990 lines
38 KiB
C
990 lines
38 KiB
C
/*
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* Stat.c - Source file for a statistical
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* dll package that exports eleven
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* entry points:
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* a) TestStatOpen
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* b) TestStatInit
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* c) TestStatConverge
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* d) TestStatValues
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* e) TestStatClose
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* f) TestStatRand
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* g) TestStatUniRand
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* h) TestStatNormDist
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* i) TestStatShortRand
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* j) TestStatFindFirstMode
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* k) TestStatFindNextMode
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*
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* Entry point a) is an allocating routine
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* that is called by an application program
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* that desires to automatically compute
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* convergence.
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*
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* Entry point b) initializes all variables that
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* are used by entry points c) and d) in computing
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* convergence and statistical information.
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*
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* Entry point c) automatically computes the
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* the number of passes that the application has to
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* go through for a 95% confidence data.
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* This routine has to be called by the application
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* after each pass.
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*
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* Entry point d) automatically computes the
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* various statistical values eg. mean, SD etc.
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* This function has to be called only after the
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* application has called c) several times and has
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* either converged or reached the iteration limit.
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*
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* Entry point e) deallocates all instance data
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* data structures that were allocated by entry
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* point a).
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*
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* Entry point f) returns a Random Number in a
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* given range.
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*
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* Entry point g) returns a uniformly distributed
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* number in the range 0 - 1.
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*
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* Entry point h) returns a normally distributed
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* set of numbers, with repeated calls, whose
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* mean and standard deviation are approximately
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* equal to those that are passed in.
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*
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* Entry point i) is the same as g) except that
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* the range is 0 - 65535.
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*
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* The following should be the rules of calling
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* the entry points:
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*
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* Entry a) should be called before any of the others.
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* Entry c) should be preceded by at least one call
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* to entry b) for meaningful results. Entry d)
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* should be preceded by several calls to entry c).
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* A call to b) and c) after a call to e) should
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* preceded by a call to a) again.
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*
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* Created - Paramesh Vaidyanathan (vaidy)
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* Initial Version - October 29, '90
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*/
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/*********************************************************************
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*
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* Formula Used in Computing 95 % confidence level is derived here:
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*
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*
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* Any reference to (A) would imply "Experimental Design
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* in Psychological Research", by Allan Edwards.
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*
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* Any reference to (B) would imply "Statistical Methods"
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* by Allan Edwards.
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*
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* Assumptions - TYPE I Error - 5% (B)
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* TYPE II Error - 16% -do-
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*
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* Area under the curve for Type I - 1.96
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* Area under the curve for Type II - 1.00
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*
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* For a 5% deviation, number of runs,
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*
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* 2 2
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* n = 2 (c) (1.96 + 1.00)
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* ------ .....Eqn (1)
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* 2
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* (d)
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*
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* where c is the Std. Dev. and d is the absolute
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* difference bet. means [(B) Page 91].
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*
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* d = 5% X' .....Eqn (2)
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*
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* where X' is the mean of samples
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* _
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* and = >_ X
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* ----- .....Eqn (3)
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* n
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* 0
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*
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* When the number of iterations -> infinity,
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*
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* 2 2
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* S -> c .....Eqn (4)
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*
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*
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* 2
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* where S is the estimate of the common population
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* variance (Eqn. 4 is a big assumption)
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*
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* From (B) page 59, we have,
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*
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* 2 _ 2 _ 2
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* S = >_ X - ( >_ X)
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* -----
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* n
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* 0
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* ----------------- .....Eqn (5)
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* n - 1
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* 0
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*
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* Substituting Eqn (2), (3), (4) and (5) in (1), we get:
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* _ _
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* 2 | _ 2 _ 2 |
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* n = 7008 (n ) |( >_ X ) - ( >_ X) |
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* 0 | -------- |
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* | n |
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* |_ 0 _|
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* ---------------------------------------
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* _ 2
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* (n - 1) ( >_ X )
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* 0
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*
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* It should be mentioned that n is the iteration pass number.
