├── conftest.py ├── requirements.txt ├── setup.cfg ├── README.rst ├── tests ├── print_version_test.py ├── local_main_test.py └── all_test.py ├── .coveragerc ├── whereami ├── compat.py ├── mobile.py ├── __init__.py ├── learn.py ├── utils.py ├── get_data.py ├── pipeline.py ├── predict.py └── __main__.py ├── .gitignore ├── .travis.yml ├── tox.ini ├── deploy.py ├── setup.py ├── README.md └── LICENSE /conftest.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tqdm 2 | scipy 3 | numpy 4 | scikit-learn 5 | access_points 6 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [bdist_rpm] 2 | doc_files = README.rst 3 | 4 | [wheel] 5 | universal = 1 -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | whereami - Uses WiFi signals to predict where you are https://github.com/kootenpv/whereami 2 | -------------------------------------------------------------------------------- /tests/print_version_test.py: -------------------------------------------------------------------------------- 1 | from whereami import print_version 2 | 3 | 4 | def test_print(): 5 | assert print_version() 6 | -------------------------------------------------------------------------------- /.coveragerc: -------------------------------------------------------------------------------- 1 | [report] 2 | exclude_lines = 3 | pragma: no cover 4 | def __repr__ 5 | raise AssertionError 6 | raise NotImplementedError 7 | if __name__ == .__main__.: -------------------------------------------------------------------------------- /whereami/compat.py: -------------------------------------------------------------------------------- 1 | # whereami.predict 2 | try: 3 | from sklearn.model_selection import cross_val_score 4 | except ImportError: 5 | from sklearn.cross_validation import cross_val_score 6 | -------------------------------------------------------------------------------- /whereami/mobile.py: -------------------------------------------------------------------------------- 1 | # # mobile 2 | # case = {} 3 | # for row in [data['activity']] + data['available']: 4 | # key = row['SSID'] + " " + row['BSSID'] + " " + row.get('capabilities', '') 5 | # value = row.get("RSSI", row.get("level", '')) 6 | # case[key] = value 7 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | *#* 3 | *.DS_STORE 4 | *.log 5 | *Data.fs* 6 | *flymake* 7 | dist/* 8 | *egg* 9 | urllist* 10 | build/ 11 | __pycache__/ 12 | /.Python 13 | /bin/ 14 | /include/ 15 | /lib/ 16 | /pip-selfcheck.json 17 | .tox/ 18 | .cache 19 | .coverage 20 | .coverage.* 21 | .coveralls.yml -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | dist: trusty 2 | language: python 3 | matrix: 4 | include: 5 | - python: 2.7.13 6 | env: TOX_ENV=py27 7 | - python: 3.4 8 | env: TOX_ENV=py34 9 | - python: 3.5 10 | env: TOX_ENV=py35 11 | - python: 3.6 12 | env: TOX_ENV=py36 13 | before_install: 14 | - pip install --upgrade pip 15 | install: 16 | - pip install tox 17 | script: 18 | - tox -e $TOX_ENV 19 | -------------------------------------------------------------------------------- /tox.ini: -------------------------------------------------------------------------------- 1 | [tox] 2 | envlist = py27,py34,py35,py36 3 | 4 | [testenv] 5 | # If you add a new dep here you probably need to add it in setup.py as well 6 | passenv = TRAVIS TRAVIS_JOB_ID TRAVIS_BRANCH 7 | setenv = 8 | PYTHON_ENV = dev 9 | deps = 10 | pytest 11 | pytest-cov 12 | coveralls 13 | scipy 14 | numpy 15 | scikit-learn 16 | tqdm 17 | access_points 18 | commands = 19 | py.test --cov ./whereami 20 | coveralls 21 | -------------------------------------------------------------------------------- /tests/local_main_test.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from whereami.__main__ import main 3 | 4 | 5 | def test_main_learn(): 6 | sys.argv[1:] = ["learn", "-l", "bed", "-n", "1"] 7 | main() 8 | 9 | 10 | def test_main_predict(): 11 | sys.argv[1:] = ["predict"] 12 | main() 13 | 14 | 15 | def test_main_predict_proba(): 16 | sys.argv[1:] = ["predict_proba"] 17 | main() 18 | 19 | 20 | def test_main_locations(): 21 | sys.argv[1:] = ["locations"] 22 | main() 23 | -------------------------------------------------------------------------------- /whereami/__init__.