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"description": "tindetheus\n==========\n\nBuild personalized machine learning models for Tinder based on your\nhistorical preference using Python.\n\nThere are three parts to this: 1. A function to build a database which\nrecords everything about the profiles you\u2019ve liked and disliked. 2. A\nfunction to train a model to your database. 3. A function to use the\ntrained model to automatically like and dislike new profiles.\n\nHow it works\n============\n\nThe last layer of a CNN trained for facial classification can be used as\na feature set which describes an individual\u2019s face. It just so happens\nthat this feature set is related to facial attractiveness.\n\ntindetheus let\u2019s you build a database based on the profiles that you\nlike and dislike. You can then train a classification model to your\ndatabase. The model training first uses a MTCNN to detect and box the\nfaces in your database. Then a facenet model is run on the faces to\nextract the embeddings (last layer of the CNN). A logistic regression\nmodel is then fit to the embeddings. The logistic regression model is\nsaved, and this processes is repeated in automation to automatically\nlike and dislike profiles based on your historical preference.\n\n.. figure:: https://raw.githubusercontent.com/cjekel/tindetheus/master/examples/how_does_tindetheus_work.png\n :alt: Visual aid explaining tindetheus\n\n Visual aid explaining tindetheus\n\nThis `blog\npost `__\nhas a short description of how tindetheus works.\n\nFor a more detailed description of how and why this works see\nhttps://arxiv.org/abs/1803.04347\n\nExample usage\n=============\n\n.. code:: bash\n\n tindetheus browse\n\nbuild a database by liking and disliking profiles on Tinder. The\ndatabase contains all the profile information as a numpy array, while\nthe profile images are saved in a different folder.\n\n.. code:: bash\n\n tindetheus browse --distance=20\n\nby default tindetheus starts with a 5 mile radius, but you can specify a\nsearch distance by specifying \u2013distance. The above example is to start\nwith a 20 mile search radius. It is important to note that when you run\nout of nearby users, tindethesus will ask you if you\u2019d like to increase\nthe search distance by 5 miles.\n\n.. code:: bash\n\n tindetheus train\n\nUse machine learning to build a personalized model of who you like and\ndislike based on your database. The more profiles you\u2019ve browsed, the\nbetter your model will be.\n\n.. code:: bash\n\n tindetheus like\n\nUse your personalized model to automatically like and dislike profiles.\nThe profiles which you have automatically liked and disliked are stored\nin al_database. By default this will start with a 5 mile search radius,\nwhich increases by 5 miles until you\u2019ve used 100 likes. You can change\nthe default search radius by using\n\n.. code:: bash\n\n tindetheus like --distance=20\n\nwhich would start with a 20 mile search radius.\n\nInstallation and Getting started\n================================\n\nInstallation and getting started guide now stored in\n`GETTING_STARTED.md `__\n\nconfig.txt\n==========\n\nYou can now store all default optional parameters in the config.txt!\nThis means you can set your starting distance, number of likes, and\nimage_batch size without manually specifying the options each time. This\nis an example config.txt file:\n\n::\n\n facebook_token = XXXXXXX # your facebook token hash\n # alternatively you can use the XAuthToken\n # XAuthToken = xxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxx\n model_dir = 20170512-110547 # the location of your facenet model directory\n # see https://github.com/davidsandberg/facenet#pre-trained-models for other\n # pretrained facenet models\n image_batch = 1000 # number of images to load in a batch during train\n # the larger the image_batch size, the faster the training process, at the\n # cost of additional memory. A 4GB machine may struggle with 1000 images.\n distance = 5 # Set the starting distance in miles\n likes = 100 # set the number of likes you want to use\n # note that free Tinder users only get 100 likes in 24 hours\n\nUsing the validate function on a different dataset\n==================================================\n\nAs of Version 0.4.0, tindetheus now includes a validate function. This\nvalidate functions applies your personally trained tinder model on an\nexternal set of images. If there is a face in the image, the model will\npredict whether you will like or dislike this face. The results are\nsaved in validation.csv. For more information about the validate\nfunction `read\nthis `__.\n\nChangelog\n=========\n\nAll changes now stored in\n`CHANGELOG.md `__\n\nOpen source libraries\n=====================\n\ntindetheus uses the following open source libraries:\n\n- `pynder `__\n- `facenet `__\n- `numpy `__\n- `matplotlib `__\n- `scikit-learn `__\n- `tensorflow `__\n- `imageio `__\n- `pandas `__\n\nAbout the name\n==============\n\nTindetheus is a combination of Tinder (the popular online dating\napplication) and the Greek Titans:\n`Prometheus `__ and\n`Epimetheus `__.\nPrometheus signifies \u201cforethought,\u201d while his brother Epimetheus denotes\n\u201cafterthought\u201d. In synergy they serve to improve your Tinder experience.\n\nEpimetheus creates a database from all of the profiles you review on\nTinder.\n\nPrometheus learns from your historical preferences to automatically like\nnew Tinder profiles.",
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