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* 0
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*********************************************************************/
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#include <nt.h>
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#include <ntrtl.h>
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#include <nturtl.h>
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#include <windows.h>
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#include <stdio.h>
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#include <math.h>
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#include "teststat.h"
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#define SQR(A) ( (A) * (A) ) /* macro for squaring */
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#define SUCCESS_OK 0 /* weird, but OK */
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#define MIN_ITER 3 /* MIN. ITERATIONS */
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#define MAX_ITER 65535 /* max. iterations */
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#define REPEATS 14 /* repeat count for Norm. Dist. Fn. */
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/**********************************************************************/
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USHORT usMinIter; /* global min iter */
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USHORT usMaxIter; /* global max iter */
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ULONG *pulDataArray; /* a pointer to the data array for this
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package. Will be as large as the
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maximum iterations */
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double dSumOfData; /* sum of data during each pass */
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double dSumOfDataSqr; /* sum of sqr. of each data point */
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ULONG ulTotalIterCount; /* No. of iters returned by the interna;
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routine */
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USHORT cusCurrentPass; /* count of the current iteration pass */
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BOOL bDataConverged = FALSE; /* TRUE will return a precision of 5% */
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BOOL bMemoryAllocated=FALSE; /* TRUE will allow alloced mem to free */
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BOOL bPowerComputed = FALSE; /* compute 10 exp. 9 for random no. gen */
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BOOL *pbIndexOfOutlier; /* to keep track of values in
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pulDataArray, that were thrown out */
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HANDLE hMemHandle = NULL; /* handle to mem. allocated */
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HANDLE hMemOutlierFlag; /* handle to outlier flag memory */
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/**********************************************************************/
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ULONG TestStatRepeatIterations (double, double);
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VOID TestStatStatistics (PSZ, PULONG far *, USHORT,
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PUSHORT, PUSHORT);
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void DbgDummy (double, double);
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ULONG ulDataArrayAddress; /* call to mem alloc routine returns
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base address of alloced. mem. */
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BOOL bOutlierDataIndex; /* for allocating memory for outliers'
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index in data set */
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/*********************************************************************/
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/*
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* Function - TestStatOpen (EXPORTED)
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*
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* Arguments -
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* a) USHORT - usMinIterations
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* b) USHORT - usMaxIterations
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*
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* Returns -
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* 0 if the call was successful
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*
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* An error code if the call failed. The error code
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* may be one of:
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*
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* STAT_ERROR_ILLEGAL_MIN_ITER
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* STAT_ERROR_ILLEGAL_MAX_ITER
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* STAT_ERROR_ALLOC_FAILED
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*
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*
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* Instance data is allocated for the statistical package. This
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* call should precede any other calls in this dll. This function
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* should also be called after a call to TestStatClose, if convergence
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* is required on a new set of data. An error code is returned if
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* argument a) is zero or a) is greater than b). An error code is
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* also returned of one of the allocations failed.
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*
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*/
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USHORT
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TestStatOpen (
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USHORT usMinIterations,
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USHORT usMaxIterations
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)
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{
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/* check for invalid args to this function */
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if (!usMinIterations)
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return (STAT_ERROR_ILLEGAL_MIN_ITER);
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if ((usMinIterations > usMaxIterations) || (usMaxIterations > MAX_ITER))
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return (STAT_ERROR_ILLEGAL_MAX_ITER);
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/* any other parameter is allowed */
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usMinIter = usMinIterations; /* set global vars */
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usMaxIter = usMaxIterations; /* -do - */
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// change made based on request from JeffSt/Somase/JonLe
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if (hMemHandle != NULL)
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return (STAT_ERROR_ALLOC_FAILED);
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hMemHandle = GlobalAlloc (GMEM_MOVEABLE | GMEM_ZEROINIT, usMaxIter *
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sizeof(ULONG));
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if (hMemHandle == NULL)
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return (STAT_ERROR_ALLOC_FAILED);
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pulDataArray = (ULONG *) GlobalLock (hMemHandle);
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if (pulDataArray == NULL)
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return (STAT_ERROR_ALLOC_FAILED);
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bMemoryAllocated = TRUE; /* A call to TestStatClose will
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now free the mem */
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return (SUCCESS_OK);
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}
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/*
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* Function - TestStatClose (EXPORTED)
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*
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* Arguments - None
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*
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* Returns - Nothing
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*
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* Instance data allocated for the statistical package by TestStatOpen
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* is freed. Any call to entry points b) and c) following a call to
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* this function, should be preceded by a call to a).
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*
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*/
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VOID
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TestStatClose (VOID)
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{
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if (bMemoryAllocated) { /* free only if memory allocated */
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GlobalUnlock (hMemHandle);
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GlobalFree (hMemHandle);
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hMemHandle = NULL; /* Indicate released (t-WayneR/JohnOw) */
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} /* end of if (bMemoryAllocated) */
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bMemoryAllocated = FALSE; /* further calls to TestStatClose should be
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preceded by a memory allocation */
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return;
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}
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/*
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* Function - TestStatInit (EXPORTED)
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*
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* Arguments - None
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*
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* Returns - Nothing
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*
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* Initializes all the data arrays/variables for use by the convergence
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* and statistics routines. This call should precede the first call
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* to TestStatConverge for each set of data.
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*
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*/
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VOID
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TestStatInit (VOID)
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{
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USHORT usTempCtr;
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/* initialize all counters, variables and the data array itself */
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for (usTempCtr = 0; usTempCtr < usMaxIter; usTempCtr++) {
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pulDataArray [usTempCtr] = 0L;
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}
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dSumOfData = 0.0;
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dSumOfDataSqr = 0.0;
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ulTotalIterCount = 0L;
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cusCurrentPass = 0;
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bDataConverged = FALSE;
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return;
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}
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/*
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* Function - TestStatConverge (EXPORTED)
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*
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* Arguments -
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* a) ULONG - ulNewData
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* Returns -
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* TRUE if data set converged or limit on max. iters reached
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*
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* FALSE if more iterations required for converged.
