. + """ + + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.cross_correlation(ctypes.pointer(xss.arr_reference), ctypes.pointer(yss.arr_reference), + ctypes.pointer(ctypes.c_bool(unbiased)), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def auto_correlation(arr, max_lag, unbiased): + """ Calculates the autocorrelation of the specified lag for the given time series. + + :param arr: KHIVA array with the time series. + :param max_lag: The maximum lag to compute. + :param unbiased: Determines whether it divides by n - lag (if true) or n (if false). + :return: KHIVA array with the autocorrelation value for the given time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.auto_correlation(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_long(max_lag)), + ctypes.pointer(ctypes.c_bool(unbiased)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def binned_entropy(arr, max_bins): + """ Calculates the binned entropy for the given time series and number of bins. + + :param arr: KHIVA array with the time series. + :param max_bins: The number of bins. + :return: KHIVA array with the binned entropy value for the given time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.binned_entropy(ctypes.pointer(arr.arr_reference), ctypes.pointer(ctypes.c_int(max_bins)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def c3(arr, lag): + """ Calculates the Schreiber, T. and Schmitz, A. (1997) measure of non-linearity + for the given time series + + :param arr: KHIVA array with the time series. + :param lag: The lag. + :return: KHIVA array with non-linearity value for the given time series. + """ + + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.c3(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_long(lag)), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def cid_ce(arr, z_normalize): + """ Calculates an estimate for the time series complexity defined by + Batista, Gustavo EAPA, et al (2014). (A more complex time series has more peaks, + valleys, etc.) + + :param arr: KHIVA array with the time series. + :param z_normalize: Controls wheter the time series should be z-normalized or not. + :return: KHIVA array with the complexity value for the given time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.cid_ce(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_bool(z_normalize)), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def count_above_mean(arr): + """ Calculates the number of values in the time series that are higher than + the mean. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the number of values in the time series that are higher than the mean. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.count_above_mean(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def count_below_mean(arr): + """ Calculates the number of values in the time series that are lower than + the mean. + + :param arr: KHIVA array with the time series. + :return: KHIVA Array with the number of values in the time series that are lower than the mean. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.count_below_mean(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def cwt_coefficients(tss, widths, coeff, w): + """ Calculates a Continuous wavelet transform for the Ricker wavelet, also known as + the "Mexican hat wavelet". + + :param tss: KHIVA array with the time series. + :param widths: Widths. It accepts a list of lists or a numpy array with one or several widths. + :param coeff: Coefficient of interest. + :param w: Width of interest. + :return: KHIVA Array with the result of calculated coefficients. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.cwt_coefficients(ctypes.pointer(tss.arr_reference), + ctypes.pointer(widths.arr_reference), + ctypes.pointer(ctypes.c_int(coeff)), + ctypes.pointer(ctypes.c_int(w)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def energy_ratio_by_chunks(arr, num_segments, segment_focus): + """ Calculates the sum of squares of chunk i out of N chunks expressed as a ratio + with the sum of squares over the whole series. segmentFocus should be lower + than the number of segments. + + :param arr: KHIVA array with the time series. + :param num_segments: The number of segments to divide the series into. + :param segment_focus: The segment number (starting at zero) to return a feature on. + :return: KHIVA array with the energy ratio by chunk of the time series. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.energy_ratio_by_chunks(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_long(num_segments)), + ctypes.pointer(ctypes.c_long(segment_focus)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def fft_aggregated(arr): + """ Calculates the spectral centroid(mean), variance, skew, and kurtosis of the absolute fourier transform + spectrum. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the spectral centroid (mean), variance, skew, and kurtosis of the absolute fourier transform + spectrum. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.fft_aggregated(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def fft_coefficient(arr, coefficient): + """ Calculates the fourier coefficients of the one-dimensional discrete + Fourier Transform for real input by fast fourier transformation algorithm. + + :param arr: KHIVA array with the time series. + :param coefficient: The coefficient to extract from the FFT. + :return: Tuple with: + real: KHIVA array with the real part of the coefficient. + imag: KHIVA array with the imaginary part of the coefficient. + abs: KHIVA array with the absolute value of the coefficient. + angle: KHIVA array with the angle of the coefficient. + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + d = ctypes.c_void_p(0) + e = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.fft_coefficient(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_long(coefficient)), + ctypes.pointer(b), + ctypes.pointer(c), + ctypes.pointer(d), + ctypes.pointer(e) + ) + + return Array(array_reference=b), Array(array_reference=c, + khiva_type=arr.khiva_type), Array( + array_reference=d), Array(array_reference=e) + + +def first_location_of_maximum(arr): + """ Calculates the first relative location of the maximal value for each time series. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the first relative location of the maximum value to the length of the time series, for each + time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.first_location_of_maximum(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def first_location_of_minimum(arr): + """ Calculates the first location of the minimal value of each time series. The position is calculated relatively + to the length of the series. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the first relative location of the minimal value of each series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.first_location_of_minimum(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def friedrich_coefficients(arr, m, r): + """ Coefficients of polynomial :math:`h(x)`, which has been fitted to the deterministic dynamics of Langevin model: + Largest fixed point of dynamics :math:`argmax_x {h(x)=0}` estimated from polynomial :math:`h(x)`, + which has been fitted to the deterministic dynamics of Langevin model: + + .. math:: + \\dot(x)(t) = h(x(t)) + R \\mathcal(N)(0,1) + + as described by [1]. For short time series this method is highly dependent on the parameters. + + [1] Friedrich et al. (2000): Physics Letters A 271, p. 217-222 + Extracting model equations from experimental data. + + + :param arr: KHIVA array with the time series. + :param m: Order of polynom to fit for estimating fixed points of dynamics. + :param r: Number of quantiles to use for averaging. + :return: KHIVA array with the coefficients for each time seriess. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.friedrich_coefficients(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(m)), + ctypes.pointer(ctypes.c_float(r)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def has_duplicates(arr): + """ Calculates if the input time series contain duplicated elements. + + :param arr: KHIVA array with the time series. + :return: KHIVA array containing True if the time series contains duplicated elements + and false otherwise. + """ + + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.has_duplicates(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def has_duplicate_max(arr): + """ Calculates if the maximum within input time series is duplicated. + + :param arr: KHIVA array with the time series. + :return: KHIVA array containing True if the maximum value of the time series is duplicated and false otherwise. + """ + + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.has_duplicate_max(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def has_duplicate_min(arr): + """ Calculates if the minimum of the input time series is duplicated. + + :param arr: KHIVA array with the time series. + :return: KHIVA array containing True if the minimum of the time series is duplicated and False otherwise. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.has_duplicate_min(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def index_mass_quantile(arr, q): + """ Calculates the index of the mass quantile. + + :param arr: KHIVA array with the time series. + :param q: The quantile. + :return: KHIVA array with the index of the mass quantile q. + """ + + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.index_mass_quantile(ctypes.pointer(arr.arr_reference), ctypes.pointer(ctypes.c_float(q)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def kurtosis(arr): + """ Returns the kurtosis of tss (calculated with the adjusted Fisher-Pearson + standardized moment coefficient G2). + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the kurtosis of each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.kurtosis(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def large_standard_deviation(arr, r): + """ Checks if the time series within tss have a large standard deviation. + + :param arr: KHIVA array with the time series. + :param r: The threshold. + :return: KHIVA array containing True for those time series in tss that have a large standard deviation. + """ + + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.large_standard_deviation(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_float(r)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def last_location_of_maximum(arr): + """ Calculates the last location of the maximum value of each time series. The position + is calculated relatively to the length of the series. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the last relative location of the maximum value of each series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.last_location_of_maximum(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def last_location_of_minimum(arr): + """ Calculates the last location of the minimum value of each time series. The position + is calculated relatively to the length of the series. + + :param arr: KHIVA array with the time series. + :return: KHIVA array the last relative location of the minimum value of each series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.last_location_of_minimum(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def length(arr): + """ Returns the length of the input time series. + + :param arr: KHIVA array with the time series. + :return: KHIVA array the length of tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.length(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def linear_trend(arr): + """ Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to + length of the time series minus one. + + :param arr: KHIVA array with the time series. + :return a tuple with: + pvalue: KHIVA array the pvalues for all time series. + rvalue: KHIVA array The rvalues for all time series. + intercept: KHIVA array the intercept values for all time series. + slope: KHIVA array the slope for all time series. + stdrr: KHIVA array the stderr values for all time series. + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + d = ctypes.c_void_p(0) + e = ctypes.c_void_p(0) + f = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.linear_trend(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b), + ctypes.pointer(c), + ctypes.pointer(d), + ctypes.pointer(e), + ctypes.pointer(f) + ) + + return Array(array_reference=b), \ + Array(array_reference=c), \ + Array(array_reference=d), \ + Array(array_reference=e), \ + Array(array_reference=f) + + +def local_maximals(arr): + """ Calculates all Local Maximals fot the time series in array. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the calculated local maximals for each time series in arr. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.local_maximals(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def longest_strike_above_mean(arr): + """ Calculates the length of the longest consecutive subsequence in tss that is bigger than the mean of tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the length of the longest consecutive subsequence in the input time series that is bigger + than the mean. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.longest_strike_above_mean(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def longest_strike_below_mean(arr): + """ Calculates the length of the longest consecutive subsequence in tss that is below the mean of tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the length of the longest consecutive subsequence in the input time series that is below + the mean. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.longest_strike_below_mean(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def max_langevin_fixed_point(arr, m, r): + """ Largest fixed point of dynamics :math:`argmax_x {h(x)=0}` estimated from polynomial :math:`h(x)`, + which has been fitted to the deterministic dynamics of Langevin model + + .. math:: + \\dot(x)(t) = h(x(t)) + R \\mathcal(N)(0,1) + + as described by + + Friedrich et al. (2000): Physics Letters A 271, p. 217-222 + *Extracting model equations from experimental data* + + :param arr: KHIVA array with the time series. + :param m: Order of polynom to fit for estimating fixed points of dynamics. + :param r: Number of quantiles to use for averaging. + :return: KHIVA array with the largest fixed point of deterministic dynamics. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.max_langevin_fixed_point(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(m)), + ctypes.pointer(ctypes.c_float(r)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def maximum(arr): + """ Calculates the maximum value for each time series within tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the maximum value of each time series within tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.maximum(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def mean(arr): + """ Calculates the mean value for each time series within tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the mean value of each time series within tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.mean(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def mean_absolute_change(arr): + """ Calculates the mean over the absolute differences between subsequent time series values in tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the mean over the absolute differences between subsequent time series values. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.mean_absolute_change(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def mean_change(arr): + """ Calculates the mean over the differences between subsequent time series values in tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the mean over the differences between subsequent time series values. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.mean_change(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def mean_second_derivative_central(arr): + """ Calculates mean value of a central approximation of the second derivative for each time series in tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the mean value of a central approximation of the second derivative for each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.mean_second_derivative_central(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def median(arr): + """ Calculates the median value for each time series within tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the median value of each time series within tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.median(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def minimum(arr): + """ Calculates the minimum value for each time series within tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the minimum value of each time series within tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.minimum(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def number_crossing_m(arr, m): + """ Calculates the number of m-crossings. A m-crossing is defined as two sequential values where the first + value is lower than m and the next is greater, or viceversa. If you set m to zero, you will get the number of + zero crossings. + + :param arr: KHIVA array with the time series. + :param m: The m value. + :return: KHIVA array with the number of m-crossings of each time series within tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.number_crossing_m(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(m)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def number_cwt_peaks(arr, max_w): + """ This feature calculator searches for different peaks. To do so, the time series is smoothed by a ricker + wavelet and for widths ranging from 1 to :math:'max_w`. This feature calculator returns the number of peaks that + occur at enough width scales and with sufficiently high Signal-to-Noise-Ratio (SNR). + + :param arr: KHIVA array with the time series. + :param max_w: The maximum width to consider. + :return: KHIVA array with the number of peaks for each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.number_cwt_peaks(ctypes.pointer(arr.arr_reference), ctypes.pointer(ctypes.c_int(max_w)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def number_peaks(arr, n): + """ Calculates the number of peaks of at least support :math:`n` in the time series :math:`tss`. A peak of support + :math:`n` is defined as a subsequence of :math:`tss where a value occurs, which is bigger than + its :math:`n` neighbours to the left and to the right. + + :param arr: KHIVA array with the time series. + :param n: The support of the peak. + :return: KHIVA array with the number of peaks of at least support :math:`n`. