double num2_Combination (int n, int s)
{
double Res;
int i;
int Diff;
if (s == 0 || s == n)
return 1.0;
if (s < 0) {
util_Warning (1, "num2_Combination: s < 0");
return 0.0;
}
if (s > n) {
util_Warning (1, "num2_Combination: s > n");
return 0.0;
}
if (s > (n / 2))
s = n - s;
if (n <= NLIM) {
Res = 1.0;
Diff = n - s;
for (i = 1; i <= s; i++) {
Res = (Res * (double) (Diff + i)) / (double) (i);
}
return Res;
} else {
Res = (num2_LnFactorial (n) - num2_LnFactorial (s))
- num2_LnFactorial (n - s);
return exp (Res);
}
}
void svaria_CollisionArgMax (unif01_Gen * gen, sres_Chi2 * res,
long N, long n, int r, long k, long m)
{
if (m > 1) {
svaria_CollisionArgMax_00 (gen, res, N, n, r, k, m);
} else if (m == 1) {
double ValDelta[] = { -1.0 };
smultin_Param *par;
if (swrite_Basic) {
printf (
"***********************************************************\n"
"Test svaria_CollisionArgMax calling smultin_Multinomial\n\n");
}
par = smultin_CreateParam (1, ValDelta, smultin_GenerCellMax, -3);
if (NULL == res) {
smultin_Multinomial (gen, par, NULL, N, n, r, 0, k, TRUE);
} else {
smultin_Res *resm;
resm = smultin_CreateRes (par);
smultin_Multinomial (gen, par, resm, N, n, r, 0, k, TRUE);
sres_InitChi2 (res, N, -1, "svaria_CollisionArgMax");
statcoll_SetDesc (res->sVal1, "CollisionArgMax sVal1");
res->sVal1->NObs = resm->Collector[0]->NObs;
tables_CopyTabD (resm->Collector[0]->V, res->sVal1->V, 1, N);
tables_CopyTabD (resm->sVal2[0], res->sVal2, 0, gofw_NTestTypes - 1);
tables_CopyTabD (resm->pVal2[0], res->pVal2, 0, gofw_NTestTypes - 1);
smultin_DeleteRes (resm);
}
smultin_DeleteParam (par);
} else {
util_Warning (m <= 0, "svaria_CollisionArgMax: m <= 0");
}
}
double num2_Factorial (int n)
{
util_Assert (n >= 0, "num2_Factorial: n < 0");
if (n <= 170)
return Factorials[n];
util_Warning (1, "num2_Factorial: n > 170: return inf");
return 1.0 / 0.0;
}
void ftab_SetDesc (ftab_Table *T, char *Desc)
{
size_t len;
util_Assert (T != NULL, "ftab_SetDesc: ftab_Table is a NULL pointer");
len = strlen (Desc);
if (len > MAXLEN) {
len = MAXLEN;
util_Warning (1, "ftab_Table->Desc truncated");
}
if (T->Desc != NULL)
T->Desc = util_Free (T->Desc);
T->Desc = util_Calloc (len + 1, sizeof (char));
strncpy (T->Desc, Desc, (size_t) len);
T->Desc[len] = '\0';
}
double num2_EvalCheby (const double A[], int N, double x)
{
int j;
double xx;
double b0, b1, b2;
util_Warning (fabs (x) > 1.0,
"Chebychev polynomial evaluated at x outside [-1, 1]");
xx = 2.0 * x;
b0 = 0.0;
b1 = 0.0;
for (j = N; j >= 0; j--) {
b2 = b1;
b1 = b0;
b0 = (xx * b1 - b2) + A[j];
}
return (b0 - b2) / 2.0;
}
void ffam_PrintFam (ffam_Fam *fam)
{
int i;
if (fam == NULL) {
util_Warning (TRUE, "ffam_PrintFam: fam is NULL");
return;
}
printf ("-------------------------------------------------\n");
printf ("Family: %s\nNumber of generators: %d\n\n",
fam->name, fam->Ng);
printf ("LSize Resol Generator\n");
printf ("-------------------------------------------------\n");
for (i = 0; i < fam->Ng; i++) {
printf ("%3d %3d %s\n", fam->LSize[i], fam->Resol[i],