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*
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* Computes the number of iterations required for a 95% confidence
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* in the data received (please see teststat.txt under \ntdocs on
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* \\jupiter\perftool for an explanation of the confidence.
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* If the current iteration count is larger than the maximum specified
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* with the call to TestStatOpen, or if the data set has converged
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* this function returns a TRUE. The calling application should test
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* for the return value.
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*/
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BOOL
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TestStatConverge (
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ULONG ulNewData
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)
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{
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dSumOfData += (double)ulNewData; /* sum of all data points in the set */
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dSumOfDataSqr += SQR ((double) ulNewData);
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/* sqr of data needed for the computation */
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if (cusCurrentPass < (USHORT) (usMinIter-(USHORT)1)) { /* do nothing if current iter
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< min specified value */
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ulTotalIterCount = (ULONG)usMaxIter + 1; /* bogus value */
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pulDataArray [cusCurrentPass++] = ulNewData;
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/* register this data into the array and return FALSE */
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return (FALSE);
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}
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if ((cusCurrentPass == usMaxIter) ||
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(cusCurrentPass >= (USHORT) ulTotalIterCount)) {
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/* either the limit on the max. iters. specified has been reached
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or, the data has converged during the last iter; return TRUE */
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if (cusCurrentPass >= (USHORT) ulTotalIterCount)
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bDataConverged = TRUE; /* set to determine if precision
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should be computed */
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return (TRUE);
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}
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if ((usMinIter < MIN_ITER) &&
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(usMinIter == usMaxIter) && ((USHORT)(cusCurrentPass+(USHORT)1) >= usMaxIter))
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/* don't call convergence algorithm, just return a TRUE */
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/* It does not make any sense in calling the convergence
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algorithm if less than 3 iterations are specifed for the
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minimum */
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return (TRUE);
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pulDataArray [cusCurrentPass++] = ulNewData; /* register this data into
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the array */
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if (dSumOfData == 0.0) { /* possible if data points are all zeros */
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bDataConverged = TRUE;
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return (TRUE);
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}
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ulTotalIterCount = TestStatRepeatIterations (dSumOfData,
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dSumOfDataSqr);
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if (ulTotalIterCount <= cusCurrentPass)
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return (TRUE);
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return (FALSE);
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}
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/*
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* Function - TestStatValues (EXPORTED)
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*
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* Arguments -
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* a) PSZ - pszOutputString
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* b) USHORT - usOutlierFactor
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* c) PULONG - *pulData
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* d) PUSHORT - pcusElementsInArray
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* e) PUSHORT - pcusDiscardedElements
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*
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* Returns -
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* Nothing
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*
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* Computes useful statistical values and returns them in the string
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* whose address is passed to this function. The returned string
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* has the following format :
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* ("%4u %10lu %10lu %10lu %6u %5u %10lu %4u %2u")
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* and the arg. list will be in the order: mode number, mean,
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* minimum, maximum, number of iterations, precision,
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* standard deviation, number of outliers in the data set and the
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* outlier count. (Please refer to \ntdocs\teststat.txt for
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* a description of precision. This is on \\jupiter\perftool.
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*
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*/
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VOID
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TestStatValues(
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PSZ pszOutputString,
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USHORT usOutlierFactor,
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PULONG *pulFinalData,
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PUSHORT pcusElementsInArray,
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PUSHORT pcusDiscardedElements
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)
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{
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ULONG far * pulArray = NULL;
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USHORT Count =0;
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/* Call the low-level routine to do the statistics computation */
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/* doing this ,'cos, there is a possibility that the low-level
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routine may be used for some apps, within the perf. group. This
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may not be fair, but that is the way life is */
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TestStatStatistics (pszOutputString, &pulArray,
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usOutlierFactor, pcusElementsInArray,
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pcusDiscardedElements);
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*pulFinalData = pulArray;
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return;
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}
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/***********************************************************************
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ROUTINES NOT EXPORTED, BEGIN
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***********************************************************************/
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/*
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* Function - TestStatRepeatIterations (NOT EXPORTED)
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* Arguments -
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* (a) double - Sum of Individual Data Points thus far
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* (b) double - Sum of Squares of Indiv. data points
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*
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* Returns - ULONG - value of no. of iterations required for 95%
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* confidence,
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*
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* Computes the number of iterations required of the calling program
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* before a 95% confidence level can be reached. This will return
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* a zero if the application calls this routine before 3 passes
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* are complete. The function normally returns the total number of
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* iterations that the application has to pass through before
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* offering a 95% confidence on the data.
