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copied!<p><strong>Approach 1</strong></p> <p>If the assignment what products belong to the three price segments never changes, why don't you simply build 3 lists, one for the products in each price segment (assuming these sets are disjoint). Then you may pick from these lists randomly (<a href="http://en.wikipedia.org/wiki/Sampling_%28statistics%29#Replacement_of_selected_units" rel="nofollow">either with or without replacement</a> - as you like). The number of items for each class is given by the ratios.</p> <p><strong>Approach 2</strong></p> <p>If the product-price-segment assignment is intended to be pre-specified, e.g., by passing corresponding price values for each segment on function call, you may want to have the products sorted by price and use a binary search to select the <a href="http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm" rel="nofollow">m-nearest-neighbors</a> (for example). The parameter <code>m</code> could be specified according to the ratios. If you specify a maximum distance you may reject products that are outside the desired price range.</p> <p><strong>Approach 3</strong></p> <p>If the product-price-segment assignment needs to be determined autonomously, you could apply your <a href="http://en.wikipedia.org/wiki/Cluster_analysis" rel="nofollow">clustering</a> algorithm of choice, e.g., <a href="http://en.wikipedia.org/wiki/K-means" rel="nofollow">k-means</a>, to assign your products to, say, <code>k = 3</code> price segments. For the actual product selection you may proceed similarly as described above.</p>

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