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.number_peaks(ctypes.pointer(arr.arr_reference), ctypes.pointer(ctypes.c_int(n)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def partial_autocorrelation(arr, lags): + """ Calculates the value of the partial autocorrelation function at the given lag. The lag :math:`k'` partial + autocorrelation of a time series :math:`\\lbrace x_t, t = 1 \\ldots T \\rbrace` equals the partial correlation of + :math:`x_t` and :math:`x_{t-k}`, adjusted for the intermediate variables :math:`\\lbrace x_{t-1}, \\ldots, x_{t-k+1}\\rbrace` + ([1]). Following [2], it can be defined as: + + .. math:: + \\alpha_k = \\frac{ Cov(x_t, x_{t-k} | x_{t-1}, \\ldots, x_{t-k+1})} + {\\sqrt{ Var(x_t | x_{t-1}, \\ldots, x_{t-k+1}) Var(x_{t-k} | x_{t-1}, \\ldots, x_{t-k+1} )}} + + with (a) :math:`x_t = f(x_{t-1}, \\ldots, x_{t-k+1})` and (b) :math:`x_{t-k} = f(x_{t-1}, \\ldots, x_{t-k+1})` + being AR(k-1) models that can be fitted by OLS. Be aware that in (a), the regression is done on past values to + predict :math:`x_t` whereas in (b), future values are used to calculate the past value :math:`x_{t-k}`. + It is said in [1] that "for an AR(p), the partial autocorrelations :math:`\\alpha_k` will be nonzero for :math:`k<=p` + and zero for :math:`k>p`." + With this property, it is used to determine the lag of an AR-Process. + + [1] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). + Time series analysis: forecasting and control. John Wiley & Sons. + [2] https://onlinecourses.science.psu.edu/stat510/node/62 + + :param arr: KHIVA array with the time series. + :param lags: KHIVA array with the lags to be calculated. + :return: KHIVA array with the partial autocorrelation for each time series for the given lag. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.partial_autocorrelation(ctypes.pointer(arr.arr_reference), + ctypes.pointer(lags.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def percentage_of_reoccurring_datapoints_to_all_datapoints(arr, is_sorted): + """ Calculates the percentage of unique values, that are present in the time series more than once. + + .. math:: + + len(different values occurring more than once) / len(different values) + + This means the percentage is normalized to the number of unique values, in contrast to the + percentage_of_reoccurring_values_to_all_values. + + :param arr: KHIVA array with the time series. + :param is_sorted: Indicates if the input time series is sorted or not. Defaults to false. + :return: KHIVA array with the percentage of unique values, that are present in the time series more than once. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.percentage_of_reoccurring_datapoints_to_all_datapoints(ctypes.pointer(arr.arr_reference), + ctypes.pointer( + ctypes.c_bool(is_sorted)), + ctypes.pointer( + b)) + + return Array(array_reference=b) + + +def percentage_of_reoccurring_values_to_all_values(arr, is_sorted): + """ Calculates the percentage of unique values, that are present in the time series more than once. + + .. math:: + + \\frac{\\textit{number of data points occurring more than once}}{\\\\textit{number of all data points})} + + This means the percentage is normalized to the number of unique values, in contrast to the + percentage_of_reoccurring_datapoints_to_all_datapoints. + + :param arr: KHIVA array with the time series. + :param is_sorted: Indicates if the input time series is sorted or not. Defaults to false. + :return: KHIVA array with the percentage of unique values, that are present in the time series more than once. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.percentage_of_reoccurring_values_to_all_values(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_bool(is_sorted)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def quantile(arr, q, precision=1e8): + """ Returns values at the given quantile. + + :param arr: KHIVA array with the time series. + :param q: Khiva array with the percentile(s) at which to extract score(s). One or many. + :param precision: Number of decimals expected. + :return: Values at the given quantile. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.quantile(ctypes.pointer(arr.arr_reference), ctypes.pointer(q.arr_reference), + ctypes.pointer(ctypes.c_float(precision)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def range_count(arr, min, max): + """ Counts observed values within the interval [min, max). + + :param arr: KHIVA array with the time series. + :param min: Value that sets the lower limit. + :param max: Value that sets the upper limit. + :return: KHIVA array with the values at the given range. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.range_count(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(min)), + ctypes.pointer(ctypes.c_float(max)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def ratio_beyond_r_sigma(arr, r): + """ Calculates the ratio of values that are more than :math:`r*std(x)` (so :math:`r` sigma) away from the mean of + :math:`x`. + + :param arr: KHIVA array with the time series. + :param r: Number of times that the values should be away from. + :return: KHIVA array with the ratio of values that are more than :math:`r*std(x)` (so :math:`r` sigma) away from + the mean of :math:`x`. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.ratio_beyond_r_sigma(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_float(r)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def ratio_value_number_to_time_series_length(arr): + """ Calculates a factor which is 1 if all values in the time series occur only once, and below one if this is + not the case. In principle, it just returns: + + .. math:: + \\frac{\\textit{number_unique_values}}{\\textit{number_values}} + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the ratio of unique values with respect to the total number of values. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.ratio_value_number_to_time_series_length(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def sample_entropy(arr): + """ Calculates a vectorized sample entropy algorithm. + https://en.wikipedia.org/wiki/Sample_entropy + https://www.ncbi.nlm.nih.gov/pubmed/10843903?dopt=Abstract + For short time-series this method is highly dependent on the parameters, but should be stable for N > 2000, + see: Yentes et al. (2012) - The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets + Other shortcomings and alternatives discussed in: + Richman & Moorman (2000) - Physiological time-series analysis using approximate entropy and sample entropy. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the same dimensions as tss, whose values (time series in dimension 0) + contains the vectorized sample entropy for all the input time series in tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.sample_entropy(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def skewness(arr): + """ Calculates the sample skewness of tss (calculated with the adjusted Fisher-Pearson standardized + moment coefficient G1). + + :param arr: KHIVA array with the time series. + :return: KHIVA array containing the skewness of each time series in tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.skewness(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def spkt_welch_density(arr, coeff): + """ Estimates the cross power spectral density of the time series array at different frequencies. To do so, the + time series is first shifted from the time domain to the frequency domain. + + Welch's method computes an estimate of the power spectral density by dividing the data into overlapping + segments, computing a modified periodogram for each segment and averaging the periodograms. + [1] P. Welch, "The use of the fast Fourier transform for the estimation of power spectra: A method based on time + averaging over short, modified periodograms", IEEE Trans. Audio Electroacoust. vol. 15, pp. 70-73, 1967. + [2] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra", Biometrika, vol. 37, pp. 1-16, 1950. + [3] Rabiner, Lawrence R., and B. Gold. "Theory and Application of Digital Signal Processing" Prentice-Hall, pp. + 414-419, 1975. + + :param arr: KHIVA array with the time series. + :param coeff: The coefficient to be returned. + :return: KHIVA array containing the power spectrum of the different frequencies for each time series in arr. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.spkt_welch_density(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(coeff)), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def standard_deviation(arr): + """ Calculates the standard deviation of each time series within tss. + + :param arr: KHIVA array with the time series. + :return: KHIVA array with the standard deviation of each time series within tss. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.standard_deviation(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def sum_of_reoccurring_datapoints(arr, is_sorted=False): + """ Calculates the sum of all data points, that are present in the time series more than once. + + :param arr: KHIVA array with the time series. + :param is_sorted: Indicates if the input time series is sorted or not. Defaults to false. + :return: KHIVA array with the sum of all data points, that are present in the time series more than once. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.sum_of_reoccurring_datapoints(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_bool(is_sorted)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def sum_of_reoccurring_values(arr, is_sorted=False): + """ Calculates the sum of all values, that are present in the time series more than once. + + :param arr: KHIVA array with the time series. + :param is_sorted: Indicates if the input time series is sorted or not. Defaults to false. + :return: KHIVA array with the sum of all values, that are present in the time series more than once. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.sum_of_reoccurring_values(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_bool(is_sorted)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def sum_values(arr): + """ Calculates the sum over the time series arr. + + :param arr: KHIVA array with the time series. + :param is_sorted: Indicates if the input time series is sorted or not. Defaults to false. + :return: KHIVA array with the sum of values in each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.sum_values(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def symmetry_looking(arr, r): + """ Calculates if the distribution of tss *looks symmetric*. This is the case if + + .. math:: + + | mean(tss)-median(tss)| < r * (max(tss)-min(tss)) + + + :param arr: KHIVA array with the time series. + :param r: The percentage of the range to compare with. + :return: KHIVA array denoting if the input time series look symmetric. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.symmetry_looking(ctypes.pointer(arr.arr_reference), ctypes.pointer(ctypes.c_float(r)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def time_reversal_asymmetry_statistic(arr, lag): + """ This function calculates the value of: + + .. math:: + + \\frac{1}{n-2lag} \\sum_{i=0}^{n-2lag} x_{i + 2 \\cdot lag}^2 \\cdot x_{i + lag} - x_{i + lag} \\cdot x_{i}^2 + + which is + + .. math:: + + \\mathbb{E}[L^2(X)^2 \\cdot L(X) - L(X) \\cdot X^2] + + where :math:`\\mathbb{E}` is the mean and :math:`L` is the lag operator. It was proposed in [1] as a promising + feature to extract from time series. + + :param arr: KHIVA array with the time series. + :param lag: The lag to be computed. + :return: KHIVA array containing the count of the given value in each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.time_reversal_asymmetry_statistic(ctypes.pointer(arr.arr_reference), + ctypes.pointer(ctypes.c_int(lag)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def value_count(arr, v): + """ Counts occurrences of value in the time series tss. + + :param arr: KHIVA array with the time series. + :param v: The value to be counted. + :return: KHIVA array containing the count of the given value in each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.value_count(ctypes.pointer(arr.arr_reference), ctypes.pointer(ctypes.c_float(v)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def variance(arr): + """ Computes the variance for the time series array. + + :param arr: KHIVA array with the time series. + :return: KHIVA array containing the variance in each time series. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.variance(ctypes.pointer(arr.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def variance_larger_than_standard_deviation(arr): + """ Calculates if the variance of array is greater than the standard deviation. In other words, if the variance of + array is larger than 1. + + :param arr: KHIVA array with the time series. + :return: KHIVA array denoting if the variance of array is greater than the standard deviation. + """ + b = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.variance_larger_than_standard_deviation(ctypes.pointer(arr.arr_reference), + ctypes.pointer(b)) + + return Array(array_reference=b) diff --git a/khiva/library.py b/khiva/library.py new file mode 100644 index 0000000..3cd6f60 --- /dev/null +++ b/khiva/library.py @@ -0,0 +1,134 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +from enum import Enum +import ctypes +import platform + + +######################################################################################################################## + +class KhivaLibrary(object): + class __KhivaLibrary: + def __init__(self): + if platform.system() == 'Darwin': + self.c_khiva_library = ctypes.CDLL('libkhiva_c.dylib') + elif platform.system() == 'Windows': + self.c_khiva_library = ctypes.CDLL('C:/Program Files/Khiva/v0/lib/khiva_c.dll') + elif platform.system() == 'Linux': + self.c_khiva_library = ctypes.CDLL('libkhiva_c.so') + + instance = None + + def __new__(cls): + if not KhivaLibrary.instance: + KhivaLibrary.instance = KhivaLibrary.__KhivaLibrary() + return KhivaLibrary.instance + + def __getattr__(self, name): + return getattr(self.instance, name) + + def __setattr__(self, name): + return setattr(self.instance, name) + + +class KHIVABackend(Enum): + """ + KHIVA Backend. + """ + KHIVA_BACKEND_DEFAULT = 0 + """ + Default Backend. + """ + KHIVA_BACKEND_CPU = 1 + """ + CPU Backend. + """ + KHIVA_BACKEND_CUDA = 2 + """ + CUDA Backend. + """ + KHIVA_BACKEND_OPENCL = 4 + """ + OPENCL Backend. + """ + + +def info(): + """ Get the devices info. + """ + KhivaLibrary().c_khiva_library.info() + + +def set_backend(backend): + """ Set the KHIVABackend. + + :param backend: The desired backend. KHIVABackend type. + """ + KhivaLibrary().c_khiva_library.set_backend(ctypes.pointer(ctypes.c_int(backend.value))) + + +def get_backend(): + """ Get the active backend. + + :return: The active backend. KHIVABackend type. + """ + backend = (ctypes.c_int * 1)(*[0]) + KhivaLibrary().c_khiva_library.get_backend(ctypes.pointer(backend)) + + return KHIVABackend(backend[0]) + + +def get_backends(): + """ Get the available backends. + + :return: The available backends. + """ + backends = (ctypes.c_int * 1)(*[0]) + KhivaLibrary().c_khiva_library.get_backends(ctypes.pointer(backends)) + return backends[0] + + +def set_device(device): + """ Set the device. + + :param device: The desired device. + """ + KhivaLibrary().c_khiva_library.set_device(ctypes.pointer(ctypes.c_int(device))) + + +def get_device_id(): + """ Get the active device. + + :return: The active device. + """ + device = (ctypes.c_int * 1)(*[0]) + KhivaLibrary().c_khiva_library.get_device_id(ctypes.pointer(device)) + return device[0] + + +def get_device_count(): + """ Get the devices count. + + :return: The devices count. + """ + device_count = (ctypes.c_int * 1)(*[0]) + KhivaLibrary().c_khiva_library.get_device_count(ctypes.pointer(device_count)) + return device_count[0] + + +def version(): + """ Returns a string with the current version of the library. + + :return: A string with the current version of the library. + """ + v = ctypes.c_char_p((" " * 40).encode('utf8')) + KhivaLibrary().c_khiva_library.version(ctypes.pointer(v)) + return v.value.decode('utf8') diff --git a/khiva/linalg.py b/khiva/linalg.py new file mode 100644 index 0000000..349bf1b --- /dev/null +++ b/khiva/linalg.py @@ -0,0 +1,31 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + +def lls(a, b): + """ Calculates the minimum norm least squares solution :math:`x` :math:`(||A·x − b||^2)` to :math:`A·x = b`. This + function uses the singular value decomposition function of Arrayfire. The actual formula that this function computes + is :math:`x = V·D\\dagger·U^T·b`. Where :math:`U` and :math:`V` are orthogonal matrices and :math:`D\\dagger` contains + the inverse values of the singular values contained in :math:`D` if they are not zero, and zero otherwise. + + :param a: KHIVA array with the coefficients of the linear equation problem to solve. It accepts a list of lists or a numpy array + with one or several time series. + :param b: KHIVA array with the measured values. + :return: KHIVA array with the solution to the linear equation problem minimizing the norm 2. + """ + c = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.lls(ctypes.pointer(a.arr_reference), ctypes.pointer(b.arr_reference), ctypes.pointer(c)) + + return Array(array_reference=c) diff --git a/khiva/matrix.py b/khiva/matrix.py new file mode 100644 index 0000000..fe9dc65 --- /dev/null +++ b/khiva/matrix.py @@ -0,0 +1,101 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + +def find_best_n_discords(profile, index, n): + """ This function extracts the best N motifs from a previously calculated matrix profile. + + :param profile: KHIVA array with the matrix profile containing the minimum distance of each subsequence. + :param index: KHIVA array with the matrix profile index containing where each minimum occurs. + :param n: Number of discords to extract. + :return: KHIVA arrays with the discord distances, the discord indices and the subsequence indices. + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + d = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.find_best_n_discords(ctypes.pointer(profile.arr_reference), + ctypes.pointer(index.arr_reference), + ctypes.pointer(ctypes.c_long(n)), + ctypes.pointer(b), + ctypes.pointer(c), + ctypes.pointer(d)) + + return