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*/
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ULONG
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TestStatRepeatIterations(
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double dSumOfIndiv,
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double dSumOfSqrIndiv
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)
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{
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double dSqrSumOfIndiv = 0;
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ULONG ulRepeatsNeeded = 0L;
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/* dSqrSumOfIndiv. stands for the square of the Sum of Indiv. data
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points,
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dSumOfSqrIndiv stands for the sum of the square of each entry point,
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dSumOfIndiv. stands for the sum of each data point in the set, and
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uIter is the iteration pass count
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*/
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if (cusCurrentPass < MIN_ITER)
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/* not enough passes to compute convergence count */
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return (MAX_ITER);
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dSqrSumOfIndiv = SQR (dSumOfIndiv);
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/* use the formula derived at the beginning of this file to
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compute the no. of iterations required */
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ulRepeatsNeeded = (ULONG) (7008 *
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(dSumOfSqrIndiv - dSqrSumOfIndiv/cusCurrentPass)
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* SQR (cusCurrentPass) /
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((cusCurrentPass - 1) * dSqrSumOfIndiv));
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return (ulRepeatsNeeded);
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}
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/***************************************************************************/
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/*
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* Function - TestStatStatistics
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* Arguments -
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* a) PSZ - pszOutputString
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* b) PULONG far * - pulFinalData
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* c) USHORT - usOutlierFactor
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* d) PUSHORT - pcusElementsInArray
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* e) PUSHORT - pcusDiscardedValues
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*
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* Returns - Nothing
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*
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* Computes the max, min, mean, and std. dev. of a given
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* data set. The calling program should convert the values obtained
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* from this routine from a "ULONG" to the desired data type. The
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* outlier factor decides how many data points of the data set are
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* within acceptable limits. Data is returned to the buffer whose
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* address is the first argument to this call.
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*
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*/
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VOID
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TestStatStatistics (
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PSZ pszOutputString,
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PULONG *pulFinalData,
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USHORT usOutlierFactor,
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PUSHORT pcusElementsInArray,
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PUSHORT pcusDiscardedValues
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)
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{
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static USHORT uArrayCount = 0; /* local variable that may be reused */
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USHORT uTempCt = 0; /* local variable that may be reused */
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double dSqrOfSDev = 0; /* sqr of the std. deviation */
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double dSumOfSamples = 0; /* sum of all data points */
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double dSumOfSquares = 0; /* sum of squares of data points */
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ULONG ulMean = 0L;
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ULONG ulStdDev = 0L;
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ULONG ulDiffMean = 0L; /* to store the diff. of mean and SD,
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outlier factor */
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BOOL bAcceptableSDev = TRUE ; /* flag to determine if SDev. is
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acceptable */
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ULONG ulMax = 0L; /* pilot value */
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ULONG ulMin = 0xffffffff; /* largest possible ULONG */
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USHORT usPrecision = 0; /* to obtain precision */
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USHORT uModeNumber = 0; /* DUMMY VALUE until this is
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supported */
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/* compute mean by adding up all values and dividing by the no.
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of elements in data set - might need to recompute the
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mean if outlier factor is selected. However, the min. and max. will
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be selected from the entire set */
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USHORT Count = 0;
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*pcusDiscardedValues = 0; /* init. this variable */
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|
|
if (cusCurrentPass == 0)
|
|
return; /* get out without doing anything - this is a weird
|
|
case when the user calls this routine without
|
|
calling a converge routine */
|
|
|
|
*pcusElementsInArray = cusCurrentPass;
|
|
|
|
/* every iteration produces one data point */
|
|
uArrayCount = 0;
|
|
while (uArrayCount < *pcusElementsInArray) {
|
|
if (pulDataArray[uArrayCount] > ulMax)
|
|
ulMax = pulDataArray[uArrayCount]; /* new Max. value */
|
|
if (pulDataArray[uArrayCount] < ulMin)
|
|
ulMin = pulDataArray[uArrayCount]; /* new min. value */
|
|
|
|
ulMean += pulDataArray [uArrayCount++];
|
|
}
|
|
if (*pcusElementsInArray)
|
|
ulMean /= *pcusElementsInArray; /* this is the mean */
|
|
else
|
|
ulMean = 0;
|
|
/* the standard deviation needs to be computed */
|
|
|
|
for (uArrayCount = 0; uArrayCount < *pcusElementsInArray; uArrayCount++) {
|
|
dSumOfSamples += (double) pulDataArray [uArrayCount];
|
|
dSumOfSquares += SQR ((double) pulDataArray [uArrayCount]);
|
|
}
|
|
|
|
if (*pcusElementsInArray) {
|
|
dSqrOfSDev = ((*pcusElementsInArray * dSumOfSquares) -
|
|
SQR (dSumOfSamples)) /
|
|
(*pcusElementsInArray * (*pcusElementsInArray - 1));
|
|
}
|
|
ulStdDev = (ULONG) sqrt (dSqrOfSDev);
|
|
|
|
/* the standard deviation has been computed for the first pass */
|
|
/* Use the outlier factor and the S.D to find out if any of
|
|
individual data points are abnormal. If so, throw them out and
|
|
increment the discard value counter */
|
|
if (usOutlierFactor) { /* if outlier factor is zero, do not go
|
|
through with the following */
|
|
/*** here is what we do....