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | __project__ = "whereami" 4 | __version__ = "0.4.80" 5 | __repo__ = "https://github.com/kootenpv/whereami" 6 | 7 | from whereami.learn import learn 8 | from whereami.pipeline import get_pipeline 9 | from whereami.predict import predict, predict_proba, crossval, locations 10 | 11 | 12 | def print_version(): 13 | sv = sys.version_info 14 | py_version = "{}.{}.{}".format(sv.major, sv.minor, sv.micro) 15 | version_parts = __version__.split(".") 16 | s = "{} version: [{}], Python {}".format(__project__, __version__, py_version) 17 | s += "\nMajor version: {} (breaking changes)".format(version_parts[0]) 18 | s += "\nMinor version: {} (extra feature)".format(version_parts[1]) 19 | s += "\nMicro version: {} (commit count)".format(version_parts[2]) 20 | s += "\nFind out the most recent version at {}".format(__repo__) 21 | return s 22 | -------------------------------------------------------------------------------- /whereami/learn.py: -------------------------------------------------------------------------------- 1 | import time 2 | import json 3 | 4 | from tqdm import tqdm 5 | 6 | from whereami.get_data import sample 7 | 8 | from whereami.pipeline import train_model 9 | from whereami.utils import ensure_whereami_path 10 | from whereami.utils import get_label_file 11 | 12 | 13 | def write_data(label_path, data): 14 | with open(label_path, "a") as f: 15 | f.write(json.dumps(data)) 16 | f.write("\n") 17 | 18 | 19 | def learn(label, n=1, device=""): 20 | path = ensure_whereami_path() 21 | label_path = get_label_file(path, label + ".txt") 22 | for i in tqdm(range(n)): 23 | if i != 0: 24 | time.sleep(15) 25 | try: 26 | new_sample = sample(device) 27 | if new_sample: 28 | write_data(label_path, new_sample) 29 | except KeyboardInterrupt: # pragma: no cover 30 | break 31 | train_model() 32 | -------------------------------------------------------------------------------- /deploy.py: -------------------------------------------------------------------------------- 1 | """ File unrelated to the package, except for convenience in deploying """ 2 | import re 3 | import sh 4 | import os 5 | 6 | commit_count = sh.git('rev-list', ['--all']).count('\n') 7 | 8 | with open('setup.py') as f: 9 | setup = f.read() 10 | 11 | setup = re.sub("MICRO_VERSION = '[0-9]+'", "MICRO_VERSION = '{}'".format(commit_count), setup) 12 | 13 | major = re.search("MAJOR_VERSION = '([0-9]+)'", setup).groups()[0] 14 | minor = re.search("MINOR_VERSION = '([0-9]+)'", setup).groups()[0] 15 | micro = re.search("MICRO_VERSION = '([0-9]+)'", setup).groups()[0] 16 | version = '{}.{}.{}'.format(major, minor, micro) 17 | 18 | with open('setup.py', 'w') as f: 19 | f.write(setup) 20 | 21 | with open('whereami/__init__.py') as f: 22 | init = f.read() 23 | 24 | with open('whereami/__init__.py', 'w') as f: 25 | f.write( 26 | re.sub('__version__ = "[0-9.]+"', 27 | '__version__ = "{}"'.format(version), init)) 28 | 29 | py_version = "python3.7" if sh.which("python3.7") is not None else "python" 30 | os.system('{} setup.py sdist bdist_wheel upload'.format(py_version)) 31 | -------------------------------------------------------------------------------- /whereami/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | 4 | def get_whereami_path(path=None): 5 | if path is None: 6 | _USERNAME = os.getenv("SUDO_USER") or os.getenv("USER") or "/" 7 | path = os.path.expanduser('~' + _USERNAME) 8 | path = os.path.join(path, ".whereami") 9 | return os.path.expanduser(path) 10 | 11 | 12 | def ensure_whereami_path(): 13 | path = get_whereami_path() 14 | if not os.path.exists(path): # pragma: no cover 15 | os.makedirs(path) 16 | return path 17 | 18 | 19 | def get_model_file(path=None, model="model.pkl"): 20 | path = ensure_whereami_path() if path is None else path 21 | return os.path.join(path, model) 22 | 23 | 24 | def get_label_file(path, label): 25 | return os.path.join(get_whereami_path(path), label) 26 | 27 | 28 | def rename_label(label, new_label, path=None): 29 | path = ensure_whereami_path() if path is None else path 30 | from_path = os.path.join(path, label + ".txt") 31 | new_path = os.path.join(path, new_label + ".txt") 32 | os.rename(from_path, new_path) 33 | print("Renamed {} to {}".format(from_path, new_path)) 34 | -------------------------------------------------------------------------------- /tests/all_test.py: -------------------------------------------------------------------------------- 1 | from random import randint 2 | from whereami.get_data import aps_to_dict 3 | from whereami.pipeline import get_pipeline 4 | from whereami.predict import crossval 5 | 6 | 7 | def mock_get_train_data(): 8 | X = [aps_to_dict([ 9 | {"quality": randint(0, 130), "bssid": "XX:XX:XX:XX:XX:84", "ssid": "X", "security": "XX"}, 10 | {"quality": randint(0, 130), "bssid": "XX:XX:XX:XX:XX:90", 11 | "ssid": "X", "security": "XX"}, 12 | {"quality": randint(0, 130), "bssid": "XX:XX:XX:XX:XX:d1", 13 | "ssid": "X", "security": "XX"}, 14 | {"quality": randint(0, 130), "bssid": "XX:XX:XX:XX:XX:c8", "ssid": "X", "security": "XX"}]) 15 | for _ in range(50)] 16 | y = [0] * 25 + [1] * 25 17 | return X, y 18 | 19 | 20 | def mock_get_model(): 21 | return get_pipeline() 22 | 23 | 24 | def test_train_model(): 25 | X, y = mock_get_train_data() 26 | pipeline = mock_get_model() 27 | pipeline.fit(X, y) 28 | return pipeline, X, y 29 | 30 | 31 | def test_crossval(): 32 | X, y = mock_get_train_data() 33 | pipeline = mock_get_model() 34 | assert crossval(pipeline, X, y, folds=2, n=1) 35 | 36 | 37 | def test_predict(): 38 | pipeline, X, y = test_train_model() 39 | assert pipeline.predict(X[0])[0] == y[0] 40 | -------------------------------------------------------------------------------- /whereami/get_data.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from access_points import get_scanner 4 | 5 | from whereami.utils import ensure_whereami_path 6 | 7 | 8 | def aps_to_dict(aps): 9 | return {ap['ssid'] + " " + ap['bssid']: ap['quality'] for ap in aps} 10 | 11 | 12 | def sample(device=""): 13 | wifi_scanner = get_scanner(device) 14 | if not os.environ.get("PYTHON_ENV", False): 15 | aps = wifi_scanner.get_access_points() 16 | else: 17 | aps = [{"quality": 100, "bssid": "XX:XX:XX:XX:XX:84", 18 | "ssid": "X", "security": "XX"}] 19 | return aps_to_dict(aps) 20 | 21 | 22 | def get_external_sample(path): 23 | data = [] 24 | with open(os.path.join(path, "current.loc.txt")) as f: 25 | for line in f: 26 | data.append(json.loads(line)) 27 | return data 28 | 29 | 30 | def get_train_data(folder=None): 31 | if folder is None: 32 | folder = ensure_whereami_path() 33 | X = [] 34 | y = [] 35 | for fname in os.listdir(folder): 36 | if fname.endswith(".txt"): 37 | data = [] 38 | with open(os.path.join(folder, fname)) as f: 39 | for line in f: 40 | data.append(json.loads(line)) 41 | X.extend(data) 42 | y.extend([fname.rstrip(".txt")] * len(data)) 43 | return X, y 44 | -------------------------------------------------------------------------------- /whereami/pipeline.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | from sklearn.ensemble import RandomForestClassifier 4 | from sklearn.feature_extraction import DictVectorizer 5 | from sklearn.pipeline import make_pipeline 6 | from whereami.get_data import get_train_data 7 | from whereami.utils import get_model_file 8 | 9 | 10 | class LearnLocation(Exception): 11 | pass 12 | 13 | 14 | def get_pipeline(clf=RandomForestClassifier(n_estimators=100, class_weight="balanced")): 15 | return make_pipeline(DictVectorizer(sparse=False), clf) 16 | 17 | 18 | def train_model(path=None): 19 | model_file = get_model_file(path) 20 | X, y = get_train_data(path) 21 | if len(X) == 0: 22 | raise ValueError("No wifi access points have been found during training") 23 | # fantastic: because using "quality" rather than "rssi", we expect values 0-150 24 | # 0 essentially indicates no connection 25 | # 150 is something like best possible connection 26 | # Not observing a wifi will mean a value of 0, which is the perfect default. 