added (prev_data ~ number) check to diff;

added ($x ~ number) check to lin_reg;
added gaussian expressions gaussian;

3 files changed, 63 insertions, 54 deletions

diff --git a/diff.awk b/diff.awk
index 1d1f72d..ba452c7 100644
--- a/diff.awk
+++ b/diff.awk
@@ -5,6 +5,7 @@
 
 BEGIN {
     OFS = FS
+    # OFMT = "%.9g"
     sign = "[+-]?"
     decimal = "[0-9]+[.]?[0-9]*"
     fraction = "[.][0-9]*"
@@ -13,7 +14,7 @@ BEGIN {
 }
 
 NR == 1 {
-    ### orig data columns
+    # orig data columns
     for (n=1; n<=NF; n++) {
         if ($n !~ number) {
             header[n] = $n
@@ -27,7 +28,7 @@ NR == 1 {
         printf(OFS)
     }
 
-    ### diff columns
+    # diff columns
     for (n=1; n<=NF; n++) {
         printf(dheader[n])
         printf(n < NF ? OFS : ORS)
@@ -38,29 +39,31 @@ NF {
     if (NF > nf_max)
         nf_max = NF
 
-    ### data columns
-    for (y=1; y<=nf_max; y++) {
-        if ($y == header[y] || $y == dheader[y])
+    # data columns
+    for (n=1; n<=nf_max; n++) {
+        if ($n == header[n] || $n == dheader[n])
             continue
-        if ($y ~ number)
-            printf(OFMT, $y)
+        if ($n ~ number)
+            printf(OFMT, $n)
         else
             printf("")
         printf(OFS)
     }
 
-    ### diff columns
-    for (y=1; y<=nf_max; y++) {
-        if ($y == header[y] || $y == dheader[y])
+    # diff columns
+    for (n=1; n<=nf_max; n++) {
+        if ($n == header[n] || $n == dheader[n])
             continue
-        if ($y ~ number) {
-            data[y] = $y
-            diff[y] = data[y] - data_prev[y]
-            data_prev[y] = data[y]
-            printf(OFMT, diff[y])
+        if ($n ~ number) {
+            data[n] = $n
+            if (data_prev[n] ~ number)
+                diff[n] = data[n] - data_prev[n]
+            else
+                diff[n] = ""
+            data_prev[n] = data[n]
         }
-        else
-            diff[y] = ""
-        printf(y < nf_max ? OFS : ORS)
+        printf(diff[n])
+        printf(n < nf_max ? OFS : ORS)
     }
 }
+
diff --git a/gaussian.awk b/gaussian.awk
index dfb5fb4..dc62d37 100644
--- a/gaussian.awk
+++ b/gaussian.awk
@@ -25,7 +25,10 @@ BEGIN {
 

     for (n=0; n<N; n++) {

         if (N > 1 && M > 0) {

-            w[n] = gaussian(n)

+            # sigma <= 0.5

+            sigma = 0.4

+            en[n] = (n - M)/(sigma*M)

+            w[n] = exp(-0.5*e[n]*e[n])

             print n, w[n]/M

         }

         else {

diff --git a/lin_reg.awk b/lin_reg.awk
index 52e55d3..0213c7f 100644
--- a/lin_reg.awk
+++ b/lin_reg.awk
@@ -17,7 +17,7 @@ NR == 1 {
         ($n ~ number) ? header[n] = "col" n : header[n] = $n
 }
 
-NF > 0 {
+NF {
     if (NF > nf_max)
         nf_max = NF
 
@@ -25,6 +25,7 @@ NF > 0 {
     for (y=1; y<=nf_max; y++) {
         if ($y == header[n])
             continue
+
         if ($y ~ number) {
 
             ### mean
@@ -42,45 +43,47 @@ NF > 0 {
 
             # x = row, y = col, trendline: y = A + Bx
             for (x=1; x<=nf_max; x++) {
-                count[x,y] += 1
-                sum_xy[x,y] += $x*$y
-                sum_delta_xy[x,y] += delta0[x]*delta1[y]
+                if ($x ~ number) {
+                    count[x,y] += 1
+                    sum_xy[x,y] += $x*$y
+                    sum_delta_xy[x,y] += delta0[x]*delta1[y]
 
