diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
new file mode 100644
index 0000000..5e6bb08
--- /dev/null
+++ b/.github/workflows/python-package.yml
@@ -0,0 +1,21 @@
+name: Python Package using Poetry
+
+on: [push]
+
+jobs:
+ build-linux:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python 3.11
+ uses: actions/setup-python@v5
+ with:
+ python-version: '3.11'
+ - name: Install Poetry
+ run: pip install poetry==2.1.3
+ - name: Install dependencies
+ run: poetry install --no-interaction --no-root
+ - name: Lint with flake8
+ run: poetry run flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics && poetry run flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+ - name: Test with pytest
+ run: poetry run pytest
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..97b39a1
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,16 @@
+repos:
+ - repo: https://github.com/psf/black
+ rev: 25.1.0
+ hooks:
+ - id: black
+ - repo: https://github.com/pycqa/flake8
+ rev: 7.2.0
+ hooks:
+ - id: flake8
+ - repo: local
+ hooks:
+ - id: pytest
+ name: pytest
+ entry: pytest
+ language: system
+ pass_filenames: false
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..eca6fe1
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,15 @@
+cff-version: 1.2.0
+message: >
+ If you use the sample BBC dataset included with this project,
+ please cite the following publication:
+ Greene, D., and Cunningham, P. (2006). Practical Solutions to the Problem of
+ Diagonal Dominance in Kernel Document Clustering. Proceedings of the
+ 23rd International Conference on Machine Learning.
+title: "BBC News Dataset"
+authors:
+ - family-names: Greene
+ given-names: Derek
+ - family-names: Cunningham
+ given-names: P.
+version: "1.0"
+date-released: 2004-01-01
diff --git a/LICENSE.txt b/LICENSE.txt
index 9cecc1d..3e23ddc 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,674 +1,19 @@
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- 14. Revised Versions of this License.
-
- The Free Software Foundation may publish revised and/or new versions of
-the GNU General Public License from time to time. Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
- Each version is given a distinguishing version number. If the
-Program specifies that a certain numbered version of the GNU General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation. If the Program does not specify a version number of the
-GNU General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
- If the Program specifies that a proxy can decide which future
-versions of the GNU General Public License can be used, that proxy's
-public statement of acceptance of a version permanently authorizes you
-to choose that version for the Program.
-
- Later license versions may give you additional or different
-permissions. However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
-later version.
-
- 15. Disclaimer of Warranty.
-
- THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
-IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
- 16. Limitation of Liability.
-
- IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
- 17. Interpretation of Sections 15 and 16.
-
- If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
- To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
- {one line to give the program's name and a brief idea of what it does.}
- Copyright (C) {year} {name of author}
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see .
-
-Also add information on how to contact you by electronic and paper mail.
-
- If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
- {project} Copyright (C) {year} {fullname}
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
- You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-.
-
- The GNU General Public License does not permit incorporating your program
-into proprietary programs. If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library. If this is what you want to do, use the GNU Lesser General
-Public License instead of this License. But first, please read
-.
+Copyright (c) 2025 Joe Wandy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/Pipfile b/Pipfile
deleted file mode 100644
index 8238ec9..0000000
--- a/Pipfile
+++ /dev/null
@@ -1,16 +0,0 @@
-[[source]]
-url = "https://pypi.org/simple"
-verify_ssl = true
-name = "pypi"
-
-[packages]
-numpy = "*"
-pandas = "*"
-matplotlib = "*"
-
-[dev-packages]
-jupyterlab = "*"
-twine = "*"
-
-[requires]
-python_version = "3"
diff --git a/Pipfile.lock b/Pipfile.lock
deleted file mode 100644
index bff010c..0000000
--- a/Pipfile.lock
+++ /dev/null
@@ -1,961 +0,0 @@
-{
- "_meta": {
- "hash": {
- "sha256": "643bdc442bf891831099bc5ce212a0c0741eebbce83e1145a9bcad3cbd5f15e2"
- },
- "pipfile-spec": 6,
- "requires": {
- "python_version": "3"
- },
- "sources": [
- {
- "name": "pypi",
- "url": "https://pypi.org/simple",
- "verify_ssl": true
- }
- ]
- },
- "default": {
- "cycler": {
- "hashes": [
- "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
- "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"
- ],
- "version": "==0.10.0"
- },
- "kiwisolver": {
- "hashes": [
- "sha256:0cd53f403202159b44528498de18f9285b04482bab2a6fc3f5dd8dbb9352e30d",
- "sha256:1e1bc12fb773a7b2ffdeb8380609f4f8064777877b2225dec3da711b421fda31",
- "sha256:225e2e18f271e0ed8157d7f4518ffbf99b9450fca398d561eb5c4a87d0986dd9",
- "sha256:232c9e11fd7ac3a470d65cd67e4359eee155ec57e822e5220322d7b2ac84fbf0",
- "sha256:31dfd2ac56edc0ff9ac295193eeaea1c0c923c0355bf948fbd99ed6018010b72",
- "sha256:33449715e0101e4d34f64990352bce4095c8bf13bed1b390773fc0a7295967b3",
- "sha256:401a2e9afa8588589775fe34fc22d918ae839aaaf0c0e96441c0fdbce6d8ebe6",
- "sha256:44a62e24d9b01ba94ae7a4a6c3fb215dc4af1dde817e7498d901e229aaf50e4e",
- "sha256:50af681a36b2a1dee1d3c169ade9fdc59207d3c31e522519181e12f1b3ba7000",
- "sha256:563c649cfdef27d081c84e72a03b48ea9408c16657500c312575ae9d9f7bc1c3",
- "sha256:5989db3b3b34b76c09253deeaf7fbc2707616f130e166996606c284395da3f18",
- "sha256:5a7a7dbff17e66fac9142ae2ecafb719393aaee6a3768c9de2fd425c63b53e21",
- "sha256:5c3e6455341008a054cccee8c5d24481bcfe1acdbc9add30aa95798e95c65621",
- "sha256:5f6ccd3dd0b9739edcf407514016108e2280769c73a85b9e59aa390046dbf08b",
- "sha256:72c99e39d005b793fb7d3d4e660aed6b6281b502e8c1eaf8ee8346023c8e03bc",
- "sha256:78751b33595f7f9511952e7e60ce858c6d64db2e062afb325985ddbd34b5c131",
- "sha256:834ee27348c4aefc20b479335fd422a2c69db55f7d9ab61721ac8cd83eb78882",
- "sha256:8be8d84b7d4f2ba4ffff3665bcd0211318aa632395a1a41553250484a871d454",
- "sha256:950a199911a8d94683a6b10321f9345d5a3a8433ec58b217ace979e18f16e248",
- "sha256:a357fd4f15ee49b4a98b44ec23a34a95f1e00292a139d6015c11f55774ef10de",
- "sha256:a53d27d0c2a0ebd07e395e56a1fbdf75ffedc4a05943daf472af163413ce9598",
- "sha256:acef3d59d47dd85ecf909c359d0fd2c81ed33bdff70216d3956b463e12c38a54",
- "sha256:b38694dcdac990a743aa654037ff1188c7a9801ac3ccc548d3341014bc5ca278",
- "sha256:b9edd0110a77fc321ab090aaa1cfcaba1d8499850a12848b81be2222eab648f6",
- "sha256:c08e95114951dc2090c4a630c2385bef681cacf12636fb0241accdc6b303fd81",
- "sha256:c5518d51a0735b1e6cee1fdce66359f8d2b59c3ca85dc2b0813a8aa86818a030",
- "sha256:c8fd0f1ae9d92b42854b2979024d7597685ce4ada367172ed7c09edf2cef9cb8",
- "sha256:ca3820eb7f7faf7f0aa88de0e54681bddcb46e485beb844fcecbcd1c8bd01689",
- "sha256:cf8b574c7b9aa060c62116d4181f3a1a4e821b2ec5cbfe3775809474113748d4",
- "sha256:d3155d828dec1d43283bd24d3d3e0d9c7c350cdfcc0bd06c0ad1209c1bbc36d0",
- "sha256:f8d6f8db88049a699817fd9178782867bf22283e3813064302ac59f61d95be05",
- "sha256:fd34fbbfbc40628200730bc1febe30631347103fc8d3d4fa012c21ab9c11eca9"
- ],
- "markers": "python_version >= '3.6'",
- "version": "==1.3.1"
- },
- "matplotlib": {
- "hashes": [
- "sha256:09225edca87a79815822eb7d3be63a83ebd4d9d98d5aa3a15a94f4eee2435954",
- "sha256:0caa687fce6174fef9b27d45f8cc57cbc572e04e98c81db8e628b12b563d59a2",
- "sha256:27c9393fada62bd0ad7c730562a0fecbd3d5aaa8d9ed80ba7d3ebb8abc4f0453",
- "sha256:2c2c5041608cb75c39cbd0ed05256f8a563e144234a524c59d091abbfa7a868f",
- "sha256:2d31aff0c8184b05006ad756b9a4dc2a0805e94d28f3abc3187e881b6673b302",
- "sha256:3a4c3e9be63adf8e9b305aa58fb3ec40ecc61fd0f8fd3328ce55bc30e7a2aeb0",
- "sha256:5111d6d47a0f5b8f3e10af7a79d5e7eb7e73a22825391834734274c4f312a8a0",
- "sha256:5ed3d3342698c2b1f3651f8ea6c099b0f196d16ee00e33dc3a6fee8cb01d530a",
- "sha256:6ffd2d80d76df2e5f9f0c0140b5af97e3b87dd29852dcdb103ec177d853ec06b",
- "sha256:746897fbd72bd462b888c74ed35d812ca76006b04f717cd44698cdfc99aca70d",
- "sha256:756ee498b9ba35460e4cbbd73f09018e906daa8537fff61da5b5bf8d5e9de5c7",
- "sha256:7ad44f2c74c50567c694ee91c6fa16d67e7c8af6f22c656b80469ad927688457",
- "sha256:83e6c895d93fdf93eeff1a21ee96778ba65ef258e5d284160f7c628fee40c38f",
- "sha256:9b03722c89a43a61d4d148acfc89ec5bb54cd0fd1539df25b10eb9c5fa6c393a",
- "sha256:a4fe54eab2c7129add75154823e6543b10261f9b65b2abe692d68743a4999f8c",
- "sha256:b1b60c6476c4cfe9e5cf8ab0d3127476fd3d5f05de0f343a452badaad0e4bdec",
- "sha256:b26c472847911f5a7eb49e1c888c31c77c4ddf8023c1545e0e8e0367ba74fb15",
- "sha256:b2a5e1f637a92bb6f3526cc54cc8af0401112e81ce5cba6368a1b7908f9e18bc",
- "sha256:b7b09c61a91b742cb5460b72efd1fe26ef83c1c704f666e0af0df156b046aada",
- "sha256:b8ba2a1dbb4660cb469fe8e1febb5119506059e675180c51396e1723ff9b79d9",
- "sha256:c092fc4673260b1446b8578015321081d5db73b94533fe4bf9b69f44e948d174",
- "sha256:c586ac1d64432f92857c3cf4478cfb0ece1ae18b740593f8a39f2f0b27c7fda5",
- "sha256:d082f77b4ed876ae94a9373f0db96bf8768a7cca6c58fc3038f94e30ffde1880",
- "sha256:e71cdd402047e657c1662073e9361106c6981e9621ab8c249388dfc3ec1de07b",
- "sha256:eb6b6700ea454bb88333d98601e74928e06f9669c1ea231b4c4c666c1d7701b4"
- ],
- "index": "pypi",
- "version": "==3.3.3"
- },
- "numpy": {
- "hashes": [
- "sha256:012426a41bc9ab63bb158635aecccc7610e3eff5d31d1eb43bc099debc979d94",
- "sha256:06fab248a088e439402141ea04f0fffb203723148f6ee791e9c75b3e9e82f080",
- "sha256:0eef32ca3132a48e43f6a0f5a82cb508f22ce5a3d6f67a8329c81c8e226d3f6e",
- "sha256:1ded4fce9cfaaf24e7a0ab51b7a87be9038ea1ace7f34b841fe3b6894c721d1c",
- "sha256:2e55195bc1c6b705bfd8ad6f288b38b11b1af32f3c8289d6c50d47f950c12e76",
- "sha256:2ea52bd92ab9f768cc64a4c3ef8f4b2580a17af0a5436f6126b08efbd1838371",
- "sha256:36674959eed6957e61f11c912f71e78857a8d0604171dfd9ce9ad5cbf41c511c",
- "sha256:384ec0463d1c2671170901994aeb6dce126de0a95ccc3976c43b0038a37329c2",
- "sha256:39b70c19ec771805081578cc936bbe95336798b7edf4732ed102e7a43ec5c07a",
- "sha256:400580cbd3cff6ffa6293df2278c75aef2d58d8d93d3c5614cd67981dae68ceb",
- "sha256:43d4c81d5ffdff6bae58d66a3cd7f54a7acd9a0e7b18d97abb255defc09e3140",
- "sha256:50a4a0ad0111cc1b71fa32dedd05fa239f7fb5a43a40663269bb5dc7877cfd28",
- "sha256:603aa0706be710eea8884af807b1b3bc9fb2e49b9f4da439e76000f3b3c6ff0f",
- "sha256:6149a185cece5ee78d1d196938b2a8f9d09f5a5ebfbba66969302a778d5ddd1d",
- "sha256:759e4095edc3c1b3ac031f34d9459fa781777a93ccc633a472a5468587a190ff",
- "sha256:7fb43004bce0ca31d8f13a6eb5e943fa73371381e53f7074ed21a4cb786c32f8",
- "sha256:811daee36a58dc79cf3d8bdd4a490e4277d0e4b7d103a001a4e73ddb48e7e6aa",
- "sha256:8b5e972b43c8fc27d56550b4120fe6257fdc15f9301914380b27f74856299fea",
- "sha256:99abf4f353c3d1a0c7a5f27699482c987cf663b1eac20db59b8c7b061eabd7fc",
- "sha256:a0d53e51a6cb6f0d9082decb7a4cb6dfb33055308c4c44f53103c073f649af73",
- "sha256:a12ff4c8ddfee61f90a1633a4c4afd3f7bcb32b11c52026c92a12e1325922d0d",
- "sha256:a4646724fba402aa7504cd48b4b50e783296b5e10a524c7a6da62e4a8ac9698d",
- "sha256:a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4",
- "sha256:a9d17f2be3b427fbb2bce61e596cf555d6f8a56c222bd2ca148baeeb5e5c783c",
- "sha256:ab83f24d5c52d60dbc8cd0528759532736b56db58adaa7b5f1f76ad551416a1e",
- "sha256:aeb9ed923be74e659984e321f609b9ba54a48354bfd168d21a2b072ed1e833ea",
- "sha256:c843b3f50d1ab7361ca4f0b3639bf691569493a56808a0b0c54a051d260b7dbd",
- "sha256:cae865b1cae1ec2663d8ea56ef6ff185bad091a5e33ebbadd98de2cfa3fa668f",
- "sha256:cc6bd4fd593cb261332568485e20a0712883cf631f6f5e8e86a52caa8b2b50ff",
- "sha256:cf2402002d3d9f91c8b01e66fbb436a4ed01c6498fffed0e4c7566da1d40ee1e",
- "sha256:d051ec1c64b85ecc69531e1137bb9751c6830772ee5c1c426dbcfe98ef5788d7",
- "sha256:d6631f2e867676b13026e2846180e2c13c1e11289d67da08d71cacb2cd93d4aa",
- "sha256:dbd18bcf4889b720ba13a27ec2f2aac1981bd41203b3a3b27ba7a33f88ae4827",
- "sha256:df609c82f18c5b9f6cb97271f03315ff0dbe481a2a02e56aeb1b1a985ce38e60"
- ],
- "index": "pypi",
- "version": "==1.19.5"
- },
- "pandas": {
- "hashes": [
- "sha256:050ed2c9d825ef36738e018454e6d055c63d947c1d52010fbadd7584f09df5db",
- "sha256:055647e7f4c5e66ba92c2a7dcae6c2c57898b605a3fb007745df61cc4015937f",
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- "sha256:324e60bea729cf3b55c1bf9e88fe8b9932c26f8669d13b928e3c96b3a1453dff",
- "sha256:37443199f451f8badfe0add666e43cdb817c59fa36bceedafd9c543a42f236ca",
- "sha256:47ec0808a8357ab3890ce0eca39a63f79dcf941e2e7f494470fe1c9ec43f6091",
- "sha256:496fcc29321e9a804d56d5aa5d7ec1320edfd1898eee2f451aa70171cf1d5a29",
- "sha256:50e6c0a17ef7f831b5565fd0394dbf9bfd5d615ee4dd4bb60a3d8c9d2e872323",
- "sha256:5527c5475d955c0bc9689c56865aaa2a7b13c504d6c44f0aadbf57b565af5ebd",
- "sha256:57d5c7ac62925a8d2ab43ea442b297a56cc8452015e71e24f4aa7e4ed6be3d77",
- "sha256:9d45f58b03af1fea4b48e44aa38a819a33dccb9821ef9e1d68f529995f8a632f",
- "sha256:b26e2dabda73d347c7af3e6fed58483161c7b87a886a4e06d76ccfe55a044aa9",
- "sha256:cfd237865d878da9b65cfee883da5e0067f5e2ff839e459466fb90565a77bda3",
- "sha256:d7cca42dba13bfee369e2944ae31f6549a55831cba3117e17636955176004088",
- "sha256:fe7de6fed43e7d086e3d947651ec89e55ddf00102f9dd5758763d56d182f0564"
- ],
- "index": "pypi",
- "version": "==1.2.1"
- },
- "pillow": {
- "hashes": [
- "sha256:165c88bc9d8dba670110c689e3cc5c71dbe4bfb984ffa7cbebf1fac9554071d6",
- "sha256:1d208e670abfeb41b6143537a681299ef86e92d2a3dac299d3cd6830d5c7bded",
- "sha256:22d070ca2e60c99929ef274cfced04294d2368193e935c5d6febfd8b601bf865",
- "sha256:2353834b2c49b95e1313fb34edf18fca4d57446675d05298bb694bca4b194174",
- "sha256:39725acf2d2e9c17356e6835dccebe7a697db55f25a09207e38b835d5e1bc032",
- "sha256:3de6b2ee4f78c6b3d89d184ade5d8fa68af0848f9b6b6da2b9ab7943ec46971a",
- "sha256:47c0d93ee9c8b181f353dbead6530b26980fe4f5485aa18be8f1fd3c3cbc685e",
- "sha256:5e2fe3bb2363b862671eba632537cd3a823847db4d98be95690b7e382f3d6378",
- "sha256:604815c55fd92e735f9738f65dabf4edc3e79f88541c221d292faec1904a4b17",
- "sha256:6c5275bd82711cd3dcd0af8ce0bb99113ae8911fc2952805f1d012de7d600a4c",
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diff --git a/README.md b/README.md
index 993b406..2751bfe 100644
--- a/README.md
+++ b/README.md
@@ -5,22 +5,133 @@ Hierarchical Latent Dirichlet Allocation
---
-Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant process, which allows for arbitrarily large branching factors and readily accommodates growing
-data collections. The hLDA model combines this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation.
+Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic
+hierarchies from data. The model relies on a non‑parametric prior called the nested
+Chinese restaurant process, which allows for arbitrarily large branching factors and
+easily accommodates growing data collections. The hLDA model combines this prior with a
+likelihood based on a hierarchical variant of Latent Dirichlet Allocation.
-[Hierarchical Topic Models and the Nested Chinese Restaurant Process](http://www.cs.columbia.edu/~blei/papers/BleiGriffithsJordanTenenbaum2003.pdf)
+The original papers describing the algorithm are:
-[The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies](http://cocosci.berkeley.edu/tom/papers/ncrp.pdf)
+- [Hierarchical Topic Models and the Nested Chinese Restaurant Process](http://www.cs.columbia.edu/~blei/papers/BleiGriffithsJordanTenenbaum2003.pdf)
+- [The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies](http://cocosci.berkeley.edu/tom/papers/ncrp.pdf)
-Implementation
---------------
+## Overview
-- [hlda/sampler.py](hlda/sampler.py) is the Gibbs sampler for hLDA inference, based on the implementation from [Mallet](http://mallet.cs.umass.edu/topics.php) having a fixed depth on the nCRP tree.
+This repository contains a pure Python implementation of the Gibbs sampler for hLDA.
+It is intended for experimentation and as a reference implementation. The code follows
+the approach used in the original [Mallet](http://mallet.cs.umass.edu/topics.php)
+implementation but with a simplified interface and a fixed depth for the tree.
+Key features include:
-Installation
-------------
+- **Python 3.11+** support with minimal third‑party dependencies.
+- A small set of example scripts demonstrating how to run the sampler.
+- Utilities for visualising the resulting topic hierarchy.
+- Test suite for verifying the sampler on synthetic data and a small BBC corpus.
-- Simply use `pip install hlda` to install the package.
-- An example notebook that infers the hierarchical topics on the BBC Insight corpus can be found in [notebooks/bbc_test.ipynb](notebooks/bbc_test.ipynb).
+## Installation
+
+The package can be installed directly from PyPI:
+
+```bash
+pip install hlda
+```
+
+Alternatively, to develop locally, clone this repository and install it in editable mode:
+
+```bash
+git clone https://github.com/joewandy/hlda.git
+cd hlda
+pip install -e .
+pre-commit install
+```
+
+## Usage
+
+The easiest way to get started is by using the sample BBC dataset provided in the
+`data/` directory. You can run the full demonstration from the command line:
+
+```bash
+python examples/bbc_demo.py --data-dir data/bbc/tech --iterations 20
+```
+
+If you installed the package from PyPI you can run the same demo via the
+`hlda-run` command:
+
+```bash
+hlda-run --data-dir data/bbc/tech --iterations 20
+```
+
+To write the learned hierarchy to disk in JSON format, pass
+`--export-tree ` when running the script:
+
+```bash
+python scripts/run_hlda.py --data-dir data/bbc/tech --export-tree tree.json
+```
+
+If you make use of the BBC dataset, please cite the publication by Greene and
+Cunningham (2006) as detailed in [`CITATION.cff`](CITATION.cff).
+
+Example scripts for the BBC dataset and synthetic data are available in the
+[`examples/`](examples) directory.
+
+Within Python you can also construct the sampler directly:
+
+```python
+from hlda.sampler import HierarchicalLDA
+
+corpus = [["word", "word", ...], ...] # list of tokenised documents
+vocab = sorted({w for doc in corpus for w in doc})
+
+hlda = HierarchicalLDA(corpus, vocab, alpha=1.0, gamma=1.0, eta=0.1,
+ num_levels=3, seed=0)
+hlda.estimate(iterations=50, display_topics=10)
+```
+
+### Integration with scikit-learn
+
+The package provides a `HierarchicalLDAEstimator` that follows the scikit-learn API. This allows using the sampler inside a standard `Pipeline`.
+
+```python
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import FunctionTransformer
+from sklearn.pipeline import Pipeline
+from hlda.sklearn_wrapper import HierarchicalLDAEstimator
+
+vectorizer = CountVectorizer()
+prep = FunctionTransformer(
+ lambda X: (
+ [[i for i, c in enumerate(row) for _ in range(int(c))] for row in X.toarray()],
+ list(vectorizer.get_feature_names_out()),
+ ),
+ validate=False,
+)
+
+pipeline = Pipeline([
+ ("vect", vectorizer),
+ ("prep", prep),
+ ("hlda", HierarchicalLDAEstimator(num_levels=3, iterations=10, seed=0)),
+])
+
+pipeline.fit(documents)
+assignments = pipeline.transform(documents)
+```
+
+
+## Running the tests
+
+The repository includes a small test suite that checks the sampler on both the BBC
+corpus and synthetic data. After installing the development dependencies you can run:
+
+```bash
+pytest -q
+```
+
+All tests should pass in a few seconds.
+
+## License
+
+This project is licensed under the terms of the MIT license. See
+[`LICENSE.txt`](LICENSE.txt) for details.
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diff --git a/examples/bbc_demo.py b/examples/bbc_demo.py
new file mode 100644
index 0000000..e064e0b
--- /dev/null
+++ b/examples/bbc_demo.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""Run hierarchical LDA on the included BBC tech dataset."""
+
+import argparse
+import os
+
+from scripts.run_hlda import run_hlda
+
+
+def main():
+ parser = argparse.ArgumentParser(
+ description=("Run hierarchical LDA on the BBC tech dataset"),
+ )
+ parser.add_argument(
+ "--data-dir",
+ default=os.path.join(
+ os.path.dirname(__file__),
+ "..",
+ "data",
+ "bbc",
+ "tech",
+ ),
+ help="Directory containing BBC .txt files",
+ )
+ parser.add_argument(
+ "--iterations",
+ type=int,
+ default=100,
+ help="Number of Gibbs samples",
+ )
+ parser.add_argument(
+ "--display-topics",
+ type=int,
+ default=50,
+ help="Report topics every N iterations",
+ )
+ parser.add_argument(
+ "--n-words",
+ type=int,
+ default=5,
+ help="Number of words to display per topic",
+ )
+ parser.add_argument(
+ "--num-levels",
+ type=int,
+ default=3,
+ help="Depth of the topic hierarchy",
+ )
+ parser.add_argument(
+ "--alpha",
+ type=float,
+ default=10.0,
+ help="Alpha hyperparameter",
+ )
+ parser.add_argument(
+ "--gamma",
+ type=float,
+ default=1.0,
+ help="Gamma hyperparameter",
+ )
+ parser.add_argument(
+ "--eta",
+ type=float,
+ default=0.1,
+ help="Eta hyperparameter",
+ )
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=0,
+ help="Random seed",
+ )
+
+ args = parser.parse_args()
+ run_hlda(args)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/synthetic_data.py b/examples/synthetic_data.py
new file mode 100644
index 0000000..75fe17a
--- /dev/null
+++ b/examples/synthetic_data.py
@@ -0,0 +1,159 @@
+from numpy import int64
+from pandas.core.frame import DataFrame
+
+import numpy as np
+import pylab as plt
+import pandas as pd
+
+
+class HldaDataGenerator(object):
+
+ def __init__(self, alpha, make_plot=False):
+ self.alpha = alpha
+ self.make_plot = make_plot
+
+ def generate_word_dists(self, n_topics, vocab_size, document_length):
+
+ width = vocab_size // n_topics
+ word_dists = np.zeros((n_topics, vocab_size))
+
+ for k in range(n_topics):
+ temp = np.zeros((n_topics, width))
+ temp[k, :] = int(document_length / width)
+ word_dists[k, :] = temp.flatten()
+
+ word_dists /= word_dists.sum(axis=1)[:, np.newaxis]
+ # turn counts into probabilities
+ if self.make_plot:
+ self._plot_nicely(word_dists, "Topic Words", "N", "K")
+ return word_dists
+
+ def generate_document(
+ self,
+ word_dists,
+ n_topics,
+ vocab_size,
+ document_length,
+ ):
+
+ # sample topic proportions with uniform dirichlet parameter alpha
+ # of length n_topics
+ theta = np.random.mtrand.dirichlet([self.alpha] * n_topics)
+
+ # for every word in the vocab for this document
+ d = np.zeros(vocab_size)
+ for n in range(document_length):
+
+ # sample a new topic index
+ k = np.random.multinomial(1, theta).argmax()
+
+ # sample a new word from the word distribution of topic k
+ w = np.random.multinomial(1, word_dists[k, :]).argmax()
+
+ # increase the occurrence of word w in document d
+ d[w] += 1
+
+ return d
+
+ def generate_input_df(
+ self,
+ n_topics,
+ vocab_size,
+ document_length,
+ n_docs,
+ vocab_prefix=None,
+ df_outfile=None,
+ vocab_outfile=None,
+ ):
+
+ print("Generating input DF")
+
+ # word_dists is the topic x document_length matrix
+ word_dists = self.generate_word_dists(
+ n_topics,
+ vocab_size,
+ document_length,
+ )
+
+ # generate each document x terms vector
+ docs = np.zeros((vocab_size, n_docs), dtype=int64)
+ for i in range(n_docs):
+ docs[:, i] = self.generate_document(
+ word_dists,
+ n_topics,
+ vocab_size,
+ document_length,
+ )
+
+ # build vocabulary and use it as column names
+ vocab = []
+ for n in range(vocab_size):
+ if vocab_prefix is None:
+ word = "word_" + str(n)
+ else:
+ word = vocab_prefix + "_word_" + str(n)
+ vocab.append(word)
+
+ df = DataFrame(docs.T, columns=vocab)
+ print(df.shape)
+ if self.make_plot:
+ self._plot_nicely(df, "Documents X Terms", "Terms", "Docs")
+
+ if df_outfile is not None:
+ df.to_csv(df_outfile)
+ print("Generating vocabularies")
+
+ # save to txt
+ vocab = np.array(vocab)
+ if vocab_outfile is not None:
+ np.savetxt(vocab_outfile, vocab, fmt="%s")
+
+ return df, vocab
+
+ def generate_from_file(self, df_infile, vocab_infile):
+ """Load a document-term matrix and vocabulary from disk.
+
+ Column names are kept as words. When using this matrix with the
+ sampler, map the words to integer token IDs before constructing the
+ corpus.