|
|
allocate space for an array of BOOLs. Each of these is a flag
|
|
corresponding to a data point. Initially, these flags will be
|
|
all set to FALSE. We then go thru each data point. If a data
|
|
point does not satisfy the condition for throwing out outliers,
|
|
we set the flag corresponding to that data point to TRUE. That
|
|
point is not used to recompute the mean and SDev. We recompute
|
|
the mean and SDev after each round of outlier elimination. When
|
|
we reach a stage where no points were discarded during a round,
|
|
we get out of the while loop and compute the statistics for the
|
|
new data set ****/
|
|
|
|
hMemOutlierFlag = GlobalAlloc (GMEM_MOVEABLE | GMEM_ZEROINIT,
|
|
*pcusElementsInArray * sizeof(BOOL));
|
|
pbIndexOfOutlier = (BOOL FAR *) GlobalLock (hMemOutlierFlag);
|
|
if (!pbIndexOfOutlier) {
|
|
return;
|
|
}
|
|
|
|
for (uArrayCount = 0; uArrayCount < *pcusElementsInArray;
|
|
uArrayCount ++)
|
|
pbIndexOfOutlier [uArrayCount] = FALSE;
|
|
|
|
while (1) { /* begin the data inspection round */
|
|
bAcceptableSDev = TRUE; /* set this flag to TRUE. If we
|
|
hit an outlier, this flag will
|
|
be reset */
|
|
for (uArrayCount = 0; uArrayCount < cusCurrentPass;
|
|
uArrayCount++) {
|
|
/*** check the individual data points ***/
|
|
if (ulMean < (ulStdDev * usOutlierFactor))
|
|
/* just make sure that we are not comparing with a
|
|
negative number */
|
|
ulDiffMean = 0L;
|
|
else
|
|
ulDiffMean = (ulMean - (ulStdDev * usOutlierFactor));
|
|
if (!pbIndexOfOutlier [uArrayCount]) {
|
|
if ((pulDataArray [uArrayCount] < ulDiffMean)
|
|
|| (pulDataArray [uArrayCount] >
|
|
(ulMean + (ulStdDev * usOutlierFactor)))) {
|
|
/* set the flag of this data point to TRUE to
|
|
indicate that this data point should not be
|
|
considered in the mean and SDev computation */
|
|
pbIndexOfOutlier [uArrayCount] = TRUE;
|
|
/*** increment the discarded qty ***/
|
|
(*pcusDiscardedValues)++;
|
|
/*** decrement the count of good data points ***/
|
|
|
|
// uncomment next line if outliers should be part of mean - vaidy
|
|
|
|
// (*pcusElementsInArray)--;
|
|
bAcceptableSDev = FALSE;
|
|
} /*** end of if statement ***/
|
|
} /*** end of if !pbIndexOfOutlier ***/
|
|
} /*** end of for loop ***/
|
|
if (!bAcceptableSDev) { /*** there were some bad data points ;
|
|
recompute S.Dev ***/
|
|
// Starting at next statement, uncomment all lines until you see
|
|
// "STOP UNCOMMENT FOR OUTLIERS IN MEAN", if you want outliers to be
|
|
// part of mean. vaidy Aug. 1991.