fam->Gen[i]->name);
}
printf ("\n\n");
}
static void InitAppear (int r, int s, int L, long Q, double E[], double KV[])
{
util_Assert (r >= 0, "svaria_AppearanceSpacings: r < 0");
util_Assert (s > 0, "svaria_AppearanceSpacings: s <= 0");
/* if (L >= s && L % s) {
util_Error ("svaria_AppearanceSpacings: L mod s != 0");
}*/
if (L < s && s % L) {
util_Error ("svaria_AppearanceSpacings: s mod L != 0");
}
util_Warning (Q < 10.0 * num_TwoExp[L],
"svaria_AppearanceSpacings: Q < 10 * 2^L");
/* Theoretical mean E and variance KV for different L given by Maurer */
if (L > 16) {
/* For L > 16 and near 16, the 6-th decimal of E[L] could be
erroneous. For KV [L], the 4-th decimal. */
E[L] = L - 8.32746E-1;
KV[L] = 3.423715;
} else {
E [1] = 0.73264948; KV[1] = 0.68977;
E [2] = 1.53743829; KV[2] = 1.33774;
E [3] = 2.40160681; KV[3] = 1.90133;
E [4] = 3.31122472; KV[4] = 2.35774;
E [5] = 4.25342659; KV[5] = 2.70455;
E [6] = 5.21770525; KV[6] = 2.95403;
E [7] = 6.19625065; KV[7] = 3.12539;
E [8] = 7.18366555; KV[8] = 3.23866;
E [9] = 8.17642476; KV[9] = 3.31120;
E [10] = 9.17232431; KV[10] = 3.35646;
E [11] = 10.1700323; KV[11] = 3.38409;
E [12] = 11.1687649; KV[12] = 3.40065;
E [13] = 12.1680703; KV[13] = 3.41043;
E [14] = 13.1676926; KV[14] = 3.41614;
E [15] = 14.1674884; KV[15] = 3.41943;
E [16] = 15.1673788; KV[16] = 3.42130;
}
}
void sknuth_Gap (unif01_Gen *gen, sres_Chi2 *res,
long N, long n, int r, double Alpha, double Beta)
{
int len;
int t;
long m; /* Number of observed Gaps */
long Seq; /* Current replication number */
double p; /* Probability of U01 in (Alpha, Beta) */
double X2;
double U;
double Mult;
double V[1]; /* Number of degrees of freedom for Chi2 */
char str[LENGTH + 1];
lebool localRes = FALSE;
chrono_Chrono *Timer;
char *TestName = "sknuth_Gap test";
Timer = chrono_Create ();
p = Beta - Alpha;
t = (int)(log (gofs_MinExpected / n) / num2_log1p (-p));
len = (int)(1 + log (gofs_MinExpected / (n*p)) / num2_log1p (-p));
t = util_Min(t, len);
t = util_Max(t, 0);
Mult = p * n;
if (swrite_Basic)
WriteDataGap (gen, TestName, N, n, r, Alpha, Beta);
util_Assert (Alpha >= 0.0 && Alpha <= 1.0,
"sknuth_Gap: Alpha outside interval [0..1]");
util_Assert (Beta <= 1.0 && Beta > Alpha,
"sknuth_Gap: Beta outside interval (Alpha..1]");
if (res == NULL) {
localRes = TRUE;
res = sres_CreateChi2 ();
}
sres_InitChi2 (res, N, t, "sknuth_Gap");
sprintf (str, "The N statistic values (a ChiSquare with %1d degrees"
" of freedom):", t);
statcoll_SetDesc (res->sVal1, str);
res->degFree = t;
if (res->degFree < 1) {
util_Warning (TRUE, "Chi-square with 0 degree of freedom.");
if (localRes)
sres_DeleteChi2 (res);
chrono_Delete (Timer);
return;
}
/* Compute the probabilities for each gap length */
res->NbExp[0] = Mult;
res->Loc[0] = 0;
for (len = 1; len < t; len++) {
Mult *= 1.0 - p;
res->NbExp[len] = Mult;
res->Loc[len] = len;
}
res->NbExp[t] = Mult * (1.0 - p) / p;
res->Loc[t] = t;
if (swrite_Classes)
gofs_WriteClasses (res->NbExp, res->Count, 0, t, 0);