+ """
+
+ # read data frame with word columns intact
+ df = pd.read_csv(df_infile, index_col=0)
+
+ vocab = np.genfromtxt(vocab_infile, dtype="str")
+ return df, vocab
+
+ def _plot_nicely(self, mat, title, xlabel, ylabel, outfile=None):
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+ im = ax.matshow(mat)
+ ax.set_title(title)
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+ ax.set_aspect(2)
+ ax.set_aspect("auto")
+ plt.colorbar(im)
+ if outfile is not None:
+ plt.savefig(outfile)
+ plt.show()
+
+
+def main():
+
+ gen = HldaDataGenerator(0.01, make_plot=True)
+
+ n_topics = 5
+ vocab_size = 25
+ document_length = 1000
+ n_docs = 100
+ df, vocab = gen.generate_input_df(
+ n_topics,
+ vocab_size,
+ document_length,
+ n_docs,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/notebooks/bbc_hlda.p b/notebooks/bbc_hlda.p
deleted file mode 100644
index 3c20a82..0000000
Binary files a/notebooks/bbc_hlda.p and /dev/null differ
diff --git a/notebooks/bbc_test.ipynb b/notebooks/bbc_test.ipynb
deleted file mode 100644
index 93539a7..0000000
--- a/notebooks/bbc_test.ipynb
+++ /dev/null
@@ -1,931 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Hierarchical LDA Demo"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This notebook demonstrates how we can load the BBC Insight Dataset (http://mlg.ucd.ie/datasets/bbc.html), preprocess them via NLTK and run hierarchical LDA inference on the data. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
- "source": [
- "%load_ext autoreload\n",
- "%autoreload 2\n",
- "%matplotlib inline\n",
- "\n",
- "import sys\n",
- "basedir = '../'\n",
- "sys.path.append(basedir)\n",
- "\n",
- "import pylab as plt\n",
- "from nltk.tokenize import word_tokenize\n",
- "from nltk.corpus import stopwords\n",
- "from nltk.stem.porter import PorterStemmer\n",
- "from wordcloud import WordCloud\n",
- "from hlda.sampler import HierarchicalLDA\n",
- "from ipywidgets import widgets\n",
- "from IPython.core.display import HTML, display\n",
- "\n",
- "import string\n",
- "import glob"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Load test corpus"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load and preprocess text using NLTK. Below we load all tech articles from the BBC Insight corpus."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "stopset = stopwords.words('english') + list(string.punctuation) + ['will', 'also', 'said']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "corpus = []\n",
- "all_docs = []\n",
- "vocab = set()\n",
- "\n",
- "stemmer = PorterStemmer()\n",
- "for filename in glob.glob('../bbc/tech/*.txt'):\n",
- " with open(filename) as f:\n",
- " try:\n",
- " \n",
- " doc = f.read().splitlines() \n",
- " doc = filter(None, doc) # remove empty string\n",
- " doc = '. '.join(doc)\n",
- " doc = doc.translate(None, string.punctuation) # strip punctuations\n",
- " doc = doc.translate(None, '0123456789') # strip numbers \n",
- " doc = doc.decode(\"utf8\").encode('ascii', 'ignore') # ignore fancy unicode chars\n",
- " all_docs.append(doc) \n",
- "\n",
- " tokens = word_tokenize(str(doc))\n",
- " filtered = []\n",
- " for w in tokens:\n",
- " w = stemmer.stem(w.lower()) # use Porter's stemmer\n",
- " if len(w) < 3: # remove short tokens\n",
- " continue\n",
- " if w in stopset: # remove stop words\n",
- " continue\n",
- " filtered.append(w)\n",
- " \n",
- " vocab.update(filtered)\n",
- " corpus.append(filtered) \n",
- " \n",
- " except UnicodeDecodeError:\n",
- " print 'Failed to load', filename"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create an inverted index for the words to position in the sorted vocab"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "vocab = sorted(list(vocab))\n",
- "vocab_index = {}\n",
- "for i, w in enumerate(vocab):\n",
- " vocab_index[w] = i"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Total number of documents in the corpus"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "401\n"
- ]
- }
- ],
- "source": [
- "print len(all_docs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Total number of vocab. Also print the first 500 words in the sorted vocab."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "8185\n",
- "[u'aaa', 'aac', 'aadc', u'aarhu', 'aaron', u'abacu', u'abandon', u'abat', 'abbott', 'abensur', u'abet', u'abid', u'abil', u'abl', u'ablebodi', u'aboutroughli', u'abov', u'aboveaverag', 'abraham', u'abramovich', 'abroad', u'abruptli', u'absenc', u'absolut', u'absorb', 'abstain', 'abstract', 'abtahi', u'abund', u'abundantli', u'abus', u'academ', u'academi', 'academia', u'acceler', 'accept', u'access', u'accessori', u'accid', u'accident', 'acclaim', u'acclim', u'accolad', u'accommod', u'accompani', u'accomplic', u'accomplish', u'accord', u'accordingli', 'account', u'accumul', u'accur', u'accuraci', u'accus', u'achiev', 'acid', u'acknowledg', u'acquiesc', u'acquir', u'acquisit', u'acquit', u'acr', u'acrobat', u'across', 'act', 'action', 'actionfest', u'actionpack', u'activ', 'activcard', u'actor', 'actual', u'acut', 'adam', 'adamind', u'adapt', 'adaptor', u'adawar', u'add', u'addict', 'addin', u'addit', u'addon', u'address', 'adept', u'adequ', u'adher', u'adict', u'adida', u'adjudg', u'adjust', u'administ', u'administr', u'admit', u'admittedli', u'adolesc', 'adopt', u'adrenalin', 'adrian', u'adsens']\n"
- ]
- }
- ],
- "source": [
- "print len(vocab)\n",
- "print vocab[0:100]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Visualise the data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Make some pretty word cloud using the Python Word Cloud package: https://github.com/amueller/word_cloud"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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vUZmYT2Nk2by9z//2P/57Fs7UeP23LnH3+g6H2x0CL+QHP7rGP/2Tt/jpX33K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z3v351oSPsFASnPw6RRb8WtkGQZZdPGeIngENsxufLOBfafdLj/m8ds3Nnip//hV3T3+jTPzeEU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\nKI0WF7uaW67HR9YG77XWgJLQcua/CytegK0Uvfj/Z+/NeyS58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9Q1gK1M5q0axTOcOTIg\nyVLGsUcnGNGy1LF+yVEW0w/HTGP/XO+/v7BwlHX2xs8QtqrQcl6hF96hG37OrvcBpizh6ubzZQf3\nHCCvJH/BnfF7RFmALQtUjTleKP+INfdVSrqB/hIe04vLNebmKyglEFLQmi+TzW5cQgj6/SnDwZRq\n1eXlV5dzBwgpvlWV5IW5Cq5j8vntXbZ2Bvzq3S94sNnj/kaPetVFa3UQOPLp51tUKy4vv7jEG6+u\nsrbyUDKmtaRadblzf49bd3ZZXKiyuFDBth6e80IIbEsz1yqxszfk7nqXl15YoFZxT3HoeAiBxJRF\nktRnEu8C2Sz6vXwk/v07/H4jSlIedAfc2+2x2R3ihxGZgPlKiRcbLearRW5utfmrD29jKkW1YLPS\nqOBFMRP/5KFuy1AsVEt4YUR7OM0DUc65wDsNUkoMQ6OkJNUZ1apDkiSEYUK1ki/St3cGhGGMZRmk\naYoQguHQp9+f0G6P2GsPme+UaTZLp5LkfvAxD8b/NxkpBb3CnPtOTnQBP2kTJSMy4kPyiH3Yao6q\n9TI7018wie6TZPudzv1OUEZKTJB0GAW3abk/oWxeQYnDksUg6bM1+SvG0V0yUubsd6haLyHQxOkY\nP2mTZhFaOMf6HQMUjBVWS39KliVM403C3v9BlE6Zc9+hZr1Oybx8aHtLPbxeZVlElA7Y9X7Jg9H/\nhakblM0rNOy3Z+Q6YxB8Qj/8hPboPxGlQxw9j6MXMR/pLqckDKNbCKGZd9+hH3zMxvi/sDP9G4rG\nBebcdzBVlST1GEd3EAjq9htYNE7/QZyA544BFLTJUAbcGnQIkvhAR3naLXAcBrT9CTcHHe4N+4yj\ngCBJSLLjzcOPwx8uXeKdxQsUdRlbOeRrq7xVbUjziVflOeE7v3RgGvsMownxCZIELwnYDXpnBojs\nI9ckV55IbmEpk3m7dm5njnHsseN3KRkOJY4S6zhN6EdjJvH59tlRFnN2Decp5BZfBoYsUDbWaFjX\nmMTbjKMt1ie/IEzHFI2lg4ElyEiykDAZkWQhDfs6Bf3NG9B/nTClw/XyOyw4Vw+0qrbKI6/LRhMl\n9JeqYEkpkGZ+gd5/nUw+PIfdgsnaxSaOM7N8E9+sbvxpsD9s9aO3LlEq2nzy+SbjccDtL3a5KyVC\n5BUtgJeuLfHStQVeurZIq1k6VP3VSlGtuNiWwWQaUik7LC1UsczD34FhahbnK6xv9Lj/oItpampV\nF30OoiHIifL+UKtAIpUmOyG6/nnEh3e3+cv3bvLi6hwrzQqTIKQ9mLDVGRLEMWRQsE0WG2WuLjVo\nlotUCsfMRGR51W5vMObvPrpLdzSlPZwSJQkCsExNq1xgoV7m8kKdetk90OPuI4hiRl7AvZ0eX2x3\n6U8epjKWXJtG2eWFpSaLjTIF2zzkoLHRHvAX792kVnJ44/ISneGEvcGEnd6YiR8SxTGWoSk6FvPV\nEmtzVS4t1lFCHllETvyQ3mjKra0O290R3dGUJM2QUlBxLZabFa4sNqmXHIrO+YoWWkoWKkUsrVio\nFg+6wiXbomhbuHYupWiV82RXx9RUCjZxkhLGCdWCQ9mxkFJwab6OVpLFWok88U7SKLmzbc62LTwN\ntmWwtpoPExYK5kEq4tpqg1azRBynuK6JYSgqFZckSVFKkCT5Qr1SdqhWXWzbpD4bND6rKxOlI/y4\nTcm8RNG8TNm8elB1ddLF2RxMcqzEwdZNqtbLdPz3idIR4+geWpZwde5hn2Q+XryFF++SEuHqRUrG\n5Vlq7EOkWYgft0mzhJJ5mZJ5hZJ5FYEkzUIK6ZSMBClMDHl8N1YJC0s1Zu8bIIWJFCGGLGGp+hF3\ni0dJf5B06fjvMonuI6VJy/kRdftNCnoFJSwyMixVx1BlgqRLELfZ837FvPtPMNVDiYtAYMoaBWOV\nknmVOJ3i6kWkNDFVnZJxBVNW8kVDeIsg6ZJlTz+M//yRZMPE0Zpdb0ySZlhKo8TpsaDdwOOD9jZ/\nu3WP37Q3GYY+XhzlU+Ocy2GJqunw06VLuM8oEcbVOeE7H0nO8hS7cEKSHl8l9pKQHb/HNDm9KivJ\nIzTLs2jnJ/EatqRBy65S0g4SQXrGkZskHrvByX7JUTqrJJ+xz/twlMn8TG7xdUJLGy1tGvZ1vKTH\njvc+veAWQTKkbK5RMpbQs5M4Tn38pE+aRRT03O8hSba5Wnr7K3v9s6QglmWwtFxDKvm1EmSlJM1G\nkfEkoNko4jjHn9dSCEpFm1azjOdHlMtHF49S5kN3r720zFyzRKVkc/P2LnfX24ynAVma4TgmSwsV\nLq01+d5ra1y60MQ0FfIR0mQYima9yPxchVazxOpyndXl2pGgE8vUrCzV2dkbsb0zYGGuTKtRwjyH\nfVZ+/cyLDXImA8uydOZc93Ag9HnGrc02/+Yvf8PPXr/M65cWmQQhm90hdza7TIPcXcS1TC4vNhhN\nA167uIBrGXkozCPkMs0yxn7A/d0+nwV7bHaHbPfyhENBHvc8Xy1ycb6OFAKtFWXXQs0GY5M0YzDx\nubPV4f07m/z2zia9kYcXRGRApWAzVy0y8ULiJOXCfA1nth8Au/0xf/b3HzFfLaGEYKMz5EF7wIO9\nPsNpQBDlJLnsWiw1yrx5ZZlywabsWrhW/nvNA25Sdnojbm62+fVn69zd6dEZTonTFCkFtYLDlaUG\n0yDixZUWa4ZGy6NE+3FoJWlVijRKbv5aQiBn2uP987RWcLm60DjIPhAnnMMrjQrL9TJhnLA3HJOk\nKbWCS6PkUnafbKj7cZimZnHhqKZ3Yb6Sd62zh/s113oo9YTD+7q6cv5kvIx8cC8fvCvMhuOKKGFh\nq7lTh8pMWaVkXMZS1Rnx+wJbzR2Q5DjzGEf3CJI2Uhg4egHXWDkkddjfizSLEMjZQJuDFg5K2IiZ\nG9FZ19NHh+m0dBEoBDIfkpfuieQaIEh7tP3f4Cd7mKpO036LpvMDBA8X9QVjGSEU/eBj/KRNx3+X\nqvUyJR5WqAUSS9Up6BVcvUSg2zjGAiCxVZOCsZzPGUXrjMLbxNmE9Bzd95Pw3JFkAZQMi0W3TJjE\ndANvZs92Mu6P+vyHOx+xPu4zDAIqls2cU8SYuaaf5xLecp5t63C/knwewpcBk9hnEE9PlFLsV5Kn\nZ1RlLWVQNYoUtYMh9bmGsfZhSE1ZFyhpF1tZBGl46rDdOPbZ8Xsn+h9H2X4l+bxyC4uWdd6FxbNH\nzbqKKUsUjUV6wS1G0Qbd4DPa/kdA7hkrhMaURQp67ltVTftdgZQCyz7bseVZo1y0+Vd/8n08L8SZ\nVZCOg2VpfvjWJV58YQHPC2k1Tx7y0VrSrBf54VuXeOX6ElMvX9iTgVIC2zJwHZNy2cEw1JHPbFua\nC6t1KmWb77+2Sr1WoFpxjxB4xzZ48eo883Nl/uAHV2YBIwXcc1jmZSQEyQCJZn6WuBd8SxP3Pl3f\nZTgNmK+VaFWKXF+ZywM3/JAvtrvs9cf8h1/cIAhjygWbWtHBNh924vww4sO7O2yWhhQdi+VGhe9d\nXcYyFGGU0Bt73Nnq8Pef3iPNMqZhxA+ureBa5kyv6/PJ/R3+7JcfMwlCXMvg+ktzVGeOFDu9Eevt\nAf/1xh3u7vb4H37yMiutKkX78D3k9laH//TLj/JQEtfm7WurlFwLQyl64yk7vRGfb7SZBhFRkvKD\naytcXcorlGEcM/ZD/ubGHX716X0AFuplfvLyRQwliZKUre6QvcGYf/dff8t/99a1PPSj7OKY5+tK\n7ofkwCkDi+f42aRZxkZnwHpnQG/ssdKscGm+fu79eFp8FZcWW7UoGhdmbhBDomSUkz/zKqYsH5FG\nPIp8DqCIq5cJkg7j6C4FY406bwCQpFOG4S3idELBWMPWTQxZOOJAo6RNybxEP/yEnenPidMpftKm\nYl7H0fMY8skHEp8EcTphEt0DoGhcnMlNjlbglXBw9CJB0mUabxJn3mNbCJSwUNJmPwpbiXyQOB8c\n3B/Yny0Mv0QVGZ5DkuzFEX4Ss1qsMolCNqZDojQ5dXDPSyK2pyNAsFKs8GpjnpZTxFTy3FWOK5Wn\n06ucBFuZ1MwSjjJRQp7p7DBJTpdb+LNK8llyC3smWShq54ldQZTIPTmLhkPVLNILh3inBIBMYu9U\nkhynMf1ocqqd3D4EAkfbNK3yiST5OCskQxZZdN8myxIq5sVDbZknha2qGMJFCo2jGgzCu/hJjzAd\nk9vMyFm7KXfRML/ii8p3OAoxq9B93TBNzdVLZ3cNlJIszFVYmDvdFmo/Tc9xzBOr0mdBa0W55FAu\nOaytnHz9ypP3CtSqBVh78vdJs4Qki0iygDjzCdMJ43gbAEuVZy3X59sJBnKJgRdGLNZLvLjSYqVZ\nQQiBF0bMVYv85tYGv/jwLp9vtFltVXnl4vwhkpxXgj1qRYcXlppcW2mxNlfDNhRhnKfJRXHCTm/M\n7a0O1aLDG5cWcS1IkpT7u30+ub/Lra0Oa3NVvn91hcuLDRplF7KMjc6QSsHhHz67z+cP9vj4/i6G\nVhQXD5Pk0TRgfW/Am1eWePXiAleWmtSKNpah6Y6m3N7qsNMb0x1N+c3NB1ycrx2Q5P7Y4+Zmm08f\n7LLdG/HWCyu8cmGBaytNTJ17F6/v9Xnv9gafPdjj5kab+VqJNy4vPRFJPp0cn5+FKiUp2iaLtRLz\nlSKVL1lFPgtfZqj5NBTNC8w5P6YbfECY9OkFHxIkXcbRfVy9hGss4uhFlLCPzMHsD6UXjFW8eJtJ\nvJ47QqQhQgiidMw4/II0C6mY17AO5IGHYcgSdft7IAQ9/8Fu6IoAACAASURBVAbTeIvEC/HiLVy9\ngmss4ehFbFWfVXefratYmoUESZc82bZI2/8103jjyHZ+ssc03iRIOoTJ4CD2+uEBya3i8s+YC8KE\nUChMJOZscSAe2f/z6gmOx3NHkruBR9ufcKFUYxQF3Bv1aNlF0gInOnPaStO0C1RMi6uVJv/8wjUu\nlKpPVOMwnnGOvSUNKmYBV9kYQh/49p2EyUyTfJJrhZcE7Hq9M4fgHGWyYNcpnuFQcRpK2mXOrjKN\n/VNJ8nifJJ8QqR1lCYNwcuY+CwRaKlxlUTdLyFNOzmwmAsmXPwJXN3m19r8BINEnDhycF1IYVMwL\nlI0Vlgs/mi3ODn9vQshcm/nddP93+J2HQAoDL26zHW8Tpx5JFhGnHqExpG5fw5KVbwVJLtgma60q\nP3v9MleXmiiZ32CzLOOF5RaOZfLx/V02uwN+fXOd5VaFRvlhx0AIsAzN1eUG//Mfvk6t5KIPCjEZ\naZYRRAnjWWX6QbtPPPPvDuOEj+/v8Mn6LkoKXr+0yP/0zqt5YMhMTnFhvs5rFxfpDCfc+GKbX3++\nTtG2uLJ4WKeqlMCxDN555SL/zRtXMJQ6kDUsNyo0ywX6Y58bX2zx6fou/fHDStx2b8zPP/yCnd6Y\nZqXAf/v9a7xyYR7L2G95Z1xZamCbOifb/TH/+Pk6F+ZqtCpf76CmFIKVRoWlWpksyw5Jjb5tKJvX\ncPQijl6i479Hx3+XXvARWRZTtq7RtN9mqfDHOHr+2GFxIRRF4wLTeIO2/y5evEOcTZGZJkpynbKp\natSsVzBl7Zg9AFNWmHd/mpNhtUjHf5eu/x47059jqxZl6xpLhT+iab89k1I82+OdZQlx5hHEHSbR\nOh3/13DcdSNLZ97JCVoWc3nXkQNytAAqhPzS9//j8NyR5LrtEKYxN/ttvCSiaFhYSp1KeFeKFf7k\n4nU+6+9xa9AhvvspDdvF0Xm09UPT8ZNf5aX6HC/Xn52+VAiBRFIyXKpmkU4wJDql7D+JfQbRhPgx\nTfKjgRztcIh3AiHdh6ssFp0GRf30K+6i4TBv1dnyunBK/oefhDP3ioAojdHicEs4ThOG0fjM6reW\nKpeIGO6pdnthGtMJB/hJQJKltKwqVbOEFM8uLz6vgmgQxxnxfYfv8PuFPIF0GVuVibNwFj6QIWey\nI0MWvzUOMLWSy1KjTMmxD8UZ71t+NcouF+drDCc+290RQXj4em1oxVKjzEqzSsm1sY2jQ6pl16Je\ndPg8Thh7IemsA5qkGdu9Ef2Jx0K9xHy1iDtzIdl/DSUFmWOyWC9zf6/PZmdEe3jUEqxacLi62KBV\nKeBY5iHNrwIc06BecjG1oj/2CKKHn2M49bm12UHJPB2vXnJwLOPQgKFWUCk4rDQrPGgP2GgP8MPT\ng6C+CgghUEJwRt7NtwJSGJiyTNW6jqkqVMwXmMQPmEYb+EmbXnADgKbzNg37e0eeL5A4eoGCXkVL\nmzDNrdKUMJnGG8SZR1FdpGK9dKyHcv4iEiVsXL2McDSuscQkesA0WsdLdpmEd9kTf0+SerScH2Hr\n42eNnhoi1y7bem6mHV7FlKd33JS0KRqrR1/qkf89+vizxXN3dauaDpMo5NP+HlkGc04RexbDe+Jz\nLIdXG/PcGrT5uLfDjc42llKUTRtDSfRBZfLk1xCCZ0qS89cUlI0CNbPEIBoTnaIdnyYBo9ibxVhn\nBxe+jIwoTZjG50utc5TF4jOqJNtnOGOEacwonjKJPfwkxNU2alaZgXxwbxhPzyTJhlDUzBIl7Zza\n2oqzhH44ohcN8ZIAW5lUzaOWeVmaEccJaZKSJilSyZk5vSBNU5I4Rap8qCSdRQJn2cxZYXZFztKM\nNEkRUqKUIJtNtu8/JlX+ekmSEPoR2lCYtoFU8rmveOx/P0GcMPYDXNM4GOw5DSM/YBpEVGYE4Tzw\no5j+xMsHihzroBWbzLSgkyCk6trnev/v8PVDCoOiMQ/Mn7nt845qwWGuWsR8zIngYGjIMlltVflk\nukNnNCWMD1+wtZIs1HKCq+XxQ06WoSk4FkmaEkTxwbmWZind4ZSpH3J5oUG1ePRaJ0RuZdisuFQL\nNh+2dw5VgfdRdm0uLtQpu/ax98V9iYLWikkQHlSzIZecPNjrs9ys4FgGg0nAdnd05DVGUx/bNPCD\nmOE0OHIsvsOTIXfpMigYq7jGCjhvM4nWGQSfsjn5c8bhXcKkj6mqx5JkEFiqiauXMGWNJPUYBJ+g\nhIOf7AHkgSTGxWNt5Pb3Id8uT8GrWNeI0gmj8DZ73i/ZjP6cXvAhceZRsV7E5iySLA7+ZeeQNEg0\nhighlUXJuMS8+1MKxr7+6yQv8XwY75vEc0eSgyTGS/KVr60UJdM6Uwpxe9Dh3928wYfdbXam44N2\n/J43eUQfdfoa4w+XLj6L3T8EQe5XXDOLrE9P/wxxmoeFeHFAlEYH1m3xbPhtGOdSjLMs7ZxZJbnw\npUiyw8I5nTnSLE8B7EdjLPUwHTHJUsI0YhL7BGcQe0Nq6maJsnF6RVgJSUHbTJIp/XB0ojQlimI6\nW30GnTGToUex7OCWHKSSeGOfzs6AYsXBLdl444DAj4jDmELFoVDOb16BFzLsTiiUHYoVhyRO8L2I\n6dDDLlgUyy5CCgadEV989IDWcp1Lr6xQrhew3a/XneNpkGUZd3Y7/MWNW/zo6io/unq2WPWXn9/n\nV7fX+dc/fIWXl89Hmr7Y7fJv//Y9Xlqa41987zqumQ/djf2An396l7/86Bb/6ztv8uNzvP93+A5f\nBqZW2KaBOmEhrpTEMY2DBVySHm7zSiFwLRPHMs4c7nqcMmRZPjQXJym2qU/0qBaAqfO/h1FCdAw5\nNbWi5FgHw3Gn7UT22O0iTlKmQcTd7S7twYSP7+8cm1w38SO6oynDqU+j5JKmT6/p/A7HQWCrFsJS\nBEkPEAzCz4jS4SnPkBiqRNm8RpB0GISfooRLRjKTciwgxZNkOgi0sCkZF0nSKVEyoh9+gp/sHiTg\nnfkKQs8s5OLjZRGPQMsCReMiQdohTPso6cws404pXiJOJP1fF547kjyJQ4ahf+CP7McRcXb64N4w\nDHJ5Rhwx5xRZKJQoGvnA3Hl/LkvFs/PXnxQCQdlwqZulE6Om95GSEaUR49jDS8KHJDlN6IUjhtH0\noHV3ErRQFLRD3Sx/KRu1ouHQsmpY5yHJpAyiCb1wTNN6eAyDNMRLAoI0OnNo0RCahlWmdIY/c+5Z\nrQmSiA1vF0NqRtH00KDIgt3EjW0GnTHD7pg0ybWCSZLS3u4z6uXE2Rv7WI6JN/aRSlKqukwGU4ad\n8SycQqIMxWQ4ZdSbtTxnNk6WYyJVHjgTBTHdnSGFGWn+Nk37KyGxDI0+Zz9zsz/kg/tb/NGrV879\nHmM/4JONPbRU7AxGLFbLKCnYG064s9vlowc79Cfncz/5XUKcpny+1yZMEq41G7jms7kR7F8n9yYT\n7vb6XKhWmS8dtWVK0pRJGCKFoGA+uQ/8N4mCLvBy+WXm7KOdvwvuhROflz3236Mb5KE16ezf4xAI\ntJIYSp1NRI4prO0Pa2bZMez16K7sP+nI36QUmIaaaarPxnHvJIRAirxDdhwBdkzNcqPMcqPMXLVI\n6Vuw8N9Hy2rxVu2tE//2TcCLd/DjNoYqoYWDFMaBteLDRAZ1og74QAojS1TMa3T93+YkWTqYskzB\nWMm9gk/hGXE6ZRpvQpZhqBJSGAhUTnDJcktN1Ozf2b8tgcKQJaJ0yDTexEu2cZPFA8eKjAQlnIOo\nalNVqNmv0fXfZxpvMYnuYcoKlqohRT5wl5Kn8iWpfzD3Y6rqNyrpeu5I8iD06fpTDKmI0pQHkyFz\nbomUkwf3tJC42qBq2awUK/zx6lUuV+oHMo3zXEoc/eyHsIQQs0py+VwRmnGWE85J7FMxCrPHErrh\nkGE04bR2hkTiKJOidqgYhSeKo34cReXkQSjSRBy0Uo5HlmUMovGsspuT4YyMaZzLR87jT6ilom6c\nTZL30YtGfDi4w+3xJq6y89bn7Fv+4/kf8opxOSe/UjC32qDaLJFlGZ+/d5dBd0y1UcSbBAy7Y+Io\nYX6tyYvfv8Sdjx5w64P7SCmYv9Dk+tuXuPfJJrc+WMct2VSaJeZW6tRaZUq1Qp4El2Y0l6o0l6rU\n5yunyoKeJwghuDxfZ7lexvwK3SIMpagVHeI05V67T9mxMbXiQXeAH8XMV4o436JI6WeFMI75z598\nStfz+N//4MfPjCRD3t35bK/Nv33/t/wvr792LEkOk4St0QhTKRzj5Orq84iW1eJPl/6U9JjFt3HK\ndS+KE4IwPrHYEKcp0yCELMMy9NFz+Uv4cgshDhakQRQTJscXDjLywJEoSbAMfey5OZvnf6r90LNq\n+UqzwgsrTb5/dSV31zgFtqFZanx7nHxeLL3IRffisX+zvmYP/n30g4/Zm/6SsvkCjrGIqSqkWUSQ\ndOkFHzKNN7FU40yNriEKVKzrjKIvmEQPUMIEc42W8SNcvXTqc8NZ4h5ZloeZqApKWsTplH7wCV3/\nt0gMHL16jMfyUUihsVUTL96kF9zAUnVMWUVJOy8oZT6usXxAki3VZM79CV68TTf4LVuTv2ISbVC3\nX58RZZs4nRImfbxkG4nGVDXq1usHr/FN4Lm7O5UMm4rl0PZy2YSjjENZ7sehbFq8UG2yMRlwb9Tn\nl9v3+WLYw9UzQ/hzXFBeqrW4/qw1yXCgST6rkgw5IR7Fk0PhG1Ga0A3ySvJptQctJXWzTNUonhm+\ncha0VNjKpGIUKGqHSeydGCySZhn9cEIvGh+0JzNyS7tx7JGcUTGBmdzCKlE6Iw5bC0lJF3ixtIYW\nCiXkbFjw4Te85DQhzqsjQkps18ScRYq6JRvfC0niFNs1cUs2na0+w86I9VvbjAdTDFOjtCQOYjbv\n7DEZepi2Roi83Wo5JqZtoLSEDIQURGHMdOwzHkxxCham9WwWXFmWcXu3y0Z3wNDzieK88mAoRa3g\n8trqPNVCfsx2B2O2ByM2e0PGs1Zx1XWYqxS4PNc4sE4a+yG9ice9do/d4Zj+1OftS8u8vrZ46L07\n4yk7/RF32z28MKJk29zZ7eYLpifovColKVomWZbRG08J4hglBZNZiINjGIdSxT7f2uM3dzepFRws\nrZkEYf5dCnh9bZG15glDKd8yZMA0ipiE4ZkdoieFEILVaoX//sUXuFA9/nhNo4j3t7YomRbLlcq3\nakhVCUXhKUKfeuMpW70hYXR4IG+/+j72Qu7v9gGO1S4fwhNeXpUUtCoFNtoWW90RvdH0SHc0DxxJ\n2euP6Y28A23ys0TBNlmbr6KkJIpT5qtFVudOd4KSUp5o/9YNvuDu+BcUdIuSMc8k7hAmY1JiGtZV\nquYF9vzPGMdbRKlHxVilYV3B0TUM+fB6nw+op7m9YLTLJN5jGreJ0ilxFpBmMVJolDAp6CZFY4Ga\neQFDukeqp73gFvfGf0fdukTJWEQLCy/p0gvvE6ceaRbnIR66QlG3qJirFHULHgvTCJIhG9P3GUc7\npFnEkvsmLecl4PAiZb+I1A/vsz75hzw5T5VZcr9HyVicfb6EKB3RCd5HBB8cuDDsP140LlC1XqZs\nXjv1+1PSwtHz2KqJlu4sytqgYKxhqdOr5BkpaRrkVdx4fbYPuaNJnHpoWaRqvkTNehVTnn2d1cKl\n6byNFIpecINReJMg2QPUQabAgvuHB+Q9t09t0nR+iBQGfrKHn+yyPfmb2XHfP6b5PWa/Ov5VOFY8\nCZ47klw2LaqmzYNxnyyDipn7/Z5WobOUpuG43Bl2+LzX5sF4gGsYuNp8zN3iZPzrK69+BSRZUNIu\nVaN4LpKcpCmj2DvkKxxn8SOV5JOhhaJhlamZxS/dOpVCYkhN1SxSNQp4SXBiRTjj0Upyvk2WZUxi\nn1HsnSm1ADClpm6eXUlWUlGSLkW9xtXiKunM+UOJw+4n3jjIh+iEwLRyQitSQX2+kmsD/ZBKo4jt\nWgw7IyYjn+37bZSSVFollMovlnubPQRQbZaBDMs1sWYEWQiR+zVKgdKKJE7xRj6mpeFZkWTg5lab\nX3/xgJEXEKcpQoBtGKw1qlyeqx2Q5P7U4+5ejxvr27Rnfq0V1+ZCs0qjWDggyX4U0R5N+Hhjlxvr\nW3y22cYxjCMkebs/4rf3tvjwwXbuI1spsdkbkqbnDXrPoYTANjQZGSM/JI5TUp0RxvmAqlbq0Pn5\n+XaHf/+rG7y0NEezVGAw9YiTPAlssVr+RkjyNIwIkpiCaZKmGYPAxzUMCqbJJMy1ewXDQEpJlmXE\naUqUJPjxftVSYGuFqdThzysgTjOGfkBnmsupbK0xlcptvWateT+OCZOEOE0PJZUZUuKaJnq2yEjS\nlChNCeKYomny49U1SscMRHpRxM54zHsbW9QchzeXFrG0Rs2kF8ZM6xqnKfHsc8SzBbCpFJbOE9jU\ncz6g+jh6I4+N9oDOcEqt5GKqPNY8TTOCKGa3P+bebo9Wpchaq/pMQyuUlCw3KqzvDbi12WarO6Q3\n8rDMh3KnMEoYTDw2OkNG04Aryw1a1ZMTzJ4G1aLDiytz3Nvt0Z94xGmKoRTWLKxGiFzqkaQ5YReC\nY2Ot99EP73Oj9++pW1eYs19iEK4zifcIkhGXSv8UEDyY/gM73sdM4l2W3bcQCFrixUMkGXLC6Mc9\ndrwP6QS36AX3CNIRUTolyUKkUGhhUTXXaNkvoYVF2VjEVIePUdv/nPe6/4aLxXeYt1/BkC6DaINt\n7wOCZEicBihpUtLz1KxLrBV+jCkLGNLJnY1mCNMpG9N32Z5+gJ/0UdKkbl3JZQqP3mMzSInoBXf5\nuP8f8+NsrlEx1w5IspIOWpaYxA8Ikg5J5iHRaFmgYKxStV5h3v0n2GcQXSlMLFXHUjUMWSLJAgxZ\nxNFLJ7taHDxXY6gKWbzBOLpHknqkJLPUvxYl4xJN521q56zcKulQt99ACk2cTpnE9+n5H5KSIIWF\nKUvUrNcOvb8UxYMo6j3v7+kFHzGO7hGnY1JClLAwZBlbtVDCxlIN1CNVbUMWcdQChqwgMAnjlDQ1\nMGUFJRyUdBFCIUV+bPcjtL+MXetzR5LTLCPJMkylSdKUOE3OrLRsTob8f/c/Z3syYhyFjKIAJeWZ\n5PpR7Pmnk9CnhastyoaLrUy0UCcm6kE+AT2J/UPhHPua5FE8PVX2oKWmaVaomqWnbsU9CiUkdbNE\n3SqzG/RP3O80SxnONMmH5BaJn1egzyDJEoEl85RA95y2dWmWEmXxzDIvpmFVMaUmzbKcGDsGa9cW\nAIFTsFCGgixj5eo8reU6SZxgOQZxlLCz3qEkBNfevIBhaZjpBgW5w8WjLVZtKOyChX6kwlRtlfn+\nP30J0zYoVlyMZykdyDIedAe0hxN+8sIaK/UKpdlEu2sa1IoPL2QLlRKuafLCQpMgjknTjL/66Daf\nbbX56fVLB9uVbItLrRoV18Y1De7sdo996zu7Xd6/t8n3Li6x2qjimgb/OYp50BkcGWg69SOQn9Mi\nm92AZ4/vn5aP/6anQcjuYMzPrl/ixy+sYRsaNfOAXax9My3f9zY3+WRvjz+6coVh4PN/vv8BP710\nkZ9dvsRf375DBvyzK5cpWRZJlrE3mXCn0+W9rS0mYYiWilfn53ih2WClUsHS+W8ky2BvMuYvbt1G\nChgEAa8vLHC91WStWsUxDNIs4+PdXT7b69CeTAjimAywtWapXOJnly7RKuZV1YEfsDkccmN7h/VB\nn93xhP/xlZf5JxcP63RvbO/wt3fv8Wm7jaUUWsrchtGx+WdXLrNazVu+fc/jwWDIe5ub7Izzzt61\nRpPrcy1WK2XK9lcb7PCsMfR8vtju8lfv32SzO+TifA0pBF4Q8flGm/dvb9Abe7xxeYnvXV2mUnj6\n4efHYWjFS2tzDKc+Nzf2uPHFNoa6wbWVFs2Z//D6Xp/P1ve4tdXBNjVvXV3m8sL5o4/Pg4VaiT98\n9RL/zz8EfPpgl//y7ufc3+1xdbGBY5loJfGjmOHUpzuaYmlNpeBwdalxkAx4HIbhBlmWYMrCrEo4\nYHP6Ll6SX19sVWYcbdMP7rEu/pGC0aRgHCaEGSnTpMuD6T/iJwO0tCkYDbSwEULiJwMmcZtBtEGQ\njgFYcr/Hovv6MXuUMQw3idOQJPNRwqZiriAxSLOYcbyDnwxZn/wDAklGxpz9Eo5+SDSVMKmZFxlH\nO4yiLSbRHqNom6LROmQ7mhIzidqMo12CZEzFWKFhXsWUD7sdFfNFLNUgyXLCn2UJ+2lxWrgYqoyt\nmk8wpCaQQmHrZYrGJbRwzvQ1NmWVBfdnNOzvEWceWZbAzOZASQstHCzVRMuzX2u2B2hZpGK+hKWa\nJLPFTDYL3pJC4+rlI8/Lq/hN5tw/oGq/SpxOZ9aS6YEOOSfZZUxVRT+ymGo5P54FnrSIoiL3dic4\n1hyrlX85O5YOWhaQwqQlfkjJyOOsbf30BdDnjiR7cUScJgex1H6Si8pPG9wrGCYXSzUW3aN2YOfF\nl3nuiRD5UJqjHhLlcXzU0mcfSZbM7NTySnKWZQfuFqPo5OfBfiW5kleSnyVJNkunJvdlZIxij2E0\nIcpi0iwXZkzjXG5xFkm2lUlBO7jaPhhWPAn5AiqhFw7Z8tp0wxFS5El9QSrZ9bs0Z97J5cbR41Cu\nF2d2b9mBg0VruYZSiuZSDcPSh1qf+6m7p1Xmbddk8ULrS+kVT4ag4to0SgVsI6+Mp2lGpWBTcXN7\nw33sVy+zLEPPLOrGfsjucEwYP2wvW4bGMjTVgsO9du/E4JbOaMJGd8i/ePM6r68tooTg74r3nupT\nZIAx00JOgpAsy7C0xjLUwd/3kaYZUZJQL7pcnW9QcqyDyuY3hfZ0yp1ujx+vhvQ9n99ubXOlUSdK\nEjaG+TT6fqU1ThJutjvcbHfwo5gsmw3ptTtEaUqzUMDSmox8ARwl6UERIJo9N0lTWoUCjmGQZXCr\n0+WDrW0cQ2PNKs0HvraP/OT2bSOVFLQnU/7+/jo/XjveY1QIQZQkecqmlCiZe9I++hNe7w/4YHub\nvuczK4izPujjxREl0/zWkeSSY1FwLHb7E5J0m63uEDLwgoj1vT6Dqc+VxQbXV+e4utSkYD+7SrKW\nkuVmheurc9ze6tCf+Hx0b4fe2KNScBDA3mDMVndIxbW4MF/j+uocc8+4klxxba6ttLi702MahHRH\nUz68u017MMHQGiXz34Ufxkz8kIVaCSXlwe/7JETplCAdU7cuY6VlOv5NhtEWGbDivoUjK4zCLfxk\nSDe4TZCMj7yGEDInxrqFqxq4uoGja48Q7x7DaIuN6W8YRdvs+B9RNOZY5DiSDNO4Q5JF2KpCxZxj\nzr6OIZ1ZYWeTXf8TNqe/oR3cxFIlquYaDg9JshYWNesCg2idjem7TOI9+uE6lipiPFJpTbOYUbTF\nKNoiSQNc3aBhX8WSD787Wzex9Ze3MkuzmDQLiNIxcepRMV+mbFzJI5qPuf9kWUpGSpwGZKS4xjKC\nNQ6GBR+935HNBucyhEgRWXZwD83ISLNkZqggZ6+bIYTEVDUs9ehiTpx6L8wryrMq+hN+/oKxQsFY\nAWDPH7PR3mShVuLy3MVD2ylhYsgCrnGUpD8pnjuSPAwDgiTm9cYioyjkRmdrX6JyIvV7s7k0i5V+\nem2fq786mxFTaupWmZLvnkGS80qy91gleRBOmJzyPMi9hptWOa8kPwOyJkWuca6b5TMS8HJCPIo9\nwiRvL2dZxjQJmMQeCSdfXAVQ1C41s4ghTw+MgfyE95OAO5NNfr73HuPYo2aWuVhYxE9Dfr73Hj+s\nv3Ksd/Kh950dH8PUrFyZR0hxqDp8aAfPg69o5kkI+MGVFepFh4/Wd/j1nQdMw4i3L6/w2toCl1q1\nAwK53hnw4YMd7rd7BFGMbRh8vrVHlKbn0oU/jijJ2/amVhQtE0QeUuAY+qmGvBzTpF502RtN0FJS\ncixK9tHhEEPnHq+1gsP/z96bPUlypdl9v7v47rFnRm6VtQJVKAANdPegu2coDjlDM4ock0wmSiYz\nmh70IDOZZHrVk/4b6YV6ksmMT5JIjjhbs6e7pxd0YwdqzczKPfYIX6/rwT0js6pyq0KhG+DgwFAZ\nGRmLx3UP93O/e75zGmf4wP6u4WlNw3FQsnQ2aHmlXtoURWlph5hvZ5zn/N3mFoMo4l+8dZeFICDO\nMv7Vr37Nzzem/ODKGg23bGxJspzAtviH16+xXAsZJwn/528+4Gcbm7x3ZY2272MoeNwfsD+d8F+/\n/RZ3Fhdoez5QoKTEs46JXN11CGyba82ygfQnjx6f+nm+s7KMb9t8uLvLQhDwL999B69qVDvZwPzx\n3j4/fbzJf/nWXd5YXEBLyb/+6GP+4t597nYXudr6ZunDX1td4M2rSwynMU8Oh/z4wwfziUwzcHlj\nvcsf3r3GnYqcniUxeBkIAaFrc2e9S+Da/PyzDX76ySN+8tFDxrMEBLRCj5V2jT968zrv3Fhh/RVL\nPgBsS6G1y5+8c5PrSy3+9uPHfLa5x6++eEKUpGTG4FiKuu/Sqfl0GyGNwL1woiqFhaearPrfI8qH\nPJr8BwoMUmiWvLdRwqaXPKSfPGKa98iL521BJRZt+wbfbf+3VZXSqaqt5fXHFDlpMSUzMx5Nfsog\n3WSc7Z25TbEZ4aga18J/wIr/Lm37xtxdJC9ivHGT/fhTptk+h/E9UvP0NVZLh5Z9nUPrPlJoxtke\nh/EXtJxr+BzHwOdFyiDdYJhuYcgJ9SId57XnZCCvAqZIiLJ9Ztk2Ub6Pp7vU7ddR4vQJqyEnLxKm\n2T55kVK311BCUpA/5WJRChdzUhNVmm270oCriiAbsiICBBqHzJQ6cSUdlNCVLrxkabKKhf6qEac5\n273RqRaGrxJfO5Ic5Sn70ZTPBgfEecYwjUlNfu6Qu1rj6q/dR5kfgJbUdOw6Ne3x5JzH54WpLODK\nSnKUJ4yyKZFJzpVpCAS2KnW9DSs4UUF9lhxd/sBVqhQHswAAIABJREFUQtK6RCUZSvu6OE/pp2Pa\neQ1HWicqyecRtNIir2XXsMTz6VXPIq2cPkxhWPeX2I16OMpCCokpDInJSIuMvDAo5Kkf9+R7CCnx\na15ZBZZPz35P3i6e+wzHFbev2jqrE/oIBJ5t05/MmMQJ24MR/+GzR7QDj7Aimp/v7PPR5i531xZp\nBR5SCDZ7A570Ry81dzwihFmlcZVCkOQ5SX6x/Ok01H2H1WadXz7YIs1zfnhrnab//PJtSThLDeTX\nRfPqWhY11yEvSo3mci3E1RazNMNVFlody7pMUXAwnfKg1+MnjzdouC5ZnrM/ndLyTn5ega5IbtNz\nWQwCQtsmLwzDOCKvbLmkELy11MVSir3JlFG8iZaSq80mq/VaqXOuxqmsCFd+wFqf+XV3tSawLbSU\nZeiS6+Bbz5OxQRTxoN/jF1tb7E8mSCl53B98o+ziTsKxNAuNgNfXFkiyZfYGE7I8BwSha7HcrnN9\nqU2r5j1ni/iHd6+Wx3CjjpsL7n+yTRA4eL6NthRJnNE/nBDHCdcbdf7ZO6/hSMXWvX3i1gw/LCdG\n01lCejhj2Xb5w+tX6LWnjCYRcZTQrPusLDZYCwLENGPz3h5ZkpNnOd2VJm4h+OHyMp1OyJvXlsin\nKfc+3SbPcmxb4wXluWA8jRGjlBtewD+/fYOrjeOiwVGKXSv0S5tLKXlttcPhuOxjMKZA63LVJ3Rt\nVhcarLbrFxIRJSxsGeCq+lxKIIXClgG2DFBCVxpkQV5E1VL/MY4TA2183eZITnASBQXKWHi6gyND\nJtkeqZmeu02uatJ2btCw1rCkPyfJGhdPtQj0AlE+JMoHmGeIu0Biy4BAd2hYa2Qm4jC5z3o+rjyB\ny5WcvEgYJJtMs0M81cK3FvBUE/UldLAnkZoJaT6iICfO9ziM3meWbZWNbdY6ru6eqbkdJpvsRr8l\nyvtY0sdVDfKix0H8KR3ndWp22VQ3zXocJhvEpuwtCnQLW3ooLBIzIzETIjNGCQtfNdGiJMdZliLm\nY13HVSHFGda7e3tDPv7kCZ5n0+mEdBfrBMGXcBupeoIe7w/4t7/+DMfS89XV5WaN60uvRqr09WOW\nQGwyHowO53rGgq+ejHyVKEly48LGtPwZTfIkL6OqE5Oe6S4BpeuDpxwadkiovWOKXACY6n+Ay2eb\ny4okX9aZIy1SDpMRS1kL27aY5jHjLDpXblG6f1Q+0pewyMuLnGE2RQrJjWAViSCtPLQzk89dTExh\nStJyZMRQUC0NHS8tiYro6urkXz6ueOq2KcoKkECQZjkFpYWSEOULn4xy/SoRODZ3VxfJcsMszfg/\nfvwrPts+4B/fPdYabx4OeXzQ55+/e5vXlzsURcHP722wM5w8tY1plpPmOZkpmMYppiiI0pThLCq1\nqUphKYnvWNQ8h/EsZmcwwtGaaZyUzWMv8RlqrsNKs8a/G08ZRTFN3503Ez4FcTTmXx/4lkXNcZgk\nCUmWs1qvo6XgYDrFUorQtuckuSgKZlnK1mjEzzc28Sw9n7QuBsGc0CLKY+moSc/Rep78mOZH/qnl\nMfa91VWarsvPNjd5MhwxThLe6nZ5a6mLb9lzjfPLYP79KIrnjuU4zziYTnn/yTaP+4P5Z1yp1b4S\ny8yvGkIIQtfh3Zurcx3wZfGjN67xB7eucLg/YvdJn4f3dwhrLo1WgOPZRNOEna0+jZbPzcUmtxZa\nTMYRu5t9smlKa7FGEqXMpjHjUUTbs1i/sozJcuIoZTKOCUKXZicgiTNG+xPGwxnD/pTJOOJtIag1\nPN5b7rK01uLN9S4f/2aDRxuV5tezCWsuQgqyJEOOEm74IW+80WK9+byW37E1XTt8ZXIOJSy0dNHC\nmRM2LWzsI/cJUWpNEYLcZOfbilJgipTEpPNl/aNrWF4ckTKbzMTk5uzQC0v6+LpFzVrB0635/WXP\niSr/rjrE+YjETCmeWfUUQqKFg6fatJ2bHMRfMEgeE5sheZGihF25U8wYpVskZkTNWibQC9jqxd1X\nzkKSHzJK7mGKjGm2we70xyAETfvtMtr5nIa9SbbLxuSnJGZMoLus+u8xyw55PPkJjqoRWssIJLN8\nwPbsY2ZmSFEUNO1VXBkihWKaD5hlfWZmjCUcanqRurWEq2tMs0OyIkGiadtXcM+pnu/ujfj3f/Ex\niws13nhjlSBwvhRJPlqR3DgY8HD3kNBz5kmw+dXiP16S/Eazy1rQmDf5FEVBx311B9zvA/ZRWMYF\niXIlSZ7NSfI4m9FPxqTmfK/hmhWw4DRwpfWM3Z2hKCYUxRRjpijVRojLLZFKBL52qVk+vnYZZlNS\nk535+MRk9JIRkyyiaYdMs7Jx73x3C/FCFnlalNXyUTrhk9FD+ukYieSLySZRHjPJo9IxQZTLSKYo\nSLOyQ9+YAstSFEXpQ2prhVZynmilVFlVkUKQ5YYky4iSrGwcU5KtgwG5Keg2w8rEX+A71pnJWa8C\nBWXK3W8eb6OVnOtbs9zw1lq3lEFUWG7W6IQ+f/HRfX71cIvQdTgYz+iE3lPa5UcHA+7vHrIzHPPb\nx9sMpxG/uL+FEoKVVukecbPb5ma3zeF4yvuPt/lwc4eFWsCT/ohW4L2URviIeK+260yiBM8+P2r+\n64SaY1N3HH6zvUOWG5ZrIQfTKVvDEUthSNv35hIUIQQ12+H1Toc/u/06S7Vw3oBXsx1Cp7ooFJBk\nOdM0ZVZZwU3TFCkErtbz77Go3v9mp00n8BnHCeMk4T88fMS/v3ef9WaDpvfi2uCioArNMGdOwH3L\nYq1e509v3eTOwsLcy9mSkrXGN8c39whF5YbzZZ6fpTkU4AcO0TRhPJwhpaTeCnjjO1fww3L/3v90\nmyzNuf32GkFYktcvPn5ClmTcurNCb3/E5sN9HMfC820Wlxs02wFBzeXzj7aYTRNu3l5md3vA/U+3\n8Xwb17NLulg5zMymMSY33Lq7SjRN2Hi4j2VrpBQIKbFtiePapV3lVwwhZGX7deRZLxBIVJXIVt5z\ndFSfvQ9SM2Oc7TBINugnj4nyAYmZkBVRpcdNGaZPmKT7ZEV85usAWNLDliHyDINDcaJafRzs8Tw8\n1aTr3mWS7TNMtxgmT6hba9SsFWZ5n2GyySwfoIRNx3kNX3VOfZ2XxSD+hEej/4u8am6TQtJ03qLr\n/SGBvnLucwPdZc1/j0H6GCUclLCq/WJVn7vUGZeV4BqOClFC46kGhpxZNkIg8HUTn9Z8hcDXDWrW\nIlpYjLMDxuk+OSlSHBcFnsVslrD1pI/n2VjW5U0VzkLNc3j76jLXui3iNMNSElkVIRbqr44zfu1I\n8oIXsOB9s0nxszgptzgPBsM0j4nyskN0nM7opeNzpRZQVmMXnCaOejY5q5yBGzMmz7ehmFGoBCkb\niAvMwoUQ2EITKJeWHTJIJ+eS5NRk9JJh5WhRapKnWXTu0rwQL1ZJ1lJStwJqVoAjbVxpV00YY7RQ\nXPG61K1grqGezkqnhCw3ZcpQ1ZhyZHN0ZNcFzCuotlZYliJOMg6GU3RFnrcOhmS5IU5STFWhXl9s\n0Ar96jW/KmHy8SqKEOUYrC91uLrQJHSPSfLNbpsozRhMI6QopQqvLXdwtHqmYlvMK+Wd0OcHN6+w\nWA+euzxcaTfIcsMHGztM4gQlJdcXW1xpN+iElzd2r7kOd1e7rLUbOJbmtaUOUZLhORbdRsh31pdp\nn3ARWG7U+NGtdRbrr17P97KoOy4t1+WnjzfmTha/ebLNBzu7/PM7r9P2vfnJWUvJ9VYL19JopY7a\nY+aWcXM9d1VJTvOc+4c9etMZoyTGUoqrzcY8RKIAdsZj9idT9FEDVQGzLCNK06fS0nqzGcMoZpom\nPB4MSHPD5nDIR7t7BLZN3bFpuGWDj5aSpueS5oZfbm0R2DaBZbNWr88nXyu1Gne7i4QVORaUUg2v\nkmr8fYMxBeNRxGQSI4AkyZiO49L1xrMx1b4pKMqo+zSvCG15wkiTjCzNEQLy3BBNEyjA821qDY96\n069epyCJs/mqytF7Z2n5fpNRxHQcEc9ShBC0OyGHRWll6fk2lq3nEgw/dNBnTOSLqhE6MQmJiXGU\ni12dU8vmzuN9LLnIe19Uj39asnbskvDsc58+4xSFmRPO3ehDBskmk2yvJK+FwVA6MRTFURPakTPD\n2dcXJWy0dJ/6HM9t89H2nVPMcVSNBfc2O7MP6CUPGaabjNIrhHqJWdajnzwmycdY0qfjvo6vL0eS\nJ9mYSTZGCokWGi2tys5UEJsYUxhsaZMUOQabmUkRwqZhXUWrG+QskhUWcR6TFSlaaBz19IS5TFbU\nVWJdwSzrEZshhry6EpT7xZY+TXu10iFbWNIhNTGSUm+sqvvL1f0MXzdxZEAsJmhho2VJwM/rX8qy\nnNFwRp4bHMdCXjLp9SxYWtEMXUxRWjjWPBfPscjy/JUGVH3tSPJ/jLCkpuPULlVJnuUxkUnIC8Mo\nm9JPLkOSA7qVDdrTkBUZNhjzBGP2UGYXbb2NusCP8QiOsllyWhzE53s1pyajl44Z56XEYpYnTPMY\nc27j3lEluX6pSrJEEiiXq/4SofJ4NN3mIBlgC4uu2+JqsEx4QtJyOJryqy+2Sk9ZVZ7AQ89msRFy\nOJrSG8+q2WfZeGVrTeDaXFksCeLuYFw2s+SGKMlIspwnhyPGs5gsN/zx2zfwHRvPvlhP/TIQwD+8\nfZ0f3Hy6WnAUjXsyjevtK0u8vrwwn5RIUVbSBWWAwBHWO02WmzWy3JQWi7lBSYlWsnpdiZaSbj2g\nFXi8udbFFGWT2FFT5klyfhFWmjX+qx++NdfOvnVliaIoU7zeWutys9vGP9Gc9L3rq7y+vEDtBd7j\nq0bdcej4AXuTCUpIVms1frG5xb3eIbbWdHx/Tn4drXnvyhq/2d7mz7+4R5JlOJZmtVbjbrfLH1+/\nVnogQ9nAB/z44SOiLONwOuXdlWW+u7JCUBHT3Bj+6sFDfvLo0bw3wBQFi0HA28tL88cBPOj1+GBn\nl0f9Ph/t7jFJEv728QaTJOVmu80biwt8Z3kJJQSO1rzW7vDR7i7/289/QeDYXG02+RdvvknolMuU\nd7tdCuBnGxv8xf372EqxFIZcb7X4h9evPfXefx+QZzkHeyMO90bYdumNrpQkrHtkWc6Hv3xEe7FG\nsxNi2ZokzvjoV49oLYa0OrXSFzvLuffJNnluygpzNQm2LI1SpVex41j08wn3Pt1hMpqRpTnj4Yxo\nGnOwU7qp7G0PSNKsbDg+ce7RlkIpyXgYlbaUde9MW0qDIcoj+mmP/WSfJWeZtt0iMQkCgaXsuTRN\nS4164cgZwWWFU6bIOIy/YGPycx6M/xoAXy+w4LxO3V7FUbXKx1jyaPITtqe/YZhunvuaUqhK+vHl\nzs2OqtES1wmsBcRMMEg2aFhXWPbeZpLtcZjcJzVTAmuRBefWpUnybrzN/cnnONIl0CF1q4ErPZRQ\n7MU7pCalabXQ6iZX6v89O/ETUpNSs5eIjeHe5AFrniHQNcbZiECHz5HkaXbAzuw3JGaCli4H8Wek\nZkJmplAUyIoC+rqJM5eIiKoaXGAcM7/nJKTQVSNlhBSKtr2Gp85fXTpavbJtRRi6z+n+XxRZbhjP\nEj7e3OX9B0/4wevrXOk0GM9iigI6tVdTbP3GkuTxYEpvb8TGvV3GwxkU0L3SYulKm+ZCjcHBmE9/\n/Ygrt7qs31qqZi0FeW54+MkTdjf73Hn3Kp3l0hN0MpwxOByzcW+XweEEClhYabK8Xr7eUVPE5x9s\n8NmvHxHUPaSSTMcxfujQ7NS4cnOR5sLzzgpaKOpWQKA9tFDkxdlLO3nVgDbNY/rpmH46IjunggvQ\n0Eck+VmdYFH6D5oRebaJlA0KWePpGfj5JxBHlgT08fTsLmKAtMjoJWMGyZhpHpOY9FyphaDUPb9I\n2IqhIMnTcoatLK74XZbcNkqo6nWebjLM8pxJVFqO2VqVnd3Ko1XzGE1j4iQlFgLX1tQ9lyTLScYz\nGqFLmhkOBpPSe1lKoiQjNwatJIFro5TEd8umLb6iKrIQgsC1CbiYjLi2hXuJTnhbq0vFUOsq+OLL\ndtdbWtE8sYJy8vVO22bPtl55R/+Xha0V3VrAn925jRSCpTDkj66u0/I87ix08Kxj6YgSgrV6DSHK\ngJEkz1FS0nBdlmthGWBBGdf9n1y7xo1Wi9BxSLKMcZKAgl4+49P+Pk3XxdcWy/WQ76wuczCblgl5\nYR2tBJ5t8XgyYD8pHUMsrbizsEA3CLjRavPD9XU6vsdCENDxfRaCYD6ZC2yL966ssdao05vNsCvX\njsYJx5HFwOetpS6OUvSjcoWi7ji0fZ+a8/uJ9/19Qlua1fU2rXaA0hKTG4wpShlEURDNEvzQJQgd\ngtAljlOiaYwfuPi1sskvTfIT/uvl69rOEZlVSClYWmsR1EqykyYZaZLT6dYQUvCd967jhy71VsC1\nSrzvehbNTsidt6+wvztkNo5ptHy0Ujx5fIgfONSbzxdoBAJLltW/1KTM8im9VLAX7ZAXOZ4um4Zt\nadN1lvAv6Kn5MjBkHMRfsBt9TGLGLLp3uRb+A2p6uUrncysP4YKd2QeXLEqI58jdy6DUL7uEeoma\ntcw0P2SYPiEvEibZHoPkMVp6hLqLq1poebnvRl7kZEXGotXAlS79pIevYgIdVlkACQfJPkoqtNA0\nrXVc5eHrkIN4j/2kx1a0SU3XaNsLuOr5lWpPtVl07zLJ9siLlAKDp9us639Q6pGPVilRyMrJ4uTo\nHeFZvnL0t0C1MKpWNW6ef3xYlqJec5FSMpulL+S3fxqmccK9nUOmUcpKq46WktEsZrc/xtJqHkL1\nZWUd31iSfLg35IsPNvnNTz7nYKcUm7/21hVuf/cqd1yLvSd9fvz/vM97f/ImiytNXN8pm2omMZ+9\nv8HHv3rI4mpzTpL7B2Puf7TJ+3/7BTuPDymKguu3V7jz3avcfvfqnCR/+utH/Ov//a+4+toSnu/Q\n2x8R1D2W1zt4gX0qSVZSEkiPQLu4yiHK43Orw4lJGWdTesmo1CRfUElu2AFdt4VzBkk2xRhj9hDC\n5XiXn2eqdwxX2XTd1oVV8NTkDJJx6el8gX4ZSoJsS01Ne9Qsr7KNOR+mqrRP84goj+cTD1VFVD+b\nvHfUhicq+YFnWzQCl24jZDCJ2BtYpHmOozWduk9vPCvJc5qTpBlRpV12bU2a5WglqfsunlNWnFs1\nH0tfbF33Lb7ZsJRiMQj4F2+9Ob/vj29c549vXH/usUpK2r5P2/d5e2np3Nd8NuQjNTl//ugL7g0P\niYuMZuTRcj1eW+xwo9Pitwc7rAZ1frR8hQfDPhvjAQ9GvXlS3zsLy7zdPfs9TyKwbb67usJ3WTnz\nMXXXpe663Gy/2kCL3zUcS9OqeYSeg63VS6/62I7m2q3uqR7qJ/3XL7rvIv/15bUWxWpzXscQJ6zo\nVtePq5QL3ePKnevZtNohv/jJ54yHMzrdOtE0YfPRAatXT99/RyRZCV2dW2dEecT96QPiPKJm1VBC\nEVZVTp+vkCQXOf34Ef3kIVJouu4b3Gn82VznDKUkIytihJAV4Xt5ffmLoNxPitBaomlfY2v2K0bZ\nNnE+mgeN1K1VatYKtvQv7WohEFhC07EXUEKxHW2RF1m5T6REGBhmffIixxKa12p36TrLSCTjtOQ8\n+/EucT5jxbuCp57fP75eYNn/Hr34PtN8vwr4KC3qtDzdXeisbX0WSmhC6/L6a8fRdLt1lBT0ehNW\nV5vn5l9chFmScn/nEEcrXlvpYCnFuJJY1jynSoz88kWsbyxJ/uK3m3z8ywfc/f4Nmgs1pBJ8/MuH\n/OIvP2H5SodGK+DOd69BUXDvoy2u3V7G5IaHn26jLMXr76wT1o8PkoefPuGXf/Mpr7+9zg/+5C5S\nSj7/YIO//fMP6Sw3WVwtu2OjacJ4MOP6nVXufv86UPDbn93ng5/d4zs/unXuNvvKpes02IsHjLKz\nrWtSkzFMphzGQ3rJmOyCxr265bN4qtxCIISNlCFCtkAoiiLmsgQZwFEWS07rQj11arKy8p2My2CR\nC0iyq2w6Th1POZciyNWnQQnBIB3z+fgxiclQQrHktlh2O6y4C9jSmlelHUvTDH2WmiHLrRqWlviO\nPY+Irfsuxhi0UviOxVKrRpbn+I6NKQo6db/UiylBklWxvLqs9mglCV3nW4L8LV4ppJQ0HY93FpYZ\npQkPh32ajkdgWQiYN9mN05hBXFZ3baXwtIW+wKrx7yvevbnK//ov/wmLjZDFRkDovQKZyAX2kufd\nd+mTxoueXASs31ik2Q4QQlKreyyttWgtnK7vN+RMsynDtM8g7ROZqLLTzMuwGZMipPgdUVHm0ryy\nwcyuGu6OByErIqZZj0m6xyzrYYrzrzGvGqG1zILzGrvRR0yyPTanv6SXPMIUGXVrlbq99pxt3Xko\nMCQmYTvaQgsLKRSJSdiPdxFCkFfFMUe6eMplL95mnA6pW00m+RghBEvuKr7y2Yt3yUxG111+6j1S\nM2GUbJKYEabIKIqccfqExIzoOLepW6uvdIzOQ7sV8P3vXefJkz6//e1jVlebLHRe3o9cUBa/kiyn\nN55ha8UsSRlOI+Ise8qa88vgG0uSdzYPuffRFldvr2A7GqUlk+GM3c0ecZSwtN7m9e+s8+ThPo8+\n3aa72iJNcx588oR6K2D91hrBCZK896TP57/d4MrNJexKVD6bxOxsHBLNjm1mjDEUpqC71uL2u1ex\nXc3je7vsbfXKRoxTcDQL85XDottklM3OJclxXtqpHVaE86yqsxKylHLogIYVoE8lyaUuWco6oKCI\nKe10LkmSpc2i27ywkpwVOaN0Si8piXJinjeLPwlPOSzaTdznmg3PhhBl5cORFra0SExGlMfsRj2m\nWcRe3KeufepWQMOqYduSKwsNVto1llq1qhmlfK9G4FL3naemC89uR7tWfuaiKJ4Tpxw975tsTfgt\nvp4oT/4KUxSM04Q0z8iUYpwm+EnMKE2YZSmJycmNwVGK0LIv1fz69xGrnTqrnVfnxnFZMnxp0nzJ\n97gM2gshYc1lOomxbE1Y984kIUdFbS0talYdLXTpUe50S09iobCEha8DrFfk+Xs2BJb00NKjIGeW\n9+knj+eBIqZIy0jqZINJtv+cXdvvAr5q07Cv4qo60+yQJ7NfMUy3EEjq9hp1a/XSFqsAngpo2wto\nYaGlRcvukBcZpsjRQgMCTwXY0saVLtN8Sl7kFIXBlR5te4G23cGWDqNseGplPTUzJtkeQsgqAfAo\n3XPynC/0Vw0/cLh+fYHD3oTNrR6ffbZNnhnCmoNlqXN98W1bU6t56BNOLY6lWGnV6E8jpnFKnJZh\nZo3Ao+65r8xn/xtLkmeTmO1HB/z4/36fsOkjqvvChofSirDuc+PuKlsP9nn8xS5v/eAmSZLx+Itd\n3v2j17h2ewXHPf740TRhb6vP3/67D/j4Vw8R1X1h3Xsqjc2yStP2oObi+TZCCqSU5ZLYBec1Tzl0\nnSZPZgfnPi4yCfvJgH5aWqqdlVpnS4uGFRBaHo6yzlgqKbPZhXBLfXIRn9vJ+ywcabFoN6hpD4k4\n0y4qL0pnjkE6Zj8eEF1Akn3l0HWbeOry2kaJwFU26/4yS25n7pv82egRn4we8snoIctuh5vBGu82\nX2fJXeCN9cVzJREvW9T5lhp/i68CpjAMkhkfHOxApWue5RnTacr2dEReGLYmQxJjUEIwzBIcrWnY\nLs7vOb77W/z+obTCVRLbtUpng3OqdEooAh3gKpclZ/lEBeC4JCBEadymn5PyvVoIIQn1Ir5q00vu\nsxt9iK18Ar2IFg5RPuQg/py96GOAKhTjbI/krwKOqlGzlgj0IuN0l+3Zb0jyKY6q0bCuULdWzrSb\nOw1dZ5m23eG4jFY2y51EdfV+qrKshZ6n4B25YbTs9qkrsnmREJsRDfsKnnpaduPI85NpXzW0Uvi+\nQ73mobXir//mU375q4fcuL5Ave7hntOs3emEvHl3Fa2P+ULDd/n+rTXu7Rzy2dY+cZwRuDbfv7nK\narvx6rb7lb3S7xiWram1Aq7fWWFpvY3tWECBF7q0FmsoLXGVQ1B3cX2bg90hWZrjeDZeWN53crJu\nWYqw7nH19SWu3OhiObp0RXAsFleOvYWFECgtkUrOLUzEiX/Pg68dum4L9wJi2E/GfDx8xGEyOjfW\n2VM2S26LQHvnWK+cqH0WCQXRC83CpZA4yibUPg07ZJLNSM6QUhQU7MdDPh1tMErPrpRDJT1xW7jq\nxZY+CyAyMYfJsNJsD+knI2o64J3G67TsGqEunS8Sk/Fm/caZFbavuqrzLb7Fi0AiuFFvU7ddXK1R\nVXJV0yltjt5bukJg2TRtl+3JiCjPaLkeWkoej/vUbJuWe74s6ltcDrkxjKOEURQzmsV4toVjKeK0\nDOPJjSkbTy1d2sFlOdOkrGYh4GqneWqi5FeJY6tIwWWKaEIIFAolFLa0z2za+l1AoVl03yArolJW\nVGQ8mb5faXzL66UUikX3DYRQpZVZPvqdbqMUCkt6NO11xuk2B/EXWNKrwkqalcb38uNmSQv9DAUT\niFOb5IqiwDrx0pe9JsVmSC++V8Zwq95T+9WWIZ7+3fUb7O4N+Yu//IQHD/bY3u4znsRoJdnfH+G6\nNrZ19gTj1q0uN24s4vvH3ClKMzYPBuz2x4yjmLxybNo8HOI59isLyvnGkuSw7rF6bYHv/OgmN98s\npRNHDRK2o6vb0OiEdJbq7G72MKZgYblBreE/N8P2ax5L623eeu8Gb3z32ty9AkFFwCuUa+wvtc2l\nJrmFdwEx7KUjPhw+oJecfxLwlMOK1yFUF4cJlMlFaSW3OPKYhIu+1FKUhuuh5bFg10lNdiZJBjhI\nBqTDjME5dnEAnnZYdF6sklxQkFWhJV+MN3g83aGfjGjZda75K9ytX0dLxSAd89d7vybOU96oX4MX\nmN2fjKAuTvn36M/FhfcXF7qSnPkZi5wkz+YI+JMSAAAgAElEQVShMkeTMDH/5fhUN69BnHH/Eb4p\nRP/p8b/c2FOceEz13MQk59oPnoWyqzwnzpMT43/+GIvjPz69r44ed8mxV1Jyu7XwlLznpKbuWr1V\nbWPBJ709MmNY9kImWcr94SFXa5cLCjoLpx37xfEf57cvd+yf30dx1vtnRV7JqH6/x36WG/bHE7Z6\nw3mITs1zGE5joiyjMAU1z6FW9SXM0pTeZEZ/WnrD11znd06SvyxehhhLobFliC0DtHArlwSNo6r7\npFvpdAWWdLFlSFGY52KUpbDoem9gSQ9T5OzHnzFINqpkO42vOyx777Dm/wGRmXAY36/kGM9f+5Sw\ncGS92qZSrnH6tqt54Igl3UtVgZWwaVjr9PUjdqOPqallWs41HFVHiRenU6eN+an3veT5O8knDNMN\n8iJmqg6eGoumfe2cZ5buUHGeIyjPQ5Y6XxJxEfb3R/z133xCv/90AW08OT8UBkAqSZI8fT0dRwkf\nbeyyP5yS5mXIT5zmDKcxvmNzZ+1yNrcX4RtLkl97+wrGFHz8q0d8+IuHOK5Fox2yuNrk9jtXaS2W\nSwnLVzpkSc5P//xDlKV47x+/weLq8xeT63dWiCYxDz/d5osPNrFdi3oroLPU4Pa7V5+qJr8sjiQG\nF1VP4zzlMB6eS0bL13NZcTsEZzbVFRRFGe0phAdkCGxeRiwQao8lr116IWezMx8X5UllY3c5ucWL\nVJLzwjDOpkR5jBKStxo3CZSHq2xC7VG3QkT139VgiUB5nJ4ifz5MdZHPiqz6mZMWOanJyUxW/l79\nPPn3zGSkJietnvfpaOOF33uWJzyYbCMRHCRDLKnQQqErGyAtVHmfVFjP3K+lmj/eEhotVVWN/GYQ\nZCgJVl4UJ8b+aExPjLUp/5YWz+yD+b7J2Yl6DJLzJ2qnYS/u88HgPpMsomWHWPJ4zEuNpkKfuO/k\nvjn5uyXK20K8zBF4/jdUAK81O3S8ACkEDcfjStig6395X9Bnj/20eHbMT34H8mr8j38/Ova/GJ/v\nYXsapnnE/ckToNwPx+OpTxzrJ499PR9nLU/sh+r3L3PsJ1nOg70e+6MplpZM4oRxnDBLUlq+x521\nRfZGYzZ7A0xR0PRd3r6yzIP9Hlu94TcmTfLLYsG5zR91/2eUsHFVg5q1hKeb/OHi/1SSZdmooo8F\ntxv/jKvhH5EXKQvOa8+9lhI2dXuVW7U/ZS34A1IzK69dCJR08FQTRzXZGt5jkHX5Tuu/o+tefe51\nVvzv8sfL/wuODPFUE0+ffu1u2te42/wvSPIxQijq9uWa2KRQc+1xoLu07VvY8usZgBboRdb892g5\nNwmtJThxRQz18nlP5d5hj7958AgpBR3f54fra3TDl6/O3rrZ5X/8H/6UJHnxCXSr5dNsPN0XJYTA\n1orAtRGAOmH39ip99r8RJDk3hijP2JtNcJRiya+xen0RIQQf/Pwe/f0xJje4XuVDeaIiUm8HrOQd\nmp0QbWvWri9Sbz5