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        "author": "sqarrt",
        "author_email": "sqarrt1337@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Natural Language :: Russian",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# Ingine - a cross-platform AI toolbox\n\n### Features\n\n- Artifical Neural Networks with:\n   - Classifier builder;\n   - Regressor  builder;\n   - Custom layer builder;\n   - Custom keras access.\n- Evolutional algorithms;\n\n### Ready examples\n\n- 8 queens puzzle;\n- Seabatlle ANN;\n- Handwritten digit recognition ANN;\n- Boston Housing;\n- Salesman issue resolving;\n\n# HOW TO?\n\n## Classify:\n```python\nfrom ingine import ann\n\n\"\"\"getting dataset\"\"\"\n(X_train, Y_train), (X_test, Y_test) = get_dataset()\n\ncategorizer = ann.get_categorizer(X_train, y_train, num_cat = 10)\n\n\"\"\"check out how it works!\"\"\"\nprint(categorizer(X_train[0]), Y_train[0])\n```\n\n## Regression:\n```python\nfrom ingine import ann\n\n\"\"\"getting dataset\"\"\"\n(X_train, Y_train), (X_test, Y_test)**** = get_dataset()\n\nregression = ann.get_regression(X_train, y_train)\n\n\"\"\"check out how it works!\"\"\"\nprint(regression(X_train[0]), Y_train[0])\n```\n\n## Custom layer configuration:\n```python\nfrom ingine import ann\nfrom keras.layers import Dense\n\n\"\"\"getting dataset\"\"\"\n(X_train, Y_train), (X_test, Y_test) = get_dataset()\n\n# defining layers\nlayers = [Dense(100, input_dim = 100, activation = \"softsign\", kernel_initializer = \"normal\"),\n          Dense(20, activation = \"softsign\", kernel_initializer = \"normal\"),\n          Dense(10, activation = \"softsign\", kernel_initializer = \"normal\"),\n          Dense(100, activation = \"softsign\", kernel_initializer = \"normal\")]\n\ncustomnn = ann.get_customnn(X_train, Y_train, layers = layers)\n\n\"\"\"check out how it works!\"\"\"\nprint(customnn(X_train[0]), Y_train[0])\n```\n\n## Evolutional optimizer:\n```python\nfrom ingine import ga\nimport random as rnd\n\n\"\"\"define an example creature\"\"\"\ndata = [1, 2, 3, 4]\n\n\"\"\"define a representation function of creature\"\"\"\ndef create_individual(data):\n    return data[:]\n\n\"\"\"define a crossover function\"\"\"\ndef crossover(creature1, creature2):\n    r1 = [rnd.randint(1, 10) for _ in range(4)]\n    r2 = [rnd.randint(1, 10) for _ in range(4)]\n    return r1, r2\n\n\"\"\"define an mutation function\"\"\"\ndef mutate(creature):\n    a = rnd.randint(0, len(creature)-1)\n    b = rnd.randint(0, len(creature)-1)\n    creature[a], creature[b] = creature[a] + 1, creature[b] + 1\n\n\"\"\"define a selection function\"\"\"\ndef selection(population):\n    return rnd.choice(population)\n\n\"\"\"define a fitness function\"\"\"\ndef fitness(creature, data):\n    return abs(sum(creature) - 100)\n\n\"\"\"getting an optimizer\"\"\"\noptimiser = ga.get_optimizer(data,\n                             fitness,\n                             maximise_fitness = False,\n                             create_individual = create_individual,\n                             mutate = mutate,\n                             crossover = crossover)\n\n\"\"\"check out how it works!\"\"\"\nres = optimiser()[1]\n```\n\n\n",
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