27 | lp = get_pipeline() 28 | lp.fit(X, y) 29 | with open(model_file, "wb") as f: 30 | pickle.dump(lp, f) 31 | return lp 32 | 33 | 34 | def get_model(path=None): 35 | model_file = get_model_file(path) 36 | if not os.path.isfile(model_file): # pragma: no cover 37 | msg = "First learn a location, e.g. with `whereami learn -l kitchen`." 38 | raise LearnLocation(msg) 39 | with open(model_file, "rb") as f: 40 | lp = pickle.load(f) 41 | return lp 42 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import find_packages 2 | from setuptools import setup 3 | 4 | MAJOR_VERSION = '0' 5 | MINOR_VERSION = '4' 6 | MICRO_VERSION = '80' 7 | VERSION = "{}.{}.{}".format(MAJOR_VERSION, MINOR_VERSION, MICRO_VERSION) 8 | 9 | setup(name='whereami', 10 | version=VERSION, 11 | description="Uses WiFi to tell you where you are", 12 | author='Pascal van Kooten', 13 | url='https://github.com/kootenpv/whereami', 14 | author_email='kootenpv@gmail.com', 15 | install_requires=[ 16 | 'scipy', 'numpy', 'scikit-learn', 'tqdm' 17 | ], 18 | entry_points={ 19 | 'console_scripts': ['whereami = whereami.__main__:main'] 20 | }, 21 | classifiers=[ 22 | 'Intended Audience :: Developers', 23 | 'Intended Audience :: Customer Service', 24 | 'Intended Audience :: System Administrators', 25 | 'Operating System :: Microsoft', 26 | 'Operating System :: MacOS :: MacOS X', 27 | 'Operating System :: Unix', 28 | 'Operating System :: POSIX', 29 | 'Programming Language :: Python', 30 | 'Programming Language :: Python :: 2', 31 | 'Programming Language :: Python :: 2.7', 32 | 'Programming Language :: Python :: 3', 33 | 'Programming Language :: Python :: 3.4', 34 | 'Programming Language :: Python :: 3.5', 35 | 'Programming Language :: Python :: 3.6', 36 | 'Topic :: Software Development', 37 | 'Topic :: Software Development :: Libraries', 38 | 'Topic :: Software Development :: Libraries :: Python Modules', 39 | 'Topic :: System :: Software Distribution', 40 | 'Topic :: System :: Systems Administration', 41 | 'Topic :: Utilities' 42 | ], 43 | license='MIT', 44 | packages=find_packages(), 45 | zip_safe=False, 46 | platforms='any') 47 | -------------------------------------------------------------------------------- /whereami/predict.py: -------------------------------------------------------------------------------- 1 | import json 2 | from collections import Counter 3 | 4 | from access_points import get_scanner 5 | 6 | from whereami.get_data import get_train_data, get_external_sample 7 | from whereami.get_data import sample 8 | from whereami.pipeline import get_model 9 | from whereami.get_data import aps_to_dict 10 | from whereami.compat import cross_val_score 11 | 12 | 13 | def predict_proba(input_path=None, model_path=None, device=""): 14 | lp = get_model(model_path) 15 | data_sample = sample(device) if input_path is None else get_external_sample(input_path) 16 | print(json.dumps(dict(zip(lp.classes_, lp.predict_proba(data_sample)[0])))) 17 | 18 | 19 | def predict(input_path=None, model_path=None, device=""): 20 | lp = get_model(model_path) 21 | data_sample = sample(device) if input_path is None else get_external_sample(input_path) 22 | return lp.predict(data_sample)[0] 23 | 24 | 25 | def crossval(clf=None, X=None, y=None, folds=10, n=5, path=None): 26 | if X is None or y is None: 27 | X, y = get_train_data(path) 28 | if len(X) < folds: 29 | raise ValueError('There are not enough samples ({}). Need at least {}.'.format(len(X), folds)) 30 | clf = clf or get_model(path) 31 | tot = 0 32 | print("KFold folds={}, running {} times".format(folds, n)) 33 | for i in range(n): 34 | res = cross_val_score(clf, X, y, cv=folds).mean() 35 | tot += res 36 | print("{}/{}: {}".format(i + 1, n, res)) 37 | print("-------- total --------") 38 | print(tot / n) 39 | return tot / n 40 | 41 | 42 | def locations(path=None): 43 | _, y = get_train_data(path) 44 | if len(y) == 0: # pragma: no cover 45 | msg = "No location samples available. First learn a location, e.g. with `whereami learn -l kitchen`." 