/* Beginning of test */
for (Seq = 1; Seq <= N; Seq++) {
for (len = 0; len <= t; len++)
res->Count[len] = 0;
for (m = 1; m <= n; m++) {
/* Process one gap */
len = 0;
U = unif01_StripD (gen, r);
while ((U < Alpha || U >= Beta) && len < n) {
++len;
U = unif01_StripD (gen, r);
}
if (len >= n) {
util_Warning (TRUE,
"sknuth_Gap: one gap of length > n\n********* Interrupting the test\n");
printf ("\n\n");
res->pVal2[gofw_Mean] = res->pVal2[gofw_AD]
= res->pVal2[gofw_KSM] = res->pVal2[gofw_KSP] = 0.0;
if (localRes)
sres_DeleteChi2 (res);
chrono_Delete (Timer);
return;
}
if (len >= t)
++res->Count[t];
else
++res->Count[len];
}
if (swrite_Counters)
tables_WriteTabL (res->Count, 0, t, 5, 10, "Observed numbers:");
X2 = gofs_Chi2 (res->NbExp, res->Count, 0, t);
statcoll_AddObs (res->sVal1, X2);
}
V[0] = t;
gofw_ActiveTests2 (res->sVal1->V, res->pVal1->V, N, wdist_ChiSquare, V,
res->sVal2, res->pVal2);
sres_GetChi2SumStat (res);
if (swrite_Collectors)
statcoll_Write (res->sVal1, 5, 14, 4, 3);
if (swrite_Basic) {
swrite_AddStrChi (str, LENGTH, res->degFree);
gofw_WriteActiveTests2 (N, res->sVal2, res->pVal2, str);
swrite_Chi2SumTest (N, res);
swrite_Final (gen, Timer);
}
if (localRes)
sres_DeleteChi2 (res);
chrono_Delete (Timer);
}
void svaria_SumLogs (unif01_Gen * gen, sres_Chi2 * res,
long N, long n, int r)
{
const double Eps = DBL_EPSILON / 2.0; /* To avoid log(0) */
const double Epsilon = 1.E-100; /* To avoid underflow */
long i;
long Seq;
double u;
double Prod;
double Sum;
double V[1];
lebool localRes = FALSE;
chrono_Chrono *Timer;
char *TestName = "svaria_SumLogs test";
char chaine[LEN1 + 1] = "";
char str[LEN2 + 1];
Timer = chrono_Create ();
if (swrite_Basic) {
swrite_Head (gen, TestName, N, n, r);
printf ("\n\n");
}
util_Assert (n < LONG_MAX/2, "2n > LONG_MAX");
if (res == NULL) {
localRes = TRUE;
res = sres_CreateChi2 ();
}
sres_InitChi2 (res, N, -1, "svaria_SumLogs");
strncpy (chaine, "SumLogs sVal1: chi2 with ", (size_t) LEN1);
sprintf (str, "%ld", 2 * n);
strncat (chaine, str, (size_t) LEN2);
strncat (chaine, " degrees of freedom", (size_t) LEN1);
statcoll_SetDesc (res->sVal1, chaine);
res->degFree = 2 * n;
if (res->degFree < 1) {
util_Warning (TRUE, "Chi-square with 0 degree of freedom.");
if (localRes)
sres_DeleteChi2 (res);
return;
}
for (Seq = 1; Seq <= N; Seq++) {
Prod = 1.0;
Sum = 0.0;
for (i = 1; i <= n; i++) {
u = unif01_StripD (gen, r);
if (u < Eps)
u = Eps;
Prod *= u;
if (Prod < Epsilon) {
Sum += log (Prod);
Prod = 1.0;
}
}
statcoll_AddObs (res->sVal1, -2.0 * (Sum + log (Prod)));
}
V[0] = 2 * n;
gofw_ActiveTests2 (res->sVal1->V, res->pVal1->V, N, wdist_ChiSquare, V,
res->sVal2, res->pVal2);
res->pVal1->NObs = N;
sres_GetChi2SumStat (res);
if (swrite_Collectors)
statcoll_Write (res->sVal1, 5, 14, 4, 3);
if (swrite_Basic) {
swrite_AddStrChi (str, LEN2, res->degFree);
gofw_WriteActiveTests2 (N, res->sVal2, res->pVal2, str);
swrite_Chi2SumTest (N, res);
swrite_Final (gen, Timer);
}
if (localRes)
sres_DeleteChi2 (res);
chrono_Delete (Timer);
}