Array(array_reference=b), Array(array_reference=c), Array( + array_reference=d) + + +def find_best_n_motifs(profile, index, n): + """ This function extracts the best N discords from a previously calculated matrix profile. + + :param profile: KHIVA array with the matrix profile containing the minimum distance of each subsequence. + :param index: KHIVA array with the matrix profile index containing where each minimum occurs. + :param n: Number of motifs to extract. + :return: KHIVA arrays with the motif distances, the motif indices and the subsequence indices. + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + d = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.find_best_n_motifs(ctypes.pointer(profile.arr_reference), + ctypes.pointer(index.arr_reference), + ctypes.pointer(ctypes.c_long(n)), + ctypes.pointer(b), + ctypes.pointer(c), + ctypes.pointer(d)) + + return Array(array_reference=b), Array(array_reference=c), Array( + array_reference=d) + + +def stomp(first_time_series, second_time_series, subsequence_length): + """ Stomp algorithm to calculate the matrix profile between `ta` and `tb` using a subsequence length of `m`. + + :param first_time_series: KHIVA array with the first time series. + :param second_time_series: KHIVA array with the second time series. + :param subsequence_length: Length of the subsequence. + :return: KHIVA arrays with the profile and index. + """ + + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.stomp(ctypes.pointer(first_time_series.arr_reference), + ctypes.pointer(second_time_series.arr_reference), + ctypes.pointer(ctypes.c_long(subsequence_length)), + ctypes.pointer(b), + ctypes.pointer(c)) + + return Array(array_reference=b), Array(array_reference=c) + + +def stomp_self_join(time_series, subsequence_length): + """ Stomp algorithm to calculate the matrix profile between `t` and itself using a subsequence length of `m`. + This method filters the trivial matches. + + :param time_series: The query and reference time series in KHIVA array format. + :param subsequence_length: Lenght of the subsequence + :return: KHIVA arrays with the profile and index. + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.stomp_self_join(ctypes.pointer(time_series.arr_reference), + ctypes.pointer(ctypes.c_long(subsequence_length)), + ctypes.pointer(b), + ctypes.pointer(c)) + + return Array(array_reference=b), Array(array_reference=c) diff --git a/khiva/normalization.py b/khiva/normalization.py new file mode 100644 index 0000000..71610d0 --- /dev/null +++ b/khiva/normalization.py @@ -0,0 +1,135 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + +def decimal_scaling_norm(tss): + """ Normalizes the given time series according to its maximum value and adjusts each value within the range (-1, 1). + + :param tss: KHIVA array with the time series. + + :return: KHIVA array with the same dimensions as tss, whose values (time series in dimension 0) have been + normalized by dividing each number by :math::`10^j` , where j is the number of integer digits of the max number in + the time series.` + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.decimal_scaling_norm(ctypes.pointer(tss.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def decimal_scaling_norm_in_place(tss): + """ Same as decimal_scaling_norm, but it performs the operation in place, without allocating further memory. + + :param tss: KHIVA array with the time series. + """ + KhivaLibrary().c_khiva_library.decimal_scaling_norm_in_place(ctypes.pointer(tss.arr_reference)) + + +def max_min_norm(tss, high=1.0, low=0.0, epsilon=0.00000001): + """ Normalizes the given time series according to its minimum and maximum value and adjusts each value within the + range [low, high]. + + :param tss: KHIVA array with the time series. + :param high: Maximum final value (Defaults to 1.0). + :param low: Minimum final value (Defaults to 0.0). + :param epsilon: Safeguard for constant (or near constant) time series as the operation implies a unit scale + operation between min and max values in the tss. + + :return: KHIVA array with the same dimensions as tss where the time series have been adjusted for zero mean and + one as standard deviation. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.max_min_norm(ctypes.pointer(tss.arr_reference), ctypes.pointer(ctypes.c_double(high)), + ctypes.pointer(ctypes.c_double(low)), + ctypes.pointer(ctypes.c_double(epsilon)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def max_min_norm_in_place(tss, high=1.0, low=0.0, epsilon=0.00000001): + """ Same as max_min_norm, but it performs the operation in place, without allocating further memory. + + :param tss: KHIVA array with the time series. + :param high: Maximum final value (Defaults to 1.0). + :param low: Minimum final value (Defaults to 0.0). + :param epsilon: Safeguard for constant (or near constant) time series as the operation implies a unit scale + operation between min and max values in the tss. + """ + KhivaLibrary().c_khiva_library.max_min_norm_in_place(ctypes.pointer(tss.arr_reference), + ctypes.pointer(ctypes.c_double(high)), + ctypes.pointer(ctypes.c_double(low)), + ctypes.pointer(ctypes.c_double(epsilon))) + + +def mean_norm(tss): + """ Normalizes the given time series according to its maximum-minimum value and its mean. It follows the following + formulae: + + .. math:: + \\acute{x} = frac{x - mean(x)}{max(x) - min(x)}. + + :param tss: KHIVA array with the time series. + + :return: An array with the same dimensions as tss, whose values (time series in dimension 0) have been + normalized by substracting the mean from each number and dividing each number by :math:`max(x) - min(`x)`, + in the time series. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.mean_norm(ctypes.pointer(tss.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) + + +def mean_norm_in_place(tss): + """ Normalizes the given time series according to its maximum-minimum value and its mean. It follows the following + formulae: + + .. math: + \\acute{x} = frac{x - mean(x)}{max(x) - min(x)}. + + :param tss: KHIVA array with the time series. + """ + KhivaLibrary().c_khiva_library.mean_norm_in_place(ctypes.pointer(tss.arr_reference)) + + +def znorm(tss, epsilon=0.00000001): + """ Calculates a new set of time series with zero mean and standard deviation one. + + :param tss: KHIVA array with the time series. + :param epsilon: Minimum standard deviation to consider. It acts as a gatekeeper for those time series that + may be constant or near constant. + + :return: KHIVA array with the same dimensions as tss where the time series have been adjusted for zero mean and + one as standard deviation. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.znorm(ctypes.pointer(tss.arr_reference), ctypes.pointer(ctypes.c_double(epsilon)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def znorm_in_place(tss, epsilon=0.00000001): + """ Adjusts the time series in the given input and performs z-norm + in place (without allocating further memory). + + :param tss: KHIVA array with the time series. + :param epsilon: epsilon Minimum standard deviation to consider. It acts as a gatekeeper for + those time series that may be constant or near constant. + """ + KhivaLibrary().c_khiva_library.znorm_in_place(ctypes.pointer(tss.arr_reference), + ctypes.pointer(ctypes.c_double(epsilon))) diff --git a/khiva/polynomial.py b/khiva/polynomial.py new file mode 100644 index 0000000..b2bcd62 --- /dev/null +++ b/khiva/polynomial.py @@ -0,0 +1,51 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + +def polyfit(x, y, deg): + """ Least squares polynomial fit. Fit a polynomial :math:`p(x) = p[0] * x^{deg} + ... + p[deg]` of degree + :math:`deg` to points :math:`(x, y)`. Returns a vector of coefficients :math:`p` that minimises the squared error. + + :param x: KHIVA array with the x-coordinates of the M sample points :math:`(x[i], y[i])`. + :param y: KHIVA array with the y-coordinates of the sample points. + :param deg: Degree of the fitting polynomial + + :return: KHIVA array with the polynomial coefficients, highest power first. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.polyfit(ctypes.pointer(x.arr_reference), ctypes.pointer(y.arr_reference), + ctypes.pointer(ctypes.c_int(deg)), + ctypes.pointer(b)) + + return Array(array_reference=b) + + +def roots(p): + """ Calculates the roots of a polynomial with coefficients given in :math:`p`. The values in the rank-1 array + :math:`p` are coefficients of a polynomial. If the length of :math:`p` is :math:`n+1` then the polynomial is + described by: + + .. math:: + p[0] * x^n + p[1] * x^{n-1} + ... + p[n-1] * x + p[n] + + :param p: KHIVA array with the polynomial coefficients. + + :return: KHIVA array with the roots of the polynomial. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.roots(ctypes.pointer(p.arr_reference), ctypes.pointer(b)) + + return Array(array_reference=b) diff --git a/khiva/regression.py b/khiva/regression.py new file mode 100644 index 0000000..84b91a4 --- /dev/null +++ b/khiva/regression.py @@ -0,0 +1,49 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + + +def linear(xss, yss): + """Calculates a linear least-squares regression for two sets of measurements. Both arrays should have the same + length. + + :param xss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + :param yss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + + :return: slope Slope of the regression line. + intercept Intercept of the regression line. + rvalue Correlation coefficient. + pvalue Two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero, using Wald + Test with t-distribution of the test statistic. + stderrest Standard error of the estimated gradient. + """ + b = ctypes.c_void_p(0) + c = ctypes.c_void_p(0) + d = ctypes.c_void_p(0) + e = ctypes.c_void_p(0) + f = ctypes.c_void_p(0) + + KhivaLibrary().c_khiva_library.linear(ctypes.pointer(xss.arr_reference), + ctypes.pointer(yss.arr_reference), + ctypes.pointer(b), + ctypes.pointer(c), + ctypes.pointer(d), + ctypes.pointer(e), + ctypes.pointer(f) + ) + return Array(array_reference=b), Array(array_reference=c), Array(array_reference=d), Array( + array_reference=e), Array(array_reference=f) diff --git a/khiva/regularization.py b/khiva/regularization.py new file mode 100644 index 0000000..dee9d44 --- /dev/null +++ b/khiva/regularization.py @@ -0,0 +1,46 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + +def group_by(tss, aggregation_function, n_columns_key=1, n_columns_value=1): + """ Group by operation in the input array using n_columns_key columns as group keys and n_columns_value columns as + values. The data is expected to be sorted. The aggregation function determines the operation to aggregate the + values. + + :param tss: KHIVA array with the time series. + :param aggregation_function: Function to be used in the aggregation. It receives an integer which indicates + the function to be applied. + 0 : mean, + 1 : median + 2 : min, + 3 : max, + 4 : stdev, + 5 : var, + default : mean + :param n_columns_key: Number of columns conforming the key. + :param n_columns_value: Number of columns conforming the value (they are expected to be consecutive to the column + keys). + + :return: KHIVA array with the values of the group keys aggregated using the aggregation_function. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.group_by(ctypes.pointer(tss.arr_reference), + ctypes.pointer(ctypes.c_int(aggregation_function)), + ctypes.pointer(ctypes.c_int(n_columns_key)), + ctypes.pointer(ctypes.c_int(n_columns_value)), + ctypes.pointer(b)) + + return Array(array_reference=b) diff --git a/khiva/statistics.py b/khiva/statistics.py new file mode 100644 index 0000000..80ab40f --- /dev/null +++ b/khiva/statistics.py @@ -0,0 +1,154 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import ctypes +from khiva.library import KhivaLibrary +from khiva.array import Array + + +######################################################################################################################## + + +def covariance(tss, unbiased=False): + """ Returns the covariance matrix of the time series contained in tss. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + :param unbiased: Determines whether it divides by n - 1 (if false) or n (if true). + :return: The covariance matrix of the time series. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.covariance_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(ctypes.c_bool(unbiased)), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def kurtosis(tss): + """ Returns the kurtosis of tss (calculated with the adjusted Fisher-Pearson standardized moment coefficient G2). + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + :return: The kurtosis of tss. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.kurtosis_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def ljung_box(tss, lags): + """ The Ljung–Box test checks that data whithin the time series are independently distributed (i.e. the correlations in + the population from which the sample is taken are 0, so that any observed correlations in the data result from + randomness of the sampling process). Data are no independently distributed, if they exhibit serial correlation. + The test statistic is: + + .. math:: + Q = n\left(n+2\\right)\sum_{k=1}^h\\frac{\hat{\\rho}^2_k}{n-k} + + where ''n'' is the sample size, :math:`\hat{\\rho}k` is the sample autocorrelation at lag ''k'', and ''h'' is the + number of lags being tested. Under :math:`H_0` the statistic Q follows a :math:`\chi^2{(h)}`. For significance level + :math:`\alpha`, the :math:`critical region` for rejection of the hypothesis of randomness is: + + .. math:: + Q > \chi_{1-\alpha,h}^2 + + where :math:`\chi_{1-alpha,h}^2` is the :math:`\alpha` -quantile of the chi-squared distribution with ''h'' degrees of + freedom. + + [1] G. M. Ljung G. E. P. Box (1978). On a measure of lack of fit in time series models. + Biometrika, Volume 65, Issue 2, 1 August 1978, Pages 297–303. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + :param lags: Number of lags being tested. + :return: Array containing the Ljung-Box statistic test. + """ + ljung_box_out = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.ljung_box(ctypes.pointer(tss.arr_reference), ctypes.pointer(ctypes.c_long(lags)), + ctypes.pointer(ljung_box_out)) + return Array(array_reference=ljung_box_out) + + +def moment(tss, k): + """ Returns the kth moment of the given time series. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + :param k: The specific moment to be calculated. + :return: The kth moment of the given time series. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.moment_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(ctypes.c_int(k)), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def quantile(tss, q, precision=1e8): + """ Returns values at the given quantile. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. NOTE: the time series should be sorted. + :param q: Percentile(s) at which to extract score(s). One or many. + :param precision: Number of decimals expected. + :return: Values at the given quantile. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.quantile_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(q.arr_reference), + ctypes.pointer(ctypes.c_float(precision)), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def quantiles_cut(tss, quantiles, precision=1e-8): + """ Discretizes the time series into equal-sized buckets based on sample quantiles. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. NOTE: the time series should be sorted. + :param quantiles: Number of quantiles to extract. From 0 to 1, step 1/quantiles. + :param precision: Number of decimals expected. + :return: Matrix with the categories, one category per row, the start of the category in the first column and + the end in the second category. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.quantiles_cut_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(ctypes.c_float(quantiles)), + ctypes.pointer(ctypes.c_float(precision)), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def sample_stdev(tss): + """ Estimates standard deviation based on a sample. The standard deviation is calculated using the "n-1" method. + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. + :return: The sample standard deviation. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.sample_stdev_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + + +def skewness(tss): + """ Calculates the sample skewness of tss (calculated with the adjusted Fisher-Pearson standardized moment + coefficient G1). + + :param tss: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension + one indicates the number of time series. NOTE: the time series should be sorted. + :return: Array containing the skewness of each time series in tss. + """ + b = ctypes.c_void_p(0) + KhivaLibrary().c_khiva_library.skewness_statistics(ctypes.pointer(tss.arr_reference), + ctypes.pointer(b)) + return Array(array_reference=b) + diff --git a/rdocs-requirements.txt b/rdocs-requirements.txt new file mode 100644 index 0000000..bc4449a --- /dev/null +++ b/rdocs-requirements.txt @@ -0,0 +1,2 @@ +Sphinx >= 1.7.0 +sphinx_rtd_theme >= 0.2.4 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 9dff893..5ec8d65 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,2 @@ -pandas -numpy +pandas >= 0.22.0 +numpy >= 1.14.0 \ No newline at end of file diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..797016c --- /dev/null +++ b/setup.cfg @@ -0,0 +1,28 @@ +[metadata] +name = khiva +summary = khiva performs big sequence analytics by using GPU. +license = MPL +author = Shapelets.io +description-file = README.md +# Add here all kinds of additional classifiers as defined under +# https://pypi.python.org/pypi?