|
|
|
|
// dSumOfSamples = 0.0; /* init these two guys */
|
|
// dSumOfSquares = 0.0;
|
|
// for (uArrayCount = 0;
|
|
// uArrayCount < cusCurrentPass;
|
|
// /* check all elements in the data array */
|
|
// uArrayCount++) {
|
|
// /* consider only those data points that do not have the
|
|
// pbIndexOfOutlier flag set */
|
|
|
|
// if (!pbIndexOfOutlier [uArrayCount]) {
|
|
// dSumOfSamples += (double) pulDataArray [uArrayCount];
|
|
// dSumOfSquares += SQR ((double)pulDataArray
|
|
// [uArrayCount]);
|
|
// }
|
|
// }
|
|
// if (*pcusElementsInArray > 1)
|
|
// /* compute StdDev. only if there are atleast 2 elements */
|
|
// dSqrOfSDev = ((*pcusElementsInArray * dSumOfSquares) -
|
|
// SQR (dSumOfSamples)) /
|
|
// (*pcusElementsInArray *
|
|
// (*pcusElementsInArray - 1));
|
|
// ulStdDev = (ULONG) sqrt (dSqrOfSDev);
|
|
// /* since some data points were discarded, the mean has to be
|
|
// recomputed */
|
|
// uArrayCount = 0;
|
|
// ulMean = 0;
|
|
// while (uArrayCount < cusCurrentPass) {
|
|
// /* consider only those data points that do not have the
|
|
// bIndexOfOutlier flag set */
|
|
// if (!pbIndexOfOutlier [uArrayCount++])
|
|
// ulMean += pulDataArray [uArrayCount - 1];
|
|
// }
|
|
// if (*pcusElementsInArray > 0) /* only then compute mean */
|
|
// ulMean /= *pcusElementsInArray; /* this is the new mean */
|
|
// else
|
|
// ulMean = 0L;
|
|
// "STOP UNCOMMENT FOR OUTLIERS IN MEAN"
|
|
} /*** end of if (!bAcceptableSDev) ***/
|
|
else /*** if the for loop completed without
|
|
a single bad data point ***/
|
|
break;
|
|
} /* end of while */
|
|
/**** free the memory for the bIndexOfOutiler flag */
|
|
GlobalUnlock (hMemOutlierFlag);
|
|
GlobalFree (hMemOutlierFlag);
|
|
} /* end of if (iOutlierFactor) */
|
|
/* so, now an acceptable Standard deviation and mean have been obtained */
|
|
|
|
if ((!bDataConverged) &&
|
|
(usMaxIter < MIN_ITER)) {
|
|
/* set precision to 0% if max iters chosen is less than 3 */
|
|
usPrecision = 0;
|
|
} else { /* need to compute precision */
|
|
/* using eqn. 1. above, it can be shown that the precision, p,
|
|
can be written as:
|
|
1
|
|
_ _ /
|
|
| 2 2 | 2
|
|
| 2 * SD * 2.96 |
|
|
p = | ----------------- |
|
|
| 2 |
|
|
| n * Mean |
|
|
|_ _|
|
|
*************************************************************/
|
|
if (ulMean > 0 && *pcusElementsInArray) {
|
|
usPrecision = (USHORT) (sqrt((double) ((2 *
|
|
SQR ((double)ulStdDev) *
|
|
SQR (2.96) /(*pcusElementsInArray *
|
|
SQR ((double) ulMean))))) * 100.0
|
|
+ 0.5);
|
|
|
|
} else
|
|
usPrecision = (USHORT)~0;
|
|
} /* end of else need to compute precision */
|
|
sprintf (pszOutputString,
|
|
"%4u %10lu %10lu %10lu %6u %5u %10lu %4u %2u ",
|
|
uModeNumber, ulMean, ulMin, ulMax, cusCurrentPass,
|
|
usPrecision, ulStdDev, *pcusDiscardedValues,
|
|
usOutlierFactor);
|
|
|
|
*pcusElementsInArray = cusCurrentPass;
|
|
*pulFinalData = pulDataArray;
|
|
return;
|
|
}
|
|
|
|
|
|
/*
|
|
* The following is the source for generating random numbers.
|
|
* Two procs are provided: TestStatRand and TestStatUniRand.
|
|
*
|
|
* a) TestStatRand is called as follows: TestStatRand (Low, High)
|
|
* The result is a number returned in the range Low - High (both
|
|
* inclusive.
|
|
*
|
|
* A given intial value of Seed will yield a set of repeatable
|
|
* results. The first call to TestStatRand should be with an odd seed
|
|
* in the range of 1 - 67108863, both inclusive. The following
|
|
* 9 seeds have been tested with good results:
|
|
*
|
|
* 32347753, 52142147, 52142123, 53214215, 23521425, 42321479,
|
|
* 20302541, 32524125, 42152159.
|
|
*
|
|
* The result should never be equal to the seed since this would
|
|
* eliminate the theoretical basis for the claim for uniform
|
|
* randomeness.
|
|
*
|
|
* b) TestStatUniRand is called as follows:
|
|
* NormFrac = TestStatUniRand ();
|
|
* NormFrac is uniformaly distributed between 0 and 1 with
|
|
* a scale of 9 (values range bet. 0 and 0.999999999).
|
|
*
|
|
* The basis for this algorithm is the multiplicative congruential
|
|
* method found in Knuth (Vol.2 , Chap.3). Constants were selected
|
|
* by Pike, M.C and Hill, I.D; Sullivans, W.L. provides the
|
|
* the list of tested seeds.
|
|
*
|
|
* The code here has been adapted from Russ Blake's work.