void sentrop_EntropyDiscOver2 (unif01_Gen * gen, sentrop_Res * res,
long N, long n, int r, int s, int L)
{
long i, j; /* Indices */
unsigned long B2, B1, B0; /* Blocks of bits */
long Seq; /* Replication number */
double Entropy; /* Value of the entropy S */
double tempPrev; /* Previous value of the entropy */
double SumSq; /* To compute the covariance */
double Corr; /* Empirical correlation */
double Var; /* Empirical variance */
double Mean; /* Empirical mean */
double Sigma, Mu; /* Parameters of the normal law */
double Sum2, Sum; /* Temporary variables */
unsigned long d; /* 2^s */
long C; /* 2^L */
unsigned long CLC; /* 2^L */
long m0; /* m0 = ceil (L/s) */
long m; /* m = n/s */
double xLgx[NLIM + 1]; /* = -i/n * Lg (i/n) */
double NLR = N;
double temp, E1;
lebool localRes = FALSE;
chrono_Chrono *Timer;
char *TestName = "sentrop_EntropyDiscOver2 test";
Timer = chrono_Create ();
InitExactOver (n, L, &Mu, &Sigma);
if (swrite_Basic)
WriteDataDisc (gen, TestName, N, n, r, s, L, Mu, Sigma);
util_Assert (L <= n, "sentrop_EntropyDiscOver2: L > n");
util_Assert (L <= 15, "sentrop_EntropyDiscOver2: L > 15");
util_Assert (r <= 31, "sentrop_EntropyDiscOver2: r > 31");
util_Assert (s <= 31, "sentrop_EntropyDiscOver2: s > 31");
util_Assert (L + s <= 31, "sentrop_EntropyDiscOver2: L+s > 31");
util_Assert (n % s == 0, "sentrop_EntropyDiscOver2: n % s != 0");
d = num_TwoExp[s];
m = n / s;
m0 = L / s;
if (m0 * s < L)
++m0;
/* B0 must not be larger than LONG_MAX (31 bits) */
util_Assert (m0 * s <= 31, "sentrop_EntropyDiscOver2: m0 * s > 31");
C = num_TwoExp[L];
CLC = num_TwoExp[L];
if (res == NULL) {
localRes = TRUE;
res = sentrop_CreateRes ();
}
InitRes (res, N, C - 1, "sentrop_EntropyDiscOver2");
tempPrev = SumSq = Sum2 = Sum = 0.0;
CalcLgx (xLgx, n);
for (Seq = 1; Seq <= N; Seq++) {
for (i = 0; i < C; i++)
res->Count[i] = 0;
B0 = unif01_StripB (gen, r, s);
for (j = 2; j <= m0; j++)
B0 = B0 * d + unif01_StripB (gen, r, s);
/* B0 now contains the bits 0,...,0,b_1,...,b_{m0*s} */
B2 = B0;
/* Count the blocks of L bits in b_1,...,b_{m0*s} */
for (i = 0; i <= m0 * s - L; i++) {
++res->Count[B2 % CLC];
B2 >>= 1;
}
B1 = B0 % CLC;
B0 = B2 % CLC;
/* B1 contains 0,...,0,b_{m0*s-L+1},...,b_{m0*s} */
/* B0 contains 0,...,0,b_1,...,b_{L-1} */
for (j = 1; j <= m - m0; j++) {
B1 = B1 * d + unif01_StripB (gen, r, s);
B2 = B1;
B1 %= CLC;
/* B1 and B2 contain L bits and L+s bits, resp. */
for (i = 1; i <= s; i++) {
++res->Count[B2 % CLC];
B2 >>= 1;
}
}
/* B1 contains 0,...,0,b_{m*s-L+1},...,b_{m*s}. */
/* Her we must have 2 * L <= 31. */
B2 = B0 + B1 * (CLC / 2);
/* B2 contains 0,..,0,b_{m*s-L+1},..,b_{m*s},b_1,...,b_{L-1}. */
/* Now count blocks with overlap. */
for (i = 1; i < L; i++) {
++res->Count[B2 % CLC];
B2 >>= 1;
}
/* Compute entropy */
Entropy = 0.0;
for (i = 0; i < C; i++) {
util_Assert (res->Count[i] <= NLIM,
"sentrop_EntropyDiscOver2: NLIM is too small");
Entropy += xLgx[res->Count[i]];
}
#ifdef STABLE
/* Ideally, we should use the moving average for numerical stability.