%3Aaction=list_classifiers +classifier = + Programming Language :: Python + Operating System :: Unix + Operating System :: Microsoft :: Windows + Operating System :: MacOS + Intended Audience :: Science/Research + Topic :: Scientific/Engineering + Topic :: Software Development + +[entry_points] +# Add here console scripts like: +# console_scripts = +# script_name = khiva.module:function + + +[files] +# Add here 'data_files', 'packages' or 'namespace_packages'. +# Additional data files are defined as key value pairs of source and target: +packages = + khiva \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..f56a897 --- /dev/null +++ b/setup.py @@ -0,0 +1,45 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +######################################################################################################################## +# IMPORT +######################################################################################################################## +from setuptools import setup, find_packages +import subprocess +import platform +import logging +from pathlib import Path +######################################################################################################################## + + +library_installed = False + +if platform.system() == 'Darwin': + khivalibrary = Path("/usr/local/lib/libkhiva_c.dylib") + library_installed = khivalibrary.is_file() +elif platform.system() == 'Windows': + khivalibrary = Path("C:\\Program Files\\KHIVA\\lib\\khiva_c.dll") + library_installed = khivalibrary.is_file() +elif platform.system() == 'Linux': + khivalibrary = Path("/usr/local/lib/libkhiva_c.so") + library_installed = khivalibrary.is_file() + +if library_installed: + setup( + author="Shapelets.io", + author_email="info@shapelets.io", + name="khiva", + version=subprocess.check_output(["git", "describe"]).strip().decode("utf-8")[1:].split('-')[0], + description="Python bindings for khiva", + license="MPL 2.0", + url="https://shapelets.io", + packages=find_packages() + ) +else: + logging.error("C++ KHIVA library not installed. Please, follow the steps for installing the library in: ") \ No newline at end of file diff --git a/test-requirements.txt b/test-requirements.txt new file mode 100644 index 0000000..0b43859 --- /dev/null +++ b/test-requirements.txt @@ -0,0 +1 @@ +pytest >= 3.4.1 \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..987223f --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. diff --git a/tests/unit_tests/__init__.py b/tests/unit_tests/__init__.py new file mode 100644 index 0000000..987223f --- /dev/null +++ b/tests/unit_tests/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. diff --git a/tests/unit_tests/array_unit_tests.py b/tests/unit_tests/array_unit_tests.py new file mode 100644 index 0000000..246ccc3 --- /dev/null +++ b/tests/unit_tests/array_unit_tests.py @@ -0,0 +1,286 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +import numpy as np +from khiva.array import Array, dtype +import logging +import sys + + +######################################################################################################################## + +class ArrayTest(unittest.TestCase): + DELTA = 1e-6 + DECIMAL = 6 + + def setUp(self): + pass + + def test_real_1d(self): + a = Array([1, 2, 3, 4, 5, 6, 7, 8]) + expected = np.array([1, 2, 3, 4, 5, 6, 7, 8]) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_real_2d(self): + a = Array([[1, 2, 3, 4], [5, 6, 7, 8]]) + expected = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_real_3d(self): + a = Array([[[1, 5], [2, 6]], [[3, 7], [4, 8]]]) + expected = np.array([[[1, 5], [2, 6]], [[3, 7], [4, 8]]]) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_real_4d(self): + a = Array([[[[1, 9], [2, 10]], [[3, 11], [4, 12]]], [[[5, 13], [6, 14]], [[7, 15], [8, 16]]]]) + expected = np.array([[[[1, 9], [2, 10]], [[3, 11], [4, 12]]], [[[5, 13], [6, 14]], [[7, 15], [8, 16]]]]) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex64_1d(self): + a = Array(np.array([1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j]).astype(np.complex64), khiva_type=dtype.c32) + expected = np.array([1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j]).astype(np.complex64) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex64_2d(self): + a = Array( + np.array([[1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j], [9 + 13j, 10 + 14j, 11 + 15j, 12 + 16j]]).astype(np.complex64), + khiva_type=dtype.c32) + expected = np.array([[1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j], [9 + 13j, 10 + 14j, 11 + 15j, 12 + 16j]]).astype( + np.complex64) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex64_3d(self): + a = Array( + np.array([[[1 + 1j, 5 + 5j], [2 + 2j, 6 + 6j]], [[3 + 3j, 7 + 7j], [4 + 4j, 8 + 8j]]]).astype(np.complex64), + khiva_type=dtype.c32) + expected = np.array([[[1 + 1j, 5 + 5j], [2 + 2j, 6 + 6j]], [[3 + 3j, 7 + 7j], [4 + 4j, 8 + 8j]]]).astype( + np.complex64) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex64_4d(self): + a = Array( + np.array([[[[1 + 1j, 9 + 9j], [2 + 2j, 10 + 10j]], [[3 + 3j, 11 + 11j], [4 + 4j, 12 + 12j]]], + [[[5 + 5j, 13 + 13j], [6 + 6j, 14 + 14j]], [[7 + 7j, 15 + 15j], [8 + 8j, 16 + 16j]]]]) + .astype(np.complex64), + khiva_type=dtype.c32) + expected = np.array([[[[1 + 1j, 9 + 9j], [2 + 2j, 10 + 10j]], [[3 + 3j, 11 + 11j], [4 + 4j, 12 + 12j]]], + [[[5 + 5j, 13 + 13j], [6 + 6j, 14 + 14j]], + [[7 + 7j, 15 + 15j], [8 + 8j, 16 + 16j]]]]).astype( + np.complex64) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex128_1d(self): + a = Array(np.array([1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j]).astype(np.complex128), khiva_type=dtype.c64) + expected = np.array([1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j]).astype(np.complex128) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex128_2d(self): + a = Array( + np.array([[1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j], [9 + 13j, 10 + 14j, 11 + 15j, 12 + 16j]]).astype(np.complex128), + khiva_type=dtype.c32) + expected = np.array([[1 + 5j, 2 + 6j, 3 + 7j, 4 + 8j], [9 + 13j, 10 + 14j, 11 + 15j, 12 + 16j]]).astype( + np.complex128) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex128_3d(self): + a = Array( + np.array([[[1 + 1j, 5 + 5j], [2 + 2j, 6 + 6j]], [[3 + 3j, 7 + 7j], [4 + 4j, 8 + 8j]]]).astype( + np.complex128), + khiva_type=dtype.c32) + expected = np.array([[[1 + 1j, 5 + 5j], [2 + 2j, 6 + 6j]], [[3 + 3j, 7 + 7j], [4 + 4j, 8 + 8j]]]).astype( + np.complex128) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_complex128_4d(self): + a = Array( + np.array([[[[1 + 1j, 9 + 9j], [2 + 2j, 10 + 10j]], [[3 + 3j, 11 + 11j], [4 + 4j, 12 + 12j]]], + [[[5 + 5j, 13 + 13j], [6 + 6j, 14 + 14j]], [[7 + 7j, 15 + 15j], [8 + 8j, 16 + 16j]]]]) + .astype(np.complex128), + khiva_type=dtype.c32) + expected = np.array([[[[1 + 1j, 9 + 9j], [2 + 2j, 10 + 10j]], [[3 + 3j, 11 + 11j], [4 + 4j, 12 + 12j]]], + [[[5 + 5j, 13 + 13j], [6 + 6j, 14 + 14j]], + [[7 + 7j, 15 + 15j], [8 + 8j, 16 + 16j]]]]).astype( + np.complex128) + np.testing.assert_array_equal(a.to_numpy(), expected) + + def test_get_type(self): + a = Array([[1, 2, 3, 4], [5, 6, 7, 8]], khiva_type=dtype.s64) + expected = dtype.s64 + self.assertEqual(a.get_type(), expected) + + def testPlus(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 4]) + c = a + b + np.testing.assert_array_equal(c.to_numpy(), np.array([2, 4, 6, 8])) + + def testTimes(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 4]) + c = a * b + np.testing.assert_array_equal(c.to_numpy(), np.array([1, 4, 9, 16])) + + def testMinus(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 4]) + c = a - b + np.testing.assert_array_equal(c.to_numpy(), np.array([0, 0, 0, 0])) + + def testRDivide(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 4]) + c = a / b + expected = np.array([1., 1., 1., 1.]) + for d, e in zip(expected, c.to_numpy()): + self.assertAlmostEqual(d, e, delta=1e-5) + + def testMod(self): + a = Array([1, 2, 3, 4]) + b = Array([2, 2, 2, 2]) + c = a % b + np.testing.assert_array_equal(c.to_numpy(), np.array([1, 0, 1, 0])) + + def testPower(self): + a = Array([1, 2, 3, 4]) + b = Array([2, 2, 2, 2]) + c = a ** b + np.testing.assert_array_equal(c.to_numpy(), np.array([1, 4, 9, 16])) + + def testLt(self): + a = Array([1, 2, 3, 4]) + b = Array([2, 2, 2, 2]) + c = a < b + np.testing.assert_array_equal(c.to_numpy(), np.array([True, False, False, False])) + + def testGt(self): + a = Array([1, 2, 3, 4]) + b = Array([2, 2, 2, 2]) + c = a > b + np.testing.assert_array_equal(c.to_numpy(), np.array([False, False, True, True])) + + def testLe(self): + a = Array([1, 2, 3, 4]) + b = Array([2, 2, 2, 2]) + c = a <= b + np.testing.assert_array_equal(c.to_numpy(), np.array([True, True, False, False])) + + def testGe(self): + a = Array([1, 2, 3, 4]) + b = Array([2, 2, 2, 2]) + c = a >= b + np.testing.assert_array_equal(c.to_numpy(), np.array([False, True, True, True])) + + def testEq(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 5]) + c = a == b + np.testing.assert_array_equal(c.to_numpy(), np.array([True, True, True, False])) + + def testNeq(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 5]) + c = a != b + np.testing.assert_array_equal(c.to_numpy(), np.array([False, False, False, True])) + + def testAnd(self): + a = Array([True, True, True, True], dtype.b8) + b = Array([True, False, True, False], dtype.b8) + c = a & b + np.testing.assert_array_equal(c.to_numpy(), np.array([True, False, True, False])) + + def testOr(self): + a = Array([True, True, True, True], dtype.b8) + b = Array([True, False, True, False], dtype.b8) + c = a | b + np.testing.assert_array_equal(c.to_numpy(), np.array([True, True, True, True])) + + def testXor(self): + a = Array([True, True, True, True], dtype.b8) + b = Array([True, False, True, False], dtype.b8) + c = a ^ b + np.testing.assert_array_equal(c.to_numpy(), np.array([False, True, False, True])) + + def testBitshift(self): + a = Array([2, 4, 6, 8], dtype.s32) + c = a >> 1 + np.testing.assert_array_equal(c.to_numpy(), np.array([1, 2, 3, 4])) + + def testBitsra(self): + a = Array([2, 4, 6, 8], dtype.s32) + c = a << 1 + np.testing.assert_array_equal(c.to_numpy(), np.array([4, 8, 12, 16])) + + def testCtranspose(self): + a = Array([[0 - 1j, 4 + 2j], [2 + 1j, 0 - 2j]], khiva_type=dtype.c32) + b = a.transpose(True) + expected = [[0 + 1j, 2 - 1j], [4 - 2j, 0 + 2j]] + np.testing.assert_array_equal(b.to_numpy(), expected) + + def testTranspose(self): + a = Array([[1, 3], [2, 4]], dtype.s32) + c = a.transpose() + np.testing.assert_array_equal(c.to_numpy(), np.array([[1, 2], [3, 4]])) + + def testCol(self): + a = Array([[1, 3], [2, 4]], dtype.s32) + c = a.get_col(0) + np.testing.assert_array_equal(c.to_numpy(), np.array([1, 3])) + + def testCols(self): + a = Array(np.transpose([[1, 2, 3], [4, 5, 6]]), dtype.s32) + c = a.get_cols(0, 1) + np.testing.assert_array_equal(c.to_numpy(), np.transpose(np.array([[1, 2], [4, 5]]))) + + def testRow(self): + a = Array(np.transpose([[1, 2], [3, 4]]), dtype.s32) + c = a.get_row(0) + np.testing.assert_array_equal(c.to_numpy(), [1, 2]) + + def testRows(self): + a = Array(np.transpose([[1, 2], [3, 4], [5, 6]]), dtype.s32) + c = a.get_rows(0, 1) + np.testing.assert_array_equal(c.to_numpy(), np.transpose(np.array([[1, 2], [3, 4]]))) + + def testMtimes(self): + a = Array([1, 2, 3, 4]) + b = Array([1, 2, 3, 4]) + d = a.transpose() + c = b.matmul(d) + expected = np.transpose([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12], [4, 8, 12, 16]]) + np.testing.assert_array_equal(c.to_numpy(), expected) + + def testAs(self): + a = Array([1, 2, 3, 4], khiva_type=dtype.s32) + b = a.as_type(dtype.u32) + expected_data = [1, 2, 3, 4] + np.testing.assert_array_equal(b.to_numpy(), expected_data) + self.assertEqual(b.khiva_type, dtype.u32) + + def testCopy(self): + a = Array([1, 2, 3, 4], khiva_type=dtype.s32) + b = a.copy() + np.testing.assert_array_equal(a.to_numpy(), b.to_numpy()) + self.assertEqual(b.khiva_type, b.khiva_type) + + def testArrayfire(self): + try: + import arrayfire as af + except ModuleNotFoundError: + logging.error("In order to use `to_arrayfire()` function, you need to install the Arrayfire Python library") + sys.exit(1) + a = af.Array([1, 2, 3, 4]) + b = Array.from_arrayfire(a) + np.testing.assert_array_equal(np.asarray(a.to_list()), np.asarray(b.to_list())) + np.testing.assert_array_equal(np.asarray(a.to_list()), b.to_arrayfire()) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/dimensionality_unit_tests.py b/tests/unit_tests/dimensionality_unit_tests.py new file mode 100644 index 0000000..46d9a62 --- /dev/null +++ b/tests/unit_tests/dimensionality_unit_tests.py @@ -0,0 +1,76 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.dimensionality import * +from khiva.array import Array +import numpy as np + + +######################################################################################################################## + + +class DimensionalityTest(unittest.TestCase): + DELTA = 1e-6 + DECIMAL = 6 + + def setUp(self): + pass + + def test_ramer_douglas_peucker(self): + a = Array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0]]) + ramer_douglas_peucker_result = ramer_douglas_peucker(a, 1.0).to_numpy() + expected = np.array([[0, 2, 3, 6, 9], [0, -0.1, 5.0, 8.1, 9.0]]) + np.testing.assert_array_almost_equal(ramer_douglas_peucker_result, expected, decimal=self.DECIMAL) + + def test_visvalingam(self): + a = Array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0]]) + visvalingam_result = visvalingam(a, 5).to_numpy() + expected = np.array([[0, 2, 5, 7, 9], [0, -0.1, 7.0, 9.0, 9.0]]) + np.testing.assert_array_almost_equal(visvalingam_result, expected, decimal=self.DECIMAL) + + def test_paa(self): + a = Array( + [[0.0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0], [0.0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0]]) + paa_result = paa(a, 5).to_numpy() + expected = np.array([[0.05, 2.45, 6.5, 8.55, 9.0], [0.05, 2.45, 6.5, 8.55, 9.0]]) + np.testing.assert_array_almost_equal(paa_result, expected, decimal=self.DECIMAL) + + def test_sax(self): + a = Array([[0.05, 2.45, 6.5, 8.55, 9.0], [0.05, 2.45, 6.5, 8.55, 9.0]]) + sax_result = sax(a, 3).to_numpy() + + expected = np.array([[0, 0, 1, 2, 2], [0, 0, 1, 2, 2]], dtype=np.int32) + np.testing.assert_array_almost_equal(sax_result, expected, decimal=self.DECIMAL) + + def test_pip(self): + a = Array( + [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], [0.0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0]]) + pip_result = pip(a, 6).to_numpy() + + expected = np.array([[0.0, 2.0, 3.0, 6.0, 7.0, 9.0], [0.0, -0.1, 5.0, 8.1, 9.0, 9.0]]) + np.testing.assert_array_almost_equal(pip_result, expected, decimal=self.DECIMAL) + + def test_pla_bottom_up(self): + tss = Array([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], + [0.0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0]]) + expected = [[0, 1, 2, 3, 4, 7, 8, 9], [0, 0.1, -0.1, 5, 6, 9, 9, 9]] + result = pla_bottom_up(tss, 1).to_numpy() + np.testing.assert_array_almost_equal(result, expected, decimal=self.DECIMAL) + + def test_pla_sliding_window(self): + tss = Array([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], + [0.0, 0.1, -0.1, 5.0, 6.0, 7.0, 8.1, 9.0, 9.0, 9.0]]) + expected = [[0, 2, 3, 7, 8, 9], [0, -0.1, 5, 9, 9, 9]] + result = pla_sliding_window(tss, 1).to_numpy() + np.testing.assert_array_almost_equal(result, expected, decimal=self.DECIMAL) + + if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/distances_unit_tests.py b/tests/unit_tests/distances_unit_tests.py new file mode 100644 index 0000000..36d6db3 --- /dev/null +++ b/tests/unit_tests/distances_unit_tests.py @@ -0,0 +1,54 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.distances import * +from khiva.array import Array +import numpy as np + + +######################################################################################################################## + + +class DistancesTest(unittest.TestCase): + + def setUp(self): + pass + + def test_euclidean(self): + euclidean_result = euclidean(Array(data=[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])).to_numpy().flatten() + expected = np.array([0, 0, 0, 8, 0, 0, 16, 8, 0]) + np.testing.assert_array_almost_equal(euclidean_result, expected, decimal=1) + + def test_squared_euclidean(self): + squared_euclidean_result = squared_euclidean( + Array(data=[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])).to_numpy().flatten() + expected = np.array([0, 0, 0, 64, 0, 0, 256, 64, 0]) + np.testing.assert_array_almost_equal(squared_euclidean_result, expected, decimal=1) + + def test_dtw(self): + euclidean_result = dtw(Array( + data=[[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5]])).to_numpy() + expected = np.array([[0, 0, 0, 0, 0], [5, 0, 0, 0, 0], [10, 5, 0, 0, 0], [15, 10, 5, 0, 0], [20, 15, 10, 5, 0]]) + np.testing.assert_array_almost_equal(euclidean_result, expected, decimal=1) + + def test_hamming(self): + result = hamming(Array(data = [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5]])).to_numpy() + expected = np.array([[0, 0, 0, 0, 0], [5, 0, 0, 0, 0], [5, 5, 0, 0, 0], [5, 5, 5, 0, 0], [5, 5, 5, 5, 0]]) + np.testing.assert_array_almost_equal(result, expected, decimal=1) + + def test_manhattan(self): + result = manhattan(Array(data = [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5]])).to_numpy() + expected = np.array([[0, 0, 0, 0, 0], [5, 0, 0, 0, 0], [10, 5, 0, 0, 0], [15, 10, 5, 0, 0], [20, 15, 10, 5, 0]]) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/features_unit_tests.py b/tests/unit_tests/features_unit_tests.py new file mode 100644 index 0000000..c64a54c --- /dev/null +++ b/tests/unit_tests/features_unit_tests.py @@ -0,0 +1,553 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +import numpy as np +from khiva.features import * +from khiva.array import Array, dtype +import logging + + +######################################################################################################################## + +class FeaturesTest(unittest.TestCase): + DELTA = 1e-6 + + def setUp(self): + pass + + def test_cid_ce(self): + cid_ce_result = cid_ce(Array(data=[[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), + False).to_numpy() + self.assertAlmostEqual(cid_ce_result[0], 2.23606797749979, delta=self.DELTA) + self.assertAlmostEqual(cid_ce_result[1], 2.23606797749979, delta=self.DELTA) + + cid_ce_result = cid_ce(Array(data=[[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), + True).to_numpy() + self.assertAlmostEqual(cid_ce_result[0], 1.30930734141595, delta=self.DELTA) + self.assertAlmostEqual(cid_ce_result[1], 1.30930734141595, delta=self.DELTA) + + def test_c3(self): + c3_result = c3(Array(data=[[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), 2).to_numpy() + self.assertEqual(c3_result[0], 7.5) + self.assertEqual(c3_result[1], 586.5) + + def test_abs_sum_of_changes(self): + abs_sum_of_changes_result = absolute_sum_of_changes( + Array(data=[[0, 1, 2, 3], [4, 6, 8, 10], [11, 14, 17, 