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Since the sum of the average number of messages lost per time unit and the average number of messages transmitted per time unit equals the arrival rate 1 + 2 , we have the identity 1 1 (L) + 2 2 (L) + T (L) = 1 + 2 This relation is useful as an accuracy check for the calculated values of the p(i1 , i2 ) As an illustration, we consider the following numerical data: c = 10, 1 = 10, 2 = 7, 1 = 10, 2 = 1 Table 541 gives the values of T (L), 1 (L) and 2 (L) for L = 7, 8 and 9 The L-policy with L = 8 maximizes the long-run average throughput among the class of L-policies The above analysis restricted itself to the easily implementable L-policies, but other control rules are conceivable The question of how to compute the overall optimal control rule among the class of all conceivable control rules will be addressed in the s 6 and 7, which deal with Markov decision processes The best L-policy s in general not optimal among the class of all possible control rules However, numerical investigations indicate that using the best L-policy rather than the overall optimal policy often leads to only a small deviation from the theoretically maximal average throughput For example, for the above numerical data the average throughput of 15209 for the best L-policy is only 016% below the theoretically optimal value of 15233 This optimal value is achieved by the following control rule Each arriving message of type 1 is accepted as long as not all channels are occupied A message of type 2 nding i messages of type 1 present upon arrival is accepted only when less than Li other messages of the same type 2 are present and not all of the channels are occupied The optimal values of the Li are L0 = L1 = 8, L2 = L3 = 7, L4 = 6, L5 = 5, L6 = 4, L7 = 3, L8 = 2 and L9 = 1 The insensitivity property is no longer exactly true for the Li -policy, but numerical investigations indicate that the dependency on the distributional form of the transmission times is quite weak The above Li -policy 2 was simulated for lognormally distributed transmission times Denoting by ci the squared coef cient of variation of the transmission time for type i messages, we 2 2 varied (c1 , c2 ) as (1, 1), (2, 05) and (05, 2) For these three examples the average. print document pdf c# NuGet Gallery | EvoPdf.PdfPrint 7.1.0
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Draw and save QR Code bar codes in gif jpeg, png, tiff, and bitmap formats with C#.NET programming.Related: Barcode Generator Crystal C# , Crystal Barcode Generator , Generate Barcode SSRS C# For example, if there are n inconsistent objects for a 2-class dataset, c1 such objects belong to class 1, c2 to class 2, then c1 + c2 = n If the largest of these is, say, c1 then the inconsistency count will be n c1 The inconsistency rate is simply the sum of all inconsistency counts divided by the number of objects Every time a better subset is encountered, it is outputted Once the maximum number of iterations has been reached, the loop terminates and the algorithm returns the best subset found overall A problem with this approach is that it will tend to take longer to locate an optimal subset than algorithms that employ heuristic generation procedures Additionally, when datasets are huge, checking the consistency of the dataset takes a long time. Returning to the example, LVF randomly chooses feature subsets from among the six features present, such as the subset {a, b, c} For class 1 there are 3 inconsistent objects, for class 2 there are also 3, so the inconsistency count will be (6 3)/12 = 1 If no inconsistency is allowed ( = 0), then this subset will not 4 be kept Given suf cient time, the algorithm should eventually reach the subset {a, d, f }, which is the smallest subset that produces the required inconsistency rate. A feature subset is randomly chosen; if the subset has a smaller cardinality than the current best and its inconsistency rate is less than or equal to a threshold , it is considered to be the new best subset Here the inconsistency count is de ned as the sum of the number of inconsistent objects minus the number of inconsistent objects with the most frequent class label For example, if there are n inconsistent objects for a 2-class dataset, c1 such objects belong to class 1, c2 to class 2, then c1 + c2 = n If the largest of these is, say, c1 then the inconsistency count will be n c1 The inconsistency rate is simply the sum of all inconsistency counts divided by the number of objects Every time a better subset is encountered, it is outputted. 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