But we shall use the first observed value of instead; it should be
typical and will prevent loss of precision (unless it is 0). */
if (1 == Seq)
E1 = Entropy;
temp = Entropy - E1;
Sum += temp;
Sum2 += temp * temp;
SumSq += temp * tempPrev;
tempPrev = temp;
#else
/* The naive unstable method */
Sum += Entropy;
Sum2 += Entropy * Entropy;
SumSq += Entropy * tempPrev;
tempPrev = Entropy;
#endif
if (swrite_Counters)
tables_WriteTabL (res->Count, 0, C - 1, 5, 10, "Counters:");
if (swrite_Collectors) {
printf ("Entropy = ");
num_WriteD (Entropy, 15, 6, 1);
printf ("\n");
}
}
/* We now test the correlation between successive values of the */
/* entropy. Corr should have mean 0 and variance 1. */
#ifdef STABLE
Mean = Sum / NLR + E1;
Var = Sum2 / NLR - (E1 - Mean) * (E1 - Mean);
Var *= NLR / (NLR - 1.0);
temp = (Entropy + E1 * NLR - 2.0 * NLR * Mean) * E1 / (NLR - 1.0);
Corr = SumSq / (NLR - 1.0) - temp - Mean * Mean;
if (Var <= 0.0) {
Corr = 1.0e100;
util_Warning (TRUE,
"Empirical variance <= 0. Correlation set to 1e100.");
} else
Corr /= Var;
#else
/* Naive calculations. Here, there could be huge losses of precision
because Mean*Mean, Sum2/NLR, and SumSq/(NLR - 1.0) may be very close. */
Mean = Sum / NLR;
Var = (Sum2 / NLR - Mean * Mean) * NLR / (NLR - 1.0);
Corr = (SumSq / (NLR - 1.0) - Mean * Mean) / Var;
#endif
if (Sigma > 0.0) {
/* We know the true values of Mu and Sigma */
res->Bas->sVal2[gofw_Mean] = (Mean - Mu) * sqrt (NLR) / Sigma;
res->Bas->pVal2[gofw_Mean] = fbar_Normal1 (res->Bas->sVal2[gofw_Mean]);
} else
res->Bas->sVal2[gofw_Mean] = -1.0;
res->Bas->sVal2[gofw_Cor] = Corr * sqrt (NLR);
res->Bas->pVal2[gofw_Cor] = fbar_Normal1 (res->Bas->sVal2[gofw_Cor]);
if (swrite_Basic) {
WriteResultsDiscOver (res, NLR, Sum2, SumSq, Mu, Sigma, Mean, Var,
Corr);
swrite_Final (gen, Timer);
}
if (localRes)
sentrop_DeleteRes (res);
chrono_Delete (Timer);
}
void sentrop_EntropyDiscOver (unif01_Gen * gen, sentrop_Res * res,
long N, long n, int r, int s, int L)
{
long i; /* Index */
unsigned long Block1, Block0; /* Blocks of bits */
long Seq; /* Replication number */
double Entropy; /* Value of the entropy S */
double tempPrev; /* Previous value of entropy */
double SumSq; /* To compute the covariance */
double Corr; /* Empirical correlation */
double Var; /* Empirical variance */
double Mean; /* Empirical mean */
double Sigma, Mu; /* Parameters of the normal law */
double Sum2, Sum; /* Temporary variables */
long d; /* 2^s */
long C; /* 2^L */
long nSurs; /* n / s */
double xLgx[NLIM + 1]; /* = -i/n * Lg (i/n) */
double NLR = N;
double temp, E1;
lebool localRes = FALSE;
chrono_Chrono *Timer;
char *TestName = "sentrop_EntropyDiscOver test";
Timer = chrono_Create ();
InitExactOver (n, L, &Mu, &Sigma);
if (swrite_Basic)
WriteDataDisc (gen, TestName, N, n, r, s, L, Mu, Sigma);
util_Assert (L <= n - L, "sentrop_EntropyDiscOver: L > n-L");
util_Assert (n <= 31, "sentrop_EntropyDiscOver: n > 31");
util_Assert (r <= 31, "sentrop_EntropyDiscOver: r > 31");
util_Assert (s <= 31, "sentrop_EntropyDiscOver: s > 31");
util_Assert (n % s == 0, "sentrop_EntropyDiscOver: n % s != 0");