20]])).to_numpy() + self.assertEqual(abs_sum_of_changes_result[0], 3) + self.assertEqual(abs_sum_of_changes_result[1], 6) + self.assertEqual(abs_sum_of_changes_result[2], 9) + + def test_abs_energy(self): + abs_energy_result = abs_energy(Array(data=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])).to_numpy() + self.assertAlmostEqual(abs_energy_result, 385, delta=self.DELTA) + + def test_cross_correlation(self): + cross_correlation_result = cross_correlation(xss=Array(data=[1, 2, 3, 4]), + yss=Array(data=[4, 6, 8, 10, 12]), + unbiased=False).to_numpy() + self.assertAlmostEqual(cross_correlation_result[0], 0.790569415, delta=self.DELTA) + self.assertAlmostEqual(cross_correlation_result[1], 0.790569415, delta=self.DELTA) + self.assertAlmostEqual(cross_correlation_result[2], 0.079056941, delta=self.DELTA) + self.assertAlmostEqual(cross_correlation_result[3], -0.395284707, delta=self.DELTA) + self.assertAlmostEqual(cross_correlation_result[4], -0.474341649, delta=self.DELTA) + + def test_auto_covariance(self): + auto_covariance_result = auto_covariance( + Array(data=[[0, 1, 2, 3], [10, 11, 12, 13]])).to_numpy() + self.assertAlmostEqual(auto_covariance_result[0][0], 1.25, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[0][1], 0.3125, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[0][2], -0.375, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[0][3], -0.5625, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[1][0], 1.25, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[1][1], 0.3125, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[1][2], -0.375, delta=self.DELTA) + self.assertAlmostEqual(auto_covariance_result[1][3], -0.5625, delta=self.DELTA) + + def test_cross_covariance(self): + cross_covariance_result = cross_covariance( + xss=Array(data=[[0, 1, 2, 3], [10, 11, 12, 13]]), + yss=Array(data=[[4, 6, 8, 10, 12], [14, 16, 18, 20, 22]]), + unbiased=False).to_numpy().flatten() + for i in range(4): + self.assertAlmostEqual(cross_covariance_result[(i * 5)], 2.5, delta=self.DELTA) + self.assertAlmostEqual(cross_covariance_result[(i * 5) + 1], 2.5, delta=self.DELTA) + self.assertAlmostEqual(cross_covariance_result[(i * 5) + 2], 0.25, delta=self.DELTA) + self.assertAlmostEqual(cross_covariance_result[(i * 5) + 3], -1.25, delta=self.DELTA) + self.assertAlmostEqual(cross_covariance_result[(i * 5) + 4], -1.5, delta=self.DELTA) + + def test_approximate_entropy(self): + approximate_entropy_result = approximate_entropy( + Array(data=[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]], + khiva_type=dtype.f32), 4, 0.5).to_numpy() + + self.assertAlmostEqual(approximate_entropy_result[0], 0.13484281753639338, delta=self.DELTA) + self.assertAlmostEqual(approximate_entropy_result[1], 0.13484281753639338, delta=self.DELTA) + + def test_auto_correlation(self): + auto_correlation_result = auto_correlation( + Array(data=[[0, 1, 2, 3], [10, 11, 12, 13]]), 4, False).to_numpy().flatten() + self.assertAlmostEqual(auto_correlation_result[0], 1, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[1], 0.25, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[2], -0.3, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[3], -0.45, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[4], 1.0, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[5], 0.25, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[6], -0.3, delta=self.DELTA) + self.assertAlmostEqual(auto_correlation_result[7], -0.45, delta=self.DELTA) + + def test_binned_entropy(self): + binned_entropy_result = binned_entropy(Array(data=[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, + 14, 15, 16, 17, 18, 19, 20], + [1, 1, 3, 10, 5, 6, 1, 8, 9, 10, 11, 1, 13, 14, 10, 16, + 17, 10, 19, + 20]]), 5).to_numpy() + self.assertAlmostEqual(binned_entropy_result[0], 1.6094379124341005, delta=self.DELTA) + self.assertAlmostEqual(binned_entropy_result[1], 1.5614694247763998, delta=self.DELTA) + + def test_count_above_mean(self): + count_above_mean_result = count_above_mean(Array(data=[[0, 1, 2, 3, 4, 5], + [6, 7, 8, 9, 10, 11]])).to_numpy() + self.assertAlmostEqual(count_above_mean_result[0], 3, delta=self.DELTA) + self.assertAlmostEqual(count_above_mean_result[1], 3, delta=self.DELTA) + + def test_count_below_mean(self): + count_below_mean_result = count_below_mean(Array(data=[[0, 1, 2, 3, 4, 5], + [6, 7, 8, 9, 10, 11]])).to_numpy() + self.assertAlmostEqual(count_below_mean_result[0], 3, delta=self.DELTA) + self.assertAlmostEqual(count_below_mean_result[1], 3, delta=self.DELTA) + + def test_energy_ratio_by_chunks(self): + energy_ratio_by_chunks_result = energy_ratio_by_chunks(Array([[0, 1, 2, 3, 4, 5], + [6, 7, 8, 9, 10, 11]]), + 2, 0).to_numpy() + self.assertAlmostEqual(energy_ratio_by_chunks_result[0], 0.090909091, delta=self.DELTA) + self.assertAlmostEqual(energy_ratio_by_chunks_result[1], 0.330376940, delta=self.DELTA) + energy_ratio_by_chunks_result = energy_ratio_by_chunks(Array([[0, 1, 2, 3, 4, 5], + [6, 7, 8, 9, 10, 11]]), 2, + 1).to_numpy() + self.assertAlmostEqual(energy_ratio_by_chunks_result[0], 0.909090909, delta=self.DELTA) + self.assertAlmostEqual(energy_ratio_by_chunks_result[1], 0.669623060, delta=self.DELTA) + + def test_first_location_of_maximum(self): + first_location_of_maximum_result = first_location_of_maximum( + Array([[5, 4, 3, 5, 0, 1, 5, 3, 2, 1], [2, 4, 3, 5, 2, 5, 4, 3, 5, 2]])).to_numpy() + self.assertAlmostEqual(first_location_of_maximum_result[0], 0.0, delta=self.DELTA) + self.assertAlmostEqual(first_location_of_maximum_result[1], 0.3, delta=self.DELTA) + + def test_first_location_of_minimum(self): + first_location_of_minimum_result = first_location_of_minimum( + Array([[5, 4, 3, 0, 0, 1], [5, 4, 3, 0, 2, 1]])).to_numpy() + self.assertAlmostEqual(first_location_of_minimum_result[0], 0.5, delta=self.DELTA) + self.assertAlmostEqual(first_location_of_minimum_result[1], 0.5, delta=self.DELTA) + + def test_friedrich_coefficients(self): + friedrich_coefficients_result = friedrich_coefficients( + Array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]), 4, 2).to_numpy() + expected = np.array([[-0.0009912563255056738, -0.0027067768387496471, -0.00015192681166809052, + 0.10512571036815643, 0.89872437715530396], + [-0.0009912563255056738, -0.0027067768387496471, -0.00015192681166809052, + 0.10512571036815643, 0.89872437715530396]]) + np.testing.assert_array_almost_equal(friedrich_coefficients_result, expected, decimal=6) + + def test_has_duplicates(self): + has_duplicates_result = has_duplicates( + Array([[5, 4, 3, 0, 0, 1], [5, 4, 3, 0, 2, 1]])).to_numpy() + self.assertEqual(has_duplicates_result[0], True) + self.assertEqual(has_duplicates_result[1], False) + + def test_has_duplicate_max(self): + has_duplicate_max_result = has_duplicate_max( + Array([[5, 4, 3, 0, 5, 1], [5, 4, 3, 0, 2, 1]])).to_numpy() + self.assertEqual(has_duplicate_max_result[0], True) + self.assertEqual(has_duplicate_max_result[1], False) + + def test_index_mass_quantile(self): + index_mass_quantile_result = index_mass_quantile(Array([[5, 4, 3, 0, 0, 1], [5, 4, 0, 0, 2, 1]]), + 0.5).to_numpy() + + self.assertAlmostEqual(index_mass_quantile_result[0], 0.333333333, delta=self.DELTA) + self.assertAlmostEqual(index_mass_quantile_result[1], 0.333333333, delta=self.DELTA) + + def test_kurtosis(self): + kurtosis_result = kurtosis(Array([[0, 1, 2, 3, 4, 5], [2, 2, 2, 20, 30, 25]])).to_numpy() + self.assertAlmostEqual(kurtosis_result[0], -1.2, delta=1e-4) + self.assertAlmostEqual(kurtosis_result[1], -2.66226722, delta=1e-4) + + def test_large_standard_deviation(self): + large_standard_deviation_result = large_standard_deviation( + Array([[-1, -1, -1, 1, 1, 1], [4, 6, 8, 4, 5, 4]]), 0.4).to_numpy() + self.assertEqual(large_standard_deviation_result[0], True) + self.assertEqual(large_standard_deviation_result[1], False) + + def test_last_location_of_maximum(self): + last_location_of_maximum_result = last_location_of_maximum( + Array([[0, 4, 3, 5, 5, 1], [0, 4, 3, 2, 5, 1]])).to_numpy() + self.assertAlmostEqual(last_location_of_maximum_result[0], 0.8333333333333334, delta=self.DELTA) + self.assertAlmostEqual(last_location_of_maximum_result[1], 0.8333333333333334, delta=self.DELTA) + + def test_last_location_of_minimum(self): + last_location_of_minimum_result = last_location_of_minimum( + Array([[0, 4, 3, 5, 5, 1, 0, 4], [3, 2, 5, 1, 4, 5, 1, 2]])).to_numpy() + self.assertAlmostEqual(last_location_of_minimum_result[0], 0.875, delta=self.DELTA) + self.assertAlmostEqual(last_location_of_minimum_result[1], 0.875, delta=self.DELTA) + + def test_length(self): + length_result = length(Array([[0, 4, 3, 5, 5, 1], [0, 4, 3, 2, 5, 1]])).to_numpy().flatten() + self.assertEqual(length_result[0], 6) + self.assertEqual(length_result[1], 6) + + def test_linear_trend(self): + pvalue, rvalue, intercept, slope, stderr = linear_trend(Array([[0, 4, 3, 5, 5, 1], [2, 4, 1, 2, 5, 3]])) + + pvalue = pvalue.to_numpy() + self.assertAlmostEqual(pvalue[0], 0.6260380997892747, delta=self.DELTA) + self.assertAlmostEqual(pvalue[1], 0.5272201945463578, delta=self.DELTA) + rvalue = rvalue.to_numpy() + self.assertAlmostEqual(rvalue[0], 0.2548235957188128, delta=self.DELTA) + self.assertAlmostEqual(rvalue[1], 0.3268228676411533, delta=self.DELTA) + intercept = intercept.to_numpy() + self.assertAlmostEqual(intercept[0], 2.2857142857142856, delta=self.DELTA) + self.assertAlmostEqual(intercept[1], 2.1904761904761907, delta=self.DELTA) + slope = slope.to_numpy() + self.assertAlmostEqual(slope[0], 0.2857142857142857, delta=self.DELTA) + self.assertAlmostEqual(slope[1], 0.2571428571428572, delta=self.DELTA) + stderr = stderr.to_numpy() + self.assertAlmostEqual(stderr[0], 0.5421047417431507, delta=self.DELTA) + self.assertAlmostEqual(stderr[1], 0.37179469135129783, delta=self.DELTA) + + def test_has_duplicate_min(self): + has_duplicate_min_result = has_duplicate_min( + Array(data=[[5, 4, 3, 0, 0, 1], [5, 4, 3, 0, 2, 1]])).to_numpy() + self.assertEqual(has_duplicate_min_result[0], True) + self.assertEqual(has_duplicate_min_result[1], False) + + def test_longest_strike_above_mean(self): + longest_strike_above_mean_result = longest_strike_above_mean( + Array(data=[[20, 20, 20, 1, 1, 1, 20, 20, 20, 20, 1, 1, 1, 1, + 1, 1, 1, 1, 20, 20], + [20, 20, 20, 1, 1, 1, 20, 20, + 20, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 20, 20]])).to_numpy() + self.assertEqual(longest_strike_above_mean_result[0], 4) + self.assertEqual(longest_strike_above_mean_result[1], 3) + + def test_longest_strike_below_mean(self): + longest_strike_below_mean_result = longest_strike_below_mean( + Array([[20, 20, 20, 1, 1, 1, 20, 20, 20, 20, 1, 1, 1, 1, + 1, 1, 1, 1, 20, 20], + [20, 20, 20, 1, 1, 1, 20, 20, + 20, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 20, 20]], dtype.f32)).to_numpy() + self.assertEqual(longest_strike_below_mean_result[0], 8) + self.assertEqual(longest_strike_below_mean_result[1], 9) + + def test_maximum(self): + maximum_result = maximum(Array([[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, + 20]])).to_numpy() + self.assertEqual(maximum_result[0], 50) + self.assertEqual(maximum_result[1], 30) + + def test_mean_absolute_change(self): + mean_absolute_change_result = mean_absolute_change( + Array([[0, 1, 2, 3, 4, 5], [8, 10, 12, 14, 16, 18]])).to_numpy() + r = 5 / 6 + self.assertAlmostEqual(mean_absolute_change_result[0], r, delta=self.DELTA) + self.assertAlmostEqual(mean_absolute_change_result[1], r * 2, delta=self.DELTA) + + def test_fft_coefficient(self): + fftCoefficient_result = fft_coefficient( + Array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), 0) + a = fftCoefficient_result[0].to_numpy() + self.assertAlmostEqual(a[0], 15, delta=self.DELTA) + self.assertAlmostEqual(a[1], 51, delta=self.DELTA) + b = fftCoefficient_result[1].to_numpy() + + self.assertAlmostEqual(b[0], 0, delta=self.DELTA) + self.assertAlmostEqual(b[1], 0, delta=self.DELTA) + c = fftCoefficient_result[2].to_numpy() + + self.assertAlmostEqual(c[0], 15, delta=self.DELTA) + self.assertAlmostEqual(c[1], 51, delta=self.DELTA) + d = fftCoefficient_result[3].to_numpy() + + self.assertAlmostEqual(d[0], 0, delta=self.DELTA) + self.assertAlmostEqual(d[1], 0, delta=self.DELTA) + + def test_aggregated_autocorrelation_mean(self): + aggregated_autocorrelation_result = aggregated_autocorrelation( + Array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]), 0).to_numpy() + self.assertAlmostEqual(aggregated_autocorrelation_result[0], -0.6571428571428571, delta=self.DELTA) + self.assertAlmostEqual(aggregated_autocorrelation_result[1], -0.6571428571428571, delta=self.DELTA) + + def test_aggregated_autocorrelation_median(self): + aggregated_autocorrelation_result = aggregated_autocorrelation( + Array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]), 1).to_numpy() + self.assertAlmostEqual(aggregated_autocorrelation_result[0], -0.54285717010498047, delta=self.DELTA) + self.assertAlmostEqual(aggregated_autocorrelation_result[1], -0.54285717010498047, delta=self.DELTA) + + def test_aggregated_autocorrelation_min(self): + aggregated_autocorrelation_result = aggregated_autocorrelation( + Array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]), 2).to_numpy() + self.assertAlmostEqual(aggregated_autocorrelation_result[0], -2.142857142857143, delta=1e-4) + self.assertAlmostEqual(aggregated_autocorrelation_result[1], -2.142857142857143, delta=1e-4) + + def test_aggregated_autocorrelation_max(self): + aggregated_autocorrelation_result = aggregated_autocorrelation( + Array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]), 3).to_numpy() + self.assertAlmostEqual(aggregated_autocorrelation_result[0], 0.6, delta=self.DELTA) + self.assertAlmostEqual(aggregated_autocorrelation_result[1], 0.6, delta=self.DELTA) + + def test_aggregated_autocorrelation_stdev(self): + aggregated_autocorrelation_result = aggregated_autocorrelation( + Array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]), 4).to_numpy() + self.assertAlmostEqual(aggregated_autocorrelation_result[0], 0.9744490855905009, delta=self.DELTA) + self.assertAlmostEqual(aggregated_autocorrelation_result[1], 0.9744490855905009, delta=self.DELTA) + + def test_aggregated_autocorrelation_var(self): + aggregated_autocorrelation_result = aggregated_autocorrelation( + Array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]), 5).to_numpy() + self.assertAlmostEqual(aggregated_autocorrelation_result[0], 0.9495510204081633, delta=self.DELTA) + self.assertAlmostEqual(aggregated_autocorrelation_result[1], 0.9495510204081633, delta=self.DELTA) + + def test_aggregated_linear_trend_mean(self): + aggregated_linear_trend_result = aggregated_linear_trend( + Array([[2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]]), 3, 0) + self.assertAlmostEqual(aggregated_linear_trend_result[0].to_numpy(), 1, delta=self.DELTA) + self.assertAlmostEqual(aggregated_linear_trend_result[1].to_numpy(), 2, delta=self.DELTA) + self.assertAlmostEqual(aggregated_linear_trend_result[2].to_numpy(), 1, delta=self.DELTA) + self.assertAlmostEqual(aggregated_linear_trend_result[3].to_numpy(), 0, delta=self.DELTA) + self.assertAlmostEqual(aggregated_linear_trend_result[4].to_numpy(), 0, delta=self.DELTA) + + def test_aggregated_linear_trend_min(self): + aggregated_linear_trend_result = aggregated_linear_trend( + Array([2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]), 3, 2) + self.assertAlmostEqual(aggregated_linear_trend_result[0].to_numpy(), 1, delta=1e-3) + self.assertAlmostEqual(aggregated_linear_trend_result[1].to_numpy(), 2, delta=1e-3) + self.assertAlmostEqual(aggregated_linear_trend_result[2].to_numpy(), 1, delta=1e-3) + self.assertAlmostEqual(aggregated_linear_trend_result[3].to_numpy(), 0, delta=1e-3) + self.assertAlmostEqual(aggregated_linear_trend_result[4].to_numpy(), 0, delta=1e-3) + + def test_cwt_coefficients(self): + cwt_coefficients_result = cwt_coefficients(Array([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]), + Array(data=[1, 2, 3], khiva_type=dtype.s32), 2, 2).to_numpy() + self.assertAlmostEqual(cwt_coefficients_result[0], 0.26517161726951599, delta=self.DELTA) + self.assertAlmostEqual(cwt_coefficients_result[1], 0.26517161726951599, delta=self.DELTA) + + def test_mean_second_derivative_central(self): + mean_second_derivative_central_result = mean_second_derivative_central( + Array([[1, 3, 7, 4, 8], [2, 5, 1, 7, 4]])).to_numpy() + self.assertAlmostEqual(mean_second_derivative_central_result[0], 1.0 / 5.0, delta=self.DELTA) + self.assertAlmostEqual(mean_second_derivative_central_result[1], -3.0 / 5.0, delta=self.DELTA) + + def test_minimum(self): + minimum_result = minimum( + Array([[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 13, 15, 5, 16, 20, 20], + [20, 20, 20, 2, 19, 4, 20, 20, 20, 4, 15, 6, 30, 7, 9, 18, 4, 10, 20, 20]])).to_numpy() + self.assertAlmostEqual(minimum_result[0], 1, delta=self.DELTA) + self.assertAlmostEqual(minimum_result[1], 2, delta=self.DELTA) + + def test_number_crossing_m(self): + number_crossing_m_result = number_crossing_m( + Array([[1, 2, 1, 1, -3, -4, 7, 8, 9, 10, -2, 1, -3, 5, 6, 7, -10], + [1, 2, 1, 1, -3, -4, 7, 8, 9, 10, -2, 1, -3, 5, 6, 7, -10]]), 0).to_numpy() + + self.assertAlmostEqual(number_crossing_m_result[0], 7, delta=self.DELTA) + self.assertAlmostEqual(number_crossing_m_result[1], 7, delta=self.DELTA) + + def test_mean(self): + mean_result = mean(Array([[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, 20]])).to_numpy() + self.assertAlmostEqual(mean_result[0], 18.55, delta=self.DELTA) + self.assertAlmostEqual(mean_result[1], 12.7, delta=self.DELTA) + + def test_median(self): + median_result = median(Array([[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, + 20]])).to_numpy() + self.assertAlmostEqual(median_result[0], 20, delta=self.DELTA) + self.assertAlmostEqual(median_result[1], 18.5, delta=self.DELTA) + + def test_mean_change(self): + mean_change_result = mean_change(Array([[0, 1, 2, 3, 4, 5], + [8, 10, 12, 14, 16, 18]])).to_numpy() + self.assertAlmostEqual(mean_change_result[0], 5 / 6, delta=self.DELTA) + self.assertAlmostEqual(mean_change_result[1], 10 / 6, delta=self.DELTA) + + def test_max_langevin_fixed_point(self): + max_langevin_fixed_point_result = max_langevin_fixed_point( + Array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]), 7, 2).to_numpy() + self.assertAlmostEqual(max_langevin_fixed_point_result[0], 4.562970585, delta=1e-4) + self.assertAlmostEqual(max_langevin_fixed_point_result[1], 4.562970585, delta=1e-4) + + def test_fft_aggregated(self): + fft_aggregated_result = fft_aggregated( + Array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], + dtype.f32)).to_numpy().flatten() + self.assertAlmostEqual(fft_aggregated_result[0], 1.135143, delta=1e-4) + self.assertAlmostEqual(fft_aggregated_result[1], 2.368324, delta=1e-4) + self.assertAlmostEqual(fft_aggregated_result[2], 1.248777, delta=1e-4) + self.assertAlmostEqual(fft_aggregated_result[3], 3.642666, delta=1e-4) + + self.assertAlmostEqual(fft_aggregated_result[4], 1.135143, delta=1e-4) + self.assertAlmostEqual(fft_aggregated_result[5], 2.368324, delta=1e-4) + self.assertAlmostEqual(fft_aggregated_result[6], 1.248777, delta=1e-4) + self.assertAlmostEqual(fft_aggregated_result[7], 3.642666, delta=1e-4) + + def test_number_cwt_peaks(self): + result = number_cwt_peaks(Array([[1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1]]), + 2).to_numpy() + self.assertEqual(result[0], 2) + self.assertEqual(result[1], 2) + + def test_number_peaks(self): + result = number_peaks(Array([[3, 0, 0, 4, 0, 0, 13], [3, 0, 0, 4, 0, 0, 13]]), 2).to_numpy() + self.assertAlmostEqual(result[0], 1, delta=1e-4) + self.assertAlmostEqual(result[1], 1, delta=1e-4) + + def test_partial_autocorrelation(self): + numel = 3000.0 + step = 1 / (numel - 1) + a = [step * i for i in range(int(numel))] + a = Array([a, a]) + lags = Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], khiva_type=dtype.s32) + result = partial_autocorrelation(a, lags).to_numpy() + expected = np.array([[1.0, 0.9993331432342529, -0.0006701064994559, -0.0006701068487018, -0.0008041285327636, + -0.0005360860959627, -0.0007371186511591, -0.0004690756904893, -0.0008041299879551, + -0.0007371196406893], + [1.0, 0.9993331432342529, -0.0006701064994559, -0.0006701068487018, -0.0008041285327636, + -0.0005360860959627, -0.0007371186511591, -0.0004690756904893, -0.0008041299879551, + -0.0007371196406893]]) + np.testing.assert_array_almost_equal(result, expected, decimal=3) + + def test_percentage_of_reocurring_datapoints_to_all_datapoints(self): + result = percentage_of_reoccurring_datapoints_to_all_datapoints( + Array([[3, 0, 0, 4, 0, 0, 13], [3, 0, 0, 4, 0, 0, 13]]), False).to_numpy() + self.assertAlmostEqual(result[0], 0.25, delta=1e-4) + self.assertAlmostEqual(result[1], 0.25, delta=1e-4) + + def test_percentage_of_reocurring_values_to_all_values(self): + result = percentage_of_reoccurring_values_to_all_values( + Array([[1, 1, 2, 3, 4, 4, 5, 6], [1, 2, 2, 3, 4, 5, 6, 7]]), False).to_numpy() + self.assertEqual(result[0], 4 / 8) + self.assertEqual(result[1], 2 / 8) + + def test_quantile(self): + result = quantile(Array([[0, 0, 0, 0, 3, 4, 13], [0, 0, 0, 0, 3, 4, 13]]), + Array([0.6], dtype.f32)).to_numpy() + self.assertAlmostEqual(result[0], 1.79999999, delta=1e-4) + self.assertAlmostEqual(result[1], 1.79999999, delta=1e-4) + + def test_range_count(self): + result = range_count(Array([[3, 0, 0, 4, 0, 0, 13], [3, 0, 5, 4, 0, 0, 13]]), 2, 12).to_numpy() + self.assertEqual(result[0], 2) + self.assertEqual(result[1], 3) + + def test_ratio_beyond_r_sigma(self): + result = ratio_beyond_r_sigma(Array([[3, 0, 0, 4, 0, 0, 13], [3, 0, 0, 4, 0, 0, 13]]), + 0.5).to_numpy() + self.assertAlmostEqual(result[0], 0.7142857142857143, delta=1e-4) + self.assertAlmostEqual(result[1], 0.7142857142857143, delta=1e-4) + + def test_ratio_value_number_to_time_series_length(self): + result = ratio_value_number_to_time_series_length( + Array([[3, 0, 0, 4, 0, 0, 13], [3, 5, 0, 4, 6, 0, 13]])).to_numpy() + self.assertAlmostEqual(result[0], 4 / 7, delta=1e-4) + self.assertAlmostEqual(result[1], 6 / 7, delta=1e-4) + + def test_sample_entropy(self): + result = sample_entropy(Array([[3, 0, 0, 4, 0, 0, 13], [3, 0, 0, 4, 0, 0, 13]])).to_numpy() + self.assertAlmostEqual(result[0], 1.2527629, delta=1e-4) + self.assertAlmostEqual(result[1], 1.2527629, delta=1e-4) + + def test_skewness(self): + result = skewness(Array([[3, 0, 0, 4, 0, 0, 13], [3, 0, 0, 4, 0, 0, 13]])).to_numpy() + self.assertAlmostEqual(result[0], 2.038404735373753, delta=1e-4) + self.assertAlmostEqual(result[1], 2.038404735373753, delta=1e-4) + + def test_spkt_welch_density(self): + result = spkt_welch_density(Array([[0, 1, 1, 3, 4, 5, 6, 7, 8, 9], [0, 1, 1, 3, 4, 5, 6, 7, 8, 9]]), + 0).to_numpy() + self.assertAlmostEqual(result[0], 1.6666667, delta=1e-5) + self.assertAlmostEqual(result[1], 1.6666667, delta=1e-5) + + def test_standard_deviation(self): + result = standard_deviation( + Array([[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, 20]])).to_numpy() + self.assertAlmostEqual(result[0], 12.363150892875165, delta=1e-4) + self.assertAlmostEqual(result[1], 9.51367436903324, delta=1e-4) + + def test_sum_of_reoccurring_datapoints(self): + result = sum_of_reoccurring_datapoints(Array([[3, 3, 0, 4, 0, 13, 13], [3, 3, 0, 4, 0, 13, 13]])).to_numpy() + self.assertEqual(result[0], 32) + self.assertEqual(result[1], 32) + + def test_sum_of_reoccurring_values(self): + result = sum_of_reoccurring_values(Array([[4, 4, 6, 6, 7], [4, 7, 7, 8, 8]])).to_numpy() + self.assertEqual(result[0], 10) + self.assertEqual(result[1], 15) + + def test_sum_values(self): + result = sum_values(Array([[1, 2, 3, 4.1], [-1.2, -2, -3, -4]])).to_numpy() + self.assertAlmostEqual(result[0], 10.1, delta=self.DELTA) + self.assertAlmostEqual(result[1], -10.2, delta=self.DELTA) + + def test_symmetry_looking(self): + result = symmetry_looking(Array([[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, 20]]), + 0.1).to_numpy() + self.assertEqual(result[0], bool(1)) + self.assertEqual(result[1], bool(0)) + + def test_time_reversal_asymmetry_statistic(self): + result = time_reversal_asymmetry_statistic(Array( + [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, 20]]), 2).to_numpy() + self.assertEqual(result[0], 1052) + self.assertEqual(result[1], -150.625) + + def test_value_count(self): + result = value_count(Array(data=[[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, 20]]), + 20).to_numpy() + self.assertAlmostEqual(result[0], 9, delta=1e-4) + self.assertAlmostEqual(result[1], 8, delta=1e-4) + + def test_variance(self): + result = variance(Array(data=[[1, 1, -1, -1], [1, 2, -2, -1]])).to_numpy() + self.assertEqual(result[0], 1) + self.assertEqual(result[1], 2.5) + + def test_variance_larger_than_standard_deviation(self): + result = variance_larger_than_standard_deviation( + Array(data=[[20, 20, 20, 18, 25, 19, 20, 20, 20, 20, 40, 30, 1, 50, 1, 1, 5, 1, 20, 20], + [20, 20, 20, 2, 19, 1, 20, 20, 20, 1, 15, 1, 30, 1, 1, 18, 4, 1, 20, 20]])).to_numpy() + self.assertEqual(result[0], True) + self.assertEqual(result[1], True) + + def test_concatenated(self): + try: + import arrayfire as af + a = Array([[1, 2], [1, 3], [1, 4], [1, 5], [1, 6], [1, 7], [1, 8], [1, 9], [1, 10], [1, 11]]) + b = absolute_sum_of_changes(a).to_arrayfire() + c = af.transpose(b) + d = Array(array_reference=c.arr) + e = abs_energy(d).to_numpy() + self.assertAlmostEqual(e, 385, delta=self.DELTA) + except ModuleNotFoundError: + logging.error("This test needs the Arrayfire package in order to be executed.") + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/library_unit_tests.py b/tests/unit_tests/library_unit_tests.py new file mode 100644 index 0000000..ee6311c --- /dev/null +++ b/tests/unit_tests/library_unit_tests.py @@ -0,0 +1,66 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.library import * + + +######################################################################################################################## + +class LibraryTest(unittest.TestCase): + + def setUp(self): + pass + + def test_set_backend(self): + backends = get_backends() + cuda = backends & KHIVABackend.KHIVA_BACKEND_CUDA.value + opencl = backends & KHIVABackend.KHIVA_BACKEND_OPENCL.value + cpu = backends & KHIVABackend.KHIVA_BACKEND_CPU.value + if cuda: + set_backend(KHIVABackend.KHIVA_BACKEND_CUDA) + self.assertEqual(get_backend(), KHIVABackend.KHIVA_BACKEND_CUDA) + if opencl: + set_backend(KHIVABackend.KHIVA_BACKEND_OPENCL) + self.assertEqual(get_backend(), KHIVABackend.KHIVA_BACKEND_OPENCL) + if cpu: + set_backend(KHIVABackend.KHIVA_BACKEND_CPU) + self.assertEqual(get_backend(), KHIVABackend.KHIVA_BACKEND_CPU) + + def test_get_device(self): + backends = get_backends() + cuda = backends & KHIVABackend.KHIVA_BACKEND_CUDA.value + opencl = backends & KHIVABackend.KHIVA_BACKEND_OPENCL.value + cpu = backends & KHIVABackend.KHIVA_BACKEND_CPU.value + + if cuda: + set_backend(KHIVABackend.KHIVA_BACKEND_CUDA) + for i in range(get_device_count()): + set_device(i) + self.assertEqual(get_device_id(), i) + if opencl: + set_backend(KHIVABackend.KHIVA_BACKEND_OPENCL) + for i in range(get_device_count()): + set_device(i) + self.assertEqual(get_device_id(), i) + + if cpu: + set_backend(KHIVABackend.KHIVA_BACKEND_CPU) + for i in range(get_device_count()): + set_device(i) + self.assertEqual(get_device_id(), i) + + def test_version(self): + v = version() + self.assertEqual(v, '0.0.1') + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/linalg_unit_tests.py b/tests/unit_tests/linalg_unit_tests.py new file mode 100644 index 0000000..7de7532 --- /dev/null +++ b/tests/unit_tests/linalg_unit_tests.py @@ -0,0 +1,33 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.linalg import * +import numpy as np +from khiva.array import * + + +######################################################################################################################## + +class LinalgTest(unittest.TestCase): + DELTA = 1e-6 + + def setUp(self): + pass + + def test_lls(self): + lls_result = lls(Array(np.array([[4, 3], [-1, -2]])), Array([3, 1], dtype.f32)) + a = lls_result.to_numpy() + self.assertAlmostEqual(a[0], 1, delta=self.DELTA) + self.assertAlmostEqual(a[1], 1, delta=self.DELTA) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/matrix_unit_tests.py b/tests/unit_tests/matrix_unit_tests.py new file mode 100644 index 0000000..678e83f --- /dev/null +++ b/tests/unit_tests/matrix_unit_tests.py @@ -0,0 +1,72 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.matrix import * +from khiva.array import * + + +######################################################################################################################## + +class MatrixTest(unittest.TestCase): + DELTA = 1e-6 + + def setUp(self): + pass + + def test_stomp_self_join(self): + stomp_self_join_result = stomp_self_join( + Array(data=[[10, 10, 11, 11, 10, 11, 10, 10, 11, 11, 10, 11, 10, 10], [ + 11, 10, 10, 11, 10, 11, 11, 10, 11, 11, 10, 10, 11, 10]]), 3) + expected_index = [6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 9, 10, 11, 6, 7, 8, 3, 4, 5, 0, 1, 2] + + for i in range(6): + self.assertAlmostEqual(stomp_self_join_result[0].to_numpy()[0][i], 0.0, delta=1e-2) + self.assertEqual(stomp_self_join_result[1].to_numpy()[0][i], expected_index[i]) + + def test_stomp(self): + stomp_result = stomp(Array([[10, 11, 10, 11], [10, 11, 10, 11]]), + Array([[10, 11, 10, 11, 10, 11, 10, 11], [10, 11, 10, 11, 10, 11, 10, 11]]), 3) + expected_index = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + a = stomp_result[0].to_numpy().flatten() + b = stomp_result[1].to_numpy().flatten() + + for i in range(24): + self.assertAlmostEqual(a[i], 0, delta=1e-2) + self.assertAlmostEqual(b[i], expected_index[i]) + + def test_find_best_n_motifs(self): + + stomp_result = stomp(Array([10, 10, 10, 10, 10, 10, 9, 10, 10, 10, 10, 10, 11, 10, 9], dtype.f32), + Array([10, 11, 10, 9], dtype.f32), + 3) + + find_best_n_motifs_result = find_best_n_motifs(stomp_result[0], stomp_result[1], 2) + a = find_best_n_motifs_result[1].to_numpy() + b = find_best_n_motifs_result[2].to_numpy() + self.assertAlmostEqual(a[0], 12, delta=self.DELTA) + self.assertAlmostEqual(a[1], 11, delta=self.DELTA) + self.assertAlmostEqual(b[0], 1, delta=self.DELTA) + self.assertAlmostEqual(b[1], 0, delta=self.DELTA) + + def test_find_best_n_discords(self): + stomp_result = stomp(Array(np.array([11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11])), + Array(np.array([9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9])), + 3) + + find_best_n_discords_result = find_best_n_discords(stomp_result[0], + stomp_result[1], 2) + a = find_best_n_discords_result[2].to_numpy() + self.assertEqual(a[0], 0) + self.assertEqual(a[1], 9) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/normalization_unit_tests.py b/tests/unit_tests/normalization_unit_tests.py new file mode 100644 index 0000000..691fccd --- /dev/null +++ b/tests/unit_tests/normalization_unit_tests.py @@ -0,0 +1,84 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +import numpy as np +from khiva.normalization import * +from khiva.array import Array, dtype + + +######################################################################################################################## + +class NormalizationTest(unittest.TestCase): + DELTA = 1e-6 + DECIMAL = 6 + + def setUp(self): + pass + + def test_znorm(self): + znorm_result = znorm(Array([[0, 1, 2, 3], [4, 5, 6, 7]]), 0.00000001).to_numpy().flatten() + expected = [-1.341640786499870, -0.447213595499958, 0.447213595499958, 1.341640786499870] + for i in range(len(expected)): + self.assertAlmostEqual(znorm_result[i], expected[i], delta=self.DELTA) + self.assertAlmostEqual(znorm_result[i + 4], expected[i], delta=self.DELTA) + + def test_znorm_in_place(self): + tss = Array(data=[[0, 1, 2, 3], [4, 5, 6, 7]]) + znorm_in_place(tss) + tss = tss.to_numpy() + self.assertAlmostEqual(tss[0][0], -1.341640786499870, delta=self.DELTA) + self.assertAlmostEqual(tss[0][1], -0.447213595499958, delta=self.DELTA) + self.assertAlmostEqual(tss[0][2], 0.447213595499958, delta=self.DELTA) + self.assertAlmostEqual(tss[0][3], 1.341640786499870, delta=self.DELTA) + + self.assertAlmostEqual(tss[1][0], -1.341640786499870, delta=self.DELTA) + self.assertAlmostEqual(tss[1][1], -0.447213595499958, delta=self.DELTA) + self.assertAlmostEqual(tss[1][2], 0.447213595499958, delta=self.DELTA) + self.assertAlmostEqual(tss[1][3], 1.341640786499870, delta=self.DELTA) + + def test_max_min_norm(self): + max_min_norm_result = max_min_norm(Array([[0, 1, 2, 3], [4, 5, 6, 7]]), 2.0, 1.0).to_numpy() + expected = np.array([[1.0, 1.3333333333333, 1.66666667, 2.0], [1.0, 1.3333333333333, 1.66666667, 2.0]]) + np.testing.assert_array_almost_equal(max_min_norm_result, expected, decimal=self.DECIMAL) + + def test_max_min_norm_in_place(self): + tss = Array([[0, 1, 2, 3], [4, 5, 6, 7]]) + max_min_norm_in_place(tss, 2.0, 1.0) + tss = tss.to_numpy() + expected = np.array([[1.0, 1.3333333333333, 1.66666667, 2.0], [1.0, 1.3333333333333, 1.66666667, 2.0]]) + np.testing.assert_array_almost_equal(tss, expected, decimal=self.DECIMAL) + + def test_decimal_scaling_norm(self): + decimal_scaling_norm_result = decimal_scaling_norm(Array([[0, 1, -2, 3], [40, 50, 60, -70]])).to_numpy() + expected = np.array([[0.0, 0.1, -0.2, 0.3], [0.4, 0.5, 0.6, -0.7]]) + np.testing.assert_array_almost_equal(decimal_scaling_norm_result, expected, decimal=self.DECIMAL) + + def test_decimal_scaling_norm_in_place(self): + tss = Array([[0, 1, -2, 3], [40, 50, 60, -70]]) + decimal_scaling_norm_in_place(tss) + tss = tss.to_numpy() + expected = np.array([[0.0, 0.1, -0.2, 0.3], [0.4, 0.5, 0.6, -0.7]]) + np.testing.assert_array_almost_equal(tss, expected, decimal=self.DECIMAL) + + def test_mean_norm(self): + result = mean_norm(Array([[0, 1, 2, 3], [4, 5, 6, 7]])).to_numpy() + expected = np.array([[-0.5, -0.166666667, 0.166666667, 0.5], [-0.5, -0.166666667, 0.166666667, 0.5]]) + np.testing.assert_array_almost_equal(result, expected, decimal=self.DECIMAL) + + def test_mean_norm_in_place(self): + a = Array([[0, 1, 2, 3], [4, 5, 6, 7]]) + mean_norm_in_place(a) + expected = np.array([[-0.5, -0.166666667, 0.166666667, 0.5], [-0.5, -0.166666667, 0.166666667, 0.5]]) + np.testing.assert_array_almost_equal(a.to_numpy(), expected, decimal=self.DECIMAL) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/polynomial_unit_tests.py b/tests/unit_tests/polynomial_unit_tests.py new file mode 100644 index 0000000..f06fa5b --- /dev/null +++ b/tests/unit_tests/polynomial_unit_tests.py @@ -0,0 +1,44 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +import numpy as np +from khiva.polynomial import * +from khiva.array import Array + + +######################################################################################################################## + +class PolynomialTest(unittest.TestCase): + DELTA = 1e-6 + DECIMAL = 6 + + def setUp(self): + pass + + def test_polyfit1(self): + polyfit_result = polyfit(Array([0, 1, 2, 3, 4, 5]), Array([0, 1, 2, 3, 4, 5]), 1).to_numpy() + expected = np.array([1.0, 0.0]) + np.testing.assert_array_almost_equal(polyfit_result, expected, decimal=self.DECIMAL) + + def test_polyfit3(self): + polyfit_result = polyfit(Array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]), Array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0]), + 3).to_numpy() + expected = np.array([0.08703704, -0.81349206, 1.69312169, -0.03968254]) + np.testing.assert_array_almost_equal(polyfit_result, expected, decimal=self.DECIMAL - 1) + + def test_roots(self): + roots_result = roots(Array([5, -20, 5, 50, -20, -40])).to_numpy() + expected = np.array([2 + 0j, 2 + 0j, 2 + 0j, -1 + 0j, -1 + 0j]) + np.testing.assert_array_almost_equal(roots_result, expected, decimal=2) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/regression_unit_tests.py b/tests/unit_tests/regression_unit_tests.py new file mode 100644 index 0000000..2dbc9dd --- /dev/null +++ b/tests/unit_tests/regression_unit_tests.py @@ -0,0 +1,66 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.regression import * +from khiva.array import Array +import numpy as np + + +######################################################################################################################## + + +class RegressionTest(unittest.TestCase): + DELTA = 1e-6 + + def setUp(self): + pass + + def test_linear(self): + euclidean_result = linear(Array([0.24580423, 0.59642861, 0.35879163, 0.37891011, 0.02445137, + 0.23830957, 0.38793433, 0.68054104, 0.83934083, 0.76073689]), + Array([0.2217416, 0.06344161, 0.77944375, 0.72174137, 0.19413884, + 0.51146167, 0.06880307, 0.39414268, 0.98172767, 0.30490851])) + self.assertAlmostEqual(euclidean_result[0].to_numpy(), 0.344864266, delta=self.DELTA) + self.assertAlmostEqual(euclidean_result[1].to_numpy(), 0.268578232, delta=self.DELTA) + self.assertAlmostEqual(euclidean_result[2].to_numpy(), 0.283552942, delta=self.DELTA) + self.assertAlmostEqual(euclidean_result[3].to_numpy(), 