|
|
*
|
|
* Created : vaidy - Nov. 29, 90
|
|
*/
|
|
|
|
#define MODULUS 67108864 /* modulus for computing random no */
|
|
#define SQRTMODULUS 8192 /* sqrt of MODULUS */
|
|
#define MULTIPLIER 3125
|
|
#define MAX_UPPER 67108863
|
|
#define MAX_SEEDS 8 /* 8 good starting seeds */
|
|
#define SCALE 65535
|
|
|
|
ULONG aulSeedTable [] = { /* lookup table for good seeds */
|
|
32347753, 52142147, 52142123, 53214215, 23521425, 42321479,
|
|
20302541, 32524125, 42152159};
|
|
|
|
USHORT uSeedIndex; /* index to lookup table */
|
|
ULONG ulSeed = 32347753; /* the seed chosen from table (hardcoded here)
|
|
and recomputed */
|
|
|
|
/*********************************************************************/
|
|
/*
|
|
* Function - TestStatRand (EXPORTED)
|
|
*
|
|
* Arguments -
|
|
* a) ULONG - ulLower
|
|
* b) ULONG - ulUpper
|
|
*
|
|
* Returns -
|
|
* a random number in the range ulLower to ulUpper
|
|
*
|
|
* An error code if the call failed. The error code
|
|
* will be:
|
|
*
|
|
* STAT_ERROR_ILLEGAL_BOUNDS
|
|
*
|
|
*
|
|
* Calls TestStatUniRand and returns a random number in the range passed
|
|
* in (both inclusive). The limits for the lower and upper bounds
|
|
* are 1 and 67108863. The start seed index looks up into the array
|
|
* of seeds to select a good, tested starting seed value. The returned
|
|
* values will be uniformaly distributed within the boundary. A start
|
|
* seed has been hardcoded into this dll.
|
|
*
|
|
*/
|
|
|
|
ULONG
|
|
TestStatRand (
|
|
ULONG ulLower,
|
|
ULONG ulUpper
|
|
)
|
|
{
|
|
double dTemp;
|
|
double dNormRand;
|
|
LONG lTestForLowBounds = (LONG) ulLower;
|
|
|
|
/* check args */
|
|
if ((lTestForLowBounds < 1L) ||
|
|
(ulUpper > MAX_UPPER) || (ulUpper < ulLower))
|
|
return (STAT_ERROR_ILLEGAL_BOUNDS);
|
|
dNormRand = TestStatUniRand (); /* call TestStatUniRand */
|
|
dTemp = (double) ((ulUpper - ulLower) * dNormRand); /* scale value */
|
|
return (ulLower + (ULONG) dTemp);
|
|
}
|
|
|
|
/*
|
|
*
|
|
* Function - TestStatUniRand () EXPORTED
|
|
*
|
|
* Accepts - nothing
|
|
*
|
|
* Returns a uniformaly distrib. normalized number in the range 0 - 0.9999999
|
|
* (both inclusive). Modifies the seed to the next value.
|
|
*
|
|
*/
|
|
|
|
double
|
|
TestStatUniRand (VOID)
|
|
{
|
|
ULONG ulModul = MODULUS; /* use the modulus for getting remainder
|
|
and dividing the current value */
|
|
double dMult = MULTIPLIER;
|
|
double dTemp = 0.0; /* a temp variable */
|
|
double dTemp2 = 0.0; /* a temp variable */
|
|
ULONG ulDivForMod; /* used for obtaining the remainder of
|
|
the present seed / MODULUS */
|
|
|
|
/* the following long-winded approach has to be adopted to
|
|
obtain the remainder. % operator does not work on floats */
|
|
|
|
/* use a temp variable. Makes the code easier to follow */
|
|
|
|
dTemp = dMult * (double) ulSeed; /* store product in temp var. */
|
|
DbgDummy (dTemp, dMult); // NT screws up bigtime for no reason
|
|
// if this is not used - possible compiler
|
|
// bug
|
|
dTemp2 = (double) ulModul; // more compiler problems reported
|
|
// on Build 259 by JosephH.
|
|
// April 13, 1992.
|
|
ulDivForMod = (ULONG) (dTemp / dTemp2);
|
|
|
|
// ulDivForMod = (ULONG) (dTemp / ulModul); /* store quotient of present
|
|
// seed divided by MODULUS */
|
|
dTemp -= ((double)ulDivForMod * (double)ulModul);
|
|
/* dTemp will contain the remainder of present seed / MODULUS */
|
|
|
|
ulSeed = (ULONG) dTemp; /* seed for next iteration obtained */
|
|
/* return value */
|
|
return ((dTemp)/(double)ulModul);
|
|
}
|
|
|
|
/*
|
|
*
|
|
* Function - TestStatNormDist () EXPORTED
|
|
*
|
|
* Accepts -
|
|
* a) ULONG - ulMean
|
|
* b) USHORT - usStdDev
|
|
*
|
|
* Returns - LONG - A LONG that allows the mean of the generated
|
|
* points to be approximately ulMean and the SD of the
|
|
* set to be ulStdDev.
|
|
*
|
|
* Formula used here is: REPEATS
|
|
* _
|
|
* Return Value = ulMean + (-7 + [ >_ TestStatUniRandRand ()] * ulStdDev
|
|
* i = i
|
|
*
|
|
* This formula is based on 'Random Number Generation and Testing',
|
|
* IBM Data Processing Techniques, C20-8011.