46 | print(msg) 47 | else: 48 | occurrences = Counter(y) 49 | for key, value in occurrences.items(): 50 | print("{}: {}".format(key, value)) 51 | 52 | 53 | class Predicter(): 54 | def __init__(self, model=None, device=""): 55 | self.model = model 56 | self.device = device 57 | self.clf = get_model(model) 58 | self.wifi_scanner = get_scanner(device) 59 | self.predicted_value = None 60 | 61 | def predict(self): 62 | aps = self.wifi_scanner.get_access_points() 63 | self.predicted_value = self.clf.predict(aps_to_dict(aps))[0] 64 | return self.predicted_value 65 | 66 | def refresh(self): 67 | self.clf = get_model(self.model) 68 | self.wifi_scanner = get_scanner(self.device) 69 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## whereami 2 | 3 | [![Build Status](https://travis-ci.org/kootenpv/whereami.svg?branch=master)](https://travis-ci.org/kootenpv/whereami) 4 | [![Coverage Status](https://coveralls.io/repos/github/kootenpv/whereami/badge.svg?branch=master)](https://coveralls.io/github/kootenpv/whereami?branch=master) 5 | [![PyPI](https://img.shields.io/pypi/v/whereami.svg?style=flat-square)](https://pypi.python.org/pypi/whereami/) 6 | [![PyPI](https://img.shields.io/pypi/pyversions/whereami.svg?style=flat-square)](https://pypi.python.org/pypi/whereami/) 7 | 8 | Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters. 9 | 10 | Your computer will known whether you are on Couch #1 or Couch #2. 11 | 12 | ## Cross-platform 13 | 14 | Works on OSX, Windows, Linux (tested on Ubuntu/Arch Linux). 15 | 16 | The package [access_points](https://github.com/kootenpv/access_points) was created in the process to allow scanning wifi in a cross platform manner. Using `access_points` at command-line will allow you to scan wifi yourself and get JSON output. 17 | `whereami` builds on top of it. 18 | 19 | ### Installation 20 | 21 | git clone https://github.com/ramonfontes/whereami 22 | cd whereami && sudo python setup.py install 23 | 24 | git clone https://github.com/ramonfontes/access_points 25 | cd access_points && sudo python setup.py install 26 | 27 | ### Usage 28 | 29 | ```bash 30 | # in your bedroom, takes a sample 31 | whereami learn -l bedroom 32 | 33 | # in your kitchen, takes a sample 34 | whereami learn -l kitchen 35 | 36 | # get a list of already learned locations 37 | whereami locations 38 | 39 | # cross-validated accuracy on historic data 40 | whereami crossval 41 | # 0.99319 42 | 43 | # use in other applications, e.g. by piping the most likely answer: 44 | whereami predict | say 45 | # Computer Voice says: "bedroom" 46 | 47 | # probabilities per class 48 | whereami predict_proba 49 | # {"bedroom": 0.99, "kitchen": 0.01} 50 | ``` 51 | 52 | If you want to delete some of the last lines, or the data in general, visit your `$USER/.whereami` folder. 53 | 54 | ### Python 55 | 56 | Any of the functionality is available in python as well. Generally speaking, commands can be imported: 57 | 58 | from whereami import learn 59 | from whereami import get_pipeline 60 | from whereami import predict, predict_proba, crossval, locations 61 | 62 | ### Accuracy 63 | k 64 | Generally it should work really well. I've been able to learn using only 7 access points at home (test using `access_points -n`). At organizations you might see 70+. 65 | 66 | Distance: anything around ~10 meters or more should get >99% accuracy. 67 | 68 | If you're adventurous and you want to learn to distinguish between couch #1 and couch #2 (i.e. 2 meters apart), it is the most robust when you switch locations and train in turn. E.g. first in Spot A, then in Spot B then start again with A. 69 | Doing this in spot A, then spot B and then immediately using "predict" will yield spot B as an answer usually. No worries, the effect of this temporal overfitting disappears over time. And, in fact, this is only a real concern for the very short distances. Just take a sample after some time in both locations and it should become very robust. 70 | 71 | Height: Surprisingly, vertical difference in location is typically even more distinct than horizontal differences. 72 | 73 | ### Related Projects 74 | - The [wherearehue](https://github.com/DeastinY/wherearehue) project can be used to toggle Hue light bulbs based on the learned locations. 