0.427239418, delta=self.DELTA) + self.assertAlmostEqual(euclidean_result[4].to_numpy(), 0.412351891, delta=self.DELTA) + + def test_linear_multiple_time_series(self): + euclidean_result = linear(Array([[0.24580423, 0.59642861, 0.35879163, 0.37891011, 0.02445137, + 0.23830957, 0.38793433, 0.68054104, 0.83934083, 0.76073689], + [0.24580423, 0.59642861, 0.35879163, 0.37891011, 0.02445137, + 0.23830957, 0.38793433, 0.68054104, 0.83934083, 0.76073689]]), + Array([[0.2217416, 0.06344161, 0.77944375, 0.72174137, 0.19413884, + 0.51146167, 0.06880307, 0.39414268, 0.98172767, 0.30490851], + [0.2217416, 0.06344161, 0.77944375, 0.72174137, 0.19413884, + 0.51146167, 0.06880307, 0.39414268, 0.98172767, 0.30490851]])) + slope = euclidean_result[0].to_numpy() + intercept = euclidean_result[1].to_numpy() + rvalue = euclidean_result[2].to_numpy() + pvalue = euclidean_result[3].to_numpy() + stderrest = euclidean_result[4].to_numpy() + + self.assertAlmostEqual(slope[0], 0.344864266, delta=self.DELTA) + self.assertAlmostEqual(intercept[0], 0.268578232, delta=self.DELTA) + self.assertAlmostEqual(rvalue[0], 0.283552942, delta=self.DELTA) + self.assertAlmostEqual(pvalue[0], 0.427239418, delta=self.DELTA) + self.assertAlmostEqual(stderrest[0], 0.412351891, delta=self.DELTA) + + self.assertAlmostEqual(slope[1], 0.344864266, delta=self.DELTA) + self.assertAlmostEqual(intercept[1], 0.268578232, delta=self.DELTA) + self.assertAlmostEqual(rvalue[1], 0.283552942, delta=self.DELTA) + self.assertAlmostEqual(pvalue[1], 0.427239418, delta=self.DELTA) + self.assertAlmostEqual(stderrest[1], 0.412351891, delta=self.DELTA) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/regularization_unit_tests.py b/tests/unit_tests/regularization_unit_tests.py new file mode 100644 index 0000000..c6edc6a --- /dev/null +++ b/tests/unit_tests/regularization_unit_tests.py @@ -0,0 +1,51 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +import numpy as np +from khiva.regularization import * +from khiva.array import Array + + +######################################################################################################################## + +class RegularizationTest(unittest.TestCase): + DELTA = 1e-6 + DECIMAL = 6 + + def setUp(self): + pass + + def test_group_by_single_column(self): + group_by_result = group_by(Array([[0, 1, 1, 2, 2, 3], [0, 3, 3, 1, 1, 2]]), 0).to_numpy() + expected = [0, 3, 1, 2] + np.testing.assert_array_almost_equal(group_by_result, expected, decimal=self.DECIMAL) + + def test_group_by_double_key_column(self): + group_by_result = group_by(Array([[0, 1, 1, 2, 2, 3], [1, 2, 2, 3, 3, 4], [0, 3, 3, 1, 1, 2]]), + 0, 2, 1).to_numpy() + expected = [0, 3, 1, 2] + np.testing.assert_array_almost_equal(group_by_result, expected, decimal=self.DECIMAL) + + def test_group_by_double_key_column_2(self): + group_by_result = group_by(Array([[0, 0, 1, 1, 1], [0, 1, 0, 0, 1], [1, 2, 3, 4, 5]]), + 0, 2, 1).to_numpy() + expected = [1, 2, 3.5, 5] + np.testing.assert_array_almost_equal(group_by_result, expected, decimal=self.DECIMAL) + + def test_group_by_double_key_double_value_column(self): + group_by_result = group_by(Array([[0, 0, 0, 2, 2], [2, 2, 2, 4, 4], [0, 1, 2, 3, 4], [1, 1, 1, 1, 1]]), + 0, 2, 2).to_numpy() + expected = [[1, 3.5], [1, 1]] + np.testing.assert_array_almost_equal(group_by_result, expected, decimal=self.DECIMAL) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/unit_tests/statistics_unit_tests.py b/tests/unit_tests/statistics_unit_tests.py new file mode 100644 index 0000000..ff8646a --- /dev/null +++ b/tests/unit_tests/statistics_unit_tests.py @@ -0,0 +1,102 @@ +# Copyright (c) 2018 Shapelets.io +# +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +######################################################################################################################## +# IMPORT +######################################################################################################################## +import unittest +from khiva.statistics import * +from khiva.array import Array +import numpy as np +import arrayfire as af + + +######################################################################################################################## + + +class StatisticsTest(unittest.TestCase): + + def setUp(self): + pass + + def test_covariance_unbiased(self): + result = covariance(Array(data=[[-2.1, -1, 4.3], [3, 1.1, 0.12], [3, 1.1, 0.12]]), True).to_numpy().flatten() + expected = np.array([11.70999999, -4.286, -4.286, -4.286, 2.14413333, + 2.14413333, -4.286, 2.14413333, 2.14413333]) + + np.testing.assert_array_almost_equal(result, expected, decimal=6) + + def test_covariance_biased(self): + result = covariance(Array(data=[[-2.1, -1, 4.3], [3, 1.1, 0.12], [3, 1.1, 0.12]]), False).to_numpy().flatten() + expected = np.array([7.80666667, -2.85733333, -2.85733333, -2.85733333, 1.42942222, + 1.42942222, -2.85733333, 1.42942222, 1.42942222]) + np.testing.assert_array_almost_equal(result, expected, decimal=6) + + def test_kurtosis(self): + result = kurtosis(Array(data=[[0, 1, 2, 3, 4, 5], [2, 2, 2, 20, 30, 25]])).to_numpy().flatten() + expected = np.array([-1.2, -2.66226722]) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + def test_ljung_box(self): + result = ljung_box(Array(data =[[0, 1, 2, 3], [4, 5, 6, 7]]), 3).to_numpy().flatten() + expected = np.array([6.4400, 6.4400]) + np.testing.assert_array_almost_equal(result, expected, decimal=6) + + + def test_moment(self): + result = moment(Array(data=[[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]), 2).to_numpy().flatten() + expected = np.array([9.166666666, 9.166666666]) + np.testing.assert_array_almost_equal(result, expected, decimal=6) + result = moment(Array(data=[[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]), 4).to_numpy().flatten() + expected = np.array([163.1666666666, 163.1666666666]) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + def test_quantile(self): + result = quantile(Array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), Array([0.1, 0.2])).to_numpy().flatten() + expected = np.array([0.5, 1.0, 6.5, 7.0]) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + def test_quantile_cut_2(self): + result = quantiles_cut(Array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), 2) + a = af.transpose(result.to_arrayfire()) + b = Array(array_reference=a.arr) + expected = np.array([-0.00000001, 2.5, -0.00000001, 2.5, -0.00000001, 2.5, 2.5, 5.0, 2.5, 5.0, 2.5, 5.0, + 6.0, 8.5, 6.0, 8.5, 6.0, 8.5, 8.5, 11.0, 8.5, 11.0, 8.5, 11.0]) + np.testing.assert_array_almost_equal(b.to_numpy().flatten(), expected, decimal=6) + + def test_quantile_cut_3(self): + result = quantiles_cut(Array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), 3) + a = af.transpose(result.to_arrayfire()) + b = Array(array_reference=a.arr) + expected = np.array( + [-0.00000001, 1.66666667, -0.00000001, 1.6666667, 1.6666667, 3.3333333, 1.6666667, 3.3333333, + 3.3333333, 5.0, 3.3333333, 5.0, 5.9999999, 7.66666667, 5.9999999, 7.6666667, + 7.6666667, 9.3333333, 7.6666667, 9.3333333, 9.3333333, 11.0, 9.3333333, 11.0]) + np.testing.assert_array_almost_equal(b.to_numpy().flatten(), expected, decimal=6) + + def test_quantile_cut_7(self): + result = quantiles_cut(Array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]), 7) + a = af.transpose(result.to_arrayfire()) + b = Array(array_reference=a.arr) + expected = np.array([0, 0.7142857, 0.7142857, 1.4285715, 1.4285715, 2.1428573, 2.8571429, 3.5714288, + 3.5714288, 4.2857146, 4.2857146, 5, 5.9999999, 6.7142857, 6.7142857, 7.4285715, + 7.4285715, 8.1428573, 8.8571429, 9.5714288, 9.5714288, 10.2857146, 10.2857146, 11]) + np.testing.assert_array_almost_equal(b.to_numpy().flatten(), expected, decimal=6) + + def test_sample_stdev(self): + result = sample_stdev(Array(data=[[0, 1, 2, 3, 4, 5], [2, 2, 2, 20, 30, 25]])).to_numpy().flatten() + expected = np.array([1.870828693, 12.988456413]) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + def test_skewness(self): + result = skewness(Array(data=[[0, 1, 2, 3, 4, 5], [2, 2, 2, 20, 30, 25]])).to_numpy().flatten() + expected = np.array([0.0, 0.236177069879499]) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + +if __name__ == '__main__': + unittest.main() diff --git a/tsa/__init__.py b/tsa/__init__.py deleted file mode 100644 index 481e89f..0000000 --- a/tsa/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" \ No newline at end of file diff --git a/tsa/analyser.py b/tsa/analyser.py deleted file mode 100644 index 1985183..0000000 --- a/tsa/analyser.py +++ /dev/null @@ -1,104 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## -import ctypes -import os -import tsa.tsa_libraries -from tsa.tsa_algorithms.stomp import _stomp -from tsa.tsa_algorithms.stomp_self_join import _stomp_self_join -from tsa.tsa_algorithms.binding_test import binding_test -from tsa.tsa_algorithms.find_best_n_motifs import _find_best_n_motifs -from tsa.tsa_algorithms.find_best_n_discords import _find_best_n_discords -from tsa.tsa_features.absolute_sum_of_changes import _absolute_sum_of_changes -from tsa.tsa_features.abs_energy import _abs_energy - -######################################################################################################################## - -class analiser: - - def __init__(self): - self._c_tsa_library = ctypes.CDLL(os.path.join(tsa.tsa_libraries.__path__[0], 'libTSALIB.dylib')) - - def stomp(self, first_time_series_list, second_time_series_list, subsequence_length): - """ - - Stomp algorithm to calculate the matrix profile between two time series using a subsequence_length. - - :param first_time_series_list: list of doubles representing the first time series. - :param second_time_series_list: list of doubles representing the second time series. - :param subsequence_length: Subsequence length. - :return: Dict with the Matrix Profile. - """ - return _stomp(first_time_series_list, second_time_series_list, subsequence_length, self._c_tsa_library) - - def stomp_self_join(self, first_time_series_list, subsequence_length): - """ - - Stomp algorithm to calculate the matrix profile between two time series using a subsequence_length. - - - :param first_time_series_list: list of doubles representing the time series. - :param subsequence_length: Subsequence length. - :return: Dict with the Matrix Profile. - """ - return _stomp_self_join(first_time_series_list, subsequence_length, self._c_tsa_library) - - def find_best_n_motifs(self, profile_list, index_list, n): - """ - - This function extracts the best N motifs from a previously calculated matrix profile - - :param profile_list: The matrix profile containing the minimum distance of each subsequence - :param index_list: The matrix profile index containing where each minimum occurs - :param n: Number of motifs to extract - :return: Dict with the motif distances, motif indices and indices in the other sequence. - """ - return _find_best_n_motifs(profile_list, index_list, n, self._c_tsa_library) - - def find_best_n_discords(self, profile_list, index_list, n): - """ - - This function extracts the best N discords from a previously calculated matrix profile - - :param profile_list: The matrix profile containing the minimum distance of each subsequence - :param index_list: The matrix profile index containing where each minimum occurs - :param n: Number of discords to extract - :return: Dict with the discord distances, discord indices and indices in the other sequence. - """ - return _find_best_n_discords(profile_list, index_list, n, self._c_tsa_library) - - def absolute_sum_of_change(self,list_of_time_series): - """ - This function extracts the absolute sum of change of a list of time series. - - :param list_of_time_series: - :return: Absolute sum of change. - """ - return _absolute_sum_of_changes(list_of_time_series,self._c_tsa_library) - - def abs_energy(self,list_of_time_series): - """ - - This function extracts the Absolute Energy of a list of time series. - - :param list_of_time_series: list of lists of doubles containing the time series. - :return: Abs Energy. - """ - return _abs_energy(list_of_time_series,self._c_tsa_library) - - def binding_test(self, first_time_series_list): - """ - - This is a function just to check the bindings performance. - - :param first_time_series_list: - :return: Dict with the distance, indices and indices in the other sequence. - """ - return binding_test(first_time_series_list, self._c_tsa_library) diff --git a/tsa/tsa_algorithms/__init__.py b/tsa/tsa_algorithms/__init__.py deleted file mode 100644 index 481e89f..0000000 --- a/tsa/tsa_algorithms/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" \ No newline at end of file diff --git a/tsa/tsa_algorithms/binding_test.py b/tsa/tsa_algorithms/binding_test.py deleted file mode 100644 index 8ea3e16..0000000 --- a/tsa/tsa_algorithms/binding_test.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -import ctypes -import numpy as np - - -######################################################################################################################## -def binding_test(time_series_list, c_tsa_library): - - first_time_series_double_array = (ctypes.c_double * len(time_series_list))(*time_series_list) - - initialized_mp_numpy_array = np.zeros(len(time_series_list)).astype(np.double) - - initialized_c_mp_array = (ctypes.c_double * (len(time_series_list)))\ - (*initialized_mp_numpy_array) - - c_tsa_library.binding_test(ctypes.pointer(first_time_series_double_array), - ctypes.pointer(ctypes.c_int(len(time_series_list))), - ctypes.pointer(initialized_c_mp_array)) - - np_array_mp = np.array(initialized_c_mp_array) - - return np_array_mp \ No newline at end of file diff --git a/tsa/tsa_algorithms/find_best_n_discords.py b/tsa/tsa_algorithms/find_best_n_discords.py deleted file mode 100644 index a49a7bd..0000000 --- a/tsa/tsa_algorithms/find_best_n_discords.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" - -######################################################################################################################## -# IMPORT -######################################################################################################################## -import ctypes -import numpy as np -######################################################################################################################## - -def _find_best_n_discords(profile_list, index_list, n, c_tsa_library): - """ - Primitive of the findBestNDiscords function - - :param profile_list: The matrix profile containing the minimum distance of each subsequence - :param index_list: The matrix profile index containing where each minimum occurs - :param n: Number of discords to extract - :param c_tsa_library: tsa Library - :return: Dictionary with the discord distances, the discord indices and the subsequence indices - """ - profile_c_double_array = (ctypes.c_double * len(profile_list))(*profile_list) - index_c_double_array = (ctypes.c_uint32 * len(index_list))(*index_list) - - initialized_discord_distance_array = np.zeros(n).astype(np.double) - initialized_discord_index_array = np.zeros(n).astype(np.int) - initialized_subsequence_index_array = np.zeros(n).astype(np.int) - - c_discord_distance = (ctypes.c_double * n)(*initialized_discord_distance_array) - c_discord_index = (ctypes.c_int * n)(*initialized_discord_index_array) - c_subsequence_index = (ctypes.c_int * n)(*initialized_subsequence_index_array) - - c_tsa_library.find_best_n_discords(ctypes.pointer(profile_c_double_array), - ctypes.pointer(index_c_double_array), - ctypes.pointer(ctypes.c_long(len(profile_list))), - ctypes.pointer(ctypes.c_long(n)), - ctypes.pointer(c_discord_distance), - ctypes.pointer(c_discord_index), - ctypes.pointer(c_subsequence_index)) - - np_discord_distance = np.array(c_discord_distance) - np_discord_index = np.array(c_discord_index).astype(int) - np_subsequence_index = np.array(c_subsequence_index).astype(int) - - return {'discord_distance': np_discord_distance, 'discord_index': np_discord_index, - 'subsequence_index': np_subsequence_index} diff --git a/tsa/tsa_algorithms/find_best_n_motifs.py b/tsa/tsa_algorithms/find_best_n_motifs.py deleted file mode 100644 index 29db9ff..0000000 --- a/tsa/tsa_algorithms/find_best_n_motifs.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" - -######################################################################################################################## -# IMPORT -######################################################################################################################## -import ctypes -import numpy as np -######################################################################################################################## - -def _find_best_n_motifs(profile_list, index_list, n, c_tsa_library): - """ - Primitive of the findBestNMotifs function - - :param profile_list: The matrix profile containing the minimum distance of each subsequence - :param index_list: The matrix profile index containing where each minimum occurs - :param n: Number of motifs to extract - :param c_tsa_library: tsa Library - :return: Dictionary with the motif distances, the motif indices and the subsequence indices. - """ - profile_c_double_array = (ctypes.c_double * len(profile_list))(*profile_list) - index_c_int_array = (ctypes.c_uint32 * len(index_list))(*index_list) - - initialized_motif_distance_array = np.zeros(n).astype(np.double) - initialized_motif_index_array = np.zeros(n).astype(np.int) - initialized_subsequence_index_array = np.zeros(n).astype(np.int) - - c_motif_distance = (ctypes.c_double * n)(*initialized_motif_distance_array) - c_motif_index = (ctypes.c_int * n)(*initialized_motif_index_array) - c_subsequence_index = (ctypes.c_int * n)(*initialized_subsequence_index_array) - - c_tsa_library.find_best_n_motifs(ctypes.pointer(profile_c_double_array), - ctypes.pointer(index_c_int_array), - ctypes.pointer(ctypes.c_long(len(profile_list))), - ctypes.pointer(ctypes.c_long(n)), - ctypes.pointer(c_motif_distance), - ctypes.pointer(c_motif_index), - ctypes.pointer(c_subsequence_index)) - - np_motif_distance = np.array(c_motif_distance) - np_motif_index = np.array(c_motif_index).astype(int) - np_subsequence_index = np.array(c_subsequence_index).astype(int) - - return {'motif_distance': np_motif_distance, 'motif_index': np_motif_index, - 'subsequence_index': np_subsequence_index} diff --git a/tsa/tsa_algorithms/stomp.py b/tsa/tsa_algorithms/stomp.py deleted file mode 100644 index 9da78ab..0000000 --- a/tsa/tsa_algorithms/stomp.