/QAfLIfU32nz0412GB2OuNpo4rk2alFWDIyyuNLn7vWs02sev0Vlu8uYfXKfZ\nPv8AKh0dGvjKOVffmxU5WX7xgeTrqpKsz6skH1msSIrCAmHDJd0kTqKmfZacFg/U9rmPu+y2e8ph\nwXkxkgzlp3GUTcdusOx26DiNOTGeQ8O6v4wt9XNpS6N0yl7cJzU5BlMFZJTRvKYoMJgy+vmI9FYX\n/eSIrJlsfv/894pEpEc/K7L8ZLb/Qp8NIDYJm7M9ZnnM1uygIr4lAbPmJLkiZBVJOCLEpz1OS1U2\n41CeQI6Ss6Qo7YdsqWnaIXXr+e/Eye8RfPlqtCkMu3GfUTolL8w8Erk4EY+cF4a8MPNxPCbJx+N9\ncqyzZyYnSbV/LmqOPQuHyQjGmxwmI3zlPD2m85/VfXOSdvz70WOOSJwSshz/StspqxQyKQSOtAi0\nS8uuYUldJaDFZEUy11sKZNW8JEjNDCVsbBnSciSuUkS5QksIrAItU+I8Iy9iQJSJY8JByfI7NqyO\n/ezEsW8qz/aj21lhTkw4Toz5iWM7e2b8U3NyH5W3N2fn+6qfhihP2JjuMc0iNqf7x8f4Kcf+fOxP\n/jwa+6OJ4wXHvqMsmlZ4alOyKQqmSRkZbumywdeYgqSyglxt1hjMIgazCADftsqoeym/Ktv0l4ap\n9mmcJxgKJBJHWVhV5dNUq16pyTAUONJCyzIaPasmSUdWdkrI8txZ5LjKwdMd1tQfUlTf48RkIHxW\n/B+ihJqTltwYAn0dR+WYqpKcVe5EBjP31C9w0WqZhl6df69URUjjPGWaRyTGQYkF1oK7NKzwxOcs\nEwS17NBx6jjSqizujq8Bx+fnDIRF3bqF5VhYzzW9n47SkWJSyUIkoe7Sdm5gXeAR/PuCljauas4r\n/Sfr1Bc5cWyPxvzF/QfUHYdbnTZvL3W/1LYsLNRYOMUi96VRFOSmwLU0dd9BKzUPdar5F6+uXxbf\nCJKcmJzt6Yh/+/gLul7Af37jDertAD90WL2xiMkMiLJpwbIUzgmLH60VrcU6//S/+WEZzlD3UKck\nvfRVwqMwYvcti4XvrfGf3n6Xju+jLYXjH7/eO3/0Gq+9fYXaCaL9nR/d4uabq6catp+Eo2xahPjK\nRUtFanJO60i9LEq5RfucSrJACAchNEWRVr9bvAxJDrXHktvGlV9+hnaUnLdg108JQDkbWiiaVo2a\nDjBuZ255VUajVJMBBLbUrHvdedPJSWzO9vl/t3/OIB0T5cn8hJmYjMSkJEVWEraiqE78R699fLt4\n9jZHhPL4dkFB+hJLzqnJOIiH9JNx2ZQhmE8Cjtw0jpeZn79fVGNwRGhl5YBiSY0tdPlTlT8bVVLj\ne+07vNW4cer2fBkfy+c/W87PDj/ht/37RHlMbNIT41+OfWby4wtmcWJ8q4vwyduccf8R6btoNeM0\njLIZszxmO+rNG2bmS/7i6fGd36Zs2p3vpxO3lSwjZ+2j8a/G3pEWXafJa7U1ftR5k4asYmPzHtNs\nj2leerVr4RBay0ih6ScP8dUCi+4bRGaftJjSdK6SFRN6ySMcVUMgGKc7KKHxdIdQL+FV39lH0x3+\nzfbPGWez+bGfVOMfm5T0jGP/KFb5vGP/2cddZAF5GhKTsR8P6CWjc4/9I3nLixz7jtQVwS7/b1oh\nXafJDzpvcNd6ftlZSUE78EjynDQ3dEKfwLHZ7A2eChA6SuqMs5wPN3c4nMxIc0Px8qf1V47M5Iyz\nGdvRAWmRYUtN12nTtGtIROnLn045TIbEJmXZbVO3AhxpM8tjRumUxKRoqQi1Rz8dM8tjVr0FXGkz\ny5M5ET9MhgghWLAbBNqbF0Eik7AX9xinUxKT0XVbNK0avnZJTMYsiykomOUxh8kAR9k0rRp1K8Cr\nGlJH2YQn0QGB9lhQz4dn5UXOJI/oJUMO4gFdt0XbKm1Gj4j2LI/op2MO4gGSMvG14zQvTZLzImWQ\nbDJOd5FCE1iLNO11rFM8h78OiPMx/SpK+9nY8Bvhn7DkvX3mc8dJwv3DHrc6bWz18v7iXxUyYxjN\nYmqew1qngZbHlm+Nv28keX824d7gkIfDHpnJOZhNaTgujmdT2HKe3DLOEoZ5TM2An1nYSpEUOSk5\num4Tm5zN2RBPW4SWM4/KBej4ATc6HT6f9rBsh2a3RtM9Jr2ZMcR5xljlzMICJTPIJLZSBDV3npB0\nHqQQWFITWh51K7hU5PRpEBx1Jrt07Po51dgqcW/+HjkUKefl3Z+F0PJYeolGu2ehq+0OtIcjrecq\nvefhSK+7HR1wb7xJVpSWL5bUCASmMNyuXeVqsIxzxnYO0ymfjTbopSOSPK1Sjwz50dLxOWmIvwsU\nMK/ewKux6CnJQhm2cvRTCUXTDplkEdf9FaZeWj1WoKQgNwVxnjGYRSgp6fj+POo6rVYKbK2qKnCB\npcrbUZahpcLRZRXp5InVYHgy2+eT0SOiPCEr8mrcTbUf8qqy+fuDKQxJYYBX48FaenuXZE0JWaVn\nSSypGadTQu3NtesFBYkZM8v7ZKasVBmhy+VdJKmZksmo2s6ErBiTFX1SMyMzU1IzwhQ5eZEiUeRF\niiNreJRa5mE64dPRY0bZjCRPy/EvTDUx+Toc+8VXduzr6pg/2gdtq8Ykm3Gnvn7q82ytWO80afge\naZ5Tcx1cS1NzHTxbo6WgWw94a23pKT/WThhQUFD3Lt9rkeQ5oyRmezxiczxiGEVM0tLirzzXS3zL\nouG4dIOA5SBkOXza0vI0HE14Phs/Zjvax67S9QSChlXDFIZpnrATH/Jw8gRbapTQfDi8T8du8npt\nna3ZPluzXTzlkhYZg3SMEmVTfFEUOMoiyVOG2YRJFuEpB1MUPJg84Wawyq1wjQKI86Qk4RWh/mBw\nj6Zd453Ga0zyGduzAw7TIanJCLXHQTLg8/EGbzduseYuooQsq/mFYSc6xJKaFW8Br9qOxKT00zH3\nJpukJsORNvfGm2zKPe7UrhJWqwX3J1s8me0TaB9X2gzSCbVTVtFOQ2oiZlmPYbpJama07OuEuosl\n/TN1zy+D3Bj2Z1MeDQd8tL/LNH3x78JSEPKP1q/j6w5L3ttE+bCqfpc6bEfVcNX5EtLMmOp8LvFt\n65VZqr0qSFGmjs6SlAe7vadIcgEst15N1fobQZK3pyM+HxwwTCLqicPWZIiWsowhTGKiPCMvDPuz\nCZM0ZckPWfAC2spjlmWMkoi8KBglMbuzMS3Ho+uHtF1/Xp5fDet42uLv9jY56Z17VKWK84zDaMrO\nbMxhNGM1qNP1AlpumSd/mVnWUb2jbgW07BrjbHYpacJzrzN3nPBoWCGOOqsaW1AUCeUFR0CRURAB\nMUWRAfrSS4NlJbn1Qo12p6GsBoeElod6QV/XvDBMsxmfjx/zb7b/lmkeUxTFXCoQ5UlZRQ7K1LHT\n9IhRHrMTHdJPx+fGZv/HhLOIX1lVUhxGEwZRRAFYUuJaFlGaMohiNgcDbKVwtMZWitwYRnE57oHj\nYIqyo9i3ygCW3mxG4Nho5Z2owpYoioJeMmYnOiQ12e+VDP+u8DTxo+yZpVQGWkIzSCdkJ47D1ExJ\nzbQi1zaWcLFE1TVfmHLSW4pUyIuUcbZTTYIFk2yf1EzxVQdDwTQ7oG4fk8BpFrMdHZa2kn/Pjv3k\nmWM/NzmWVMzy023EbK252mlWk8CycCyF4MoJa6m1VoO1Vvn70XXiIuJ6EkcTzFES83DQ5++2t/jp\n1gaPBgN2p2MmaYoAbKVZ9AOuNhq8s7jE95dXqTkOrtLzIs9p71n2hmR8NHzAF+PHvNN8nVD7pKYs\nDuSFoZ8OeTR9wm8Gn3MrvELLqvPB8D5dp8UVf5GN6Q6fjB5y1V9mkI75Rf8TrnhdrvkrJHlZXS4o\n2JrtMUgmvNm4QZJn/Lz3EQK46i8hEMQmYZiOOTor/2bwOTXt83q4zjib8ni2wxfjTZSQfK91h924\nx4fD+yw4DbpOCyksLKFwpMXj2Q6ZyXi7cXMut5jmMdvRAe/3PyfULndq1/h8vEGcJyw4DWxZWuJ9\nPtrg/mSL77Xu4CqbWR6d2WBd7tPj1d4o7zNKtxmmTygwdN27hNbSCwWIXAZ5UbA5GvJXjx7wrz54\nn/3Zi8vGvre0wp3OArfbXWw/ZJQ+ITZDpLDwVZuatYK8oNHw6Ji3tcKzrDlXelkcrf7luSGvIt3L\nVMqLn6uUxLb1U4YLllY0Q5eHu30e7vZQSs6Jsmtr3rp6ufTRi/CNIMk70wmH0ZTvLq7gKYuPe7v4\n2sLTFpuTIY9GfZ5Mhix6AYHl8Jeb91kOavyT9VvszcY8GPbYmpTNFCtBnQ8Pd/npzgb/ZP0WN+vt\nc3d+AUzShEejPn+3t4mnNG3X5+e7G4SWzR+v3qDteFgv4FFa1z5tu3ahX/JZsIRiwa7TtMMLGkSO\n5RVFEVO6Wxiy7B5QoNQ14HJyB0da1C2fQLs40iYxLxcLaktNx6lfmOB3GlKTsZ8MsKXmvfbdufvH\nndo1pnnMx8MHBPrrqQ37OmN3POG38Q6TOCF0HK63W2wMBmz0hzha4VsWH+3ssVqv0fI9DqczDqcz\n4jxDVV3PtqrCL+KEa+0mHe/rufz4dYfEmleNpbKxVUhWJCRmzCTbw2AYZ7s4qlbaZM1+jacarHjf\nRQrNJNujDIiw8WTna9tQ9E3CZQPBXpRC5EXBw0Gf93e3+f8e3uPBoM/edMokSYjyjKxaITVFWpHm\nhO3xiC/6h3y0v8s/unqDNzoLZ14DpnnMXtTDUzbXq6ruUQW5bgWkJmVrtk9mct6q32TdX8JTLofJ\nACkkO9EBo2yKEoorfpdWVuPRdJtVb4E1b4FBNiE2KZZQtOw6bbvBNX+l7KeI9lBIDpMhDSus9MmS\nSTZjkkWMsxmOtMuVo6Kslq94HWra56q/RGZy9uM+IJjlMZbQc3eoQLmMiul8xEvr0R6b013G2ZTY\nJHwx3uQgHiCAw2RI3Qpp23U87SCFYDc6RAvNut/FP6OnJytmHMT3iPMBBXAY32M/+pQo79O011kL\nvk/NOr/57feNfvKAjcnPsKSPEjYFhiEbbM/eZ9l7l5Zz/cznhrbN1WY5CdyfTEheoqB3EsYU5HnO\np5/t8MknT9jbHzEczkjTp/vITsP164v8Z3/2Lq3W8fnMsy3WO01Gs4Sd/og8zdGOZLVdp1N7dTzg\na02STTXTPogm7M8mvNFaJDOGDw53uF5vsxrWGcQzdqdjnkxGdNwAX1vszMakhWEYx/TiGbuzMTvT\nMXXbpWG73Bsccm94yDuzZVb8GoFln3mCM4XhMJryYNTjg4Mdul6IFJJPenu4yuJuq4uvrUuTZCHK\nSnLbrmG95AxUC8WC06Bl1Z5vWnv63RDCQsoQpRarqrKgJM0vtoRjSY2nHOpWUOnSMvKXEN5ZUtO2\n6xcm+J2GgoI4T9BCs+otllrsAq74XQ6TIa6ykaL0lDzSiX6LizFNEvaTKf3ZjCCOcbTmcW/A4/6A\nlXq5rDtLMwLbIrAtDqcztkcjoizD0RrPKidaWZ4zSzMWw+C5KvK3uBwK8soiq/TfzYoYXbgIBI5q\nYEmPoshwVAOhBUpYWNInsJYquyybvIixpI+n2t+S5Esgz3Km45gsK0mAlBKpypCooigwuSFLc/Lc\nzK1FOTq/FAVZFVlv2WXzZWEKpBJorbAdC20rtH76XJ8bwyxN+WBvl7989IC/evSQfhydWnYwRUGa\nJIyThJ3JmL3phK3RiAU/YNH3aboe9inXn9SkjLMpSiqado2O3aTjHFfC+8mIUTrBFIYVb5EFp1kF\nVIVEecw4mxGbFCkkLbuGKyu/fCukYdUYZlMyk6OVwlMunnRo2TWczKKmfYQQTLMIURHd3Bi0UHjK\nKZ1f5ltSIIWkaYW07QYtq07TGhJqHwrmBRlbWYTCx5JPB2cVwCSLmOQRjrRxVJnK2nVbpSNN9Xgt\nFF2nxdgviX9sEnrJiJr2T60VpSZiL/qYQbJBganS/vbwVJsF5zYd59aFkoXfN8rEvR0a9jVs6ZOT\nMs32GaabNOyrNIurHPVbPIu27/Hd1WX2JlM2h0P2JhM6vkdgXz4l9yRmUcLe3ogPPtjgZz+/z2gU\nEccpxhQMRxHjcUSz6WNZmiROiZOMLDMsLtao1bz59+wIR6s3Ndfh6kITUxRoJfGcUhryqvppvtYk\nOatcLfpxxM5swuZkxCxN+aS3z4+Wjq1fGo5LYNncaS2w4AU8HPXIi4K9WTn7tqVivdZkxa/xerPD\nIJ4xSUu/y148w9X6zCWTzBRsjAc8HPYYJwm5GRLlKf8/e2/6JEl63/d9MvPJOyvr7up7pufamd1Z\n7AEIBEEQJCVQsiiHJL9zyOEX9iv/F/5rHA45wg7J4aBpioQsmgewxLHA7tw9V99d1XVX5X34RVb3\ndE/P0TM7O9gF9huxG9PdVVl5VD75e37P9+j4U1zNYM8bU9ENStrZaAgS4KoWVa302jGyqixo6BUq\nmvOCAvnw0zQUZQlZrvGEi6zMOsyvdvllZKpqiZpWYpx4r7Vke1gkO6/RSZYlGVMYeGnIKCmEbUg5\nDyfbtMM+nbCHlxY+pfI3RdqZoQmBqxR+r0mWsTMcMYkiNEVGyFLBY5QgTlNGQUjX85hGMWVDx9F1\nTFUwjWKSLEOZLXd91UQeXw/khOmYJA8oqUskuU8/fMi8+S3K2jlsMYcsKSiSMfN4NbhU+hGyJJAR\nlNRFbNEkJ0OSZGTUly6pfgMIw4TH9/YZDz0kWUbTBbqhopsqWZrjTULGgyneNEQIBUUpElYlqeiO\njYcesixTqTukSUocpeiGSqli0Vyo4FYtHPfkeBelKf0g4O+3NvjbrQ3GcXTmdblBEBKmXX65v0vd\nNPl4fumZRbIsyQUdYuZaciqkQpIQsiDNshkFIzsSXWZ5hiqpR4E2R2LVI+Fwfuy/QjQX5/HMIehQ\nuFn8bdvvMI6neKnPijXPnF5lL+geOWbkx/ZXkZ5BXXzJiTnU6DjCxLQXmDfqXHSWSfMUCQlz5lID\ncM1dY8WaZxRPWZ9s8pd7P+FP579L06ie2m6SBXSCO7T9m6R5hJAMLLXBqv095s3rGEoF5UuP6/5i\nsEWDBesjKtp5bNEouu7BHbykgwRkpDN/6NPj9XK5zL985wr/4cYtbnc63NrvYGsaVxr116JedLtT\nfvLT+3z66WM2NrqsrTWZb5VxXZMbN7e5fWeX6+8tU6vZtNsj9vZHTCYB/+xP3uW7371I6Snd1ySI\nuLmxT8O1+FffuYokSRyMpvzjvS26Y488f+1IixP4So+g4zhkazJEkWSW7TIVzUACDEUUoqIwIMky\nJAoupa6IGU9LIU/Tmb1XcYcJSUJXFEyhoilFRv2hCv7FyEnyDFVWWHJcmqbNkuOy5tYwhcqi7WKJ\nV7tRik6yO8tnf3UIuegkV1TnhcVI8TcJ0JGkL8YlhoKfVNNK1HWXrdeweIKiSC7oFq/eSRaSQlUt\nOvBOUqQMJllCO+xjKDpr9hIVtfRNF/MVUbdMLpZqhElKkhVin6UkIUlTHF1HF8UgWtI1TFXlYr1G\nkCQ4moYuBLIksTkY4sdxUTjkOVGaosryF+ax/W5BwhZNFElFU0qouYUq2bMOsoF6KjhBOZGYJSSd\nHO2pLX5z/l+GPMtJkhRvEhAGCdWGg6oq9Npj4rC4D6ajAN8LUVUFRShFkUyR4JwmGamUMx54aLpA\n1RTiKCEM4hP2ocfRC3zu9Q/YHA/p+f4rNRzSPMOLY+71uiw6Ja7Wm1SM05QBQ9Zo6GW2vDa9aMCt\n0UNsYZIDS2YTRzGZN+q0gx4b3h5eEqArGgfRYOb64GL6OgPGcFQqPxvj2KOTRSiSQpKljBOPZWkO\nV7WZpgFhFrMf9JCQibIYLwkK8d+xbRz6kzx1dUjzjCCNOAgHtIPeEQ3k3niDOEuo62UqmkOQhWx6\n+3TCPmJmLWcqOitmi5wcLw3YD3r0ohFBGjGMJ4RZ9Nxzr8oWi9ZHlMQ8WZ6gKmbRRTYu4ajzKNKr\nCc9/E4jzAC/pYokGelYiIyXJfJI8IiefCQ6ffVUPVzt0UQheP9naYns04nytiq2q6EIcOdA8Dxfr\nNdZqxQRkMglYX98nTXMuXWrx4QerrK7UsSyd4dDnwcMOly61uHJ5ntHI5/btXdbv76OqCklympKR\nZhnTMKKamRiailBkxn5IECdF8uUbwle6SB6EPrd6bWxV5f16i9VSlYNgyqLtzjrFU8IsJc1zojQh\nTAuPzihNyCgED4pcqGKjNMVPY6I0JcwK43tFklFnwocky2b+j4cz4ezINUOTCx5yWTe4Umnwbq0F\nPPGNfJXHUCHc+4KdZEnQ0N1ZJ/ntoVh2c6hppRPek68CTRbUNZeS+uqdZCEr1GbHneYZiiTjJQFB\nFh9RQeaN+mvv2+8qGrbN1bkinejkQ+vZaJVO+oGHScLA9wvx34wiFcYJsqa+QtbhN5CQKWur2Nkc\nQTZClUxMUX2lQvebovjVIUmgiOI5EXgRkiShaoL2zoAwiNANFUkq6BRCKChCPiqSkcBydPIsJwxi\nbNekXHMYdCcIoaBqyjMtRzvelBudNl3/9Vbkshmf+U73AD9Jjrqyx2EKHVPoaPJDBtGEX8XrQEFb\n+379W7zrnmfFauGlAT8f3GHX76LJKnGeUNVc5owaJdWiEypH/tLHvb8PecYSMEqm7PtdRtEUWZLw\n0gBNVqlqLkFa2L8dhAPGicde0GUYT7BE0fQ67CAf+onDoSvJk2aWn4Zs+R1uDh+wF/QIspCbo4cg\ngS0MappLnufcGD5gy9tn09snyRNqWpmy6lDCxkt97o43eOTtokkqaZ7SeEHqqykqXC3/2QuvQ5Il\npHmKLD3x5P4qraIFyZBucA8hmTPnm5BJ0iHND92Mnj9CD4OQO50DshwcXeOTzS38OKFqmtQsk7Jh\nnAjweBb+7XvXjopkz4vY2uqxuFjl44/O8e2Pz7O4WPztsxubaJrg3Gqdb398HlmWcBwdP4gYDDw2\nN3ucP9fguNRFQkIoMnGa0p/4aEJhGkRHddubug5f6SJ5GAbc6Xd4vzHP1WqTim5SD4qZcJpl3Bt0\nkSUYRQGbkyFeElPWDTYmQ5bsousbJDFxlrI1GbLnjen6PtvTEX6SUNJ0bFVjFIVsTobc6bdZH3bJ\ngf93+wHv1eZYLVVYdsokWco/trfo+h7rwy5pntEwbL49t0RJ1V+pY2YpBq6wMWQNRZJfaZCUmBXt\nmktZtd/qQ/GQm1Z0wV+fKlLVSi/wdn45JIoOpYQ06yAvHHniPs/67Rt8eRCyzPlalbmSQ5bnOFrR\ncRbfTFZeGbIkUGWz4CRLynO6a9/gTSJNM4a9KVmas7TWIAoTOrsDltcaWCUDRZbJsvwJjUvmRAy5\nJEuQQxKn6KaKbmjU50rIsoxV0lG104/ZURiyPRnjJ6/f8RpFIV3fw49j0jxHPOcZdM1do2XUj+gW\nsiQxp9dQZRXIOWct8M9bxbh5WFgcigP4oKYAACAASURBVO3eKZ1j0WhS01zSPOWPm9+mpFo4iklJ\ntehHI/bDHk29Ql0rs2g0cYRJRsacXkWRZGp6mWvSeRpaBUUqgky8JMQSBo6w0GUNQy54rrpchJws\nmk0MRaes2piKjizJrNkLVFWH9ysXSWfiw4pawlJMFEmiprv8QeMDwizi8OqYik5FLR0F/bxXLgSK\nR89NCeaN+mtfg096n3JzdJfLzhrn7RWWzPnXXiH+MuCocyzZ/4QsjxlFW0iShKlUuVT6Ea66/ML3\nbg2H/N937jEKQyazxMlk5mDkxRH7k8lLkyX/8PwTD/I0TfH8CEMXzLfKGMaTFXhZlhFCnon7MiRJ\nodEocflSizt39tjc7J6KpS7bOh9fWOJgPOVXj3bJZ/fnxYU655qn6TOvi6/O1XwGhCxjqxqLtssF\nt4aqKFhCQ8gK25PhkTVKnhed4EkcocgyZc2gZTmUNYP2jKuV5hlxkjEIfTRZZt4uUTNMTKEyikL8\nJMJLYuozb2QvjvCTovBqmBZBGuOqOkGa0A080jxHVwTpc5bTXgRdVqnrJd5xV5Al6MdDSsLBVF7u\ntSwhsWDWjuzYMgorFeBoNv5lQUGiprmcs1u8655jdMZEs3yWZJcDF+wFavqLvJ2fjychGsVPUBQV\nDb3COJ5yEA1IZ+lQaZYRJSlhlKBrAk0opFmOKRlcKa0wijziNCHLn1gxqUJBE2+u95nnhfBzZzAm\nTlNaroOhilN+k34UMw5CpmGEF8XESUrFNlmsuCiydOr14yBkEhSDVhjHREnKQsWl4Vgoxzxbn94X\nyNkfTRj5IS3XoeWUaRkFt/3JOX51KLJM03m5QEyWZJatJu+654vr9Bqf9XVGlhXfyzwvCpV5q8qC\nWUeb8SULmpACkoLCm5/sVTSHa+65o/CH32U4wqShl4tGgyQV9lIliVrTZdidEEcJzcUq1YbDobfb\nizpTh4I9SZKKovkl8OKYA88j/AJFcpAkTKIIP0mI0/TIDu4Qh+Plgtlg3qiT5ilIEgryUfgNQF0v\nU9fLR2OnfCwpb96onygijyfclXEQksJBNMQVDrYwWLMXqaqlmY6h2IYjTCzFoGXUjzrSx/fPULRT\nQu6qVqKqnfS5PUxpfR4cWXC5ZM1SO9MTz0MJCRXBktlkwWiQkSHzHA70K+DhdIN/6P58tkJcYsF8\nM7ZjbwqmUqVpXGUUbxMkA2RJ4KhzVLU1FPnFFExZktAUBVfXcXWdRffVfYdL+pPPyGcTSaEqOI5x\nQsyqKDJCKCRpRhQlCCFTdk2WFqt8+ukGUZSQpCfHLF0IWlWHKE3pjQs7XVNXma+UqDrmG0u+/EoX\nyZfKdRrv2JRUHU0p1N6OqnFuJsIbRkWnOQcqhsFaqca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rH9qz4+/x57s/pmU0ebd8mRVz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sFa7HFI\nyZLATrqZPPDZHXkBO80efhCxWC1k/tGtbGjvqBtOOh3e6w29TAonCqzNlVio5X+vtHiBEAQBSZYw\n7OO/d1mWsGWJc0tVzi0dJWgHN/ziEyzSoiTBiyLarkvbdR97M3US8prOjGWR07Qj8xyCoCPJazzg\nAgKQQ5QejthOkeTVh5YDUSo/stwDa6UjoqxnRkHXWckXjpD608CUVV7Kz/CVyhKXCsdbsKVA0x+e\nKJeKk4QoSVjvdLjRaPHhzg63Wy12+gPc8GAGRkCRsvPAbC7HarFAz/W4VKuyViqiT7rqA8+nMRjR\nmyQDW5oyjcT2wghNltFkmZGfpRaPgiy5WFNk4iSz6Cznjp6vfS+k3xkxGni4I3/Sfcuek2URRZWp\nzRdwnmAI8CR86UiypatotQJFx+Td19eIkzQr9YsCpq4iKgKyL7AxSrjtNsgpOmVRRhKf3b84jmPC\nIELRFHwvZH+ng2FpLKxVqcwViMKIykwew9YJvJDKXAHfCyjP5knjlFHfQ1ZlREmkudsliRNESeLC\n60vMr1amrhqQsnFzj1a9x5vfvvhM05gp6bRde3jrOkOXvfYgIxW6cmTbJUlktpwjb5/hrVeXH0R5\np1kVWJUz32lFkYiiKq+em8O2HtVZnV2uMFd1CKOYutvmk85NLMVkwaxhqTqGqiLrKYEQICcCURIT\nphFBEhIm2d9F2aFWtXnHXuTK5XnSWCCMw6wKKKn04yF1v8mykyNvqURiSBAL6FJ2gVQVmblqnmLe\n5LWXFjKv7CTNXDwkCVWVJh7JsDxfRJalx+q/BSFz0UgmLSJDCfHCCDcMjxDlII75ZLdOz/P47996\ng5Jh0PM8Pm80j/UsTlOmEoTjxguTNOV6vcHddod/+9YbLOQdojirVm/3TvYHzWs6F0plCs/hvSMF\nHhoAACAASURBVLw/GuJF2RDV3nDI98+ep2ZayKfUo82WchRtgzCOCaOEIIozvbAsTQbiMonSG+cW\nMqJ2yB7q9bPzrM6UKD7kqy0I8IdvnCeMYoon2EnZuoZWk6nkLfxwLdu25oDb9xpYsoIuy5TyJhcX\nq/xPf/YuzgkONI6l86++8Wpmk6TKfP3lVV4/M5+R+RT+8vtfxZ7cjKZpSiVv8b0rZwknv70D68Yo\nSY6tJK/MFqkWbKI4O+5EQTiy3IGrxvmFCuHE3jBJ0+n+OxgmlMSMwBdsg9cmKZySKGJqyhF5ScWx\n+G++8/oRDXYpZ/IX33ptOqdwGkiTMITTVJKfBEHIvodGd8ivrt/H0BQWa3nu17tEcczZhQq5Q+FD\nWdKqzspsif/ymy9jG5ljSZKmGJr6G9Xa/x4nI4wSgjDG0GTEE/x5gYwgey79wMONTpeyVzUtFnLO\nCXKDp/09vOjlHo+8prPkPD9JthWNy8V5Vu2HCf0DrNhFakbWYT8OfhTT9zx+cPsOf3vzDs3xiCDK\ntMKmqlDQDVIhk42NgoDbzRb32h0+2dvn3ZUl/s3rl5m17azjpCrkDX0qlRAEgTDKpFQ5XcPSVHRF\nRpFFLE1lThIpWgaOqbHZ7E0KLhyxxe13Rnz4s1vc/GSLjVt1fC8kmpgmWDmdcs3h+//mHV7/2rnn\n2pdfOpIsSSKSJE5T7B5GlMRUBXuSDJVVnAxJQRGkZ9ZZjgc+rXo2dBdFMaGf6ULVic1IGMV4bohu\naqh6NskZhTG+G+KNfZp7PeI4zl4bRBOtsEi/M0ZWOtiOge9l72nnDSzHYNAZoekKxeqTiXK1YPHO\npWX8IOKnV+9h6iqKnA0u9YYeg7HH+cUq5xYrR1IAAVRVRn3KYcF87nhScmCRBCB7MQ3VAVJQfAzN\nRBMFBtGQntcH0kyrJgiIgkSSJsRpjBf7xGnCKHWRFRFD1yfLiaRCghAFKGrCWB6yH8WIsYgjW5S1\nTGMuidI01fBJXpz2MYl+D0OTJC5UKuwMBvzo3jpFwyAlxYsiSoYx3X8i2Ymi7bpcrzcoGvo09c7W\ntEcuyoYis1TI03U9/u9PP0dTJKqWxcVqFXuSFmiqCiICNxstOq6HImZarZymIp/w27UUlSUnz7zt\nUDYM+r5P+IzT5GGS0PV8brSbE2sun3OlMmuFIgu2Q0HXUcQnWxJO9+GEBD8JOVM7QnQgq/zmjvHL\nFgSBUu74NmaSpsRxQrs7YrveQyBzeSgWTMRUIAxj9vb7jEY+hq7g5HRKeYt0op/f3e8zdn1SoFyw\nyFk67ijIBuzChDDMQmuaQYxpqNQKNlGS0HM9+q6PABRMI7tJnwykwGQgJYVxEKBIEmmaad5lUSRv\n6VPbQFWWj/xeDlvAPXFfi9m+ftjq8TAUWTriw3zSY88KQcgGjp5Hv37ie5P5FOuqwt2dNsszBb5y\naZlP7+3R6bu8fm7hyDbbpsZr5+YZewE3NvYxJimclq4yU8rka5AFsNTbAzqDMf2Rz/29Dt2hy8e3\ntvGDiELOoJK3MHWFens4Wc5jfadNf+Rx9fYOcZJQsA0qBeuRjsfv8Xg0u0P2WgPOzJcp5U/ed+Mw\npDEaMz5lDLUAlAyDGcuezlkcffZp3+VFLvdkWIrKjGlhKJls8rT+FrIgYikqhqxMO7hhkjCOApJJ\nd12XFGxFm85qPYzGaMSHO7t8sltnfzhkuZBnIe8w7zhYqjKtzgdxzDgM2esP2O4P2Or1+LS+z3v3\nN3lrcYEzpSIz+RyGqhCEk7meNCsiiJN5q4OunR9m+RSyJGZSjCgiZ2hYmvKILW6WuFdHEAQuv73G\n3c93GQ89FlarRGHMsD+eFCGfD186khwnWdLe4bb1EQhgyTq2orNql2l4Awahd6rW8Xjo0ar30YyM\nbMuKhCSLmZYmSvDGAYPuCN1UMW2dKErw3JBBd8ywN6a510MzMmsrSZbQLZVcwaJV79Kqd5lbruCO\nfCRZYmEt0y03drIErqchyfOVPH/89kv87JN1/u5XN6etrDSFvG2wWC3w3TfOcmG5miXSfYEQBRFD\n1ukGPbbdOoakgWzQCXr0wiGjyEWXVEzZIK/YSEJG5utei1bQZRCNUAUFR7HQJA1VlAGBJI0Jk4xM\ni4KILMjUtBKWbCKgnZgidVpossyV+VmirYR/uH2XnKZRNI0szcwyp+b8siSyWizSHrv8/e07WKqK\no2kkaUrVMh/5vdmqysu1Kp/tN/g/Prqama7P1JjN2RiTm5XFvMN2r8+P1zfQJJmSaeBHEbWcPSFZ\n6QPB0ORkYSqZXnrZybOYy3M3bhOeYuAjThMa4xEtd8yHeztcmZnjawuLvLuwzJliiZyqTd0vDtwf\nfleQJilhGLO52+Un799GliWKjsGFtRmiKJN2NNoDNnfbRHFCuWBxbrlKtWIjSyLXbm6z38wCT86v\n1ZifybO12yFJoFqyGY49BiMfQ1eplm00TcGPI7ojl/utLoIAa9USOUObtgzj5KBVmJ2vTE3JfF/H\nWTS5rWmMgwBBECiYIsIXRDa/WAhTz+QX/s4C5C0D29Bo98cs1PIsVPOEUUJv5JK39COe6TlT452X\nV/jgxiZ//6tbSFJm87dQcXj9/AKLtTyCIOAFIfd2WtzabHC/3mVjr8PIDfjV9U1avTFnFsq8sjaL\nLNms77a4eT9bbn23jeuH/PrzLboDl7XJcl9mkpymKcmk+5GmKdLEVQgeHN9Z6mpCEiekSXb+EQUB\nQcrmVw6WOxjQOujkZQ8++plb9S6f3tmjmDMeS5JHYUBjPHyusJmibjBjWScmyv0uwlSUzDFGyeQI\n0TMWPB7GwTXDj2OGoce+NyRKElRRpKrnsJRHCzoH2O0P+Ifbd7nX7qDLMt9YXeabayu8OjuLcmg4\nL510Rz/b3+dXm1v8h2ufsd3r8Y931ymbJi9VKyyUHBZwHlkv4JHrycFzu50+222fSs4kb+koD13r\nAz9if7vLpTdW+Bf/7Vf5h//rAzqtId/+/utsbzT5xT9cf2pbw8fhS0eSN/e6fPD5Js3uiP7If+Q4\n1E2ZlZcLOKXsBNrwBoRJPLWCexbMLJXQLW1a4o+TFKdgIisyF15fJvQj8mUbw1KRZZELl5cI/JB8\n2aZcc6jMFaZ54nGcTHUyB9VjM6cTRwlzy2WsnI6syjhFe0rKnwTH0jm3WMGxdL768kpmQH6gPVZk\nLF1ltpTL4mm/4AuwKIiogowsyETEpECYRPTCIVESY8smpqxjSSaObOPFHp2wjySIFFWHgpKbVpAN\nSUcWJfzYx00igiRAFVUkAcIkzDTJgvCFtFAVSWK5UMBUVV6qVlAkEU2WGQYBjqZha1l1T5Uk3l5a\n4Ey5iHcQjS1KeFGIrao4+tFqaMk0+faZVV6bm2UchiiSiCyL7Po9vEFARbe5OFOlmrMY+P7E/1jF\niyJ0WSZv6rhxSJDEGJLyiJ3hhVKZt+cWaLpjBs8xFZ2kWcXhbrfNIPC51thnxSlwtljKqsv5Io6m\nfeEm8c+CNM2cZPwgxA8iVhZKnFupUivnaHZGCEKHUsHCNlXyOZOR63NzfZ+dRg/H1pkpO6zMl9F1\nhb1Gn48+3SKMYhRZmkaf+0HEcOQzCgIGaYhtasiiwNAPcMPMOaWSs8jpGrf3WwxcH1NTUCUJefIb\nStKUkZ9p7QQEeq6Hrau8sTJP0To+LOafM1RF4sxCmb/8869SzJk4psaffesVxm6QzRkckomoisxM\nyeadl5dZnilOApkkTF2h5JjT85+hKby0UmOu4jB0AwZjnyCMcUyNnJV1Ngo5A02VubBUY6aUY+QF\nDEaZ/VzO1KcdkMIJHbYvC9yRz8bNXerbXbrNAa++fYazLy8ctTds9NndaHLjow1aez3CMKY6V2B+\ntcrFK8uUZ/JTv+SRG/Czq+tsN3qZe80xp+fNepfuwOUrlx7vquGGIc3x+NT+yJBJF8qGdazN4+8q\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Yh8ZRNqC2v9Nl+16DpXMznHl5nlIth25k+0c3VMycQRxnA28PI0kyKU9v6NIdeCST36Q4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lVcW0dK416oL6RSdDaPLd/gdcb2yx0z/gi+ldfj+7y8N0n0k5e6JlMdUZD9N9RsWUz6e3WQ8H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\nQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "wordcloud = WordCloud(background_color='white').generate(' '.join(all_docs))\n",
- "plt.figure(figsize=(12, 12))\n",
- "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
- "plt.axis(\"off\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Run hLDA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "8185 401 378 210\n"
- ]
- }
- ],
- "source": [
- "print len(vocab), len(corpus), len(corpus[0]), len(corpus[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Convert words in the corpus into indices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "new_corpus = []\n",
- "for doc in corpus:\n",
- " new_doc = []\n",
- " for word in doc:\n",
- " word_idx = vocab_index[word]\n",
- " new_doc.append(word_idx)\n",
- " new_corpus.append(new_doc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "8185 401\n",
- "['ink', u'help', 'drive', u'democraci', 'asia', 'kyrgyz', u'republ', 'small', u'mountain', 'state']\n",