|
|
*/
|
|
|
|
LONG
|
|
TestStatNormDist (
|
|
ULONG ulMean,
|
|
USHORT usSDev
|
|
)
|
|
{
|
|
LONG lSumOfRands = 0L; /* store the sum of the REPEATS calls here */
|
|
USHORT cuNorm; /* a counter */
|
|
LONG lMidSum = 0L;
|
|
LONG lRemainder = 0L;
|
|
|
|
for (cuNorm = 0; cuNorm < REPEATS; cuNorm++)
|
|
lSumOfRands += (LONG) TestStatShortRand ();
|
|
|
|
/* we now do a lot of simple but ugly mathematics to obtain the
|
|
correct result. What we do is as follows:
|
|
|
|
Divide the lSumOfRands by the scale factor.
|
|
Since we are dealing with short and long integers, we are
|
|
likely to lose precision. So, we get the remainder of this
|
|
division and multiply each of the values by the standard division.
|
|
|
|
Eg. if lSumOfRands = 65534 and std.dev is 10,
|
|
lQuotient = 0, lRemainder = 65534.
|
|
|
|
lMidSum = (-7 * 10) + (0 * 10) + (65534 * 10/65535) = -61,
|
|
which is pretty accurate. We then add the mean and return.
|
|
Actually, we do not return right away. To be more precise,
|
|
we need to find out if the third element in the above term
|
|
yields a remainder of < 0.5. If so, we do not do anything.
|
|
Else, we add 1 to the result to round off and then return.
|
|
In the above example, the remainder = 0.99. So we add 1 to
|
|
-61. The result is -60 and this is accurate. */
|
|
|
|
lRemainder = (lSumOfRands * usSDev) % SCALE;
|
|
/* the above remainder is the one to determine the rounding off */
|
|
|
|
lMidSum = ((-7 + (lSumOfRands / SCALE)) * usSDev) +
|
|
((lSumOfRands % SCALE) * usSDev / SCALE);
|
|
|
|
if (lRemainder >= (SCALE / 2L)) /* need to roundup ? */
|
|
lMidSum += 1L;
|
|
|
|
return (lMidSum + ulMean);
|
|
}
|
|
|
|
/*
|
|
*
|
|
* Function - TestStatShortRand () EXPORTED
|
|
*
|
|
* Accepts - nothing
|
|
*
|
|
* Returns a normalized number in the range 0 - 65535
|
|
* (both inclusive). Modifies the seed to the next value.
|
|
*
|
|
*/
|
|
|
|
USHORT
|
|
TestStatShortRand (VOID)
|
|
{
|
|
ULONG ulTemp = SCALE / SQRTMODULUS;
|
|
|
|
ulSeed = (MULTIPLIER * ulSeed) % MODULUS;
|
|
/* seed for next iteration obtained */
|
|
|
|
/* note: the return value should be (ulSeed * SCALE / MODULUS).
|
|
However, the product of the elements in the numerator, far exceeds
|
|
4 Billion. So, the math is done in two stages. The value of
|
|
MODULUS is a perfect square (of 8192). So, the SCALE is first
|
|
divided by the SQRT of the MODULUS, the product of ulSeed and the
|
|
result of the division is divided by the SQRT of the MODULUS again */
|
|
|
|
/* return scale value - add one to ulTemp for correction */
|
|
return ((USHORT) ((ulSeed * (ulTemp + 1)) / SQRTMODULUS));
|
|
}
|
|
|
|
/*
|
|
*
|
|
* Function - TestStatFindFirstMode () EXPORTED
|
|
*
|
|
* Accepts - a) PSZ - pszOutputString
|
|
* b) USHORT - usOutlierFactor
|
|
* c) PULONG - *pulData
|
|
* d) PUSHORT - pcusElementsInArray
|
|
* e) PUSHORT - pcusDiscardedElements
|
|
*
|
|
* Returns -
|
|
* Nothing
|
|
*
|
|
* Computes useful statistical values and returns them in the string
|
|
* whose address is passed to this function. The returned string
|
|
* has the following format :
|
|
* ("%10lu %10lu %10lu %10lu %5u %10lu %4u %2u")
|
|
* and the arg. list will be in the order: mean,
|
|
* minimum, maximum, number of iterations, precision,
|
|
* standard deviation, number of outliers in the data set and the
|
|
* outlier count. (Please refer to \ntdocs\teststat.txt for
|
|
* a description of precision. This is on \\jupiter\perftool.
|
|
*
|
|
* Returns
|
|
* TO BE COMPLETED.....
|
|
*
|
|
*/
|
|
|
|
/*++
|
|
Had to call this routine in TestStatUniRand - compiler screws up
|
|
--*/
|
|
void
|
|
DbgDummy (
|
|
double dTemp,
|
|
double dLocal
|
|
)
|
|
{
|
|
dTemp = 0.0;
|
|
dLocal = 0.0;
|
|
}
|