75 | 76 | ### Almost entirely "copied" from: 77 | 78 | https://github.com/schollz/find 79 | 80 | That project used to be in Python, but is now written in Go. `whereami` is in Python with lessons learned implemented. 81 | 82 | ### Tests 83 | 84 | It's possible to locally run tests for python 2.7, 3.4 and 3.5 using tox. 85 | 86 | git clone https://github.com/kootenpv/whereami 87 | cd whereami 88 | python setup.py install 89 | tox 90 | -------------------------------------------------------------------------------- /whereami/__main__.py: -------------------------------------------------------------------------------- 1 | from whereami.predict import predict 2 | from whereami.predict import predict_proba 3 | from whereami.predict import crossval 4 | from whereami.predict import locations 5 | from whereami.learn import learn 6 | from whereami.pipeline import train_model 7 | 8 | from whereami import print_version 9 | from whereami.utils import rename_label 10 | 11 | 12 | def get_args_parser(): 13 | import argparse 14 | from argparse import RawTextHelpFormatter 15 | desc = 'Uses WiFi signals and machine learning to predict where you are.' 16 | desc += '\nFeel free to try out commands, if anything is missing it will print help.' 17 | desc += '\n\nYou will want to start with `whereami learn`' 18 | p = argparse.ArgumentParser(description=desc, formatter_class=RawTextHelpFormatter) 19 | p.add_argument('--version', '-v', action='version', version=print_version()) 20 | subparsers = p.add_subparsers(dest="command") 21 | 22 | predict_parser = subparsers.add_parser('predict') 23 | predict_parser.add_argument('--input_path', '-ip', default=None, 24 | help='The directory containing current.loc.txt') 25 | predict_parser.add_argument('--model_path', '-mp', default=None, 26 | help='The directory of the model / trained data') 27 | predict_parser.add_argument( 28 | '--device', '-d', default="", help='Change the wifi device to use') 29 | 30 | predict_proba_parser = subparsers.add_parser('predict_proba') 31 | predict_proba_parser.add_argument( 32 | '--input_path', '-ip', default=None, help='The directory containing current.loc.txt') 33 | predict_proba_parser.add_argument( 34 | '--model_path', '-mp', default=None, help='The directory of the model / trained data') 35 | predict_proba_parser.add_argument( 36 | '--device', '-d', default="", help='Change the wifi device to use') 37 | 38 | crossval_parser = subparsers.add_parser('crossval') 39 | crossval_parser.add_argument('--model_path', '-mp', default=None, 40 | help='The directory of the model / trained data') 41 | 42 | ls_parser = subparsers.add_parser('ls') 43 | ls_parser.add_argument('--model_path', '-mp', default=None, 44 | help='The directory of the model / trained data') 45 | 46 | locations_parser = subparsers.add_parser('locations') 47 | locations_parser.add_argument('--model_path', '-mp', default=None, 48 | help='The directory of the model / trained data') 49 | 50 | learn_parser = subparsers.add_parser('learn') 51 | learn_parser.add_argument('--location', '-l', required=True, 52 | help='A name-tag for location to learn.') 53 | learn_parser.add_argument('--device', '-d', default="", 54 | help='Change the wifi device to use') 55 | learn_parser.add_argument('--num_samples', '-n', type=int, 56 | default=1, help='Number of samples to take') 57 | 58 | rename = subparsers.add_parser('rename') 59 | 60 | rename.add_argument('--label', help='Label to rename') 61 | rename.add_argument('--new_label', help='New label name') 62 | rename.add_argument('--model_path', '-mp', default=None, 63 | help='The directory of the model / trained data') 64 | 65 | train_parser = subparsers.add_parser('train') 66 | train_parser.add_argument('--model_path', '-mp', default=None, 67 | help='The directory of the model / trained data') 68 | return p 69 | 70 | 71 | def main(): 72 | try: 73 | parser = get_args_parser() 74 | args = parser.parse_args() 75 | if args.command == "predict_proba": 76 | predict_proba(args.input_path, args.model_path, args.device) 77 | elif args.command == "predict": 78 | print(predict(args.input_path, args.model_path, args.device)) 79 | elif args.command == "learn": 80 | learn(args.location, args.num_samples, args.device) 81 | elif args.command == "crossval": 82 | crossval(path=args.model_path) 83 | elif args.command in ["locations", "ls"]: 84 | locations(args.model_path) 85 | elif args.command == "rename": 86 | rename_label(args.label, args.new_label) 87 | print("Retraining model...") 