- "[3587, 3212, 2029, 1788, 401, 3928, 5974, 6595, 4609, 6831]\n"
- ]
- }
- ],
- "source": [
- "print len(vocab), len(new_corpus)\n",
- "print corpus[0][0:10]\n",
- "print new_corpus[0][0:10]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create hierarchical LDA object and run the sampler."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "n_samples = 500 # no of iterations for the sampler\n",
- "alpha = 10.0 # smoothing over level distributions\n",
- "gamma = 1.0 # CRP smoothing parameter; number of imaginary customers at next, as yet unused table\n",
- "eta = 0.1 # smoothing over topic-word distributions\n",
- "num_levels = 3 # the number of levels in the tree\n",
- "display_topics = 50 # the number of iterations between printing a brief summary of the topics so far\n",
- "n_words = 5 # the number of most probable words to print for each topic after model estimation\n",
- "with_weights = False # whether to print the words with the weights"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "HierarchicalLDA sampling\n",
- "\n",
- ".................................................. 50\n",
- "topic=0 level=0 (documents=401): peopl, thi, use, technolog, get, \n",
- " topic=1 level=1 (documents=148): user, mobil, network, servic, softwar, \n",
- " topic=2 level=2 (documents=78): email, secur, viru, net, firm, \n",
- " topic=3 level=2 (documents=53): appl, music, patent, mac, law, \n",
- " topic=12 level=2 (documents=10): game, yahoo, learn, sim, educ, \n",
- " topic=27 level=2 (documents=7): ink, print, elect, film, cinema, \n",
- " topic=4 level=1 (documents=40): mobil, phone, top, game, like, \n",
- " topic=5 level=2 (documents=10): radio, podcast, listen, hiphop, world, \n",
- " topic=16 level=2 (documents=16): player, librari, blog, survey, american, \n",
- " topic=23 level=2 (documents=14): game, award, play, titl, mobil, \n",
- " topic=6 level=1 (documents=151): mobil, phone, use, music, peopl, \n",
- " topic=7 level=2 (documents=30): site, attack, websit, traffic, data, \n",
- " topic=8 level=2 (documents=71): game, soni, gadget, consol, comput, \n",
- " topic=9 level=2 (documents=12): laser, firm, system, light, broadband, \n",
- " topic=11 level=2 (documents=24): robot, game, tag, rfid, argonaut, \n",
- " topic=15 level=2 (documents=14): blogger, card, comput, websit, net, \n",
- " topic=13 level=1 (documents=17): mobil, text, phone, handset, cebit, \n",
- " topic=14 level=2 (documents=9): messag, email, address, send, sent, \n",
- " topic=20 level=2 (documents=8): dvd, game, bluray, format, technolog, \n",
- " topic=17 level=1 (documents=13): phone, mobil, thi, doe, feel, \n",
- " topic=18 level=2 (documents=13): game, play, hour, time, addict, \n",
- " topic=21 level=1 (documents=14): servic, broadband, speed, price, dtt, \n",
- " topic=22 level=2 (documents=14): local, secur, project, commun, wifi, \n",
- " topic=25 level=1 (documents=18): microsoft, version, news, browser, program, \n",
- " topic=26 level=2 (documents=18): search, blog, site, engin, ask, \n",
- "\n",
- ".................................................. 100\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, one, \n",
- " topic=1 level=1 (documents=137): user, softwar, phone, mobil, secur, \n",
- " topic=2 level=2 (documents=57): email, viru, machin, firm, domain, \n",
- " topic=3 level=2 (documents=53): music, appl, patent, mac, legal, \n",
- " topic=12 level=2 (documents=9): game, yahoo, learn, research, children, \n",
- " topic=27 level=2 (documents=8): print, film, cinema, vodafon, world, \n",
- " topic=28 level=2 (documents=7): human, robot, opera, voic, browser, \n",
- " topic=30 level=2 (documents=3): vehicl, transport, disabl, car, passeng, \n",
- " topic=4 level=1 (documents=51): phone, mobil, game, broadband, servic, \n",
- " topic=5 level=2 (documents=7): radio, podcast, say, listen, hiphop, \n",
- " topic=16 level=2 (documents=12): librari, player, survey, blog, found, \n",
- " topic=23 level=2 (documents=14): game, award, play, mobil, titl, \n",
- " topic=29 level=2 (documents=6): iptv, messag, map, satellit, blog, \n",
- " topic=31 level=2 (documents=6): ink, uwb, use, elect, tremor, \n",
- " topic=32 level=2 (documents=6): sport, espn, nielsen, piero, fan, \n",
- " topic=6 level=1 (documents=144): mobil, phone, music, peopl, system, \n",
- " topic=7 level=2 (documents=30): site, attack, net, dvd, campaign, \n",
- " topic=8 level=2 (documents=62): game, gadget, soni, consol, first, \n",
- " topic=9 level=2 (documents=13): broadband, laser, line, light, servic, \n",
- " topic=11 level=2 (documents=20): game, robot, gadget, tag, rfid, \n",
- " topic=15 level=2 (documents=13): card, blogger, seafar, protect, comput, \n",
- " topic=35 level=2 (documents=6): date, videoondemand, music, telewest, servic, \n",
- " topic=13 level=1 (documents=19): mobil, text, phone, show, oper, \n",
- " topic=14 level=2 (documents=10): email, messag, internet, sent, send, \n",
- " topic=20 level=2 (documents=9): dvd, game, bluray, technolog, format, \n",
- " topic=17 level=1 (documents=10): thi, like, doe, charact, feel, \n",
- " topic=18 level=2 (documents=10): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=17): phone, servic, camera, mobil, user, \n",
- " topic=22 level=2 (documents=11): secur, local, commun, alert, wifi, \n",
- " topic=34 level=2 (documents=6): lab, hunt, shoot, autolink, nuclear, \n",
- " topic=25 level=1 (documents=23): microsoft, desktop, tool, version, program, \n",
- " topic=26 level=2 (documents=23): search, blog, web, site, user, \n",
- "\n",
- ".................................................. 150\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, one, \n",
- " topic=1 level=1 (documents=138): softwar, user, secur, compani, phone, \n",
- " topic=2 level=2 (documents=54): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=51): music, appl, patent, mac, file, \n",
- " topic=12 level=2 (documents=9): game, yahoo, learn, research, search, \n",
- " topic=27 level=2 (documents=6): mobil, phone, film, wifi, offic, \n",
- " topic=28 level=2 (documents=8): human, robot, opera, voic, featur, \n",
- " topic=30 level=2 (documents=5): vehicl, dtt, transport, box, sky, \n",
- " topic=36 level=2 (documents=5): music, per, lift, subscript, servic, \n",
- " topic=4 level=1 (documents=56): phone, mobil, media, digit, interact, \n",
- " topic=5 level=2 (documents=6): radio, podcast, hiphop, listen, say, \n",
- " topic=16 level=2 (documents=11): survey, player, librari, found, blog, \n",
- " topic=23 level=2 (documents=16): game, award, titl, play, releas, \n",
- " topic=29 level=2 (documents=9): iptv, messag, laptop, map, text, \n",
- " topic=31 level=2 (documents=7): use, ink, uwb, elect, tremor, \n",
- " topic=32 level=2 (documents=7): imag, sport, espn, print, printer, \n",
- " topic=6 level=1 (documents=137): phone, mobil, music, peopl, use, \n",
- " topic=7 level=2 (documents=23): site, attack, net, websit, firm, \n",
- " topic=8 level=2 (documents=60): game, gadget, soni, consol, comput, \n",
- " topic=9 level=2 (documents=14): broadband, laser, servic, firm, speed, \n",
- " topic=11 level=2 (documents=19): game, robot, rfid, tag, gadget, \n",
- " topic=15 level=2 (documents=15): card, blogger, softwar, net, protect, \n",
- " topic=35 level=2 (documents=6): servic, date, telewest, offer, pvr, \n",
- " topic=13 level=1 (documents=21): mobil, phone, camera, handset, text, \n",
- " topic=14 level=2 (documents=10): email, messag, address, fbi, law, \n",
- " topic=20 level=2 (documents=11): dvd, game, technolog, bluray, format, \n",
- " topic=17 level=1 (documents=10): thi, doe, game, charact, feel, \n",
- " topic=18 level=2 (documents=10): game, play, hour, time, addict, \n",
- " topic=21 level=1 (documents=16): phone, gadget, softwar, access, survey, \n",
- " topic=22 level=2 (documents=10): secur, wifi, local, wireless, govern, \n",
- " topic=34 level=2 (documents=6): laptop, power, cab, shoot, hunt, \n",
- " topic=25 level=1 (documents=23): microsoft, desktop, softwar, user, version, \n",
- " topic=26 level=2 (documents=23): search, blog, web, site, engin, \n",
- "\n",
- ".................................................. 200\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, one, \n",
- " topic=1 level=1 (documents=142): user, softwar, secur, program, compani, \n",
- " topic=2 level=2 (documents=51): email, viru, machin, domain, spam, \n",
- " topic=3 level=2 (documents=51): music, appl, patent, mac, legal, \n",
- " topic=12 level=2 (documents=10): game, yahoo, learn, research, educ, \n",
- " topic=27 level=2 (documents=10): mobil, phone, digit, camera, wifi, \n",
- " topic=28 level=2 (documents=8): human, robot, opera, voic, featur, \n",
- " topic=30 level=2 (documents=7): transport, poster, vehicl, box, dtt, \n",
- " topic=36 level=2 (documents=5): music, servic, per, lift, track, \n",
- " topic=4 level=1 (documents=55): phone, gadget, world, mobil, digit, \n",
- " topic=5 level=2 (documents=6): radio, podcast, listen, say, hiphop, \n",
- " topic=16 level=2 (documents=11): survey, librari, found, player, blog, \n",
- " topic=23 level=2 (documents=15): game, award, titl, play, releas, \n",
- " topic=29 level=2 (documents=8): iptv, laptop, messag, map, say, \n",
- " topic=31 level=2 (documents=8): ink, uwb, use, elect, tremor, \n",
- " topic=32 level=2 (documents=7): imag, sport, espn, look, print, \n",
- " topic=6 level=1 (documents=137): phone, mobil, music, use, peopl, \n",
- " topic=7 level=2 (documents=22): site, attack, websit, firm, system, \n",
- " topic=8 level=2 (documents=57): game, gadget, soni, consol, devic, \n",
- " topic=9 level=2 (documents=14): broadband, laser, firm, speed, light, \n",
- " topic=11 level=2 (documents=19): game, robot, gadget, rfid, tag, \n",
- " topic=15 level=2 (documents=17): softwar, comput, blogger, card, sun, \n",
- " topic=35 level=2 (documents=8): servic, video, date, telewest, pvr, \n",
- " topic=13 level=1 (documents=23): mobil, data, compani, system, softwar, \n",
- " topic=14 level=2 (documents=14): email, messag, microsoft, comput, send, \n",
- " topic=20 level=2 (documents=9): dvd, game, bluray, technolog, format, \n",
- " topic=17 level=1 (documents=12): thi, doe, charact, like, feel, \n",
- " topic=18 level=2 (documents=12): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=11): comput, project, would, visitor, professor, \n",
- " topic=22 level=2 (documents=7): secur, wifi, local, alert, wireless, \n",
- " topic=34 level=2 (documents=4): laptop, power, shoot, hunt, anim, \n",
- " topic=25 level=1 (documents=21): microsoft, desktop, version, internet, tool, \n",
- " topic=26 level=2 (documents=21): search, blog, web, site, engin, \n",
- "\n",
- ".................................................. 250\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, mobil, \n",
- " topic=1 level=1 (documents=137): user, softwar, secur, program, compani, \n",
- " topic=2 level=2 (documents=49): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=48): appl, music, patent, legal, mac, \n",
- " topic=12 level=2 (documents=10): game, yahoo, learn, research, search, \n",
- " topic=27 level=2 (documents=10): mobil, digit, camera, film, wifi, \n",
- " topic=28 level=2 (documents=10): human, robot, brain, opera, lab, \n",
- " topic=30 level=2 (documents=4): vehicl, box, dtt, sky, disabl, \n",
- " topic=36 level=2 (documents=6): music, download, servic, player, per, \n",
- " topic=4 level=1 (documents=53): softwar, use, help, read, colour, \n",
- " topic=5 level=2 (documents=7): radio, podcast, listen, say, hiphop, \n",
- " topic=16 level=2 (documents=9): survey, player, librari, found, broadband, \n",
- " topic=23 level=2 (documents=14): game, award, titl, play, releas, \n",
- " topic=29 level=2 (documents=7): iptv, messag, laptop, map, text, \n",
- " topic=31 level=2 (documents=7): ink, uwb, elect, problem, relay, \n",
- " topic=32 level=2 (documents=9): imag, sport, print, espn, printer, \n",
- " topic=6 level=1 (documents=140): comput, system, softwar, use, firm, \n",
- " topic=7 level=2 (documents=25): site, attack, websit, data, net, \n",
- " topic=8 level=2 (documents=52): game, gadget, soni, consol, sale, \n",
- " topic=9 level=2 (documents=16): broadband, laser, light, line, speed, \n",
- " topic=11 level=2 (documents=18): game, robot, rfid, tag, nintendo, \n",
- " topic=15 level=2 (documents=17): net, card, blogger, sun, spywar, \n",
- " topic=35 level=2 (documents=10): servic, network, mobil, speed, video, \n",
- " topic=38 level=2 (documents=2): lift, power, laptop, record, tower, \n",
- " topic=13 level=1 (documents=18): text, cebit, mda, battl, theme, \n",
- " topic=14 level=2 (documents=10): messag, email, internet, address, send, \n",
- " topic=20 level=2 (documents=8): dvd, game, bluray, format, technolog, \n",
- " topic=17 level=1 (documents=13): thi, like, charact, feel, doe, \n",
- " topic=18 level=2 (documents=13): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=19): user, system, comput, softwar, skype, \n",
- " topic=22 level=2 (documents=10): wifi, commun, network, wireless, hotspot, \n",
- " topic=34 level=2 (documents=3): hunt, shoot, anim, nuclear, old, \n",
- " topic=39 level=2 (documents=6): poster, tremor, cab, lost, lose, \n",
- " topic=25 level=1 (documents=21): microsoft, version, desktop, broadband, firm, \n",
- " topic=26 level=2 (documents=21): search, blog, site, engin, user, \n",
- "\n",
- ".................................................. 300\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, mobil, \n",
- " topic=1 level=1 (documents=141): user, softwar, secur, program, microsoft, \n",
- " topic=2 level=2 (documents=48): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=50): appl, music, patent, mac, legal, \n",
- " topic=12 level=2 (documents=10): game, yahoo, learn, research, educ, \n",
- " topic=27 level=2 (documents=10): mobil, digit, phone, film, camera, \n",
- " topic=28 level=2 (documents=12): research, human, control, robot, brain, \n",
- " topic=30 level=2 (documents=4): vehicl, dtt, box, sky, viewer, \n",
- " topic=36 level=2 (documents=7): music, download, servic, song, player, \n",
- " topic=4 level=1 (documents=58): develop, project, world, softwar, user, \n",
- " topic=5 level=2 (documents=8): radio, podcast, hiphop, listen, say, \n",
- " topic=16 level=2 (documents=11): librari, survey, player, found, blog, \n",
- " topic=23 level=2 (documents=15): game, award, titl, play, releas, \n",
- " topic=29 level=2 (documents=7): laptop, iptv, messag, say, map, \n",
- " topic=31 level=2 (documents=7): ink, uwb, elect, use, relay, \n",
- " topic=32 level=2 (documents=10): imag, sport, game, espn, print, \n",
- " topic=6 level=1 (documents=135): system, comput, firm, softwar, use, \n",
- " topic=7 level=2 (documents=26): site, attack, net, network, websit, \n",
- " topic=8 level=2 (documents=47): game, gadget, soni, consol, sale, \n",
- " topic=9 level=2 (documents=16): broadband, laser, line, light, speed, \n",
- " topic=11 level=2 (documents=16): game, robot, gadget, rfid, tag, \n",
- " topic=15 level=2 (documents=17): camera, card, blogger, sun, net, \n",
- " topic=35 level=2 (documents=9): network, servic, mobil, speed, offer, \n",
- " topic=38 level=2 (documents=4): power, laptop, lift, record, xbox, \n",
- " topic=13 level=1 (documents=16): comput, text, cebit, case, data, \n",
- " topic=14 level=2 (documents=9): messag, email, internet, address, send, \n",
- " topic=20 level=2 (documents=7): dvd, game, bluray, format, technolog, \n",
- " topic=17 level=1 (documents=12): game, thi, doe, charact, titl, \n",
- " topic=18 level=2 (documents=12): game, play, hour, time, addict, \n",
- " topic=21 level=1 (documents=18): user, comput, system, skype, softwar, \n",
- " topic=22 level=2 (documents=11): wifi, secur, commun, wireless, project, \n",
- " topic=34 level=2 (documents=2): tremor, shoot, hunt, mous, anim, \n",
- " topic=39 level=2 (documents=5): phone, mobil, poster, cab, lost, \n",
- " topic=25 level=1 (documents=21): search, microsoft, desktop, version, softwar, \n",
- " topic=26 level=2 (documents=21): blog, search, site, engin, web, \n",
- "\n",
- ".................................................. 350\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, mobil, \n",
- " topic=1 level=1 (documents=143): softwar, user, secur, program, microsoft, \n",
- " topic=2 level=2 (documents=47): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=51): music, appl, patent, mac, mini, \n",
- " topic=12 level=2 (documents=12): game, yahoo, learn, research, school, \n",
- " topic=27 level=2 (documents=12): digit, camera, mobil, wifi, film, \n",
- " topic=28 level=2 (documents=11): human, research, robot, control, comput, \n",
- " topic=30 level=2 (documents=4): vehicl, dtt, box, sky, transport, \n",
- " topic=36 level=2 (documents=6): music, download, servic, song, per, \n",
- " topic=4 level=1 (documents=55): softwar, creat, develop, data, media, \n",
- " topic=5 level=2 (documents=7): radio, podcast, listen, say, hiphop, \n",
- " topic=16 level=2 (documents=12): survey, found, secur, player, wireless, \n",
- " topic=23 level=2 (documents=14): game, award, titl, play, releas, \n",
- " topic=29 level=2 (documents=7): messag, laptop, iptv, say, map, \n",
- " topic=31 level=2 (documents=4): ink, uwb, elect, caus, use, \n",
- " topic=32 level=2 (documents=11): imag, pictur, sport, photo, print, \n",