-                # covariance
-                #(count[x,y] > 1) ? cov[x,y] = sum_delta_xy[x,y]/(count[x,y] - 1) : cov[x,y] = ""
+                    # covariance
+                    #(count[x,y] > 1) ? cov[x,y] = sum_delta_xy[x,y]/(count[x,y] - 1) : cov[x,y] = ""
 
-                # correlation
-                r_den[x,y] = sqrt(sum_delta2[x]*sum_delta2[y])
-                (r_den[x,y]) ? r[x,y] = sum_delta_xy[x,y]/r_den[x,y] : r[x,y] = 1
+                    # correlation
+                    r_den[x,y] = sqrt(sum_delta2[x]*sum_delta2[y])
+                    (r_den[x,y]) ? r[x,y] = sum_delta_xy[x,y]/r_den[x,y] : r[x,y] = 1
 
-                ab_den[x,y] = (count[x,y]*sum2[x] - sum[x]*sum[x])
-                if (ab_den[x,y]) {
-                    a[x,y] = (sum[y]*sum2[x] - sum[x]*sum_xy[x,y])/ab_den[x,y]
-                    b[x,y] = (count[x,y]*sum_xy[x,y] - sum[x]*sum[y])/ab_den[x,y]
-                }
-                else {
-                    a[x,y] = 0
-                    b[x,y] = 1
-                }
+                    ab_den[x,y] = (count[x,y]*sum2[x] - sum[x]*sum[x])
+                    if (ab_den[x,y]) {
+                        a[x,y] = (sum[y]*sum2[x] - sum[x]*sum_xy[x,y])/ab_den[x,y]
+                        b[x,y] = (count[x,y]*sum_xy[x,y] - sum[x]*sum[y])/ab_den[x,y]
+                    }
+                    else {
+                        a[x,y] = 0
+                        b[x,y] = 1
+                    }
+
+                    # error estimate
+                    err_den[x,y] = count[x,y]*(count[x,y] - 2)
+                    if (count[x,y] > 2) {
+                        err[x,y] = $y - (a[x,y] + b[x,y]*$x)
+                        sum_err2[x,y] += err[x,y]*err[x,y]
+                    }
+                    b_err_den[x,y] = (count[x,y] - 2)*sum_delta2[x]
+                    if (b_err_den[x,y])
+                        b_err[x,y] = sqrt(sum_err2[x,y]/b_err_den[x,y])
+                    a_err_den[x,y] = count[x,y]*b_err_den[x,y]
+                    if (a_err_den[x,y])
+                        a_err[x,y] = sqrt(sum2[x]/count[x,y])*b_err[x,y]
 
-                # error estimate
-                err_den[x,y] = count[x,y]*(count[x,y] - 2)
-                if (count[x,y] > 2) {
-                    err[x,y] = $y - (a[x,y] + b[x,y]*$x)
-                    sum_err2[x,y] += err[x,y]*err[x,y]
+                    # weighted mean, from HP-20S manual, pg 60
+                    xw[x,y] = sum_xy[x,y]/sum[y]
+                    yw[x,y] = b[x,y]*xw[x,y] + a[x,y]
+                    xw_dist[x,y] = (xw[x,y] - mean[x])
+                    yw_dist[x,y] = b[x,y]*(xw[x,y] - mean[x])
                 }
-                b_err_den[x,y] = (count[x,y] - 2)*sum_delta2[x]
-                if (b_err_den[x,y])
-                    b_err[x,y] = sqrt(sum_err2[x,y]/b_err_den[x,y])
-                a_err_den[x,y] = count[x,y]*b_err_den[x,y]
-                if (a_err_den[x,y])
-                    a_err[x,y] = sqrt(sum2[x]/count[x,y])*b_err[x,y]
-
-                # weighted mean, from HP-20S manual, pg 60
-                xw[x,y] = sum_xy[x,y]/sum[y]
-                yw[x,y] = b[x,y]*xw[x,y] + a[x,y]
-                xw_dist[x,y] = (xw[x,y] - mean[x])
-                yw_dist[x,y] = b[x,y]*(xw[x,y] - mean[x])
             }
         }
         else