88 | train_model() 89 | elif args.command == "train": 90 | train_model(args.model_path) 91 | else: 92 | parser.print_help() 93 | parser.exit(1) 94 | except (KeyboardInterrupt, SystemExit): 95 | exit() 96 | 97 | 98 | if __name__ == '__main__': 99 | main() 100 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | 3 | GNU AFFERO GENERAL PUBLIC LICENSE 4 | Version 3, 19 November 2007 5 | 6 | Copyright (C) 2007 Free Software Foundation, Inc. 7 | Everyone is permitted to copy and distribute verbatim copies 8 | of this license document, but changing it is not allowed. 9 | 10 | Preamble 11 | 12 | The GNU Affero General Public License is a free, copyleft license for 13 | software and other kinds of works, specifically designed to ensure 14 | cooperation with the community in the case of network server software. 15 | 16 | The licenses for most software and other practical works are designed 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Remote Network Interaction; Use with the GNU General Public License. 543 | 544 | Notwithstanding any other provision of this License, if you modify the 545 | Program, your modified version must prominently offer all users 546 | interacting with it remotely through a computer network (if your version 547 | supports such interaction) an opportunity to receive the Corresponding 548 | Source of your version by providing access to the Corresponding Source 549 | from a network server at no charge, through some standard or customary 550 | means of facilitating copying of software. This Corresponding Source 551 | shall include the Corresponding Source for any work covered by version 3 552 | of the GNU General Public License that is incorporated pursuant to the 553 | following paragraph. 554 | 555 | Notwithstanding any other provision of this License, you have 556 | permission to link or combine any covered work with a work licensed 557 | under version 3 of the GNU General Public License into a single 558 | combined work, and to convey the resulting work. The terms of this 559 | License will continue to apply to the part which is the covered work, 560 | but the work with which it is combined will remain governed by version 561 | 3 of the GNU General Public License. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU Affero General Public License from time to time. Such new versions 567 | will be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU Affero General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU Affero General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU Affero General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 2015-2016 Zack Scholl 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU Affero General Public License as published 639 | by the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU Affero General Public License for more details. 646 | 647 | You should have received a copy of the GNU Affero General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If your software can interact with users remotely through a computer 653 | network, you should also make sure that it provides a way for users to 654 | get its source. For example, if your program is a web application, its 655 | interface could display a "Source" link that leads users to an archive 656 | of the code. There are many ways you could offer source, and different 657 | solutions will be better for different programs; see section 13 for the 658 | specific requirements. 659 | 660 | You should also get your employer (if you work as a programmer) or school, 661 | if any, to sign a "copyright disclaimer" for the program, if necessary. 662 | For more information on this, and how to apply and follow the GNU AGPL, see 663 | . --------------------------------------------------------------------------------