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{"ops":[{"insert":"\nWe wish to train a machine learning algorithm on an array of floating-point numbers in the\ninterval [0.0, 1.0). The data is horribly unbalanced (not evenly distributed) and we wish to\nfilter the dataset to obtain a subset containing an equal number of values from each\ninterval\n[0, 0.2), [0.2, 0.4), ... [0.8, 1.0), throwing away as little data as possible.\nWrite a program which reads comma-separated floating-point numbers in a single line from\nstandard input and prints the filtered data to standard output in the same format\nNote: Solve this in linear time, if possible. Priority will be given to those who solve in linear\ntime.\nExplanation Example\nInput: 0.11,0.12,0.13,0.23,0.34,0.35,0.47,0.59,0.77,0.83,0.85,0.91,0.95\nOn classifying the above input data from example 4, Subset in each interval will look as below:\n\nSince the interval [0.6 - 0.8) has the minimum subset of size 1. We choose 1 element from\nthe rest of the intervals.\nOutput: 0.11,0.23,0.47,0.77,0.83\n*if the interval [0.6 - 0.8) had more than 3 elements then we would choose 2 elements from all\nsubset, since the interval with minimum subset, would be [0.4 - 0.6) and of size 2.\nInterval Data\n[0 - 0.2) 0.11,0.12,0.13\n[0.2 - 0.4) 0.23,0.34,0.35\n[0.4 - 0.6) 0.47,0.59\n[0.6 - 0.8) 0.77\n[0.8 - 1.0) 0.83,0.85,0.91,0.95\n\nSample Examples:\nExample 1\nInput: 0.1,0.3,0.5,0.7,0.9\nOutput: 0.1,0.3,0.5,0.7,0.9\nExample 2\nInput: 0.1,0.3,0.5,0.7,0.9,0.5\nOutput: 0.1,0.3,0.5,0.7,0.9\n\nExample 3\nInput: 0.15,0.12,0.35,0.38,0.55,0.56,0.57,0.75,0.77,0.9,0.94\nOutput: 0.15,0.12,0.35,0.38,0.55,0.56,0.75,0.77,0.9,0.94\n"}]}

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