- " topic=6 level=1 (documents=132): comput, system, firm, softwar, new, \n",
- " topic=7 level=2 (documents=22): site, attack, net, data, firm, \n",
- " topic=8 level=2 (documents=48): game, gadget, soni, consol, devic, \n",
- " topic=9 level=2 (documents=16): broadband, laser, speed, line, light, \n",
- " topic=11 level=2 (documents=16): game, robot, gadget, rfid, tag, \n",
- " topic=15 level=2 (documents=17): camera, card, blogger, peopl, spywar, \n",
- " topic=35 level=2 (documents=9): network, servic, mobil, speed, video, \n",
- " topic=38 level=2 (documents=4): laptop, lift, panda, power, world, \n",
- " topic=13 level=1 (documents=21): comput, search, copi, macrovis, text, \n",
- " topic=14 level=2 (documents=11): email, messag, internet, sent, send, \n",
- " topic=20 level=2 (documents=10): dvd, game, technolog, bluray, format, \n",
- " topic=17 level=1 (documents=13): thi, charact, titl, like, even, \n",
- " topic=18 level=2 (documents=13): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=17): comput, softwar, alert, system, secur, \n",
- " topic=22 level=2 (documents=8): public, commun, project, local, librari, \n",
- " topic=34 level=2 (documents=4): tremor, shoot, hunt, relay, deaf, \n",
- " topic=39 level=2 (documents=5): phone, mobil, poster, cab, lose, \n",
- " topic=25 level=1 (documents=20): search, googl, microsoft, desktop, version, \n",
- " topic=26 level=2 (documents=20): blog, site, web, search, ask, \n",
- "\n",
- ".................................................. 400\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, mobil, \n",
- " topic=1 level=1 (documents=145): softwar, user, program, secur, use, \n",
- " topic=2 level=2 (documents=47): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=51): appl, music, patent, mac, mini, \n",
- " topic=12 level=2 (documents=12): game, yahoo, learn, school, research, \n",
- " topic=27 level=2 (documents=12): mobil, camera, wifi, servic, digit, \n",
- " topic=28 level=2 (documents=11): research, human, control, robot, brain, \n",
- " topic=30 level=2 (documents=4): vehicl, dtt, box, sky, settop, \n",
- " topic=36 level=2 (documents=8): music, system, servic, download, consum, \n",
- " topic=4 level=1 (documents=57): softwar, microsoft, use, creat, help, \n",
- " topic=5 level=2 (documents=7): radio, podcast, say, hiphop, listen, \n",
- " topic=16 level=2 (documents=14): secur, found, survey, browser, wifi, \n",
- " topic=23 level=2 (documents=14): game, award, titl, play, releas, \n",
- " topic=29 level=2 (documents=7): laptop, iptv, messag, map, say, \n",
- " topic=31 level=2 (documents=4): ink, uwb, elect, intel, fund, \n",
- " topic=32 level=2 (documents=11): imag, pictur, photo, sport, print, \n",
- " topic=6 level=1 (documents=131): comput, system, new, one, softwar, \n",
- " topic=7 level=2 (documents=23): site, attack, firm, net, data, \n",
- " topic=8 level=2 (documents=48): game, gadget, soni, consol, xbox, \n",
- " topic=9 level=2 (documents=14): broadband, laser, line, light, connect, \n",
- " topic=11 level=2 (documents=16): game, robot, tag, rfid, bill, \n",
- " topic=15 level=2 (documents=15): camera, card, blogger, softwar, spywar, \n",
- " topic=35 level=2 (documents=11): servic, network, mobil, speed, data, \n",
- " topic=38 level=2 (documents=4): laptop, lift, world, panda, record, \n",
- " topic=13 level=1 (documents=19): copi, new, ripguard, macrovis, text, \n",
- " topic=14 level=2 (documents=9): email, messag, internet, address, fbi, \n",
- " topic=20 level=2 (documents=10): dvd, game, bluray, format, technolog, \n",
- " topic=17 level=1 (documents=14): game, thi, charact, control, titl, \n",
- " topic=18 level=2 (documents=14): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=15): comput, nation, afford, movement, grid, \n",
- " topic=22 level=2 (documents=6): commun, public, project, onlin, local, \n",
- " topic=34 level=2 (documents=4): tremor, shoot, hunt, relay, deaf, \n",
- " topic=39 level=2 (documents=5): phone, mobil, poster, cab, lost, \n",
- " topic=25 level=1 (documents=20): search, googl, microsoft, desktop, peopl, \n",
- " topic=26 level=2 (documents=20): blog, site, web, ask, user, \n",
- "\n",
- ".................................................. 450\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, mobil, \n",
- " topic=1 level=1 (documents=143): softwar, user, secur, program, use, \n",
- " topic=2 level=2 (documents=45): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=51): appl, music, mac, legal, mini, \n",
- " topic=12 level=2 (documents=12): game, yahoo, learn, school, student, \n",
- " topic=27 level=2 (documents=12): mobil, call, phone, camera, wifi, \n",
- " topic=28 level=2 (documents=11): human, control, robot, research, brain, \n",
- " topic=30 level=2 (documents=5): vehicl, dtt, sky, cebit, card, \n",
- " topic=36 level=2 (documents=7): music, system, servic, download, consum, \n",
- " topic=4 level=1 (documents=57): softwar, use, media, system, creat, \n",
- " topic=5 level=2 (documents=7): radio, podcast, listen, say, hiphop, \n",
- " topic=16 level=2 (documents=14): secur, user, found, browser, wifi, \n",
- " topic=23 level=2 (documents=14): game, award, titl, play, best, \n",
- " topic=29 level=2 (documents=7): iptv, laptop, messag, map, say, \n",
- " topic=31 level=2 (documents=4): ink, uwb, elect, intel, countri, \n",
- " topic=32 level=2 (documents=11): imag, pictur, photo, sport, print, \n",
- " topic=6 level=1 (documents=134): comput, softwar, microsoft, system, develop, \n",
- " topic=7 level=2 (documents=24): site, attack, messag, data, firm, \n",
- " topic=8 level=2 (documents=47): game, gadget, soni, consol, sale, \n",
- " topic=9 level=2 (documents=14): broadband, laser, line, light, speed, \n",
- " topic=11 level=2 (documents=16): game, robot, rfid, tag, gadget, \n",
- " topic=15 level=2 (documents=16): camera, spywar, card, blogger, seafar, \n",
- " topic=35 level=2 (documents=12): servic, network, speed, mobil, offer, \n",
- " topic=38 level=2 (documents=5): lift, laptop, panda, world, record, \n",
- " topic=13 level=1 (documents=18): dvd, copi, new, text, ripguard, \n",
- " topic=14 level=2 (documents=9): messag, email, internet, comput, sent, \n",
- " topic=20 level=2 (documents=9): dvd, game, technolog, bluray, format, \n",
- " topic=17 level=1 (documents=14): game, thi, charact, like, control, \n",
- " topic=18 level=2 (documents=14): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=17): search, comput, inform, movement, tool, \n",
- " topic=22 level=2 (documents=7): project, commun, local, public, librari, \n",
- " topic=34 level=2 (documents=4): tremor, shoot, relay, hunt, deaf, \n",
- " topic=39 level=2 (documents=6): phone, mobil, poster, cab, handset, \n",
- " topic=25 level=1 (documents=18): search, engin, ask, user, jeev, \n",
- " topic=26 level=2 (documents=18): blog, site, web, news, journal, \n",
- "\n",
- ".................................................. 500\n",
- "topic=0 level=0 (documents=401): peopl, use, thi, technolog, mobil, \n",
- " topic=1 level=1 (documents=145): softwar, user, use, secur, program, \n",
- " topic=2 level=2 (documents=46): email, viru, domain, spam, infect, \n",
- " topic=3 level=2 (documents=51): appl, music, patent, mac, legal, \n",
- " topic=12 level=2 (documents=12): game, yahoo, learn, school, student, \n",
- " topic=27 level=2 (documents=13): mobil, phone, camera, digit, wifi, \n",
- " topic=28 level=2 (documents=11): research, human, robot, control, brain, \n",
- " topic=30 level=2 (documents=5): vehicl, dtt, cebit, settop, transport, \n",
- " topic=36 level=2 (documents=7): music, servic, system, consum, download, \n",
- " topic=4 level=1 (documents=58): softwar, system, use, data, creat, \n",
- " topic=5 level=2 (documents=7): radio, podcast, listen, hiphop, say, \n",
- " topic=16 level=2 (documents=15): secur, microsoft, browser, internet, found, \n",
- " topic=23 level=2 (documents=14): game, award, play, titl, releas, \n",
- " topic=29 level=2 (documents=7): iptv, laptop, messag, map, satellit, \n",
- " topic=31 level=2 (documents=4): ink, uwb, elect, use, intel, \n",
- " topic=32 level=2 (documents=11): imag, pictur, sport, photo, print, \n",
- " topic=6 level=1 (documents=135): comput, use, system, softwar, microsoft, \n",
- " topic=7 level=2 (documents=25): site, attack, firm, net, messag, \n",
- " topic=8 level=2 (documents=47): game, gadget, soni, consol, xbox, \n",
- " topic=9 level=2 (documents=14): broadband, laser, connect, light, line, \n",
- " topic=11 level=2 (documents=16): game, robot, gadget, rfid, tag, \n",
- " topic=15 level=2 (documents=16): camera, spywar, card, blogger, seafar, \n",
- " topic=35 level=2 (documents=12): servic, network, speed, offer, technolog, \n",
- " topic=38 level=2 (documents=5): world, laptop, lift, panda, nuclear, \n",
- " topic=13 level=1 (documents=18): dvd, copi, text, ripguard, new, \n",
- " topic=14 level=2 (documents=9): messag, email, internet, sent, send, \n",
- " topic=20 level=2 (documents=9): dvd, game, technolog, bluray, format, \n",
- " topic=17 level=1 (documents=14): game, doe, charact, thi, like, \n",
- " topic=18 level=2 (documents=14): game, play, time, hour, addict, \n",
- " topic=21 level=1 (documents=18): search, googl, web, engin, comput, \n",
- " topic=22 level=2 (documents=7): commun, project, public, librari, reddi, \n",
- " topic=34 level=2 (documents=4): tremor, shoot, relay, hunt, deaf, \n",
- " topic=39 level=2 (documents=7): phone, mobil, poster, handset, cab, \n",
- " topic=25 level=1 (documents=13): search, site, jeev, ask, googl, \n",
- " topic=26 level=2 (documents=13): blog, web, journal, word, news, \n",
- "\n"
- ]
- }
- ],
- "source": [
- "hlda = HierarchicalLDA(new_corpus, vocab, alpha=alpha, gamma=gamma, eta=eta, num_levels=num_levels)\n",
- "hlda.estimate(n_samples, display_topics=display_topics, n_words=n_words, with_weights=with_weights)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Visualise results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "colour_map = {\n",
- " 0: 'blue',\n",
- " 1: 'red',\n",
- " 2: 'green'\n",
- "}\n",
- "\n",
- "def show_doc(d=0):\n",
- " \n",
- " node = hlda.document_leaves[d]\n",
- " path = []\n",
- " while node is not None:\n",
- " path.append(node)\n",
- " node = node.parent\n",
- " path.reverse() \n",
- " \n",
- " n_words = 10\n",
- " with_weights = False \n",
- " for n in range(len(path)):\n",
- " node = path[n]\n",
- " colour = colour_map[n] \n",
- " msg = 'Level %d Topic %d: ' % (node.level, node.node_id)\n",
- " msg += node.get_top_words(n_words, with_weights)\n",
- " output = '%s' % (n+1, colour, msg)\n",
- " display(HTML(output))\n",
- " \n",
- " display(HTML('
Processed Document
'))\n",
- "\n",
- " doc = corpus[d]\n",
- " output = ''\n",
- " for n in range(len(doc)):\n",
- " w = doc[n]\n",
- " l = hlda.levels[d][n]\n",
- " colour = colour_map[l]\n",
- " output += '%s ' % (colour, w)\n",
- " display(HTML(output))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you run this notebook locally, you'd be able to flip through the documents in the corpus and see the topic assignments of individual words of the document."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "Level 0 Topic 0: peopl, use, thi, technolog, mobil, phone, like, one, year, get, "
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Level 1 Topic 6: comput, use, system, softwar, microsoft, user, machin, chip, compani, new, "
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Level 2 Topic 7: site, attack, firm, net, messag, data, websit, spam, send, traffic,
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "
Processed Document
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "net fingerprint combat attack eighti larg net servic firm switch softwar spot stop net attack automat system creat digit fingerprint ongo incid sent everi network affect firm involv smart sens system believ help trace attack back sourc data gather pass polic help build intellig behind worm outbreak denial servic attack firm sign sens system includ mci deutsch telekom energi ntt bell canada mani creation fingerprint system broker firm arbor network signatur attack pass anyon suffer weight attack increasingli comput crimin use swarm remot control comput carri denial servic attack websit launch worm relay spam around net seen attack involv five ten gigabyt traffic rob pollard sale director arbor network behind fingerprint system attack size caus collater damag cross internet befor get destin onc attack spot signatur defin inform pass back chain network affect help everi unwit player tackl problem pollard arbor charg servic would pass fingerprint data everi network affect want help net servic firm commun push attack back around world sourc pollard arbor network technolog work build detail histori traffic network spot comput group user regularli talk type traffic pass machin workgroup ani anomali thi usual pattern spot flag network administr take action traffic due netbas attack kind thi type close analysi becom veri use net attack increasingli launch use sever hundr thousand differ machin anyon look traffic machin machin basi would unlik spot part concert attack attack get diffus sophist malcolm seagrav secur expert energi last month start get notic crimin take weve seen massiv growth although inform system exist pass inform attack often commerci confidenti got way share enough inform properli combat attack "
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "widgets.interact(show_doc, d=(0, len(corpus)-1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5. Dump the hlda object for further use later"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "https://stackoverflow.com/questions/18474791/decreasing-the-size-of-cpickle-objects"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import cPickle\n",
- "import gzip\n",
- "\n",
- "def save_zipped_pickle(obj, filename, protocol=-1):\n",
- " with gzip.open(filename, 'wb') as f:\n",
- " cPickle.dump(obj, f, protocol)\n",
- " \n",
- "def load_zipped_pickle(filename):\n",
- " with gzip.open(filename, 'rb') as f:\n",
- " loaded_object = cPickle.load(f)\n",
- " return loaded_object"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "save_zipped_pickle(hlda, 'bbc_hlda.p')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.13"
- },
- "widgets": {
- "state": {
- "03d4a94715d647d1ad07cd0817285e4f": {
- "views": [
- {
- "cell_index": 27
- }
- ]
- }
- },
- "version": "1.2.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/synth_data.py b/notebooks/synth_data.py
deleted file mode 100644
index 2c05d56..0000000
--- a/notebooks/synth_data.py
+++ /dev/null
@@ -1,129 +0,0 @@
-from numpy import int64
-from pandas.core.frame import DataFrame
-
-import numpy as np
-import pylab as plt
-import pandas as pd
-
-class HldaDataGenerator(object):
-
- def __init__(self, alpha, make_plot=False):
- self.alpha = alpha
- self.make_plot = make_plot
-
- def generate_word_dists(self, n_topics, vocab_size, document_length):
-
- width = vocab_size/n_topics
- word_dists = np.zeros((n_topics, vocab_size))
-
- for k in range(n_topics):
- temp = np.zeros((n_topics, width))
- temp[k, :] = int(document_length / width)
- word_dists[k,:] = temp.flatten()
-
- word_dists /= word_dists.sum(axis=1)[:, np.newaxis] # turn counts into probabilities
- if self.make_plot:
- self._plot_nicely(word_dists, 'Topic Words', 'N', 'K')
- return word_dists
-
- def generate_document(self, word_dists, n_topics, vocab_size, document_length):
-
- # sample topic proportions with uniform dirichlet parameter alpha of length n_topics
- theta = np.random.mtrand.dirichlet([self.alpha] * n_topics)
-
- # for every word in the vocab for this document
- d = np.zeros(vocab_size)
- for n in range(document_length):
-
- # sample a new topic index
- k = np.random.multinomial(1, theta).argmax()
-
- # sample a new word from the word distribution of topic k
- w = np.random.multinomial(1, word_dists[k,:]).argmax()
-
- # increase the occurrence of word w in document d
- d[w] += 1
-
- return d
-
- def generate_input_df(self, n_topics, vocab_size, document_length, n_docs,
- vocab_prefix=None, df_outfile=None, vocab_outfile=None):
-
- print("Generating input DF")
-
- # word_dists is the topic x document_length matrix
- word_dists = self.generate_word_dists(n_topics, vocab_size, document_length)
-
- # generate each document x terms vector
- docs = np.zeros((vocab_size, n_docs), dtype=int64)
- for i in range(n_docs):
- docs[:, i] = self.generate_document(word_dists, n_topics, vocab_size, document_length)
-
- df = DataFrame(docs)
- df = df.transpose()
- print(df.shape )
- if self.make_plot:
- self._plot_nicely(df, 'Documents X Terms', 'Terms', 'Docs')
-
- if df_outfile is not None:
- df.to_csv(df_outfile)
-
- print("Generating vocabularies")
- vocab = []
-
- # add new words
- for n in range(vocab_size):
- if vocab_prefix is None:
- word = "word_" + str(n)
- else:
- word = vocab_prefix + "_word_" + str(n)
- vocab.append(word)
-
- # save to txt
- vocab = np.array(vocab)
- if vocab_outfile is not None:
- np.savetxt(vocab_outfile, vocab, fmt='%s')
-
- return df, vocab
-
- def generate_from_file(self, df_infile, vocab_infile):
-
- # read data frame
- df = pd.read_csv(df_infile, index_col=0)
-
- # here we need to change column type from string to integer for
- # other parts in gibbs sampling to work ...
- # TODO: check why, because this means we cannot set the column
- # names in the dataframe to the words!