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## -import ctypes -import numpy as np -######################################################################################################################## - -def _stomp(first_time_series_list, second_time_series_list, subsequence_length, c_tsa_library): - """ - STOMP algorithm to calculate the matrix profile between 'ta' and 'tb' using a subsequence length - of 'm'. - :param first_time_series_list: List with the first time series. - :param second_time_series_list: List with the second time series. - :param subsequence_length: Length of the subsequence. - :param c_tsa_library: Dynamic library of TSA. - :return: Matrix profile in dictionary format. - """ - first_time_series_double_array = (ctypes.c_double * len(first_time_series_list))(*first_time_series_list) - - second_time_series_double_array = (ctypes.c_double * len(second_time_series_list))(*second_time_series_list) - - initialized_mp_numpy_array = np.zeros(len(second_time_series_list) - subsequence_length + 1).astype(np.double) - initialized_ip_numpy_array = np.zeros(len(second_time_series_list) - subsequence_length + 1).astype(np.uint32) - - initialized_c_mp_array = (ctypes.c_double * (len(second_time_series_list) - subsequence_length + 1)) \ - (*initialized_mp_numpy_array) - - initialized_c_ip_array = (ctypes.c_uint32 * ((len(second_time_series_list)) - subsequence_length + 1)) \ - (*initialized_ip_numpy_array) - - c_tsa_library.stomp(ctypes.pointer(first_time_series_double_array), - ctypes.pointer(second_time_series_double_array), - ctypes.pointer(ctypes.c_int(len(first_time_series_list))), - ctypes.pointer(ctypes.c_int(len(second_time_series_list))), - ctypes.pointer(ctypes.c_long(subsequence_length)), - ctypes.pointer(initialized_c_mp_array), - ctypes.pointer(initialized_c_ip_array)) - - np_array_mp = np.array(initialized_c_mp_array) - np_array_ip = np.array(initialized_c_ip_array).astype(int) - - return {'matrix_profile': np_array_mp, 'index_profile': np_array_ip} diff --git a/tsa/tsa_algorithms/stomp_self_join.py b/tsa/tsa_algorithms/stomp_self_join.py deleted file mode 100644 index e38bd09..0000000 --- a/tsa/tsa_algorithms/stomp_self_join.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## - -import ctypes -import numpy as np -######################################################################################################################## - -def _stomp_self_join(time_series_list, subsequence_length, c_tsa_library): - """ - STOMP algorithm to calculate the matrix profile between 't' and itself using a subsequence length - of 'm'. This method filters the trivial matches. - :param time_series_list: List with the query and reference time series. - :param subsequence_length: Lenght of the subsequence - :param c_tsa_library: Dynamic library of TSA - :return: Matrix profile in dictionary format. - """ - first_time_series_double_array = (ctypes.c_double * len(time_series_list))(*time_series_list) - - initialized_mp_numpy_array = np.zeros(len(time_series_list) - subsequence_length + 1).astype(np.double) - initializes_ip_numpy_array = np.zeros(len(time_series_list) - subsequence_length + 1).astype(np.uint32) - - initialized_c_mp_array = (ctypes.c_double * (len(time_series_list) - subsequence_length + 1)) \ - (*initialized_mp_numpy_array) - - initialized_c_ip_array = (ctypes.c_uint32 * ((len(time_series_list)) - subsequence_length + 1)) \ - (*initializes_ip_numpy_array) - - c_tsa_library.stomp_self_join(ctypes.pointer(first_time_series_double_array), - ctypes.pointer(ctypes.c_int(len(time_series_list))), - ctypes.pointer(ctypes.c_long(subsequence_length)), - ctypes.pointer(initialized_c_mp_array), - ctypes.pointer(initialized_c_ip_array)) - - np_array_mp = np.array(initialized_c_mp_array) - np_array_ip = np.array(initialized_c_ip_array).astype(int) - - return {'matrix_profile': np_array_mp, 'index_profile': np_array_ip} diff --git a/tsa/tsa_datasets/Coffee_TRAIN b/tsa/tsa_datasets/Coffee_TRAIN deleted file mode 100644 index e4be6e6..0000000 --- a/tsa/tsa_datasets/Coffee_TRAIN +++ /dev/null @@ -1,28 +0,0 @@ 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diff --git a/tsa/tsa_datasets/__init__.py b/tsa/tsa_datasets/__init__.py deleted file mode 100644 index 481e89f..0000000 --- a/tsa/tsa_datasets/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" \ No newline at end of file diff --git a/tsa/tsa_datasets/dataset_generator.py b/tsa/tsa_datasets/dataset_generator.py deleted file mode 100644 index 16a92a1..0000000 --- a/tsa/tsa_datasets/dataset_generator.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## -import random -import os - -import tsa.tsa_datasets as a - -######################################################################################################################## - -def random_dataset_generator(name,points): - """ - Generates a dataset of the desired number of points with a desired name indicated when calling the function. - The dataset is composed by random doubles between 0 and 1. - :param name: Name of the dataset - :param points: NUmber of points of the dataset. - """ - file = open(os.path.join(a.__path__[0], name), 'w') - file.write("0,") - for i in range(1,points): - print("0," + str(i)) - file.write(str(random.uniform(0, 1))) - file.write(',') - file.write('1') - file.write('\n') - file.write("1,") - for i in range(1,points): - print("1," + str(i)) - file.write(str(random.uniform(0, 1))) - file.write(',') - file.write('1') - -def section_defined_dataset_generator(name, points): - """ - Generates a dataset of the desired number of points with a desired name indicated when calling the function. - The dataset is composed by random doubles between 0 and 1 with a section in common to the two time series. - - :param name: Name of the dataset. - :param points: NUmber of points of the dataset. : - """ - file = open(os.path.join(a.__path__[0], name), 'w') - file.write("0,") - for i in range(1,points): - if (i>500 and i < 700): - print("0," + str(i)) - file.write(str(0.9)) - file.write(',') - else: - print("0," + str(i)) - file.write(str(random.uniform(0, 1))) - file.write(',') - file.write('1') - file.write('\n') - file.write("1,") - for i in range(1,points): - if (i>200 and i<400): - print("1," + str(i)) - file.write(str(0.9)) - file.write(',') - else: - print("1," + str(i)) - file.write(str(random.uniform(0, 1))) - file.write(',') - file.write('1') \ No newline at end of file diff --git a/tsa/tsa_datasets/generator.py b/tsa/tsa_datasets/generator.py deleted file mode 100644 index 92d8070..0000000 --- a/tsa/tsa_datasets/generator.py +++ /dev/null @@ -1,13 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## -from tsa.tsa_datasets.dataset_generator import section_defined_dataset_generator -######################################################################################################################## -section_defined_dataset_generator("peak_dataset_16000", 16000) \ No newline at end of file diff --git a/tsa/tsa_datasets/peak_dataset_1000 b/tsa/tsa_datasets/peak_dataset_1000 deleted file mode 100644 index 3a6ca39..0000000 --- a/tsa/tsa_datasets/peak_dataset_1000 +++ /dev/null @@ -1,2 +0,0 @@ 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\ No newline at end of file diff --git a/tsa/tsa_datasets/peak_dataset_16000 b/tsa/tsa_datasets/peak_dataset_16000 deleted file mode 100644 index 62cb1f6..0000000 --- a/tsa/tsa_datasets/peak_dataset_16000 +++ /dev/null @@ -1,2 +0,0 @@ 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\ No newline at end of file diff --git a/tsa/tsa_datasets/random_dataset_100000 b/tsa/tsa_datasets/random_dataset_100000 deleted file mode 100644 index f76e3a0..0000000 --- a/tsa/tsa_datasets/random_dataset_100000 +++ /dev/null @@ -1,2 +0,0 @@ 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\ No newline at end of file diff --git a/tsa/tsa_datasets/sel102m.mat b/tsa/tsa_datasets/sel102m.mat deleted file mode 100644 index be35905..0000000 Binary files a/tsa/tsa_datasets/sel102m.mat and /dev/null differ diff --git a/tsa/tsa_examples/ecg_example.py b/tsa/tsa_examples/ecg_example.py deleted file mode 100644 index 13b7612..0000000 --- a/tsa/tsa_examples/ecg_example.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## -from scipy.io import loadmat -import os -import time -import tsa.tsa_datasets as a -from tsa.analyser import analiser -import pandas as pd - -######################################################################################################################## - -# data preprocessing -data = loadmat(os.path.join(a.__path__[0], 'sel102m.mat')) -ta = data["val"][0] -print(ta) -print(len(ta)) -analiser_cat = analiser() -for i in range(1): - print("-----") - print("stomp") - print("-----") - start = time.time() - # data analysis - l = [] - l.append(list(ta[0:1000])) - l.append(list(ta[1000:2000])) - mp = analiser_cat.stomp(ta[0:1000], ta[1000:2000], 256) - tp = analiser_cat.find_best_n_motifs(mp['matrix_profile'], mp['index_profile'], 4) - dp = analiser_cat.find_best_n_discords(mp['matrix_profile'], mp['index_profile'], 4) - print(analiser_cat.absolute_sum_of_change([[1, 2, 3, 5, 6, 7, 8, 10], - [4, 5, 6, 24, 24, 24, 3, 3],[4, 5, 6, 24, 24, 24, 3, 3]])) - print(analiser_cat.abs_energy([[1, 2, 3, 5, 6, 7, 8, 10], - [4, 5, 6, 24, 24, 24, 3, 3]])) - print(tp) - print(dp) - print(str(time.time() - start)) diff --git a/tsa/tsa_examples/peak_dataset_checker.py b/tsa/tsa_examples/peak_dataset_checker.py deleted file mode 100644 index 1b32572..0000000 --- a/tsa/tsa_examples/peak_dataset_checker.py +++ /dev/null @@ -1,33 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORT -######################################################################################################################## -import os -import tsa.tsa_datasets as a -from tsa.analyser import analiser -import pandas as pd - -######################################################################################################################## - -data = pd.read_csv(os.path.join(a.__path__[0], 'peak_dataset_1000'), sep=',', header=None) -label = data.pop(data.columns[0]) - -ta = (data[label == 0].iloc[[0]].values.flatten()) -tb = (data[label == 1].iloc[[0]].values.flatten()) -tc = [] -for a in range(0, 220): - tc.append(0.5) -print(len(tc)) -for a in range(0, 300): - tc.append(1) - tc.append(0) -g_cat_analiser = analiser(); - -a = g_cat_analiser.stomp_self_join(ta, 200) -print(a) diff --git a/tsa/tsa_features/__init__.py b/tsa/tsa_features/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tsa/tsa_features/abs_energy.py b/tsa/tsa_features/abs_energy.py deleted file mode 100644 index 189b79b..0000000 --- a/tsa/tsa_features/abs_energy.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" - -######################################################################################################################## -# IMPORT -######################################################################################################################## -import ctypes -import numpy as np -######################################################################################################################## - -def _abs_energy(list_of_time_series, c_tsa_library): - """ - Primitive of the abs_energy function - - :param list_of_time_series: List with the time series. - :param c_tsa_library: TSA library. - :return: Numpy array with the absEnergy. - """ - list_of_time_series = list(list_of_time_series) - n = len(list_of_time_series) - time_series_length = len(list_of_time_series[0]) - c_number_of_time_series = ctypes.c_long(n) - - c_time_series_length = ctypes.c_long(time_series_length) - - initialized_result_array = np.zeros(n).astype(np.double) - - c_result_array = (ctypes.c_double * n)(*initialized_result_array) - - time_series_joint = [] - for time_series in list_of_time_series: - time_series_joint += time_series - - c_time_series_joint = (ctypes.c_double * len(time_series_joint))(*time_series_joint) - - c_tsa_library.abs_energy(ctypes.pointer(c_time_series_joint), - ctypes.pointer(c_time_series_length), - ctypes.pointer(c_number_of_time_series), - ctypes.pointer(c_result_array)) - - np_result = np.array(c_result_array) - - return np_result diff --git a/tsa/tsa_features/absolute_sum_of_changes.py b/tsa/tsa_features/absolute_sum_of_changes.py deleted file mode 100644 index 0ee1caf..0000000 --- a/tsa/tsa_features/absolute_sum_of_changes.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" - -######################################################################################################################## -# IMPORT -######################################################################################################################## -import ctypes -import numpy as np -######################################################################################################################## - -def _absolute_sum_of_changes(list_of_time_series, c_tsa_library): - """ - Primitive of the absolute_sum_of_changes function - - :param list_of_time_series: List with the time series. - :param c_tsa_library: TSA library. - :return: Numpy array with the absolute sum of changes. - """ - list_of_time_series= list(list_of_time_series) - n = len(list_of_time_series) - time_series_length= len(list_of_time_series[0]) - c_number_of_time_series = ctypes.c_long(n) - - c_time_series_length = ctypes.c_long(time_series_length) - - initialized_result_array = np.zeros(n).astype(np.double) - - c_result_array = (ctypes.c_double * n)(*initialized_result_array) - - time_series_joint = [] - for time_series in list_of_time_series: - time_series_joint += time_series - - c_time_series_joint = (ctypes.c_double * len(time_series_joint))(*time_series_joint) - - c_tsa_library.absolute_sum_of_changes(ctypes.pointer(c_time_series_joint), - ctypes.pointer(c_time_series_length), - ctypes.pointer(c_number_of_time_series), - ctypes.pointer(c_result_array)) - - np_result = np.array(c_result_array) - - return np_result diff --git a/tsa/tsa_libraries/__init__.py b/tsa/tsa_libraries/__init__.py deleted file mode 100644 index 481e89f..0000000 --- a/tsa/tsa_libraries/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" \ No newline at end of file diff --git a/tsa/tsa_libraries/libTSALIB.dylib b/tsa/tsa_libraries/libTSALIB.dylib deleted file mode 100755 index 6a49c28..0000000 Binary files a/tsa/tsa_libraries/libTSALIB.dylib and /dev/null differ diff --git a/tsa/tsa_libraries/libc_performance.dylib b/tsa/tsa_libraries/libc_performance.dylib deleted file mode 100755 index c192f65..0000000 Binary files a/tsa/tsa_libraries/libc_performance.dylib and /dev/null differ diff --git a/tsa/tsa_tests/__init__.py b/tsa/tsa_tests/__init__.py deleted file mode 100644 index 481e89f..0000000 --- a/tsa/tsa_tests/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" \ No newline at end of file diff --git a/tsa/tsa_tests/benchmarks_test/__init__.py b/tsa/tsa_tests/benchmarks_test/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tsa/tsa_tests/c_vs_python_comparison.py b/tsa/tsa_tests/c_vs_python_comparison.py deleted file mode 100644 index 009010c..0000000 --- a/tsa/tsa_tests/c_vs_python_comparison.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -Copyright (c) 2018 Grumpy Cat Software S.L. - -This Source Code Form is subject to the terms of the Mozilla Public -License, v. 2.0. If a copy of the MPL was not distributed with this -file, You can obtain one at http://mozilla.org/MPL/2.0/. -""" -######################################################################################################################## -# IMPORTS -######################################################################################################################## -import ctypes -import tsa.tsa_libraries -import os -import time -""" -c_performance_checker = ctypes.CDLL(os.path.join(tsa.tsa_libraries.__path__[0], 'libc_performance.dylib')) -start = time.time() -c_performance_checker.c_performance(ctypes.pointer(ctypes.c_int(1000))) -print(time.time() -start) -""" \ No newline at end of file diff --git a/tsa/tsa_tests/unit_tests/__init__.py b/tsa/tsa_tests/unit_tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tsa/tsa_visualisation/__init__.py b/tsa/tsa_visualisation/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tsa/tsa_visualisation/visualisation.py b/tsa/tsa_visualisation/visualisation.py deleted file mode 100644 index a1f3f57..0000000 --- a/tsa/tsa_visualisation/visualisation.py +++ /dev/null @@ -1,66 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -""" -def plot_stamp(ta, tb, matrix_profile, indexes, m): - fig_width = 16 - fig_height = 10 - fig_dpi = 100 - plt.figure(figsize=(fig_width, fig_height), dpi=fig_dpi) - fig = plt.subplots(nrows=4, ncols=1) - plt.tight_layout() - - plt.subplot(411) - plt.plot(ta) - plt.xlim((0, len(ta))) - plt.title('TimeSeries A') - - plt.subplot(412) - plt.plot(tb) - plt.plot(range(np.argmax(matrix_profile), np.argmax(matrix_profile) + m), - tb[np.argmax(matrix_profile):np.argmax(matrix_profile) + m], c='r') - plt.plot(range(np.argmin(matrix_profile), np.argmin(matrix_profile) + m), - tb[np.argmin(matrix_profile):np.argmin(matrix_profile) + m], c='k') - plt.title('TimeSeries B') - plt.xlim((0, len(tb))) - - plt.subplot(413) - plt.title('Matrix Profile AB') - plt.plot(range(0, len(matrix_profile)), matrix_profile, '#ff5722') - plt.plot(np.argmax(matrix_profile), np.max(matrix_profile), marker='x', ms=10) - plt.plot(np.argmin(matrix_profile), np.min(matrix_profile), marker='*', ms=10, c='k') - plt.xlim((0, len(tb))) - - plt.subplot(414) - plt.title('Matrix Profile Index') - plt.plot(indexes, '#ff5722') - plt.xlim((0, len(tb))) - - plt.show() - -def motif(ta, matrix_profile, indexes, m): - fig_width = 16 - fig_height = 10 - fig_dpi = 100 - plt.figure(figsize=(fig_width, fig_height), dpi=fig_dpi) - fig = plt.subplots(nrows=4, ncols=1) - plt.tight_layout() - - ##################### 1 ################### - plt.subplot(411) - plt.plot(ta) - plt.xlim((indexes[np.argmax(matrix_profile)],indexes[np.argmax(matrix_profile)] + m )) - plt.title('motif in ta') - #################### 2 ################### - - ################### 3 ##################### - plt.subplot(413) - - - plt.title('discord a') - plt.plot(ta) - plt.xlim((indexes[np.argmin(matrix_profile)], indexes[np.argmin(matrix_profile)] + m)) - ################## 4 ###################### - - #################################################### - plt.show() -""" \ No newline at end of file