util_Assert (N > 1, "sentrop_EntropyDiscOver: N <= 1");
d = num_TwoExp[s];
C = num_TwoExp[L];
nSurs = n / s;
if (res == NULL) {
localRes = TRUE;
res = sentrop_CreateRes ();
}
InitRes (res, N, C - 1, "sentrop_EntropyDiscOver");
CalcLgx (xLgx, n);
tempPrev = SumSq = Sum2 = Sum = 0.0;
for (Seq = 1; Seq <= N; Seq++) {
for (i = 0; i < C; i++)
res->Count[i] = 0;
Block0 = unif01_StripB (gen, r, s);
for (i = 2; i <= nSurs; i++)
Block0 = Block0 * d + unif01_StripB (gen, r, s);
/* Compute entropy of the block of n bits = Block0. */
/* This block has less than 31 bits. */
Block1 = Block0;
for (i = 0; i <= n - L - 1; i++) {
++res->Count[Block1 % C];
Block1 >>= 1;
}
Block1 = (Block1 % C) + C * (Block0 % C);
for (i = n - L; i < n; i++) {
++res->Count[Block1 % C];
Block1 >>= 1;
}
Entropy = 0.0;
for (i = 0; i < C; i++) {
util_Assert (res->Count[i] <= NLIM,
"sentrop_EntropyDiscOver: NLIM is too small");
Entropy += xLgx[res->Count[i]];
}
#ifdef STABLE
/* Ideally, we should use the moving average for numerical stability.
But we shall use the first observed value instead; it should be
typical and will prevent loss of precision (unless it is 0). */
if (1 == Seq)
E1 = Entropy;
temp = Entropy - E1;
Sum += temp;
Sum2 += temp * temp;
SumSq += temp * tempPrev;
tempPrev = temp;
#else
/* The naive method: it is simple but numerically unstable. It can be
used for debugging and testing the more stable calculation in the
case of small samples. */
Sum += Entropy;
Sum2 += Entropy * Entropy;
SumSq += Entropy * tempPrev;
tempPrev = Entropy;
#endif
if (swrite_Counters)
tables_WriteTabL (res->Count, 0, C - 1, 5, 10, "Counters:");
if (swrite_Collectors) {
printf ("Entropy = ");
num_WriteD (Entropy, 15, 6, 1);
printf ("\n");
}
}
/* We now test the correlation between successive values of the entropy.
Corr should have mean 0 and variance 1. We use a numerically stable
calculation. */
#ifdef STABLE
Mean = Sum / NLR + E1;
Var = Sum2 / NLR - (E1 - Mean) * (E1 - Mean);
Var *= NLR / (NLR - 1.0);
temp = (Entropy + E1 * NLR - 2.0 * NLR * Mean) * E1 / (NLR - 1.0);
Corr = SumSq / (NLR - 1.0) - temp - Mean * Mean;
if (Var <= 0.0) {
Corr = 1.0e100;
util_Warning (TRUE,
"Empirical variance <= 0. Correlation set to 1e100.");
} else
Corr /= Var;
#else
/* Naive calculations. Here, there could be huge losses of precision
because Mean*Mean, Sum2/NLR, and SumSq/(NLR - 1.0) may be very close. */
Mean = Sum / NLR;
Var = (Sum2 / NLR - Mean * Mean) * NLR / (NLR - 1.0);
Corr = (SumSq / (NLR - 1.0) - Mean * Mean) / Var;
#endif
if (Sigma > 0.0) {
/* We know the true values of Mu and Sigma */
res->Bas->sVal2[gofw_Mean] = (Mean - Mu) * sqrt (NLR) / Sigma;
res->Bas->pVal2[gofw_Mean] = fbar_Normal1 (res->Bas->sVal2[gofw_Mean]);
} else
res->Bas->pVal2[gofw_Mean] = -1.0;
res->Bas->sVal2[gofw_Cor] = Corr * sqrt (NLR);
res->Bas->pVal2[gofw_Cor] = fbar_Normal1 (res->Bas->sVal2[gofw_Cor]);
if (swrite_Basic) {
WriteResultsDiscOver (res, NLR, Sum2, SumSq, Mu, Sigma, Mean, Var,
Corr);
swrite_Final (gen, Timer);
}
if (localRes)
sentrop_DeleteRes (res);
chrono_Delete (Timer);
}