- df.rename(columns = lambda x: int(x), inplace=True)
-
- vocab = np.genfromtxt(vocab_infile, dtype='str')
- return df, vocab
-
- def _plot_nicely(self, mat, title, xlabel, ylabel, outfile=None):
- fig = plt.figure()
- ax = fig.add_subplot(111)
- im = ax.matshow(mat)
- ax.set_title(title)
- ax.set_xlabel(xlabel)
- ax.set_ylabel(ylabel)
- ax.set_aspect(2)
- ax.set_aspect('auto')
- plt.colorbar(im)
- if outfile is not None:
- plt.savefig(outfile)
- plt.show()
-
-
-def main():
-
- gen = HldaDataGenerator(0.01, make_plot=True)
-
- n_topics = 5
- vocab_size = 25
- document_length = 1000
- n_docs = 100
- df, vocab = gen.generate_input_df(n_topics, vocab_size, document_length, n_docs)
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/notebooks/synth_data_test.ipynb b/notebooks/synth_data_test.ipynb
deleted file mode 100644
index e60311c..0000000
--- a/notebooks/synth_data_test.ipynb
+++ /dev/null
@@ -1,648 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%load_ext autoreload\n",
- "%autoreload 2\n",
- "%matplotlib inline\n",
- "\n",
- "import sys\n",
- "basedir = '../'\n",
- "sys.path.append(basedir)\n",
- "\n",
- "import numpy as np\n",
- "from numpy.random import RandomState\n",
- "import pandas as pd\n",
- "from IPython.display import display\n",
- "\n",
- "from synth_data import HldaDataGenerator\n",
- "from hlda.sampler import NCRPNode"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Synthetic data test for hierarchical LDA inference."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 1. Generate Vocab"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[['w0' 'w1' 'w2' 'w3' 'w4']\n",
- " ['w5' 'w6' 'w7' 'w8' 'w9']\n",
- " ['w10' 'w11' 'w12' 'w13' 'w14']\n",
- " ['w15' 'w16' 'w17' 'w18' 'w19']\n",
- " ['w20' 'w21' 'w22' 'w23' 'w24']\n",
- " ['w25' 'w26' 'w27' 'w28' 'w29']\n",
- " ['w30' 'w31' 'w32' 'w33' 'w34']\n",
- " ['w35' 'w36' 'w37' 'w38' 'w39']\n",
- " ['w40' 'w41' 'w42' 'w43' 'w44']\n",
- " ['w45' 'w46' 'w47' 'w48' 'w49']\n",
- " ['w50' 'w51' 'w52' 'w53' 'w54']\n",
- " ['w55' 'w56' 'w57' 'w58' 'w59']\n",
- " ['w60' 'w61' 'w62' 'w63' 'w64']\n",
- " ['w65' 'w66' 'w67' 'w68' 'w69']\n",
- " ['w70' 'w71' 'w72' 'w73' 'w74']\n",
- " ['w75' 'w76' 'w77' 'w78' 'w79']\n",
- " ['w80' 'w81' 'w82' 'w83' 'w84']\n",
- " ['w85' 'w86' 'w87' 'w88' 'w89']\n",
- " ['w90' 'w91' 'w92' 'w93' 'w94']\n",
- " ['w95' 'w96' 'w97' 'w98' 'w99']]\n"
- ]
- }
- ],
- "source": [
- "n_rows = 20\n",
- "n_cols = 5\n",
- "vocab_mat = np.zeros((n_rows, n_cols), dtype=np.object)\n",
- "word_count = 0\n",
- "for i in range(n_rows):\n",
- " for j in range(n_cols):\n",
- " vocab_mat[i, j] = 'w%s' % word_count\n",
- " word_count += 1\n",
- " \n",
- "print vocab_mat"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['w0', 'w1', 'w2', 'w3', 'w4', 'w5', 'w6', 'w7', 'w8', 'w9', 'w10', 'w11', 'w12', 'w13', 'w14', 'w15', 'w16', 'w17', 'w18', 'w19', 'w20', 'w21', 'w22', 'w23', 'w24', 'w25', 'w26', 'w27', 'w28', 'w29', 'w30', 'w31', 'w32', 'w33', 'w34', 'w35', 'w36', 'w37', 'w38', 'w39', 'w40', 'w41', 'w42', 'w43', 'w44', 'w45', 'w46', 'w47', 'w48', 'w49', 'w50', 'w51', 'w52', 'w53', 'w54', 'w55', 'w56', 'w57', 'w58', 'w59', 'w60', 'w61', 'w62', 'w63', 'w64', 'w65', 'w66', 'w67', 'w68', 'w69', 'w70', 'w71', 'w72', 'w73', 'w74', 'w75', 'w76', 'w77', 'w78', 'w79', 'w80', 'w81', 'w82', 'w83', 'w84', 'w85', 'w86', 'w87', 'w88', 'w89', 'w90', 'w91', 'w92', 'w93', 'w94', 'w95', 'w96', 'w97', 'w98', 'w99']\n"
- ]
- }
- ],
- "source": [
- "vocab = vocab_mat.flatten().tolist()\n",
- "print vocab"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 2. Assign Documents to Tree"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "11\n",
- "node 0 (level=0, documents=100): \n",
- " node 1 (level=1, documents=56): \n",
- " node 2 (level=2, documents=23): \n",
- " node 5 (level=2, documents=31): \n",
- " node 10 (level=2, documents=2): \n",
- " node 3 (level=1, documents=35): \n",
- " node 4 (level=2, documents=28): \n",
- " node 6 (level=2, documents=7): \n",
- " node 7 (level=1, documents=9): \n",
- " node 8 (level=2, documents=2): \n",
- " node 9 (level=2, documents=7): \n"
- ]
- }
- ],
- "source": [
- "NCRPNode.total_nodes = 0\n",
- "NCRPNode.last_node_id = 0\n",
- "num_levels = 3\n",
- "gamma = 1\n",
- "num_docs = 100\n",
- "\n",
- "root_node = NCRPNode(num_levels, vocab)\n",
- "document_path = {}\n",
- "unique_nodes = set()\n",
- "unique_nodes.add(root_node)\n",
- "for d in range(num_docs):\n",
- "\n",
- " # populate nodes into the path of this document\n",
- " path = np.zeros(num_levels, dtype=np.object)\n",
- " path[0] = root_node\n",
- " root_node.customers += 1 # always add to the root node first\n",
- " for level in range(1, num_levels):\n",
- " # at each level, a node is selected by its parent node based on the CRP prior\n",
- " parent_node = path[level-1]\n",
- " level_node = parent_node.select(gamma)\n",
- " level_node.customers += 1\n",
- " path[level] = level_node\n",
- " unique_nodes.add(level_node)\n",
- "\n",
- " # set the leaf node for this document \n",
- " document_path[d] = path\n",
- " \n",
- "unique_nodes = sorted(unique_nodes, key=lambda x: x.node_id)\n",
- "print len(unique_nodes)\n",
- " \n",
- "def print_node(node, indent, node_topic):\n",
- " out = ' ' * indent\n",
- " out += 'node %d (level=%d, documents=%d): ' % (node.node_id, node.level, node.customers)\n",
- " if node in node_topic:\n",
- " probs, words = node_topic[node]\n",
- " out += ' '.join(words)\n",
- " print out \n",
- " for child in node.children:\n",
- " print_node(child, indent+1, node_topic) \n",
- "\n",
- "node_topic = {}\n",
- "print_node(root_node, 0, node_topic)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 3. Assign Each Node Along the Tree to a Topic"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 0.00040692 0.03688672 0.09907082 0.02081315 0.0340949 0.06334867\n",
- " 0.05575553 0.00243414 0.01760897 0.09163744 0.00026633 0.0544931\n",
- " 0.04593761 0.20827443 0.01494573 0.00392205 0.05150066 0.07604774\n",
- " 0.02063409 0.10192099]\n",
- "['w0' 'w5' 'w10' 'w15' 'w20' 'w25' 'w30' 'w35' 'w40' 'w45' 'w50' 'w55'\n",
- " 'w60' 'w65' 'w70' 'w75' 'w80' 'w85' 'w90' 'w95']\n",
- "1.0\n"
- ]
- }
- ],
- "source": [
- "def get_words(vocab_mat, eta, pos, dim):\n",
- "\n",
- " if dim == 'row':\n",
- " words = vocab_mat[pos]\n",
- " elif dim == 'col':\n",
- " words = vocab_mat[:, pos]\n",
- " \n",
- " k = len(words)\n",
- " eta = [eta] * k\n",
- " probs = np.random.dirichlet(eta)\n",
- " return probs, words\n",
- " \n",
- "pos = 0\n",
- "eta = 1\n",
- "probs, words = get_words(vocab_mat, eta, pos, 'col')\n",
- "print probs\n",
- "print words\n",
- "print np.sum(probs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "11\n"
- ]
- }
- ],
- "source": [
- "node_topic = {}\n",
- "node_topic[unique_nodes[0]] = get_words(vocab_mat, eta, 0, 'row') \n",
- "node_topic[unique_nodes[1]] = get_words(vocab_mat, eta, 1, 'row') \n",
- "node_topic[unique_nodes[2]] = get_words(vocab_mat, eta, 2, 'row') \n",
- "node_topic[unique_nodes[3]] = get_words(vocab_mat, eta, 3, 'row') \n",
- "node_topic[unique_nodes[4]] = get_words(vocab_mat, eta, 4, 'row') \n",
- "node_topic[unique_nodes[5]] = get_words(vocab_mat, eta, 5, 'row') \n",
- "node_topic[unique_nodes[6]] = get_words(vocab_mat, eta, 6, 'row') \n",
- "node_topic[unique_nodes[7]] = get_words(vocab_mat, eta, 7, 'row') \n",
- "node_topic[unique_nodes[8]] = get_words(vocab_mat, eta, 8, 'row') \n",
- "node_topic[unique_nodes[9]] = get_words(vocab_mat, eta, 9, 'row') \n",
- "node_topic[unique_nodes[10]] = get_words(vocab_mat, eta, 10, 'row') \n",
- "print len(node_topic)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "node 0 (level=0, documents=100): w0 w1 w2 w3 w4\n",
- " node 1 (level=1, documents=56): w5 w6 w7 w8 w9\n",
- " node 2 (level=2, documents=23): w10 w11 w12 w13 w14\n",
- " node 5 (level=2, documents=31): w25 w26 w27 w28 w29\n",
- " node 10 (level=2, documents=2): w50 w51 w52 w53 w54\n",
- " node 3 (level=1, documents=35): w15 w16 w17 w18 w19\n",
- " node 4 (level=2, documents=28): w20 w21 w22 w23 w24\n",
- " node 6 (level=2, documents=7): w30 w31 w32 w33 w34\n",
- " node 7 (level=1, documents=9): w35 w36 w37 w38 w39\n",
- " node 8 (level=2, documents=2): w40 w41 w42 w43 w44\n",
- " node 9 (level=2, documents=7): w45 w46 w47 w48 w49\n"
- ]
- }
- ],
- "source": [
- "print_node(root_node, 0, node_topic)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 4. Generate Words in a Document Based on Its Path"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def generate_document(topics, theta, doc_len):\n",
- "\n",
- " # for every word in the vocab for this document\n",
- " doc = []\n",
- " for n in range(doc_len):\n",
- "\n",
- " # sample a new topic index \n",
- " k = np.random.multinomial(1, theta).argmax()\n",
- "\n",
- " # sample a new word from the word distribution of topic k\n",
- " probs, words = topics[k]\n",
- " w = np.random.multinomial(1, probs).argmax()\n",
- " doc_word = words[w]\n",
- "\n",
- " doc.append(doc_word)\n",
- "\n",
- " return doc"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "corpus = []\n",
- "# alpha = [2.0, 1.0, 0.5]\n",
- "alpha = [1.0, 1.0, 1.0]\n",
- "doc_len = 50\n",
- "for d in range(num_docs):\n",
- " path = document_path[d]\n",
- " topics = [node_topic[node] for node in path]\n",
- " theta = np.random.mtrand.dirichlet(alpha)\n",
- " doc = generate_document(topics, theta, doc_len)\n",
- " corpus.append(doc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "outdir = '/Users/joewandy/Dropbox/Analysis/hLDA/data/synthetic/'\n",
- "for d in range(len(corpus)):\n",
- " doc = corpus[d]\n",
- " file_name = 'doc_%d.txt' % d\n",
- " file_path = os.path.join(outdir, file_name)\n",
- " with open(file_path, 'w') as f:\n",
- " f.write(\"%s\\n\" % ' '.join(doc))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 5. Run hLDA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "100 100 50\n"
- ]
- }
- ],
- "source": [
- "print len(vocab), len(corpus), len(corpus[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "convert corpus words into indices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "new_corpus = []\n",
- "for doc in corpus:\n",
- " new_doc = []\n",
- " for word in doc:\n",
- " word_idx = vocab.index(word)\n",
- " new_doc.append(word_idx)\n",
- " new_corpus.append(new_doc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "100 100\n",
- "['w4', 'w13', 'w7', 'w4', 'w9', 'w4', 'w1', 'w4', 'w7', 'w6', 'w14', 'w13', 'w4', 'w4', 'w7', 'w8', 'w4', 'w3', 'w3', 'w2', 'w6', 'w4', 'w8', 'w2', 'w4', 'w14', 'w4', 'w4', 'w2', 'w4', 'w12', 'w4', 'w7', 'w4', 'w3', 'w2', 'w1', 'w10', 'w1', 'w7', 'w2', 'w12', 'w9', 'w13', 'w9', 'w3', 'w6', 'w2', 'w2', 'w10']\n",
- "[4, 13, 7, 4, 9, 4, 1, 4, 7, 6, 14, 13, 4, 4, 7, 8, 4, 3, 3, 2, 6, 4, 8, 2, 4, 14, 4, 4, 2, 4, 12, 4, 7, 4, 3, 2, 1, 10, 1, 7, 2, 12, 9, 13, 9, 3, 6, 2, 2, 10]\n"
- ]
- }
- ],
- "source": [
- "print len(vocab), len(new_corpus)\n",
- "print corpus[0]\n",
- "print new_corpus[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from hlda.sampler import HierarchicalLDA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1.0, 1.0, 1.0] 1 1\n"
- ]
- }
- ],
- "source": [
- "print alpha, gamma, eta"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "HierarchicalLDA sampling\n",
- "\n",
- ".......... 10\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w9, \n",
- " topic=1 level=1 (documents=36): w4, w2, w3, w19, w17, \n",
- " topic=2 level=2 (documents=27): w20, w24, w22, w17, w23, \n",
- " topic=12 level=2 (documents=9): w12, w13, w6, w10, w14, \n",
- " topic=4 level=1 (documents=64): w29, w7, w8, w6, w25, \n",
- " topic=5 level=2 (documents=26): w6, w7, w9, w8, w5, \n",
- " topic=6 level=2 (documents=19): w25, w29, w28, w26, w6, \n",
- " topic=8 level=2 (documents=10): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=8): w49, w36, w39, w47, w46, \n",
- " topic=23 level=2 (documents=1): w43, w41, w42, w2, w44, \n",
- "\n",
- ".......... 20\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w0, \n",
- " topic=1 level=1 (documents=38): w50, w52, w6, w7, w17, \n",
- " topic=2 level=2 (documents=28): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=9): w12, w13, w10, w14, w6, \n",
- " topic=33 level=2 (documents=1): w43, w41, w42, w4, w44, \n",
- " topic=4 level=1 (documents=62): w7, w25, w8, w29, w6, \n",
- " topic=5 level=2 (documents=27): w6, w9, w7, w8, w12, \n",
- " topic=6 level=2 (documents=18): w29, w25, w28, w26, w6, \n",
- " topic=8 level=2 (documents=9): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=8): w49, w36, w39, w47, w46, \n",
- "\n",
- ".......... 30\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w0, \n",
- " topic=1 level=1 (documents=36): w50, w6, w52, w7, w9, \n",
- " topic=2 level=2 (documents=27): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=7): w12, w13, w10, w14, w6, \n",
- " topic=43 level=2 (documents=2): w43, w41, w42, w44, w40, \n",
- " topic=4 level=1 (documents=64): w6, w7, w9, w8, w29, \n",
- " topic=5 level=2 (documents=28): w6, w7, w9, w12, w8, \n",
- " topic=6 level=2 (documents=18): w25, w29, w28, w26, w27, \n",
- " topic=8 level=2 (documents=9): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=9): w49, w36, w39, w47, w35, \n",
- "\n",
- ".......... 40\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w0, \n",
- " topic=1 level=1 (documents=35): w6, w9, w7, w8, w29, \n",
- " topic=2 level=2 (documents=27): w24, w20, w22, w17, w19, \n",
- " topic=12 level=2 (documents=7): w12, w13, w10, w14, w6, \n",
- " topic=53 level=2 (documents=1): w43, w41, w42, w3, w40, \n",
- " topic=4 level=1 (documents=65): w6, w7, w9, w8, w25, \n",
- " topic=5 level=2 (documents=25): w6, w7, w9, w12, w8, \n",
- " topic=6 level=2 (documents=20): w29, w25, w28, w26, w27, \n",
- " topic=8 level=2 (documents=9): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=10): w49, w36, w39, w47, w35, \n",
- " topic=54 level=2 (documents=1): w99, w36, w26, w27, w28, \n",
- "\n",
- ".......... 50\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w25, \n",
- " topic=1 level=1 (documents=37): w7, w6, w9, w8, w5, \n",
- " topic=2 level=2 (documents=27): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=10): w12, w13, w10, w14, w6, \n",
- " topic=4 level=1 (documents=63): w6, w7, w9, w8, w5, \n",
- " topic=5 level=2 (documents=20): w6, w9, w7, w12, w13, \n",
- " topic=6 level=2 (documents=23): w29, w25, w28, w26, w27, \n",
- " topic=8 level=2 (documents=10): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=8): w49, w36, w39, w47, w35, \n",
- " topic=61 level=2 (documents=2): w43, w41, w42, w54, w39, \n",
- "\n",
- ".......... 60\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w0, \n",
- " topic=1 level=1 (documents=41): w6, w9, w7, w8, w50, \n",
- " topic=2 level=2 (documents=28): w24, w20, w22, w17, w23, \n",
- " topic=12 level=2 (documents=11): w12, w13, w10, w14, w11, \n",
- " topic=72 level=2 (documents=2): w43, w41, w42, w2, w44, \n",
- " topic=4 level=1 (documents=59): w6, w7, w9, w8, w5, \n",
- " topic=5 level=2 (documents=13): w12, w6, w13, w7, w10, \n",
- " topic=6 level=2 (documents=27): w29, w25, w28, w26, w27, \n",
- " topic=8 level=2 (documents=9): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=10): w49, w36, w39, w47, w46, \n",
- "\n",
- ".......... 70\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w0, \n",
- " topic=1 level=1 (documents=52): w6, w9, w7, w8, w5, \n",
- " topic=2 level=2 (documents=29): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=19): w12, w13, w10, w14, w11, \n",
- " topic=81 level=2 (documents=4): w43, w41, w42, w40, w39, \n",
- " topic=4 level=1 (documents=48): w6, w7, w9, w8, w5, \n",
- " topic=6 level=2 (documents=29): w25, w29, w28, w26, w27, \n",
- " topic=8 level=2 (documents=9): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=8): w49, w36, w39, w47, w35, \n",
- " topic=75 level=2 (documents=2): w50, w52, w53, w54, w3, \n",
- "\n",
- ".......... 80\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w29, \n",
- " topic=1 level=1 (documents=52): w6, w7, w9, w8, w5, \n",
- " topic=2 level=2 (documents=26): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=20): w12, w13, w10, w14, w11, \n",
- " topic=88 level=2 (documents=5): w50, w52, w53, w6, w54, \n",
- " topic=89 level=2 (documents=1): w43, w41, w42, w2, w44, \n",
- " topic=4 level=1 (documents=48): w6, w7, w9, w8, w5, \n",
- " topic=6 level=2 (documents=29): w25, w29, w28, w26, w27, \n",
- " topic=8 level=2 (documents=10): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=9): w49, w36, w39, w47, w35, \n",
- "\n",
- ".......... 90\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w9, \n",
- " topic=1 level=1 (documents=47): w6, w7, w9, w8, w5, \n",
- " topic=2 level=2 (documents=26): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=18): w12, w13, w10, w14, w11, \n",
- " topic=88 level=2 (documents=3): w50, w52, w53, w54, w3, \n",
- " topic=4 level=1 (documents=53): w6, w7, w9, w8, w5, \n",
- " topic=6 level=2 (documents=30): w29, w25, w28, w26, w27, \n",
- " topic=8 level=2 (documents=11): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=9): w49, w36, w39, w47, w35, \n",
- " topic=97 level=2 (documents=3): w43, w41, w42, w44, w39, \n",
- "\n",
- ".......... 100\n",
- "topic=0 level=0 (documents=100): w4, w3, w2, w1, w0, \n",
- " topic=1 level=1 (documents=48): w6, w7, w9, w8, w12, \n",
- " topic=2 level=2 (documents=28): w20, w24, w22, w17, w19, \n",
- " topic=12 level=2 (documents=18): w12, w13, w10, w14, w11, \n",
- " topic=105 level=2 (documents=2): w50, w52, w53, w54, w3, \n",
- " topic=4 level=1 (documents=52): w6, w7, w9, w8, w5, \n",
- " topic=6 level=2 (documents=30): w29, w25, w28, w26, w27, \n",
- " topic=8 level=2 (documents=11): w17, w16, w19, w33, w15, \n",
- " topic=9 level=2 (documents=8): w49, w36, w39, w47, w35, \n",
- " topic=101 level=2 (documents=1): w99, w36, w26, w27, w28, \n",
- " topic=103 level=2 (documents=2): w43, w41, w42, w2, w4, \n",
- "\n"
- ]
- }
- ],
- "source": [
- "n_samples = 100\n",
- "hlda = HierarchicalLDA(new_corpus, vocab, alpha=1, gamma=1.0, eta=1.0, num_levels=3)\n",
- "hlda.estimate(n_samples, display_topics=10, n_words=5, with_weights=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..1813d00
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3618 @@
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
+
+[[package]]
+name = "anyio"
+version = "4.9.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+ {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
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+]
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+groups = ["dev"]
+files = [
+ {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+ {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
+]
+
+[[package]]
+name = "webcolors"
+version = "24.11.1"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+ {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
+ {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
+]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+optional = false
+python-versions = "*"
+groups = ["dev"]
+files = [
+ {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+ {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
+
+[[package]]
+name = "websocket-client"
+version = "1.8.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+files = [
+ {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+ {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.14"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+groups = ["dev"]
+files = [
+ {file = "widgetsnbextension-4.0.14-py3-none-any.whl", hash = "sha256:4875a9eaf72fbf5079dc372a51a9f268fc38d46f767cbf85c43a36da5cb9b575"},
+ {file = "widgetsnbextension-4.0.14.tar.gz", hash = "sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af"},
+]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.11,<3.13"
+content-hash = "e57785b1181d5ce6fbc8b5064aa2c7bf8ef2460c93dc56bb74e0d4a6f876ce73"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..8991b9a
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,47 @@
+[tool.poetry]
+name = "hlda"
+version = "0.4"
+description = "Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model."
+authors = ["Joe Wandy "]
+license = "MIT"
+readme = "README.md"
+homepage = "https://github.com/joewandy/hlda"
+keywords = ["topic modelling", "hierarchical lda"]
+classifiers = [
+ "License :: OSI Approved :: MIT License",
+ "Operating System :: OS Independent"
+]
+packages = [{include = "hlda", from = "src"}]
+
+[tool.poetry.scripts]
+hlda-run = "scripts.run_hlda:main"
+
+[tool.poetry.dependencies]
+python = ">=3.11,<3.13"
+numpy = "^2.2.6"
+pandas = "^2.2.3"
+matplotlib = "^3.10.3"
+seaborn = "^0.13.2"
+tqdm = "^4.67.1"
+scikit-learn = "^1.5.0"
+
+[tool.poetry.group.dev.dependencies]
+jupyterlab = "^4.4.3"
+ipywidgets = "^8.1.7"
+flake8 = "^7.2.0"
+autopep8 = "^2.3.2"
+pytest = "^8.4.0"
+pytest-cov = "^6.1.1"
+mkdocs = "^1.6.1"
+mkdocstrings = "^0.29.1"
+black = "^25.1.0"
+pre-commit = "^3.7.0"
+
+[tool.setuptools_scm]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.black]
+line-length = 99
diff --git a/hlda/__init__.py b/scripts/__init__.py
similarity index 100%
rename from hlda/__init__.py
rename to scripts/__init__.py
diff --git a/scripts/run_hlda.py b/scripts/run_hlda.py
new file mode 100644
index 0000000..7e9d6f6
--- /dev/null
+++ b/scripts/run_hlda.py
@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+"""Command-line utility for running hierarchical LDA on a corpus of text
+files."""
+
+import argparse
+import glob
+import json
+import os
+import re
+
+from sklearn.feature_extraction.text import CountVectorizer
+
+from hlda.sklearn_wrapper import HierarchicalLDAEstimator
+
+
+# A small set of English stopwords. This keeps the script self-contained.
+STOPWORDS = {
+ "the",
+ "and",
+ "for",
+ "are",
+ "with",
+ "that",
+ "this",
+ "from",
+ "you",
+ "was",
+ "have",
+ "not",
+ "but",
+ "they",
+ "his",
+ "her",
+ "she",
+ "has",
+ "had",
+ "him",
+ "its",
+ "our",
+ "their",
+ "about",
+ "into",
+ "after",
+ "these",
+ "those",
+ "them",
+ "over",
+ "such",
+ "also",
+ "will",
+ "would",
+ "can",
+ "could",
+ "should",
+ "may",
+ "might",
+ "your",
+ "than",
+ "when",
+ "where",
+ "what",
+ "which",
+ "who",
+ "whom",
+ "why",
+ "how",
+}
+
+TOKEN_RE = re.compile(r"[a-zA-Z]{3,}")
+
+
+def load_documents(data_dir: str):
+ """Load and preprocess all text files under *data_dir*."""
+ corpus = []
+ for filename in sorted(glob.glob(os.path.join(data_dir, "*.txt"))):
+ with open(filename, "r", encoding="utf-8", errors="ignore") as f:
+ text = f.read().lower()
+ tokens = [t for t in TOKEN_RE.findall(text) if t not in STOPWORDS]
+ corpus.append(tokens)
+ return corpus
+
+
+def build_vocab(corpus):
+ vocab = sorted({word for doc in corpus for word in doc})
+ index = {w: i for i, w in enumerate(vocab)}
+ return vocab, index
+
+
+def convert_corpus(corpus, index):
+ new_corpus = []
+ for doc in corpus:
+ new_corpus.append([index[w] for w in doc])
+ return new_corpus
+
+
+def run_hlda(args):
+ corpus = load_documents(args.data_dir)
+ documents = [" ".join(doc) for doc in corpus]
+
+ vectorizer = CountVectorizer(
+ stop_words=list(STOPWORDS),
+ token_pattern=r"[a-zA-Z]{3,}",
+ )
+ dtm = vectorizer.fit_transform(documents)
+ vocab = list(vectorizer.get_feature_names_out())
+
+ estimator = HierarchicalLDAEstimator(
+ alpha=args.alpha,
+ gamma=args.gamma,
+ eta=args.eta,
+ num_levels=args.num_levels,
+ iterations=0,
+ seed=args.seed,
+ verbose=False,
+ vocab=vocab,
+ )
+ estimator.fit(dtm)
+ hlda = estimator.model_
+
+ hlda.estimate(
+ args.iterations,
+ display_topics=args.display_topics,
+ n_words=args.n_words,
+ with_weights=False,
+ )
+
+ print("\nFinal topic hierarchy:")
+ hlda.print_nodes(args.n_words, with_weights=False)
+
+ if getattr(args, "export_tree", None):
+ with open(args.export_tree, "w", encoding="utf-8") as f:
+ json.dump(hlda.export_tree(), f, indent=2)
+
+ return hlda
+
+
+def main():
+ parser = argparse.ArgumentParser(
+ description="Run hierarchical LDA on a directory of text documents"
+ )
+ parser.add_argument(
+ "--data-dir",
+ required=True,
+ help="Directory containing text files",
+ )
+ parser.add_argument(
+ "--iterations",
+ type=int,
+ default=100,
+ help="Number of Gibbs samples",
+ )
+ parser.add_argument(
+ "--display-topics",
+ type=int,
+ default=50,
+ help="Report topics every N iterations",
+ )
+ parser.add_argument(
+ "--n-words",
+ type=int,
+ default=5,
+ help="Number of words to display per topic",
+ )
+ parser.add_argument(
+ "--num-levels",
+ type=int,
+ default=3,
+ help="Depth of the topic hierarchy",
+ )
+ parser.add_argument(
+ "--alpha",
+ type=float,
+ default=10.0,
+ help="Alpha hyperparameter",
+ )
+ parser.add_argument(
+ "--gamma",
+ type=float,
+ default=1.0,
+ help="Gamma hyperparameter",
+ )
+ parser.add_argument(
+ "--eta",
+ type=float,
+ default=0.1,
+ help="Eta hyperparameter",
+ )
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=0,
+ help="Random seed",
+ )
+ parser.add_argument(
+ "--export-tree",
+ metavar="FILE",
+ help="Write the final hierarchy as JSON to FILE",
+ )
+
+ args = parser.parse_args()
+ run_hlda(args)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 224a779..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-[metadata]
-description-file = README.md
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 1b35b74..0000000
--- a/setup.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from setuptools import setup, find_packages
-
-with open("README.md", "r") as fh:
- long_description = fh.read()
-
-setup(
- name="hlda",
- version="0.3.1",
- author="Joe Wandy",
- author_email="joe.wandy@glasgow.ac.uk",
- description = 'Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model. This is based on the hLDA implementation from Mallet, having a fixed depth on the nCRP tree.',
- long_description=long_description,
- long_description_content_type='text/markdown',
- url = 'https://github.com/joewandy/hlda', # use the URL to the github repo
- classifiers=[
- "Programming Language :: Python :: 3",
- "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
- "Operating System :: OS Independent",
- ],
- python_requires='>=3',
- packages=find_packages(),
- install_requires=['numpy'],
-)
diff --git a/src/hlda/__init__.py b/src/hlda/__init__.py
new file mode 100644
index 0000000..d00cb31
--- /dev/null
+++ b/src/hlda/__init__.py
@@ -0,0 +1,6 @@
+__version__ = "0.4"
+
+from .sampler import HierarchicalLDA
+from .sklearn_wrapper import HierarchicalLDAEstimator
+
+__all__ = ["HierarchicalLDA", "HierarchicalLDAEstimator"]
diff --git a/hlda/sampler.py b/src/hlda/sampler.py
similarity index 62%
rename from hlda/sampler.py
rename to src/hlda/sampler.py
index 85a03e2..8756886 100644
--- a/hlda/sampler.py
+++ b/src/hlda/sampler.py
@@ -1,3 +1,19 @@
+"""Gibbs sampler for the Hierarchical LDA model.
