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        "author": "Ke Sang",
        "author_email": "kesang0156357@gmail.com",
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        "description": "This module brings the effective reservoir sampling method, with or without\nweight. The reservoir sampling is used when you have a very large and unknown\ndataset of size N, and you want to sampling a subset of k of these N samples,\nwith one stream or one file reading. \n\nIf the weight is not present, each sample will have equal chance to be selected\nin the final subset; if weight is used, each sample will be selected according \nto their weights.\n\n\n# to install\n    pip install weightreservoir\n\n\n# to use as a module in python\n    from weightreservoir import reservoir\n\n# to use uniform sampling\n    uniform = reservoir.UniformSampling(size = 10)\n\n    # to add one item into the stream and decide to sample it or not\n    uniform.addOne(itemValue)\n\n    # to add a list of items into the stream and decide to sample each of them or not\n    uniform.addAll(itemValueList)    \n\n    # to get all the current items of the sampled dataset, returned as a list\n    uniform.get()\n\n# to use weighted sampling\n    weight_sample = reservoir.WeightSampling(size = 10)\n\n    # to add one item into the stream and decide to sample it or not by its weight\n    weight_sample.addOne(itemValue, itemWeight)\n\n    # to add a list of items into the stream and decide to sample each of them or not by their weight\n    weight_sample.addAll(itemValueList, itemWeightList)    \n\n    # to get all the current items of the sampled dataset, returned as a list\n    weight_sample.get()   \n\n",
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