+
+This module implements a simple Gibbs sampler for hierarchical Latent Dirichlet
+Allocation (hLDA). The algorithm follows the nested Chinese restaurant process
+described in the original hLDA papers:
+
+* Blei et al.,
+ "Hierarchical Topic Models and the Nested Chinese Restaurant Process".
+* Griffiths et al.,
+ "The Nested Chinese Restaurant Process and Bayesian"
+ " Nonparametric Inference of Topic Hierarchies".
+
+It provides helper functions for loading data and the :class:`HierarchicalLDA`
+sampler.
+"""
+
import csv
from math import log
import sys
@@ -38,12 +54,21 @@ def __repr__(self):
parent_id = None
if self.parent is not None:
parent_id = self.parent.node_id
- return 'Node=%d level=%d customers=%d total_words=%d parent=%s' % (self.node_id,
- self.level, self.customers, self.total_words, parent_id)
+ return (
+ f"Node={self.node_id} level={self.level} "
+ f"customers={self.customers} "
+ f"total_words={self.total_words} parent={parent_id}"
+ )
def add_child(self):
''' Adds a child to the next level of this node '''
- node = NCRPNode(self.num_levels, self.vocab, parent=self, level=self.level+1)
+ node = NCRPNode(
+ self.num_levels,
+ self.vocab,
+ parent=self,
+ level=self.level + 1,
+ random_state=self.random_state,
+ )
self.children.append(node)
NCRPNode.total_nodes += 1
return node
@@ -55,21 +80,19 @@ def is_leaf(self):
def get_new_leaf(self):
''' Keeps adding nodes along the path until a leaf node is generated'''
node = self
- for l in range(self.level, self.num_levels-1):
+ for lvl in range(self.level, self.num_levels - 1):
node = node.add_child()
return node
def drop_path(self):
- ''' Removes a document from a path starting from this node '''
+ """Remove a document from a path starting at this leaf and moving
+ upwards."""
node = self
- node.customers -= 1
- if node.customers == 0:
- node.parent.remove(node)
- for level in range(1, self.num_levels): # skip the root
- node = node.parent
+ while node is not None:
node.customers -= 1
- if node.customers == 0:
+ if node.customers == 0 and node.parent is not None:
node.parent.remove(node)
+ node = node.parent
def remove(self, node):
''' Removes a child node '''
@@ -85,7 +108,7 @@ def add_path(self):
node.customers += 1
def select(self, gamma):
- ''' Selects an existing child or create a new one according to the CRP '''
+ """Select an existing child or create a new one using the CRP."""
weights = np.zeros(len(self.children)+1)
weights[0] = float(gamma) / (gamma+self.customers)
@@ -117,7 +140,40 @@ def get_top_words(self, n_words, with_weight):
output += '%s, ' % word
return output
+
class HierarchicalLDA(object):
+ """Collapsed Gibbs sampler for hierarchical LDA.
+
+ Parameters
+ ----------
+ corpus : Sequence[Sequence[int]]
+ Collection of documents encoded as lists of token ids.
+ vocab : Sequence[str]
+ Mapping from token id to word.
+ alpha : float, optional
+ Dirichlet prior for document-topic distributions.
+ gamma : float, optional
+ Parameter of the nested CRP controlling branching behaviour.
+ eta : float, optional
+ Dirichlet prior for topic-word distributions.
+ seed : int, optional
+ Seed for the internal random number generator.
+ verbose : bool, optional
+ Whether to print progress during sampling.
+ num_levels : int, optional
+ Depth of the topic hierarchy.
+
+ Attributes
+ ----------
+ root_node : NCRPNode
+ Root of the NCRP tree representing the topic hierarchy.
+ document_leaves : dict[int, NCRPNode]
+ Mapping from document index to the leaf node currently assigned.
+ levels : ndarray
+ Array of per-word level assignments for each document.
+ random_state : RandomState
+ Random number generator used by the sampler.
+ """
def __init__(self, corpus, vocab,
alpha=10.0, gamma=1.0, eta=0.1,
@@ -128,9 +184,12 @@ def __init__(self, corpus, vocab,
self.corpus = corpus
self.vocab = vocab
- self.alpha = alpha # smoothing on doc-topic distributions
- self.gamma = gamma # "imaginary" customers at the next, as yet unused table
- self.eta = eta # smoothing on topic-word distributions
+ # smoothing on doc-topic distributions
+ self.alpha = alpha
+ # "imaginary" customers at the next, as yet unused table
+ self.gamma = gamma
+ # smoothing on topic-word distributions
+ self.eta = eta
self.seed = seed
self.random_state = RandomState(seed)
@@ -148,23 +207,31 @@ def __init__(self, corpus, vocab,
# print 'doc_%d = %s' % (d, words)
# initialise a single path
- path = np.zeros(self.num_levels, dtype=np.object)
+ path = np.zeros(self.num_levels, dtype=object)
# initialize and fill the topic pointer arrays for
# every document. Set everything to the single path that
# we added earlier.
- self.root_node = NCRPNode(self.num_levels, self.vocab)
- self.document_leaves = {} # currently selected path (ie leaf node) through the NCRP tree
- self.levels = np.zeros(self.num_documents, dtype=np.object) # indexed < doc, token >
+ self.root_node = NCRPNode(
+ self.num_levels,
+ self.vocab,
+ random_state=self.random_state,
+ )
+ # currently selected path (i.e. leaf node) through the NCRP tree
+ self.document_leaves = {}
+ # indexed < doc, token >
+ self.levels = np.zeros(self.num_documents, dtype=object)
for d in range(len(self.corpus)):
# populate nodes into the path of this document
doc = self.corpus[d]
doc_len = len(doc)
path[0] = self.root_node
- self.root_node.customers += 1 # always add to the root node first
+ # always add to the root node first
+ self.root_node.customers += 1
for level in range(1, self.num_levels):
- # at each level, a node is selected by its parent node based on the CRP prior
+ # at each level, a node is selected by its parent node based on
+ # the CRP prior
parent_node = path[level-1]
level_node = parent_node.select(self.gamma)
level_node.customers += 1
@@ -174,8 +241,9 @@ def __init__(self, corpus, vocab,
leaf_node = path[self.num_levels-1]
self.document_leaves[d] = leaf_node
- # randomly assign each word in the document to a level (node) along the path
- self.levels[d] = np.zeros(doc_len, dtype=np.int)
+ # randomly assign each word in the document to a level (node)
+ # along the path
+ self.levels[d] = np.zeros(doc_len, dtype=int)
for n in range(doc_len):
w = doc[n]
random_level = self.random_state.randint(self.num_levels)
@@ -184,7 +252,13 @@ def __init__(self, corpus, vocab,
random_node.total_words += 1
self.levels[d][n] = random_level
- def estimate(self, num_samples, display_topics=50, n_words=5, with_weights=True):
+ def estimate(
+ self,
+ num_samples,
+ display_topics=50,
+ n_words=5,
+ with_weights=True,
+ ):
print('HierarchicalLDA sampling\n')
for s in range(num_samples):
@@ -205,9 +279,10 @@ def estimate(self, num_samples, display_topics=50, n_words=5, with_weights=True)
def sample_path(self, d):
# define a path starting from the leaf node of this doc
- path = np.zeros(self.num_levels, dtype=np.object)
+ path = np.zeros(self.num_levels, dtype=object)
node = self.document_leaves[d]
- for level in range(self.num_levels-1, -1, -1): # e.g. [3, 2, 1, 0] for num_levels = 4
+ for level in range(self.num_levels - 1, -1, -1):
+ # e.g. [3, 2, 1, 0] for num_levels = 4
path[level] = node
node = node.parent
@@ -232,7 +307,7 @@ def sample_path(self, d):
doc = self.corpus[d]
# remove doc from path
- for n in range(len(doc)): # for each word in the doc
+ for n in range(len(doc)): # for each word in the doc
# count the word at each level
level = doc_levels[n]
@@ -257,7 +332,8 @@ def sample_path(self, d):
nodes = np.array(list(node_weights.keys()))
weights = np.array([node_weights[node] for node in nodes])
- weights = np.exp(weights - np.max(weights)) # normalise so the largest weight is 1
+ # normalise so the largest weight is 1
+ weights = np.exp(weights - np.max(weights))
weights = weights / np.sum(weights)
choice = self.random_state.multinomial(1, weights).argmax()
@@ -268,11 +344,12 @@ def sample_path(self, d):
node = node.get_new_leaf()
# add the doc back to the path
- node.add_path() # add a customer to the path
- self.document_leaves[d] = node # store the leaf node for this doc
+ node.add_path() # add a customer to the path
+ self.document_leaves[d] = node # store the leaf node for this doc
# add the words
- for level in range(self.num_levels-1, -1, -1): # e.g. [3, 2, 1, 0] for num_levels = 4
+ for level in range(self.num_levels - 1, -1, -1):
+ # e.g. [3, 2, 1, 0] for num_levels = 4
word_counts = level_word_counts[level]
for w in word_counts:
node.word_counts[w] += word_counts[w]
@@ -281,12 +358,17 @@ def sample_path(self, d):
def calculate_ncrp_prior(self, node_weights, node, weight):
''' Calculates the prior on the path according to the nested CRP '''
-
for child in node.children:
- child_weight = log( float(child.customers) / (node.customers + self.gamma) )
- self.calculate_ncrp_prior(node_weights, child, weight + child_weight)
+ child_weight = log(float(child.customers) /
+ (node.customers + self.gamma))
+ self.calculate_ncrp_prior(node_weights, child,
+ weight + child_weight)
- node_weights[node] = weight + log( self.gamma / (node.customers + self.gamma))
+ if node.is_leaf():
+ node_weights[node] = weight
+ else:
+ node_weights[node] = weight + log(self.gamma /
+ (node.customers + self.gamma))
def calculate_doc_likelihood(self, node_weights, level_word_counts):
@@ -299,30 +381,56 @@ def calculate_doc_likelihood(self, node_weights, level_word_counts):
for w in word_counts:
count = word_counts[w]
- for i in range(count): # why ?????????
- new_topic_weights[level] += log((self.eta + i) / (self.eta_sum + total_tokens))
+ for i in range(count): # iterate over each occurrence
+ new_topic_weights[level] += log(
+ (self.eta + i) / (self.eta_sum + total_tokens)
+ )
total_tokens += 1
- self.calculate_word_likelihood(node_weights, self.root_node, 0.0, level_word_counts, new_topic_weights, 0)
-
- def calculate_word_likelihood(self, node_weights, node, weight, level_word_counts, new_topic_weights, level):
-
- # first calculate the likelihood of the words at this level, given this topic
+ self.calculate_word_likelihood(
+ node_weights,
+ self.root_node,
+ 0.0,
+ level_word_counts,
+ new_topic_weights,
+ 0,
+ )
+
+ def calculate_word_likelihood(
+ self,
+ node_weights,
+ node,
+ weight,
+ level_word_counts,
+ new_topic_weights,
+ level,
+ ):
+
+ # first calculate the likelihood of the words at this level
+ # given this topic
node_weight = 0.0
word_counts = level_word_counts[level]
total_words = 0
for w in word_counts:
count = word_counts[w]
- for i in range(count): # why ?????????
- node_weight += log( (self.eta + node.word_counts[w] + i) /
- (self.eta_sum + node.total_words + total_words) )
+ for i in range(count): # iterate over each occurrence
+ node_weight += log(
+ (self.eta + node.word_counts[w] + i)
+ / (self.eta_sum + node.total_words + total_words)
+ )
total_words += 1
# propagate that weight to the child nodes
for child in node.children:
- self.calculate_word_likelihood(node_weights, child, weight + node_weight,
- level_word_counts, new_topic_weights, level+1)
+ self.calculate_word_likelihood(
+ node_weights,
+ child,
+ weight + node_weight,
+ level_word_counts,
+ new_topic_weights,
+ level + 1,
+ )
# finally if this is an internal node, add the weight of a new path
level += 1
@@ -338,14 +446,15 @@ def sample_topics(self, d):
# initialise level counts
doc_levels = self.levels[d]
- level_counts = np.zeros(self.num_levels, dtype=np.int)
+ level_counts = np.zeros(self.num_levels, dtype=int)
for c in doc_levels:
level_counts[c] += 1
# get the leaf node and populate the path
- path = np.zeros(self.num_levels, dtype=np.object)
+ path = np.zeros(self.num_levels, dtype=object)
node = self.document_leaves[d]
- for level in range(self.num_levels-1, -1, -1): # e.g. [3, 2, 1, 0] for num_levels = 4
+ for level in range(self.num_levels - 1, -1, -1):
+ # e.g. [3, 2, 1, 0] for num_levels = 4
path[level] = node
node = node.parent
@@ -364,9 +473,11 @@ def sample_topics(self, d):
# pick new level
for level in range(self.num_levels):
- level_weights[level] = (self.alpha + level_counts[level]) * \
- (self.eta + path[level].word_counts[w]) / \
- (self.eta_sum + path[level].total_words)
+ level_weights[level] = (
+ (self.alpha + level_counts[level])
+ * (self.eta + path[level].word_counts[w])
+ / (self.eta_sum + path[level].total_words)
+ )
level_weights = level_weights / np.sum(level_weights)
level = self.random_state.multinomial(1, level_weights).argmax()
@@ -381,15 +492,33 @@ def print_nodes(self, n_words, with_weights):
self.print_node(self.root_node, 0, n_words, with_weights)
def print_node(self, node, indent, n_words, with_weights):
- out = ' ' * indent
- out += 'topic=%d level=%d (documents=%d): ' % (node.node_id, node.level, node.customers)
+ out = " " * indent
+ out += (
+ f"topic={node.node_id} level={node.level} "
+ f"(documents={node.customers}): "
+ )
out += node.get_top_words(n_words, with_weights)
print(out)
for child in node.children:
self.print_node(child, indent+1, n_words, with_weights)
+ def export_tree(self):
+ """Return the current hierarchy as a JSON‑serialisable structure."""
+
+ def visit(node):
+ return {
+ "id": int(node.node_id),
+ "level": int(node.level),
+ "customers": int(node.customers),
+ "total_words": int(node.total_words),
+ "children": [visit(child) for child in node.children],
+ }
+
+ return visit(self.root_node)
+
+
def load_vocab(file_name):
- with open(file_name, 'rb') as f:
+ with open(file_name, 'r', encoding='utf-8', newline='') as f:
vocab = []
reader = csv.reader(f)
for row in reader:
@@ -398,8 +527,9 @@ def load_vocab(file_name):
vocab.append(stripped)
return vocab
+
def load_corpus(file_name):
- with open(file_name, 'rb') as f:
+ with open(file_name, 'r', encoding='utf-8', newline='') as f:
corpus = []
reader = csv.reader(f)
for row in reader:
diff --git a/src/hlda/sklearn_wrapper.py b/src/hlda/sklearn_wrapper.py
new file mode 100644
index 0000000..c22e4cc
--- /dev/null
+++ b/src/hlda/sklearn_wrapper.py
@@ -0,0 +1,104 @@
+# Sklearn wrapper for HierarchicalLDA
+
+from __future__ import annotations
+
+from typing import Any, List, Sequence, Tuple
+
+import numpy as np
+from scipy import sparse
+from sklearn.base import BaseEstimator, TransformerMixin
+
+from .sampler import HierarchicalLDA
+
+
+def _dtm_to_corpus(dtm: Any) -> List[List[int]]:
+ """Convert a document-term matrix into an integer corpus."""
+ if sparse.issparse(dtm):
+ dtm = dtm.toarray()
+ else:
+ dtm = np.asarray(dtm)
+ corpus: List[List[int]] = []
+ for row in dtm:
+ doc: List[int] = []
+ for idx, count in enumerate(row):
+ if count:
+ doc.extend([idx] * int(count))
+ corpus.append(doc)
+ return corpus
+
+
+class HierarchicalLDAEstimator(BaseEstimator, TransformerMixin):
+ """Scikit-learn compatible estimator for :class:`HierarchicalLDA`."""
+
+ def __init__(
+ self,
+ *,
+ alpha: float = 10.0,
+ gamma: float = 1.0,
+ eta: float = 0.1,
+ num_levels: int = 3,
+ iterations: int = 100,
+ seed: int = 0,
+ verbose: bool = False,
+ vocab: Sequence[str] | None = None,
+ ) -> None:
+ self.alpha = alpha
+ self.gamma = gamma
+ self.eta = eta
+ self.num_levels = num_levels
+ self.iterations = iterations
+ self.seed = seed
+ self.verbose = verbose
+ self.vocab = list(vocab) if vocab is not None else None
+
+ # ------------------------------------------------------------------
+ def _prepare_input(self, X: Any) -> Tuple[List[List[int]], Sequence[str]]:
+ corpus: List[List[int]]
+ vocab: Sequence[str] | None = None
+
+ if isinstance(X, tuple) and len(X) == 2:
+ corpus, vocab = X
+ elif sparse.issparse(X) or (isinstance(X, np.ndarray) and X.ndim == 2):
+ corpus = _dtm_to_corpus(X)
+ vocab = self.vocab
+ else:
+ corpus = X # assume already integer corpus
+ vocab = self.vocab
+
+ if vocab is None:
+ raise ValueError("Vocabulary is required to fit the model")
+ return corpus, vocab
+
+ # ------------------------------------------------------------------
+ def fit(self, X: Any, y: Any | None = None): # noqa: D401
+ corpus, vocab = self._prepare_input(X)
+ self.vocab_ = list(vocab)
+ self.model_ = HierarchicalLDA(
+ corpus,
+ self.vocab_,
+ alpha=self.alpha,
+ gamma=self.gamma,
+ eta=self.eta,
+ num_levels=self.num_levels,
+ seed=self.seed,
+ verbose=self.verbose,
+ )
+ if self.iterations > 0:
+ self.model_.estimate(
+ self.iterations,
+ display_topics=self.iterations + 1,
+ n_words=0,
+ with_weights=False,
+ )
+ return self
+
+ # ------------------------------------------------------------------
+ def transform(self, X: Any) -> np.ndarray: # noqa: D401
+ if not hasattr(self, "model_"):
+ raise RuntimeError("Estimator has not been fitted")
+ n_docs = len(self.model_.document_leaves)
+ assignments = np.zeros(n_docs, dtype=int)
+ for d in range(n_docs):
+ leaf = self.model_.document_leaves[d]
+ assignments[d] = leaf.node_id
+ return assignments
diff --git a/tests/test_bbc_demo.py b/tests/test_bbc_demo.py
new file mode 100644
index 0000000..5964a6e
--- /dev/null
+++ b/tests/test_bbc_demo.py
@@ -0,0 +1,30 @@
+import argparse
+import os
+import sys
+from importlib import import_module
+
+ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+sys.path.insert(0, ROOT)
+sys.path.insert(0, os.path.join(ROOT, "src"))
+
+run_hlda = import_module("scripts.run_hlda")
+
+BBC_DIR = os.path.join(ROOT, "data", "bbc", "tech")
+
+
+def test_bbc_demo_deterministic():
+ args = argparse.Namespace(
+ data_dir=BBC_DIR,
+ iterations=2,
+ display_topics=2,
+ n_words=3,
+ num_levels=3,
+ alpha=10.0,
+ gamma=1.0,
+ eta=0.1,
+ seed=0,
+ )
+ hlda = run_hlda.run_hlda(args)
+ assert hlda.root_node.total_nodes == 17
+ assert hlda.root_node.customers == 401
+ assert hlda.num_documents == 401
diff --git a/tests/test_export_tree_json.py b/tests/test_export_tree_json.py
new file mode 100644
index 0000000..dddde38
--- /dev/null
+++ b/tests/test_export_tree_json.py
@@ -0,0 +1,36 @@
+import argparse
+import json
+import os
+import sys
+from importlib import import_module
+
+ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+sys.path.insert(0, ROOT)
+sys.path.insert(0, os.path.join(ROOT, "src"))
+
+run_hlda = import_module("scripts.run_hlda")
+
+
+def test_export_tree(tmp_path):
+ (tmp_path / "doc1.txt").write_text("First document about cats.")
+ (tmp_path / "doc2.txt").write_text("Second document about dogs.")
+
+ output_file = tmp_path / "tree.json"
+ args = argparse.Namespace(
+ data_dir=str(tmp_path),
+ iterations=1,
+ display_topics=1,
+ n_words=2,
+ num_levels=3,
+ alpha=1.0,
+ gamma=1.0,
+ eta=0.1,
+ seed=0,
+ export_tree=str(output_file),
+ )
+
+ run_hlda.run_hlda(args)
+ data = json.loads(output_file.read_text())
+
+ assert data["level"] == 0
+ assert isinstance(data["children"], list)
diff --git a/tests/test_run_hlda_utils.py b/tests/test_run_hlda_utils.py
new file mode 100644
index 0000000..ab7f94c
--- /dev/null
+++ b/tests/test_run_hlda_utils.py
@@ -0,0 +1,60 @@
+import os
+import sys
+from importlib import import_module
+
+ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+sys.path.insert(0, ROOT)
+sys.path.insert(0, os.path.join(ROOT, "src"))
+
+run_hlda = import_module("scripts.run_hlda")
+
+
+def test_load_documents(tmp_path):
+ (tmp_path / "doc1.txt").write_text(
+ "This is the first document. Hello world!"
+ )
+ (tmp_path / "doc2.txt").write_text(
+ "Second document: world is big and bright."
+ )
+
+ corpus = run_hlda.load_documents(str(tmp_path))
+ assert corpus == [
+ ["first", "document", "hello", "world"],
+ ["second", "document", "world", "big", "bright"],
+ ]
+
+
+def test_build_vocab():
+ corpus = [
+ ["first", "document", "hello", "world"],
+ ["second", "document", "world", "big", "bright"],
+ ]
+
+ vocab, index = run_hlda.build_vocab(corpus)
+ expected_vocab = [
+ "big",
+ "bright",
+ "document",
+ "first",
+ "hello",
+ "second",
+ "world",
+ ]
+ expected_index = {w: i for i, w in enumerate(expected_vocab)}
+ assert vocab == expected_vocab
+ assert index == expected_index
+
+
+def test_convert_corpus():
+ corpus = [
+ ["first", "document", "hello", "world"],
+ ["second", "document", "world", "big", "bright"],
+ ]
+
+ vocab, index = run_hlda.build_vocab(corpus)
+ int_corpus = run_hlda.convert_corpus(corpus, index)
+ expected = [
+ [index[w] for w in corpus[0]],
+ [index[w] for w in corpus[1]],
+ ]
+ assert int_corpus == expected
diff --git a/tests/test_sampler_io.py b/tests/test_sampler_io.py
new file mode 100644
index 0000000..32b0125
--- /dev/null
+++ b/tests/test_sampler_io.py
@@ -0,0 +1,34 @@
+import os
+import sys
+import csv
+from importlib import import_module
+
+ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+sys.path.insert(0, ROOT)
+sys.path.insert(0, os.path.join(ROOT, "src"))
+
+sampler = import_module("hlda.sampler")
+
+
+def test_load_vocab(tmp_path):
+ vocab_file = tmp_path / "vocab.csv"
+ with open(vocab_file, "w", encoding="utf-8", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow([0, " hello"])
+ writer.writerow([1, " world "])
+
+ vocab = sampler.load_vocab(str(vocab_file))
+
+ assert vocab == ["hello", "world"]
+
+
+def test_load_corpus(tmp_path):
+ corpus_file = tmp_path / "corpus.csv"
+ with open(corpus_file, "w", encoding="utf-8", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow(["0 hello", "1 world"])
+ writer.writerow(["1 world", "0 hello "])
+
+ corpus = sampler.load_corpus(str(corpus_file))
+
+ assert corpus == [[0, 1], [1, 0]]
diff --git a/tests/test_sklearn_wrapper.py b/tests/test_sklearn_wrapper.py
new file mode 100644
index 0000000..b05c60e
--- /dev/null
+++ b/tests/test_sklearn_wrapper.py
@@ -0,0 +1,64 @@
+import numpy as np
+from importlib import import_module
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import FunctionTransformer
+from sklearn.pipeline import Pipeline
+
+
+HierarchicalLDAEstimator = import_module(
+ "hlda.sklearn_wrapper"
+).HierarchicalLDAEstimator # noqa: E501
+
+
+def _prepare_input(vectorizer):
+ def _transform(X):
+ if hasattr(X, "toarray"):
+ arr = X.toarray()
+ else:
+ arr = np.asarray(X)
+ corpus = []
+ for row in arr:
+ doc = []
+ for idx, count in enumerate(row):
+ doc.extend([idx] * int(count))
+ corpus.append(doc)
+ vocab = list(vectorizer.get_feature_names_out())
+ return corpus, vocab
+
+ return _transform
+
+
+def test_pipeline_fit_transform():
+ docs = [
+ "apple orange banana",
+ "apple orange",
+ "banana banana orange",
+ ]
+
+ vectorizer = CountVectorizer()
+ hlda = HierarchicalLDAEstimator(
+ num_levels=2,
+ iterations=1,
+ seed=0,
+ verbose=False,
+ )
+
+ pipeline = Pipeline(
+ [
+ ("vect", vectorizer),
+ (
+ "prep",
+ FunctionTransformer(
+ _prepare_input(vectorizer),
+ validate=False,
+ ),
+ ),
+ ("hlda", hlda),
+ ]
+ )
+
+ pipeline.fit(docs)
+ result = pipeline.transform(docs)
+
+ assert result.shape[0] == len(docs)
+ assert isinstance(result[0], (int, np.integer))
diff --git a/tests/test_synthetic_hlda.py b/tests/test_synthetic_hlda.py
new file mode 100644
index 0000000..1a0c8ad
--- /dev/null
+++ b/tests/test_synthetic_hlda.py
@@ -0,0 +1,89 @@
+import os
+import sys
+from importlib import import_module
+
+import numpy as np
+
+TEST_DIR = os.path.dirname(__file__)
+ROOT = os.path.abspath(os.path.join(TEST_DIR, ".."))
+sys.path.insert(0, ROOT)
+sys.path.insert(0, os.path.join(ROOT, "src"))
+HierarchicalLDA = import_module("hlda.sampler").HierarchicalLDA
+
+
+def generate_corpus(n_topics, vocab_size, doc_len, n_docs, alpha=0.5, seed=0):
+ rng = np.random.default_rng(seed)
+ width = vocab_size // n_topics
+
+ word_dists = np.zeros((n_topics, vocab_size))
+ for k in range(n_topics):
+ start = k * width
+ word_dists[k, start:start + width] = 1.0 / width
+
+ vocab = [f"w{i}" for i in range(vocab_size)]
+ corpus = []
+ for _ in range(n_docs):
+ theta = rng.dirichlet([alpha] * n_topics)
+ doc = []
+ for _ in range(doc_len):
+ k = rng.choice(n_topics, p=theta)
+ w = rng.choice(vocab_size, p=word_dists[k])
+ doc.append(w)
+ corpus.append(doc)
+ return corpus, vocab
+
+
+def test_hlda_runs_on_synthetic_data():
+ n_topics = 3
+ vocab_size = 9
+ doc_len = 20
+ n_docs = 5
+ corpus, vocab = generate_corpus(n_topics, vocab_size, doc_len, n_docs)
+
+ hlda = HierarchicalLDA(
+ corpus,
+ vocab,
+ alpha=1.0,
+ gamma=1.0,
+ eta=1.0,
+ num_levels=3,
+ seed=0,
+ verbose=False,
+ )
+ hlda.estimate(2, display_topics=2, n_words=3, with_weights=False)
+
+ assert len(hlda.document_leaves) == n_docs
+ assert hlda.root_node.customers == n_docs
+
+
+def test_tree_invariants_during_sampling():
+ n_topics = 3
+ vocab_size = 9
+ doc_len = 20
+ n_docs = 5
+ corpus, vocab = generate_corpus(n_topics, vocab_size, doc_len, n_docs)
+
+ hlda = HierarchicalLDA(corpus, vocab, alpha=1.0, gamma=1.0, eta=1.0,
+ num_levels=3, seed=0, verbose=False)
+
+ total_nodes_history = []
+ root_cust_history = []
+ for _ in range(20):
+ for d in range(n_docs):
+ hlda.sample_path(d)
+ for d in range(n_docs):
+ hlda.sample_topics(d)
+ total_nodes_history.append(hlda.root_node.total_nodes)
+ root_cust_history.append(hlda.root_node.customers)
+ for leaf in hlda.document_leaves.values():
+ assert leaf.level == hlda.num_levels - 1
+ node = leaf
+ depth = 0
+ while node.parent is not None:
+ node = node.parent
+ depth += 1
+ assert depth == hlda.num_levels - 1
+
+ assert all(cust == n_docs for cust in root_cust_history)
+ diffs = np.diff(total_nodes_history)
+ assert (diffs > 0).any() and (diffs < 0).any()